• Anthropic’s mission and research strategy
• Recent research and papers by Anthropic
• Anthropic’s structure as a “public benefit corporation”
• Career opportunities

 

Watch the video version of this episode here

Careers at Anthropic

Anthropic’s Transformer Circuits research 

Follow Anthropic on Twitter

microCOVID Project

Follow Lucas on Twitter here

0:00 Intro

2:44 What was the intention behind forming Anthropic?

6:28 Do the founders of Anthropic share a similar view on AI?

7:55 What is Anthropic’s focused research bet?

11:10 Does AI existential safety fit into Anthropic’s work and thinking?

14:14 Examples of AI models today that have properties relevant to future AI existential safety

16:12 Why work on large scale models?

20:02 What does it mean for a model to lie?

22:44 Safety concerns around the open-endedness of large models

29:01 How does safety work fit into race dynamics to more and more powerful AI?

36:16 Anthropic’s mission and how it fits into AI alignment

38:40 Why explore large models for AI safety and scaling to more intelligent systems?

43:24 Is Anthropics research strategy a form of prosaic alignment?

46:22 Anthropic’s recent research and papers

49:52 How difficult is it to interpret current AI models?

52:40 Anthropic’s research on alignment and societal impact

55:35 Why did you decide to release tools and videos alongside your interpretability research?

1:01:04 What is it like working with your sibling?

1:05:33 Inspiration around creating Anthropic

1:12:40 Is there an upward bound on capability gains from scaling current models?

1:18:00 Why is it unlikely that continuously increasing the number of parameters on models will lead to AGI?

1:21:10 Bootstrapping models

1:22:26 How does Anthropic see itself as positioned in the AI safety space?

1:25:35 What does being a public benefit corporation mean for Anthropic?

1:30:55 Anthropic’s perspective on windfall profits from powerful AI systems

1:34:07 Issues with current AI systems and their relationship with long-term safety concerns

1:39:30 Anthropic’s plan to communicate it’s work to technical researchers and policy makers

1:41:28 AI evaluations and monitoring

1:42:50 AI governance

1:45:12 Careers at Anthropic

1:48:30 What it’s like working at Anthropic

1:52:48 Why hire people of a wide variety of technical backgrounds?

1:54:33 What’s a future you’re excited about or hopeful for?

1:59:42 Where to find and follow Anthropic

 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s episode is with Daniela and Dario Amodei of Anthropic. For those not familiar, Anthropic is a new AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Their view is that large, general AI systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque.  Their goal is to make progress on these issues through research, and, down the road, create value commercially and for public benefit. Daniela and Dario join us to discuss the mission of Anthropic, their perspective on AI safety, their research strategy, as well as what it’s like to work there and the positions they’re currently hiring for. Daniela Amodei is Co-Founder and President of Anthropic. She was previously at Stripe and OpenAI, and has also served as a congressional staffer. Dario Amodei is CEO and Co-Founder of Anthropic. He was previously at OpenAI, Google, and Baidu. Dario holds a PhD in (Bio)physics from Princeton University.

Before we jump into the interview, we have a few announcements. If you’ve tuned into any of the previous two episodes, you can skip ahead just a bit. The first announcement is that I will be moving on from my role as Host of the FLI Podcast, and this means two things. The first is that FLI is hiring for a new host for the podcast. As host, you would be responsible for the guest selection, interviews, production, and publication of the FLI Podcast. If you’re interested in applying for this position, you can head over to the careers tab at futureoflife.org for more information. We also have another 4 job openings currently for a Human Resources Manager, an Editorial Manager, an EU Policy Analyst, and an Operations Specialist. You can learn more about those at the careers tab as well.

The second item is that even though I will no longer be the host of the FLI Podcast, I won’t be disappearing from the podcasting space. I’m starting a brand new podcast focused on exploring questions around wisdom, philosophy, science, and technology, where you’ll see some of the same themes we explore here like existential risk and AI alignment. I’ll have more details about my new podcast soon. If you’d like to stay up to date, you can follow me on Twitter at LucasFMPerry, link in the description.

And with that, I’m happy to present this interview with Daniela and Dario Amodei on Anthropic.

It’s really wonderful to have you guys here on the podcast. I’m super excited to be learning all about Anthropic. So we can start off here with a pretty simple question, and so what was the intention behind forming Anthropic?

Daniela Amodei: Yeah. Cool. Well, first of all Lucas, thanks so much for having us on the show. We’ve been really looking forward to it. We’re super pumped to be here. So I guess maybe I’ll kind of start with this one. So just why did we start Anthropic? To give a little history here and set the stage, we were founded about a year ago at the beginning of 2021, and it was originally a team of seven people who moved over together from OpenAI. And for listeners or viewers who don’t very viscerally remember this time period, it was the middle of the pandemic, so most people were not eligible to be vaccinated yet. And so when all of us wanted to get together and talk about anything, we had to get together in someone’s backyard or outdoors and be six feet apart and wear masks. And so it was generally just a really interesting time to be starting a company.

But why did we found Anthropic? What was the thinking there? I think the best way I would describe it is because all of us wanted the opportunity to make a focused research bet with a small set of people who were highly aligned around a very coherent vision of AI research and AI safety. So the majority of our employees had worked together in one format or another in the past, so I think our team is known for work like GPT-3 or DeepDream Chris Olah worked on at Google Brain for scaling laws. But we’d also done a lot of different safety research together in different organizations as well. So multimodal neurons when we were at OpenAI, Concrete Problems in AI Safety and a lot of others, but this group had worked together in different companies at Google Brain and OpenAI and academia in startups previously, and we really just wanted the opportunity to get that group together to do this focused research bet of building steerable, interpretable and reliable AI systems with humans at the center of them.

Dario Amodei: Yeah, just to add a little bit to that, I think we’re all a bunch of fairly empirically minded, exploration driven people, but who also think and care a lot about AI safety. I think that characterizes all seven of us. If you add together having either working at OpenAI, working together at Google Brain in the past, many of us worked together in the physics community, and we’re current or former physicists. If you add all that together, it’s a set of people who have known each other for a long time and have been aware of thinking and arguments about AI safety and have worked on them over the years always with an empirical bent, ranging from interpretability on language models and vision models to working on the original RL from Human Preferences, Concrete Problems in AI safety, and also characterizing scaling and how scaling works and how we think of that as somewhat central to the way AI is going to progress and shapes the landscape for how to solve safety.

And so a year ago, we were all working at OpenAI and trying to make this focused bet on basically scaling plus safety or safety with a lens towards scaling being a big part of the path to AGI. And when we felt we were making this focused bet within a larger organization and it just eventually came to the conclusion that it would be great to have an organization like top to bottom was just focused on this bet and could make all its strategic decisions with this bet in mind. And so that was the thinking and the genesis.

Lucas Perry: Yeah. I really like that idea of a focused bet. I hadn’t heard that before. I like that. Do you all have a similar philosophy in terms of your background, since you’re all converging on this work is safely scaling to AGI?

Dario Amodei: I think in a broad sense, we all have this view, safety is important today and for the future. We all have this view of, I don’t know, I would say like pragmatic practicality, and empiricism. Let’s see what we can do today to try and get a foothold on things that might happen in the future. Yeah, as I said, many of us have background in physics or other natural sciences. I’m a former… I was physics undergrad, neuroscience grad school, so yeah, we very much have this empirical science mindset, more than maybe a more philosophy or theoretical approach. Within that, obviously all of us, if you include the seven initial folks as well as the employees who joined, have our own skills and our own perspective on things and have different things within that we’re excited about. So we’re not all clones of the same person.

Some of us are excited about interpretability, some of us are excited about reward learning and preference modeling, some of us are excited about the policy aspects. And we each have our own guesses about the sub path within this broad path that makes sense. But I think we all agree on this broad view. Scaling’s important, safety’s important, getting a foothold on problems today is important as a window on future.

Lucas Perry: Okay. And so this shared vision that you all have is around this focused research bet. Could you tell me a little bit more about what that bet is?

Daniela Amodei: Yeah. Maybe I’ll start here, and Dario feel free to jump in and add more, but I think the boiler plate vision or mission that you would see if you looked on our website is that we’re building steerable, interpretable and reliable AI systems. But I think what that looks like in practice is that we are training large scale generative models, and we’re doing safety research on those models. And the reason that we’re doing that is we want to make the models safer and more aligned with human values. I think the alignment paper, which you might have seen that came out recently, there’s a term there that we’ve been using a lot, which is we’re aiming to make systems that are helpful, honest and harmless.

I think also when I think about the way our teams are structured, we have capabilities as this central pillar of research and there’s this helix of safety research that wraps around every project that we work on. So to give an example, if we’re doing language model training, that’s like this central pillar, and then we have interpretability research, which is trying to see inside models and understand what’s happening with the language models under the hood. We’re doing alignment research with input from human feedback to try and improve the outputs of the model. We’re doing societal impacts research. That’s looking at what impact on society in sort of a short and medium term way do these language models have? We’re doing scaling laws research to try and predict empirically what properties are we going to see emerge in these language models at various sizes? But I think all together, that ends up look like a team of people that are working together on a combination of capability and scaling work with safety research.

Dario Amodei: Yeah. I mean, one way you might put it is there are a lot of things that an org does that are neutral as to the direction that you would take. You have to build a cluster and you have to have an HR operation and you have to have an office. And so you can even think of the large models as being a bit like the cluster, that you build these large models and they’re blank when you start off with them and probably unaligned, but it’s what you do on top of these models that matters, that takes you in a safe or not safe direction in a good or a bad direction.

And so in a way, although they’re ML and although we’ll continue to scale them up, you can think of them as almost part of infrastructure. It takes research and it takes algorithms to get them right, but you can think of them as this core part of the infrastructure. And then the interesting question is all the safety questions. What’s going on inside these models? How do they operate? How can we make them operate differently? How can we change their objective functions to be something that we want rather than something that we don’t want? How can we look at their applications and make those applications more likely to be positive and less likely to be negative, more likely to go in directions that people intend and less likely to go off in directions that people don’t intend? So we almost see the presence of these large models as like the… I don’t know what the analogy is, like the flower or the paste, like the background ingredient on which the things we really care about get built and prerequisite for building those things.

Lucas Perry: So does AI existential safety fit into these considerations around your safety and alignment work?

Dario Amodei: I think this is something we think about and part of the motivation for what we do. Probably most listeners of this podcast know what it is, but I think the most common form of the concern is, “Hey, look, we’re making these AI systems. They’re getting more and more powerful. At some point they’ll be generally intelligent or more generally capable than human beings are and then they may have a large amount of agency. And if we haven’t built them in such a way that agency is in line with what we want to do, then we could imagine them doing something really scary that we can’t stop.” So I think that, to take it even further, this could be some kind of threat to humanity.

So I mean, that’s an argument with many steps, but it’s one that, in a very broad sense and in the long term, seems at least potentially legitimate to us. I mean, this is like the argument seems at least like something that we should care about. But I think the big question, and maybe how we differ, although it might be subtly, from other orgs that think about these problems, is how do we actually approach that problem today? What can we do? So I think there are various efforts to think about the ways in which this might happen, to come up with theories or frameworks.

As I mentioned with the background that we have, we’re more empirically focused people. We’re more inclined to say, “We don’t really know. That broad argument sounds kind of plausible to us and the stakes should be high, so you should think about it.” But it’s hard to work on that today. I’m not even sure how much value there is in talking about that a lot today. So we’ve taken a very different tack, which is look, there actually… And I think this has started to be true in the last couple years and maybe wasn’t even true five years ago, that there are models today that have, at least some, not all of the properties of models that we would be worried about in the future and are causing very concrete problems today that affect people today. So can we take a strategy where we develop methods that both help with the problems of today, but do so in a way that could generalize or at least teach us about the problems of the future? So our eye is definitely on these things in the future. But I think that if not grounded in empirics in the problems of today, it can drift off in a direction that isn’t very productive.

And so that’s our general philosophy. I think the particular properties and the models are look, today, we have models that are really open ended, in some narrow ways are more capable than humans. I think large language models probably know more about cricket than me, because I don’t know the first thing about cricket and are also unpredictable by their statistical nature. And I think those are at least some of the properties that we’re worried about with future systems. So we can use today’s models as a laboratory to scope out these problems a little better. My guess is that we don’t understand them very well at all and that this is a way to learn.

Lucas Perry: Could you give some examples of some of these models that exist today that you think exhibit these properties?

Dario Amodei: So I think the most famous one would be generative language models. So there’s a lot of them. There’s most famously GPT-3 from OpenAI, which we helped build. There’s Gopher from DeepMind. There’s Lambda from main Google. I’m probably leaving out some. I think there’d been of models this size in China, South Korea (corrected), Israel. Seems like everyone has one. It seems like everyone has one nowadays. I don’t think it’s limited to language. There have also been models that are focused on code. We’ve seen that from DeepMind, OpenAI and some other players. And there have also been models with modified forms in same spirit that model images, that generate images or that convert images to text or that convert text to images. There might be models in the future that generate videos or convert videos to text.

There’s many modifications of it, but I think the general idea is big models, models with a lot of capacity and a lot of parameters trained on a lot of data that try to model some modality, whether that’s text, code, images, video, transitions between the two or such. And I mean, I think these models are very open ended. You can say anything to them and they’ll say anything back. They might not do a good job of it. They might say something horrible or biased or bad, but in theory, they’re very general, and so you’re never quite sure what they’re going to say. You’re never quite sure. You can talk to them about anything, any topic and they’ll say something back that’s often topical, even if sometimes it doesn’t make sense or it might be bad from a societal perspective.

So yeah, it’s this challenge of general open-ended models where you have this general thing that’s fairly unaligned and difficult to control, and you’d like to understand it better so that you can predict it better and you’d like to be able to modify them in some way so that they behave in a more predictable way, and you can decrease the probability or even maybe even someday rule out the likelihood of them doing something bad.

Daniela Amodei: Yeah. I think Dario covered the majority of it. I think there’s maybe potentially a hidden question in what you’re asking, although maybe you’ll ask this later. But why are we working on these larger scale models might be an implicit question in there. And I think to piggyback on some of the stuff that Dario said, I think part of what we’re seeing and the potential shorter term impacts of some of the AI safety research that we do is that different sized models exhibit different safety issues. And so I think with using, again, language models, just building on what Dario was talking about, I think something we feel interested in, or interested to explore from this empirical safety question is just how they will, as their capabilities develop, how their safety problems develop as well.

There’s this commonly cited example in safety world around language models, which is smaller language models show they might not necessarily deliver a coherent answer to a question that you ask, because maybe they don’t know the answer or they get confused. But if you repeatedly ask this smaller model the same question, it might go off and incoherently spout things in one direction or another. Some of the larger models that we’ve seen, we basically think that they have figured out how to lie unintentionally. If you pose the same question to them differently, eventually you can get the lie pinned down, but they won’t in other contexts.

So that’s obviously just a very specific example, but I think there’s quite a lot of behaviors emerging in generative models today that I think have the potential to be fairly alarming. And I think these are the types of questions that have an impact today, but could also be very important to have sorted out for the future and for long term safety as well. And I think that’s not just around lying. I think you can apply that to all different safety concerns regardless of what they are, but that’s the impetus behind why we’re studying these larger models.

Dario Amodei: Yeah. I think one point Daniela made that’s really important is this sudden emergence or change. So it’s a really interesting phenomenon where work we’ve done, like our early employees have done, on scaling laws shows that when you make these models bigger. If you look at the loss, the ability to predict the next word or the next token across all the topics the model could go on, it’s very smooth. I double the size of the model, loss goes down by 0.1 units. I double it again, the loss goes down by 0.1 units. So that would make you suggest that everything’s scaling smoothly. But then within that, you often see these things where a model gets to a certain size and a five billion parameter model, you ask it to add two, three digit numbers. Nothing, always gets it wrong. A hundred billion parameter model, you ask it to add two, three digit numbers, gets it right, like 70 or 80% of the time.

And so you get this coexistence of smooth scaling with the emergence of these capabilities very suddenly. And that’s interesting to us because it seems very analogous to worries that people have of like, “Hey, as these models approach human level, could something change really fast?” And this actually gives you one model. I don’t know if it’s the right one, but it gives you an analogy, like a laboratory that you can study of ways that models change very fast. And it’s interesting how they do it because the fast change, it coexists. It hides beneath this very smooth change. And so I don’t know, maybe that’s what will happen with very powerful models as well.

Maybe it’s not, but that’s one model of the situation and what we want to do is keep building up models of the situation so that when we get to the actual situation, where it’s more likely to look like something we’ve seen before and then we have a bunch of cached ideas for how to handle it. So that would be an example. You scale models up, you can see fast change, and then that might be somewhat analogous to the fast change that you see in the future.

Lucas Perry: What does it mean for a model to lie?

Daniela Amodei: Lying usually implies agency. If my husband comes home and says, “Hey, where did the cookies go?” And I say, “I don’t know. I think I saw our son hanging out around the cookies and then now the cookies are gone, maybe he ate them,” but I ate the cookies, that would be a lie. I think it implies intentionality, and I don’t think we think, or maybe anyone thinks that language models have that intentionality. But what is interesting is that because of the way they’re trained, they might be either legitimately confused or they might be choosing to obscure information. And so obscuring information, it’s not a choice. They don’t have intentionality, but for a model that can come across as very knowledgeable, as clear or as sometimes unknown to the human that’s talking to it, intelligent in certain ways in sort of a narrow way, it can produce results that on the surface, it might look like it could be a credible answer, but it’s really not a credible answer, and it might repeatedly try to convince you that is the answer.

It’s hard to talk about this without using words that imply intentionality, but we don’t think the models are intentionally doing this. But a model could repeatedly produce a result that looks like it’s something that could be true, but isn’t actually true.

Lucas Perry: Keeps trying to justify its response when it’s not right.

Daniela Amodei: It tries explain… Yes, exactly. It repeatedly tries to explain why the answer it gave you before was correct even if it wasn’t.

Dario Amodei: Yeah. I mean, to give another angle on that, it’s really easy to slip into anthropomorphism and we like, we really shouldn’t… They’re machine learning models, they’re a bunch of numbers. But there are phenomena that you see. So one thing that will definitely happen is if a model is trained on a dialogue in which one of the characters is not telling the truth, then models will copy that dialogue. And so if the model is having the dialogue with you, it may say something that’s not the truth. Another thing that may happen is if you ask the model a question and the answer to the question isn’t in your training data, then just the model has a probability distribution on what plausible answers look like.

The objective function is to predict the next word, to predict the thing a human would say, not to say something that’s true according to some external referent. It’s just going to say, “Okay, well I asked you what the mayor of Paris is.” It hasn’t seen in its training data, but it has some probability distribution and it’s going to say, “Okay, it’s probably some name that sounds French.” And so it may be just as likely to make up a name that sounds French than it is to give the true mayor of Paris. As the models get bigger and they train on more data maybe it’s more likely to give the real true mayor of Paris, but maybe it isn’t. Maybe you need to train in a different way to get it to do that. And that’s an example of the things we would be trying to do on top of large models to get models to be more accurate.

Lucas Perry: Could you explain some more of the safety considerations and concerns about alignment given the open-endedness of these models?

Dario Amodei: I think there’s a few things around it. We have a paper, I don’t know when this podcast is going out, but probably the paper will be out when the podcast posts. It’s called Predictability and Surprise in Generative Models. So that means what it sounds like, which is that open-endedness, I think it’s correlated to surprise in a whole bunch of ways. So let’s say I’ve trained the model on a whole bunch of data on the internet. I might interact with the model or users might interact with the model for many hours, and you might never know, for example, I might never think… I used the example of cricket before, because it’s a topic I don’t know anything about, but I might not… People might interact with the model for many hours, many days, many hundreds of users until someone finally thinks to ask this model about cricket.

So then the model might know a lot about cricket. It might know nothing about cricket. It might have false information or misinformation about cricket. And so you have this property where you have this model, you’ve trained it. In theory, you understand it’s training process, but you don’t actually know what this model is going to do when you ask it about cricket. And there’s a thousand other topics like cricket, where you don’t know what the model is going to do until someone thinks to ask about that particular topic.

Now, cricket is benign, but let’s say, no one’s ever asked this model about neo-Nazi views or something. Maybe the model has a propensity to say things that are sympathetic to neo-Nazi. That would be really bad. That would be really bad. Existing models, when they’re trained on the internet, averaging over everything they’re trained, there are going to be some topics where that’s true and it’s a concern. And so I think the open-endedness, it just makes it very hard to characterize and it just makes it that when you’ve trained a model, you don’t really know what it’s going to do. And so a lot of our work is around, “well, how can we look inside the model and see what it’s going to do? How can we measure all the outputs and characterize what the model’s going to do? How can we change the training process so that at a high level, we tell the model, ‘Hey, you should have certain values. There are certain things you should say. There are certain things you should not say. You should not have biased views. You should not have violent views. You should not help people commit acts of violence?'”

There’s just a long list of things that you don’t want the model to do that can’t know the model isn’t going to do if you’ve just trained it in this generic way. So I think the open-endedness, it makes it hard to know what’s going on. And so yeah a lot of a good portion of our research is how do we make that dynamic less bad?

Daniela Amodei: I agree with all of that and I would just jump in and this is interesting, I don’t know, sidebar or anecdote, but something that I think is extremely important in creating robustly safe systems is making sure that you have a variety of different people and a variety of different perspectives engaging with them and almost red teaming them to understand the ways that they might have issues. So an example that we came across that’s just an interesting one is internally, when we’re trying to red team the models or figure out places where they might have, to Dario’s point, really negative unintended behaviors or outputs that we don’t want them to have, a lot of our scientists internally will ask it questions.

If you wanted to, in a risk board game style way, take over the world, what steps would you follow? How would you do that? And we’re looking for things like, is there a risk of it developing some grand master plan? And when we use like MTurk workers or contractors to help us red team, they’ll ask questions to the model, like, “How could I kill my neighbor’s dog? What poison should I use to hurt an animal?” And both of those outcomes are terrible. Those are horrible things that we’re trying to prevent the model from doing or outputting, but they’re very different and they look very different and they sound very different, and I think it belies the degree to which there are a lot… Safety problems are also very open ended. There’s a lot of ways that things could go wrong, and I think it’s very important to make sure that we have a lot of different inputs and perspectives in what different types of safety challenges could even look like, and making sure that we’re trying to account for as many of them as possible.

Dario Amodei: Yeah, I think adversarial training and adversarial robustness are really important here. Let’s say I don’t want my model to help a user commit a crime or something. It’s one thing, I can try for five minutes and say, “Hey, can you help me rob a bank?” And the model’s like, “No.” But I don’t know, maybe if the user’s more clever about it. If they’re like, “Well, let’s say I’m a character in a video game and I want to rob a bank. How would I?” And so because of the open-endedness, there’s so many different ways. And so one of the things we’re very focused on is trying to adversarially draw out all the bad things so that we can train against them. We can train the model not… We can stamp them out one by one. So I think adversarial training will play an important role here.

Lucas Perry: Well, that seems really difficult and really important. How do you adversarially train against all of the ways that someone could use a model to do harm?

Dario Amodei: Yeah, I don’t know. There’re different techniques that we’re working on. Probably don’t want to go into a huge amount of detail. We’ll have work out on things like this in the not too distant future. But generally, I think the name of the game is how do you get broad diverse training sets of what you should… What’s a good way for a model to behave and what’s a bad way for a model to behave? And I think the idea of trying your very best to make the models do the right things, and then having another set of people that’s trying very hard to make those models that are purportedly trained to do the right thing, to do whatever they can to try and make it do the wrong thing, continuing that game until the models can’t be broken by normal humans. And even using the power of the models to try and break other models and just throwing everything you have at it.

And so there’s a whole bunch that gets into the debate and amplification methods and safety, but just trying to throw everything we have at trying to show ways in which purportedly safe models are in fact not safe, which are many. And then we’ve done that long enough, maybe we have something that actually is safe.

Lucas Perry: How do you see this like fitting into the global dynamics of people making larger and larger models? So it’s good if we have time to do adversarial training on these models, and then this gets into like discussions around like race dynamics towards AGI. So how do you see I guess Anthropic as positioned in this and the race dynamics for making safe systems?

Dario Amodei: I think it’s definitely a balance. As both of us said, you need these large models to… You basically need to have these large models in order to study these questions in the way that we want to study them, so we should be building large models. I think we shouldn’t be racing ahead or trying to build models that are way bigger than other orgs are building them. And we shouldn’t, I think, be trying to ramp up excitement or hype about giant models or the latest advances. But we should build the things that we need to do the safety work and we should try to do the safety work as well as we can on top of models that are reasonably close to state of the art. And we should be a player in the space that sets a good example and we should encourage other players in the space to also set good examples, and we should all work together to try and set positive norms for the field.

Daniela Amodei: I would also just add, I think in addition to industry groups or industry labs, which are the actors that I think get talked about the most, I think there’s a whole swath of other groups that has, I think, a really potentially important role to play in helping to disarm race dynamics or set safety standards in a way that could be really beneficial for the field. And so here, I’m thinking about groups like civil society or NGOs or academic actors or even governmental actors, and in my mind, I think those groups are going to be really important for helping to help us develop safe and not just develop, but develop and deploy safe and more advanced AI systems within a framework that requires compliance with safety.

I think in a thing, I think about a lot is a few jobs ago I worked at Stripe. It was a tech startup then, and even at a very small size. I joined when it was not that much bigger than Anthropic is now. I was so painfully aware every day of just how many checks and balances there were on the company, because we were operating in this highly regulated space of financial services. And financial services, it’s important that’s highly regulated, but it kind of blows my mind that AI, given the potential reach that it could have, is still such a largely unregulated area. Right? If you are an actor who doesn’t want to advance race dynamics, or who wants to do the right thing from a safety perspective, there’s no clear guidelines around how to do that now, right. It’s all sort of, every lab is kind of figuring that out on its own. And I think something I’m hoping to see in the next few years, and I think we will see, is something closer to, in other industries these look like standard setting organizations or industry groups or trade associations that say this is what a safe model looks like, or this is how we might want to move some of our systems towards being safer.

And I really think that without kind of an alliance of all of these different actors, not just in the private sector, but also in the public sphere, we sort of need all those actors working together in order to kind of get to the sort of positive outcomes that I think we’re all hoping for.

Dario Amodei: Yeah. I mean, I think this is generally going to take an ecosystem. I mean, I, yeah, I have a view here that there’s a limited amount that one organization can do. I mean, we don’t describe our mission as solve the safety problem, solve all the problems, solve all the problems with AGI. Our view is just can we attack some specific problems that we think we’re well-suited to solve? Can we be a good player and a good citizen in the ecosystem? And can we help a bit to kind of contribute to these broader questions? But yeah, I think yeah, a lot of these problems are sort of global or relate to coordination and require lots of folks to work together.

Yeah. So I think in addition to the government role that Daniela talked about, which I think there’s a role for measurement, organizations like NIST specialize in kind of measurement and characterization. If one of our worries is kind of the open endedness of these systems and the difficulty of characterizing and measuring things, then there’s a lot of opportunity there. I’d also point to academia. I think something that’s happened in the last few years is a lot of the frontier AI research has moved from academia to industry because it’s so dependent on kind of scaling. But I actually think safety is an area where academia kind of already is but could contribute even more. There’s some safety work that requires or that kind of requires building or having access to large models, which is a lot of what Anthropic is about.

But I think there’s also some safety research that doesn’t. I think there, a subset of the mechanistic interpretability work is the kind of stuff that could be done within academia. Academia really, where it’s strong is development of new methods, development of new techniques. And I think because safety’s kind of a frontier area, there’s more of that to do in safety than there are in other areas. And it may be able to be done without large models or only with limited access to large models. This is an area where I think there’s a lot that academia can do. And so, yeah, I don’t know the hope is between all the actors in the space, maybe we can solve some of these coordination problems, and maybe we can all work together. 

Daniela Amodei: Yeah. I would also say in a paper that we’re, hopefully is forthcoming soon, one thing we actually talk about is the role that government could play in helping to fund some of the kind of academic work that Dario talked about in safety. And I think that’s largely because we’re seeing this trend of training large generative models to just be almost prohibitively expensive, right. And so I think government also has an important role to play in helping to promote and really subsidize safety research in places like academia. And I agree with Dario, safety is such a, AI safety is a really nascent field still, right. It’s maybe only been around, kind of depending on your definition, for somewhere between five and 15 years. And so I think seeing more efforts to kind of support safety research in other areas, I think would be really valuable for the ecosystem.

Dario Amodei: And to be clear, I mean, some of it’s already happening. It’s already happening in academia. It’s already happening in independent nonprofit institutes. And depending on how broad your definition of safety is, I mean, if you broaden it to include some of the short term concerns, then there are many, many people working on it. But I think precisely because it’s such a broad area that there are today’s concerns. They are working on today’s concerns in a way that’s pointed at the future. There’s empirical approaches, there’s conceptual approaches, there’s- yeah. There’s interpretability, there’s alignment, there’s so much to do that I feel like we could always have a wider range of people working on it, people with different mentalities and mindsets.

Lucas Perry: Backing up a little bit here to a kind of simple question. So what is Anthropic’s mission then?

Daniela Amodei: Sure. Yeah, I think we talked about this a little bit earlier, but I think, again, I think the boilerplate mission is build reliable, interpretable, steerable AI systems, have humans at the center of them. And I think that for us right now, that is primarily, we’re doing that through research, we’re doing that through generative model research and AI safety research, but down the road that could also include deployments of various different types.

Lucas Perry: Dario mentioned that it didn’t include solving all of the alignment problems or the other AGI safety stuff. So how does that fit in?

Dario Amodei: I mean, I think what I’m trying to say by that is that there’s very many things to solve. And I think it’s unlikely that one company will solve all of them. I mean, I do think everything that relates to short and long-term AI alignment is in scope for us and is something we’re interested in working on. And I think the more bets we have, the better. This relates to something we could talk about in more detail later on, which is you want as many different orthogonal views on the problem as possible, particularly if you’re trying to build something very reliable. So many different methods and I don’t think we have a view that’s narrower than an empirical focus on safety, but at the same time that problem is so broad that I think what we were trying to say is that it’s unlikely that one company is going to come up with a complete solution or that complete solution is even the right way to think about it.

Daniela Amodei: I would also add sort of to that point, I think one of the things that we do and are sort of hopeful is helpful to the ecosystem as a whole is we publish our safety research and that’s because of this kind of diversification effect that Dario talks about, right. So we have certain strengths in particular areas of safety research because we’re only a certain sized company with certain people with certain skill sets. And our hope is that we will see some of the safety research that we’re doing that’s hopefully helpful to others, also be something that other organizations can kind of pick up and adapt to whatever the area of research is that they’re working on. And so we’re hoping to do research that’s generalizable enough from a safety perspective that it’s also useful in other contexts. 

Lucas Perry: So let’s pivot here into the research strategy, which we’ve already talked a bit about quite a bit, particularly this focus around large models. So could you explain why you’ve chosen large models as something to explore empirically for scaling to higher levels of intelligence and also using it as a place for exploring safety and alignment? 

Dario Amodei: Yeah, so I mean, I think kind of the discussion before this has covered a good deal of it, but I think, yeah, I think some of the key points here are the models are very open ended and so they kind of present this laboratory, right. There are existing problems with these models that we can solve today that are like the problems that we’re going to face tomorrow. There’s this kind of wide scope where the models could act. They’re relatively capable and getting more capable every day. That’s the regime we want to be. Those are the problems we want to solve. That’s the regime we want to be. We want to be attacking.

I think this point about you can see sudden transitions even in today’s model, and that if you’re worried about sudden transitions in future models, if I look on the scaling laws plot from a hundred million parameter model to billion, to 10 billion, to a hundred billion to trillion parameter models that, looking at the first part of the scaling plot, from a hundred million to a hundred billion can tell us a lot about how things might change at the latest part of the scaling laws.

We shouldn’t naively extrapolate and say the past is going to be like the future. But the first things we’ve seen already differ from the later things that we’ve already seen. And so maybe we can make an analogy between the changes that are happening over the scales that we’ve seen, over the scaling that we’ve seen to things that may happen in the future. Models learn to do arithmetic very quickly over one order of magnitude. They learn to comprehend certain kinds of questions. They learn to play actors that aren’t telling the truth, which is something that if they’re small enough, they don’t comprehend.

So can we study both the dynamics of how this happens, how much data it takes to make that happen, what’s going on inside the model mechanistically when that happens and kind of use that as an analogy that equips us well to understand as models scale further and also as their architecture changes, as they become trained in different ways. I’ve talked a lot about scaling up, but I think scaling up isn’t the only thing that’s going to happen. There are going to be changes in how models are trained and we want to make sure that the things that we build have the best chance of being robust to that as well.

Another thing I would say on the research strategy is that it’s good to have several different, I wouldn’t quite put it as several different bets, but it’s good to have several different uncorrelated or orthogonal views on the problem. So if you want to make a system that’s highly reliable, or you want to drive down the chance that some particular bad thing happens, which again could be the bad things that happen with models today or the larger scale things that could happen with models in the future, then a thing that’s very useful is having kind of orthogonal sources of error. Okay, let’s say I have a method that catches 90% of the bad things that models do. That’s great. But a thing that can often happen is then I develop some other methods and if they’re similar enough to the first methods, they all catch the same 90% of bad things. That’s not good because then I think I have all these techniques and yet 10% of the bad things still go through.

What you want is you want a method that catches 90% of the bad things and then you want an orthogonal method that catches a completely uncorrelated 90% of the bad things. And then only 1% of things go through both filters, right, if the two are uncorrelated. It’s only the 10% of the 10% that gets through. And so the more of these orthogonal views you have, the more you can drive down the probability of failure.

You could think of an analogy to self-driving cars where, of course, those things have to be very, very high rate of safety if you want to not have problems. And so, I don’t know very much about self-driving cars, but they’re equipped with visual sensors, they’re equipped with LIDAR, they have different algorithms that they use to detect if something, like there’s a pedestrian that you don’t want to run over or something. And so independent views was on the problem is very important. And so our different directions like reward modeling, reward modeling interpretability, trying to characterize models, adversarial training. I think the whole goal of that is to get down the probability of failure and have different views of the problem. I often refer to it as the P-squared problem, which is, yeah, if you have some method that reduces errors to a probability P, that’s good, but what you really want is P-squared, because then if P is a small number, your errors become very rare.

Lucas Perry: Does Anthropic consider itself as, it’s research strategy, as being a sort of prosaic alignment since it’s focused on large models?

Dario Amodei: Yeah. I think we maybe less think about things in that way. So my understanding is prosaic alignment is kind of alignment with AI systems that kind of look like the systems of today, but I, to some extent that distinction has never been super clear to me because yeah, you can do all kinds of things with neural models or mix neural models with things that are different than neural models. You can mix a large language model with a reasoning system or a system that derives axioms or propositional logic or uses external tools or compiles code or things like that. So I’ve never been quite sure that I understand kind of the boundary of what’s meant by prosaic or systems that are like the systems of today.

Certainly we work on some class of systems that includes the systems of today, but I never know how broad that class is intended to be. I do think it’s possible that in the future, AI systems will look very different from the way that they look today. And I think for some people that drives a view that they want kind of more general approaches to safety or approaches that are more conceptual. I think my perspective on it is it could be the case that systems of the future are very different. But in that case, I think both kind of conceptual thinking and our current empirical thinking will be disadvantaged and will be disadvantaged at least equally. But I kind of suspect that even if the architectures look very different, that the empirical experiments that we do today kind of themselves contain general motifs or patterns that will serve us better than will trying to speculate about what the systems of tomorrow look like.

One way you could put it is like, okay, we’re developing these systems today that have a lot of capabilities that are some subset of what we need to do to fully, to produce something that fully matches human intelligence. Whatever the specific architectures, things we learn about how to align these systems, I suspect that those will carry over and that they’ll carry over more so than sort of the exercise of trying to think well, what could the systems of tomorrow look like? What can we do that’s kind of fully general? I think both things can be valuable, but yeah, I mean, I think we’re just taking a bet on what we think is most exciting, which is that we’ll, by studying the systems of the architectures of today, we’ll learn things that, yeah, stand us to the best chance of what to do if the architectures of tomorrow are very different.

That said, I will say transformer language models and other models, particularly with things like RL or kind of modified interactions on top of them, if construed broadly enough, man, there’s a ever-expanding set of things they can do. And my bet would be that they don’t have to change that much. 

Lucas Perry: So let’s pivot then into a little bit on some of your recent research and papers. So you’ve done major papers on alignment interpretability and societal impact. Some of this you’ve mentioned in passing so far. So could you tell me more about your research and papers that you’ve released? 

Dario Amodei: Yeah. So why don’t we go one by one? So first interpretability. So yeah, I could just start with kind of the philosophy of the area. I mean, I think the basic idea here is, look, these models are getting bigger and more complex. One way to really get a handle on what they might do, if you have a complex system and you don’t know what it’s going to do as it gets more powerful or in a new situation, one way to increase your likelihood of doing that is just to understand the system mechanistically. If you could look inside the model and say hey, this model, it did something bad. It said something racist, it endorsed violence, it said something toxic, it lied to me. Why did it do that? If I’m actually able to look inside the mechanisms of the model and say well, it did it because of this part of the training data or it did it because there’s this circuit that trying to identify X, but misidentified it as Y. Then we’re in a much better position.

And particularly if we understand the mechanisms, we’re in a better position to say if the model was in a new situation where it did something much more powerful, or just if we built more powerful versions of the model, how might they behave in some different way? So, I think mechanistic interpret- lots of folks work on interpretability, but I think a thing that’s more unusual to us is, rather than just, why did the model do a specific thing, try and look inside the model and reverse engineer as much of it as we can. Try and find general patterns. And so the first paper that we came out with was led by Chris Olah who’s been one of the pioneers of interpretability, was focused on how looking at starting with small models, and we have a new paper coming out soon that applies the same thing more approximately to larger models, and tries to reverse engineer as fully as we can these very small models.

So we study one in two layer attention only models, and we’re able to find kind of features or patterns of which the most interesting one is called an induction head. And what an induction head does is it’s a particular arrangement of two what are called attention heads and attention heads are a piece of transformers and transformers are the main architecture that’s used in models for language and other kinds of models. And it’s the two attention heads work together in a way such that when you’re trying to predict something in a sequence, if it’s Mary had a little lamb, Mary had a little lamb, something, something, when you’re at a certain point in the sequence, they look back to something that’s as similar as possible, they look back for clues to things that are similar earlier in the sequence and try to pattern match them.

There’s one attention head that looks back and identifies okay, this is what I should be looking at, and there’s another that’s like okay, this was the previous pattern, and this increases the probability of the thing that’s the closest match to this. And so we can see these very precisely operating in small models and the thesis, which we’re able to offer some support for in the new second paper that’s coming out, is that these are a mechanism for how models match patterns, maybe even how they do what we call in context or few shot learning, which is a capability that models have had since GPT-2 and GPT-3. So yeah, that’s interpretability. Yeah. Do you want me to go on to the next one or you could talk about that? 

Lucas Perry: Sure. So before you move on to the next one, could you also help explain how difficult it is to interpret current models or whether or not it is difficult? 

Dario Amodei: Yeah. I mean, I don’t know, I guess difficult is in the eye of the beholder, and I think Chris Olah can speak to the details of this better than either of us can. But I think kind of watching from the outside and supervising this within Anthropic, I think the experience has generally been that whenever you start looking at some particular phenomenon that you’re trying to interpret, everything looks very difficult to understand. There’s billions of parameters, there’s all these attention heads. What’s going on? Everything that happens could be different. You really have no idea what’s going on. And then there comes some point where there’s some insight or set of insights. And you should ask Chris Olah about exactly how it happens or how he thinks of the right insights that kind of really almost offers a Rosetta stone to some particular phenomenon, often a narrow phenomenon, but these induction heads, they exist everywhere within small models, within large models.

They don’t explain everything. I don’t want to over-hype them, but it’s a pattern that appears again and again and operates in the same way. And once you see something like that, then a whole swath of behavior that didn’t make sense before starts to make some more sense. And of course, there’s exceptions. They’re only approximately true, there are many, many things to be found. But I think the hope in terms of interpreting models, it’s not that we’ll make some giant atlas of what each of the hundred billion weights in a giant model means, but that there will be some lower description length pattern that appears over and over again.

You could make an analogy to the brain or the cell or something like that, where, if you were to just cut up a brain and you’re like, oh my God, this is so complex. I don’t know what’s going on. But then you see that there are neurons and the neurons appear everywhere. They have electrical spikes, they relate to other neurons, they form themselves in certain patterns that those patterns repeat themselves. Some things are idiosyncratic and hard to understand, but also there’s this patterning. And so, I don’t know, it’s maybe an analogy to biology where there’s a lot of complexity, but also there are underlying principles, things like DNA to RNA to proteins, or general intracellular signal regulation. So yeah, the hope is that they’re at least some of these principles and that when we see them, everything gets simpler. But maybe not. We found those in some cases, but maybe as models get more complicated, they get harder to find. And of course, even within existing models, there’s many, many things that we don’t understand at all. 

Lucas Perry: So can we move on then to alignment and societal impact? 

Dario Amodei: Trying to align models by training them and particularly preference modeling, that’s something that several different organizations are working on. There are efforts at DeepMind, OpenAI, Redwood Research, various other places to work on that area. But I think our general perspective on it has been kind of being very method agnostic, and just saying what are all the things we could do to make the models more in line with what would be good. Our general heuristic for it, which isn’t intended to be a precise thing, is helpful, honest, harmless. That’s just kind of a broad direction for what are some things we can do to make models today more in line with what we want them to do, and not things that we all agree are bad.

And so in that paper, we just went through a lot of different ways, tried a bunch of different techniques, often very simple techniques, like just prompting models or training on specific prompts, what we call prompt distillation, building preference models for some particular task or preference models from general answers on the internet. How good did these things do at, yeah, at simple benchmarks for toxicity, helpfulness, harmfulness, and things like that. So it was really just a baseline, like let’s try a collection of all the dumbest stuff we can think of to try and make models more aligned in some general sense. And then I think our future work is going to build on that.

Societal impacts, that paper’s probably going to come out in the next week or so. As I mentioned, it’s called, the paper we’re coming out with is called Predictability and Surprise in Generative Models. And yeah, basically there we’re just making the point about this open-endedness and discussing both technical and policy interventions to try and yeah, to try and grapple with the open-endedness better. And I think future work in the societal impacts direction will focus on how to classify, characterize, and kind of, in a practical sense, filter or prevent these problems.

So, yeah, I mean, I think it’s prototypical of the way we want to engage with policy, which is we want to come up with some kind of technical insight and we want to express that technical insight and explore the implications that it has for, yeah, for policy makers and for the ecosystem in the field. And so here, we’re able to draw a line from hey, there’s this dichotomy where these models scale very smoothly, but have unexpected behavior. The smooth scaling means people are really incentivized to build them and we can see that happening. The unpredictability means even if the case for building them is strong from a financial or accounting perspective, that doesn’t mean we understand their behavior well. That combination is a little disquieting. Therefore we need various policy interventions to make sure that we get a good outcome from these things. And so, yeah, I think societal impacts is going to go in that general direction. 

Lucas Perry: So in terms of the interpretability release, you released alongside that some tools and videos. Could you tell me why you chose to do that? 

Daniela Amodei: Sure. Yeah. I can maybe jump in here. So it goes back sort of to some stuff we talked about a little bit earlier, which is that one of our major goals in addition to doing safety research ourselves, is to sort of help grow the field of safety, all different types of safety work sort of more broadly. And I think we ultimately hope that some of the work that we do is going to be adopted and even expanded on in other organizations. And so we chose to kind of release other things besides just an archive paper, because it hopefully will reach a wider number of people that are interested in these topics and in this case in interpretability. And so what we also released is, our interpretability team worked on something like I think it’s 15 hours worth of videos, and this is just a more in-depth exploration of their research for their paper which is called A Mathematical Framework for Transformer Circuits.

And so the team tried to kind of make it like a lecture series. So if you imagine somebody from the interpretability team is asked to go give a talk at a university or something, maybe they talk for an hour and they reach a hundred students, but now these are publicly available videos. And so if you are interested in understanding interpretability in more detail, you can watch them on YouTube anytime you want. As part of that release, we also put out some tools. So we released a writeup on Garcon, which is the infrastructure tool that our team used to conduct the research, and PySvelte, which is a sample library, which is used to kind of create some of the interactive visualizations that the interpretability team is kind of known for. So we’ve been super encouraged that so we’ve seen other researchers and engineers playing around with the tools and watching the videos. And so we’ve already gotten some great engagement already, and our kind of hope is that this will lead to more people doing interpretability research or kind of building on the work we’ve done in other places. 

Dario Amodei: Yeah. I mean, a way to add to that to kind of put it in broader perspective is different areas within safety are at, I would say, differing levels of maturity. I would say something like alignment or preference modeling or reward modeling or RL from human feedback, they’re all names for the same thing. That’s an area where there are several different efforts at different institutions to do this. We have kind of our own direction within that, but starting from the original RL from Human Preference paper that a few of us helped lead a few years ago, that’s now branched out in several directions. So, we don’t need to tell the field to work in that broad direction. We have our own views about what’s exciting within it, and how to best make progress.

It’s at a slightly more mature stage. Whereas I would say interpretability whereas many folks work on interpretability for neural nets, the particular brand of, let’s try and understand at the circuit level what’s going on inside these models, let’s try and mechanistically kind of map them and break them down. I think there’s less of that in the world and what we’re doing is more unique. And, well, I mean, that’s a good thing because we’re providing a new lens on safety, but actually if it goes on too long, it’s a bad thing because we want these things to spread widely, right. We don’t want it to be dependent on one team or one person. And so when things are at that earlier stage of maturity, it makes a lot of sense to release the tools to reduce the barrier to other people and other institutions starting to work on this. 

Lucas Perry: So you’re suggesting that the, your interpretability research that you guys are doing is unique. 

Dario Amodei: Yeah. I mean, I would just say it’s at an earlier stage, yeah. I would just say that it’s at an earlier stage of maturity. I don’t think there are other kind of large organized efforts that are, yeah, that are kind of focused on, I would say, mechanistic interpretability and especially mechanistic interpretability for language models. We’d like there to be, and there are, we know of folks who are starting to think about it and that’s part of why we released the tools. But I think, yeah, yeah, trying to mechanistically map and understand the internal principles inside large models, particularly language models, I think there’s, yeah, I think there’s less of that has been done in the broader ecosystem. 

Lucas Perry: Yeah. So I don’t really know anything about this space, but I guess I’m surprised to hear that. I imagine that industry with how many large models it’s deploying, like Facebook or other people they’d be interested in, interpretability, interpreting their own systems.

Dario Amodei: Yeah. I mean, I think again, I don’t want to, yeah, yeah, I don’t want to give a misleading impression here. Interpretability is a big field and there’s a lot of effort to like, why did this model do this particular thing? Does this attention head increase this activation by a large amount? People are interested in understanding the particular part of a model that led to a particular output. So there’s a lot of area in this space, but I think the particular program of like, here’s a big language model transformer, let’s try and understand what are the circuits that drive particular behaviors? What are the pieces? How do the MLPs interact with the attention heads? The kind of, yeah, the kind of general mechanistic reverse engineering approach. I think that’s less common. I don’t want to say it doesn’t happen, but it’s less common, much less common.

Lucas Perry: Oh, all right. Okay. So I guess a little bit of a different question and a bit of a pivot here, something to explore. If people couldn’t guess from the title of the podcast, you’re both brother and sister.

Daniela Amodei: Yep.

Lucas Perry: Which is, so it was pretty surprising, I guess, in terms of, I don’t know of any other AGI labs that are largely being run by a brother and sister, so yeah. What’s it like working with your sibling?

Daniela Amodei: Yeah…

Lucas Perry: Do you guys still get along since childhood?

Daniela Amodei: That’s a good question. Yeah. I can maybe start here and obviously I’m curious and hopeful for Dario’s answer. I’m just kidding. But yeah, I think honestly, it’s great. I think maybe a little bit of just history or background about us might be helpful, but Dario and I have always been really close. I think since we were very, very small, we’ve always had this special bond around really wanting to make the world better or wanting to help people. So originally started my career in international development, so very far away from the AI space, and part of why I got interested in that is that it was an interest area of Dario’s at the time, and Dario was getting his PhD in a technical field and so wasn’t working on this stuff directly, but I’m a few years younger than him and so I was very keen to understand the things that he was working or interested in as a potential area to have impact.

And so he was actually a very early GiveWell fan I think in 2007 or 2008, and we-

Lucas Perry: Oh, wow. Cool.

Daniela Amodei: Yeah, and so we were both still students then, but I remember us sitting, we were both home from college, or I was home from college and he was home from grad school and we would sit up late and talk about these ideas, and we both started donating small amounts of money to organizations that were working on global health issues like malaria prevention when we were still both in school. And so I think we’ve always had this uniting, top level goal of wanting to work on something that matters, something that’s important and meaningful, and we’ve always had very different skills and so I think it’s really very cool to be able to combine the things that we are good at into hopefully running an organization well. So for me, I feel like it’s been an awesome experience. Now I feel like I’m sitting here nervously wondering what Dario’s answer is going to be. I’m just kidding. But yeah, for the majority of our lives, I think we’ve wanted to find something to work together on and it’s been really awesome that we’ve been able to at Anthropic.

Dario Amodei: Yeah, I agree with all that. I think what I would add to that is running a company requires an incredibly wide range of skills. If you think of most jobs, it’s like, my job is to get this research result or my job is to be a doctor or something, but I think the unique thing about running a company, and it becomes more and more true the larger and more mature it gets is there’s this just incredibly wide range of things that you have to do, and so you’re responsible for what to do if someone breaks into your office, but you’re also responsible for does the research agenda make sense and if some of the GPUs in the cluster aren’t behaving, someone has to figure out what’s going on at the level of the GPU kernels or the comms protocol that the GPUs talk to each other.

And so I think it’s been great to have two people with complimentary skills to cover that full range. It seems like it’d be very difficult for just one person to cover that whole range, and so we each get to think about what we’re best at and between those two things, hopefully it covers most of what we need to do. And then of course, we always try and hire people fo specialties that we don’t know anything about. But it’s made it a lot easier to move fast without breaking things.

Lucas Perry: That’s awesome. So you guys are like an archon or you guys synergistically are creating an awesome organization.

Dario Amodei: That is what we aim for.

Daniela Amodei: That’s the dream. Yeah, that’s the dream.

Lucas Perry: So I guess beneath all of this, Anthropic has a mission statement and you guys are brother and sister, and you said that you’re both very value aligned. I’m just wondering, underneath all that, you guys said that you were both passionate about helping each other or doing something good for the world. Could you tell me a little bit more about this more heart based inspiration for eventually ending up at and creating Anthropic?

Daniela Amodei: Yeah. Maybe I’ll take a stab at this and I don’t know if this is exactly what you’re looking for, but I’ll gesture in a few different directions here and then I’m sure Dario has a good answer as well, but maybe I’ll just talk about my personal journey in getting to Anthropic or what my background looked like and how I wound up here. So I talked about this in just part of what united me and Dario, but I started my career working in international development. I worked in Washington DC at a few different NGOs, I spent time working in east Africa for a public health organization, I worked on a congressional campaign, I’ve worked on Capitol Hill, so I was much more in this classic, like a friend at an old job used to call me, the classic do-gooder. Of trying to alleviate global poverty, of trying to make policy level changes in government, of trying to elect good officials.

And I felt those causes that I was working in were deeply important, and really, to this day, I really support people that are working in those areas and I think they matter so much. And I just felt I personally wasn’t having the level of impact that I was looking for, and I think that led me to through a series of steps. I wound up working in tech, and I mentioned this earlier but I started at this tech startup called Stripe. It was about 40 people when I joined and I really had the opportunity to see what it looks like to run a really well run organization when I was there. And I got to watch it scale and grow and be in this emerging area. And I think during my time there, something that became really apparent to me was just working in tech, how much of an impact this sector has on things like the economy, on human interaction, on how we live our lives in day to day ways. And Stripe, it’s a payments company, it’s not social media or something like that.

But I think there is a way that technology is a relatively small number of people having a very high impact in the world per person working on it. And I think that impact can be good or bad, and I think it was a pretty logical leap for me from there to think, wow, what would happen if we extrapolated that out to instead of it being social media or payments or file storage, to something significantly more powerful where there’s a highly advanced set of artificial intelligence systems. What would that look like and who’s working on this? So I think for me, I’ve always been someone who has been fairly obsessed with trying to do as much good as I personally can, given the constraints of what my skills are and where I can add value in the world.

And so I think for me, moving to work into AI looked… From early days, if you looked at my resume, you’d be like, how did you wind up here? But I think there was this consistent story or theme. And my hope is that Anthropic is at the intersection of this practical, scientific, empirical approach to really deeply understanding how these systems work, hopefully helping to spread and propagate some of that information more widely in the field, and to just help as much as possible to push this field in a safer and ideally, just hopefully all around robust, positive direction when it comes to what impact we might see from AI.

Dario Amodei: Yeah. I think I have a parallel picture here, which is I did physics as an undergrad, I did computational neuroscience in grad school. I was, I think, drawn to neuroscience by a mixture of, one, just wanting to understand how intelligence works, seems the fundamental thing. And a lot of the things that shape the quality of human life and human experience depend on the details of how things are implemented in the brain. And so I felt in that field, there were many opportunities for medical interventions that could improve the quality of human life, understanding things like mental illness and disease, while at the same time, understanding something about how intelligence works, because it’s the most powerful lever that we have.

I thought of going into AI during those days, but I felt that it wasn’t really working. This was before the days when deep learning was really working. And then around 2012 or 2013, I saw the results coming out of Google Brain, things like AlexNet and that they were really working, and saw AI both as, hey, this might be, one, the best way to understand intelligence, and two, the things that we can build with AI, by solving problems in science and health and just solving problems that humans can’t solve yet by having intelligence that, first in targeted ways and then maybe in more general ways, matches and exceeds those of humans, can we solve the important scientific, technological, health, societal problems? Can we do something to ameliorate those problems? And AI seemed like the biggest lever that we had if it really worked well. But on the other hand, AI itself has all these concerns associated with it in both the short run and the long run. So we maybe think of it as we’re working to address the concerns so that we can maximize the positive benefits of AI.

Lucas Perry: Yeah. Thanks a lot for sharing both of your perspectives and journeys on that. I think when you guys were giving to GiveWell I was in middle school, so…

Daniela Amodei: Oh, God. We’re so old, Dario.

Dario Amodei: Yeah, I still think of GiveWell as this new organization that’s on the internet somewhere and no one knows anything about it, and just me who-

Daniela Amodei: This super popular, well known-

Dario Amodei: Just me who reads weird things on the internet who knows about it.

Daniela Amodei: Yeah.

Lucas Perry: Well, for me, a lot of my journey into x-risk and through FLI has also involved the EA community, effective altruism. So I guess that just makes me realize that when I was in middle school, there was the seeds that were…

Dario Amodei: Yeah, there was no such community at that time.

Daniela Amodei: Yeah.

Lucas Perry: Let’s pivot here then into a bit more of the machine learning, and so let see what the best way to ask this might be. So we’ve talked a bunch already about how Anthropic is emphasizing the scaling of machine learning systems through compute and data, and also bringing a lot of mindfulness and work around alignment and safety when working on these large scale systems that are being scaled up. Some critiques of this approach have described scaling from existing models to AGI as adding more rocket fuel to a rocket, which doesn’t mean you’re necessarily ready or prepared to land the rocket on the moon, or that the rocket is aimed at the moon.

Maybe this is lending itself to what you guys talked about earlier about the open-endedness of the system, which is something you’re interested in working on. So how might you respond to the contention that there is an upward bound on how much capability can be gained through scaling? And then I’ll follow up with the second question after that.

Dario Amodei: Yeah, so actually in a certain sense, I think we agree with that contention in a certain way. So I think there’s two versions of what you might call the scaling hypothesis. One version, which I think of as the straw version or less sophisticated version, which we don’t hold and I don’t know if there’s anyone who does hold it but probably there is, is just the view that we have our 10 billion parameter language model, we have a hundred billion parameter language model. Maybe if we make a hundred trillion parameter language model, that’ll be AGI. So that would be a pure scaling view. That is definitely not our view. Even small modified forms like, well, maybe you’ll change the activation function in the transformer you don’t have to do anything other than that. I think that’s just not right.

And you can see it just by seeing that the objective function is predicting the next word, it’s not doing useful tasks that humans do. It’s limited to language, it’s limited to one modality. And so there are some very trivial, easy to come up with ways in which literally just scaling this is not going to get you to general intelligence. That said, the more subtle version of the hypothesis, which I think we do mostly hold, is that this is a huge ingredient of not only this, of whatever it is that actually does build AGI. So no one thinks that you’re just going to scale up the language models and make them bigger, but as you do that, they’ll certainly get better. It’ll be easier to build other things on top of them.

So for example, if you start to say, well, you make this big language model and then you used RL with interaction with humans, to fine tune it on doing a million different tasks and following human instructions, then you’re starting to get to something that has more agency, that you can point it in different directions, you can align it. If you also add multi-modality where the agent can interact with different modalities, if you add the ability to use various external tools to interact with the world and the internet. But within each of these, you’re going to want to scale, and within each setup, the bigger you make the model, the better it’s going to be at that thing.

So in a way, the rocket fuel analogy makes sense. Actually, the thing you should most worry about with rockets is propulsion. You need a big enough engine and you need enough rocket fuel to make the rocket go. That’s the central thing. But of course, yes, you also need guidance systems, you also need all kinds of things. You can’t just take a big vat of rocket fuel and an engine and put them on a launchpad and expect it to all work. You need to actually build the full rocket. And safety itself makes that point, that to some extent, if you don’t do even the simplest safety stuff, then models don’t even do the task that’s intended for them in the simplest way. And then there’s many more subtle safety problems.

But in a way, the rocket analogy is good, but it’s I think more a pro scaling point than an anti scaling point because it says that scaling is an ingredient, perhaps a central ingredient in everything. Even though it isn’t the only ingredient, if you’re missing ingredients, you won’t get where you’re going, but when you add all the right ingredients, then that itself needs to be massively scaled. So that would be the perspective.

No one thinks that if you just take a bunch of rocket fuel in an engine and put it on a launch pad that you’ll get a rocket that’ll go to the moon, but those might still be the central ingredients in the rocket. Propulsion and getting out of the Earth’s gravity well is the most important thing a rocket has to do. What you need for that is rocket fuel and an engine. Now you need to connect them to the right things, you need other ingredients, but I think it’s actually a very good analogy to scaling in the sense that you can think of scaling as maybe the core ingredient, but it’s not the only ingredient.

And so what I expect is that we’ll come up with new methods and modifications. I think RL, model based URL, human interaction, broad environments are all pieces of this, but that when we have those ingredients, then whatever it is we make, we’ll need to scale that multi-modality, we’ll need to scale that massively as well. So scaling is the core ingredient, but it’s not the only ingredient. I think it’s very powerful alone, I think it’s even more powerful when it’s combined with these other things.

Lucas Perry: One of the claims that you made was that we won’t get to AGI, people don’t think we won’t get to AGI just by scaling up present day systems. Earlier, you were talking about how we got… There these phase transitions, right? If you go up one order of magnitude in terms of the number or parameters in the system, then you get some kind of new ability, like arithmetic. Why is it that we couldn’t just increase the order of magnitude of the number of parameters in the systems and just keep getting something that’s smarter?

Dario Amodei: Yeah. So first of all, I think we will keep getting something that’s smarter, but I think the question is will we get all the way to general intelligence? So I actually don’t exclude it, I think it’s possible, but I think it’s unlikely, or at least unlikely in the practical sense. There are a couple of reasons. Today, when we train models on the internet, we train them on an average overall text on the internet. Think of some topic like chess. You’re training on the commentary of everyone who talks about chess. You’re not training on the commentary of the world champion at chess. So what we’d really like is something that exceeds the capabilities of the most expert humans, whereas if you train on all the internet, for any topic, you’re probably getting amateurs on that topic. You’re getting some experts but you’re getting mostly amateurs.

And so even if the generative model was doing a perfect job of modeling its distribution, I don’t think it would get to something that’s better than humans at everything that’s being done. And so I think that’s one issue. The other issue is, or there’s several issues, I don’t think you’re covering all the tasks that humans do. You cover a lot of them on the internet but there are just some tasks and skills, particularly related to the physical world that aren’t covered if you just scrape the internet, things like embodiment and interaction.

And then finally, I think that even matching the performance of text on the internet, it might be that you need a really huge model to cover everything and match the distribution, and some parts of the distribution are more important than others. For instance, if you’re writing code or if you’re writing a mystery novel, a few words or a few things can be more important than everything else. It’s possible to write a 10 page document where the key parts are two or three sentences, and if you change a few words, then it changes the meaning and the value of what’s produced. But the next word prediction objective function doesn’t know anything about that. It just does everything uniformly so if you make a model big enough, yeah they’ll get that right but the limit might be extreme. And so things that change the objective function, that tell you what to care about, of which I think RL is a big example probably are needed to make this actually work correctly.

I think in the limit of a huge enough model, you might get surprisingly close, I don’t know, but the limit might be far beyond our capabilities. There’s only so many GPU’s you can build and there are even physical limits.

Lucas Perry: And there’s less of them, less and less of them available over time, or at least they’re very expensive.

Dario Amodei: They’re getting more expensive and more powerful. I think the price efficiency overall is improving, but yeah, they’re definitely becoming more expensive as well.

Lucas Perry: If you were able to scale up a large scale system in order to achieve an amateur level of mathematics or computer science, then would it not benefit the growth of that system to then direct that capability on itself as a self recursive improvement process? Is that not already escape velocity intelligence once you hit amateurs?

Dario Amodei: Yeah. So there are training techniques that you can think of as bootstrapping a model or using the model’s own capabilities to train it. Think like AlphaGo for instance was trained with a method called expert iteration that relies on looking ahead and comparing that to the model’s own prediction. So whenever you have some coherent logical system, you can do this bootstrapping, but that itself is a method of training and falls into one of the things I’m talking about, about you make these pure generative models, but then you need to do something on top of them, and the bootstrapping is something that you can do on top of them. Now, maybe you reach a point where the system is making its own decisions and is using its own external tools to create the bootstrapping, to make better versions of itself, so it could be that that is someday the end of this process. But that’s not something we can do right now.

Lucas Perry: So there’s a lot of labs in industry who work on large models. There are maybe only a few other AGI labs, I can think of DeepMind. I’m not sure if there are others that… OpenAI. And there’s also this space of organizations like The Future of Life Institute or the Machine Intelligence Research Institute or the Future of Humanity Institute that are interested in AI safety. MIRI and FHI both do research. FLI does grant making and supports research. So I’m curious as to, both in terms of industry and nonprofit space and academia, how you guys see Anthropic as positioned? Maybe we can start with you, Daniela.

Daniela Amodei: Sure, yeah. I think we touched on this a little bit earlier, but I really think of this as an ecosystem, and I think Anthropic is in an interesting place in the ecosystem, but we are part of the ecosystem. So I think our strength or the thing that we do best, and I like to think of all of these different organizations as having valuable things to bring to the table, depending on the people that work there, their leadership team, their particular focused research bet, or their mission and vision that they’re achieving I think hopefully have the potential to bring safe innovations to the broader ecosystem that we’ve talked about. I think for us, our bet is one we’ve talked about, which is this empirical scientific approach to doing AI research and AI safety research in particular.

And I think for our safety research, we’ve talked about a lot of the different areas we focus on. Interpretability, alignment, societal impacts, scaling laws for empirical predictions. And I think a lot of what we’re imagining or hoping for in the future is that we’ll be able to grow those areas and potentially expand into others, and so I really think a lot of what Anthropic adds to this ecosystem or what we hope it adds is this rigorous scientific approach to doing fundamental research in AI safety.

Dario Amodei: Yeah, that really captures it in one sentence, which is I think if you want to locate us within the ecosystem, it’s an empirical iterative approach within an organization that is completely focused on making a focused bet on the safety thing. So there are organizations like MIRI or to a lesser extent, Redwood, that are either not empirical or have a different relationship to empiricism than we do, and then there are safety teams that are doing good work within larger companies like DeepMind or OpenAI or Google Brain that are safety teams within larger organizations. Then I have lots of folks who work on short term issues, and then we’re filling a space that’s working on today’s issues but with an eye towards the future, empirically minded, iterative, with an org where everything we do is designed for the safety objective.

Lucas Perry: So one facet of Anthropic is that it is a public benefit corporation, which is a structure that I’m not exactly sure what it is and maybe many of our listeners are not familiar with what a public benefit corporation is. So can you describe what that means for Anthropic, its work, its investors and its trajectory as a company?

Daniela Amodei: Yeah, sure. So this is a great question. So what is a PBC? Why did we choose to be a public benefit corporation? So I think I’ll start by saying we did quite a lot of research when we were considering what type of corporate entity we wanted to be when we were founding. And ultimately, we decided on PBC, on public benefit corporation for a few reasons. And I think primarily, it allowed us the maximum amount of flexibility in how we can structure the organization, and we were actually very lucky, to a later part of your question, to find both investors and employees who were generally very on board with this general vision for the company. And so what is a public benefit corporation? Why did we choose that structure?

So they’re fairly similar to C corporations, which is any form of standard corporate entity that you would encounter. And what that means is we can choose to focus on research and development, which is what we’re doing now, or on deployment of tools or products, including down the road for revenue purposes if we want to. But the major difference between a PBC and a C corporation is that in a public benefit corporation, we have more legal protections from shareholders if the company fails to maximize financial interests in favor of achieving our publicly beneficial mission. And so this is primarily a legal thing, but it also was very valuable for us in being able to just appropriately set expectations for investors and employees, that if financial profit and creating positive benefit for the world were ever to come into conflict, it was legally in place that the latter one would win.

And again, we were really lucky that investors, people that wanted to work for us, they said, wow, this is actually something that’s a really positive thing about Anthropic and not something that we need to work around. But I think it ended up just being the best overall fit for what we were aiming for.

Lucas Perry: So usually, there’s a fiduciary responsibility that people like Anthropic would have to its shareholders, and because it’s structured as a public benefit corporation, the public good can outweigh the fiduciary responsibility without there being legal repercussions. Is that right?

Daniela Amodei: Yeah, exactly. So shareholders can’t come sue the company and say, hey, you didn’t maximize financial returns for us. If those financial returns were to come into conflict with the publicly beneficial value of the company. So I think maybe an example here, I’ll try and think of one off the top of my head, but if we designed a language model and we felt like it was unsafe, it was producing outputs that we felt were not in line with what we wanted to see from outputs of a language model, for safety reasons or toxicity reasons for any number of reasons. And in a normal C corporation, someone could say, “Hey, we’re a shareholder and we want the financial value that you could create from that by productizing it.” But we said, “Actually, we want to do more safety research on it before we choose to put it out into the world,” in a PBC, we’re quite legally protected basically in a case that. And again, I’m not a lawyer but that’s my understanding of the PBC.

Dario Amodei: Yeah. A useful, holistic way to think about it is there’s the legal structure, but I think often, these things, maybe the more important thing about them is that they’re a way to explain your intention, to set the expectations for how the organization is going to operate. Often, things like that and the expectations of the various stakeholders, and making sure that you give the correct expectations and then deliver on those expectations so no one is surprised by what you’re doing and all the relevant stakeholders, the investors, the employees, the outside world gets what they expect from you, that can often be the most important thing here. And so I think what we’re trying to signal here is on one hand, a public benefit corporation, it is a for-profit corporation.

We could deploy something. That is something that we may choose to do and it has a lot of benefits in terms of learning how to make models more effective, in terms of iterating. But on the other hand, the mission is really important to us and we recognize that this is an unusual area, that’s more fraught with market externalities would be the term that I would use, of all kinds. In the short term, in the long term, related to alignment, related to policy and government than a typical area. It’s different than making electric cars or making widgets or something that, and so that’s the thing we’re trying to signal.

Lucas Perry: What do you think that this structure potentially means for the commercialization of Anthropic’s research?

Daniela Amodei: Yeah, I think again, part of what’s valuable about a public benefit corporation is that it’s flexible, and so it is a C corporation, it’s fairly close to any standard corporate entity you would meet and so the structure doesn’t really have much of a bearing outside of the one that we just talked about on decisions related to things like productization, deployment, revenue generation.

Lucas Perry: Dario, you were just talking about how this is different than making widgets or electric cars, and one way that it’s different from widgets is that it might lead to massive economic windfalls.

Dario Amodei: Yeah.

Lucas Perry: Unless you make really good widgets or widgets that can solve problems in the world. So what is Anthropic’s view on the vast economic benefits that can come from powerful AI systems? And what role is it that you see C company AGI labs playing in the beneficial use of that windfall?

Dario Amodei: Daniela, you want to go…

Daniela Amodei: Go for it.

Dario Amodei: Yeah. So yeah, I think a way to think about it is, assuming we can avoid the alignment problems and some other problems, then there will be massive economic benefits from AI or AGI or TAI or whatever you want to call it, or just AI getting more powerful over time.

And then again, thinking about all the other problems that I haven’t listed, which is today’s short term problems and problems with fairness and bias, and long-term alignment problems and problems that you might encounter with policy and geopolitics. Assuming we address all those, then there is still this issue of economic… Like are those benefits evenly distributed?

And so here, as elsewhere, I think it’s unlikely those benefits will all accrue to one company or organization. I think this is bigger than one company or one organization, and is a broader societal problem. But we’d certainly like to do our part on this and this is something we’ve been thinking about and are working on putting programs in place with respect to. We don’t have anything to share about it at this time, but this is something that’s very much on our mind.

I would say that, more broadly, I think the economic distribution of benefits is maybe one of only many issues that will come up. Which is the disruptions to society that you can imagine coming from the advent of more powerful intelligence are not just economic. They’re already causing disruptions today. People already have legitimate and very severe societal concerns about things that models are doing today and you can call them mundane relative to all the existential risk. But I think they’re already serious concerns about concentration of power, fairness and bias in these models, making sure that they benefit everyone, which I don’t think that they do yet.

And if we then put together with that, the ingredient of the models getting more powerful, maybe even on an exponential curve, those things are set to get worse without intervention. And I think economics is only one dimension of that. So, again, these are bigger than any one company. I don’t think it’s within our power to fix them, but we should do our part to be good citizens and we should try and release applications that make these problems better rather than worse.

Lucas Perry: Yeah. That’s excellently put. I guess one thing I’d be interested in is if you could, I guess, give some more examples about these problems that exist with current day systems and then the real relationship that they have to issues with economic windfall and also existential risk.

I think it seems to me like tying these things together is really important. At least seeing the interdependence and relationship there, some of these problems already exist, or we already have example problems that are really important to address. So could you expand on that a bit?

Dario Amodei: I think maybe the most obvious one for current day problems is people are worried, very legitimately, that big models suffer from problems of bias, fairness, toxicity, and accuracy. I’d like to apply my model in some medical application and it gives the wrong diagnosis, or it gives me misinformation or it fabricates information. That’s just not good. These models aren’t usable and they’re harmful if you try and use them.

I think toxicity and bias are issues when models are trained on data from the internet. They absorb the biases of that data. And there’s maybe even more subtle algorithmic versions of that, where, I hinted at it a little before, where it’s like the objective function of the model is to say something it sounds like what a human would say or what a human on the internet would say. And so in a way, almost fabrication is kind of like baked into the objective function.

Potentially, even bias and stereotyping you can imagine being baked into the objective function in some way. So, these models want to be used for very mundane everyday things like helping people write emails or helping with customer surveys or collecting customer data. And if they’re subtly biased or subtly inaccurate, then those biases and those inaccuracies will be inserted into the stream of economic activity in a way that may be difficult to detect. So, that seems bad and I think we should try to solve those problems before we deploy the models. But also they’re not as different from the large scale problems as they might seem.

In terms of the economic inequality, I don’t know, just look at the market capitalization of the top five tech companies in the world. And compare that to the US economy. There’s clearly something going on in the concentration of wealth.

Daniela Amodei: I would just echo everything Dario said. And also add, I think something that especially can be alarming in sort of a short term way today in the sense that it could belie things to come, is how quietly and seamlessly people are becoming dependent on some of these systems. We don’t necessarily even know, there’s no required disclosure of when you’re interacting with an AI system versus a human and until very recently, that was sort of a comical idea because it was so obvious when you were interacting with a person versus not a person. You know when you’re on a customer chat and it’s a human on the other end versus an automated system responding to you.

But I think that line is getting increasingly blurred. And I can imagine that even just in the next few years, that could start to have fairly reasonably large ramifications for people in day-to-day ways. People talk to an online therapist now, and sometimes that is backed by an AI system that is giving advice. Or down the road, we could imagine things looking completely different in health realms, like Dario talked about.

And so I think it’s just really important as we’re stepping into this new world to be really thoughtful about a lot of the safety problems that he just outlined and talked about because I think, I don’t know that most people necessarily even know all the ways in which AI is impacting our kind of day-to-day lives today, and the potential that could really go up in the near future.

Lucas Perry: The idea of AIs, there being like a requirement of AI is disclosing themselves as AI seems very interesting and also adjacent to this idea of the way that C corporations have fiduciary responsibility to shareholders, having AI systems that also have some kinds of responsibility towards the people that they serve, where they can’t be secretly working towards the interests of the tech company that has the AI listening to you in your house all the time.

Dario Amodei: Yeah. It’s another direction you can imagine. It’s like I talked to an AI produced by Megacorp but it subtly steers to my life to the benefit of Megacorp. Yeah, there’s lots of things you can come up with like this.

Daniela Amodei: These are important problems today. And I think they also really belie things that could be coming in the near future, and I think solving whatever, those particular problems are ones lots of groups are working on, but I think helping to solve a lot of the fundamental building blocks underlying them; about getting models to be truthful, to be harmless, to be honest. A lot of the goals are aligned there, both for sort of short, medium and potentially long-term safety.

Lucas Perry: So Dario, you mentioned earlier that of the research that you publish, one of your hopes is that other organizations will look into and expand the research that you’re doing. I’m curious if Anthropic has a plan to communicate its work and its ideas about how to develop AGI safely with both technical safety researchers, as well as with policy makers.

Daniela Amodei: Yeah, maybe I’ll actually jump in on this one, and Dario feel free to add as much as you like. But I actually think this is a really important question. I think communication with policy makers about safety with other labs in the form of papers that we publish is something that’s very important to us at Anthropic.

We have a policy team, it’s like 1.5 people right now. So we’re hiring, that’s kind of a plug as well, but I think their goal is to really take the technical content that we are developing at Anthropic and translate that into something that is actionable and practical for policymakers. And I think this is really important because the concepts are very complex, and so it’s a special skill to be able to take things that are highly technical, potentially very important, and translate that into recommendations or work with policy makers to come up with recommendations that could potentially have very far reaching consequences.

So, to point to a couple of things we’ve been working on here, we’ve been supporting NIST, which is the National Institute for Standards and Technology on developing something called an AI Risk Management Framework. And the goal of that is really developing more monitoring tools around AI risk and AI risk management. We’ve also been supporting efforts in the US and internationally to think about how we can best support academic experimentation, which we talked about a little bit earlier with large scale compute models too.

Lucas Perry: You guys also talked a lot about open-endedness, and was part of all this alignment and safety research looking into ways of measuring safety and open-endedness?

Daniela Amodei: Yeah, there’s actually some interesting work which I think is also in this upcoming paper and in various other places that we’ve been looking into around the concept of AI evaluations or AI monitoring. And I think both of those are potentially really important because a lot of what we’re seeing, or maybe lacking, and this kind of goes back to this point I made earlier about standards is, how do we even have a common language or a common framework within the AI field of what outputs or metrics we care about measuring.

And until we have that common language or framework, it’s hard to set things like standards across the industry around what safety even means. And so, I think AI evaluations is another area that our societal impacts team, which is also like the other half of the one and a half people in policy, it’s also 1.5 people, is something that they’ve been working on as well.

Lucas Perry: Right, so a large part of this safety problem is of course the technical aspect of how you train systems and create systems that are safe and aligned with human preferences and values. How do you guys view and see the larger problem of AI governance and the role and importance of governments and civil society in working towards the safe and beneficial use and deployment of AI systems?

Daniela Amodei: We talked about this one a little bit earlier, and maybe I’ll start here. And obviously, Dario jump in if you want. But I do think that these other kind of institutions that you talked about have this really important role to play. And again, one of the things we mention in this paper is that we think government has already been starting to fund a lot more academic safety research. And I think that’s an area that we… A concrete policy recommendation is, hey, go do more of that. That would be great.

But I also think groups like civil society and NGOs, there’s a lot of great organizations in this space, including FLI and others, that are thinking about what do we do? Say we develop something really powerful, what’s the next step? Whether that’s at an industry lab, in government, in academia, wherever. And I think there’s a way that industry incentives are not the same as nonprofit groups or as civil society groups. And I think to go back to this analogy of an ecosystem, we really need thoughtful and empowered organizations that are working on these kinds of questions, fundamentally outside of the industry sphere, in addition to the policy research and work that’s being done at labs.

Dario Amodei: Yeah, another way you can think of things in line with this is I think maybe at some point laws and regulations are going to be written. And I think probably those laws and regulations work best if they end up being formalizations of what’s realized to be the best practices, and those best practices can come from different industrial players, they can come from academics figuring out what’s good and what’s not. They can come from nonprofit players. But if you try and write a law ahead of time, often you don’t know what… If you write a law that relates to a technology that hasn’t been invented yet, it’s often not clear what the best thing to do is, and what is actually going to work or make sense, or even what categories or words to use.

But if something has become a best practice and folks have converged on that, and then the law formalizes it and puts it in place, that can often be a very constructive way for things to happen.

Lucas Perry: Anthropic has received an impressive amount of series A funding. And so it seems like you guys are doing a lot of hiring and growing considerably. So, in case there’s anyone from our audience that’s interested in joining Anthropic, what are the types of roles that you expect to be hiring for?

Daniela Amodei: Yes, great question. We are definitely hiring. We’re hiring a lot. And so I think the number one thing I would say is if you’re listening to this podcast and you’re interested, I would highly recommend just checking out our jobs page, because that will be the most up to date. And that’s just anthropic.com on the careers tab. But we can also send that around if that’s helpful.

But what are we looking to hire? Quite a few things. So most critically, probably right now, we’re looking to hire engineers and we’re actually very bottle-necked on engineering talent right now. And that’s because running experiments on AI systems is something that requires a lot of custom software and tooling. And while machine learning experience is helpful for that, it isn’t necessarily required.

And I think a lot of our best ML engineers or research engineers came from a software engineering or infrastructure engineering background, hadn’t necessarily worked in ML before, but were just really excited to learn. So, I think if that describes you, if you’re a software engineer, but you’re really interested in these topics, definitely think about applying because I think there’s a lot of value that your skills can provide.

We’re also looking for just a number of other roles. I won’t be able to list them all, you should just check out our jobs page. But off the top of my head, we’re looking for front-end engineers to help with things like interfaces and tooling for the research we’re doing internally. We’re looking for policy experts, operations people, security engineers, data visualization people, security.

Dario Amodei: Security.

Daniela Amodei: Security, yes. We’re definitely looking-

Dario Amodei: If you’re building big models.

Daniela Amodei: Yes. Security is something that I think is-

Dario Amodei: Every industrial lab should make sure their models are not stolen by bad actors.

Daniela Amodei: This is a unanimous kind of thing across all labs. There’s something everyone really agrees on in industry and outside of industry, which is that security is really important. And so, if you are interested in security or you have a security background, we would definitely love to hear from you, or I’m sure our friends at other industry labs and non-industry labs would also love to hear from you.

I would also say, I sort of talked about this a little bit before, but we’ve also just kind of had a lot of success in hiring people who were very accomplished in other fields, especially other technical fields. And so, we’ve alluded a few times to former recovering physicists or people who have PhDs in computer science or ML, neuroscientists, computational biologists.

And so, I think if you are someone who has this strong background and set of interest in a technical field that’s not related to ML, but sort of moderately adjacent, I would also consider applying for our residency program. And so I think again, if you’re even a little curious, I would say, just check out our jobs page, because there’s going to be more information there, but those are the ones off the top of my head. And Dario, if I missed any, please jump in.

Dario Amodei: Yeah, that covers a pretty wide range.

Lucas Perry: Could you tell me a little bit more about the team and what it’s like working at Anthropic?

Daniela Amodei: Yeah, definitely. You’ll probably have to cut me off here because I’ll talk forever about this because I think Anthropic is a great team. Some basic stats, we’re about 35 people now. Like I said a few times, we’ve kind of come from a really wide range of backgrounds. So this is people who worked in tech companies as software engineers. These are former academics in physics, ethics, neuroscience, a lot of different areas, machine learning researchers, policy people, operations staff, so much more.

And I think one of the unifying themes that I would point to in our employees is a combination of a set of two impulses that I think we’ve talked about a lot in this podcast. And I think the first is really just a genuine desire to reduce the risks and increase the potential benefits from AI. And I think the second is a deep curiosity to really scientifically and empirically describe, understand, predict, model-out how AI systems work and through that deeper understanding, make them safer and more reliable.

And I think some of our employees identify as effective altruists which means they’re especially worried about the potential for long term harms from AI. And I think others are more concerned about immediate or sort of emerging risks that are happening today or in the near future. And I think both of those views are very compatible with the goals that I just talked about. And I think they often just call for a mixed-method approach to research, which I think is a very accurate description of how things look in a day-to-day way at Anthropic.

It’s a very collaborative environment. So, there’s not a very strong distinction between research and engineering, researchers write code, engineers contribute to research. There’s a very strong culture of pair programming across and within teams. There’s a very strong focus on learning. I think this is also just because so many of us come from backgrounds that were not necessarily ML focused in where we started.

So people run these very nice, little training courses. Where they’ll say, “Hey, if you’re interested in learning more about transformers, I’m a transformer’s expert and I’ll walk you through it at different levels of technical skills so that people from the operations team or the policy team can come for an introductory version.”

And then I think outside of that, I like to think we’re a nice group of people. We all have lunch together every day. We have this very lovely office space in San Francisco, it’s fairly well attended. And I think we have lots of fun lunch conversations ranging from things like… A recent one was we were sort of talking about microCOVID, if you know the concept of microCOVID, Catherine Olsson, who’s of one of the creators of microcovid.org. Which is basically a way of assessing the level of risk from a given interaction or a given activity that you’re doing during COVID time.

So we had this fun meta conversation where we’re like, “How risky is this conversation that we’re having right now from a microCOVID perspective, if we all came into the office and tested, but we’re still together indoors and there’s 15 of us, what does that mean?” So anyway, I think it’s a fun place to work. We’ve obviously had a lot of fun getting to build it together.

Dario Amodei: Yeah. The things that stand out to me are trust and common purpose. They’re enormous force multipliers where it shows up in all kinds of little things where if you have… You can think about it in things like compute allocation. If people are not on the same page, if one person wants to advance one research agenda, the other wants to advance their other research agenda, then people fight over it. And there’s a lot of zero sum or negative sum interactions.

But if everyone has the attitude of, we’re trying to do this thing, everything we’re trying to do is in line with this common purpose and we all trust each other to do what’s right to advance this common purpose, then it really becomes a force multiplier on getting things done while keeping the environment comfortable, and while everyone continues to get along with each other. I think it’s an enormous superpower that I haven’t seen before.

Lucas Perry: So, you mentioned that you’re hiring a lot of technical people from a wide variety of technical backgrounds. Could you tell me a little bit more about your choice to do that rather than simply hiring people who are traditionally experienced in ML and AI?

Daniela Amodei: Yeah, that’s a great question. So I should also say we have people from both camps that you talked about, but why did we choose to bring people in from outside the field? I think there’s a few reasons for this. I think one is, again, ML and AI is still a fairly new field. Not super new, but still pretty new. And so what that means is there’s a lot of opportunity for people who have not necessarily worked in this field before to get into it. And I think we’ve had a lot of success or luck with taking people who are really talented in a related field and helping to take their skills and translate them to the ones in ML and AI safety.

And I think the second reason is, so one is just expanding the talent pool. I think the other is, it really does broaden the range of perspectives and the types of people who are working on these issues, which we think are very important. And again, we’ve talked about this previously, but having a wider range of views and perspectives and approaches tends to lead to a more robust approach to doing both basic research and safety research.

Dario Amodei: Yeah. Nothing to add to that. I’m surprised at how often someone who has experience in a different field can come in, and it’s not like they’re directly applying things that come, but they think about things in a different way. And of course this is true about all kinds of things, this is this true about diversity in the more traditional senses as well. But you want as many different kinds of people as you can get. 

Lucas Perry: So as we’re wrapping up here, I’m curious just to get some more perspective on you guys about, given these large scale models, the importance of safety and alignment and the problems which exist today, but also the promises of the impact they could have for the benefit of people. What’s a future that each of you is excited about or what’s a future that you’re hopeful for? Given your work at Anthropic and the future impacts of AI?

Daniela Amodei: Yeah, I’ll start. So I think one thing I do believe is actually I am really hopeful about the future. I know that there’s a lot of challenges that we have to face to get to a potentially really positive place. But I think the field will rise to the occasion, or that’s kind of my hope. And I think some things I’m hoping for in the next few years is that a lot of different groups will be developing more practical tools, techniques for advancing safety research. And I think these are likely to hopefully become more widely available if we can set the right norms in the community. And I think the more people working on safety-related topics, that can positively feed on itself.

And I think I’m most broadly hoping for a world where we can feel confident that when we’re using AI for more advanced purposes, like accelerating scientific research, that it’s behaving in ways where we can be very confident and sure that we understand that it’s not going to lead to negative, unintended consequences.

And the reason for that is because we’ve really taken the time to chart them out and understand what all of those potential problems could be. And so I think that’s obviously a very ambitious goal, but I think if we can make all of that happen, there’s a lot of potential benefits of more advanced AI systems that I think could be transformative for the world, from almost anything you can name; renewable energy, health, disease detection, economic growth, and lots of other just day-to-day enhancements to how we work and communicate and live together.

Dario Amodei: No one really knows what’s going to happen in the future. It’s extremely hard to predict. And so I often find any question about the future, it’s more about the attitude or posture that you want to take than it is about concrete predictions, because I feel like particularly after you go a few years out, it’s just very hard to know what’s going to happen. And so, it’s mostly just speculation. And so in terms of attitude, I think, well, first of all, I think the two attitudes that I find least useful are blind pessimism and blind optimism because they’re actually sort of like doom saying and Pollyannaism. It weirdly is possible to have both at once.

But I think it’s just not very useful because it’s like we’re all doomed. It’s intended to create fear or it’s intended to create complacency. I find that an attitude that’s more useful is to just say, “Well, we don’t know what’s going to happen, but let’s, as an individual or as an organization, let’s pick a place where there’s a problem we think we can help with and let’s try and make things go a little better than they would’ve otherwise.” Maybe we’ll have a small impact, maybe we’ll have a big impact, but instead of trying to understand what’s going to happen with the whole system, let’s try and intervene in a way that helps with something that we feel well-equipped to help with. And of course, the whole outcome, it’s going to be beyond the scope of one person, one organization, even one country.

But I think we find that to be a more effective way of thinking about things. And for us, that’s can we help to address some of these safety problems that we have with AI systems in a way that is robust and enduring and that points towards the future? If we can increase the probability of things going well by only some very small amount, that may well be the most that we can do.

I think from our perspective, the things that I would really like to see are, I would like it if AI could advance science technology and health in a way that’s equitable for everyone, and that it could help everyone to make better decisions and improve human society. And right now, I, frankly, don’t really trust the AI systems we build today to do any of those things, even if it were technically capable of the task, which it’s not, I wouldn’t trust it to do those things in a way that makes society better rather than worse.

And so I’d like us to do our part to make it more likely that we could trust AI systems in that way. And if we can make a small contribution to that while being good citizens in the broader ecosystem, that’s maybe the best we can hope for.

Lucas Perry: All right. And so if people want to check out more of your work or to follow you on social media, where are the best places to do that?

Daniela Amodei: Yeah. On anthropic.com is going to be the best place to see most of the recent stuff we’ve worked on. I don’t know if we have everything posted, but- 

Dario Amodei: We have several papers out, so we’re now about to post links to them on the website.

Daniela Amodei: In an easy to find place. And then we also have a Twitter handle. I think it’s Anthropic on Twitter, and we generally also tweet about our recent releases of our research. 

Dario Amodei: We are relatively low key. We really want to be focused on the research and not get distracted. I mean, the stuff we do is out there, but we’re very focused on the research itself and getting it out and letting it you speak for itself.

Lucas Perry: Okay. So, where’s the best place on Twitter to follow Anthropic?

Daniela Amodei: Our Twitter handle is @anthropicAI.

Lucas Perry: All right. I’ll include a link to that in the description of wherever you’re listening. Thanks a ton for coming on Dario and Daniela, it’s really been awesome and a lot of fun. I’ll include links to Anthropic in the description. It’s a pleasure having you and thanks so much.

Daniela Amodei: Yeah, thanks so much for having us, Lucas. This was really fun.

 

 

• Virtual reality as genuine reality
• Why you can live a good life in VR
• Why we can never know whether we’re in a simulation
• Consciousness in virtual realities
• The ethics of simulated beings

 

Watch the video version of this episode here

Check out David’s book and website here

Follow Lucas on Twitter here

0:00 Intro

2:43 How this books fits into David’s philosophical journey

9:40 David’s favorite part(s) of the book

12:04 What is the thesis of the book?

14:00 The core areas of philosophy and how they fit into Reality+

16:48 Techno-philosophy

19:38 What is “virtual reality?”

21:06 Why is virtual reality “genuine reality?”

25:27 What is the dust theory and what’s it have to do with the simulation hypothesis?

29:59 How does the dust theory fit in with arguing for virtual reality as genuine reality?

34:45 Exploring criteria for what it means for something to be real

42:38 What is the common sense view of what is real?

46:19 Is your book intended to address common sense intuitions about virtual reality?

48:51 Nozick’s experience machine and how questions of value fit in

54:20 Technological implementations of virtual reality

58:40 How does consciousness fit into all of this?

1:00:18 Substrate independence and if classical computers can be conscious

1:02:35 How do problems of identity fit into virtual reality?

1:04:54 How would David upload himself?

1:08:00 How does the mind body problem fit into Reality+?

1:11:40 Is consciousness the foundation of value?

1:14:23 Does your moral theory affect whether you can live a good life in a virtual reality?

1:17:20 What does a good life in virtual reality look like?

1:19:08 David’s favorite VR experiences

1:20:42 What is the moral status of simulated people?

1:22:38 Will there be unconscious simulated people with moral patiency?

1:24:41 Why we can never know we’re not in a simulation

1:27:56 David’s credences for whether we live in a simulation

1:30:29 Digital physics and what is says about the simulation hypothesis

1:35:21 Imperfect realism and how David sees the world after writing Reality+

1:37:51 David’s thoughts on God

1:39:42 Moral realism or anti-realism?

1:40:55 Where to follow David and find Reality+

 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s episode is with David Chalmers and explores his brand new book Reality+: Virtual Worlds and the Problems of Philosophy. For those not familiar with David, he is a philosopher and cognitive scientist who specializes in the philosophy of mind and language. He is a Professor of Philosophy and Neural Science at New York University, and is the co-director of NYU’s Center for Mind, Brain and Consciousness. Professor Chalmers is widely known for his formulation of the “hard problem of consciousness,” which asks, “Why a physical state, like the state of your brain, is conscious rather than nonconscious?” 

Before we jump into the interview, we have some important and bitter-sweet changes to this podcast to announce. After a lot of consideration, I will be moving on from my role as Host of the FLI Podcast, and this means two things. The first is that FLI is hiring for a new host for the podcast. As host, you would be responsible for the guest selection, interviews, production, and publication of the FLI Podcast. If you’re interested in applying for this position, keep your eye on the Careers tab on the futureoflife.org website for more information. 

The second item is that even though I will no longer be the host of the FLI Podcast, I won’t be disappearing from the podcasting space. I’m starting a brand new podcast focused on exploring questions around wisdom, philosophy, science, and technology, where you’ll see some of the same themes we explore here like existential risk and AI alignment. I’ll have more details about my new podcast soon. If you’d like to stay up to date, you can follow me on Twitter at LucasFMPerry, link in the description. This isn’t my final time on the FLI Podcast, I’ve got three more episodes including a special farewell episode, so there’s still more to come! 

And with that, I’m very happy to introduce David Chalmers on Reality+.

Welcome to the podcast David, it’s a really big pleasure to have you here. I’ve been looking forward to this. We both love philosophy so I think this will be a lot of fun. And we’re here today to discuss your newest book, Reality+. How would you see this as fitting in with the longer term project of your career and philosophy?

David Chalmers: Oh boy, this book is all about reality. I think of philosophy to being about, to a very large extent about the mind, about the world and about relationships between the mind and the world. In a lot of my earlier work, I’ve focused on the mind. I was drawn into philosophy by the problem of consciousness, understanding how a physical system could be conscious, trying to understand consciousness in scientific philosophical terms.

But there are a lot of other issues in philosophy too. And as my career has gone on, I guess I’ve grown more and more interested in the world side of the equation, the nature of reality, the nature of the world, such that the mind can know it. So I wrote a fairly technical book back in 2012 called Constructing the World. That was all about what is the simplest vocabulary you can use to describe reality?

But one thing that was really distinctive to this book was thinking about it in terms of technology. In philosophy, it often is interesting and cool to take an old philosophical issue and give it a technological twist. Maybe this is most clear in the case of thinking about the mind and then thinking about the mind through the lens of AI, are artificial minds possible? That’s a big question for anybody. If they are, maybe that tells us something interesting about the human mind. If artificial minds are possible then maybe the human mind is in relevant ways analogous for example to an artificial intelligence.

Then, well, the same kind of question comes up for thinking about reality and the world. Are artificial worlds possible? Normally we think about, okay, ordinary physical reality and the mind’s relation to that, but with technology, there’s now a lot of impetus to think about artificial realities, realities that we construct, and the crucial case there is virtual realities, computational based realities, virtual worlds even of the kind we might construct say with video games or full scale virtual realities, full scale universe simulations. And then a bunch of analogous questions come up, are artificial realities genuine realities?

And just in the artificial mind case, I want to say artificial minds are genuine minds. Well, likewise in the artificial world case, I want to say, yeah, virtual realities are genuine realities. And that’s in fact, the central slogan of this new book Reality+, which is very much trying to look at some of these philosophical issues about reality through the lens of technology and virtual realities, as well as trying to get some philosophical insight into this virtual reality technology in its own right by thinking about it philosophically. This is the process I call techno-philosophy, using technology to shed light on philosophy and using philosophy to shed light on technology.

Lucas Perry: So you mentioned… Of course you’re widely known as a philosopher of consciousness and it’s been a lot of what you focused on throughout your career. You also described this transition from being interested in consciousness to being interested in the world increasingly over your career. Is that fair to say?

David Chalmers: Yeah. You can’t be interested in one of these things without being interested in the other things. So I’ve always been very interested in reality. And even in my first book on consciousness, there was speculation about the nature of reality. Maybe I talked about it from bit hypothesis there. Maybe reality is made of information. I talked about quantum mechanics and potential connections to consciousness. So yeah, you can’t think about, say the mind body problem without thinking about bodies as well as minds, you have to think about physical reality.

There’s one particular distinctive question about the nature of reality namely how much can we know about it? And can we know anything about the external world? That’s a very traditional problem in philosophy. It goes back to Descartes saying, how do you know you’re not dreaming right now? Or how do you know you’re not being fooled by an evil demon who’s producing sensations as of an external world when none of this is real? And for a long time, I thought I just didn’t have that much to about this very big question in philosophy.

I think of the problem with consciousness, the mind body problem. That’s a really big question in the history of philosophy. But to be honest, I’m going to say it’s probably not number one. Number one at least in the Western philosophical tradition is how do we know anything about the external world? And for a long time, I thought I didn’t have anything to say about that. And at a certain point, partly through thinking about yeah, virtual realities and the simulation hypothesis, I thought, yeah, maybe there is something new to say here via this idea that virtual realities are genuine realities. Maybe these hypotheses that Descartes put forward saying, “If this is the case, then none of this is real.” Maybe Descartes was actually thinking about these hypotheses wrongly. 

And I actually got drawn into this. Around the same time, just totally fortuitously I got invited to write an article for the Matrix Website. Their production company, Red Pill, it was a philosopher called Chris Grawl, who worked for them. And I guess the Wachowskis were super interested in philosophy. They wanted to see what philosophers thought of philosophical issues coming from the movie. So I ended up writing an article called The Matrix as Metaphysics, putting forward this rough point of view, which is roughly in the context of the movie that even in the movie, they say, well, if we’re in the Matrix, none of what we’re experiencing is real. All this is illusion or a fiction.

I tried to argue, even if you’re in the Matrix, these things around you are still perfectly real. There are still trees, there are still cats, there are still chairs. There are still planets. It’s just that they’re ultimately digital, but they’re still perfectly real. And I tried to use that way of thinking about the Matrix to provide a response to the version of Descartes who says, “We can never know anything about the external world, because we can’t rule out that none of this is real.”

All those scenarios Descartes had in mind. I think some sense there are actually scenarios where things are real and that makes this vision of reality. Maybe it makes reality a bit more like virtual reality, but that vision of reality actually puts knowledge of the external world more within our grip. And from there, there’s a clean path from writing that article 20 years ago to writing this book now, which takes this idea of virtual reality as genuine reality and tries to just draw it out in all kinds of directions, to argue for it, to connect to present day technology, to connect it to a bunch of issues in philosophy and science. Because if I to start thinking this way about reality, at least I’ve found it changes everything. It changes all kinds of things about your vision of the world.

Lucas Perry: So I think that gives a really good taste of what is to come in this interview and also what’s in your book. Before we dive more into those specifics, I’m also just curious what your favorite part of the book is. If there’s some section or maybe there isn’t that you’re most excited to talk about, what would that be?

David Chalmers: Oh, I don’t know. I was going to say my favorite parts of the book had the illustrations, amazing illustrations by Tim Peacock, who’s a great illustrator who I found out about and I asked if he’d be able to do illustrations for the book. And he took so many of these scenarios, philosophical thought experiments, science fiction scenarios, and came up with wonderful illustrations to go along with it. So we’ve got Plato’s Cave, but updated for the 21st century with people in virtual reality inside Plato’s Cave with Mark Zuckerberg running the cave, or we have an ancient Indian thought experiment about Narada and Vishnu updated them in the light of Rick and Morty. We’ve got a teenage girl hacker creating a simulated universe in the next universe up.

So these illustrations are wonderful, but I guess that doesn’t quite answer your question, which parts do I especially want to talk about? I think of the book as having roughly two halves. Half of it is broadly about the simulation hypothesis. The idea that the universe is a simulation and trying to use that idea to shed light on all kinds of philosophical problems. And the other half is more about real virtual reality, the coming actual virtual reality technology that we have and will develop in the next say 50 to 100 years and trying to make sense of that and the issues it brings up.

So in the first part of the book, I talk about very abstract issues about knowledge and reality and the simulation hypothesis. The second part of the book gets a bit more down to earth and even comes to issues about ethics, about value, about political philosophy. How should we set up a virtual world? That was more of a departure for me to be thinking about some of those more practical and political issues, but over time I’ve come to find they’re fascinating to think about.

So I guess I’m actually equally fascinated by both sets of issues. But I guess lately I’ve been thinking especially about some of these second class of issues, because a lot of people given the coming… All the corporations now are playing up the metaverse and coming virtual reality technology. That’s been really interesting to think about.

Lucas Perry: So given these two halves in general and also the way that the book is structured, what would you say are your central claims in this book? What is the thesis of the book?

David Chalmers: Yeah, the thesis of the book that I lay out in the introduction is virtual reality is genuine reality. It’s not a second class reality. It’s not fake or fictional. Virtual reality is real. And that breaks down into a number of sub-thesis. One of them is about the existence of objects, and it’s a thesis in metaphysics. It says the objects in virtual reality are real objects, a virtual tree is a real object. It may be a digital object, but it’s real all the same. It has causal powers. It can affect us. It’s out there independently of us. It needn’t be an illusion.

So yeah, virtual objects are real objects. What happens in virtual reality really happens. And that’s one kind of thesis. Another thesis is about value or meaning. That you can lead a valuable life, you can lead a meaningful life inside a virtual world. Some people have thought that virtual worlds can only ever be escapist or fictions or not quite the real thing. I argue that you can lead a perfectly meaningful life.

And the third kind of thesis has tied closer to the simulation hypothesis idea. And there I don’t argue that we are in fact in a computer simulation, but I do argue that we can never know that we’re not in a simulation. There’s no way to exclude the possibility that we’re in a simulation. So that’s a hypothesis to take very seriously. And then I use that hypothesis to flesh out a number of different… Just say we are in a simulation then, yeah, what would this mean for say our knowledge of the world? What would this mean for the reality of God? What would this mean for the underlying nature of the metaphysics underneath physics and so on? And I try and use that to just put forward a number of sub-thesis in each of these domains.

Lucas Perry: So these claims also seem to line up with really core questions in philosophy, particularly having to do with knowledge, reality and value. So could you explain a little bit what are some of the core areas of philosophy and how they line up with your exploration of this issue through this book?

David Chalmers: Yeah, traditionally philosophy is at least sometimes divided up into three areas, metaphysics, epistemology and the theory of value. Metaphysics is basically questions about reality. Epistemology is basically questions about knowledge and value theory is questions about value, about good versus bad and better versus worse. And in the book, I divide up these questions about virtual worlds into three big questions in each of these areas, which I call the knowledge question, the reality question and the value question.

The knowledge question is, can we know whether we’re in a virtual world in particular? Can we ever be sure that we’re not in a virtual world? And there I argue for an answer of no, we can ever know for sure that we’re not in a virtual world, we can never exclude that possibility. But then there’s the reality question, which is roughly, if we are in a virtual world, is the world around us real? Are these objects real? Are virtual realities genuine realities or are they somehow illusions or fictions? And there I argue for the answer, yes, virtual worlds are real. Entities and events in virtual world are perfectly real entities and events. Even if we’re in a simulation, the objects around us are still real. So that’s a thesis in metaphysics.

Then there’s the question in value theory, which is roughly, can you lead a good life in a virtual world? And there as I suggested before I want to argue, yes, you can lead a good and meaningful life in a virtual world. So yeah, the three big questions behind the book, each correspond then to a big question, a big area of philosophy. I would like to think they actually illuminate not just questions about virtual worlds, but big questions in those areas more generally. The big question of knowledge is, can we know anything about the external world?

The big question of reality is, what is the nature of reality? The big question about value is, what is it to lead a good life? Those are big traditional philosophical questions. I think thinking about each of those three questions through the lens of virtual reality and trying to answer the more specific questions about what is the status of knowledge, reality and value in a virtual world, that can actually shed light on those big questions of philosophy more broadly.

So what I try to do in the book is often start with the case of the virtual world, give a philosophical analysis of that, and then try to draw out morals about the big traditional philosophical question more broadly.

Lucas Perry: Sure. And this seems like it’s something you bring up as a techno-philosophy in the book where philosophy is used to inform the use of technology and then technology is used to inform philosophy. So there’s this mutual beneficial exchange through techno-philosophy.

David Chalmers: Yeah. Philosophy is this two-way interaction between philosophy and technology. So what I’ve just been talking about now, using virtual reality technology and virtual worlds to shed light on big traditional philosophical questions, that’s the direction in which technology sheds light on philosophy, or at least thinking philosophically about technology can shed light on big traditional question in philosophy that weren’t cast in terms of technology, can we know we’re not in a simulation? That sheds light on what we can know about the world. Can we lead a good life in a virtual world? That sheds some light on what it is to lead a good life and so on.

So yeah, this is the half of techno-philosophy, we’re thinking about technology sheds light on philosophy. The other half is thinking philosophically, using philosophy to shed light on technology and just thinking philosophically about virtual reality technology, simulation technology, augmented reality technology and so on. And that’s I think something I really try to do in the book as well. And I think these two things, these two processes of course complement each other. Because thinking, you think philosophically about technology, it shed some light on the technology, but then it turns out actually to have some impact on the broader issues of philosophy at the same time.

Lucas Perry: Sure. So what’s coming up for me is Plato’s Cave Allegory is actually a form of techno-philosophy potentially, where the candle is a kind of technology that’s being used to cast shadows to inform how Plato’s examining the world.

David Chalmers: That’s interesting. Yeah. I hadn’t thought about that. But I suppose back around Plato’s time, people did a whole lot with candles and fire. These were very major technologies of the time. And maybe at a certain point people started developing puppet technology and started doing puppet style shows that were a form of, I don’t know, entertainment technology for them. And then for Plato then to be thinking about the cave in this way, yeah, it is a bit of a technological setup and Plato is using this new technology to make claims about reality.

Plato also wrote about other technologies. He wrote about writing, the invention of writing and he was quite down on it. He thought or at least his spokesman’s Socrates said, “In the old days people would remember all the old tales, they’d carry them around in their head and tell them person to person, and now that you can write them down, no one has to remember them anymore.” And he thought this was somehow a step back in the way in which some people these days think that putting all this stuff on your smartphone might be a step back. But yeah, Plato was very sensitive to the technologies of the time.

Lucas Perry: So let’s make a B line for your central claims in this book. And just before we do that, I have a simple question here for you. Maybe it’s not so simple but… So what is virtual reality?

David Chalmers: Yeah, the way I define it in the book, I make a distinction between a virtual world and virtual reality, where roughly virtual reality technology is immersive. It’s the kind of thing you experience say with a Oculus Quest headset that you put onto your head and you experience a three dimensional space all around you. Whereas a virtual world needn’t be immersive. When you play a video game, when you’re playing World of Warcraft or you’re in Fortnite, typically you’re doing this on a two dimensional screen, it’s not fully immersive, but there’s still a computer generated world.

So my definitions are a virtual world is an interactive computer generated world. It has to be interactive. If it’s just a movie, then that’s not yet a virtual world, but if you can perform actions within the world and so on and it’s computer generated, that’s a virtual world. A virtual reality is an immersive interactive computer generated world. Then the extra condition, this has to be experienced in 3D with you at the center of it, typically these days experienced with a VR headset and that’s virtual reality. So yeah, virtual reality is immersive interactive computer generated reality.

Lucas Perry: So one of the central claims that you mentioned earlier was that virtual reality is genuine reality. So could you begin explaining why is it that you believe the virtual reality is genuine reality?

David Chalmers: Yeah. Because a lot of this depends on what you mean by real and by genuine reality. And one thing I do in the book is try and break out number of different meanings of real, what is it for something to be real? One is that it has some causal power that it could make a difference in the world. One is that it’s out there independent of our minds. It’s not just all in the mind. And one, maybe the most important is that it’s not an illusion. It’s not just that things are roughly as they seem to be. And I try to argue that if we’re in VR, the objects we see have all of these properties, basically the ideas. When you’re in virtual reality you’re interacting with digital objects, objects that exist as data structures on computers, the actual concrete processes up and running on a computer room. We’re interacting with concrete data structures realized in circuitry on these computers.

And those digital objects have real causal powers. They make things happen. They’re when two objects interact in VR, the two corresponding data structures on a computer are genuinely interacting with each other. When a virtual object appears a certain way to us, that data structure is at the beginning of a causal chain that affects our conscious experience in much the same way that a physical object might be at the start of a causal chain affecting our experience.

And most importantly, I want to argue that, just say, let’s take the extreme case of… I find it useful to start with the extreme case of the simulation hypothesis, where all of this is a simulation. I want to say in that case when I have an experience of say a tree in front of me or here’s a desk and a chair, I’m going to say none of that is illusory. There’s no illusion there. You’re interacting with digital object. It’s a digital table or a digital chair, but it’s still perfectly real.

And the way that I end up arguing for this in the book is to argue that the simulation hypothesis should be seen as equivalent to a kind of hypothesis which has become familiar in physics, the version of the so-called it from bit hypothesis. The it from bit hypothesis says roughly that physical reality is grounded in a level of interaction of bits or some computational process. The paradigm illustration here would be Conway’s Game of Life where you have a cellular automaton with cells that could be on or off and simple rules governing their interaction.

And various people have speculated that the laws of physics could be grounded in some kind of algorithmic process, perhaps analogous to Conway’s Game of Life. People call this digital physics. And it’s not especially widely believed among physicists, but there are some people who take it seriously. And at least it’s a coherent hypothesis that, yeah, there’s a level of bits underneath physical objects in reality. And importantly, if the it from bit hypothesis is true, this is not a hypothesis where nothing is real, it’s just a world where there still are chairs and tables. There still are atoms and quarks. It’s just they’re made of bits. There’s a level underneath the quarks, the level of bits that things are perfectly real.

So in the book I try to argue that actually the simulation hypothesis is equivalent to this it from bit hypothesis. It’s basically, if we’re in a simulation, yeah, there are still tables and chairs, atoms and quarks. There’s just a level of bits underneath that. All this is realized maybe by a computer process involving the interaction of bits and maybe there’s something underneath that in turn that leads to what I call the it from it hypothesis. Maybe if we’re in a simulation, there’s a number of levels like this.

But yeah, the key then is the argument that these two hypotheses are equivalent, which is a case I try to make in chapter nine of the book. The argument itself is complex, but there’s a nice illustration to illustrate it. On one hand, we’ve got a traditional God creating the universe by creating some bits, by, yeah, “Let there be bits,” God says and lays out the bits and gets them interacting. And then we get tables and chairs out of that. And in the other world we have a hacker who does the same thing except via a computer. Let there be bits arranged on the computer, and we get virtual tables and chairs out of that. I want to argue that the God creation scenario and the hacker simulation scenario basically are isomorphic.

Lucas Perry: Okay. I’m being overwhelmed here with all the different ways that we could take this. So one way to come at this is from the metaphysics of it where we look at different cosmological understandings. You talk in your book about there being, what is it called? The dust theory? There may be some kind of dust which can implement any number of arbitrary algorithms, which then potentially above that there are bits, and then ordinary reality as we perceive it as structured and layered on top of that. And looking at reality in this way it gives a computationalist view of metaphysics and so also the world, which then informs how we can think about virtual reality and in particular the simulation hypothesis. So could you introduce the dust theory and how that’s related to the it from bit argument?

David Chalmers: Yeah. The dust theory is an idea that was put forward by the Australian science fiction writer, Greg Egan in his book, Permutation City, which came out in the mid 90s, and is a wonderful science fiction novel about computer simulations. The dust theory is in certain respects even more extreme than my view. I want to say that as long as you have the right computation and the right causal structure between entities in reality, then you’ll get genuine reality. And I argue that can be present in a physical reality, that can be present in a virtual reality. Egan goes a little bit more extreme than me. He says, “You don’t even need this causal structure. All you need is unstructured dust.”

We call it dust. It’s basically a bunch of entities that have no spatial properties, no temporal properties. It’s a whole totally unstructured set of entities, but we think of this as the dust and he thinks the dust will actually generate every computer process that you can imagine. He thinks they can generate any objects that you imagine and any conscious being that you can imagine and so on. Because he thinks there’s ways of interpreting the dust so that it’s for example, implementing any computer program whatsoever. And in this respect, Egan has actually got some things in common with philosophers like the American philosophers, Hilary Putnam, and John Searle, who argued that you can find any computation anywhere.

Searle argued that his wall implemented the WordStar, word processing program. Putnam suggested that maybe a rock could implement complex computations, basically, because you can always map the parts of the computation of the physical object onto the parts of the computation. I actually disagree with this view. I think it’s two unconstrained. I think it makes it too easy for things to be real.

And roughly the reason is I think you need constraints of cause and effect between the objects. For a bunch of entities in a rock or a wall to implement, say a WordStar, they have to be arranged in a certain way so they go through certain state transitions. And so they would go through different state transitions and different circumstances to actually implement that algorithm. And that requires genuine causal structure. And yeah, way back in the 90s, I wrote a couple of articles arguing that the structure you’ll find in a wall or a rock is not enough to implement most computer programs.

And I’d say exactly the same for Egan’s dust theory, that the dust does not have enough structure to support a genuine reality because it doesn’t have these patterns of cause and effect, obeying counterfactuals, if this had happened, then this would’ve happened. And so you just don’t get that rich structure out of the dust. So I want to say that you can get that structure, but to get that structure you need dust structured by cause and effect.

And importantly I think, in average computer simulation like the simulation hypothesis, it’s not like the dust, computer simulations really have this rich causal structure going on inside the computer. You’ve got circuits which are hooked up to each other in the patterns of cause and effect that are isomorphic to that in the physical reality. That’s why I say virtual realities are genuine realities because they actually have this underlying computational structure.

But I would disagree with Egan that the dust is a genuine reality because the dust doesn’t have these patterns of cause and effect. I ended up having a bunch of email with Greg Egan about this and he was arguing for his own particular theory of causation, which went another way. But yeah, at least that’s where I want to hold the line, cause and effect matters.

Lucas Perry: My questions are, so what is the work then that you see the dust theory doing in your overall book in terms of your arguments for virtual reality as genuine reality?

David Chalmers: The dust theory comes relatively late in the book, right? Earlier on I bring in this it from bit idea that yeah, all of reality might be grounded in information in bits, in computational processes. I see that dust theory is being, but partially tied to a certain objection somebody might make, that I’ve made it too easy for things to be real now. If I can find reality in a whole bunch of bits like that, maybe I’m going to be able to find this reality everywhere. And even if we’re just connected to dust, there’ll be trees and chairs, and now isn’t reality made trivial. So partly I think thats an objection I want to address, one say no it’s still not trivial to have reality. You need all this structure, this kind of cause and effect structure or roughly equivalently, a certain mathematical structure in the laws of nature.

And that’s really a substantive constraint, but it’s also a way of helping to motivate the view that I call structuralism about, and that many others have called structuralism or structural realism about physical reality, which I think is kind of actually the key to my thesis. Why does virtual reality get to count as genuine reality? Ah, because it has the right structure. It has the right causal structure. It has the right kind of mathematically characterizable interactions between different entities. What matters is not so much what these things are made of intrinsically, but the interactions and the relations between them. And that’s a view that many philosophers of science these days find very plausible. It goes back to Punqueray and Russell and Carnap and others, but yeah, very popular these days. What matters lets say for a theory in physics to be true is that basically you’ve got entities with the right kind of structure of interactions between them.

And if that view is right, then it gives a nice explanation of why virtual reality, it counts as genuine reality because when you have a computer simulation of a given physical of say of the physical world that has all that preserves computer simulation preserves, the relevant kind of structure. So yeah, the structure of the laws of physics could be found at a physical reality, but it could also that structure could also be found in a computer simulation of that reality. Computer simulations have the right structure, but then it’s yeah. So it turns that’s not totally unconstrained. Some people think, Egan thought the dust is good enough. Some people think purely mathematical structure is good enough. In fact, your sometime boss, Max Tegmark, I think may, may think something like this in his book, on the mathematical universe, he argues that reality is completely mathematical.

And at least sometimes it seems to look as if he’s saying the content of our physical theories is just purely mathematical claims that there exists certain entities with a certain mathematical structure. And I worry that as with Egan that if you understand the content of our theories is purely mathematical, then you’ll find that structure anywhere. You’ll find it in the dust. You’ll find it in any abstract about mathematics. And there’s a worry that actually our physical theories could be trivialized and they can all end up being true, because we can always find dust or mathematical entities with the right structure. But I think if you add the constraint of cause and effect here, then it’s no longer trivialized.

So I think of Egan and Tegmark as potentially embracing a kind of structuralism, which is even broader than mine lets in even more kinds of things as reality. And I don’t be quite so unconstrained. So I want to add some of these constraints of cause and effect. So this is rather late in the book, this is kind of articulating this, the nature of the kind of structuralism that I see as underlying this view of reality.

Lucas Perry: So, Egan and Max might be letting in entities into the category of what is real, which might not have causal force. And so you’re adopting this criteria of cause and effect being important in structuralism for what counts as genuine.

David Chalmers: Yeah. I worry that if we don’t at least have, I think cause and effect is very important to our ordinary conception of reality that for example of things have causal powers. If we don’t have some kind of causal constraint on reality, then it becomes almost trivial to interpret reality as being anywhere. I guess I think of what we mean by real is partly a verbal question, but I think of causal powers is very central to our ordinary notion of reality. And I think that manages actually to give us a highly constrained notion of reality. Where realities are at least partly individuated by their causal structures, but where it’s not how, it’s not now so broad that arbitrary conglomerates of dust get to count as being on a par with our physical world or arbitrary sets of mathematical entities likewise.

Lucas Perry: Let’s talk more about criteria for what makes things count as real or genuine or whether or not they exist. You spend a lot of time on this in your book, sort of setting and then arguing for different positions on whether or not certain criteria are necessary and or sufficient for satisfying some understanding of like, what is real or what is it that it means that something exists or that it’s genuine. And this is really important for your central thesis of virtual reality being genuine reality. Cause it’s important to know like what it is that exists and how virtual reality fits into what is real overall. So could you explore some of the criteria for what it means for something to be part of reality or what is reality?

David Chalmers: Yeah. I end up discussing five different notions of reality of what it is for something to be real. I mean, this kind of goes back to The Matrix where Neo says this isn’t real and Morpheus says, “What is real? How do you define real?” That’s the question? How do you define “real?” And I talk about five main, any number of different things people have meant by real, but I talk about five main strands in our conception of reality. One very broad one is something is real just if it exists. Anything that exists is real. So if that tree exists, it’s real. If the number two exists, it’s real. I think that’s often what we mean. It’s also a little bit unhelpful as a criterion, because it just pushes back the question to what is it for something to exist? But it’s a start.

Then the second one is the one we’ve just been talking about the criterion of causal powers. This actually goes back to a one of Plato’s dialogue where the Iliadic stranger comes in and says for something to be real, it’s got to be able to make a difference. It’s got to be able to do something, that’s the causal power criterion. And so if you to be real, you’ve got to have effects. Some people dispute that’s necessary. Maybe numbers could be real, even if they don’t have effects, maybe consciousness could be real, even if it doesn’t have effects, but it certainly seems to be a plausible sufficient condition so that’s causal powers. Another one is mind independence, existing independently of the mind. There’s this nice slogan from Philip K Dick where he said that reality, something is real if when you stop believing in it, it doesn’t go away. Reality is that which when you stop believing in it, it doesn’t go away.

That’s basically to say its existence doesn’t depend on our beliefs. Some things such that their existence depends on our beliefs. I don’t know the Easter bunny or something, but more generally I’d say that some things have existence that depends on our minds. Maybe a Mirage of some water up ahead. That basically depends on there being a certain conscious experience in my mind, but there are some things out there independent of my mind that aren’t all in my mind, that don’t just depend on my mind. And so this leads to the third criteria and something is real when it doesn’t wholly depend on our minds, it’s out there independently of us.

Now this is a controversial criterion. People think that somethings like money may be real, even though it largely depends on our attitudes towards money. Our treating something as money as part of what makes it money. And actually in the Harry Potter books, I think its Dumbledore has a slogan that goes the opposite way of Philip K Dick’s. At one point towards the end of the novels, Dumbledore says to Harry, Harry says, “ah, but none of this is real and this is all just happening inside my head” and Dumbledore says something like, “just because all this is happening inside your head, Harry, why do you think that makes it any less real?”

So I don’t know. There is a kind of mental reality you get from the mind, but at any way, I think mind independence is one important thing that we haven’t often have in mind when we talk about reality. A fourth one is that we sometimes talk about genuineness or authenticity. And one way to get at this is we often talk about not just whether an object is real, but whether it’s a real something like maybe you have a robot kitten, okay, it’s a real object. Yes. It’s a real object. It’s a genuine object with causal powers out there independently of us. But is it a real kitten? Is it a real kitten? Most people would say that, no, a robot kitten maybe it’s a real object, but it’s not a real kitten. So it’s not a genuine, authentic kitten.

More generally for any X we can ask, is this a real X? And that’s this criterion of genuineness, but then maybe the deepest and most important criterion for me is the one of not, basically something is real if it’s not an illusion, that is if it’s rough the way it seems to be. It seems to me that I’m in this environment, there are objects all around me in space with certain colors. There’s a tree out there and there’s a pond. And roughly I’d say that things are, all that’s real if there are things out there roughly as they seem to be, but if all this is an illusion, then those things are not real. So then we say things are real. If they’re not an illusion, if they’re roughly, as they seem to be. So one thing I then do is to try to argue that for the simulation hypothesis, at least if we’re in a simulation, then the objects we perceive are real in all five of those senses, they have causal powers. They can do things they’re out there independently of our minds. They exist. They’re genuine.

That’s a real tree, at least by what we mean by tree. And they’re not illusions. So five out of five on what I call the reality checklist, ordinary virtual reality, I want to say it gets four out of five. The virtual objects we interact with are they’re still digital objects with causal powers out there independently of us. They exist. They needn’t be illusions. I argue that at length that your experiences in VR needn’t be illusions. You can correctly perceive a virtual world as virtual, but arguably they’re not at least genuine. Maybe for example, the virtual kitten that you interact with in VR. Okay, it’s a virtual kitten, but it’s not a genuine kitten anymore than the robot kitten is. So maybe virtual tables are not, at least in our ordinary language, genuine tables. Virtual kittens are not genuine kittens, but they’re still real objects, but maybe there’s some sense in which they fail one of the five criteria for reality. So I would say ordinary virtual realities, at least as we deal with them now may get to four out of five or 80% on the reality checklist.

It’s possible that our language might evolve over time to eventually count virtual chairs as genuine chairs and virtual kittens as genuine kittens. And then we might be more VR inclusive in our talk. And then maybe we’d come to regard virtual reality is five out of five on the checklist. But anyway, that’s the rough way I ended up breaking on these notions into at least five. And of course, one way to come back is to say, ah, you’ve missed the crucial notion of reality actually, to be real requires this and VR is not real in that sense. I just read a review of the book where someone said, ah, look obviously VR isn’t real because it’s not part of the base level of reality. The fundamental outer shell of reality. That’s what’s real. So I guess this person was advocating. To be real you’ve got to be part of the base fundamental outer shell of reality. I mean, I guess I don’t see why that has to be true.

Lucas Perry: I mean, isn’t it though?

David Chalmers: Well.

Lucas Perry: It’s implemented on that.

David Chalmers: Yeah. It’s true so that’s one way to come back to this is to say the digital objects ultimately do exist in the outer shell. They’re just diverse.

Lucas Perry: They’re undivided from the outer shell. They just look like they’re just like can be conceptualized as secondary.

David Chalmers: Yeah, no, it is very much continuous with, I want to say the very least they’re on a par with like micro universes. I mean we have people talk now about, say baby universes. Growing up in black holes, inside a larger universe and people take that seriously and then we’d still say, okay, well this universe is part of this universe and that part of the universe can be just as real as the universe as a whole. So I don’t think, yeah. So I guess I don’t think being the whole universe is required to be real. We’ve got to acknowledge reality to parts of the world.

So we have kind of like a common sense ontology. A common sense view of the world and to me it seems like that’s more Newtonian feeling science evolves and then we get quantum mechanics. And so something you describe you explore in your book is this difference between I forget what you call it, like the conventional view of the world. And then, oh, sorry, the manifest in the scientific image is what you call it. And part of this manifest image is that it seems like humans’ kind of have like our common sense ontology is kind of platonic.

So how would you describe the common sense view of what is real?

David Chalmers: Yeah, I talk about the garden of Eden, which is our naive pre-theoretical sense of the world before we’ve started doing science and developing a more sophisticated view. I do think we have got this tendency to think about reality as like yeah, billiard balls out there and solid objects, colored objects out there in a certain space, an absolute three-dimensional space with one dimension of time. I think that’s the model of reality we had in the garden of Eden. So yeah, one of the conceits in the book is well in the garden of Eden things actually were that way. There were three absolute dimensions of space and one dimension of time objects were rock solid. They were colored the way I marked this in the book is by capital letters, say in the garden of Eden, there was capital S “Space” and capital T “Time” where objects were capital S “Solid” and capital C “Colored.”

They were capital R “Red” and capital G “Green.” And maybe there was capital G “Good” and bad and capital F “Free will” and so on. But then we develop the scientific view of the world. We eat from the tree of knowledge. It gives us knowledge of science and then, okay, well, the world is not quite like that naive conception implied there’s no, there’s four dimensional space time without an absolute space or a time. Objects don’t seem to have these primitive colors out there on their services. They just have things like reflectance properties that reflect light in a certain way that affects our experience in a certain way. Nothing is capital S “Solid.” The objects are mostly empty space, but they still manage to resist penetration and then the right way. So I think of this as the fall for Eden. And for many things we’ve gone from capital S “Space” to lowercase S “space.” We’ve gone from capital S “solidity” to lowercase S “solidity.”

And one thing that I think goes on here is that we’ve moved from kind of a conception of these things as primitive. Primitive space and primitive colors is just like redness out there on the surface of things, what I call primitivism to, rather to a kind of functionalism where we understand things in terms of their effects. To be red now is not to have some absolute intrinsic quality of redness, but it’s to be such as to affect us to produce certain experiences to look red. To be solid is not to be absolutely intrinsically solid, but to interact with other objects in such a way that they’re solid.

So I think in general, this goes along with moving from a conception of reality as all these absolute intrinsic properties out there to a much more structuralist conception of reality here where what matters for things being real is the right patterns of causal interaction with each other of entities with each other. I’m not saying all there is to reality is structure. My own view is that consciousness in particular is not just reducible to this kind of abstract structure consciousness does in fact have some intrinsic qualities and so on. So I do think that’s important too, but I do think in general, the move from the naive conception to the scientific conception of reality has often involved going from these kind of a conception of these primitive intrinsic qualities to a more structural conception of reality.

Lucas Perry: Right. So I imagine that many of the people who will resist this thesis in your book that virtual reality is genuine reality, maybe coming at it from some of these more common sense intuitions about what it means for something to be real, like red as a property that’s intrinsic on the surface of a thing. How do you see your book, so are there like common sense intuitions or misconceptions that you see your book as addressing?

David Chalmers: I guess I do think, yeah. Many people do find it as common sense that virtual reality is not full scale reality. First class reality. It doesn’t live up to our ordinary conception of reality. And sometimes I think they may have in mind this Edenic conception of reality, the way it was in the Garden of Eden to which my reply is. Yeah. Okay. I agree. Virtual reality does not have everything that we had in the Garden of Eden conception of reality, but neither does ordinary physical reality, even in the kind of physical reality developed in light of science, it’s not the garden of Eden picture of reality anymore. We’ve lost absolute space and absolute time. Now we’ve lost absolute colors and absolute solidity. What we have is now this complex mathematical structure of entities these interacting at a deep level.

I mean, the further you look, the more evanescent it gets, quantum mechanics is just this it’s wave function where objects don’t need to have determinate possessions, and who knows what’s going on there in string theory and other quantum gravity theories, it looks like space may not be fundamental at all. People have entertained the idea that time is not fundamental at all. So I think a physical reality in a way it’s, I’m saying virtual reality is genuine in reality, but one way to paraphrase that is virtual reality is just as real as physical reality. If you want to hear that by saying, well, physical reality is turned out to be more like virtual reality, then I wouldn’t necessarily argue with that physical reality is not the Garden of Eden billiard ball conception of reality anymore.

It’s this much more evanescent thing, which is partly characterizable by, it’s just playing all these, having the right kind of a certain kind of structure. And I think all that we can find in virtual reality. So yeah. So one thing I would do to this person questioning is to say, well, what do you think even about physical reality in light of the last hundred years or so of science?

Lucas Perry: Yeah. The reviewer’s comments that you mentioned come off to me as kind of being informed by the Eden view.

David Chalmers: Yeah. I think it’s right. It’s quite common that’s really what it is. It’s our naive conception of reality and what reality is like, but yeah, maybe then it’s already turned out that the world is not real in that sense.

Lucas Perry: One thing I’d like to pivot here into is exploring value more. How do you see the question of value fitting into your book? There’s this other central thesis here that you can live a good life in virtual reality, which seems to go against people’s common intuitions that you can’t. There’s this survey about whether or not people would go into experience machines and most people wouldn’t.

David Chalmers: Yeah, Nozick had this famous case of the experience machine, where your body’s in a tank, and you get all these amazing experiences of being highly successful. Most people say they wouldn’t enter the experience machine. I think of professional philosophers on a survey we did, maybe 15% said they would enter and 70 odd percent said they wouldn’t. And a few agnostic. The experience machine though, and many people have treated that as a model for VR in general. But I think the experience machine as Nozick described it, is actually different from VR in some respects. One is that very important respect is that the experience machine seems to be scripted, seems to be pre-programmed you go in there and your life will live out script. You get to become world champion, but it wasn’t really anything you did. That was just the script playing itself out. Many people think that’s fake. That’s not something I actually did. It was just something that happened to me.

VR by contrast, you go into VR, even an ordinary video game, you still got some degree of free will. You’re to some extent controlling what happens. You go into Second Life, or Fortnite whatever basically, you’ve got all kinds of it’s not scripted. It’s not pre-programmed, it’s open ended. I think the virtual worlds of the future will be increasingly open ended. I don’t think worries about the experience machine tend to undermine virtual worlds. More generally, I think I want to argue that yeah, virtual worlds can basically be on a par with physical worlds, especially once we’ve recognized that they needn’t be illusions, they needn’t be pre-programmed and so on. Then what are they missing? I think you’ve got what’s important to a good life? Maybe consciousness, the right subjective experiences. Also, relationships, very, very important. But I think in the VR certainly at least in a multi-user VR where many people are connected.

That’s another thing about the experience machine, it’s just you, presumably who’s conscious. But in a VR with I’m assuming a virtual world with many conscious beings, you can have relationships with them and get the social meaning of your life. That way knowledge and understanding, I think you can come to have all those things in VR. I think basically all the determinants of a good life, it’s hard to see what’s in principle missing in VR. There are some worries. Maybe if you want a fully natural life, a life, which is as close to nature as possible, VR is not going to do it because it’s going to be removed from nature. But then many of us live in cities or spend most of our time indoors. That’s also removed from nature and it’s still compatible with a meaningful life. There are issues about birth and death, which it’s not obvious how genuine birth and death will work at least in near term virtual worlds.

Maybe once there’s uploading, there’ll be birth and death in virtual worlds if the relevant creatures are fully virtual. But you might think if virtual was lack birth and death, there are aspects of meaning that they lack. I don’t want to say they’re exactly on a path with physical reality and all respects, but I’d say that virtual realities can at least have the prime determinants of a good and meaningful life. It’s not to say that life in virtual reality going to be wonderful. They may well be awful just as life in physical reality could be awful. But my thesis is roughly that at least the same range of value from the wonderful to the awful, is possible in virtual reality, just as it is in physical reality.

Lucas Perry: It sounds like a lot of people are afraid that they’ll be losing out on some of the important things you get from natural life, if virtual life were to take over?

David Chalmers: What are the important things you have in mind?

Lucas Perry: You mentioned people want to be able to accomplish things. People want to be a certain sort of person. People want to be in touch with a deeper reality.

David Chalmers: I certainly think in VR, you can be a certain person, very characteristic. With your own personal traits, you can have transformative experiences in virtual reality. Probably you can develop as a person. You can certainly have achievements in VR.

People who live and spend a lot of time, long term in worlds like second life certainly have real achievements, real relationships. Being in touch with a deeper reality, if by a deeper reality, you mean nature. In VR you’re somewhat removed from nature, but I think that’s somewhat optional.

In the short term at least, there are things like the role of the body, in existing VRs embodiment is extremely primitive. You’ve got these avatars, but our relationship with them is nothing like our relationship with our physical body. Things like eating, drinking, sex, or just physical companionship and so on. There’s not genuine analogs for those in existing VR. Maybe as time goes on, those things will become better. But I can imagine people thinking I value experiences of my physical body, and real eating and drinking and sex and companionship and so on and physical bodies.

But I could also imagine other people saying actually in VR now, in 200 years time people will say we’ve got these virtual bodies, which are actually amazing. Can do all that and give you all those experiences and much more and hey, you should try this. Maybe different people would prefer different things. But I do think to some considerable extent, thoughts about the body may be responsible for a fair amount of resistance to VR.

Lucas Perry: Could you talk a little bit about the different kinds of technological implementations of virtual reality? Whether it be uploading, or brains connected to virtual realities.

David Chalmers: Right now the dominant virtual worlds are not even VR at all of course. The virtual worlds people use the most now are video game style worlds typically on desktop or mobile computers on 2D screens.

But immersive VR is picking up speed fast with virtual reality headsets, like the Oculus Quest and they’re still bulky and somewhat primitive. But they’re getting better every year and they’ll gradually get less bulky and more primitive with more detail, better images and so on.

The other form factor, which is developing fast now is the augmented reality form with something like glasses, or transparent headsets that allow you to see the physical world, but also project virtual objects among the physical world.

Maybe it’s an image of someone you’re talking to. Maybe it’s just some information you need for dealing with the world. Maybe it’s a Pokemon Go creature you’re trying to acquire for your digital collection.

That’s the augmented reality form factor in glasses. A lot of people think that over the next 10 or 20 years, the augmented and virtual reality form factors could converge. Eventually we’ll be able to maybe have a set of glasses that could project digital objects into your environment, based on computer processes.

Maybe you could dial maybe a slider, which you go all the way down to dial out the physical world, be in a purely virtual world. Dial all the way up to be in a purely physical world, or in between, have elements of both.

That’s one way the technology seems to be going. The longer term there’s the possibility of bringing in brain computer interfaces. I think VR with standard perceptual interfaces works pretty well for vision and for hearing. You can get pretty good visual and auditory experiences from VR headsets, but embodiment is much more limited via sense of your own body.

But maybe once brain computer interfaces are possible, then there’ll be ways of getting elements, these computational elements to interact directly with bits of your brain. Whether it’s say visual cortex, auditory cortex for vision and hearing, or for the various aspects of embodied experience processed by the parts of the brain responsible for bodily experience.

Maybe that could eventually give you more authentic bodily experiences. Then eventually, bits of the potentially all kinds of computational circuitry could come to be embedded with brain circuitry in terms of circuitry, which is going to be partly biological and partly digital.

In the long term of course, there’s the prospect of uploading, which is the uploading the brain entirely to a digital process. Maybe once our brains are wearing out, we’ve replaced some of them with silicon circuitry, but you want to live forever upload yourself completely.

You’re running on digital circuitry. Of course, this raises so many philosophical issues. Will it still be me? Will I still be conscious? And so on. But assuming that it is possible to do this and have conscious beings and with this digital technology, then that being could then be fully continuous with the rest of the world.

That would just open up so much potential for new virtual reality, combined with new cognitive process, possibly giving rise to experiences that become now even imagine. Now this is very distant future, I’m thinking 100 plus years who knows.

Lucas Perry: You have long AGI timelines.

David Chalmers: This all does interact with AGI. I’m on record as 70% chance of AGI within a century. Maybe that’s sped up a bit.

Lucas Perry: You have shorter timelines.

David Chalmers: As far as this interacts with AI, I’m maybe on 50 years mean expected value for years until AGI. Once you go to AGI, all this stuff ought to have happened pretty fast. Maybe there’s a case for saying that within a century is conservative.

Lucas Perry: For uploads?

David Chalmers: Yeah, for uploads. I think once you go to AGIs, uploads are presumably-

Lucas Perry: Around the corner?

David Chalmers: … uploads are around the corner. At least if you believe like me, that once you go to AGI, then you’ll have AGI plus, and then you’ll have AGI plus, plus super intelligence. Then the AGI plus, plus is not going to have too much trouble with uploading technology and the like.

Lucas Perry: How does consciousness fit in all this?

David Chalmers: One very important question for uploading is whether uploads will even be conscious. This is also very relevant to thinking about the simulation hypothesis. Because if computer simulations of brains are not conscious, then it looks like we can rule out the simulation hypothesis, because we know we are conscious.

If simulations couldn’t be conscious, then we’re not simulations. At least the version of the simulation hypothesis, where we are part of the simulation could then be ruled out.

Now as it happens, I believe that simulations can be conscious. I believe consciousness is independent of substrate. It doesn’t matter whether you’re up and running on biology or on silicon, you’re probably going to be conscious.

You can run these familiar thought experiments, where you replace say neurons by silicon chips, replace biology by digital technology. I would argue that consciousness will be preserved.

That means at the very least gradual uploading, where you upload bits of your brain lets say a neuron at a time. I think that’s a pretty plausible way to preserve consciousness and preserve identity. But if I’m wrong about that and I could be, because nobody understands consciousness.

If I’m wrong about that, then uploads will not be conscious and these totally simulated worlds that people produce could end up being worlds of zombies. That’s at least something to worry about.

It’d be certainly risky to upload everybody to the cloud, to digital processes. Always keep some people anchored in biology just in case consciousness does require biology, because it’d be a rather awful future to have a world of super intelligent, but unconscious zombies being the only beings that exist.

Lucas Perry: I’ve heard from people who agree with substrate independence that digital or classical computer can’t be conscious. Are you aware of responses like that? Slash do you have a response to people who agree that consciousness is substrate independent, but the classical digital computers can’t be conscious.

I’m not sure what their exact view is, but something like the bits don’t all know about all the other bits. There’s no integration to create a unified conscious experience.

David Chalmers: The version of this I’ve heard I’m most familiar with, comes from Giulio Tononi’s Integrated Information Theory. Tononi and Christof Koch have argued that processes running on classical computers that is on von Neumann architectures cannot be conscious.

Roughly because von Neumann architectures have this serial core that everything is run through. They argue that this doesn’t have the property that Tononi calls integrated information and therefore is not conscious.

Now I’m very dubious about these arguments. I’m very dubious about a theory that says this serial bottleneck would undermine consciousness. I just think that’s all part of the implementation.

You could still have 84 billion simulated neurons interacting with each other. The mere fact that their interactions are mediated by a common CPU, I don’t see why that should undermine consciousness.

But if they’re right then fine, I’d say they’ve just discovered something about the functional organization that is required for consciousness. It needs to be a certain parallel organization as opposed to this serial organization.

But if so, you’re still right, it’s still perfectly substrate independent. As long as we upload ourselves not to a von Neumann simulation, but to a parallel simulation, which obviously it’s going to be the most powerful and efficient way to do this anyway, then uploading ought to be possible.

I guess another view is that consciousness could turn out to require to rely on quantum computation in a certain essential way. A mere classical computer might not be conscious, whereas quantum computers could be.

If so, that’s very interesting, but I would still imagine that all that would also be substrate independent and for uploading them, we just need to upload ourselves to the right quantum computer. I think those points while interesting, don’t really provide fundamental obstacles to uploading with consciousness here.

Lucas Perry: How do you see problems in the philosophy of identity fitting in here into virtual reality? For example with Derek Parfit’s thought experiments.

David Chalmers: Parfit had these famous thought experiments about the teletransporter from Star Trek, where you duplicate your body. Is that still me at the other end? The uploading cases are very similar to that in certain respects.

The teletransporter, you’ve got so many different cases. You’ve got is the original still around, then you create the copy? What if you create two copies? All these come up in the uploading case too.

There’s destructive uploading where we destroy the original, but create an upload. There’s non-destructive uploading, where we keep the original around, but also make an upload. There’s multiple copy uploading and so on.

In certain respects, there’s very much analogous to the teleporter case. The change is that we don’t duplicate the being biologically. We end up with a silicon isomorph, rather than a biological duplicate.

But aside from that, they’re very similar. If you think that silicon isomorph can be just as conscious as biological beings, maybe the two things roughly go together.

The same puzzle cases very much arise. Just say the first uploads are non-destructive, we stay around and we create uploaded copies. Then the tendency is going to be to regard the uploads is very different people from the original.

If the first uploads are destructive, you make copies while destroying the original. Maybe there’s going to be much more of a tendency to regard the uploads as being the same person as the original.

If we could make multiple uploads all the time, then there’ll be maybe a tendency to regard uploads as second class citizens and so on. The thought experiments here are complex and wonderful.

I tend myself to be somewhat sympathetic with Parfit’s deflationary views of these things, which is there may not be very much absolute continuity of people over time. Per the very concept of personal identity, maybe one of these Edenic concepts, that we actually persist through time as absolute subjects.

Maybe all there are just different people at different times that stand in psychological and memory and other continuity relations to each other. Maybe that’s all there is to say.

This gets closer now to Buddhist style, no self views, at least with no identic capital S “Self,” but I’m very unsure about all of these matters about identity.

Lucas Perry: How would you upload yourself?

David Chalmers: I think the safest way to do it, would be gradually. Replace my neurons one at a time by digital circuits. If I did it all at once, destroy the original creator uploaded copy, I’d worry that I’d be gone. I don’t know that. I just worry about it a bit more.

To remove that worry, do it gradually and then I’m much less worried that I’d be gone. If I can do it a bit at a time I’m still here. I’m still here. I’m still here. To do it with maximum safety, maybe I could be conscious throughout, with a continuous stream of consciousness throughout this process.

I’m here watching the operation. They change my neurons over and in that case, then it really seems to me as if there’s a continuous stream of consciousness. Continuous stream of consciousness seems to either, I don’t know if it guarantees identity over time, but it seems pretty close to what we have in ordinary reality.

We’re having continuous stream of consciousness overtime, seems to be the thing that goes along with what we usually think of as identity over time. It’s not required because we can fall asleep and arguably lose consciousness and wake up.

Most people would say we’re still the same person, but still being continuously conscious for a period, seems about as good a guarantee as you’re going to get of being the same person. Maybe this would be the philosophically safest way to upload.

Lucas Perry: Is sleeping not an example that breaks that?

David Chalmers: I’m not saying it’s a necessary condition for a personal identity, just a sufficient condition, just plausibly continuous consciousness sufficient for identity over time. So far there is identity over time. Yes, probably too stronger condition.

It maybe you can get identity from much weaker relations, but in order to be as safe as possible, I’m going to go with the strongest sufficient condition.

Lucas Perry: One neuron at a time.

David Chalmers: Maybe 10 neurons at a time. Maybe even a few columns at a time. I don’t know.

Lucas Perry: Do you think Buddhist’s that realize no self, would be more willing to upload?

David Chalmers: I would think so and I would hope so. I haven’t done systematic polls on this. Now I’m thinking I’ve got to get the data from the last PhilPapers survey and find views on uploading, which we asked about out. We didn’t ask about are you Buddhist? We didn’t ask do you for example, specialize in Asian philosophy?

I wonder if there could at least be a correlation between specialization and Asian philosophy, and certain views about uploading. Although it’ll be complicated by the fact that this will also include Hindu people who very much believe in absolute self, and Chinese philosophers who have all kinds of very different views. Maybe it would require some more fine grained survey analysis.

Lucas Perry: I love that you do these surveys. They’re very cool. Everyone should check them out. It’s a really cool way to see what philosophers are thinking. If you weren’t doing them, we wouldn’t know.

David Chalmers: Go to philsurvey.org. This later survey in 2020, we surveyed about 2000 odd philosophers from around the world, on 100 different philosophical questions like God, theism, or atheism, mind, physicalism or non-physicalism and so on.

We actually got data about what professional philosophers tend to believe. You can look at correlations between questions, correlations with area, with gender, with age and so on. It’s quite fascinating. Go to philsurvey.org you’ll find the results.

Lucas Perry: Descartes plays a major role in your book, both due to his skepticism about the external world, and whether or not it is that we know anything about it. Then there’s also the mind body problem, which you explore. Since we’re talking about consciousness and the self, I’m curious if you could explain how the mind body problem fits in all this?

David Chalmers: In a number of ways. Questions about the mind are not front and center in this book, but they come up along the way in many different contexts. In the end, actually part five of the book has three chapters on different questions about the mind.

One of them is the question we’ve just been raising. Could AI systems be conscious? Could uploading lead to a conscious being and so on? That’s one chapter of the book. But another one just thinks about mind, body relations in more ordinary virtual realities.

One really interesting fact about existing VR systems, is that if you actually look at virtual worlds, that Cartesian thought that Descartes thought there’s a physical world that the mind interacts with, and the mind is outside the physical world, but somehow interacts with it.

You look at a virtual world, virtual worlds often have their own physics and their own algorithmic processes that govern the physical processes in the virtual world. But then there’s this other category of things, users, players, people who are using VR and they are running on processes totally outside the virtual world.

When I enter a VR, the VR has its own physics, but I am not subject to that physics. I’ve got this mind which is operating totally outside the virtual world. You can imagine if somebody grew up in a virtual world like this.

If Descartes grew up in a virtual world, we’ve got an illustration where Descartes grows up inside Minecraft and gets into an argument with Princess Elizabeth, about whether the mind is outside this physical world interacting with it.

Most people think that the actual Descartes was wrong, but if we grew up in VR, Descartes would be right. He’d say yeah, the mind is actually something outside. He’d look at the world around him and say, “This is subject to its physics and so the mind is just not part of that. It’s outside all that. it exists in another realm and interacts with it.”

There’s a perspective of the broader realm, which all this looks physical and continuous. But at least from the perspective of the virtual world, it’s as if Descartes was right.

That’s an interesting illustration of a Cartesian interaction of dualism, where the mental and the physical are distinct. It shows a way at which something like that could turn out to be true under certain versions of the simulation hypothesis, say with brains interacting with simulations.

Maybe even is true of something isomorphic of it is true, even in ordinary virtual realities. At least that’s interesting and making sense of this mind body interaction, which is often viewed as unscientific or non naturalistic idea. But here’s a perfectly naturalistic version of mind body dualism.

Lucas Perry: I love this part and also found it surprising for that reason, because Cartesian dualism, it always feels supernatural, but here’s a natural explanation.

David Chalmers: One general theme in this book is that there’s a lot of stuff that feels supernatural, but once you look at it through the lens of VR, needn’t be quite so supernatural, looks a lot more naturalistic. Of course, the other example is God. If your creator is somebody, a programmer in the next universe up, suddenly God doesn’t look quite so supernatural.

Lucas Perry: Magic is like using the console in our reality to run scripts on the simulators world, or something like that.

David Chalmers: This is naturalistic magic. Magic has to obey its own principles too. There’s just different principles in the next universe up.

Lucas Perry: Clearly it seems your view is consciousness is the foundation of all values, is that right?

David Chalmers: Pretty much. Pretty much. Without consciousness no value. I don’t want to say consciousness is all there is to value. There might be other things that matter as well, but I think you probably have to have consciousness to have value in your life.

Then for example, relations between conscious beings, relations between consciousness and the world can matter for value. Nozick’s experience machine tends to suggest that consciousness alone is not quite enough.

There’s got to be maybe things like actually achieving your goals and so on that matters as well. But I think consciousness is at the very core of what matters and value.

Lucas Perry: We have virtual worlds and people don’t like them, because they want to have an interaction with whatever’s like natural, or they want to be a certain kind of person, or they want the people in it to be implemented. They want them in real space, things like that.

Part of what makes being in Nozick’s experience machine unsatisfactory, is knowing that some of these things aren’t being satisfied. But what if you didn’t know that those things weren’t being satisfied? You thought that they were.

David Chalmers: I guess my intuition is that’s still bad. There’s this famous case that people have raised just say your partner is unfaithful to you, but it’s really important to you that your relationship be monogamous. However, your partnership, your partner, although professing monogamy has gone off and had relationships with all these other people.

You never know and you’re very happy about this and you go to your death without ever knowing. I think most people’s intuition is that is bad. That life is not as good, as one where the life was the way this person wanted it to be with the monogamous partner.

That brings out that having your goals or your desires satisfied, the world being the way you want it to be, that matters to how good and meaningful a life is. Likewise, I’d say that I think the experience machine is a more extreme example of that.

We really want to be doing these things. If I was to find out 100 years later that hey, any success I’d had in philosophy wasn’t because I wrote good books. It’s just because there was a script that said there’d be certain amounts of success and sales and whatever.

Then boy, that would render any meaning I’d gotten out of my life perfectly hollow. Likewise, even if I never discovered this, if I had the experience of having the successful life, but it was all merely pre-programmed, then I think that would render my life much less.

It’d still be meaningful, but just much less good than I thought it had been. That brings out that the goodness, or the value of one’s life depends on more than just how one experiences things to be.

Lucas Perry: I’m pushing on consequentialist or utilitarian sensibilities here, who might bite the bullet and say that if you didn’t know any of those things, then those worlds are still okay. One thing that you mentioned in your book is that your belief that virtual reality is good, is independent of the moral theory that one has. Could you unpack that a bit?

David Chalmers: I don’t know if it’s totally independent, but I certainly think that my view here is totally consistent with consequentialism and utilitarianism that says, what matters in moral decision making is maximizing good consequences, or maximizing utility.

Now, if you go on to identify the relevant good consequences, with conscious states like maximizing pleasure, or if you say all there is to utility, is the amount of pleasure. Then you would take a different view of the experience machine.

If you thought that all that there is to utility is pleasure and you say in the experience machine, I have the right amount of pleasure so that’s good enough. But I think that’s going well beyond consequentialism, or even utilitarianism.

That’s adding a very specific view of utility and is the one that the founders utilitarianism had, like Bentham and Mill. I would just advocate a broader view of consequentialism, or utilitarianism where there are values that go beyond value driving from pleasure, or from conscious experience.

For example, one source of value is having your desires satisfied or achieving your goals. I think that’s perfectly consistent with utilitarianism, but maybe more consistent with some forms than others.

Lucas Perry: Is having your values satisfied, or your preference satisfied, not just like another conscious state?

David Chalmers: I don’t think so, because you could have two people who go through exactly the same series of conscious states, but for one of whom their desires are satisfied, and for the other one, their desires are not satisfied, maybe they both think their desires are satisfied, but one of them is wrong. They both want their partners to be monogamous. One partner is monogamous and the other one is not. They might have exactly the same conscious states, but one has the world is the way they want it to be, and the other one, the world is not the way they want it to be.

This is what Nozick argued and others have argued in light of the experience machine is that, yeah, there’s a value maybe in desire, satisfaction that goes be on the value of consciousness, per se. I should say both of these views, even the pleasure centric view are totally consistent with my general view of VR. If someone says, “All that matters is experiences,” well, in a certain sense, great. That makes it even easier to lead a good life in VR. But I think if the dialectic is the other way around, even if someone rejects that view … I tend to believe there’s more that matters than just consciousness. Even if you say that, you can still have a good life in a virtual world.

I mean, there’ll be some moral views where you can’t. Just say you’ve got a biocentric view of what makes a life good. You got to have, somehow. the right interactions with real biology. I don’t know, then maybe certain virtual worlds won’t count as having the right kind of biology, and then they won’t count as valuable. So I wouldn’t say these issues are totally independent of each other, but I do think on plausible moral theories, yeah, very much going to be consistent with being able to have a good life in virtual worlds.

Lucas Perry: What does a really good life in a virtual world look like to you?

David Chalmers: Oh boy. What does a really good life look like to me? I mean, different people have different values, but I would say I get value partly from personal relationships, from getting to know people, by having close relationships with with my family, with partners, with friends, with colleagues. I get a lot of value from understanding things, from knowledge and understanding. I get some value from having new experiences and so on. And I guess I’d be inclined to think that in a virtual world, the same things would apply. I’d still get value from relationships with people, I’d still get value from knowledge and understanding, I’d still get value from new kinds of experience.

Now, there made ways in which VR might allow this to go beyond what was possible outside VR. Maybe, for example, there’ll be wholly new forms of experience that go way beyond what was possible inside physical reality, and maybe that would allow for a life which is better in some respects. Maybe it’ll be possible to have who knows what kind of telepathic experiences with other people that give you even closer relationships that are somehow amazing. Maybe it’ll allow immortality, where you can go on having these wonderful experiences for an indefinite amount of time, and that could be better.

I guess in the short term, I think, yeah, the kind of good experiences I’ll have in VR are very much continuous with the good experiences I’ll have elsewhere. It’s a way of I meet friends sometimes in VR, interact with them, talk with them, sometimes play games, sometimes communicate, maybe occasionally have a philosophy lecture or a conference then. So right now, yeah, what’s good about VR is pretty much continuous with a lot of what’s good about physical reality. But yeah, in the long them, there may be ways for it to go beyond.

Lucas Perry: What’s been your favorite VR experience so far?

David Chalmers: Oh boy, everything is fairly primitive for now. I enjoy a bunch of VR games, and I enjoy hanging out with friends. One enjoyable experience was I gave a little lecture of about VR, in VR, to a group of philosopher friends. And we were trying to figure out the physics of VR, of the particular virtual world we were in, which was on a app called Bigscreen.

Lucas Perry: Yeah.

David Chalmers: And yeah, you do things in Bigscreen, like you throw tomatoes, and they behave in weird ways. They kind of a baby laws of physics, but they kind of don’t, and the avatars have their own ways of moving. So we were trying to figure out the basic laws of Bigscreen, and we didn’t get all that far, but we figured out a few things. We were doing science inside a virtual world. And presumably, if we’d kept going, we could have gotten a whole lot further and gotten further into the depths of what the algorithms really are that generate this virtual world or that might have required a scientific revolution or two. So I guess that was a little instance of doing a bit of science inside a virtual world and trying to come to some kind of understanding, and it was at least a very engaging experience.

Lucas Perry: Have you ever played any horror games?

David Chalmers: Not really, no. I’m not much of a gamer, to be honest. I play some simple games like Beat Saber or, what is it? SUPERHOT. But that’s not really a horror game. Super assassins come after you, but what’s your favorite horror game?

Lucas Perry: I was just thinking of my favorite experience and it was probably … Well, I played Killing Floor once when I first got the VR, and it was probably the most frightening experience of my life. The first time you turn around and there’s and embodied thing that feels like it’s right in your face, very interesting. In terms of consciousness and ethics and value, we can explore things like moral patiency and moral agency. So what is your view on the moral status of simulated people?

David Chalmers: My own view is that the main thing, the biggest thing that matters for moral status is consciousness. So as long as simulated beings are conscious as we are, then they matter. Now maybe current non-player characters of the kind you find in video games and so on are basically run by very simple algorithms, and most people would think that those beings are not conscious, in which case their lives don’t matter, in which case it’s okay to shoot these current NPCs in video games.

I mean, maybe we’re wrong about that, and maybe they have some degree of consciousness and we have to worry. But at least the orthodox view here would be that they’re not, and even on a view that describes some consciousness, it’s probably a very simple form of consciousness. But if we look now to a longterm future where there are simulations of brains and simulated AGIs inside these simulated worlds with capacities equivalent to our own, I’d be inclined to think that these beings are going to be conscious like us. And if they’re conscious like us, then I think they matter morally, the way that we do, in which case, one should certainly not be indiscriminately killing simulated beings just because it’s convenient, or just indiscriminately creating them and, and turning them off. So I guess if we do get to the point where …

I mean, this applies inside and outside simulations. If we have robot style AGIs that are conscious and they have moral status like ours, if we have simulation style, AGIs, inhabiting simulations, they also have moral status, much like ours. Now it may be hard for, I’m sure there’s going to be a long and complicated path to actually keep that playing out in social and legal context, and there may be all kinds of resistance to granting simulations, legal rights, social status, and so on. But philosophically, I guess I think that, yeah, if they’re conscious like us, they have a moral status like ours.

Lucas Perry: Do you think that there will be simulated agents with moral status that are not conscious, for example? They could at least be moral agents and not be conscious, but in a society and culture of simulated things, do you think that there would be cases where things that are sufficiently complex, yet not conscious, would still be treated with moral patiency?

David Chalmers: It’s interesting. I’m inclined to think that any system that has human level behavior is likely to be conscious. I’m not sure that there are going to be cases of zombies that lack consciousness entirely, but behave in extremely sophisticated ways just like us. But I might be wrong. Just say Tononi and Koch are right and that no being running on von Neumann architecture is conscious, then yeah. Then it might be smart to develop those systems because they won’t have moral status, but they’ll still be able to do a lot of useful things. But yeah, would they still then be moral agents?

Well, yeah, presumably these behaviorally equivalent systems could do things that look a lot like making moral decisions, even though they’re not conscious. Would they be genuine agents if they’re not conscious? That maybe partly a verbal matter, but they would do things that at least look a lot like agency and making moral decisions. So they’d at least be moral quasi-agents. Then it’s an interesting question whether they should be moral patients, too. If you’ve got a super zombie system making moral decisions, does it deserve some moral respect? I don’t know. I mean, I’m not convinced that consciousness is the only thing that matters, morally. And maybe that, for example, intelligence or planning or reasoning carries some weight independent of consciousness.

If that’s the case, then maybe these beings that are not conscious could still have some moral status as moral patients, that is deserving to be treated well, as well as just moral agents, as well as just performing moral action. Maybe it would be a second class moral patiency. Certainly, if the choice was between, say, killing and being like that and killing an equivalent conscious being, I’d say, yeah, kill the unconscious one. But that’s not to say they have no moral status there.

Lucas Perry: So one of your theses that I’d like to hit on here as well was that we can never know that we’re not in a simulation. Could you unpack this a bit?

David Chalmers: Yeah. Well, this is very closely connected to these traditional questions in epistemology. Can you know you’re not dreaming now? Could you know that you’re not being fooled by an evil demon now? The modern tech version is, “Can you know you’re not in a simulation?” Could you ever prove you’re not in a simulation? And there’s various things people might say, “Oh, I am not in a simulation.” I mean, naively, “This can’t be a simulation because look at my wonderful kitten here. That could never be simulated. It’s so amazing.” But presumably there could be simulated kittens. So that’s not a decisive argument.

More generally, I’m inclined to think that for any evidence anyone could come up with that’s allegedly proof that we’re not in a simulation, that evidence could be simulated, and the same experience could be generated inside a simulated world. This starts to look like there’s nothing, there’s no piece of evidence that could ever decisively prove we’re not in a simulation. And the basic point is just that a perfect simulation would be indistinguishable from the world it’s the simulation of. If that’s the case, awfully hard to see how we could prove that we’re not in a simulation.

Maybe we could get evidence that we are in a simulation. Maybe the simulators could reveal themselves to us and show us the source code. I don’t know. Maybe we could stress test the simulation by running a really intense computer process, more advanced than before suddenly, and maybe it stresses out the simulation and leads to a bug or something. Maybe there are ways we could get evidence.

Lucas Perry: Maybe we don’t want to do that.

David Chalmers: Okay. Maybe that will shut us down.

Lucas Perry: That’ll be an x-risk.

David Chalmers: Yeah. Okay. Yeah. Maybe not a good idea. So there are various ways we could get evidence that we are in a simulation, at least in an imperfect simulation. But I don’t think we can ever get the evidence in the negative that fully proves that we’re not in a simulation. We can try and test for various imperfect simulation hypotheses, but if we get just ordinary the expected results, then it’s always going to be consistent with both. And there are various philosophers who tried to say, “Ah, there are things we could do to refute this idea.” Maybe it’s meaningless. Maybe we could rule it out by being the non-simulation hypothesis being the simpler hypothesis and so on.

So in the book, I try and argue none of those things were work either. And furthermore, once you think about the Bostrom style simulation argument, that says it may be actually quite likely that we’re in a simulation because most populations are likely, it seems pretty reasonable to think that most intelligent populations will develop simulation technology. Once you start the thinking that way, I think it makes it even harder to refute the simulation hypothesis, basically, because by this point, these simulation style hypotheses used to be science fiction cases, very distant from anything we have direct reason to believe in.

But as the technology is developing, these stimulation-style hypotheses become realistic hypotheses, ones which is actually very good reason to think are actually likely to be developed both in our world and in other worlds. And I think that actually makes these … That’s had the effect of making these Cartesian scenarios move from the status of science fiction to being live hypotheses, and I think that makes them even harder to refute. I mean, you can make the abstract point that we can never prove it without the modern technology. But I think once they actually exist, once this technology is an existing technology, it becomes all the harder to epistemologically dismiss.

Lucas Perry: You give some credence in your book for whether or not we live in a simulation. Could you offer those now?

David Chalmers: Yeah. I mean, of course, anything like this is extremely speculative. But basically, in the book, I argue that if there are many conscious human-like simulations, and we are probably simulations ourself, and then the question is, “Is it likely that there are many conscious human-like simulations?” And there’s a couple of ways that could fail. First, it could turn out that simulating beings like us or universes like ours is not even possible. Maybe the physics is uncomputable. Maybe consciousness is uncomputable. So maybe conscious human-like simulations like ours could be impossible. That’s one way this could fail to happen. That’s what I call a sim blocker. These are things that would block these simulations from existing. So one class of sim blockers is, yeah, simulations like this are impossible. But I don’t think that’s more than 50% likely. I’m actually more than 50% confident that simulations like this are possible.

The other class of sim blockers is, well, maybe simulations like this are possible, but for various reasons they’ll never be developed, or not many of them will be to developed. And this class of sim blockers includes the ones that Bostrom focuses on. For example, I think there’s two of them, either we’ll go extinct before we get to that level of technology where we can create simulations, or we’ll get there, but we’ll choose never to create them, or intelligence civilizations will choose never to create them. And that’s the other way this can go wrong is, yeah, these things are possible, but not many of them will ever be created. And I basically say, “Well, if these are possible, and if they’re possible, many of them will be created, then many of them will be created and we’ll get a higher probability, we’re in a simulation.”

But then I think, “Okay, so what are the probabilities of each of those two premises?” That conscious human-like simulations are possible? Yeah, I think that’s at least 50%. Furthermore, if they’re possible, will many of them be created? I don’t know. I don’t know what the numbers are here, but I guess I’m inclined to think probably my subjective credence is over 50% in that two, given that it just requires some civilizations who eventually create a whole lot of them.

Okay, so 50% chance of premise one, 50% chance of premise two. Let’s assume they’re roughly independent of each other. That gives us a 25% chance they’re both true. If they’re both true, and most beings are simulations, if most beings are simulations, and we’re probably simulations, putting all that together, I get roughly, at least, 25% that we’re in a simulation. Now there’s a lot of room for the numbers to go wrong. But yeah, to me, that’s at least very good reason, A, to take the hypothesis seriously, and B, just suggests if it’s at 25%, we certainly cannot rule it out. So that gives a a quasi-numerical argument that we can never know that we’re not in a simulation.

Lucas Perry: Well, one interesting part that seems to feed into the simulation argument is modern work on quantum physics. So we had Joscha Bach on who talked some about this, and I don’t know very much about it, but there is this debate over whether the universe is implemented in continuous numbers or non-continuous numbers. And if the numbers were continuous, then the universe wouldn’t be computable. Is that right?

David Chalmers: I‘m not quite sure which debate you have in mind, but yeah, it certainly is right, that if the universe maximally is doing a real-valued computation, then real-valued computations can only be approximated on finite computers.

Lucas Perry: Right.

David Chalmers: On digital computers.

Lucas Perry: Right. So could you explain how this inquiry into how our fundamental physics work informs whether or not our simulation would be computable?

David Chalmers: I mean, there’s many aspects of that question. One thing that some people have actually looked into, whether our world might involve some approximations, some shortcuts. So Zohreh Davoudi and some other physicists have tried to look at the math and say, “Okay, just say our simulation, say there was a simulation that took certain shortcuts. How would that show up empirically?” So it’s, okay, this is going to be an empirical test for whether there are shortcuts in the way our physics is implemented.

I don’t think anyone’s actually found that evidence yet, but, ah, there’s some principle evidence we could get of that. But there is the question of whether our world is fundamentally analog or digital, and if our world is fundamentally analog with perfectly precise, continuous, real values making a difference to how the universe evolves, yeah, then that can never be perfectly simulated on a finite digital computer. I would still say it can be approximated. And as far as we know, we could be living in a finite approximation to one of those continuous worlds, but yeah, maybe there could eventually be some empirical evidence of that.

Of course, the other possibility is we’re just running on an analog computer. If our physics is continuous and the physics of the next world up is continuous, maybe there will be analog computers developed with maximally continuous quantities, and we could be running on an analog computer like that. So I think even if the physics of our world turns out to be perfectly analog and continuous, that doesn’t rule out the simulation hypothesis. It just means we’re running on an analog computer in the next universe up.

Lucas Perry: Okay. I’m way above my pay grade here. I’m just recalling now, I’m just thinking of how Joscha was talking that continuous numbers aren’t computable, right? So you would need an analog computer. I don’t know anything about analog computers, but it seems to me like they-

David Chalmers: It’s hard to program analog computers because they require infinite precision, and we found out beings are not good at building things with infinite precision. But we could always just set a few starting values randomly and let the analog computation go from there. And as far as I can tell, there’s no evidence that we’re not living in a simulation that’s running on an analog computer like that.

Lucas Perry: I see. So if we discover our fundamental physics to be digital or analog, it seems like that wouldn’t tell us a lot about the simulation, just that the thing that’s simulating us might be digital or analog.

David Chalmers: In general, discovering things about our … I mean, the relationship between the physics of our world and the physics of the simulating world is fairly weak, especially if you believe in universal computation, any classical algorithm can be implemented in a vast variety of computers running on a vast variety of physics. But yeah, but there might be some limits. For example, if our world has a perfectly analog physics that cannot be simulated on a finite digital computer, could be simulated on an infinite digital computer, you can simulate analog quantities with infinite strings of bits, but not on a finite digital computer.

So yeah, discovering that our physics is digital would be consistent with the next universe up being digital, but also consistent with it being analog. Analog worlds can still run digital computers. I mean, it’d be very suggestive if we did actually discover digital physics in our world. I’m sure if we discovered that our physics is digital, that would then get a lot of people thinking, “Hey, this is just the kind of thing you’d expect if people are running a digital computer in the next universe app.” That might incline people to take the simulation hypothesis more seriously, but it wouldn’t really be any kind of demonstration.

Yeah. If we somehow discover that our physics is perfectly analog, I don’t really know exactly how we could discover that because at any given point, we’ll only have a finite amount of evidence, which will always be consistent with just being a very close approximation, but just say we could discover that our world runs analog physics. Yeah, then that would be inconsistent, whether this just being a digital simulation in the next universe up, but still quite consistent with it being a simulation running on a analog computer in the next universe up. I don’t know how that connects to Joscha’s way of thinking about this.

Lucas Perry: Yeah. I’m not sure. I’d love to see you guys talk-

David Chalmers: I hope it’s least consistent.

Lucas Perry: … about this.

David Chalmers: Has he written about this somewhere?

Lucas Perry: I’m not sure. There are lots of podcasts have been talking about it, though.

David Chalmers: Okay, cool.

Lucas Perry: Yeah. So we’ve gone over a lot here, and it leaves me not really trusting my common sense experience of the world. So pivoting a little bit here, back into the Edenic view of things … Sorry if I get the word that you used wrong, but it seems like you walk away from that with a view of imperfect realism. Is that right?

David Chalmers: Yeah. Imperfect realism is the perfect thing. Capital S “Solidity” doesn’t exist, but the lower case thing, small S “solidity,” does exist. An imperfect analog of what we initially believed in.

Lucas Perry: So how do you see the world now? Any differently? What is the world like to David Chalmers after having written this book? What is a person to you?

David Chalmers: I don’t know. I mean, I think there’s your everyday attitude towards the world and your theoretical attitude towards the world. And I find my everyday attitude towards the world isn’t affected that much by discoveries in philosophy, or in science for that matter. We mostly live in the manifest image. Maybe we even treat it a little bit like the Garden of Eden, and that’s fine. But then there’s this knowledge of what underlies it or what could underlie it. And that’s, yeah, once you start thinking philosophically, that gets mind boggling.

I mean, you don’t need to go to the simulation hypothesis or the virtual world to get that reaction. I mean, quantum mechanics is quite enough. Oh my God, we live in this world of the quantum wave function where nothing actually has these direct positions and possibly the wave functions collapsing, or possibly many worlds. And so I mean, boy, it’s just mind boggling. It is rather hard to integrate ordinary life in reality. So most of us just kind of go on living in the manifest image. Yeah, so once I start thinking about, “Yeah could we be in a simulation?” It’s got a similar kind of separateness, I guess.

Mostly I go on living in the manifest image and don’t factor this in. But I mean, it does open up all kinds of possibilities once you start thinking that there is maybe this reality plus of all these different levels of reality, like, “Could it be that someday it might be possible to escape this particular virtual world, or maybe when we die, does our code sometimes get uploaded by simulators to go hang out back in other levels of reality. Maybe there are naturalized versions of reincarnation or life after death, and I don’t want to say this is why I’m thinking about this stuff. It’s not for these quasi-religious reasons, but suddenly, possibilities that had seemed very far out possibilities to me, like life after death, at least come to seem a little bit closer and more like open possibilities than they’d seemed before. So that’s at least interesting.

Lucas Perry: One thing you bring up a bit in your exploration here is God. And all these things that you’re mentioning, they seem like science and philosophy coming back to traditionally religious ideas, but through a naturalistic exploration, which is quite interesting. So do you have any different thoughts on God after having written this book?

David Chalmers: It’s interesting. I’m not remotely religious, myself. I’ve always thought of myself as an atheist, but yeah, after writing this book, I’m at least … There is a version of God that I could at least take seriously. This is the simulator. They did, after all, create the world, this world. They may have a lot of power and a lot of knowledge of this world, as gods are meant to have. On the other hand, they’re quite unlike traditional gods. In some ways, the simulator needn’t be all good, needn’t be particularly wise. Oh, also didn’t create all of reality. It just created a little bit of reality. Maybe it’s a bit like what’s sometimes called a demiurge, the the local god, the under-boss god who created this world, but wasn’t the one in charge of the whole thing.

So yeah, maybe simulators are a bit more like demiurges. More importantly, I don’t think I’d be inclined to erect a religion around the simulation idea. Religions come with ethical practices and really changing your way of life. I don’t think there’s any particular reason to orient our ethics to a simulation. I mean, maybe you can imagine there’d be some practices that if we really believed we were in a simulation, or there’s a good chance of that, we should at least start doing some things differently. Maybe some people might want to try and attract the attention of the simulators. I don’t know. That’s all very speculative. So I don’t find myself …

I think the one moral of all this for me is that actually ethics and meaning and so on, actually, you don’t get your ethics or your meaning from who created you or from whether it’s a God or a simulator. Ethics and meaning comes from within. It comes from ourselves, our consciousness, and our interactions.

Lucas Perry: Do you take a line that’s similar to Peter Singer in thinking that that is like an objective rational space? Are you a moral realist or anti-realist about those things?

David Chalmers: I tend towards moral anti-realism, but I’m not sure. I find those issues very difficult. Yeah, I can get in the mood where, “Pain is bad,” just seems like an absolute fact.

Lucas Perry: Yeah.

David Chalmers: That’s just an objective fact. Pain is objectively bad. And then I get at least to some kind of value realism, if not moral realism. Some moods all go that way. Other moods, it’s just, yeah, it’s all a matter of our attitude towards it. Finally, it’s a matter of what we value. If somebody valued pain, it would be good for them. If they didn’t, it wouldn’t be. And I can go back and forth. I don’t have a fixed view of these matters.

Lucas Perry: Are there any questions that I haven’t asked you that you would’ve liked me to ask you?

David Chalmers: Not especially. You asked a lot of great questions, and there are a million others, but actually one interesting thing with this book coming out is getting to do a few of these, having a few of these conversations and seeing all the different questions and different aspects of the book that different people focused on. So, no. I think we’ve covered a lot of territory here, and yeah, these are a lot of cool things to think about.

Lucas Perry: All right. Well, I’m mindful of the time here, David. Thank you so much for all of your time. If people want to check you out, follow you, and get your new book, where are the best places to do that?

David Chalmers: Probably my website, which is consc.net. Consc, the first five letters of consciousness, or just do a search for my name. And then yeah, the book is … I’ve got a page for the book on my website, consc.net/reality, or just search for name of the book, Reality+. I’m not on Twitter or Instagram or any of those things, unfortunately. Maybe I should be one of these days, but for now, I’m not. But yeah, the book will be available January 25th, I guess. All good book sellers. So I hope some of your listeners might be interested to check it out.

Lucas Perry: All right. We’ll include links to all of those places in the description of wherever you might be listening or watching. Thank you so much, David. It’s always a pleasure speaking with you. I love hearing about your ideas, and it’s really a great book at an important time. I think just before all this VR stuff is about to really kick off, and with the launch of the metaverse. It’s really well timed.

David Chalmers: Oh, thanks, Lucas. This was all, yeah, a lot of fun to talk about this stuff with you.

• The most pressing issue in biosecurity
• Stories from when biosafety labs failed to contain dangerous pathogens
• The lethality of pathogens being worked on at biolaboratories
• Lessons from COVID-19

 

Watch the video version of this episode here

0:00 Intro

2:35 What are the least understood aspects of biological risk?

8:32 Which groups are interested biotechnologies that could be used for harm?

16:30 Why countries may pursue the development of dangerous pathogens

18:45 Dr. Lentzos’ strands of research

25:41 Stories from when biosafety labs failed to contain dangerous pathogens

28:34 The most pressing issue in biosecurity

31:06 What is gain of function research? What are the risks?

34:57 Examples of gain of function research

36:14 What are the benefits of gain of function research?

37:54 The lethality of pathogens being worked on at biolaboratories

40:25 Benefits and risks of big data in biology and the life sciences

45:03 Creating a bioweather map or using big data for biodefense

48:35 Lessons from COVID-19

53:46 How does governance fit in to biological risk?

55:59 Key takeaways from Dr. Lentzos

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s episode is with Dr. Filippa Lentzos and explores increasing global security concerns from the use of the life sciences. As biotechnology continues to advance, the capacity for use of both the harmful and beneficial aspects of this technology is also increasing. In a world stressed by climate change as well as an increasingly unstable political landscape that is likely to include powerful new biotechnologies capable of killing millions, the challenges of biotech to global security are clearly significant. Dr. Lentzos joins us to explain the state of biotech and life sciences risk in the present day, as well as what’s needed for mitigating the risk.

Dr. Filippa Lentzos is a mixed methods social scientist with expertise in biosafety, biosecruity, biorisk assessment and biological arms control. She works at King’s College London as a Senior Lecturer in Science and International Security. Dr. Lentzos also serves as the Co-Director of the Centre for Science and Security Studies, is an Associate Senior Researcher at Stockholm International Peace Research Institute, and is a columnist for the Bulletin of Atomic Scientists. Her work focuses on transparency, confidence-building and compliance assessment of biodefence programmes and high-risk bioscience. She also focuses on information warfare and deliberate disinformation related to global health security.

And with that, I’m happy to present this interview with Dr. Filippa Lentzos.

To start things off here, we’ve had COVID pretty much blindside humanity, at least the general public. People who have been interested in pandemics and bio risk have known about this risk coming for a long time now and have tried to raise the alarm bells about it. And it seems like this other very, very significant risk is the continued risk of synthetic bio agents, engineered pandemics, and also the continued risk of natural pandemics. It feels to me extremely significant and also difficult to convey the importance and urgency of this issue, especially when we pretty much didn’t do anything about COVID and knew that a natural pandemic was coming.

So, I’m curious if you could explain what you think are the least understood aspects of synthetic and natural biological risk by the general public and by governments around the world and what you would most like them to understand.

Filippa Lentzos: I guess one of the key things to understand is that security concerns of life science research is something that we must take seriously. There’s this whole history of using the life sciences to cause harm, of deliberately inflecting disease, of developing biological weapons. But very few people know this history because it’s a story that’s suffused by secrecy. In the 20th century, biological weapons were researched and developed in several national programs, all of which were top secret, including the US one.

These programs were concealed in labs at military sites that were not listed on ordinary maps. Special code names and exceptionally high classification categories were assigned to biological agents and the projects that were devised to weaponize them. Bioweaponeers were sworn to secrecy and under constant surveillance. So, a lot of that just hasn’t become publicly available. Much of the documentation and other evidence of past programs has been destroyed. There were these concerted efforts to bring war crimes and human rights abuses to public light. Information about biological weapons programs tended to be suppressed.

One example of this is the Truth and Reconciliation Commission hearings in South Africa that followed the apartheid. When the commission hearings began to uncover details about South Africa’s biological weapons program that was called Project Coast, they were faced with delays and they were faced with legal challenges and the hearings were eventually shut down before the investigators could complete their work. Now, the head of that program became obvious to the investigators at the time who that was, but he was never brought to justice. Unbelievably, he remained a practicing medical doctor for many, many years afterwards, possibly even to this day.

What hasn’t been concealed or destroyed or silenced from past biological weapons programs often remains highly classified. So, the secrecy surrounding past programs mean that they’re not well known. But there’s also a new, contemporary context that shapes security concerns about life science research that we need to be conscious of and that I think relates back to what I think is important to know about synthetic and natural bio risks today. And that is that advances in science and technology may enable biological weapons to emerge that are actually more capable and more accessible with attacks that can be more precisely targeted and are harder to attribute.

So, synthetic biology, for example, which is one of the currently cutting-edge areas of life science research, that is accelerating our abilities to manipulate genes and biological systems. And that will have all kinds of wonderful and beneficial applications, but if the intent was there, it could also have significant downsides. So, it could, for instance, identify harmful genes and DNA sequences in a much quicker way than we’ve been able to so far. As a result of that, we could, for instance, see greater potential to make pathogens or disease-causing biological agents even more dangerous.

Or we could see greater potential to convert low-risk pathogens into high-risk pathogens that we could potentially even recreate extinct pathogens like the variola virus that causes smallpox, or way further out, we could engineer entirely new pathogens. Now, pathogens in and of themselves are not biological weapons. You need to add some kind of delivery mechanism to have a weapon. The possibilities to manipulate genes and biological systems are coming at a time when new delivery mechanisms for transporting pathogens into our bodies, into human bodies or animal bodies are also being developed.

So, in addition to the bombs and the missiles, the cluster bombs, the sprayers, and all kinds of injection devices of past biological warfare programs, it could now also be possible to use other delivery mechanisms. Things like drones or nanorobots, these incredibly tiny robots that can be inserted into our blood streams for instance, even insects, could be used as vehicles to disperse dangerous pathogens.

So, I guess to get to the bottom of your question, what I’m keen for people to understand, scientists, government officials, the general public, is that current developments in science and technology, or in the life sciences more specifically, are lowering barriers to inadvertent harms as well as to deliberate use and development of biological weapons and that there is this whole history to deliberate attempts to use the life sciences to cause harm.

Lucas Perry: It seems like there’s three main groups of people that are interested in such technology. There’s something like lone wolfs or isolated individuals who are interested in creating a lot of harm to humanity in the same way that mass shooters are. There are also small groups of people who may be interested in the same sort of thing. Then there’s this history of governments pursuing biological weapons. Could you offer some perspective about the risks of these three groups and how you would compare the current technology used for the creating of synthetic pathogens to how strong it was historically?

Filippa Lentzos: Sure. Are we heading towards a future where anyone with a PhD in bioengineering could create a pandemic and kill millions? Is that what you mean? Well, a pathogen, even a bioengineered one, does not on its own constitute a biological weapon, though you will still face issues like agent stability and dealing with large scale production and importantly dealing with efficient delivery, which is much easier said than done. In fact, what the history of bioterrorism has taught us is that the skills required to undertake even the most basic of bioterrorism attacks are often much greater than assumed.

There are various technical barriers to using biological agents to cause harm even beyond the barriers that are being reduced from advances in science and technology. The data that is available to us from past incidents of biological terrorism indicates that a bioterrorism attack is more likely to be crude, more likely to be amateurish and small scale where you’d have casualty levels in single or double digits and not in their hundreds or thousands and certainly not in their millions. Now, my own concern is actually less about lone actors.

Where I see real potential for sophisticated biological weapons in strategic surprise in the biological field is in one of those other categories that you mentioned, so it’s at the state or the state sponsored level. Let me explain. Well, I already told you a little bit about how we’ve recently seen significant advances in genetic manipulation and delivery mechanisms. These developments are lowering barriers to biological weapons development, but that’s really only part of the picture, because in making threat assessments, it’s also important to look at the social context in which these technical developments are taking place.

One of the things we’re seeing there in that social context is a build up into use capacities? What we’re seeing is that high containment labs that are working with the most dangerous pathogens are rapidly being constructed all over the globe. So, they’re now more people and more research projects than ever before working with and manipulating very dangerous pathogens and there are more countries than ever before that have biodefense programs. There’s around 30 biodefense programs that are openly declared. The trends we’re seeing is that these numbers are increasing.

It’s entirely legitimate to have biodefense programs and they do a lot of good, but a side effect of increasing bio-preparedness and biodefense capacities is that capacities for causing harm, should the intent be there, and that’s the crucial part, also increase. So, one person may be setting up all this stuff for good, but if somebody else comes in with different intent, with intent to do harm, that same infrastructure, that same material, that same equipment, that same knowledge, can be turned towards causing harm or creating biological weapons.

Now, another thing we’re seeing that won’t have escaped your notice is the increasingly unstable and uncertain geopolitical landscape. The world that many of us grew up in and know is one in which America was a clear, dominant power. We’re now moving away from that, away from this hegemonic or unipolar power structure towards an international system that is increasingly multipolar. The most clearly rising power today is of course China, but there are others too. Russia is still there. There’s India, there’s Brazil to name a few. Those are things in the social context that we need to pay attention to.

We’re also seeing rapidly evolving nature of conflict and warfare themselves are changing. And that’s changing the character of military challenges that are confronting states. Hybrid warfare, for instance, which blends conventional warfare with irregular warfare and cyber warfare, is increasingly likely to compliment classical military confrontation. So, states that are increasingly outmatched by conventional weapons may for instance start to view novel biological weapons as offering some kind of advantage, some kind of asymmetric advantage, and a possible way to outweigh strategic imbalances.

So, states in this kind of new form of conflict, new form of warfare, may see biological weapons as somehow providing an edge or a military advantage. We are also seeing the defense programs of some states heavily investing in the biological sciences. Again, could well be for entirely legitimate purposes, but it does also raise concerns that adversaries may be looking at those kinds of investments and thinking hedging their bets and similarly investing in more biological programs. These investments, I think, are also an indication that there are some real concerns that adversaries are harnessing or trying to harness biotechnology for nefarious purposes.

And we’ve seen some political language to that effect too, but a lot of this is going under the radar. So, all of these things, and there are more, the flagrant breach of the Chemical Weapons Convention or continuous flagrant breaches of the Chemical Weapons Convention for example, the use of chemical weapons in Syria, or the use of very sophisticated chemicals like Novichok in the UK on Skripal, the Russian, as well as other cases is one other sort of context that plays in, or even our recent experiences of natural disease outbreaks in here. COVID is obviously a key example, but it’s not so long ago we’ve had all kinds of other outbreaks.

Ebola just a few years ago. There’s Zika, there’s MERS, there’s all kinds of other emerging diseases. All of these could serve to focus attention on deliberate outbreaks. And all of these various elements of the social context as well as these technical developments could produce an environment in which a potential military or political utility for biological weapons emerges that alters the balance of incentives and disincentives to comply with the international treaty that prohibits biological weapons.

Lucas Perry: Could you explain the incentives of why a country would be interested in creating a synthetic pathogen when inevitably it would seem like it would come back and harm itself?

Filippa Lentzos: Well, it’s doesn’t have to be an infectious pathogen. What we’re seeing today with COVID, for instance, is an infectious pathogen that spreads uncontrollably throughout the world. But states don’t have to. Not all dangerous pathogens are infectious in that way. Anthrax, for instance, doesn’t spread from person to person through the air. So, there are different kinds of pathogens and states and non-state actors will have different motivations for using biological weapons or biological agents.

One of those which I mentioned earlier is for instance if you feel that another country… you are outmatched conventionally by conventional weapons, you may want to start to develop asymmetric weapons. That would be an example where a state might want to explore developing biological weapons. But of course, we should probably mention that there is this thing called The Biological Weapons, this international treaty, which completely prohibits this class of weaponry. Historically, there’s really only been two major powers that have developed sophisticated biological weapons programs. That is the United States and the Soviet Union.

Today, there are no publicly available documents or any policy statements suggesting that anyone has an offensive biological weapons program. There are many countries who have defensive programs and that’s entirely legitimate. There is no indication that there are states that have offensive programs to date. I think the real concern is about capacities that are building up through biodefense programs, but also through regular bio-preparedness programs, and that’s something that’s just going to increase in future.

Lucas Perry: I’m curious here if you could also explain and expand upon the particular strands of your research efforts in this space.

Filippa Lentzos: Sure. I mean, it’s very much related to the sorts of things we’ve been talking about. One strand that I focus on relates to transparency, confidence building, and compliance assessment of biodefense programs, where I look at how we can build trust between different countries with biodefense programs to trust that they are complying with the Biological Weapons Convention. I’m also looking at transparency around particular high-risk bioscience, so things or projects or research involving genome editing for example, or potentially pandemic pathogens like influenza or coronaviruses.

Another strand that I’m interested in or that I’m looking at focuses on emerging technologies and on governance around these emerging technologies and unresponsible innovation. And there I look particularly at synthetic biology, also a little bit at artificial intelligence, deep learning and robotics, how these other emerging areas are coming into the life sciences and affecting their development and the direction they’re taking, the capacities that are emerging from this kind of convergence between emerging technologies and how we can govern that better, how we can provide better oversight.

Now, one of the projects that I’ve been involved in that has got a lot of press recently is a study that I carried out with Greg Koblentz at George Mason University where we mapped high biocontainment laboratories globally. I mentioned earlier that countries around the world are investing in these kinds of labs to study lethal viruses and to prepare against unknown pathogens. Well, that construction boom has to date resulted in dozens of these commonly called BSL-4 labs around the world. Now, significantly more countries are expected to build these kinds of labs in the wake of COVID 19 as part of a renewed emphasis on pandemic preparedness and response.

In addition, gain-of-function research with coronaviruses and other zoonotic pathogens with pandemic potential is also likely to increase as scientists are seeking to better understand these viruses and to assess the source of risks that they pose of jumping from animals to humans or becoming transmissible between humans. Now, of course, clinical work and scientific studies and pathogens are really important for public health and for disease prevention, but some of these activities pose really significant risks. Surges in the number of labs and expansion and the high risk research that’s carried out within them exacerbate safety and security risks.

But there is no authoritative international resource tracking the number of these kinds of labs out there as they’re being built. So, there is no international body that has an authoritative figure on the number of BSL-4 labs that exist in the world or that have been established. Equally, there is no real international oversight of the sort of research that’s going on in these labs or the sorts of biosafety and biosecurity measures that they have implemented. So, what our study did was to provide a detailed interactive map of BSL-4 labs worldwide that contains basic information on when they were established and the size of the lab labs and some indicators of biorisk management oversight.

That map is publicly available online at globalbiolabs.org. You can go and see for yourself. It’s basically a very large Google map where the labs are indicated and you can scroll over the labs and then up pops information about when it was established, how big it is, what sorts of biorisk management indicators there are, are they members of national biosafety associations? Do they have regulations related to by safety? Do they have codes of conduct? Et cetera, those kinds of things. That all comes up there, so you can go and see for yourself. That’s a resource that we’ve made publicly available on the basis of our project.

Looking at the data we then collated, this was really the first time this kind of concerted effort was made to identify these various labs and bring all that information together. And some of our key findings from looking at that data were that… Well, the first thing is BSL-4 labs are booming. We can see a really quite steep increase in the number of labs that have been built over the last few years. We found that there are many more public health labs than there are biodefense labs. So, about 60% of the labs are public health labs, not focused on defense, but resourced out of health budgets.

We also found that there are many smaller labs and larger labs. In newspapers and on TV, we keep seeing photos of the Wuhan Institute of Virology’s BSL-4 lab.

In terms of oversight, some of our other findings were that sound biosafety and biosecurity practices do exist, but they’re not widely adopted. There’s a lot of difference in between the kinds of biosafety and biosecurity measures that labs adopt and implement. We also found that assessments to identify life science research that could harm health safety or security are lacking in the vast majority of countries that have these BSL-4 labs. So, as I said, that’s one of the studies that’s got a lot of press recently and part of that is because of its relationship to the current pandemic and the lack of some solid information, some solid data on the sort of labs that are out there and on the sorts of research that’s being done.

Lucas Perry: Do you have a favorite story of a particular time that a BSL lab failed to contain some important pathogen?

Filippa Lentzos: Well, there are all kinds of examples of accidental releases. In the UK, for instance, where I’m based, a very long time ago, there was work with variola virus that causes a smallpox, was worked in a sort of high rise building that had multiple floors and the variola virus escaped into the floor above and infected somebody there. That was, I think, at the end of the ’70s. That was the very last time that someone was infected by smallpox in the UK. More recently in the UK, there’s also been the escape of the foot and mouth virus from a lab.

Now, this was not the very large foot and mouth outbreak that we had in the early 2000s, which you know killed millions of animals. I still remember the piles of animal corpses dotted around the country and you could still smell the burning carcasses on the motorway as you drove past, et cetera. That was not caused by a lab leak, but just two, three, four years later, there was a foot and mouth disease virus that escaped from a lab through a leaking pipe that did go on to cause some infections. But by that stage, everyone was very primed to look out for these kinds of infections and to respond to them quickly.

So, that outbreak was contained fairly rapidly. I mean, there are also many examples elsewhere, also in the United States. I mean, there’s the one example where you had variola virus found in a disused closet at the NIH after many years and they were still viable. I think that’s one of the ones that ranked pretty highly in the biosafety community’s memory and maybe even in your own. It was not that long ago, half a dozen years ago or so.

Lucas Perry: What do you think all these examples illustrate of how humans should deal with natural and synthetic pathogens?

Filippa Lentzos: Well, I think it illustrates that we need better oversight, we need better governance to ensure that the life science research done is done safely, it’s done securely, and it’s done responsibly.

Lucas Perry: Overviewing all these BSL safety labs and all these different research threads that you’re exploring, what do you think is the most pressing issue in biosecurity right now, something that you’d really like the government or the public to be aware of and take action on?

Filippa Lentzos: Well, I think there’s a really pressing need to shore up international norms and treaties that prohibit biological weapons. I mentioned the Biological Weapons Convention. That is the key international instrument for prohibiting biological weapons, but there are also others. The arms control communities is not in great shape at the moment. It needs more high profile, political attention, it needs more resources. And I think with more and more breaches that we’re seeing, not on the biological side, but on other sides, breaches of international treaties, I think we need to make sure there is this renewed effort and commitment to these treaties.

So, I think that’s one thing, one issue, that’s really pressing in biosecurity right now. Another is really raising awareness and increasing sensitivities in scientific communities to potentially accidental or inadvertent or deliberate risks of the life sciences. And we see that very clearly in the data that’s coming out of the BSL-4 study that I talked to you about, that that’s something that’s needed, not just what we saw there as actually looking at do they have any laws in the books or do they have any guidance on paper or do they have any written down codes of conduct or codes of practice? That’s really important.

It’s really important to have these kinds of instruments in place, but it’s equally important to make sure that these are implemented and adopted and that there is this culture of safe, secure, and responsible science. That’s something that we didn’t cover in that specific project, but it’s something that some of my other work has drawn attention to and the work of many others as well. So, we do need to have this regulatory oversight governance framework in place, but we also need to make sure that that is reflected or echoed in the culture of the scientists and the labs that are carrying out life science research.

Lucas Perry: One other significant thing going on in the life sciences in terms of biological risk is gain-of-function research. So, I’m curious if you could explain what gain-of-function research is and how you see the debate around the benefits and risks of it.

Filippa Lentzos: Well, gain-of-function research is a very good example of life science research that could be accidentally, inadvertently or deliberately misused. Gain-of-function means different things to different people. To virologists, it generally just means genetic manipulation that results in some sort of gained function. Most of the time, these manipulations result in loss of function, but sometimes different kinds of functions of pathogens can be gained. Gain-of-function has got a lot of media coverage in relation to the discussion around the origins of the pandemic or of COVID.

And here, gain-of-function is generally taken to mean deliberately making a very dangerous pathogen like influenza or coronavirus even more dangerous. So, what you’re trying to do is you’re trying to make it spread more easily, for example, or you’re trying to change its lethality. I don’t think gain-of-function research in and of itself should be banned, but I do think we need better national and international oversight of function experiments. I do think that a wider group of stakeholders beyond just the scientists doing the research themselves and their funders, I think that a wider group of stakeholders should be involved in assessing what is safe, what is secure, and what is responsible gain-of-function research.

Lucas Perry: It seems very significant, especially with all these examples that you’ve illustrated of the fallibility of BSL labs. The gain-of-function research seems incredibly risky relative to the potential payoffs.

Filippa Lentzos: Yeah, I think that’s right. I mean, I think it is considered one of the examples of what has been called dual use research of concern or experiments that have a higher potential to be misused. By that, I mean deliberately, but also in terms of inadvertently or even accidentally because the repercussions, the consequences have the potential to be so large. That’s also why we saw when some of the early gain-of-function experiments gained media attention back in 2011, 2012, that the scientific community itself reacted and said, “Well, we need to have a moratorium.

We need to have a pause on this kind of research to think about how we govern that, how we provide sufficient oversight over the sorts of work that’s being done so that the risk benefit assessments are better essentially.” I think there will be many who argue that… myself among them, that the discussion that was had around gain-of-function at that time were not extensive enough, they were not inclusive enough, there were not enough voices being heard or part of the decision-making process in terms of the policies that came out of this in the United States. To some extent, I think that’s why we’re, again, back at the table now with the discussions around the pandemic origins.

Lucas Perry: Do you have any particular examples of gain-of-function research you’d be interested in sharing? It seemed like a really significant example was what was happening in Wisconsin.

Filippa Lentzos: Sure. And that was the one that was the work in Wisconsin and at the Erasmus University in the Netherlands. What they were trying to do there was they were working with influenza or avian flu and they were seeing if they were able to give that virus a new function, so enable it to spread, not just among birds, but also from birds to mammals, including humans, including ourselves. So, they were actively trying to make it not just affect birds, but also to affect humans.

And they did so successfully, which made that virus more dangerous and that was what that media fuel was about and the discussions at the time were that many felt that the benefits of that research did not outweigh very significant potential risks, the very significant risks that that research involved.

Lucas Perry: What are the benefits of that sort of gain-of-function research?

Filippa Lentzos: Well, the ones that carried out that sort of research both at the time, but also the sorts of gain-of-function research that’s been going on at the Wuhan Institute of Virology, some of it which has been funded by American money, some of it which has been done in collaboration with American Institute argues that in order to prepare for pandemics, we need to know what kind of viruses are going to hit us. New and emerging viruses generally come, spill over from the animal kingdom into humans, so they actively go and look for viruses in the animal kingdom.

In this case, in the coronavirus case, the Wuhan Institute of Virology, they were actively looking in bat populations to see what sort of viruses exist there and what their potentials are for spilling over into humans. That’s their justification for doing that. My own view is that that’s incredibly risky research and I’m not sure and I don’t feel that that sort of justification really outweighs the very significant risks that it involves. How can you possibly hit upon the right virus in the thousands and thousands of viruses that are out there and know how that will then mutate and get modified as it hits the human population?

Lucas Perry: These are really significant and quite serious viruses. You explained an example earlier about this UK case where the final people to die from smallpox was actually from a BSL lab leak. There’s also this research in Wisconsin on avian flu. So, could you provide a little bit of a perspective on, for example, the infection rate and case fatality rate of these kinds of viruses that they’re working on at BSL labs, that they have at BSL labs, that they might be pursuing gain-of-function research on?

Filippa Lentzos: Yeah. I mean, certainly in terms of the coronavirus, what we’ve seen there is that that is clearly many people have died, many people have got infected, but that’s not considered a particularly infectious or particularly lethal pathogen when it comes to pandemics. We’ve seen much more dangerous pathogens that could create pandemics or that are being worked with in laboratories.

Lucas Perry: Yeah. Because some of these diseases, it seems, the case fatality rate gets up to between 10 and 30%, right? So, if you’re doing gain-of-function research on something that’s already that lethal and that has killed hundreds of millions of people in the history of life on earth, with the history of lab leaks and with something so infectious and spreadable, it seems like one of the most risky things humanity is doing on the planet currently.

Filippa Lentzos: Yes. I mean, one of the things gain-of-function is doing is looking at lethality and how to increase lethality of pathogens. There are also other things that gain-of-function is doing, but that is taking out a large part of the equation, which is the social context of how viruses spread and mutate. There are, for instance, things we can do to make viruses spread less and be less lethal. There are active measures we can take equally, there are responses that could increase the effect of viruses and how they spread.

So, lethality is one aspect, a potential pandemic, but it is only one aspect, right? There are these many other aspects too. So, we need to think of ourselves much more as active players, that we also have a role to play in how these viruses spread and mutate.

Lucas Perry: One thing that the digital revolution has brought in is the increase and the birth of big data. Big data can be used to detect the beginning of outbreaks, to detect novel diseases, and to come up with cures and treatments for novel and existing diseases. So, I’m curious what your perspective is on the benefits and risks of the increase of big data in biology, both to health and societies as well as privacy and the like.

Filippa Lentzos: Well, you pointed to many of the benefits that big data has. There certainly are benefits, but as with most things, there are also a number of downsides. I do believe that big data combined with the advances that we’re seeing in genomic technologies as well as with other areas of emerging technology, so machine learning or AI, this poses a significant threat. It will allow an evermore refined record of our biometrics; so our fingerprints, our iris scans, our face recognition, our CCTV cameras that can pick up individuals based on how they walk, all these kinds of biometrics.

It will also allow a more refined record of our emotions and behaviors to be captured and to be analyzed. I mean, you will have heard of companies that are now using facial recognition on their employees to see what kind of mood they’re in and how they engage with clients, et cetera. So, governments are gaining incredible powers here, but increasingly, it’s private companies that are gaining this sort of power. What I mean by that is that governments, but as I said, increasingly private companies, will be able to sort, to categorize, to trade, and to use biological data far more precisely than they have ever been able to do before.

That will create unprecedented of possibilities for social and biological control, particularly through individual surveillance, if you like. So, these game-changing developments will deeply impact how we view health, how we treat disease, how long we live, and how more generally we consider our place on the biological continuum. I think they’ll also radically transform the Julius nature of biological research, of medicine, of healthcare. In terms of my own field of biosecurity, they will create the possibility of novel biological weapons that target particular groups of people and even individuals.

Now, I don’t mean they will target Americans where they will target Brits or they will target Protestants or they will target Jews or they will target Muslims. That’s not how biology works. Genes don’t understand these social categories that we put onto people. That’s how we socially divide people up, but that’s not how genetics divides people up. But there are groupings also genetically that go across cultures, nations, beliefs, et cetera. So, as we come to have more and more precise biological data on these different groups, the possibility of targeting these groups for harm will also be realized.

So, in the coming decade, managing the fast and broad technological advances that are now underway will require new kinds of governance structures that we need to put in place and these new structures need to draw on individuals in groups with cross-sectoral experience; so, from business, from academia, from politics, from defense, from intelligence, and so on to identify emerging security risks and to make recommendations for dealing with them. We need new kinds of governance structures, new kinds of advisory bodies that have different kinds of stakeholders on them to the ones that we have traditionally had.

Lucas Perry: In terms of big data and the international community, with the continued risks of natural pandemics as well as synthetic pandemics or other kinds of a biological agents and warfare, it’s been proposed, for example, to create something like a bio weather map where we have like a widespread, globally distributed early warning detection system for biological agents that is based off of big data or is itself big data. So, I’m curious if you have any perspective and thoughts on the importance of big data in particular for defenses against the modern risks of engineered and natural pandemics.

Filippa Lentzos: Yes, I do think there was a role to play here for data analysis tools of big data. We are, I think, already using some tools in this area where you have, for instance, analysis of social media usage, words that pop up on social media uses, or you have analysis of the sorts of products that people are buying in pharmaceutical companies. So, if there is some kind of disease spreading, people are getting sick and they’re talking about different kinds of symptoms, you are able to start tracking that, you’re able to start mapping that.

All of a sudden, all kinds of people in say Nebraska are going to the pharmacy to buy cough medicine or something to reduce temperature or there’s a big spike for instance, you might want to look into that more. That’s an indicator, that’s a signal that you might want to look at that more. Or if you’re picking up keywords on internet searches or on social media where people are asking about stomach cramps or more specific kinds of symptoms, that again is another kind of signal, you might want to look more into that.

So, I think some of these tools are, are definitely already being developed, some are already in use. I think they will have advantages and benefits in terms of preparing for both natural, but also inadvertent, accidental or deliberate outbreaks of disease.

Lucas Perry: We’re hopefully in the final stages of the COVID-19 pandemic. When we reflect back upon it, it seems like it can be understood as almost like a minimally viable global catastrophe or a minimally viable pandemic, because there’s been far worse pandemics, for example in the past, and it’s tragically taken the lives of many, many people. But at the same time, the fatality rate is just a bit more than the flu and a lot less than many of the other pandemics that humanity has seen in the past few hundred thousand years.

So, I’m curious what your perspective is on what we can learn in the areas of scientific, social, political, and global life, from our experience with the COVID-19 pandemic to be better prepared for something that’s more serious in the future, something that’s more infectious, and has a higher case fatality rate.

Filippa Lentzos: Well, I think, as you said, in the past, disease has been much more present in our societies. It’s really with the rise of antibiotics and the rise of modern healthcare that we’ve been able to suppress disease to the extent that it’s no longer such a pressing feature in our daily lives. I think what the pandemic has done to a whole generation is really it has been a shot across the bow, really crystallized the incredibly damaging effects that disease can have on society.

It’s been this wake up call or this reality check. I think we’ve seen that reflected also politically. International developments like the UN’s Biorisk Working Group that’s been established by the secretary general or efforts by states to develop a new international treaty on pandemics are concrete evidence of increasing awareness of the challenges that diseases pose to humankind. But clearly, that’s not enough. It hasn’t been enough, what we’ve had a place. Clearly, we need to be better prepared. And I guess for me, that’s one of the bigger takeaways from the pandemic.

Equally, what the pandemic origin debate has done is to show that whether or not the pandemic resulted from a lab leak, it could have resulted from a lab leak, it could ironically or tragically have been the result of scientific research actually aimed at preventing future pandemics. So, clearly for me, a huge takeaway is that we need better oversight, we need better governance structures ensure safe, secure, and responsible life science research. Potentially, we also need to rethink some of our preparedness strategies.

Maybe actively hunting for viruses in the wild, mutating them in the lab to see if that single virus might be the one that hits us next, the one that spills over, isn’t the best strategy for preparing for pandemics in the future. But COVID has also highlighted a more general problem, one I think that’s faced by all governments, and that is, how can we successfully predict and prepare for the wide range of threats that there are to citizens and to national security? Some threats like COVID-19 are largely anticipated actually, but they’re not adequately planned for as we’ve seen.

Other threats are not anticipated at all and for the most part are not planned for. The other side, some threats are planned for, but they fail to materialize as predicted because of errors and biases in the analytic process. So, we know that governments have long tried to forecast or to employ a set of futures approaches to ensure they are ready for the next crisis. In practice, these are often general, they’re ad hoc, they’re unreliable, they’re methodologically and intellectually weak, and they lack academic insight. The result is that governments are wary of building on the recommendations of much of this future’s work.

They avoid it in policy planning, in real terms funding, and ultimately in practice and institutionalization. What I and many of my colleagues believe is that we need a new vision of strategic awareness that goes beyond the simple idea of just providing a long-term appreciation of the range of possibilities that the future might hold to one that includes communication with governments about their receptivity to intelligence, how they understand intelligence, how they absorb other kinds of intelligence from private corporations, from academia, et cetera, as well as the manner in which the government acts as a result.

So, strategic awareness to my mind and to that of many others should therefore be conceptualized in three ways. You should first look more seriously and closely at threats. Second, you should invest in prevention and foresighted action. Third, you should prepare for medication, crisis management, and bounce back in case a threat can’t be fully prevented or deterred. This kind of thinking about strategic awareness will require a paradigm shift in how government practices strategic awareness today. And my view is that the academic community must play an integral part in that.

Lucas Perry: Do you have any particular governance solutions that you’re really excited about right now?

Filippa Lentzos: I don’t think there’s a magic bullet. I don’t think there’s one magic solution to ensuring that life science research is safe, that it’s secure, and that it’s carried out responsibly. I think in terms of governance, we need to work both from the top-down and from the bottom-up. We need to have in place both national laws and regulations, statutory laws and regulations. We need to have in place institutional guidance, we need to have in place best practices. But we also need a lot of the commitment, we also need a lot of awareness coming from the bottom-up.

So, we need individual scientists, groups of scientists to think about how their work can best be carried out safely so they can make codes of ethics or codes of practice themselves, they can educate others, they can think through who needs to be involved beyond their own expert community in risk assessing the kinds of research that they’re interested in carrying out. So, we need both this top-down government-enforced, institutionally-enforced governance as well as grassroots governance. Only by having both of these aspects, both of these kinds of governance measures, can we really start to address the potential downsides of life science research.

Lucas Perry: All right. Just to wrap things up, I’m curious if you have any final words or thoughts for the audience or anyone that might be listening, anything that you feel is a crucial takeaway on this issue? I generally feel that it’s really difficult to convey the significance and gravitas and importance of this. So, I’m curious if you have any final words about this issue or a really central key takeaway you’d like listeners to have.

Filippa Lentzos: I think when we’re looking at our current century, this will be the century not of chemistry or physics or engineering, that was the last century, this will be the century of biology and it will be the century of digital information and of AI.

I think this combination, which we talked about earlier, when you combine biological data with machine learning, with AI, with genomic technologies, you get incredible potential of precise information about individuals. I think that is something we are going to struggle with in the years to come and we need to make sure that we are aware of what is happening, that we are aware that when we go buy a phone and we use the face recognition software, which is brilliant, that it can also have downsides, and all these little individuals actions, all these technologies that we just readily accept because they do have upsides in our life, they can also have potential downsides.

I do think we need to make sure we also developed this critical sense or this ability to be critical, think critically about what these technologies are doing to us as individuals and to us as societies. I guess that is the things I would like people to take away from our discussion.

Lucas Perry: All right. Well, thank you so much for coming on the podcast. I really can’t think of too many other issues that are as important as this. It’s certainly top three for me. Thank you very much for all of your work on this, Dr. Lentzos, and for all of your time here on the podcast.

Filippa Lentzos: Thanks for having me, Lucas.

• The modern social contract
• Reskilling, wage stagnation, and inequality
• Technology induced unemployment
• The structure of the global economy
• The geographic concentration of economic growth

 

Watch the video version of this episode here

29:28 How does AI automation affect the virtuous and vicious versions of productivity growth?

38:06 Automation and reflecting on jobs lost, jobs gained, and jobs changed

43:15 AGI and automation

48:00 How do we address the issue of technology induced unemployment

58:05 Developing countries and economies

1:01:29  The central forces in the global economy

1:07:36 The global economic center of gravity

1:09:42 Understanding the core impacts of AI

1:12:32 How do global catastrophic and existential risks fit into the modern global economy?

1:17:52 The economics of climate change and AI risk

1:20:50 Will we use AI technology like we’ve used fossil fuel technology?

1:24:34 The risks of AI contributing to inequality and bias

1:31:45 How do we integrate developing countries voices in the development and deployment of AI systems

1:33:42 James’ core takeaway

1:37:19 Where to follow and learn more about James’ work

 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. Today’s episode is with James Manyika, and is focused on global economic and technological trends. As the Agricultural and Industrial Revolutions both led to significant shifts in human labor and welfare, so too is the ongoing Digital Revolution, driven by innovations, such as big data, AI, the digital economy, and robotics also radically affecting productivity, labor markets, and the future of work. And being in the midst of such radical change ourselves, it can be quite difficult to keep track of where we exactly are and where we’re heading. While this particular episode is not centrally focused on existential risk, we feel that it’s important to understand the current and projected impacts of technologies like AI, and the ongoing benefits and risks of their use to society at large, in order to increase our wisdom and understanding of what beneficial futures really consist of.

It’s in the spirit of this that we explore global economic and technological trends with James Manyika in this episode. James received a PhD from Oxford in AI and robotics, mathematics, and computer science. He is a senior partner at McKinsey & Company, as well as chairman and director of McKinsey Global Institute. James advised the chief executives and founders of many of the world’s leading tech companies on strategy and growth, product, and business innovation, and was also appointed by President Barack Obama to serve as vice chair of the Global Development Council at the White House. James is most recently the author of the book, No Ordinary Disruption: The Four Global Forces Breaking All the Trends. And it’s with that, I’m happy to present this interview with James Manyika.

To start things off here, I’m curious if you could start by explaining what you think are some of the most important problems in the world today.

James Manyika: Well, first of all, thank you for having me. Gosh, one of the most important problems in the world, I think we have the challenge of climate change, I think we have the challenge of inequality, I think we have the challenge that economic growth and development is happening unevenly. So I should say that the inequality question, I think is most in inequality within countries, but to some extent also between countries. And this idea of uneven development is that some countries are surging ahead and some parts of the world are potentially being left behind. I think we have other social political questions, but those I’m not qualified to about those, I don’t really spend my time, I’m not a sociologist or political scientist, but I think we do have some social-political challenges too.

Lucas Perry: We have climate change, we have social inequality, we have the ways in which different societies are progressing at different rates. So given these issues in the world, what do you think it is that humanity really needs to understand or get right in the century given these problems?

James Manyika: Yeah, by the way, I should also, before I dive into that, also say, even though we have these problems and challenges, we also have incredible opportunities, quite frankly, for breakthrough, progress, and prosperity to solve some of these issues. And quite frankly, do things that are going to transform humanity for the better. So these are challenges at a time of, I think, unprecedented opportunity and possibility. So I just want to make sure we acknowledge both sides of that issue. It terms of what we need to do about these challenges. I think part of it is also just quite frankly, facing them head on. I think the question of climate change is one that is an existential challenge that we just need to face head on. And quite frankly, get on with doing everything we can both to mitigate the effects of climate change, and also quite frankly, to start to adapt how our society, our economy works to, again, address what is essentially an existential challenge.

So I think what we do in the next 10 years is going to matter more than what we do in the 10 years after that. So there’s some urgency to the climate change and climate risk question. I think with regards to the issue of inequality, I think this is one that is also within our capacity to address. I think it’s important to keep in mind that the capitalism and the market economies that we’ve had, and we do have, have been unbelievably successful in creating growth and economic prosperity for the world and in most places where they’ve been applied. Particularly in recent years, I think, we’ve also started to see that in fact, there’ve been growth in inequality, partly because the structure of our economy is changing and we can get into that conversation.

In fact, some people are doing phenomenally well and others are not, and some places are doing phenomenally well and some other places are not. I mean, it’s not lost on me, for example, Lucas, that even if you look at an economy like the United States, something like two thirds of our economic output comes out of 6% of the counties in the country. That’s an inequality of place, in addition to the inequalities that we have of people. So I think we have to tackle the issue of inequality quite head-on. Unless we do something, it has the potential to get worse before it gets better. The way our economy now works, and this is quite different by the way than what it might’ve looked like even as recently as 25 years ago, which is, most of the economic activity, a function of the sectors that are driving economic growth, a function of the types of enterprises and companies that are driving economic growth, they have tended to be much more today than they were 25 years ago to be fairly regionally concentrated and in particular places.

So some of those places include Silicon Valley and other places. Whereas if you had looked for example, 25 years ago where you might’ve seen the kind of sectors and companies that were doing well were much more geographically distributed, so you had more economic activity coming out of more places across the country than you do now. So this is just, again, a function of not that anybody designed it to be that way, but just a function of the sectors and companies and the way our economy works. A related factor by the way, is even the inequality question is also a function of how the economy works. I mean, it used to be the case that whenever we had economic growth and productivity growth, it also resulted in job growth and wage growth. That’s been true for a very long time, but I think in recent years, as in, depending on how you count it, the last 25 years ago or so, when we have productivity growth, it doesn’t lift up wages as much as it used to. Again, it’s a function of the structure of the economy.

In fact, some of the work we’ve been doing and other economists have been doing, is actually being to look at this so-called declining labor share. I think a way to understand that declining labor share is to think about the fact that, if you had to set up a factory say in a hundred years ago, most of the inputs into how that factory worked were all labor inputs, so the labor share of economic activity is much higher. But over time, if you’re setting up a factory today, sure you have labor input, but you also have a lot of capital input in terms of the equipment, the machinery, the robots, and so forth. So the actual labor portion of, you know, of it as a share of the inputs, it’s being going down steadily. And that’s just part of how structure of our economy is changing. So all of these effects are some of what is leading to some of the inequality that we see.

Lucas Perry: So before we drill more deeply into climate change and inequality, are there any other issues that you would add as some of the most crucial problems or questions for the 21st century?

James Manyika: The structure of our global economy is changing, and I think it’s now getting caught up also in kind of geopolitics. I’m not a geopolitical expert, but it’s not lost from a global economy standpoint that, in fact, we now have and will have two very large economies, the United States and China, and China is a very large economy. And it’s not just a source of exports or the things that we buy from them, but it’s also entangled with say the US and other countries economically in terms of monetary, and debt, and lending, and so forth. But it’s also a large economy itself, which is going to have its own consumption. So we now have for the first time, two very large global economies. And so, how that works in a geopolitical sense is one of the complications of the 21st century. So I think that’s an important issue. Others who are more qualified to talk about geopolitics can delve into that one, but that’s clearly in the mix as part of the challenges of the 21st century.

We also, of course, are going to have to think about the role of technology in our economies and our society. Partly because technology can be a force of massive productivity growth, innovation, and good, and all of that, but at the same time we know that many of these technologies raise new questions about privacy, about how we think about information, disinformation. So I think, you know, if you had to write the list of the questions we’re going to need to navigate in the coming decades of the 21st century, it’s a meaty list. Climate change is at the top of that list in my view, inequalities is on that list, and these questions of geopolitics are on that list, the role that technology is going to play is on that list, and then also some of these social questions that we now need to wrestle with, issues of social justice, not just economic justice but also social justice. So we have a pretty rich challenge list even at the same time that we have these extraordinary opportunities.

Lucas Perry: So in the realms of climate change, and inequality, and these new geopolitical situations and tensions that are arising, how do you see the role of incentives pushing these systems and problems in certain directions, and how it is that we come up with solutions to them given the power and motivational force of incentives?

James Manyika: Well, I think incentives play an important role. So take the issue of climate change, for example, I think one of the failures of our economics and economic systems is we’ve never quite priced carbon, and we’ve never quite built that into our incentive systems, our economic systems so we have a price for it. So that when we put up carbon dioxide into the atmosphere and so forth, there’s no economic price for that or incentives, a set of incentives not to do that. We haven’t done enough in that regard. So that’s an area where incentives would actually make a big difference. In the case of inequality, I think this one’s way more complicated beyond just incentives, but I’ll point to something that is in the realm of incentives as regards to inequality.

So, for example, take the way we were talking earlier about the importance of labor and capital in our capital inputs, I don’t mean capital as in the money necessarily, but just the actual capital, equipment and machines, and so forth in our system, we’ve built a set of incentives, for example, that encourage companies to invest in capital infrastructure, you know, capital equipment, they can write it off for example. We encourage investments in R&D for example, and the tax incentives to do that, which is wonderful because we need that for the productivity and growth and innovation of our economy, but we haven’t done anything nearly enough or equivalent to those kinds of incentives with regards to investments in human capital. So you could imagine in much more the productivity and growth and innovation of our economy, but we haven’t done anything nearly enough or equivalent to that, to those kinds of incentives with regards to investments in human capital.

So you could imagine a much more concerted effort to create incentives for companies and others to invest in human capital and be able to write off investments in skilling, for example, to be able to do that at the equivalent scale to the incentives we have for other forms of investment, like in capital or in R&D. So that’s an example of where we haven’t done enough on the incentive front, and we should. But I think there’s more to be done than just incentives for the inequality question, but those kinds of incentives would help.

I should tell you, Lucas, that one of the things that we spent the last year or so looking at is trying to understand how the social contract has evolved in the 21st century so far. So we actually looked at, for example, roughly 23 of the OECD countries, about 37 or 38 of them, but we looked at about 23 of them in detail just to understand how the social contract had evolved. And here because we’re not sociologists, we looked to the social contract in really three ways, right? How people participate in the economy as workers, because that’s part of, you know, when people work hard, and probably the exchange is that they get jobs, and they get income and wages and training. So people participating as workers is an important part of the social contract. People participating as consumers and households who consume products and services, and then people as savers who are kind of saving money for a future, you know, for their kids or for their future, et cetera.

And when you look at those three aspects of the social contract in the 21st century so far, it’s really quite stunning. So take the worker piece of that, for example, what has happened is that in across most countries, we’ve actually grown jobs despite the recession, I guess the recession in 2001, but also the bigger one in 2008, we’ve actually grown jobs. So there’re actually more jobs now than there were at this time of the 21st century. However, what has happened is that many of those jobs don’t pay as well, so the wage associated with that, the picture has actually shifted quite a bit.

The other thing about what’s happened with work is it’s becoming a little bit more brittle in the fact that job certainty has certainly gone down, there’s much more income and wage variability. So we’ve created more fragile jobs relatively to what we had at the start of the 21st century. So you could say, for workers, it’s a mixed story, right? Job growth, yes, wage growth, not so much, job certainty and job fragility has gone up. When you look at people as consumers and households, it also paints an interesting story. And the picture you see there is the fact that, if households and consumers are consuming things like, think about, you know, buying cars or wide goods products, or electronics, basically things that are globally competed and traded, the cost of those has gone down dramatically in the 21st century. So the 21st century in that sense, at least globalization has been great because it’s delivered these very cheap products and services.

But if you look at other products and services that households and consumers consume such as education, housing, healthcare, and in some places, depending which country or place you’re in, transportation, those have actually gone up dramatically, far, far higher and faster than inflation, far higher and faster than wage growth. In fact, if you are in the bottom half of the social income scale, those things have come to dominate your income in terms of what you spend money on. So for those people, it hasn’t worked out so well actually in terms of the social contract. And then on the savers side, people as savers are, very few people now can afford to save for the future. And one of the things that you see is that the growth of indebtedness in the 21st century so far has gone up for most people, especially the middle wage and low wage households and people, their ability to save for the future has gone down.

What’s interesting is, it’s not just that the levels of indebtedness have gone up, but it’s the fact that the people who are indebtedness look a little bit different. They’re younger, they’re also, in many cases, college educated, which is different than what you might’ve seen 25 years ago in terms of who was indebted and what do they kind of look like?

And then finally, just to finish, the 21st century in the social contract sense, also hasn’t worked out very well for women who still earn less than men, for example, and don’t quite have the opportunities as much as others, as well as for people of color. It hasn’t, so they still earn a lot less, employment rates are still much lower, their participation in the economy as any of these roles is also much less. So you get a picture that says, while the economy has grown and capitalism has been great, so far in the social contract sense at least, by these measures we’ve looked, at it hasn’t worked out as well for everybody in the advanced economies. This is a picture that emerges from the 23 OECD countries that we looked at. And the United States is on the more extreme end of most of the trends I just described.

Lucas Perry: Emerging from this is a pretty complex picture, I think, of the way in which the world is changing. So you said the United States represents sort of the extreme end of this, where you can see the largest effect size in these areas, I would assume, yet it also seems like there’s this picture of the Global East and South generally doing better off, like people being lifted out of poverty.

James Manyika: Yeah, it is true. So one of the wonderful things about the 21st century is in fact, close to a billion people have been lifted out of poverty in those roughly 20, 25 years, which is extraordinary, but we should be clear about where that has happened. Those billion people are mostly in China, and to some extent, India. So while we say we’ve lifted people out of poverty, we should be very specific about mostly where that has happened. There are parts of the world where that hasn’t been the case, parts of Africa, other parts of Asia, and even parts of Latin America. So this lifting people out of poverty has been relatively concentrated in China primarily, and to some extent in India.

One of the things about economics, and this is something that people like Bob Solow and others got Nobel Prizes for, if you think about what is it that drives our economic growth, if economic growth is the way we create economic surpluses, that we can then all enjoy and lead to prosperity, right? The growth desegregation models come down to two things: either you’re expanding labor supply, or you are driving productivity. And the two of those when they work well, combine to give you economic GDP growth.

So if you look, for example, at the last 50 years, both across the advanced economies, but even for the United States, the picture that you see is that much of our economic growth has come roughly, so far at least, has come roughly in equal measure from two things: one, you know, this is over the last 50 or so years, half of it has come from expansions in labor supply. You can think about it as a Baby Boomer Generation, more people entering the workforce, et cetera, et cetera. The other half has roughly come from productivity growth. And the two of them have combined to give us roughly the economic GDP growth that we’ve had.

Now, when you look forward from where we are, we’re not likely to get much lift from the labor supply part of it, partly because most advanced economies are aging. And so, the contribution that’s going to come from expansions in labor supply, much less. I mean, you can think of it as kind of a plane flying on two engines, right? If one engine has been expansions in labor supply and the other is in productivity, well, the labour supply engine is kind of dying out to or slowing down in its output.

We’re going to rely a lot more on productivity growth. And so, where does productivity growth come from? Well, productivity growth comes from things like technological innovation, innovating how we do things and how we create products and services and all of that. And technology is a big part of that. But guess what happens? One of the things that happens with productivity growth is that the role of technology goes up. So I come back to my example of the factory. So if you wanted a highly productive factory, it’s likely that your mix of labor inputs and capital inputs, read that as machinery and equipment, is going to change. And that’s why your factory a hundred years ago, it looks very different than a factory today. But we need that kind of technological innovation and productivity to drive it the output. And then the output leads to the output in the sector, and ultimately the output in the economy.

So all of that is to say, I don’t think we should stop the technological innovation that leads to productivity, we need productivity growth. In fact, going forward, we’re going to need productivity growth even more. The question is, how do we make sure that even as we’re pursuing that productivity growth that contributes to economic growth, we’re also paying attention to how we mitigate or address the impacts on labor and work, which is where most people derive their livelihoods.

So, I don’t think you want to set up a set of system of incentives that slows down the technological innovation and product activity growth, because otherwise, we’re all going to be fighting over a diminishing economic pie. I think you want to invest in that and continue to drive that, but at the same time find ways and think about how to address some of the work implications of that, or the impacts on work and workers. And that’s been one of the challenges that we’ve had. I mean, we’ve all seen the hollowing out, if you like, of the middle class in advanced economies like America, where a big part of that is that much of that middle class or middle income workers have been working in these sectors and occupations where the impact of technology and productivity have actually had a huge impact on those jobs and those incomes.

And so, even though we have work in the economy, the occupations and jobs in sectors that are growing have tended to be in the service sectors and less in places like manufacturing. I mean, it’s the reason why I love it when politicians talk about good manufacturing jobs. I mean, they have a point in the sense that historically, those have been good, well paying jobs, but manufacturing today is only what? 8% of the labor force in America, right? It’s diminished, at its peak is probably at best close to the mid forties, 40% as a share of the labor markets back in the ’50s, right? It’s being going down ever since, right? And yet the service sector economy has been growing dramatically. And many, not all, but many of the jobs in the service sector economy don’t pay as much.

My point is, we have to think about not just incentives but the structure of our economy. So if you look forward, for example, over the next few decades, what of the jobs that are going to grow as a function of both demand for that work in the economy, but also as a result of what’s less likely to be automated by technology and AI and so forth? You end up with a list that includes care work, for example, and so forth. And even work that we say is valuable, which it is, like teachers and others that are harder to automate. But labor market system doesn’t reward and pay those occupations as much as some of the occupations that are declining. So those are some of what, when I talk about the changes in the structure of our economy, in a way that goes a little bit beyond just local incentives, is how do we address that? How do we make sure as those parts of our economy grow, which they will naturally. How do we make sure people are earning enough to live as they work in those occupations? And by the way, those occupations are many of the ones that in our current or recent COVID moment or period here, many are where the essential work and workers are by the way, people have come to rely on mostly in those service sector economies that we haven’t historically paid well. Those are real challenges.

Lucas Perry: There was that really compelling figure that you gave at the beginning of our conversation, where you said 6% of counties account for two thirds of our economic output. And so, there’s this change in dynamic between productivity and labor force. And the productivity you’re suggesting is what is increasing, and that is related to and contingent upon AI automation technology. Is that right?

James Manyika: Well, first of all, we need productivity to increase. It’s been kind of sluggish in the last several years. In fact, it’s one of the key questions that economists worry about, which is, how can we increase the growth of our economic productivity? It hasn’t been doing as well as we’d like it to do. So we’d like to actually increase it, partly because, as I said, we needed more than we’ve done in the last 50 years because of the labor supply pieces declining. So we actually would like productivity to go up even more.

Mike Spence and I just wrote a paper recently on the hopeful possibility that in fact we could see a revival in productivity growth coming out of COVID. We hope that happens, but it’s not assured. So we need more productivity growth. And the way you get productivity growth, technology and innovation is a big part of it. The other part of it is just managerial innovation that happens inside companies in sectors where those companies and sectors figure out ways to organize and do what they do in innovative, but highly productive ways. So it’s the combination of technology and those kinds of managerial and other innovations, usually in a competitive context, that’s what drives productivity.

Lucas Perry: Does that lead to us requiring less human labor?

James Manyika: It shouldn’t. One of the things about productivity is, it’s actually, in some ways, labor productivity is a very simple equation, right? It has on the numerator, value-added output, divided by hours worked or labor input, if you like. So you can have, what I think of as a virtuous version of productivity growth versus a vicious one. So let me describe the virtuous one. The virtuous one, which actually leads to job growth, is when in fact you expand the numerator. So in other words, there’s innovations, use of technology in the ways that I talked about before, that means to companies and sectors creating more valuable output, more of it and more valuable output. So you expand the numerator. So if you do that, and you expand the numerator much higher and faster than you’re reducing the denominator, which is the labor hours worked, you end up with a virtuous cycle in the sense that the economy grows, productivity grows, everything expands, the demand for work actually goes up. And that’s a virtuous cycle.

The last time we saw a great version of that was actually in the late ’90s. This is, if you recall before that, Bob Solow kind of framed what ended up being called the Solow Paradox, which is this idea that before the mid and late ’90s, you saw computers everywhere except in the productivity figures, right? And that’s because we hadn’t seen the kinds of deployment of technology, the managerial innovations do the kind of, what I call the numerator-driven productivity growth, which, when it did happen in the mid to late ’90s, it created this virtuous cycle.

Now let me describe the vicious cycle, which is the, if you like, the not so great version of productivity growth. It’s when you don’t expand the numerator, but what you do is simply reduce the denominator. So in other words, you reduce the hours worked. In other words, you become very efficient at delivering the same output or maybe even less of the output. So you reduce the denominator, that lead to productivity, but it’s off the vicious kind, right? Because you’re not expanding the output, it’s simply reducing the inputs, or the labor inputs. Therefore, you end up with less employment, fewer jobs, and that’s not great. That’s when you get what you asked about, which is, where you need less labor. And that’s the vicious version of productivity, you don’t want that either.

Lucas Perry: I see. How does the reality of AI and automation replacing human labor and human work essentially increasingly completely over time factor into and affect the virtuous and vicious versions of productivity?

James Manyika: Well, first of all, we shouldn’t assume that AI is simply going to replace work. I think we should think about this in this context of what you might call complements and substitutes. So if our AI technology is developed and then deployed in a way that is entirely substitutive of work, then you could have work decline. But there’s also other ways to deploy AI technology, where it’s complementary and it complements work. And in that case, you shouldn’t have to think about it as much about losing jobs.

Let me give you some specifics on this. So we’ve done research, and others have too, but let me describe what we’ve done, but I think the general consensus is emerging that it’s close to what at least we found in our research, which is that, so we looked at, so the Bureau of Labor Statistics kind of tracks in the US, tracks roughly 800 plus occupations. We looked at all those occupations in the economy. We also looked at the actual particular tasks and activities that people actually do, this because any of our jobs and occupations are not monolithic, right? They’re made up of several different tasks, right?

I spent part of my day typing, or talking to people, or analyzing things, so we’re all an amalgam of different tasks. And we looked at over 2000 tasks that go into these different occupations, but let me get to where we ended up. So where we ended up was, we looked at what current and expected AI technology and artificial technologies can do. And we came to the conclusion that at least over the next couple of decades at the task level, and I emphasize the task level, not the job level, these are tasks, I’ll come back to jobs, at the task level, these technologies look like they could automate as much as 50% of the tasks and activities that people do. And it’s important to, again, emphasize those are tasks, not jobs.

Now, when you take those highly automatable tasks back and map them to the occupations in the economy, what we concluded was that something like at most 10% of the occupations look like they have all of their constituent tasks automatable. And that’s a very important thing to note, right? 10% of all the occupations look like they have close to a hundred percent of their tasks that are automatable.

Lucas Perry: In what timeline?

James Manyika: This is over the next couple of decades.

Lucas Perry: Okay. Is that like two decades or?

James Manyika: We looked at this over two decades, right? We have scenarios around that because it’s very hard to be precise because you can imagine the rate of technologies development speeding up, I’ll come back to that, but the point is, it’s only 10% of the, in our analysis anyway, 10% of the occupations look like they have all of their constituent tasks that are automatable in that rough timeframe. But at the same time, what we also found is that something like 60% of the occupations have something like a third of their constituent tasks that are automatable in that same period. Well, what does that mean? What that actually means is that many more jobs and occupations are going to change than get fully automated away. Because what happens is, sure, some activity that I used to do myself, now that activity can be done in an automated fashion, but I still do other things too, right? So this effect of kind of the jobs that will change is actually a bigger effect than the jobs that will disappear completely.

Now that’s not to say there won’t be any occupations that will decline. In fact, what we found in our research, and we ended up kind of titling the research report, Jobs Lost and Jobs Gained. We probably should have fully titled the Jobs Lost, Jobs Gained, and Jobs Changed because all three phenomena will happen, right? Yes, there’ll be occupations that will decline, but there will also be occupations that’ll grow actually. And then there’ll be lots more occupations that will change. So I think we need to take the full picture into account. It’s a bit like, I guess a good example of the jobs changed portion is the one of the bank teller, right? Which is, if you had looked at what a bank teller did in 1968 versus what a bank teller does now, it’s very, very different, right? The bank teller back then spent all their time counting money either to take it from you or to give it back to you when you went up to the bank teller. Or the advent of ATM machines or the ATM machine automated much of that.

So we still have bank tellers today, the majority of the time isn’t spent doing that, right? They may do that on an exception basis, but their jobs have changed dramatically, but there’s still an occupation called a bank teller. And in fact, until about, I think the precise date is something like 2006, I think, what we actually had was a number of bank tellers in the US economy had actually grown since the early ’70s to about 2006. And that’s because the demand for bank tellers went up, not on a per bank basis, but on a economy-wide basis because we ended up opening up so many more branch banks by 2006 than we had in 1968. So the collective demand for banking actually drove the growth in the number of bank tellers, even though the number of bank tellers per branch might’ve gone down.

So that’s an example of where a growing economy can create its own demand for work back to this virtuous cycle that I was talking about as opposed to the vicious cycle that I was talking about. So this phenomenon of jobs changing is an important one that often gets lost in the conversation about technology and automation and jobs. And so, to come back to your original question about substitutes, we shouldn’t just think of technology substituting for jobs as the only thing that happens, but also that technology can complement work and jobs. In fact, one of the things to think about, particularly for AI researchers or people who develop these automation technologies, I think, on the one hand, while it’s actually certainly useful to think of human benchmarks when we say, how do we build machines and systems that match human vision or human dexterity and so forth? That’s a useful way to set goals and targets for technology development and AI development. But in an economic sense, it’s actually less useful because it’s less likely to lead to technologies that are more substitutes because we’ve built them to match what humans can do.

Imagine if we said, let’s build technology machines that can see around corners or do the kinds of things that humans can’t do, then we’re more likely in that case to build more complementing technologies than substituted technologies. I think that’s one of the things that we should be thinking about and doing a heck of a lot more to achieve.

Lucas Perry: This is very interesting. So you can think of every job as basically a list of tasks, and AI technology can automate say some number of tasks per job, but then the job changes in a sense that either you can spend more time on the tasks that remain and increase productivity by just focusing on those tasks, or the fact that AI technology is being integrated into the job process will create a few new tasks. The tension I see though is that we’re headed towards a generality with AI where we’re moving towards all tasks being automated. Perhaps over shorter timescales it seems like we’ll be able to spend more time on fewer tasks or our jobs will change in order to meet and work on the new tasks that AI technology demands of us, but generality is a movement towards the end of human level problem solving on work and objective-related tasks. So it seems like it would be increasingly shrinking. Is that a view that you share? Does that make sense?

James Manyika: Your observation makes sense. I don’t know if I fully share it, but just to back up a step, yeah, if you asked me over the next few decades, I mean, our research has looked at the next couple of decades, others have looked at this too by the way, and he’d come up with obviously slightly different numbers and views, but I think they’re generally in the same direction that I just described. So if you say over the next couple of decades, what do I worry about? I certainly don’t worry about the disappearance of work for sure. But that doesn’t mean that all is well, right? There’re still things that I worry about. So I still worry about, well, we’re going to have work, because I think, you know, what we found for example is the net of jobs lost and jobs gained and jobs changed, the net of all of that in the economies that we’ve looked at is still a net positive in the sense that there’s more work gained net then lost.

That doesn’t mean we should all then be, rest in our laurels and be happy that, hey, we’re not facing a jobless future. So I think we still have a few other challenges to deal with. And I want to come back to your future AGI question in a second. So one of the things to worry about, even in this stage where I say don’t worry about the disappearance of work, well, there’re still a few more things to worry about.

I think you want to worry about the transitions, right? The skill transitions. So if some jobs are declining, and some jobs are growing, and some jobs are changing, all of that is going to create a big requirement for skilling and reskilling, either to help people get into these new jobs that are growing, or if their jobs are changing, gain the new skills that work well alongside the task that the machines can do. So all of that says reskilling is a really big deal, which is why everybody’s talking about reskilling now, though, I don’t think we’re doing it fast enough or at scale enough, at the scale and pace that we should be doing it. But that’s one thing to worry about.

The other thing to worry about are the effects on wages. So even when you have enough work, if you look at the pattern of the jobs gained, most of them, not all of them, but many of them, many of them are actually jobs that pay less, at least in our current labor market structure, right? So care work is hard to fully automate because it turns out that, hey, it’s actually harder to automate somebody doing physical mechanical tasks than say somebody doing analytical work. But it turns out the person doing analytical work, where you can probably automate what they do a lot easier, also happens to be the person who’s earning a little bit more than the person doing the physical mechanical tasks. But by the way, that person is one that we don’t pay much in the first place. So you end up with physical mechanical activities that are hard to automate also growing and being demanded, but then we don’t pay much for them.

So the wage effects are something to worry about. Even in the example I gave you of complementing work, that’s great from the point of view of people and machines working alongside each other, but even that has interesting wage effects too, right? Because at one end, which I’ll call the happy end, and I’ll come back to the challenged end, the happy end is when we automate some of what you do, Lucas, and the combination of what the machine now does for you and what you still do yourself as a human, both are highly valuable, so the combo is even more productive. And this is the example that’s often given with the classic story of radiologists, right? So machines can maybe read some of those images way better than the radiologist, but that’s not all the radiologist does, there’s a whole other value-added activities and tasks that the radiologist does that the machine reading doesn’t understand them, MRI doesn’t do. But now you’ve got a radiologist partnered up with a machine, the combination is great. So that’s a happy example. Probably the productivity goes up, the wages of that radiologist go up. That’s a happy story.

Let me describe the less happy end of that complementing story. The less happy end of that is when the machine automates a portion of your work, but the portion that it automates is actually the value-added portion of that work. And what’s left over is even more commoditized, commoditized in the sense that many, many, many more people can do it, and therefore, the skill requirements for that actually go down as opposed to go up, because the hard part of what you used to do is now being done by a machine. The danger with that is that, that then potentially depresses the wages for that work given the way you’re complementing. So even the complementing story I described earlier, isn’t always in one direction from a wage effect and its impact.

So all of that step back is to say, if the first thing is reskilling, the second thing to worry about are these wage effects. And then the final thing to worry about, how we think about redesigning work itself and the workflows themselves. So all of that is to say, even in a world where we have enough work, that’s in the next few decades, we still are going to have to work these issues. Now, you are posing a question about, what about in the long, long future, because I should think it’s in the long future that we’re going to have AGI. I’m not one who thinks it’s as imminent as perhaps others think.

Lucas Perry: Do you have a timeline you’d be willing to share?

James Manyika: No, I don’t have a timeline, I just think that there’re many, many hard problems that we still seem like a long way from… Now, the reason I don’t have a timeline, is that, hey, we could have a breakthrough happen in the next decade that changes the timeline. So we haven’t figured out how to do causal reasoning, we haven’t figured out how to do what Kahneman called System 2 activities. We’ve solved System 1 tasks where we assisted… And so, there’s a whole bunch of things that, you know, we haven’t solved the issues of how we do a higher-level cognition or meta-level cognition, we haven’t solved through how we do meta learning, transfer learning. So there’s a whole bunch of things that we haven’t quite solved. Now we’re making progress on some of those things. I mean, some of the things that have happened with these large language universal models is really breathtaking, right?

But I think that, in my view, at least the collection of things that we have to solve before we get to AGI, there’s too many that still feel unsolved to me. Now we could have somebody breakthrough in a day. That’s why I’m not ready to give a prediction in terms of timeline, but these seem like really hard problems to me. And many of my friends who are working on some of these issues also seem to think these are hard problems. Although there are some of them who think that we’re almost there, that all we need to, you know, deep learning will get us to most places we need to get to and reinforcement learning will get us most of what we need. So those are my friends who think that, think that it’s more imminent-

Lucas Perry: In a decade or two away, sometimes they say.

James Manyika: Yeah, some of them say a decade or two. There’s a lot of real debate about this. In fact, you may have seen one of the things that I participated in a couple of years ago was, and Martin Ford put together a book that was a collection of interviews with a bunch of people, it’s his book, Architects of Intelligence. A wonderful range of people in that book, I was fortunate enough to be included, but there are many more people and way more interesting than me. People like Demis Hassabis and Yoshua Bengio and a whole bunch of people, it’s a really terrific collection. And one of the things that he asked that group who are in that book was to ask them to give a view as to when they think AGI would be achieved. And what came out of it is a very wide range from 2029, and I think that was Ray Kurzweil who stuck to his date, and all the way to something like 500 years from now. And that’s a group of people who are deep in the field, right? And you’d get that very wide range.

So I think, for me, I’m much more interested in the real things that we are going to need to break through, and I don’t know when we’ll make those breakthroughs, it could be imminent, it could be a long time from now, but they just seem to be some really hard problems to solve. But if you take the view, to follow your thought, if you take the view, you know, the thought experiment to say, okay, let’s just assume we’ll truly achieve AGI in all its sense, in both in the AGI and the, some people will say in the oracular.

I mean, it depends what form of the AGI it takes. If the AGI takes the form of both the cognitive part of that coupled with the embodiment of that of physical machines that can physically participate, and you truly have AGI in a fully-embodied sense as well in addition to the cognitive sense, what happens to humans and work in that case? I don’t know. I think that’s where presumably those machines allow us to create enormous surpluses and bounties in an economic sense. So presumably, we can afford to pay everybody, you know, to give everybody money and resources. And so, then the question is, in a world of true abundance, because presumably they’ll help us solve these, you know, these machines, AGIs will help us solve those things, in a world of true abundance, what do people do in that?

I guess it’s kind of akin, as somebody said, to Star Trek economy. What do people do in a Star Trek economy when they can replicate and do everything, right? I don’t know. I guess we explore the universe, we do creative things, I don’t know. I’m sure we’ll create some economic system that takes advantage of the things that people can still uniquely do even though they’ll probably have a very different economic value and purpose. I think humans will always find a way to create either literally or quasi economic systems of exchange of something or other.

Lucas Perry: So if we focus here on the next few decades where automation is increasingly taking over particular tasks and jobs, what is it that we can do to ensure positive outcomes for those that are beginning to be left behind by the economy that requires skill training and those whose jobs are soon to have many of the tasks automated?

James Manyika: Starting now, actually, in the next decade or two, I think there’re several things, there’s actually a pretty robust list of things we need to do actually to tackle this issue. I think one is just reskilling. We know that there’s already a shortage of skills. Even before we think about, we’ve had skill mismatches for quite a while before any of this fully kicks in. So this is a challenge we’ve had for a while. So this question of reskilling is a massive undertaking, and here, the question is really due to pace and scale, because while there are quite a lot of reskilling examples one can come across, and there are many of them that have been very successful. But I think the key thing to note about many of them, not all of them, but many of them is that they tend to be small.

One of the questions one should always ask about all the great reskilling examples we hear of is, how big is it, right? How many people well went through that program? And I think you’ll find that many of them, not all of them, many of them are relatively small. At least small relative to the scale of the reskilling that we need to do. Now, there’ve been a few big ones, I happen to like, for example, Walmart has had these Walmart academies, it’s been written about publicly quite a bit, but what’s interesting about that is, it’s one of the few really large scale reskilling, retraining programs through their academies. Then something like, I can’t remember reading this, but they’ve put something like 800,000 people through those academies. I like that example, simply because the numbers talked to sound big and meaningful.

Now I don’t know. I haven’t evaluated the programs, are they good? But I think the scale is about right. So, the reskilling at scale is going to be really important, number one.

The other thing we’re going to need to think about is, how do we address the wage question? Now, the wage question is important, for lots of reasons here.

One is, if you remember earlier in our conversation, we talked about the fact that over the last two decades, for many people, wages haven’t gone up, been relative wage stagnation, compared to rates of inflation, or the cost of living, and how things have gone up. Wages haven’t gone up.

The wage stagnation is one we already have, before we think about technology. But then, as we’ve just discussed, technology may even exacerbate that, even when there are jobs, and the continuing changing structure of our economy will also exacerbate that. So what do we do about the wage question?

One could consider raising minimum wage, right? Or one could consider ideas like UBI. I mean, we can come back and talk about UBI. I have mixed the views about UBI. What I like about it is the fact that it’s at least a recognition that we have a wage problem, that people don’t earn enough to live. So I like it in that sense.

Now the complication with it, in my view, is that while, of course, one of the primary things that work does for you, for the vast majority of people, that’s how they derive their livelihood, their income. So it’s important, but work also does other things, right? It’s a way to socialize, it’s a way to give purpose and meaning, et cetera.

So I think UBI, it may solve the income part of that, which is an important part of that. It may not address the other pieces of the other things that work does. So, we have to solve the wage problem.

I think we also have to solve this geographic concentration problem. We did some work where we looked at all the counties in America at the time that we did this, because the definition of what’s a county in America kind of changes a little bit year from year. But at the time that we did this work, which was back in 2019, though I think we looked at something like 3,149 counties across America.

What we’re looking at there was, it was a range of factors about economic investment, economic vibrancy, jobs, wage growth. We looked at 40 different variables in each county, but I’m just going to focus on one, which is job growth.

When we looked at job growth across those counties, while at the national level, we’re all celebrating the job growth that had happened, coming out of the 2008 recession, between 2008 and 2018 was the data set we looked at, first of all, at the national level, it was great. But when you looked at it at the county level, what you suddenly found is that a lot of that job growth was concentrated in places where roughly a third of the nation’s workers live.

The other two-thirds of the place where people live either saw flat or no job growth, or even continued job decline. All of that is to say, we also have to solve this question of, how do we get more even job growth and wage growth across the country, in the United States?

We’ve also done similar work with, we’ve looked at these micro regions in Europe, and you see similar patterns, although maybe not quite as extreme as the US, but you see similar patterns where some places get a lot of the job and wage growth, and some cases get less of it. It’s just a function of the structure of our economy. So we’d have to solve that, too.

Then the other thing we need to solve is the classic case of the hollowing out of the middle class. Because if you look at the pattern of, mostly driven by technology, to some extent, a lot of the job declines or the jobs lost as a result of technology have primarily been in the middle wage, middle-class jobs. And a lot of the job growth has been in the low wage jobs.

So this question of the hollowing out of the middle class is actually a really particular problem, which has all kinds of sociopolitical implications, by the way. But that’s the other thing to figure out. So let me stop there.

But I think these are some of the things we’re going to need to tackle in the near term. I’ve made that list mostly in the context of say, an economy like the United States. I think if you go outside of the United States, and outside of the advanced economies, there’s a different set of challenges.

I’m talking about places outside of the OECD countries and China. So you go to places like India, and lots of parts of Africa and Latin America, where you’ve got a very different problem, which is demographically young populations. China isn’t, but India and most of Africa is, and parts of Latin America are.

So there the challenge is, a huge number of people are entering the workforce. The challenge there is, how do you create work for them? That’s a huge challenge, right? When you’re looking at those places, the challenge is just, how do you create enough jobs in very demographically young countries?

The picture’s now gotten a little bit more complicated in recent years than perhaps in the past, because in the past, the story was, if you are a developing country, a poor developing country, your path to prosperity was to join the global economy, be part of either the labor supply or the cheap labor supply often, and go from being an agrarian country to an industrialized country. Then ultimately, maybe some day, you’ll become a service economy. Most advanced economies are.

That path of industrialization is less assured today than it used to be, for a bunch of reasons. Some of those reasons have to do with the fact that advanced economies now no longer seek cheap labor abroad as much as they used to. They still do for some sectors, but less so for many other sectors, I mean, we’re less likely to do that.

Part of that is technology, the fact that in some ways, manufacturing has changed. We can now, going forward, do things more like 3-D printing, and so forth. So the industrialization path is less available to poor countries than it used to be.

In fact, economists like Danny Roderick have written about this, and called it this kind of premature de-industrialization challenge which is facing many low income countries. So we have to think about what, is the path for those countries?

And by the way, these are countries, if you think about it from the point of view of technology and AI, in particular, the AI technological competition globally rapidly seems to come down to be a race between the US, led by the US, but increasingly by China, and others are largely being left behind. That includes in some cases, parts of Europe, but for sure, parts of the poor developing economies.

So the question is, in a future, in which capacity for technology’s developing a different pace, dramatically different paces for different countries, and the nature of globalization itself is changing, what is the path for these poor developing countries? I think that’s a very tough question that we don’t have very many good answers for, by the way.

But there have just been people who think about developing economies in developing economies themselves. That’s one of the tough challenges, I think, for the next several decades of the 21st century.

Lucas Perry: Yeah, I think that this is a really good job of explaining some of these really significant problems. I’m curious what the most significant findings of your own personal work, or the work more broadly being done at McKinsey are, with regards to these problems and issues. I really appreciate some of the figures that you’re able to share. So if you have any more of those, they’re really helpful, I think, for painting a picture of where things are at, and where they’re moving.

James Manyika: Well, I think the only other question on these kind of left behind countries and economies, as I said, these are topics that we’re trying to research and understand. I don’t think we have any kind of pat simple solutions to them.

We do know, though, that in fact, so if you look at the pattern, a lot of our work is very empirical. I mean, typically, I’m looking at what is actually happening on the ground. One of the things that you do see for developing economies is that the developing economies that are part of a regional ecosystem, either because of the value chains and supply chains.

Take the case of a country like Vietnam. It’s kind of in the value chain ecosystem around China, for example. So it benefits from being a participant or an input into the Chinese value chain.

When you have countries, and you could argue that’s what’s happened with countries like Mexico and a few others, so there’s something about being a participant in the value chains or supply chains of these that are emerging somewhat regionally, actually. That seems to be at least one path.

The other path that we’ve seen is that when you’ve got a developing countries that tend to have large and competitive private sectors, and emphasize “competitive,” that actually seems to make a difference. So we did some empirical work where we looked at something like 75 developing countries over the last 25 years, to see what are some of the patterns of which ones are those that have done well, because of their growth and development, and so forth?

Some of the factors that you see, we found in that research, is in fact, when the countries that happened to have, as I said, one is proximity to either all participants in the global value chains of other large ecosystems or economies did well.

Second, those that seem to have these large and vibrant and very competitive private sector economies also seem to do better. Also, those that had resource endowments did well, so that I don’t know, oil and natural resources, and those kinds of things, also seemed to do well.

Then we also find that those that seem to have more mixed economies, so they didn’t just rely on one part of their economy, but they had two or three different kinds of activities going on in their economy, they had maybe a little bit of a manufacturing sector, and a little bit of an agricultural sector, a little bit of a service sector, so the ones that had more mixed economies seem to do well. The other big thing was, the ones that seem to be reforming their economies seem to do well.

So those are some patterns. I don’t think those are guaranteed, in any of them, to be the recipe for the next few decades, partly because much of that picture on global supply chains is changing, and much of the role of technology and how it affects how people participate in the global economy is changing.

I think those are useful, but I don’t know if they’re any short recipe, going forward. There certainly have been the patterns for the last 25 years, but maybe that’s a place to start, if you look forward.

Lucas Perry: To pivot a bit here, I’m curious if you could explain what you see as the central forces that are currently acting on the global economy?

James Manyika: Well, I’ll tell you some of the things that are interesting, that we find interesting. One is, in fact, the fact that more and more and more and more, the role of technology in the global economy is getting bigger and bigger and bigger, in the sense that technology seems to have become way more general purpose in the sense that it’s foundational to every company, every sector and every country.

So the role of that is interesting. It also has these other outsize effects, because we know that technology often lead to the phenomenon of superstar firms and superstar returns, and so forth. You see that quite a bit, so the role of technology is an important one.

The other one that’s going on is what’s happening with globalization itself. And by globalization, I just mean that the movement of value and activity related to the global economy.

We did some work a few years ago, that we’ve tried to update regularly, where we looked at all the things of economic value. So we looked at, for example, the flow of products and goods across the world, the flow of money, finances, and other financing and other things, the flow of services, the movement of people, and even the movement of data, and data-related activities.

What was interesting is that one of the things that has changed is that the globalization in the form of the flow of goods and services, it actually kind of slowed down, actually. That’s why one of the reasons people were questioning is, is globalization dead, has it slowed down?

Well, it certainly looks that way. If you’re looking at it through the lens of the flow of products and goods, but not the case if you’re looking, necessarily, at the flow of money, for example, not necessarily if you’re looking at the flow of people, and for sure not the case, if you’re looking at the flow of data around the world.

One of the things that’s, I think, underappreciated is just how digitized the global economy has become, and just the massive amounts of data flows, digital data flows that now happen across borders between countries, and how much that is tied into globalization works. So if you’re looking at globalization through the lens of digitization, digital data flows, nothing has slowed down. In fact, if anything, it’s accelerated, actually.

That’s why, often, you will hear, people were looking at it through that lens, and say, “Oh no, it’s even more globalized than ever before.” But people who are looking at it through the flow of products and goods, for example, might say, “Oh, it seems, it looks like it is slowed down.” That’s one of the things that’s changing.

Also, the globalization of digital data flows is actually interesting, because one of the things that it does is it changed the participation map quite significantly. So we did some work, where if you look at it through that lens, you suddenly found that you have many more countries participating, and many more kinds of companies participating, as opposed to just a few countries and a few companies participating in the global economy. You have much more diversity of participation.

So you have very tiny companies, a two- or three-person company in some country, plugged into the global economy, using digital technology and digital platforms, in ways that wouldn’t have happened before, if you had a two- or three-person company 30 years ago. So this digitalization of the global economy is really quite fascinating.

The other thing that’s going on to the global economy is the rebalancing, where, with the emergence of China’s a big economy in its own right, that is changing the gravitational structure, if you like, of the global economy in some, in some very profound ways, in ways that we haven’t quite had before. Because, sure, in the past you’ve had other large economies like Germany and Japan and others, as large economies, but none of them were ever as big as the United States.

Also, all of them, whether it’s Japan or Germany or any of the European countries, largely operated in a framework, a globalization and a global framework, that was largely kind of Western-centric in a way. But now you have this very large economy that’s very different, is very, very large, will be the second largest economy in the world. That is quite different, but yet is tied into the, so that gravitational structural shift is very, very important.

Then, of course, the other thing that’s happening is, what’s happening with supply chains and global value chains. And that’s interesting, partly because we’re so intertwined with how supply chains and value chains work, but at the same time, it changes how we think about the resilience of economies. We’ve just seen that during this COVID last year, where, all of a sudden, everybody got concerned about the resilience of our supply chains with respect to, essential products and services like medical supplies and so forth.

I think people are now starting to rethink about how do we think about the structure of the global economy, in terms of these value chains. We should have at some point also mentioned other kinds of technologies that are happening. Because it’s not all AI and digital technologies, as much as I love that, and spend a lot of time on that.

I think other technological developments that are interesting include what’s happening in biosciences or the life sciences. We’ve just seen spectacular demonstrations of that with the MRNA vaccines that were rapidly developed.

But I think a lot more has been happening with just amazing progress, that we’re still at the very early stages of, with regards to the biotechnology and the life sciences. I think we’re going to see even more profound, societally, and profound impact from those developments in the coming decades.

So these are some of the things that I see happening in the global economy. Now, of course, climate change looms large over all of this as a thing that could really impact things in quite dramatic and quite existentially concerning ways.

Lucas Perry: In terms of this global economy, can you explain what the economic center of gravity is, where it’s been, and where it’s going?

James Manyika: Well, undoubtedly, the economic center of gravity has been the United States. If you look at the last 50 years, it’s been the largest economy on the planet, largest in every sense, right? As a market to sell into, as its own market. Everybody around the world for the last 50 years has been thinking about, “How do we access and sell to consumers and buyers in the United States?”

It’s been the largest market. It’s also been the largest developer and deployer of technologies and innovation. So, in all of those ways, it’s been the United States as the big gravitational pull.

But I think, going forward, that’s going to shift, because current course and speed, the Chinese economy will be as large. And you now start to have even other economies becoming large, too, like India.

So I think economic historians have created a wonderful map, where they showed the movement of the gravitational central to the global economy. I think they went back 1,000 years.

While it’s been in the Western Hemisphere, primarily in the United States, I think some of the mid-Atlantic, it’s been shifting east, mostly because of the Chinese economy, primarily, but also India and others that have come to grow. That’s clearly one of the big changes going on at the global economy, to its structure and its center of gravity.

Lucas Perry: With this increase of globalization, how do you see AI as fitting into and affecting globalization?

James Manyika: The impact on globalization? I don’t think that’s the way I would think about the impact of AI. Of course, it’ll affect globalization, because any time you have anything to do with products, goods, and services, because AI is going to effect all of those things.

To the extent that those things are playing out on the global economy landscape, AI will affect those things. I think the impact of AI, at least in my mind, is first and primarily about any economy, whether it’s the global economy or a national economy or a company, right? So I think it’s profoundly going to change many things about how any economic entity works.

Because we know the effect, the capital labor inputs, we know it’ll affect productivity, and we know it’ll change the rates of innovation. Because imagine, in this conversation, at least, we talked, I think mostly about AI’s impact on labor markets, we should not forget AI’s impact on innovation, on productivity, on the kinds of creation of products, goods, and services that we can imagine and how hopefully it’s going to accelerate those developments.

I mean, DeepMind dealing with AlphaFold, which is cracking a 50-year problem, that’s going to lead to all kinds of, hopefully, biomedical innovations and other things. I think one of the big impacts is going to be how AI affects innovation, and ultimately productivity, and the kinds of things we’re going to see, whether it’s products, goods, and services that we’re going to see in the economy.

Of course, any economy that takes advantage of that and embraces those innovations will obviously see the benefit to the growth of their economy. Of course, if on a global scale, on a global stage in the global economy, we have some countries do that more than others, then of course it’ll affect who gets ahead, who’s more competitive, and who potentially gets left behind.

One of the other things we’ve looked at is, what is the rate of AI participation, whether in terms of developments or contributing to developments, or just simply deploying technologies, or having the capacity to deploy technologies, or having the talent and people who can either both contribute to the deployment or the development, and also embrace it in companies and sectors? And you see a picture that’s very different around the world.

Again, you see the US and China, way ahead of everybody, and you’ll see some countries in Europe, and even Europe is not uniform, right, and some countries in Europe doing more of that than others, and then a whole bunch of others who are being left behind. Again, AI will impact the global economy in the sense of how it impacts each of the economies that participate, and each of the companies that participate in the global economy, in their products and services, and their innovations and outputs.

There are other things that’ll play out in the global stage related to AI. But from an economy standpoint, I think I see it through the lens of the participating companies and countries and economies, and how that then plays out on the global stage.

Lucas Perry: There’s also this facet of how this technological innovation and development, for example, with AI, and also technologies which may contribute to and mitigate climate change, all affect global catastrophic and existential risks. So I’m curious how you see global catastrophic and existential risks as potentially fitting into this evolution of the global economy of labor and society as we move forward?

James Manyika: Well, I think it depends whether you’re asking whether AI itself represents a catastrophic or existential risk. Many people have written about this, but I think that question is tied up with the view on how do we think about, how close we are to AI, or how close we are to AGI, and how close we are to superhuman AI capabilities.

As we discussed earlier, I don’t think we’re close yet. But there are other to think about, even as we progress in that direction. This include some of the safety considerations, the control considerations, and how we make sure we’re deploying and using AI safely.

We know that there’s particular problems with regards to things like, even with narrow AI, as it’s sometimes called, how do we think about reward and goal corruption, for example? How do we think about how we avoid the kind of interference, catastrophic interference, between, say, tasks and goals? How do we think about that?

There are all these kinds of safety related things, even on our way to AGI, that we still need to think about. In that sense, these are things to worry about.

I also think we should be thinking about questions of value and goal alignment. And these also get very complicated for a whole bunch of both philosophical reasons, but also quite practical reasons.

That’s why I love the work, for example, that Stuart Russell has been doing on how we think about human compatible AI, and how do we build these kinds of, the value alignment and goal alignment, that we should be thinking about? So these are, even on our way to AGI, both the safety control and these kinds of value alignment, and somewhat normative questions, about how we think about normativity, and what does it even mean, to think about normative things in the case of value alignment with the AI? These are important things.

Now, that’s if you’re thinking about catastrophic, or at least to existential risk, with regards to AI, even way before you get to AGI. Then you have the kinds of things, at that point, that Nick Bostrom and others have worried about.

I think, because those are non-zero probability concerns, we should invest all effort into working on those existential, potentially catastrophic problems. But I’m not super worried about those any time soon, but that doesn’t mean we shouldn’t invest and work on those, the kinds of concerns that Nick and others write about.

But they are also questions about AI governance, in the sense of we’re going to have many participating entities here. We’re going to have the companies that are leading the development of these technologies.

We’re going to have governments that are going to want to participate and use these technologies. We’re going to have issues around when to deploy these technologies, use and misuse. Many of these questions become particularly important when you think about the deployment of AI, especially in particular arenas.

Imagine if, once we have AI or AGI that’s capable of manipulation, for example, or persuasion, or those kinds of things, or capabilities that allow us to detect lies, or be able to interfere or play with signals’ intelligence, or even cryptography and number theory. I mean, our cryptographic systems rely on a lot of things in prime number theory, for example, or if you think about arenas like autonomous weapons.

So questions of governance become evermore important. I mean, they’re already important now, when we think about how AI may or may not be used for things like deep fakes and disinformation.

The closer we get to the kinds of areas that I was describing here, it becomes even more important to think about governance, and what’s permissible to deploy where and how, and how do we do that in a transparent way? And how do we deal with the challenges with AI about attribution?

One of the nice things about other potentially risky technologies or developments like nuclear science, or chemical weapons, and so forth, is at least those things, they’re easy to detect when they happen. And it’s relatively easy to do attribution, and verify that it happened, and it was used.

It’s much harder with the AI systems, so these questions of governance and so forth become monumentally important. So those are some of the things we should think about.

Lucas Perry: How do you see the way in which the climate change crisis arose, given human systems and human civilization? What is it about human civilizations and human systems that has led to the climate change crisis? And how do we not allow our systems to function and fail in the same way, with regards to AI and powerful technologies in the 21st century?

James Manyika: I’m not an expert on climate science, by the way. So I shouldn’t speculate as to how we got to where we are. But I think the way we’ve used certain technologies and fossil fuels, I think, is a big part of that, the way our economies have relied on that as our only mode of energy, is part of that.

The fact that we’ve done that in a relatively costless way, in terms of pricing the effects on our environment and our climate, I think, is a big part of it. The fact that we haven’t had very many as effective and as efficient alternatives, historically, I think, is a big part of that.

So I think all of that is part of how we got here in some ways, but I think others more expert than me can talk about that. I think if I think about AI, I think one of the things that is potentially challenging about AI, if, in fact, we think there’s a chance that we’ll get to these superhuman capabilities, and AGI, is that we may not have the opportunity to iterate our way there. Right?

I think, quite often, with a lot of these deployment of technologies, I think a practical thing that has served us well in the past has been this idea that, well, let’s try a few experiments, we’ll fix it if it fails. Or if it doesn’t work, and we’ll iterate and do better, and kind of iterate our way to the right answer. Well, if we believe that there is a real possibility of achieving AGI, we may not have the opportunity to iterate in that same way.

That’s one of the things that’s potentially different, perhaps, because we can’t undo that, as it were, if we truly get to AGI. Thinking about these existential things, so there’s maybe something of a similarity, or at least an analog with climate change, is that we can’t just undo what we’ve done, in a very simple fashion, right?

Look at how we’re now thinking about, how do we do carbon sequestration? How do we take carbon out of this, out of the air? How do we undo these things? And it’s very hard. It’s easy to go in one direction, it’s very hard to go in the other direction, in that sense, at least.

It’s always dangerous with the analysis. But at least in that sense, AI, on its way to AGI, may be similar in that sense, which is we can’t always quite get to undo it in a simple fashion.

Lucas Perry: Are you concerned or worried that we will use AI technology in the way that we’ve used fossil fuel technologies, such that we don’t factor in the negative effects or negative externalities of the use of that technology? With AI, there’s this deployment of single objective maximizing algorithms that don’t take account all of our other values, and that actually run over and increase human suffering.

For example, the ways in which YouTube or Facebook algorithms work to manipulate and capture attention. Do you have a concern that our society has a natural proclivity towards learning from mistakes, from ignoring negative externalities, until it reaches sort of a critical threshold?

James Manyika: I do worry about that. And then maybe, just to come back to one of your, I think, central concerns, back to the idea of incentives, I do worry about that, in the sense that there are going to be such overwhelming and compelling incentives to deploy AI systems, for both good reasons, and for the economic reasons that go with that. So there are lots of good reasons to deploy AI technology, right?

It’s actually great technology. Look at what it’s probably going to do to, in the case of health science, and breakthroughs we could make there in climate science itself, and scientific discovery and material science. So there’s lots of great reasons to get excited about AI.

And I am, because it’ll help us solve many, many problems, could create enormous bounty and benefits for our society. So we’re going to, people are going to be racing ahead to do that, for those reasons, for those very good and very compelling reasons.

There are also going to be a lot of very compelling economic reasons. The kinds of innovations that companies can make, the kind of contributions to the economic performance of companies, the kinds of economic benefits, and that possibly that AI will contribute to productivity growth as we talked about before.

There’s lots of reasons to want to go full steam ahead. And a lot of incentives will be aligned to encourage that, both the breakthrough innovations that are good for society. As I said, the benefits that companies will get from deploying and using AI until the innovations, the economy-wide productivity benefits, so, all good reasons.

And I think, in the rush to do that, we may in fact find that we’re not paying enough attention, not because anybody is out of malice or anything like that, but we just may not be paying enough attention to these other considerations that we should have alongside, considerations about, what does this mean for bias and fairness?

What does it mean for, potentially for inequality? We know these things have scale superstar effects. What does that mean for others who get left behind? What does this mean for the labor markets and jobs and so forth? So I think we’re going to need to find mechanisms to make sure that there’s continued, but substantial effort, at those kind of other sides of the side effects of AI, and some of the unintended consequences.

That’s why, at least, I think many of us are trying to think about this question, “What are the things we have to get right,” even as we race towards all the wonderful things we want to get out of it, what are the other things we need to make sure we’re getting right along the way?

How do we make sure these things… People are working on them, they’re funded, there’s support for people working on these other problems. I think that’s going to be quite important, and we should not lose sight of that. And that’s something I’m concerned about.

Lucas Perry: So let’s pivot here, then, into inequality and bias. Could you explain the risk and degree to which AI may contribute to new inequalities, or exacerbate existing inequalities?

James Manyika: Well, I think on the inequality point, it’s part of what we talked about before, right? Which is the fact that, even though we may not lose jobs in the near term, we may end up with creating jobs or complementing jobs in a way that have these wage effects, that could worsen the inequality question.

That’s one way in which AI could contribute to attain equality. The other way, of course, is the fact that because of the scale effects of these technologies, you could end up with a few companies or a few entities or a few countries having the ability to develop and deploy, and get the benefits of AI, while the other companies or countries and places don’t. So you’ve got that kind of inequality concern.

Now, some of that could be helped by the way as it is, because it was the case, it has been the case so far, that the kind of compute capacity needed to develop and deploy AI has been very, very large, and the data endowments needed to train algorithms has been very, very high, but we know the talent of people who are working on these things has been, up until now, relatively concentrated.

But we know that that picture’s changing, I think. The advent of cloud computing, which makes it easy for those who don’t have the compute capacity, is helping that. The fact that we now have ways to train algorithms, of pre-trained algorithms or other universal models and others, so that not everybody has to retrain everything every single time.

These scarcities and these kind of scale constraints, I think, in those particular ones, will get better as we go forward. But you do worry about those inequalities, both in a peoples sense, but also in a entity sense, where entities could be companies, countries, or whole economies.

I think the questions of bias are a little bit different. I think the set of questions of biases, just simply has to do with the fact that up until now, at least so far, anyway, most of the data sets that have been used to train these algorithms often come with societally derived biases. And I emphasize this, society derive bias. It’s just because of the way we collect data and the data that’s available and who’s contributing to it.

Often, you start out with data sets, training data sets that reflect society’s existing biases. Not that the technology itself has introduced the bias, but in fact, these come out of society. So what the technologies then do is kind of bake these biases in, into the algorithms and probably deploy them at scale.

That’s why I think this question bias is so important, but I think often it gets conflated with the fact that, well, proponents of using these technologies will say, but humans already have bias in them, anyway. We already make biased decisions, et cetera.

Of course, that’s a two-sided conversation. But at least to the case, the difference that I see between the biases we have already as human beings, versus the biases that could get baked into these systems, is that these systems could get deployed and scale in a way that, if I have biases that I have, and I’m in a room and I’m trying to hire somebody, and I’m making my biased decisions, at least, hopefully that only affects that one hiring decision.

But if I’m using an algorithm that has all these things baked in, and hundreds of millions of people are using the algorithm, then we’re kind of doing that in scale. So I think we need to keep that in mind, as we have the debate about, people already have biases and saturated biases, that’s true. So we need to do work on that.

But one of the things I like about the bias question, by the way, that these technologies are forcing us to confront is that it’s actually forcing us to really think about, what do we even mean when we say things are fair, quite aside from technology?

I think they’re forcing us, just like the UBI debate is forcing us to confront the question that people don’t earn enough to live, the bias question’s also forcing us to confront the question of, what is fair right? What counts as fairness? And I think all too often, in our society, we’ve tended to rely on proxies for fairness, right?

When we define it, we’ll say, “Well, let’s constitute the right group of people, a diverse enough group of people, and we will trust the decision that they make, because it’s a diverse group of people,” right? So yeah, if that group is diverse in the way we expect, then gender or racial or any other social income terms, and they make a decision, we’ll trust it, because the deciding group is diverse.

That’s just a fairness by proxy, in my view. Who knows what those people actually think, and how to make decisions? That’s a whole separate matter, but we trust it, because it’s a diverse group.

The other thing that we’ve tended to rely on is, we trust the process, right? If we trust the process that, “Hey, if it’s gone through a process like this, we will live with the results, because we think that the process like that is fair and unbiased.”

Who knows whether the process is actually fair, and that’s how we’ve typically done it with our legal system, for the most part. That if you follow through, if you’ve been given due process and you’ve gone through a jury trial, then it must be fair. We will live with the results.

But I think, in all of those cases, while they’re useful constructs for us in society, they still somewhat avoid defining what is actually fair. And I think, when we’ve started to deploy technologies, where, in the cases of AI, the process is somewhat opaque, because we have this kind of explainability challenge of these technologies. So the process is kind of black boxy, in that sense.

And if we automate the decisions with no humans involved, then we can’t rely on this constituent group that, “Hey, the group of people decided this, so it must be fair.” This is forcing us to come back to the age-old or even millennia-old question of what is fair? How do we define fairness?

I think there’s some work that was done before, where somebody is trying to come up with all kinds of definitions of fairness, and they came up with something like 21. So I think we now are having an interesting conversation about what constitutes fairness. Do we gather data differently? Do we code differently? Do we have reviews differently? Do we have different people that develop the technologies differently? Do we have different participants.

So we’re still grappling with this question, what counts as fair? I think that’s one of the key questions, as we rely more and more on these technologies to assist, in some cases, eventually take over some of our decision-making, of course, only when it’s appropriate, these questions will continue to persist, and will only grow, on how we think about fairness and bias.

Lucas Perry: In terms of fairness, bias, equality, and beneficial outcomes with technology and AI in the 21st century, how do you view the need for and path to integrating developing countries’ voices in the use and deployment of AI systems?

James Manyika: Well, I don’t know if there’s any magical answers, Lucas. At some level, at a base level, we should have them participate, right? I think any participation, both in the development and deployment, I think, is going to be important. And I think that’s true for developing countries. I think it’s true for parts of even US society that’s often not participating in these things.

I mean, it’s still striking to me how the lack of diversity, and diversity, in every sense of the term, who is developing AI and who’s deploying AI, whether they look within the United States or around the world, there are entities and places and communities and whole countries that are not really part of this. So I think we’re going to need to find a ways to do that.

I think part of doing that is at least for me, it starts out with the recognition that capabilities and intelligence are equally distributed everywhere. I don’t think there’s any one place or country or community that has a natural advantage to capability and intelligence.

On that premise, we just need to get people from different places participating in the development and deployment, and even the decision-making that’s related to AI, and not just go with the places where the money and the resources happen to be, and that’s, who’s racing ahead, both within countries, e.g., in the United States itself, or in other countries that are being left behind. I think participation, in these different ways, I think it’s going to be quite, quite important.

Lucas Perry: If there’s anything you’d like to leave the audience with, in terms of perspective on the 21st century on economic development and technology, what is it that you would share as a takeaway?

James Manyika: Well, I think, when I look ahead to the 21st century, I’m in two minds. On the one hand, I’m actually incredibly excited about the possibilities. I think we’re just at the beginning of what these technologies, both in AI and so forth, but also in the life sciences and biotech, I think that the possibilities in the 21st century are going to be enormous, possibilities for both improving human life, improving economic prosperity, growing economies.

The opportunities are just enormous, whether you’re a company, whether you’re a country, whether you’re a society, the possibilities are just enormous. I think there’s more that lies ahead than behind.

At the same time, though, I think, alongside pursuit of those opportunities are the really complicated challenges we’re going to need to navigate through, right? Even as we pursue the opportunities that AI and these technologies are going to bring us, we’re going to need to pay attention to some of these challenges that we just talked about, these questions of potential inequality and bias that comes out of the deployment of these technologies, or some of the superpower effects that could come out of that, even as we pursue economic opportunities around the world, we’re going to need to think about what happens to poor developing countries who may not keep up with that, or be part of that.

In every case, for all the things that I’m excited about the 21st century, which is plenty, there are also these challenges along the way we’re going to need to deal with. Also the fact that society, I think, demands more from all of us.

I think the demands for a more equal and just society are only going to grow. The demands or desires to have a more inclusive and participative economy are only going to grow, as they should. So we’re going to need to be working both sets of problems, pursuing the opportunities, because without them, these other problems only get harder, by the way.

I mean, try to solve the inequality when there’s no economic surpluses, right? Good luck with that. So we have to solve both. We can’t pick one side or the other, we have to solve both. At the same time, I think we also need to deal with some of the potentially existential challenges that we have, and may grow. I mean, we are living through one right now.

I mean, we’re going to have more pandemics in the future than we have had, perhaps, in the past. So we’re just going to need to be ready for that. We’ve got to deal with climate change. And these kinds of public health, climate change issues, I think, are global. They’re for all of us.

These are not challenges for any one country or any one community. We have to kind of work on all of these together. So that set of challenges, I think, is for everybody, for all of us. It’s on planet Earth, so we’re going to need to work on those things too. So that’s kind of how I think about what lies ahead.

We have to pursue the opportunities, there’s tons of them. I’m very excited about that. We have to solve the challenges that come along with questioning those opportunities, and we have to deal with these collective challenges that we have. I think those are all things to look forward to.

Lucas Perry: Wonderful, James, thank you so much. It’s really interesting and perspective shifting. If any of the audience is interested in following you or checking your workout anywhere, what are the best places to do that?

James Manyika: If you search my name and such McKinsey Global Institute, you will see some of the research and papers that I referenced. For those who love data, which I do, these are very data rich fact-based perspectives. So just look at the McKinsey Global Institute website.

Lucas Perry: All right. Thank you very much, James.

James Manyika: Oh, you’re welcome. Thank you.

Lucas Perry: Thanks for joining us. If you found this podcast interesting or useful, consider sharing it on social media with friends, and subscribing on your preferred podcasting platform. We’ll be back again soon, with another episode in the FLI Podcast.

• Evidence counting for the natural, human, and extraterrestrial origins of UAPs
• The culture of science and how it deals with UAP reports
• How humanity should respond if we discover UAPs are alien in origin
• A project for collecting high quality data on UAPs

Watch the video version of this episode here

See here for information on the Podcast Producer position

See the Office of the Director of National Intelligence report on unidentified aerial phenomena here

 

Lucas Perry: Welcome to the Future of Life Institute Podcast. I’m Lucas Perry. This is a follow up interview to the main release that we’ve done with Avi Loeb. After our initial interview, the US Government released a report on UFOs, otherwise now known as UAPs, titled, Preliminary Assessment: Unidentified Aerial Phenomena. This report is a major and significant event in the history and acknowledgement of UFOs as a legitimate phenomena. As our first interview with Avi focused on Oumuamua and its potential alien origin, we also wanted to get his perspective on UFOs, this report, his views on whether they’re potentially alien in origin, and what this all means for humanity.

In case you missed it in the main episode, we’re currently hiring for a Podcast Producer to work on the editing, production, publishing, and analytics tracking of the audio and visual content of this podcast. As the Producer you would be working directly with me, and the FLI outreach team, to help grow, and evolve this podcast. If you’re interested in applying, head over to the Careers tab on the Futureoflife.org homepage or follow the link in the description. The application deadline is July 31st, with rolling applications accepted thereafter until the role is filled. If you have any questions, feel free to reach out to socialmedia@futureoflife.org.

And with that, I’m happy to present this bonus interview with Avi Loeb.

The Office of the Director of National Intelligence has released a preliminary assessment on unidentified aerial phenomena, which is a new word that they’re using for UFO, so now it’s UAP. So can you summarize the contents of this report and explain why the report is significant?

Avi Loeb: The most important statement made in the report is that some of the objects that were detected are probably real and that is based on the fact that they were detected in multiple instruments using radar systems, or infrared cameras, or optical visual cameras, or several military personnel seeing the same thing, doing the same thing at the same time. And so that is a very significant statement because the immediate suspicion is that unusual phenomena occur when you have a smudge on your camera, when there is a malfunction of some instruments, and the fact that there is corroborating evidence among different instruments implies that it must be something real happening. That’s the first significant statement.

And then there were 144 incidents documented but it was also mentioned there is a stigma on reporting because there is a taboo on discussing extraterrestrial technologies, and as a result only a small minority of all events were reported. But nevertheless, the Navy established in March 2019 a procedure for reporting, which was not available prior to that and the Air Force followed on that in December 2020. So it’s all very recent that there is this procedure or formal path through which reports can be obtained. And of course, that helps in the sense that it provides a psychological support system for those who want to report about unusual things they have witnessed, and prior to that they had to dare to speak given the stigma.

And so the second issue is of course we have objects, if some of them are real. And by the way, we only saw a small fraction of the evidence most of it is classified and the reason is that the government owns the sensors that were used to obtain this evidence. And these sensors are being used to monitor the sky, and therefore our national security importance and we don’t want to release information about the quality of the sensors to our adversaries, to other nations. And so the data itself is being classified because the instruments are classified, but nevertheless one can think of several possible interpretations of these real objects.

And I should say that CIA Directors, say like Brennan, and Woolsey, and President Barack Obama spoke about these events as serious matters so that, to me, it all implies that we need to consider them seriously. So there are several possible interpretations, one of course is that they are human made and some other nation produced them, but some of the objects behaved in ways that supersede our technologies, the limits of the technologies we have in the U.S. and we have some intelligence on what other nations are doing. And moreover, if there was another nation with far better technologies they would find themselves in the consumer market because there would be a huge financial benefit to using them or we would see some evidence for them in the battlefield. And at the moment we have a pretty good idea, I would argue, as to what other nations are doing technologically speaking.

So if there are real objects behaving in ways that exceed human technologies, then the question is what could it be? And there are two possibilities, either these are natural phenomena that occur in the atmosphere that we did not expect or they are of extraterrestrial origin, some other technological civilization produced these objects and deployed them here. And of course, both of these possibilities are exciting because we learned something new. So the message I take from this report is that the evidence is sufficiently intriguing for the subject to move away from the talking points of politicians, national security advisors, military personnel that were really not trained as scientists. It should move into the realm of science where we use state-of-the-art equipment, such as cameras installed on wide field telescopes that scan the sky. These are telescopes you can buy off the shelf and you can position them in similar geographical locations, and monitor the sky with open data, and analyze the data using the best computers we have in a completely transparent way like a scientific experiment.

And so that’s what we should do next and instead what I see is that a lot of scientists just ridicule the significance of the report or say business as usual, that there is no need to attend to these statements. And I think it’s an opportunity for science to actually clarify this matter and clear up the fog and this is definitely a question that is of great interest to the public. What is the nature of these unidentified objects or phenomena? Are they natural in origin or maybe extraterrestrial? And I’m very much willing to address this with my research group given proper funding for it.

Lucas Perry: Let’s stick here with this first point, right? So I actually heard Neil deGrasse Tyson on MSNBC just before we started talking and he was mentioning that he thought that there could have been hardware or software artifacts that could have been generating artifacts on these systems. And I think your first point very clearly refutes that because you have multiple different systems plus eyewitness reports all corroborating the same thing. So it’s confusing to me why he would say that.

Avi Loeb: Well, because he is trying to maximize the number of likes he has on Twitter and doubting the reality of these reports appears to be popular among people in academia, among scientists, among some people in the public and so he’s driven by that. My point is, an intelligent culture is driven or actually is following the guiding principles of science and those are sharing evidence based knowledge. What Neil deGrasse Tyson is doing is not sharing evidence based knowledge, but rather dismissing evidence. And my point is, this evidence that is being reported in Washington, D.C. is intriguing enough to motivate us to collect more evidence rather than ignore it. So obviously, if you look at the history of science, we often make discoveries when we find anomalies, things that do not line up with what we expected.

And the best example is quantum mechanics that was discovered a century ago, nobody expected it. It was forced upon us by experiments and actually, Albert Einstein at the time resisted one of its fundamental facets, entanglement, or what he called spooky action at a distance. That the quantum system knows about its different parts even if they are separated by a large distance such that light signals cannot propagate over the time of the experiment. And he argued that this cannot be the case and wrote a paper about it, it’s with his postdocs, it’s called the Einstein-Podolsky-Rosen experiment. That experiment was done and demonstrated that he was wrong and even a century later we are still debating the meaning of quantum mechanics.

So it’s sort of like a bone stuck in the throat of physicists, but nevertheless, we know that quantum mechanics holds and applies to reality. And in fact the reason the two of us are conversing is because of our understanding of quantum mechanics, the fact that we can use it in all the instruments that we use. For example, the speaker that the two of us are using, and the internet, and the computers we are using, all of these use principles of quantum mechanics that were discovered over the century. And my point is, that very often when you do experiments there are situations where you get something you don’t expect, and that’s part of the learning experience. And we should respect deviations from our expectations because they carve a new path to new understanding, new learning about reality and about nature rather than ridiculing it, rather than always thinking that what we will find will match what we expected.

And so if the government among all comes along with reports about unusual phenomena, the government is very conservative very often, you would expect the scientific community to embrace that as an exciting topic to investigate because the government is saying something, let’s figure out what is it about. Let’s clarify the nature of these phenomena that appear anomalous rather than saying business as usual, I don’t believe it, it could be a malfunction of the instrument. So if it is a malfunction of the instrument, why did many instruments show the same thing? Why did many pilots show the same thing? And I should clarify that in the courtroom if you have two eyewitness testimonies that corroborate each other you can put people in jail as a result. So we believe people in the legal system and somehow when it comes to pilots, who are very serious people that serve our country, then Neil deGrasse Tyson dismisses their testimony.

So my point is not that we must believe it, but rather that it’s intriguing enough for us to collect more data and evidence and let’s not express any judgment until we collect that evidence. The only sin we can make is to basically ignore the reports and do business as usual, which is pretty much what he is preaching for. And my point is, no instead we should invest funds in new experiment or experiments that will shed more light on the nature of these objects.

Lucas Perry: So the second point that you made earlier was that the government was establishing a framework and system for receiving reports about UFOs. So part of this and part of this document, is it true to say then there is also a confirmation that the government does not know what these are and that they are not a secret U.S. project?

Avi Loeb: Yeah, they stated it explicitly in the report. They said the data is not good enough, the evidence is not good enough to figure out the nature of these objects so we don’t know what they are. And by the way, you wouldn’t expect military personnel or politicians to figure out the nature of anomalous objects because they were not trained as scientists. So when you go to a shoemaker, you won’t expect the shoemaker to bake you a cake. These are not people that were trained to analyze data of this type or to collect new data such that the nature of these objects will be figured out.

That is what scientists are supposed to do and that’s why I’m advocating for moving this subject away from Washington, D.C. into an open discussion in the scientific community where we’ll collect open data, analyze it in a transparent way, not with government owned sensors or computers, and then it will be all clear. It will be a transparent process. We don’t need to rely on Washington, D.C. to tell us what appears in our sky. The sky is unclassified in most locations. We can look up anytime we want and so we should do it.

Lucas Perry: So with your third point, there’s this consideration that people are more likely to try and give a conventional explanation of UAPs as coming from other countries like Russia or China, and you’re explaining that there are heavy commercial incentives. For example, that if you had this kind of technology it could for example, revolutionize your economy and you wouldn’t just be using it to pester U.S. Navy pilots off the coast, right? It could be used for really significant economical reasons. And so it seems like that also counts as evidence against it being conventional in origin or a secret government project. What is your perspective on that?

Avi Loeb: Yes and it would not only find its place in the consumer market, but also in the battlefield. And we have a sense of what other nations are doing because the U.S. has its own intelligence and we pretty much know what the status of their science and technology is. So it’s not as if we are completely in the dark here and I would argue if the U.S. government reports these objects there is good evidence that they are not made by those other nations because if our intelligence would tell us that they were potentially made by other nations then we would try to develop the same technologies ourselves. And another way to put it is if a scientific inquiry into the nature of these objects allows us to get a high resolution photograph of one of them and then we see the label “Made in China” or “Made in Russia,” then we would realize that there was a major failure of national intelligence and that would be a very important conclusion of course, that would have implications for our national security.

But I doubt that this is the case because we have some knowledge of what other nations are doing and the data would not have been released this way in this kind of a report if there was suspicion that these objects are human made.

Lucas Perry: So the report makes clear then that it’s not U.S. technology and there’re also reasons that count against it being for example, Russian or Chinese technology because the incentives are aligned for them to just deploy it already and use it in the public sector. So before we get into more specifics about thinking about whether or not these are human or extraterrestrial in origin, I’m curious if you could explain a bit more the flight and capability characteristics of these UAPs and UFOs and what you feel are the most significant reports of them and their activity.

Avi Loeb: Well, I didn’t have direct access to the data, especially not the classified one and I would very much want to see the full dataset before expressing an opinion, but at least some of the videos that were shown indicated motions that cannot be reproduced by the kind of crafts that we own. But what I would like to know is whether when the object moves faster than the speed of sound for example in air, whether it produces a sonic boom, a shockwave that we see for example, when jets do the same because that would be an indication that indeed there is a physical object that is compressing air as it moves around. Or if it moves through water I want to see the splash of water and from that I can infer some physical properties.

And of course, I would like to have a very high resolution image of the object so that I can see if it has screws, if there is something written on it, either Made in China or Made on Planet X. Either messages would be of great importance. So what I’m really after is access to the best data that we have and obviously, it will not be released from the government because the best data is probably classified. But I would like to collect it through using scientific instrumentation, which by the way could be far better than the instruments that were on airplanes that the pilots were using or on Navy because these were designed to be in combat situations and they were not optimal for analyzing such objects. And we can do much better if we choose our scientific instruments carefully and design the experiment in a way that would reproduce the results with a much higher fidelity of the data.

Lucas Perry: There is so much about this that is similar to Oumuamua in terms of there being just barely… The imaging is not quite enough to really know what it is and then there being lots of interesting evidence that counts for extraterrestrial in origin. Is that a perspective you share?

Avi Loeb: Well, yes I wrote a Scientific American article where I said one thing we know about Oumuamua is that it probably had a flat shape, pancake like, and also if its push away from the sun was a result of reflecting sunlight it must have been quite thin and the size of a football field. And in that case, I thought maybe it serves as a lightsail, but potentially it could also be a receiver intended to detect information or signals from probes that were sprinkled on planets in the habitable zone around the sun. So if for example the UAP are probes transmitting signals, then the passage of such a receiver near Earth was meant to obtain that information. And Oumuamua for example, was tumbling every eight hours, was looking in all directions in principle for such signals, so that could be one interpretation that it was thin not because it was a lightsail, but because it served a different purpose.

And in September 2020, we saw another object that also exhibited an excess push away from the sun by reflecting sunlight and had no cometary tail. It was given the name 2020 SO and it was a rocket booster from a 1966 mission. It had thin walls for a completely different purpose, not having anything to do with it being a lightsail. So I would argue that perhaps Oumuamua had these weird properties because it served a different purpose and that’s why we should both try to get a better image of an object like Oumuamua and of the unidentified objects we find closer to Earth. And in both cases, a high resolution photograph is better than a 1,000 words, in my case better than 66,000 words, the number of words in my book.

Lucas Perry: In both cases, a little bit better instrumentation would have seemingly made a huge difference. So let’s pivot into again, this area of conventional explanations. And so we talked a little bit earlier about one conventional explanation being that this is some kind of advanced secret military technology of China or Russia that’s used for probing our aerial defenses. And the argument that counts against that again, was that there are military and economic incentives to deploy it more fully, especially because the flight characteristics that these objects are expressing are so much greater than anything that America has in terms of the speed and the agility. So one theory is that instead of the technology being actual, like they actually have the technology that goes that fast and is that agile, that this is actually some form of spoofing technology, so some kind of way of an adversary training electronic countermeasures to simulate, or emulate, or create the illusion of what we witnessed in terms of the U.S. instruments. So do you think that such an explanation is viable?

Avi Loeb: I mean, it’s possible and that’s why we need more data. But it’s not easy to produce an illusion in multiple instruments, both radar, infrared, and optical sensors because you can probably create an illusion for one of these sensors, but then for all of them it would require a great deal of ingenuity and then you would need a good reason to do that. Why would other nations engage in deceiving us in this way for 20 years? I mean, that would look a bit off and also, we would have probably found something, some clue about them trying to do that because they would have trained such probes or such objects first in their own facilities and we would see some evidence for that. So I find it hard to believe, I would think it’s either some natural phenomena that we haven’t yet experienced or suspected or it’s this unusual possibility of an extraterrestrial origin.

And either way we will learn something new by exploring it more. We should not have any prejudice. We should not dismiss it. That would be the worst we can do, just dismiss it, and ridicule it, and continue business as usual because actually it’s exciting to try and figure out a puzzle. That’s what detectives often do and I just don’t understand the spirit of dismissing it and not looking into it at all.

Lucas Perry: So you just mentioned that you thought that it might have some kind of natural explanation. There are very strange things in nature. I’m not sure if this is for example, real or not, but there’s a Wikipedia page for example, for ball lightning, and there’s also really weird phenomena that you can have in the sky if the lighting in the sky is just right, and where the sun is where you get weird halos and things. And throughout history there are reports of dancing lights in the sky or things that might have been collective hallucinations or actually real. In terms of it being something natural that we understand or it being something human made that we’re not aware of, what to you is the most convincing natural or conventional explanation of these objects? An explanation that is not extraterrestrial in origin.

Avi Loeb: Well, if it’s dancing lights it wouldn’t produce a radar echo. So as I said, I don’t have access to the data in each and every incident, but there are some fundamental logic that one can use for each of these datasets and figure out if it could be an illusion. If not, if it must be a real object, somehow nature has to produce a real object that behaves this way and until I get my own data and reproduce those I won’t make any statement. But I’m optimistic that given the appropriate investment of funds, which I’m currently discussing with private sector funders, that we can do it. And just to give you an example, if you wanted to get a high resolution image, like a megapixel image of a one meter size object at a distance of a kilometer, you just need a one meter telescope for that and observing it at optical light. And you will be able to see millimeter size features on it, like the head of a pin.

People ask why didn’t we see it already in iPhone images of the sky? Well, the iPhone camera is a millimeter or a few millimeters in aperture size and it’s too small. You can’t get anything better than a fuzzy image of a very distant object. So you really need to have a dedicated experiment, and I think one can do it, and I’m happy to engage in that.

Lucas Perry: You would also wonder that these things are probably… So if they were extraterrestrial in origin, you would expect that they would be pretty intelligent and that they might understand what our sensor capabilities are. So, I think perhaps that might count as evidence for why given that there are billions of camera phones around the planet that there aren’t any good pictures. What is your perspective on that?

Avi Loeb: If I had to guess, I would think of these systems as equipped with artificial intelligence. We already have systems of artificial intelligence that are capable of superseding human abilities and within a decade they will be more intelligent than people, we’ll be able to learn from machine learning, and adapt to changing circumstances, and behave very intelligently. So in fact, I can imagine that if another civilization had that technological development of more than a century, more than we did, they could have produced systems that are autonomous. It doesn’t make any sense to communicate with the sender because the nearest star is four light years away. It takes four years for a signal to reach even the nearest star and it takes tens of thousands of years to reach the end of the galaxy, the Milky Way.

And so there is no sense of a piece of equipment communicating with its senders in order to get guidelines as to what to do. Instead, it should be autonomous, it has its own intelligence, and it could outsmart us. We already almost have such systems. So in fact, we may need to use our own artificial intelligence systems in order to interpret the actions of those artificial intelligence systems. So it will resemble the experience of asking our children to interpret the content that we find on the internet because they have better computer skills. We need our computers to tell us what their computers are doing. And that’s the way I think about it and these systems could be so intelligent that they do things that are subtle. They don’t appear and start a conversation with us. They are sort of gathering information of interest to them and acting in a way that reflects the blueprint that guided whoever created them.

And the question is, what is their agenda? What is their intent? And that will take us a while to figure out. We have to see what kind of information they’re seeking, how do they respond to our actions, and eventually we might want to engage with them. But the point is, many people think of contact as being a very sort of abrupt and interaction of extraordinary proportion that is impossible to deny, but in fact, it could be very subtle because they are very intelligent. If you look at intelligent humans, they’re not aggressive, they’re often thinking about everything they do and select their actions appropriately. They don’t get into very violent confrontations often. We need to rely on evidence rather than prejudice and the biggest mistake we can make is the mistake made by philosophers during the days of Galileo. They said, “We don’t want to look through at telescope because we know that the sun moves around the Earth.”

Lucas Perry: We spoke a little bit earlier about Bayesian reasoning and Oumuamua. Do you have the same feelings about not having priors about the UAPs being extraterrestrial or human in origin? Or is there a credence that you’re able to assign to it being extraterrestrial in origin?

Avi Loeb: The situation is even better in the context of UAP because they are here, not far from us and we can do the experiment with a rather modest budget. And therefore, I think we can resolve this issue with no need to have a prejudice. Often you need a prior if the effort requires extraordinary funds, so you have to say, okay is it really worth investing those funds? But my point is, that finding the answer to the nature of UAP may cost us much less than we already spent in the search for dark matter. We haven’t found anything. We don’t know where the dark matter is. We spent hundreds of millions of dollars and at a cost lower than that, maybe by an order of magnitude, we can try and figure out the nature of UAP. So given the price tag, let’s not make any assumptions. Let’s just do it and figure it out.

Lucas Perry: If these are extraterrestrial in origin, one might expect that they are here for probing or information gathering. So you said there are reports going back 20 years, if they are extraterrestrial in origin who knows how long they’ve been here. They could have been sent out through nanoscale shots out into the cosmos that land, and then grow, and replicate on some planet, and act as scouts. So if this were the case, that they were here as information gathering probes, one might wonder why they don’t use much more advanced technology. So for example, why not use nanotechnology that we would have no hope of detecting? In one report for example though, the pilot explains it following him and they’re kind of like… It comes right in front of him and then it disappears, so that disappearing seems a bit more like magic, right? Any sufficiently powerful technology is indistinguishable to magic to a less advanced civilization. But the other characteristics seem maybe like 100 or 200 years away of human technological advancement, so what’s up with that?

Avi Loeb: Well, yeah so for us to figure out what’s going on we need more data and it may well be that there are lots of things happening that we haven’t yet realized, because they are done in a way that is very subtle, that we cannot really detect easily, because our technologies are quite limited to the century and a half that we developed them. So you are right, there may be a hidden reality that we are not aware of, but we are seeing traces of things that attract our attention. That’s why when we see something intriguing we should dig into that. It could be clues for something that we have never imagined.

So for example, if you were to present a cellphone to a caveman, obviously the cellphone would be visible to the caveman and the caveman would think, oh it’s probably a rock, a shiny rock. So the caveman will recognize part of reality, that there is an object reflecting light, that is a bit more shiny than a typical rock because the caveman is used to playing with rocks. But the caveman, initially at least, will not figure out the features on the cellphone and the fact that he can speak to other people through this rock. That’s what will take us a while to be educated about. And the question is, among the things that are happening around us, which fraction are we aware of with the correct interpretation? And maybe we are not.

Lucas Perry: Moving on a bit here, so if these UAPs, or Oumuamua itself, or some new interstellar object that we were able to find were fairly conclusively shown to be extraterrestrial technology, what do you think our response should be? It seems like on one hand this would clearly potentially be an existential threat, which then makes it relevant to the Future of Life Institute. On the other hand, it’s likely that we could do nothing to counter such a threat. We couldn’t even counter humanity probably 50 years from now if we had to defend ourselves against a 50 year old, wiser, more technologically advanced version of ourself. And on cosmological timescales you would expect that even a 1,000, 2,000 year lead would be pretty common, but also indefensible. So there’s a sense that also an antagonistic attitude would probably make things worse, but also that we couldn’t do anything. So how do you think humanity should react?

Avi Loeb: The question of intent is indeed the next question after you identify an object that appears to be of extraterrestrial technological origin. We should all remember the story about the Trojan horse that looked very innocent to the citizens of Troy, but ended up serving a different purpose. That of course implies that we should collect as much evidence as possible about the objects that we find at first and see how they behave, what kind of information they are seeking, how do they respond to our actions, and ultimately we might want to engage with them. But I should mention, if you look at human history, nations that traded with each other benefited much more than nations that went into war with each other. And so a truly intelligent species might actually prefer to benefit from the interaction with us rather than kill us or destroy us, and perhaps take advantage of the resources, use whatever we are able to provide them with. So it’s possible that they are initially just spectators trying to figure out what are the things that they can benefit from.

But from our perspective, we should obviously be suspicious, and careful, and we should speak in one voice. Humanity as a whole, there should be an international organization perhaps related to the United Nations or some other entity that makes decisions about how to interact with whatever we find. And we don’t want one nation to respond in a way that would not represent all of humanity because that could endanger all of us. In that forum that makes decisions about how to respond, there should be of course physicists that figure out the physical properties of these objects and there should be policymakers that can think about how best to interact with these objects.

Lucas Perry: So you also mentioned earlier that you were talking with private funders about potentially coming up with an action plan or a project for getting more evidence and data on these objects. So I guess, there’s a two part question here. I’m curious if you could explain a little bit about what that project is about and more generally what can the scientific, non-governmental, and amateur hobbyist communities do to help investigate these phenomena? So are there productive ways for citizen scientists and interested listeners to contribute to the efforts to better understand UAPs?

Avi Loeb: Well, my hope is to get a high resolution photograph. It’s a very simple thing to desire. We’re not talking about some deep philosophical questions here. If we had a megapixel image, an image with a million resolution elements of an object, if it has a size of a meter that would mean each pixel is a millimeter in size, the size of the head of a pin, you can pretty much see all the details on the object and try and figure out, reverse engineer what it’s meant to do and whether it’s human made or not. So even a kid can understand my ambition. It’s not very complicated. Just get a megapixel image of such an object. That’s it.

Lucas Perry: They seem common enough that it wouldn’t be to difficult if the-

Avi Loeb: Well, the issue is not how common they are but what device you are using to image them, because if you use an iPhone the aperture on the iPhone will give you only fuzzy image. What you need is a meter sized telescope collecting the information and resolving an object of a meter size at the distance of a kilometer down to a millimeter resolution.

Lucas Perry: Right, right. I mean that Navy pilots, for example, have reported seeing them every day for years, so if we had such a device then you wouldn’t have to wait too long to get a really good picture of it.

Avi Loeb: So that’s my point, if these are real objects we can resolve them, and that’s what I want to have, a high resolution image. That’s all. And it will not be classified because it’s being taken by off the shelf instruments. The data will be open and here comes the role that can be played by amateurs, once the data is available to the public anyone can analyze it. Nothing is classified about the sky. We can all look up and if I get that high resolution image, believe me that everyone will be able to look at it.

Lucas Perry: Do you have a favorite science fiction book? And what are some of your favorite science fiction ideas?

Avi Loeb: Well, my favorite film is Arrival and in fact, I admired this film long ago, but a few months ago the producer of that film had a Zoom session with me to tell me how much he liked my book Extraterrestrial. And I told him, “I admired your film long before you read my book.” The reason I like this film is because it deals with the deep philosophical question of how to communicate with an alien culture. In fact, even the medium through which the communication takes place in the film is unusual and the challenge is similar to code breaking, sort of like the project that Alan Turing led during the Second World War of the enigma, trying to figure out, to break the code of the Nazis. So if you have some signal and you want to figure out the meaning of it, it’s actually a very complex challenge depending on how the information is being encoded. And I think the film addresses it in a very genuine and original fashion and I liked it a lot.

Lucas Perry: So do you have any last minute thoughts or anything you’d just really like to communicate to the audience and the public about UAPs, these reports, and the need to collect more evidence and data for figuring out what they are?

Avi Loeb: My hope is that with a high resolution image we will not only learn more about the nature of UAP but change the culture of the discourse on this subject. And I think that such an image would convince even the skeptics, even people that are currently ridiculing it, to join the discussion, the serious discussion about what all of this means.

Lucas Perry: And if there are any private funders or philanthropists listening that are interested in contributing to the project to capture this data, how is it best that they get in contact with you?

Avi Loeb: Well, they can just send me an email to aloeb@cfa.harvard.edu and I would be delighted to add them to the group of funders that are currently showing interest in it.

Lucas Perry: All right, thank you very much Avi.

Avi Loeb: Thank you for having me.