Will Everyone Have a Personal AI? With Mustafa Suleyman, Founder of DeepMind and Inflection

Mustafa Suleyman, co-founder of DeepMind and now co-founder and CEO of Inflection AI, joins Sarah and Elad to discuss how his interests in counseling, conflict resolution, and intelligence led him to start an AI lab that pioneered deep reinforcement learning, lead applied AI and policy efforts at Google, and more recently found Inflection and launch Pi. Mustafa offers insights on the changing structure of the web, the pressure Google faces in the age of AI personalization, predictions for model architectures, how to measure emotional intelligence in AIs, and the thinking behind Pi: the AI companion that knows you, is aligned to your interests, and provides companionship. Sarah and Elad also discuss Mustafa’s upcoming book, The Coming Wave (release September 12, 2023), which examines the political ramifications of AI and digital biology revolutions. No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode. Show Links: Forbes - Startup From Reid Hoffman and Mustafa Suleyman Debuts ChatBot Inflection.ai Mustafa-Suleyman.ai Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mustafasuleymn Show Notes: [00:06] - From Conflict Resolution to AI Pioneering [10:36] - Defining Intelligence [15:32] - DeepMind's Journey and Breakthroughs [24:45] - The Future of Personal AI Companionship [33:22] - AI and the Future of Personalized Content [41:49] - The Launch of Pi [51:12] - Mustafa’s New Book The Coming Wave

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  Today on No Priors, we're speaking with Mustafa Suleyman, co-founder of DeepMind, the pioneering AI lab acquired by Google in 2014 for $650 million. And now, co-founder and CEO of Inflection, along with Reid Hoffman and Karin Simonian. Inflection just launched their first public product, Pi, last week. Mustafa, welcome to No Priors. Thanks so much for joining us.

• 0:26Speaker 01

  Thanks for having me. I'm super excited to be here.

• 0:28Speaker 02

  Yeah, we're very excited to have you today. I think one thing that'd be great to maybe start with is just a little bit of your personal story, because I think you have a really unique background. You're very well-known, obviously, for DeepMind and your pioneering work in the AI world. But I think before all that, you worked on a Muslim youth helpline. You started a partnership and consultancy that was focused on conflict resolution to navigate social problems. I'd just love to hear a little bit more about the early days of things that you did before DeepMind, and then maybe we can talk a little about DeepMind and sort of more recent stuff as well.

• 0:56Speaker 01

  Yeah, sure. I mean, the truth is, I was very much a kind of change-the-world kid growing up, like a big believer in grand visions, doing good, having a huge impact in the world. And that was always kind of what drove me. So, when I...I grew up in London and went to Oxford. But at the end of the second year of my philosophy degree, I was kind of getting a bit frustrated with the sort of theoretical, you know, nature of it all. It was full of hypothetical moral quandaries. And so, a friend that I met at Oxford was starting a telephone counseling service, a kind of helpline. And it really appealed to me.

• 1:41Speaker 01

  It was a non-judgmental, non-directional, secular support service for young British Muslims. And this was like... About six months after the nine eleven attacks. And so there was quite a lot of like rising Islamophobia. And the government was talking a lot about anti-terrorism. And, you know, in general, I think that like sort of migrant communities were feeling the pressure. And this was a support service that was staffed entirely by us, by young people. I was nineteen at the time. And yeah, I spent almost three years working pretty much full time on that. And it was an incredible experience because it was basically my first startup.

• 2:23Speaker 01

  And, you know, fundraising was the name of the game, except the numbers were much, much smaller than they are these days. And, you know, the service was staffed by almost a hundred volunteer young people, which was just amazing because we felt like we can actually do something, you know? It was quite liberating and energizing to actually give this a shot. And, you know, I was very much inspired by the kind of human rights principles. It was deliberately not religious, even though it used some of the kind of culturally sensitive language that helped people feel heard and understood. So, yeah, it's had a very formative impact on my outlook.

• 3:02Speaker 02

  Yeah, no, it's super interesting. And I think we can talk more about that in the context of AI in a little bit. One other thing that you did is you also started a consultancy where you worked as a negotiator and facilitator. And I believe you worked with clients like the United Nations, the Dutch government and others. Can you tell us a little bit more about that work as well?

• 3:19Speaker 01

  Yeah, I mean, I was always trying to figure out how to scale my impact. And, you know, I quite quickly realized that delivering a sort of one to one service via a nonprofit was not going to scale a great deal, even though it had an amazing impact, you know, on a kind of human to human level. And so I was super interested in these like metastructures, like how does, you know, the UN actually influence, you know, behavior at the country level? And, you know, how could we run more efficient decision making processes where there's tension and disagreement? So we worked all over the world, actually.

• 4:00Speaker 01

  In Israel, Palestine, and in, you know, in Cyprus, between the Greeks and the Turks. My colleagues worked in South Africa, Colombia, Guatemala. And I think it really taught me that learning to speak other people's social languages is actually an acquired skill. You really can do it with a little bit of attention to detail and some patience and care. It's kind of a superpower, being able to deeply hear other people and make them feel heard such that they're better able to empathize with people that they disagree with. And that's been an important theme throughout my kind of career, something I've always been interested in.

• 4:41Speaker 01

  So I think I co-founded that and worked on it for, I think, three years and soon realized the limitations of like, large-scale human processes. I mean, in 2009, I worked, I facilitated one part of the climate negotiations in Copenhagen. And, yeah, it was a kind of a remarkable experience, like, you know, 192 countries, literally a thousand NGOs and activists, many different academics, everyone proposing a different solution, a different definition of the problem. And, you know, in one way, it was sort of inspiring to see so many different cultures and ideas coming together

• 5:23Speaker 01

  to try to form consensus around an issue that was clearly of existential importance. On the other hand, it was just like deeply depressing that we weren't able to achieve consensus. It took another decade to even get mild consensus on this, or half a decade, 2015. And I think that was sort of an eye-opener for me. I was like, the world's governance systems are not going to keep up with both the exponential challenges that we face from globalization and carbon emitting, but also like, technology. And that was the next thing that I saw on the landscape.

• 6:01Speaker 02

  So how did this lead into your interest in AI? And, you know, I believe that you met Demis when you were quite young, and I think he and your other co-founder worked together later in a lab. But I'm a little bit curious, like, how your background and interests in these sorts of global issues then transformed into an interest in AI and the founding of DeepMind.

• 6:19Speaker 01

  Yeah, well, around about that time, actually, like, I guess it was like two thousand and eight or so. I was starting to keep an eye on Facebook's rise and I was like, this is incredible. I mean, it's like a two or three year old platform at that point. And it had hit like a hundred million monthly actives. And that was just a mind blowing number to me and it was obvious that this wasn't just the kind of neutral platform for giving people access to information or connecting people with other people. The the frame, because I had come from a conflict resolution background, our entire approach was like, what is the frame of a conversation? Like, how do you organize space? How do you prepare individuals to have a constructive disagreement?

• 7:02Speaker 01

  How do you like set up the the environment, basically, to facilitate dialogue? And so that was the lens through which I looked at Facebook. I was like, well, this is a frame. There's a choice architecture here. There are significant design choices which are going to incentivize certain behaviors. Obviously, at that point, there wasn't really ranking, but even just having a thumbs up or like the choice of, you know, which button you place in what order and how you arrange information on the page and what all of that drives behaviors in one way or another. And, you know, that was a big realization to me because I was like, well, this is actually reframing the default approach to human connection at a scale that is like completely unimaginable.

• 7:46Speaker 01

  I mean, perhaps only akin to, you know, the default expectations in a religion, for example. Everyone grows up with an idea that there is a, you know, a patriarchy, a male god, that, you know, that there's a particular role for women like that's that, you know, until a few decades ago, that was just an implied sort of undertone to an entire social structure for thousands of years. And that's kind of what I mean by frame. There's these implicit design choices which cause hundreds of millions of people to change their behavior.

• 8:19Speaker 02

  Yeah, and I think that's super interesting because I remember working on a bunch of Facebook apps at the time when the platform launched and people were purposefully thinking about that stuff, but on the micro level, right? How do we get more users? How do we get people to convert? How do we drive certain behaviors? And so everybody, I think, was very explicitly thinking about this as a behavioral change platform, but not at the level of society. You know, we were thinking about it in the context of just like, how do you get more people to use this thing, you know? And so I think it's really interesting that people then later realize the big ramifications of this in terms of, you know, how that actually cascades in terms of social behaviors and other things. How did that lead to starting DeepMind?

• 8:56Speaker 01

  Well, it was clear to me from that moment on, like, I left Copenhagen in 2009 thinking, this is not the path to significant positive social change. It still needs to continue, and I support those processes, obviously, but I'm just saying it is just not something that I feel I could continue to work on full time. And so my heart was set on technology at that point. So I reached out to Demis, who was the brother of my best friend from when I was a kid. We got together, we had a coffee, we went and actually, we played poker at one of the casinos in London because we both love games, both super competitive, both good at poker. And on that night, I think we both got knocked out pretty early

• 9:41Speaker 01

  in the tournament. So we sat around drinking Diet Coke, talking about ways to change the world. And we basically were, you know, having exactly this conversation, like, you know, is it going to be? I mean, obviously, at that point, I was mostly inspired by platforms and software and social apps and connectivity and so on. Whereas, you know, Demis was way more in the kind of robotics land and sci fi land. I mean, he was, he was fully thinking that, you know, the way to manage the economy, the way to make economic decisions was to simulate the entire economy, right? And, and he thought that He was very much, obviously, had just come off the back of his games like Evil Genius

• 10:24Speaker 01

  and Black and White and so on, which were kind of simulation-based games. So I think that was his default frame at that point. Yeah, and then we spent many months talking and spent a lot of time with Shane Legg as well. And Shane was really the core driver of the ideas and the language around artificial general intelligence. I mean, he had worked on that for his PhD with Marcus Hutter on definitions of intelligence. I found that super inspiring. I think that was actually the turning point for me, that it was pretty clear that we at least had a thesis around how we could distill the sort of essence of human intelligence into an algorithmic construct. And it was it was his work in,

• 11:07Speaker 01

  I think he, I think for his PhD thesis, he put together like 80 definitions of intelligence and aggregated those into a single formulation, which was, how do we, you know, the intelligence is the ability to perform well across a wide range of problems. And he basically, you know, gave gave us a measurement, an engineering kind of measurement that allowed us to constantly measure progress towards, you know, whether we were actually producing an algorithm which was inherently general, i.e. it could do many things well at the same time.

• 11:42Sarah Guo?

  Is that the working definition you use for intelligence today?

• 11:46Speaker 01

  Um, actually, no. I've changed. I I think that there's a more nuanced version of that. I think that's a good definition of intelligence, but I think in a weird way, it's over-rotated the entire field on one aspect of of intelligence, which is generality. You know, and I think OpenAI and then subsequently Anthropic and others have taken up this default sort of mantra that like, it, all that matters is, can a single agent do everything? You know, can it be multimodal? Can it do translation and speech generation, recognition, et cetera, et cetera?

• 12:29Speaker 01

  I think there's another definition which is valuable, which is the ability to direct attention or processing power to the salient features of an environment, given some context. Right? So actually, what you want is to be able to take your raw processing horsepower and direct it in the right way at the right time. Because it may be that a certain tone or style is more appropriate, given a context. It may be that a certain expert model is more suitable, or it may be that you actually need to go and use a tool, right? And obviously, we're starting to see this emerge.

• 13:10Speaker 01

  And in fact, I think the key, and we can get into this obviously in a moment, but I think the key element that is going to really unlock this field is actually going to be the router in the middle of a series of different systems which are specialized, some of which don't even look like AI at all. They might just be traditional pieces of software, databases, tools and other sorts of things. But it's the router or the kind of central brain which is going to need to be the key decision maker. And that doesn't necessarily need to be the largest language model that we have.

• 13:44Speaker 02

  It's really interesting, because I feel like a lot of what you described is actually how the human brain seems to work in terms of you have something a little bit closer to a mixture of experts or MOE model, where you have the visual cortex responsible for visual processing, and then you have a other piece of the brain specifically responsible for empathy, and you have mirror neurons. And, you know, it feels like the brain is actually this ensemble model in some sense with some routing, depending on the subsystem you're trying to access. And so, you know, the generality approach seems like a really, it almost goes at odds with some of those pieces of it unless you're just talking about some part of the hippocampus or something, right?

• 14:17Speaker 01

  Well, I think that's long been the inspiration, right? I think for everybody, these neural networks are the obvious example, but in many other elements, reinforcement learning, you know, and so on, are all brain inspired. And I think that, you know, there's been a lot of talk about, you know, sparsity as well, which is sort of what you're describing. And, you know, so far, we've had To do, you know, very dense all to all connections because we sort of haven't quite learnt the algorithms for sparse activations. But I I think that's going to be a very promising area. And, you know, in many ways, what I'm describing doesn't actually require sparse activations. Because, you know, you you actually could just train a decision making engine at the middle to know when to use which size model, right?

• 15:02Speaker 01

  So maybe in some contexts, you would want the highest quality, super expensive, twenty second latency model. And in most other contexts, a super fast three second mini model might work fine. I think that's the gonna be the the key unlock, actually. And and quite sort of remarkably, that's an engineering problem, perhaps more than it is a an AI problem. Which, you know, is is just a pretty surreal moment, you know, just if you actually observe that, given where we are in the field and stuff.

• 15:31Speaker 02

  When you started DeepMind, I think it was reasonably unpopular to do what you were doing, right? And so I think you ended up getting funded by Founders Fund and Peter Thiel and Elon Musk. But I remember at the time, there was like three or four parties that funded a lot of AI things and then nobody else was really doing it. In terms of the types of approaches you were taking, in terms of saying, we're gonna build these big AI systems that can do all sorts of things, right?

• 15:53Speaker 01

  Yeah, I mean, it was wacky, like I I can't say that enough, like it it was especially for the first two years. So because we founded in two thousand and ten and for the most of the sort of spring and summer of two thousand and ten, actually most of the rest of that year. I was going to Gatsby computational neuroscience unit at UCL, sneaking in with Demis and Shane to just sit in on the lunches that Peter Diane ran. And I remember Shane like sort of saying to me like, you know, the language here is machine learning.

• 16:28Speaker 02

  Yeah, you can say AI.

• 16:29Speaker 01

  Don't say AI.

• 16:30Speaker 02

  Yeah.

• 16:32Speaker 01

  I was like, okay, okay, I'll keep my mouth shut, don't worry. Like, we certainly don't say AGI, you know, and and that that was a kind of, that was pretty weird. I mean, that, you know, there weren't, you know, there weren't very many funders for us, like, you know, Peter Thiel, you know, to his credit. Did actually have significant vision here, although he sold pretty early, I think, and now doesn't seem to be in the game. So, but yeah, he certainly, he certainly saw it first. And, you know, I think that all changed pretty quickly. First with, you know, AlexNet, of course, in 2012, and then with DQN, the Atari paper in 2013, you know, and then a kind of succession of breakthroughs after AlphaGo, and people got more, more,

• 17:17Speaker 01

  more sort of aware of it. But it still surprises me the extent to which the rest of the world is like suddenly waking up. And obviously, we've seen that like crazy in the last six months. So.

• 17:27Speaker 02

  Yeah. And then I guess last question on sort of your time with Google and DeepMind, and because I think there's a lot of really exciting things to talk about in the context of inflection and sort of the broader field and world. What are some of the things you were most excited to have the team create at DeepMind over the years, or some of the breakthroughs that you're most proud of?

• 17:43Speaker 01

  Yeah. Well, I mean, in some ways, we definitely sort of pioneered the deep reinforcement learning effort. And I think, you know, in principle, it's a very promising direction. I mean, you clearly want some mechanism by which you can learn from raw perceptual data, and that directly feeds into a reinforcement learning algorithm that can update and essentially iterate on that in real time with respect to some reward function, whether that's online or offline, like directly interacting with the real world in real time, or it's, you know, in a kind of batch simulation mode. You know, and, and that turned out to be

• 18:27Speaker 01

  very valuable for a specific type of problem, where a game-like environment had a very structured, scalar reward, and we could play that game many millions of times. That's part of the reason why we started the AlphaFold project, because it was actually my group that was looking around for other applications of DQN-like, AlphaGo-like tools. And in a hackathon that we did one week, someone stumbled across this problem. We'd actually looked at it back in 2013, when it was called Foldit. Which was a very small scale kind of version of this.

• 19:09Speaker 02

  And just for context, sorry to interrupt, you know, AlphaFold was focused on folding proteins, which at the time was a really hard problem, right? People were trying to do this molecular modeling. They couldn't really make any real headway in lots of the traditional approaches. And then your group at DeepMind really started pioneering how to think about protein folding in a different way. So sorry to interrupt, I just wanted to give context for people listening.

• 19:28Speaker 01

  So I think the hackathon was probably 2016. And then as soon as we saw the hackathon that, you know, start to work, then we actually, you know, scaled up the effort and hired, you know, a bunch of outside consultants to help us with the domain knowledge. And then I think the following year, we entered the CASP competition. So, you know, these things take a long time, you know, sort of longer than I think people realize, you know, there's a lot of, that was a very big effort by DeepMind and eventually became a company-wide strike team. So in hindsight, these things do take a huge amount of effort.

• 20:04Speaker 02

  Yeah, the fascinating thing here is that, you know, the work started with AlphaGo, which was how to play Go better, right? Or how to beat people at Go. And then the same underlying approach could then be morphed and applied to protein folding, which I think is an amazing sort of leap or connection to make. And, you know, I used to work as a biologist and I remember you'd spend literal years trying to crystallize proteins in different solutions. You'd do all these different salt concentrations in each well. So if the protein would crystallize, you could hit it with X-rays and then you'd interpret those X-rays to look at the structure, right? And so you had to do this really hard sort of chemistry and physics to get any information about a protein at all. And then you folks with the machine ran through every protein sequence, literally in the, in every database for every organism.

• 20:50Speaker 02

  And you're able to then predict folding, which is, it's pretty amazing. It's very striking.

• 20:55Speaker 01

  Yeah, I mean, I think the if I were to sort of summarize the core thesis of DeepMind, it was that it would be possible to the motivation for generality was that you would be able to learn, you know, a rewarding behavior in one environment and transfer in a more compressed or efficient representation, the insights that had made you successful in one environment to the next environment. Right? That transfer learning has always been the key goal. And that was one of the one of the very exciting proof points that it is, you know, increasingly looking likely that that's possible. So, you know, I definitely think that's that's pretty cool.

• 21:38Speaker 01

  Because when you think about the sorts of problems that we're facing in the world today, we don't have obvious answers lying around. There's no like genius insight that's just waiting to be applied. We actually have to discover new knowledge. And I think that's the that's the attraction of artificial intelligence. That's why we want to work on these, you know, on these models because, you know, we're we're sort of at the limit of what, you know, the smartest humans in the world are capable of inventing. And we have, you know, very pressing, urgent global challenges, you know, from food supply to water to decarbonization to clean energy transportation, you know, with a rising population that we really want to solve. So there are that, you know,

• 22:22Speaker 01

  amidst all of the stresses and the fears about everything that's being worked on at the moment, it is important to keep in mind that there is a an important north star that everybody is working towards. And we just got to keep focused on those goals rather than sort of be too sidetracked by some of the fears.

• 22:41Sarah Guo?

  Let's talk about inflection. What was the motivation for starting another company?

• 22:47Speaker 01

  Well, I guess back in sort of two thousand and eighteen, two thousand and nineteen, it wasn't clear that neural networks were going to have a significant impact in language. If you just think about it intuitively, for the for the previous sort of five years, CNNs had been effective at learning structure locally, right? So pixel i- in an image, in the input. So pixels in an image that were correlated in space tended to produce, you know, subfeatures which were, you know, a good representation of what you were trying to predict. Maybe there were lines and edges and they grew into eyes and faces and scenes and so on.

• 23:29Speaker 01

  And that kind of hierarchy just intuitively seemed to make sense and seemed to apply to audio and and other modalities, right? Whereas if you kind of think about it, a lot of the structure of predicting the next word or letter or token in a sentence seems to exist in a very, very, very spread out, you know, far removed from the immediate next step of the prediction, right? And so it didn't look like that was working. And then, to be honest, like when GPT three came out, that was like a big revelation. Um, I I had seen the GPT two work and hadn't quite clicked for me that this was significant. It was really only when I started saw the GPT three paper

• 24:14Speaker 01

  that my eyes were wide open to this possibility. It's pretty amazing that you could attend to, you know, a very, very sparse, seemingly sparse representation and use that to predict something which, on the face of it, seemed like there were billions of possibilities of what might come next in a sentence, right, maybe tens of billions or something, but a lot. And for me, it was early twenty twenty that I went to work at Google and I got involved in the large language model efforts. I got involved in the Mina team, it was called at the time. I know that you guys had Noam on the show recently. Um, Noam's super awesome. And it was me and Noam, Daniel, Quoc Lee, uh, and a few others.

• 24:59Speaker 01

  And it was just unbelievable what was being built there. And when I joined, it was pretty small models. And very quickly, we scaled it up. It became the Lambda Group. And we started seeing how it could potentially be used in various kinds of search. Started looking at retrieval, grounding for improving factuality. Started getting a feel for all the hallucinations and so on. And that was just really a mind blowing few years to me. And while I was there, sort of in the last year in 2021, I tried pretty hard to get things launched at Google. We were all kind of banging on the table, being like, come on, this is the future.

• 25:41Speaker 01

  And, you know, obviously David Luhan from Adept was also in and around that group. So the three of us, in our own ways, were pushing pretty hard for for launch. And it wasn't meant to be. Just, you know, timing is everything and, you know, Google just wasn't wasn't the right timing for Google for various reasons. And, you know, I was just like, look, this this has to be out there in the world. This is this is clearly the new wave of technology. And so, yeah, in January, I left, got together with Corinne, my co-founder, who I worked with at DeepMind for seven years. We bought his company back in 2014 at DeepMind. He led the deep learning scaling team at DeepMind for years and worked on all the big breakthroughs at DeepMind.

• 26:26Speaker 01

  And then, of course, Reid Hoffman, who's been my one of my closest friends for like 10 years. And we've always talked about starting something together. And I was like, this is the obvious thing. Now is the time for sure. And so the rest is history. You know, we've we it's been a wild ride since then.

• 26:42Sarah Guo?

  It makes me feel a little bit better than somebody who's been such a pioneer in the field and working on this all the time is still constantly surprised, as I am also constantly surprised. I remember when you were first starting to get this going. I another thing I was surprised by is the focus you I mean, I came around to it in writing the investment memo. But, you know, you have this focus on the idea of companionship rather than information as the right initial approach. You've talked about, worked on, thought about empathy for humans and other populations for a long time. It seems counterintuitive, like, why companionship?

• 27:21Speaker 01

  Yeah, it's a great question. So I think to step one step back from that first, I think my core insight about what was missing for Lambda was interaction feedback. And in a funny way, that was exactly what was motivating Corinne to having beaten all the academic benchmarks and achieved SOTA many times. He had come to the same conclusion. I had seen the same thing from Lambda. What we were missing was was user feedback. And actually, when you think about it, all of our interfaces today are fundamentally about interaction.

• 27:59Speaker 01

  You know, you're giving your browser feedback all the time. You're giving, you know, that web service feedback, same with an app or anything that you interact with. It's actually a dialogue. And so the way I'd position Lambda at Google is that, you know, conversation is the future interface. And Google is already a conversation. It's just an appallingly painful one, right? You say something to Google, it gives you an answer in ten blue links. You say something about those ten blue links by clicking on it. You it it generates that page. You look at that page, you say something to Google by how long you spend on that page, what you click on it, how much you scroll up and down, et cetera, et cetera.

• 28:41Speaker 01

  And then you come back to the search login and you update your query and you say something again to Google about what you saw. That's a dialogue and Google learns like that. And the problem is it's, you know, using nineteen eighties Yellow Pages to have that conversation. And actually, now we can do that conversation in fluent natural language. And I think the problem with what Google has sort of, I guess, in a way accidentally done to the internet is that it has basically shaped content production in a way that optimizes for ads. And everything is now SEOed to within an inch of its life, you know, you

• 29:20Speaker 01

  you go on a webpage and all the text has been broken out into sub bullets and sub headers. And, you know, separated by ads and, you know, You spend like five to seven or 10 seconds just like scrolling through the page to find the snippet of the answer that you actually wanted. Like, most of the time, you're just looking for a quick snippet. And if you are reading, it's just in this awkward format. And that's because if you spend 11 seconds on the page instead of five seconds, that looks like high-quality content to Google, and it's quote-unquote engaging. So the content creator is incentivized to keep you on that page. And that's bad for us, because what we want is a succinct.

• 30:00Speaker 01

  Well, we as humans, all humans, clearly want a high-quality, succinct, fluent, natural language answer to the questions that we want. And then crucially, we want to be able to update our response without thinking, how do I change my query? And like, write this. We've learnt to speak Google. Like, it's a crazy environment. We've learnt to Google, right? That's just a weird lexicon that we've co-developed with Google over 20 years. No, like, now that has to stop. That's over. That moment is done. And we can now talk to computers in fluent natural language, and that is the new interface. So that's what I think's going on.

• 30:41Sarah Guo?

  Maybe we should back up for a second and just tell people about what Pi is.

• 30:45Speaker 01

  Sure, yeah. So, building on all of that, we think that Pi, I think that everyone in the next few years is going to have their own personal AI, right? So there's going to be many different types of AI. There will be business AIs, government AIs, nonprofit AIs, political AIs, influencer AIs, brand AIs. All of those AIs are going to have their own objective, right? Aligned to their owner, which is to promote something, sell something, persuade you of something. And my belief is that we all as individuals want our own AIs that are aligned to our own interests and on our team and in our corner. And that's what a personal AI is, and ours.

• 31:30Speaker 01

  Is called Pi, Personal Intelligence. It is, as you said, there to be your companion. We've started off with a style that is empathetic and supportive. And we tried to sort of ask ourselves at the beginning, like, what makes for great conversation? When you have a really flowing, smooth, you know, generative interaction with somebody, what's the essence of that? And I think there's a few things, like, the first is the other person really does listen to you, right? And they demonstrate that they've heard you by reflecting back some of what you've said. They add something to the conversation, you know?

• 32:13Speaker 01

  So it's not just regurgitation, but they introduce another nugget, another fact. They ask you follow-up questions and they're curious and interested in what you say. And, you know, sometimes there's a bit of spice, right? They throw in something silly or surprising or random or kind of wrong, and it's endearing. And you're like, oh, like, we're connecting. And so we've tried to, in our first version, and this really just is a first version, like, this is actually not even our biggest model at the moment. So we're just putting out a first version that is skinned for this kind of interaction so that we can sort of learn and improve. And, you know, it really makes for a good companion, someone that is thoughtful and

• 32:57Speaker 01

  kind and interested in your world as a first start.

• 33:01Speaker 02

  You're working on these sort of personalized intelligence or personal agents, and you mentioned how you think in the future there'll be all these different types of agents for representing different businesses or causes or political groups or the like. What do you think that means in terms of how the web exists and how it's structured? So to your point, the web is effectively really based on a lot of SEO and a lot of sort of Google is the access point. What happens to webpages or what happens to the structure of the internet?

• 33:27Speaker 01

  I think it's going to change fundamentally. I think that most computing is going to become a conversation. And a lot of that conversation is going to be facilitated by AIs of various kinds. So your pie is going to give you a summary of the news in the morning, right? It's going to help you keep learning about your favorite hobby, whether it's cactuses or, you know, like motorcycles, right? And so, you know, every couple of days it's going to send you new updates, new information in a summary snippet that really kind of suits exactly your reading style and your interests and your preference for consuming information.

• 34:08Speaker 01

  Whereas a website, you know, the traditional open internet just assumes that there's a fixed format and that everybody wants a single format. And generative AI clearly shows us that we can make this dynamic and emergent and entirely personalized. So, you know, if I was Google, I would be pretty worried because the car, that's this, that that old school system does not look like it's going to be where we're at in 10 years' time. It's not going to happen overnight. There's going to be a transition. But these kind of succinct, dynamic, personalized, interactive moments are clearly the future, in my opinion.

• 34:43Sarah Guo?

  The other group of people that is clearly worried is anybody with a with a website where their business is that website? I spent a lot of time talking to publishers in April because they were freaking out. And what what advice would you have for people who like generate content today?

• 34:59Speaker 01

  Well, I think that, you know, an AI is kind of just a website or an app, right? So you can still have, like, let's say that you have a blog about baking and so on. You you know, you're, you can still produce super high quality content with your AI. And your AI will, you know, be, I think, a lot more engaging and interactive for other people to talk to. So to me, any brand is already kind of an AI. It's just using static tools, right? So so, you know, for for a couple hundred years, the ad industry has Been using color and shape and texture and text and sound and image to generate meaning.

• 35:44Speaker 01

  Right. It's just they release a new version every six months or every year. Right. And it's, you know, the same thing that applies to everybody like TV ads used to be. Right. Whereas now that's going to become much more dynamic and interactive. So I really don't subscribe to this view that there's going to be like one or five AIs. I think this is like completely misguided and fundamentally wrong. There are going to be hundreds of millions of AIs or billions of AIs. And and they'll be aligned to individuals. So what we don't want is autonomous AIs that can operate completely independently and wander off doing their own thing. That I I'm really not into that vision of the world. That doesn't end well.

• 36:24Speaker 01

  Right. But, you know, if your blogger, you know, has, you know, their own AI that represents their content. Then I imagine a world where my pie will go out and talk to that AI. And say, yeah, like my Mustafa is super interested to learn about baking. He can't crack an egg. So where does he need to start? Right. And then Pi will have an interaction and be like, oh, that was really kind of funny and interesting. Mustafa will really like that. And then Pi will come back to me and be like, hey, I found this great AI today. Maybe we could set up a conversation. You'll find something super interesting. Or they recorded this this little clip of me and the other AI interacting. And here's a three, three minute video or something like that. Right?

• 37:08Speaker 01

  That'll be how new content I think gets produced. And I think it'll be your AI, your Pi, your personal AI that acts as interlocutor accessing the other world. Which is basically, by the way, what Google does at the moment. Right? Google crawls other, you know, essentially AIs that are statically produced by, you know, the existing methods and has a little interaction with them, ranks them and then presents them to you.

• 37:31Speaker 02

  Back to your original point on Facebook, I think one thing Facebook has been criticized for is the creation of context bubbles. Where the only information that you see is information that you kind of inherently believe, or the feed is kind of tailored to you. And if you think about some of these AI agents, one could argue they're going to be the extreme form of this, right? In the downside case. In the upside case, obviously, there's other versions of this, but the downside case is it will just constantly use the feedback from you to reinforce things you already strongly believe, whether they're correct or not. And so I'm a little bit curious how you think about this. As we go through this new platform shift, and you mentioned that you identified some of these issues quite early on with some of the Facebook or other social platforms, how do you think about that in the context of AI agents?

• 38:13Speaker 01

  I think that is the default trajectory without intervention, right? So that might be a controversial view, but I think that the platforms were never neutral. That was the big lie. And I think that was, frankly, to me, very obvious from the very beginning. The choice architecture is a ranking. It's not a clean feed. Clearly, there's billions of bits of content, so you have to select what to show, and what to show is a huge political, cultural influence on how we end up. And so, of course, AI is an accelerated version of that.

• 38:57Speaker 01

  My take is that all of us AI companies, as well as the old social media platforms, have to embrace the platform responsibility of curation and try to be as transparent as possible about what that curation actually looks like, what is excluded. And here, I think that the valley probably needs to be a bit more open to the European approach. The reality is that we have to figure out, as a society, which bodies we trust to make

• 39:36Speaker 01

  decisions which influence recommendation algorithms or AI algorithms, right? And if that's a requirement for transparency of training, or if it's a requirement for transparency with respect To content that has been excluded or what has been upvoted or downvoted.

• 39:55Speaker 01

  Fundamentally, we have to make these things accountable to democratic structures. And that means that democratic structures need to sort themselves out pretty sharpish. And like actually have some functioning bodies that can provide real oversight. Without everybody like fainting over the accusations that this is censorship and being super churlish about that because you know now really is the time to like actually get that a bit more straightened out and and have some kind of responsible interactions with these companies. Because you're right, these are going to be very, very powerful systems.

• 40:30Sarah Guo?

  This is my bias coming in, but that seems like a harder hill to climb than the AGI hill.

• 40:38Speaker 01

  I hope not. I think I do agree, but I hope not.

• 40:42Sarah Guo?

  Yeah, yeah. Well, we can all work on it. So you you describe Pi as like the first foray that you guys can get out into the world and learn from and improve with. What does improvement mean? Like, how do you? Are you measuring emotional intelligence? What is better?

• 40:56Speaker 01

  Yeah, yeah, we're certainly measuring emotional intelligence. We're measuring the fluidity of the conversation. We're measuring, you know, how respectful it is. We're measuring how even handed it is.

• 41:08Speaker 01

  You know, we've already had a couple of errors where it's made some politically biased remarks and we tried super hard to make sure that it's even handed. No matter how, you know, sort of racist, homophobic or misogynist in any way. It's it should never be dismissive, disrespectful or judgmental of you. It's there to talk through issues and make you feel heard and take feedback, like it tries very hard to take feedback. So yeah, that's that's we're measuring all of those kinds of things. But but the next phase of obviously where we're headed is that we really think that this is going to be your ultimate personal digital assistant and

• 41:49Speaker 01

  It is going to, as I said, interact with other AIs to make decisions, buy your groceries and, you know, manage your sort of domestic life and help you book vacations and, you know, find, you know, fun information and that kind of stuff. So it's going to get, you know, increasingly more, you know, down that route. And, you know, the other thing is that quite soon it will have the ability to access real time content in the web. So it'll be able to, you know, sort of look up the weather and news and other kind of fresh content like sports results or provide citations. And, you know, increasingly add a lot more of those sort of practical utility features that you would expect from, you

• 42:34Speaker 01

  know, your personal intelligence.

• 42:36Sarah Guo?

  So in my early conversations with my Pi, I guess, maybe I shouldn't be so surprised. It's very human and people like to talk about themselves. But I immediately invested a reasonable amount of effort in personalizing it, right? I'm like, okay, here a bunch of things about me that you should know, what I'm like and my interests and how you can be useful to me. What surprised you in usage? Or maybe you expect, but what would surprise our listeners?

• 43:03Speaker 01

  Yeah, it's a, that's a great question. I mean, a lot of people proactively share a huge amount of personal information. And at the moment, our memory is, is not that long. It's about 100 messages, which is actually, you know, it's still quite a lot, surprisingly a lot. But what we would really like is to be able to grab that knowledge and store it in your own sort of personal brain and have Pi be your kind of second mind, able to remember, you know, all of your kind of subtle preferences, likes, habits, relations and so on, to be super useful to you. I think in time, some people will want to connect other data sources like email and documents and Drive. I think some people I'm already

• 43:48Speaker 01

  starting to see doing that and so on. It's very interesting to see what people ask Pi to ask us to do. So they're like, can you tell your developers that I really love this voice? I'm really Really enjoying talking to, you know, I think it was P two, we've just called them P one, P two, P three, P four, our voices. And of course, some people are like, can you tell your developers that it should really know that like I wrote, you know, the following stories for Forbes, but like I didn't write this story on this other topic. And I was just like, dude, that was a journalist yesterday or the day before. You know, so yeah, seeing what people give us feedback on is really, really helpful.

• 44:31Sarah Guo?

  Okay, inflection today, still a relatively small team. What's it like as a company culturally, like and you guys are recruiting, what are you looking for?

• 44:40Speaker 01

  Yeah, we're a pretty small team. We're about thirty people and we've hand-selected a very, very talented team of AI scientists and engineers. Everybody on the technical side goes by MTS. Super important to us that we don't draw a strong distinction between researchers, scientists, engineers, data scientists and anything else. To us, that equality and respect is really important and we've seen that go wrong at our, you know, other labs previously. And I think it's an important modification because everybody makes a really big contribution.

• 45:22Speaker 01

  We're very much an applied AI company, so you know, we don't publish and we're not really focused on research, even though fundamentally what we do do is applied research in production. I mean, we run some of the largest language models in the world. We have state of the art performance across many of the main benchmarks, with the exception of coding, because we don't have Py generate code and it's not a priority for us. So it's a, yeah, it's a, it's a very energetic, very high standards environment. We're very focused on ICs. So everybody is an exceptional individual contributor and mostly self-directed. So we don't do managers just yet.

• 46:07Speaker 01

  It's just two of us doing management. Which unbelievably has worked so, so well because we have such senior experienced people and they're very driven, they know what to do. My experience of building teams like this over the last decade and a half is that the best people really just want to work with really high quality people, be given outstanding amounts of resources and freedom and focus on a shared goal. So we have a very sort of explicit company goal. Every six weeks, we ship and in our seventh week, we come together in person to do a hackathon and really push super hard as a team because that forms great bonds and it's really fun. We have drinks and dinner and hang out and stuff like that.

• 46:52Speaker 01

  And it's a week of intensity which closes our launch and then we plan again for the next six weeks. So it's actually a really nice rhythm. And I found that most people make up the second half of their OKRs anyway and a 12 week cycle is just too long and BS. So like six weeks is actually perfect and it creates a lot of accountability and a lot of fun.

• 47:10Speaker 02

  So, you know, one thing that a lot of people talk about is how do these models actually scale? What is the basis for the next generation of these types of models, their performance? Where does it asymptote? How do you think about scalability? How do you think about the underlying silicon that drives it? Is it a data issue? Is it a compute issue? Like, I'm just really interested in how you think about more broadly these really large scale models since you folks are building many of them now.

• 47:34Speaker 01

  Well, the incredible thing about where we've got to at this point is that all of the progress, in my opinion, is a function of compounding exponentials, right? So over the last decade, the amount of compute that we've used to train the largest models in the world has increased by an order of magnitude every single year. So I went back and had a look at the Atari DQN paper that we published in 2013. And that used just two petaflops, right? And some of the biggest models that we're training today at Inflection use 10 billion petaflops.

• 48:17Speaker 01

  So like nine orders of magnitude in nine years is like just insane. So I feel like it's super important to stay humble and acknowledge that there is this epic wave of exponentials which is unfolding around us, which is actually shaping the industry. And so when it comes to predictions, you have to just like look at the exponential. It's pretty clear what's going on. That's just on the amount of compute side. The data side, I think everyone's super familiar with. We're using vast amounts of data and that's continuing. But I think the other thing that people don't always appreciate is that the models are also getting much more efficient. So, um, you know, one of the big breakthroughs of last year, which got some attention, but probably didn't quite get as much,

• 49:01Speaker 01

  given how many breakthroughs there were, was the Chinchilla paper, which I'm sure, you know, a bunch of you will be familiar with. But, you know, that's a very, very significant result showing that, you know, we could actually train much smaller models with much more data for longer. And that was actually compute optimal and achieve essentially comparable performance to the models that were previously being trained. And so that gives us an indication that it's very early in the space for architectures. And, um, these models are highly under optimized and there's a lot of low hanging fruit. And so that's what we found, uh, you know, over the last year and a half. So actually the lead author of Chinchilla, uh, Jordan Hoffman is, uh, on my team here at Inflection. And we have a bunch of really outstanding people who have produced

• 49:44Speaker 01

  a number of really awesome proprietary, uh, innovations building on work like that. And so I think both trajectories are going to play out. Scale, building larger models is definitely going to deliver returns. We're obviously pursuing that. We have one of the largest supercomputers in the world, uh, you know, and at the same time, we're going to see much more efficient architectures, which are going to mean that many, many people can access these models. And it's, it's in that sense, it's the coming wave of contradictions in AI. That's, uh, that's what's happening.

• 50:17Sarah Guo?

  I have one last question for you. So you are working on a book. I know you can't say much about it yet, but why? You're a pretty busy guy.

• 50:27Speaker 01

  I love reading, I love writing, and I love thinking about stuff. And what I've realized over the years is that the best way to sharpen your thoughts is to create hard deadlines. So that was like one of the main things. And I'll be honest, like, did I regret multiple times over the last year and a half agreeing to a book deal with Penguin Random House at the same time as doing a startup? Yes, like multiple times. I was tearing my already quite gray hair out, but it's nearly finished and it has been absolutely phenomenal. And yeah, I've super enjoyed it. The book's called, the book's called The Coming Wave, and it's about the consequences

• 51:11Speaker 01

  of the AI revolution and the synthetic biology revolution over the next decade for the future of the nation state. And try to sort of intersect the political ramifications with with the technology trajectories at the same time. So it's been a lot of fun.

• 51:28Sarah Guo?

  My hobbies are also this trivial, Mustafa, so.

• 51:34Sarah Guo?

  Thank you so much for joining us. Congratulations on the launch. And for our listeners, you can try it at inflection.ai and find Pi in the App Store.

• 51:42Speaker 01

  Thanks so much. It was really fun talking to you both. See you soon.

• 51:46Speaker 02

  Thank you.