This Week in Startups - Inflection AI CEO Mustafa Suleyman on building modern AI, DeepMind origins, and much more | E1794

Episode Date: August 18, 2023

This Week in Startups is brought to you by… OpenPhone. Create business phone numbers for you and your team that work through an app on your smartphone or desktop. TWiST listeners can get an extra 20% off any plan for your first 6 months at openphone.com/twist Crowdbotics. Great ideas can change the world, and Crowdbotics is the fastest way to turn those ideas into code. Get a free scoping session for your next big app idea at crowdbotics.com/twist Carta now lets you launch and administer SPVs for your syndicate. Share your knowledge, capital, and network to launch your syndicate SPVs through Carta. Get 10% off your first SPV at carta.com/twist with promo code TWIST. * Today’s show: Inflection AI CEO Mustafa Suleyman joins Jason to discuss DeepMind’s influence on AI (1:17), the hardware revolution (26:27), the foundation of Inflection AI (49:45), and much more! * Time stamps: (0:00) Inflection’s CEO Mustafa Suleyman joins Jason (1:17) Life before DeepMind (8:34) OpenPhone - Get 20% off your first six months at https://openphone.com/twist (10:05) DeepMind’s origin story (15:58) The pitch to Peter Thiel and feedback from early investors (19:34) DeepMind’s first project (24:59) Crowdbotics - Get a free scoping session for your next big app idea at crowdbotics.com/twist (26:27) The hardware revolution and growth in computing (30:15) DeepMind’s acquisition by Google and strategy for building an elite team (34:14) Major achievements at Google (37:49) Carta - Get 10% off your first SPV at https://carta.com/twist with promo code TWIST (39:22) Google’s cautious approach to releasing an AI product (44:59) Regulatory issues and fair compensation (49:45) The foundation of Inflection AI (59:50) The downsides of AI and Mustafa’s book, The Coming Wave (1:08:58) Complacency in tech and Mustafa’s thoughts on remote work * Follow Mustafa: https://twitter.com/mustafasuleyman Check out Inflection AI: https://inflection.ai * Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four Apply for Funding: https://www.launch.co/apply Buy ANGEL: https://www.angelthebook.com Great recent interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland, PrayingForExits, Jenny Lefcourt Check out Jason’s suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

Apparently, if I fancy getting married anytime soon, you're available for that too, right? Apparently, the world's greatest officiant is available. If you can find a woman who will marry you, Mustafa. But you've got a startup. Oh, you already got that accomplished? No, no, I'm struggling with that. I'm very much single. So, I mean, if you want to marry me to my startup, inflection.

You are married to your startup. You raise a billion dollars. I can tell you who you're married to for the next 10 years. Absolutely. Infliction AI and you're 40 people over there. This week in startups is brought to you by OpenFone brings your team's business calls, texts, and contacts into one delightful app that works anywhere. Get 20% off your first six months at openphone.com slash twist.

CrowdBotics. Great ideas can change the world. And crowdbotics is the fastest way to turn those ideas into code. Get a free scoping session for your next big app idea at crowdbotics.com slash Twist. And Carda now lets you launch and administer SPVs for your syndicate. Share your knowledge, capital, and network to launch your syndicate SPVs through Carta. Get 10% your first SPV at Carta.com slash twist with promo code twist. All right. We got a big treat for you today on this week and star us, Mustafa. Soleiman is here. He's with inflection AI, but very famous for having been the co-founder of Deep Mind. Welcome to the program, Mustafa.

Great to be here. Thank you, J.C. Thanks for having me. Of course, of course. You know, I wanted to start with the origins of deep mind because it seems like so much of what we're seeing in AI stands on the shoulders of that organization. And I don't think most people know the history of it. I happen to know a little bit of the history of it because I remember when Peter T.L. and Elon, I think, were two of the early funders of it and we're talking about it and we met, I think, at a couple of different industry events over time. Tell me, what, what? What was the origin of deep mind? And then how did it, you know, originate and start to tackle AI, general AI, vertical AI, all these different things that are coming to fruition. And I guess that was 2010, right? 2011. It was 2010 that we started the company.

Yeah, exactly, which seems kind of insane, like almost 15 years ago. And it's just quite surreal to see because in the last sort of, what is it, to 12 months, it feels like the kind of large language model revolution has come out of nowhere and exploded onto the scene. But in fact, it has been the kind of steady march of many, many years and a huge amount of failure and a lot of risk and a lot of persistence that I often think gets slightly neglected in the story of the perfect explosion of a new technology. In fact, for most of the last decade, we didn't have language models. I mean, the Transformer was really only popularized in 2017.

I mean, people often say that it was invented then. It was certainly not invented then. It was invented a good 15 or 20 years earlier by Oshoa Benjio. And then many other people developed the ideas. But it was really only, you know, four or five years ago that the idea started to get traction again. and then it wasn't until GPT3 that people started to get a glimpse of, you know, what it looked like at scale instead of just in a test environment. So, yeah, it's been a crazy journey.

How do we start the company? So, 2010, I was actually playing poker with Demis, Hasabas, who was my longtime friend since we were quite a bit younger. In London, I assume, at those high-rate casino. That's right. It was at the Victoria Casino in London, which is on Edgeware Road. Not the biggest game in the world. I seem to remember. It was probably a 250 pound tournament. Only 120 people. But, you know, so we would play at these things regularly. Both of us were very passionate about poker. I was playing. I was one of these people that was doing like eight table poker stars back in the day. my friends were doing 16 table, but I didn't have the actions per minute speed to be able to manage that.

You're on a clock, yeah. It's not easy to multitable. Although it's something about multitabling becomes like a flow experience. And you start to see patterns, right? Because you're playing so fast that you have no choice but to kind of play instinct, right? And now it seems like GTO and all these theories

are people are able to really deploy it very quickly. I hate online poker. I like in person because the only edge I have is my ability to read people, which is such a critical part of the game. And it's so hard for me to read people online. It's also the fun part of the game, right? Like pushing people off parts and teasing people for their losses. I mean, that's the fun part.

So, yeah, but I mean, getting through a lot of hands is also a very great way to practice. I mean, because you end up developing heuristics. And so you just see that. That's the problem is it's such a high variance game in your career. If you only ever play live, you never get to see the volume, which gives you the range of experiences. So the good thing about really having a short stint of abusing online poker is that you just get to see depth and breath, which is cool. But you can pick up bad habits because it can make you too cautious. Ah, interesting.

I haven't heard that before. So you don't, is that the reason you get too cautious is because everybody's really. reading each other's like statistics and you're just like, I'm going to be too easy to read here. I can't make a non-traditional play. I'm going to get caught. Yeah, because everybody sees so much more volume, then they play in a much more predictable and structured way. So you learn to predict everybody else's moves. And also, they end up being, because they see more volume, they are more deliberate with their hands and more cautious. Whereas in a home game, you may only see a couple hundred hands, even in a six to eight hour game, right? And so your, your range,

is clearly much lower. You're playing cards that you would otherwise leave behind because you're seeing more throughput online, right? So, you know, the classic is the knit. You know, we used to call them the nits when they would come to the live tables and they'd like clearly just playing this robotic game and driving themselves nuts because they weren't seeing enough volume. It's pretty funny.

Yeah. Yeah. It's such a fascinating game. And playing live in a casino, you get to see like a real broad spectrum of humanity. I was just talking to somebody about my friend Sky Dayton and I used to play at Hollywood Park and commerce in L.A. And we would play at the lowest tables. And at one point, I was trying to figure out how to read people better.

And I came up with the idea of Jedi poker where I would pull my cards up and put my thumb on it. But I'd make a bit of a show of looking at my cards, but I would have them covered. So I didn't know what cards I had, Musa. That's the best way. And then I would only play the, person and I'd be like, this person seems very strong. This person seems pretty scared.

Let me see, get this person off the hand. And then I get to the river and I would literally, if somebody called me down, I would turn over my cards and be embarrassed like, oh, I have a set. I didn't know it or I had bottom pair. And they'd be like, how would you bet like that? It makes no sense. And making no sense is part of poker because you have to break the ability for people to be able to read you. A hundred percent. This is one of my favorite. ways of doing it. The other way I like to do it is to represent a hand off the flop that I don't have, assuming that it is the opposite hand or a better hand and whatever I place that person on. So, you know, that's actually a very good way of doing it because then you bet consistently across

the three, you know, uh, streets, streets, but you, um, but you know, so you're not being ridiculous and wacky, but you're telling a story. You're representing a narrative. You've convinced yourself that you have 10 Jack. And when the board. comes down, you know, nine, king, queen, you're like, I'm playing 10 Jack. And I'm going to play it like 10 Jack would play this. You just got to make sure you know how to lay down if your opponent actually ends up having the hand that you're trying to represent. That can get pretty sticky.

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So you're playing cards and you get bounced out of this tournament and you're sitting there doing your post bounce or did you make it to the final table and you're just feeling like gods. Clearly, you nailed it. So now you're trying to explain your bad luck and how bad everybody else is to each other. Right. We've gone over the whinging about our bad beats. That took up the first half an hour running through our knockout hands. And we're sitting there eating chocolate cake and vanilla ice cream and Diet Coke because

obviously we're super cool and we're not getting pissed. We're talking about the future of the world. And, you know, both of us have always been interested in like how do we impact the world? What does the future look like? We've both been very, very long-term thinkers, and just instinctively, that is just one of our kind of gifts, I think. And I was particularly interested in, you know, how you do good in the world and how, you know, politics shapes our future and stuff like that. We were both talking about robotics and, you know, is now the time for, you know, robots to come on and automate everything. And I think we both agreed that actually that was way further away than people realized. But the thing that was likely to be, you know, more pressure is teaching machines to learn their own representations of what is valuable in a space. Like, surely a machine could learn to play poker or a machine could learn a set of heuristics and then reproduce those patterns. And at the time, Demis was just finishing up his PhD and

postdoctoral work in neuroscience at UCL at the computational neuroscience unit. And so he invited me to join the luncheon learns, which I did for almost six months, I think, pretty much every day went down to, you know, basically smuggled in the back door of the Gatsby computation neuroscience union and just listen to the lunch and learn. So that's where we met Shane Legg, our third co-founder, and then we all went for lunch. What is this lunch and learn? I mean, I can, there's lunch and then somebody speaks and you learn. Yeah, it's like a brown bag lunch, you know, like where, you know, it'll be like at the lab. So there's 40 or 50 people at the lab and people invite different people and so on. And so there'll be speakers or there'll be postdocs or

every lunch basically someone gives a talk about their work and takes questions and it's a bit of a bear pit I mean you know they don't take prisoners if you if you're not on your toes then you get some pretty rough questions like and it was just an amazing way to learn and be thrown in the deep end and really experience it firsthand I was only 24 at the time um so then basically a few months after that um Shane Legg got invited to the Singularity Summit um in um um um 2010 to be a speaker because he was on the less wrong forums back in the day and was a bit of a transhumanist, to be honest with you at that time. And then, you know, we decided to go because Peter was one of the sponsors. I think it was the main sponsor of the summit.

And then we got invited to the drinks afterwards. And we used that as an opportunity to pitch Peter on AGI. You know, he was the only person in the valley, to his credit, talking about AGI or even AI. in any form. To everybody else, AI was a weird taboo word and everyone was sort of talking about machine learning, but even not really, like it was mostly in the labs, in the academic labs that people talk about machine learning. Yeah, and then we went to his office in, uh, in the big park is the Presidio. Yeah, the Presidio. Presidio. Yeah, we went there and to the founders fund office and yeah, he made a decision on the spot. I was pretty easy. I think he gave us like

$2 million. Yeah. $10 million valuation? Not even, dude. It was like, Wayne, it was like half that. We were really, well,

because we were like randoms from London. I mean, he, like, he, he joked that it might as well be Somalia.

That was his view. It was literally what he said, you might as well be investing in Somalia. I was like, London's a serious place. But apparently not to Peter. Well, you've got to also put in context.

He had just done the, he had done the Facebook investments, probably feeling pretty good about himself. That was going well. And, you know, five to eight million dollars was what a seed round would,

evaluation would be. And AI, at the time, to be honest, as you said, nobody thought there was a commercial application or that it was going to work, right? Like, that was kind of the big question. Is this actually going to come up with an answer that is going to have some application in the real world?

Because you had deep blue, right? We had Kasparov got beat. And so narrow AI had proven itself. but IBM had spent hundreds of millions of dollars and they had no product. I mean, that was the playing field, right? It was like, this is a money pit. Right. And of course, that was a decade before us as well, you know, so that that had proven to not have serious commercial applications.

So that was actually a kind of non-goal in our pitching is to not bring up. Yeah, don't bring up to you. Don't bring it up because it was like a cool research thing but never quite had it had had the impact that we hoped. And yeah, you know, for the first two or three years, it was, it was very tough going because, you know, deep learning just didn't seem to be, you know, catching on. And then all of a sudden, you know, we had the cat classification paper from Alex Ruskevsky, Alex Net in 2012. And then in 2013, we had the Atari game player, DQN, which we published. And that was really the thing that changed everything for us. Because, you know, Larry Page had seen. seen the demo and just emailed us cold page at google.com and was like, you know, you guys should come and come and be part of us. I've spent my entire career building the infrastructure to enable a company like you guys to come and work on on AGI. Stepping back, what was the pitch to Peter? We're going to build reinforcement learning.

We don't know if there's an application. It's a science project. Your two million is going to be gone in three years. Was there any path to commercialization? that you pitched him on or was it, let's see what we can do in the lab? There was, yeah.

So, I mean, we actually didn't pitch him on reinforcement learning because at that point that was really early. We pitched him on deep learning.

And what we were working on was a visual image search tool for fashion and furniture and clothing and so on. And we actually, I held the first pattern for deep learning

in this area. which actually takes the shape and the texture and the color of one item of clothing, like ideally a more affordable high street version, and then uses that to find the more expensive, you know, equivalent that you could then, you know, go on, you know, find a comparison for. And, you know, that was a big moment, actually, because it was the beginnings of, you know, the generative AI movement. I mean, you know, it's now called Gen AI, but it was never called that at the time.

It was really just deep learning classification. Yeah, at that time, forget about generating something. You were trying to identify something. This is a hot dog. This is a dog. These are two different things. And that framework hadn't actually happened yet. What Google was doing at the time, would they put two or three low wage people on a group of images?

And they would say, you know, describe five tags for this image. And then whichever three or four came, you know, in common with two different people, that was what the image was about, right? That was the state of the Google Index at the time, right? Spot on. And they would have them draw bounding boxes around certain parts of the image. So this area of the image contains a penguin, this one contains an iceberg. And it turned out that was exactly the kind of thing that this hierarchical neural network

representation was pretty good at doing. Like it would essentially cluster together pixels which were correlated around a particular region. And then where there was a sharp distinction, like an edge or a line or, you know, a break in a cluster, then that would end up being a sub-representation. And then if then the next layer would absorb that sub-representation and increasingly build more and more symbolically representative ideas. Like it would go from, you know, basically, you know, a tiny little area of the iris to a wider eye, to an eyebrow, to the side of the face, to the full face, to the background. and you could kind of think of that as a way of understanding how the hierarchical neural network representation was formed. And obviously, now that we had made so much progress over the last 10 years on the classification

side, you then use those classifications to generate novel predictions. And that's basically what image generation is doing is saying, given this sentence, find the sort of optimal representation of all the competing points in this big space that best represents this long sentence as a new image. And that's the transformer model that we hear about in that 2017 paper from Google, yeah? Exactly. Yeah.

That's deep learning, but then there's lots of other generative AI components that were, you know, pushing at that direction. But they did it on, they made it work first for the language side of things. That was really the big deal. So you get a couple of years into this. You figured a couple of things out. and you start getting into reinforcement learning. So, and that's when Larry Page, was Larry on the board?

Or was just Elon on the board at that time at Peter? No, so we had first Peter invest, then Elon, then we were the first check out of, sorry, no, we were the third check out of the first fund of Mark Stad's Dragonia in 2012, I think it was. it was such a great time period to be an investor because only lunatics were starting companies after the great financial crisis. It was like this five-year period where if you started a company, you had no choice because you were a lunatic who had to start that company because everything in the world was telling you don't start a company. It's going to be pain and suffering. So you raised this money. What was the first project that you guys started to work on how did you pick it?

And then, you know, what clicked? Because I remember AlphaGo was one, and then there was this clock that became sentient. There were just all these little projects that we would hear about inside of DeepMind. But DeepMind kind of kept a lot close to the vest, I think. I mean, we operated in stealth for most of our entire period, and we actually didn't even announce our investors. I mean, there was a bunch of other, like we had Selena Chow as another investor from Horizons, LeKashing's Fund. And, you know, there was a, we had a very good group of people who were lucky. I think we raised $45 million in the end.

So each year we went back, I think we raised like, we raised two or three and then 10 and then 30. And what did you show each time to keep people investing in the vision during a time when people didn't believe in the vision? Yeah. And most people didn't. Yeah.

I mean, so we showed in the second time that we raised, we showed Flatland, which was our little agent-based environment, like a 2D grid world. where, you know, the model had kind of learned a way to navigate through the environment using purely the pixels. And we then said, okay, for our next, you know, milestone, we're going to basically teach the model to learn arbitrary games of Atari. And in the end, we played 56 games at, you know, which is pretty incredible.

It's 24 frames per second, and it's learning to basically correlate actions where it can basically go up, down, left, right, or shoot. Yeah. The original Atari 2,600 controller. Yeah, exactly. Which had five actions. Exactly, exactly. And, you know, so it's basically got to figure out which of those actions. It's randomly kind of moving them around at the beginning.

And then it stumbles on a rewarding, you know, moment. It luckily gets some, you know, get some score. and then it realizes, okay, that's a useful thing to do. Next time I see the ball bouncing towards me in that position, I'll move the paddle left or right. And it's just kind of incredible that purely through self-play and reinforcement learning, just very simple heuristic exploration and then exploit the strategy that turns out to be useful for generating score.

And suddenly you can learn to play all the games to basically superhuman performance. I mean, that was mind-blowing to me. And that was all done. Atari 2,600 emulator. Obviously, you're not taking a physical choice they can put a robot on it. It's able to run very quickly in the cloud, right? You've figured out a way to accelerate it

so that it could just be playing whatever, Pong or Tank, whatever those early games were adventure, play them, you know, millions of them, right? How many runs did it have to do to be, to perfect them? Did you track that?

Like, how many, how many quarters until you perfect the game and you get high score? I mean, it's interesting. You mentioned cloud, right? Because this was 2012-13, so there wasn't really any cloud to speak. We actually ran it on-prem. We had our own little cluster in the office.

And it used to train Atari DQN, it used two petter flops of computation. So a flop is a floating point operation. This is a unit of computation. It's like one calculation. Think of it. And obviously, petter is a million billion. So it's two million billion. calculations to train the entire model over the course of about two weeks.

So put that into perspective, and then obviously at the time, that was, you know, one of the largest. I mean, we don't know for sure, but there weren't any other big training runs of those kinds of things at that time. So it was fair to say it was probably the largest. That was a decade ago. And, you know, you roll forward in the models that we train today at inflection and, you know, the other frontier model companies use 10 billion petaflops.

Wow. 10 billion million billion floating operations, which is insane. It's a human brain cannot even conceive of what that is. It's kind of like when we start talking about there's a billion suns in our galaxy and right. And there's billions of galaxies. The human mind is not designed to even comprehend millions of billions of millions of millions. Yeah.

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That's a $499 value just for the Twist listeners. You get that for free. That's C-R-O-W-D, B-O-T-I-S-T-S-T-S-T-T-S for a free build plan. The hardware did start to catch up here, and hardware seems to have been part of the enabling. Maybe you could talk a little bit about what the infrastructure looked like at that time, the hardware footprint, versus what we see today and what you're doing in inflection and the hardware footprint. It's a great point. I mean, it's really the hardware revolution rather than the AI revolution.

I mean, it's funny because people fixate on the algorithms. Obviously, the algorithms are critical. but they really have not evolved at the exponential rate that computing has evolved at. So those 10 billion million billion petapflops I described, that's a, that that is the equivalent of one order of magnitude. So 10x increase in the total amount of compute used for the cutting edge models every year for 10 years. The 10 to the power of 10. I mean, it's insane. It's truly insane.

So, yeah, that is basically about hardware. And that's why I think actually this revolution has been easier to predict than I think people realize. I mean, this trajectory has been continuing for a long time. And we can look out at what the next three, four, five doublings look like. Oh, sorry, three or four, five, ten Xs look like. They're not doublings anymore like Moore's Law. their orders of magnitude increase in compute.

And that's a very predictable trajectory. I mean, obviously, it's unclear exactly what the, what capabilities emerge from that. But, you know, you can certainly predict what we're going to be able to build. Steve Jervison has a lot of charts on this where he's been tracking. I don't know if you've seen Steve's charts on just, you know, the amount of computing power and, you know, the sort of tipping point is somewhat predictable.

And now we've got heat. and power friction, I guess, is the limit right now, or how much we can connect these supercomputers together? What's the gaining factor now? It's a good point. Yeah. It's a good point. That is going to become the constraint.

So the A100 uses 700 watt per chip. The H100 is twice that, like 1,200 watt. Wow. So the next per chip, right? So obviously you have eight of these on a node, then the chassis and the node itself has some additional power constraints. So they're actually, it's a different data center design to, you know, what it was two or three years ago where, you know, there's actually spaces in between racks.

They're not like completely stacked up. They have to be like really large gaps in between. And some of the designs I've seen for new cooling systems are that there will actually be fans in between the node layers. So. And all fiber optics, all glass. glass, photonic, computing to transfer data from one to the other because the amount of data being moved now can't be moved over copper.

It can't be moved over Ethernet cables. It's just too much, right? Being moved around. Oh, for sure. For sure. All of it is a fibroctic cable. It's actually called Infiniband, the Melanox Nvidia cabling. And that's like 900 gigabyte a second, which is pretty nuts for, you know, direct chip-to-ship

connections. So it is really driven by all the hardware innovations, and those hardware innovations are very predictable because they're actually laid out three years in advance. Yeah, because they're planning on building. They're building those schematics and getting the fabs and the factories ready to actually build them. So there starts to be a little controversy inside of Deep Mind, I guess, at a certain point. Larry Page is like, we need this team inside of Google.

Google, maybe Peter T.L. Elon watched you to stay independent. Maybe you could explain that moment in time and the decision making there. Yeah. I mean, I think this was way back in 2014 that we were acquired. And, you know, I think that Elon and Peter, all of our investors, you know, wanted us to stay independent. And I think that the challenging decision for us was just the scale. of investment that we could see that would be required going forward. I mean, we'd raised $40 million and, you know, we could see a path to spending $500 million in three to five years. And in fact, that's what we ended up doing exactly that. You know, DeepMind now has, I think, 12, 1,300 people and spends over a billion dollars on compute a year. So, I mean, that's public information. So, you know, the, the, it's, it's pretty remarkable the trajectory. And so one of the things that we were focused on, you know, Larry made us an incredible offer to be able to do that.

We were acquired for $650 million, um, pre-revenue, obviously. Yeah, it's a pretty great deal. There's a pretty good deal. Especially at the time, I mean, the world has changed dramatically in the last decade, but at the time, this was, people were shaking their heads, like, what did they buy? I mean, in fact, the conversation was, I think you had maybe a hundred people at the time. Less. Yeah.

Yeah. Yeah, exactly. The conversation was, because Larry lost his mind. He just paid $10 million per engineer. And then that became, well, engineers in Silicon Valley are worth $10 million each. It was like, well, these are different types of engineers. You hired a very elite group of people. Maybe you could talk about the recruiting of bringing together the deep mind team at the

time because it was a lot of PhDs, a lot of people who had some, you had a pretty deep bench there, yeah? We were extremely focused on hiring the best PhDs and postdoc. actually, and I've carried that through to how I hire an inflection. I mean, you know, talent is the differentiator at the end of the day. I mean, you can be first to get access to compute. You can have the most amount of capital, but selecting a very, very high quality team is really the only thing that makes the real difference. And that means you have to be very deliberate about who you don't hire. You know, it was actually amazing at that time how many people

who were fundamental to the deep learning revolution we had around us, right? So, you know, Jeff Hinton was one of our consultants for two years before he set up his company that he then sold to Google. So was Ilyas Satskiva, the chief scientist of Open AI now. Vojchev was an intern at DeepMind, who was one of the co-founders of Open AI. It's going to be like the PayPal Mafia. It's going to be the DeepMind. turned out to be the deep mind mafia basically you got a whole group of alumni

who are just creating the future here. Was it a looking back on it? Was it a mistake to sell? You regret selling to Google? Should you have taken Elon's advice and stayed independent or anything about it? Elon was certainly keen for us to come and do the Tesla thing be part of his ecosystem.

Yeah. But, you know, I'll be honest, I was a bit... I mean, back then, you know, he's an incredible person, Sure. I mean, it was a very uncertain bet in 2014. Would be the definition of uncertain. I mean, Model 3 almost killed him. Almost killed the company.

I mean, that company's had a near-death experience with each launch of a product. I mean, you want to talk about hard, hardware plus software and manufacturing at scale and building a public brand. I mean, the degree of difficulty is absurd. Inside of Google, to the extent you can't talk about it, you guys worked on. a lot of theoretical things, but you also worked on a lot of practical stuff. What were the big wins inside of Google that you can talk about,

that DeepMind participated in it? Yeah, I mean, we deployed DeepMind technologies on all of the main products other than search, actually, and YouTube. So I think we did seven PAs in the end on everything from data centers to healthcare, to Play Store, to Android battery optimization,

to Android operating system. I mean, we reduced the amount of energy you needed to cool the Google data center fleet by 30%. Wow. That was a three-year collaboration. It's a huge project. We made the Google wind turbines 20% more efficient, which Google has the largest wind turbine farm in the world. Which is pretty crazy. Yeah, we designed the activity classification algorithms for all the wearable devices.

that would basically tell whether you're sleeping or running a walk in. They wouldn't let you touch the two biggest franchises. They wouldn't let you touch search. They wouldn't let you touch YouTube. Why would they, you got this incredible thousand folks and you don't let them touch the two biggest franchises. Why? Well, we, the truth is. No, I mean, we tried and we actually tried YouTube in 2015 and we failed.

It was too early and it was just super hard. We were trying to optimize watch next time, actually. and we were trying to use reinforcement learning for it and it was just too early we didn't succeed search is a different story I mean search is just so difficult

to ship editing and they're super conservative they also they like the fact that all of the rules are very transparent so they can see exactly why a page is being recommended and really have much more transparency on the algorithm which is very understandable

so in fact there were some you know deploy of deep learning systems, which ended up causing regressions over time because of drift, you know, over a six-month period. In other words, quality would go down. Well, it would go up initially at the beginning. And then come down. And then come down, exactly.

Why does that, why does that drift happen? People were talking about that with chat GPT4, that results have deprecated. Well, I didn't understand why that would occur. Is it garbage-out kind of situation? What's happening in something like that happens? I think there's slightly different problems. I think with the chat GPT thing, it's probably that they basically serve their best model, which is expensive to serve, right, because it's the biggest and best and uses the most number of GPUs.

And then once people are coming back frequently, they'll serve a smaller model, which is cheaper. There'd be a less well-trained model. Quality is basically, as always the case, quality is cost, right? So we can serve a cheaper model for, you know, quicker, but it won't be as good. So that's probably what's going on, I think. I've never heard that theory, but that would track and make sense. And as more people use it, they may have no choice but to give everybody a little bit of an easier model to use or a more basic model because they don't have a choice. Well, and the other variable would be speed.

So if you want it really fast, then you have to get a smaller model or you have to use more chips to serve a super large model. So you can't have all three. And so if you want a super high quality one, you could have it really slow and cheap, but that would be really slow for 20 seconds or something per response. Listen, if you're in the tech industry, you know about Carta. Carta is the leading venture capital and equity management platform. And they have huge news to share here on this week in startups. Carter now lets you syndicate an SPV.

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Make sure you use the promo code twist for 10% off. Just wrapping up your time of Google, they never launched any of the stuff until OpenAI did, but they clearly had it sitting there. It makes sense that Google has, more responsibility with their brand name and they can't put stuff out there that's shalucky or confusing under the Google brand name, but eventually, I guess, opening eye and Microsoft forced their hands.

Right. Why did it go down that way? Yeah. I mean, people say Google was asleep at the wheel and all the rest of it, but it, you know, it's not quite true. I think, so I was there at Google and working on the Lambda team, right? So I spent a year and a half working on that team and we are. You know, basically had chat GPT before chat GPT.

It was incredible. I mean, summer of 2020, and we had it. It was working. It was amazing. And was that what we saw in Gmail auto-complete? Was that model? It wasn't Gmail auto-complete, but it was featured by Sundar in May at I.O. The annual developer conference at, yeah, in 2020.

And it was actually featured as Lambda. You can see it up there now. And he actually had a conversation we designed. It was so stupid. He had a conversation with a paper airplane about what it's like to be a paper airplane. And then he had a conversation with Pluto. And then the language model pretended it was Pluto and like, you know, talked about the weather and stuff. Well, you know what we always say.

Examples matter. And they pick terrible examples. Literally, you know, when you're pitching your startup, you're pitching a new product, you want the most evocative, interesting, applicable example. And they pick two inane ones. And I can tell you, it was deline. deliberate because we didn't want it to look like a person or sound like a person who wanted it to be kind of like, you know, sharing the cool technology, but it was just, you know, the first small step in that direction.

Don't be scared. It's not taking your job. It's just Pluto. I mean, if you make it a doctor or you make it a librarian or you make it a copy editor or, you know, all of a sudden, it's like, hmm. And that's what's happened today, which I think is a good pivot point here. So anyway, suffice it to say. Google is a large organization, they're conservative, and so they just took a measured approach, and they have the goods.

Right. I think there was just a confidence that, you know, we don't have to go first on this, and we could take more time to get it right, and that, you know, search is just this phenomenal lock-in in distribution and data. And I think that's going to pay dividends, because I, you know, I think Google's going to be just fine. I mean, Google was going to be fine. I agree. I bought Google shares when I saw all this going down because I was like, I looked at Bard. Me too. And I'm watching Bard and I'm like, you've got so much clickstream data and you got so much

local data that it's all of a sudden doing links, tables, it's putting in photos. I mean, I've seen this movie before. I watched Google from 10 blue links to, you know, comprehensive search, content, shopping, maps, everything. And that happened over a decade or two. and it's obviously going to happen in there. And I also think the ad model, you know, there is a theory like the more confusing it is, the more you click on ads. But if you do a search for travel, there's no reason that links inside the barred result

cannot be monetizable. In fact, they will. Right. I think that's true. What do you think? I think where Google is going to struggle is that Google has developed an incredible expertise for getting in its own way, right? It's just almost like the master.

of like internal chaos. And so there's loads of, you know, amazing teams and projects which just block each other because there's huge amounts of duplication. It's a very chaotic place. It really is. And so I think that's going to be challenging for them. And I think the second thing is the ad model may not be the model of the future, right? It may be the case that people cannot tolerate having a, you know, an AI in your pocket that

is funded by whoever is the highest bidder trying to sell you something. Because these models are so persuasive, because they're so personal, because they'll get to know you, because you end up having conversations with them and sharing information that you wouldn't normally type in a regular search query where it's just like, you might say, like, you know, something sensitive about your cancer or your, you know, whatever, your heartbreak, you know, but it's not the same as having a fluent, continuous natural language conversation as though you and, just like you and I are now. Right.

And so I think people are not going to want, you know, your AI to suddenly turn around and say, by the way, ta-da, like I'm, you know, so we'll see how that turns out. And I think Google's going to struggle with that one. Yeah, it could be affiliate links. You know, if I was talking, I was talking to my AI and I'm suffering, I'm melancholy. I got depression. I'm feeling sad. and it knows my AI knows I'm sad.

It could be like, you know, maybe exercise, cold plunge bath, go see a psychiatrist. All of those things are monetizable links in some way. And so, you know, if it gives you the perfect answer, the question is, is it possible to monetize if you just got the answer? And Larry always said, like, eventually we're going to give you the answer. We're just going to give you the answer. And so the mind does wonder if that screws up the ad all. auction in a major way. Well, and that's precisely the problem number three for Google, which is that if Google

always gives you the answer, then what is the future for the open web? Because Google is going to disintermediate the third party content creator. Like if you're a regular mom and pop shop with your bakery on a website or you have a blog post and you rely on that display ad income, well, Google's just going to give you the perfect recipe. So why would you ever go to that kind of third-party blog post? And that's actually a problem for Google and the regulator, because Google has been telling the regulator for the best part of 15 years that the reason it can crawl all of these websites is because it's only indexing so that it can redirect the user to the third party page. It feels fair. It feels fair.

Right. It's a yellow pages, they always used to say. It's a lookup table. Whereas if it's now cutting out that source of information and giving you the perfect answer, that's a big problem with the regulated, certainly in the European context, because many Google exec have been on the witness stand claiming that they'll never do that, right? And now the models are doing that. They've been trained on the web. It's obvious. It's been proven.

And you used to be able to ask Open AI chat GPT, like, hey, where is this answer trained from? It would actually tell you some of the training data. I think it doesn't do that now. what's the fair outcome here for pools of data, lakes, oceans of data, and who gets to leverage them to build these models? What do you think is the outcome here? Because we're starting to see the lawsuits pile up. We're starting to see, you know, Elon say, hey, Twitter data is not available. Reddit saying it's available at a price, Kora saying it's available at a price, or maybe with a link back.

Stack Overflow built their own language model. This new CEO just emailed me to say, like, look, I know who keeps stack. Overflow keeps coming up. We're building our own co-pilot. Nobody else can use our data set. So talk to me a little bit about what you think will happen in the industry because I feel like it's tremendously unfair to take gourmet or whatever recipe database and then just give the answer and not give a citation at least. What's going to happen? Here's the tricky thing. I mean, the reality is that the information was placed on the open web and the open source crawling engines have gathered up their information under perfectly legal,

you know, acceptable terms. And that crawler, you know, the common crawl crawler, you know, collects the information and clearly says that it would be used for, you know, research and development purposes and, you know, be used for experimentation by other, you know, people trying to build other products off the, on top of the open source search engines. So the crawling data that everyone's collected is just a well-established status quo. So I don't think that is going to be undone or there's going to be any compensation. People sometimes talk about this data trust idea where, you know, each individual data contributor gets like one cent or something.

I mean, this is not going to happen, I think. Why not? Too hard to execute on? I think it's impossible to generate sufficient revenues to make the payment to the end. you know, produce sort of data material, right? So maybe in the case of a very large data owner, like, you know, the opening I just didn't deal with Associated Press, right? But that's actually not for historic data.

That's actually for fresh, real-time news. See, that's where I think there is a possibility of this, if we think as an industry collectively, that this could actually be a benefit. You remember, Minitel in France, used to charge a certain amount per hour. and they would share that with the data sources. AOL used to charge three, four, five bucks an hour comp you serve, and they would share that with the data provider. So if you were on some data site that had to do with weddings or whatever,

they would just give them 50 cents of the hour, right? You actually had a model there. I think if we took Robots.t. And we put in a license and said, hey, listen, these are my recipes. I'm Gordon Ramsey. If you want them in your index, Mazel Tov, there's a thousand recipes. It's a minimum payment each year,

$10 a recipe, it's $10,000 a year to put it into your index, plus I want something on top of

it, whatever it is. And that might be enough to incentivize people to start putting more recipes online. It's possible. It's possible.

I mean, I think the challenge of these things is that the creative tools are now going to be so widely available that the models are going to be,

you know, better at generating new recipes. So the cat's out of the bag. I mean, in a way, it's true. Yeah.

So tell me, you, you leave, Google and you start inflection with Reid Hoffman, who's just on the pod, you raised a bunch of money. What is inflection AI? What is the goal here?

You obviously got to see everything up close and personal that's happened with open AI and with DeepMind. Where do you sit in that sort of pantheon of elite AI offerings? You got BART over here, you got Open AI over here,

you got OpenAAA over here. Where are you going to sit? And what market are you going to try to carve out? So we're developing a personal AI. I believe there are going to be lots of different types of AIs. There'll be business AIs. You know, there'll be medical, legal. You know, every digital influencer will be an AI.

Every brand and big platform that's trying to sell stuff will have their own AI that, you know, that is more than marketing AI. I think, you know, wherever you see a website or an ad, app, expect that in the next five years, that's going to become a conversational interface that you might as well just call an AI, right? It'll be able to produce video and text and audio and talk to you just as I'm talking to you now. In that world where everything becomes an AI, I think you as an individual consumer want to have a personal AI that is on your team, right? It is fiduciary aligned to your interests in your course. corner, helping you find information, identify credible sources, negotiate with other AIs for the

best bargains, plan and prioritize your day and, you know, your thoughts, your ideas, follow up on your research interests, find you entertaining information. And it is super important that is personalized to you because you're going to end up sharing a lot of sensitive, personal intimate information in order that it can then go out and be your representative, right? whether it's in gaming environment and it's kind of in the metaverse or whether it is, you know, looking for sports news, you know, on your behalf and coming back to you and talking about it. The way I think about it is, is kind of like imagine if everybody had a chief of staff, right? A digital chief of staff that was a coordinator, scheduler, prioritiser, summariser, you know, you wake up in the morning and it gives you the perfect briefing of everything you've got on in your day, what's happened with the news, happen with the sports, the companies that you're tracking.

That is what I think of a person. It reminds me of, remember General Magic? Yeah. We're dating ourselves, but Sony and a company called General Magic made a PDA personal digital assistant device long before Palm. I think there was a documentary on it, but they had a concept of agents and the, this is before search, really, on the internet, search engines even existed. And the agent would go on your behalf and go find you flights or go find you

reservations, go do tasks for you. And so you see this AI as being autonomous in some ways and being able to put it on repeat tasks. Hey, I'm trying to lose weight. I want to be 165 pounds. What should I be doing? And then it's going to counsel me every day about that. Right. And if you're going to put an AI in that kind of position, which it will be incredibly effective at doing, because it's not going to nag you and moan, it's going to be inspiring, it's going to be reassuring, it's going to be gentle and polite and respectful. I mean, it's not going to be an asshole about it

unless that's obviously what you want. Whatever you're into. Whatever you're into. Please fat shame me. I am not worthy. It could get weird. It could get weird. But to your point,

it's going to be personalized and it's going to be your agent. And so this framework is critically important in terms of your vision is that it's your agent. It's working on your behalf, not the corporations,

have not open AIs, not Bing search results or Google search results. This is your AI. And whatever you talk to it about, we don't have any insights into. And if you put data into it, we're not sharing that with advertiser or anybody else. So that means I have to pay you 100 bucks a year for this. Yeah. I mean, at the end of the day, if you want to have full trust, you need to not be the product. And if you're not paying for it, somebody else is paying for it. And if you're putting that amount of attention and sensitive information into a place, the only way to make sure that it's on your team is for you to pay for it in some way. You wouldn't rock up and be like, oh, my accountant is actually being funded by this insurance

company. Yeah. And so I'm going to go and speak to my accountant and you're trying to get your tax return done or you're trying to decide on how to make some investment. And you're like, well, are you working for me or are you trying to sell some. an insurance product or whatever, right? Yeah, I think understanding the intent and the business model is so critical. And consumers are super savvy now.

Like, they understand it. You can't get over on customers now. They expect that like Alexa's listening to them, even, you know, in serving them up ads, even when that's not what it's actually happening. But, you know, they are pretty empowered and they understand this concept of you're the, if you're not paying, you're the product. So I've used it a bit, quite delightful, beautifully designed. What can we expect to be the beachhead markets or tasks that you think it's going to delight people with in the early days here?

Well, so far, you know, we've actually only got a small model that shipped in production, right? So, you know, only founded the company a short while ago, sort of 15 months ago now, and we're just bringing up our super, cluster. So, you know, just a few months ago, we raised a pretty large round. And, you know, we're building out the largest cluster of H-100s that's in operation in the world today. So today we have the largest operational cluster of H-100s. By the end of the year, we will have 22,000 H-100s, which is an equivalent of about

80,000, A-100s in a single cluster. And Invidia, you had to go with. on their doorstep and beg them to buy these? I mean, maybe you talk a little bit about the scarcity of these H-100s. Yeah, they're extremely scarce. I mean, InVidio is one of our investors. That works. So is Microsoft.

Yeah, I mean, you know, I think that we were just very lucky to get to the top of the supply chain with them. And they've been great to us. So it's been incredible. We've also helped them optimize their cluster for ML Perf. They have an open source benchmark that stress tests their cluster. So we've invested a huge amount over the last six months to optimize their cluster. So it was kind of a good quid pro quo that we were both for guinea pigs and also the beneficiaries of the first big shipment. When this $1.3 billion raise was announced, it was a little confusing to people because Microsoft has this big bet on OpenAI and then they're making this big bet here.

What should we take away from Microsoft's behavior here investing in you and Open AI? It was kind of confusing for folks. I think the way to think about it is that Microsoft is a platform of platforms. It's traditionally been very good at doing deals with lots and lots of third parties interacting with a whole range of different suppliers. And I think that's probably how they're going to continue. They want to back lots of the best teams. And we have one of the strongest teams in the world right now, if not the second

best team in the world. Then we have the co-creators of GPD2, GPT3, Lama, Chinchilla, Gopher, Palm, Lambda. How much of the $1.3 billion goes to hardware, just to get a curiosity. Really? So we just ship it right to Nvidia and build out this gigantic data center to do that. And then that becomes a massive competitive advantage, yeah? It will be a huge advantage because we will train models that are very, very much larger than GPT4 before anybody else in the while.

So, you know, by the spring for sure, maybe even a little bit earlier. So, you know, all of it goes to compute, basically. We're only 40 people. Oh, wow. And so did you consider using Google Cloud or Amazon Web Services or Azure? Or do you need to control the hardware in order to get the gains that you need to see? No, we wouldn't use TPUs. They're difficult for other reasons.

But we, you know, so we certainly wanted invidia. So we did look at AWS and Oracle and stuff. And, you know, we actually do use Azure for some workloads, but we wanted to make sure we designed the architecture for the H-100s. And we've really optimized everything, you know, down to the lowest levels in terms of how that operates and try and get maximum performance out of it. How long does it take to build out this cluster? It's going to take a year or two?

It takes a while. also, I mean, we're currently operational with 7,000 H-100s. We'll be 22,000 fully operational by the beginning of this ever. So it's pretty quick. That's unbelievable. And this is just in just different data centers around the world. You co-locate in and you just start racking them? No, it's just one data center because we need it all to be in the same place.

So it's actually the size of like three football pitches. Where's the data center? curious? It's in the US. Oh, it's in the US. I don't want to say it. We can't say, yeah. Got to be near something that's got hydroelectric or some nuclear power plant.

Exactly. Nailed it. That's exactly what it is. Or solar. Or solar. You got to be near something. So tell me when we look at the downside to AI. Obviously, this has been a big debate.

And, you know, there's job compression. It does seem to me. I asked a lot of smart people on the program from Brian Chesky at Airbnb to Aaron Levy up box. I asked everybody like, what kind of gains are you seeing internally on your team? Almost universally, people say 30%. Everybody's 30% more effective. Whether there's a developer, copywriter, customer support, whatever.

Which means every two years, people become twice as good at their job or efficient. Rule of 72-ish. There's job compression. And then there's like scary scenarios. People are going to use this to hack things or, you know, build. super biological weapons. How concerned are you about each of those, or those two specific scenarios, and how do you think society should think about them? Terrorism, crazy people, and then just job loss or maybe displacement? It's a good question. I mean, I'm very concerned

about it. It's something that I've worked on my entire career, the ethics and safety of AI. In fact, our business plan back in 2010, which I wrote was had the strapline building safe and ethical AGI. So I think we saw a lot of these risks right from the outset. And I'm still, I think it's appropriate to be pretty concerned around them. I don't agree with a lot of the timelines. I think people are very anxious that we're about to have this intelligence explosion and somehow I'm going to present an existential risk to our world. but I've actually just written a book called The Coming Wave, and it basically looks at all of the threats, basically,

that AI might create over the next 10 to 15 years, as well as the synthetic biology threats. And I think the labor market risk is a real one. I think for the next 10 years, people will get more productive. But the challenge is that the increases in that productivity are going to generate surplus value, which will be captured by capital and not labor,

which means that we probably won't see an average increase in wages, certainly for the middle. Those who are doing, you can think of it as like cognitive manual labor, back office administration, basic telephone calls. They're new factory workers, right?

They become the, and so the steam engine, the factory, the robots can replace them. We were very dismissive, I think, about factory workers losing their jobs, but now that it's white collar, yeah, it's, I think people are like,

wait a second, you can make a logo better than the designer. I mean, we're kind of there right now that you can create a logo or a tagline as good or better than a marketing agency. And I think when people see that,

it doesn't take a genius to say, not going to need as many marketing agencies or not going to need as many logo creators. And the question is, that's exactly right. I mean, everything is going to cost less, which is amazing. That is going to drive the biggest productivity explosion we've

seen in the history of our species. It is truly going to be an incredible couple of decades. But the reality is that those who have their jobs displaced are not going to be able to retrain, adapt their role, and then compete against man plus machine in the labor market in good enough time. Like if you're a designer, there's only X number of design slots in the world, right? Jobs, right? And if suddenly the work of that X number is being done by 70% of the humans because they're aided and augmented and accelerated by, you know, good AI, then there's going to be people who are basically graphic designers who are squeezed out and they'll have to then do the next tier down of work. And that will squeeze out the next tier below that.

So you're going to get this tiering where the bottom is squeezed out more and more. And I don't see how those bottom are going to be able to adapt quickly enough. And that's why there's a tough remedy, which a lot of people don't like, but you've got to face the facts, which is if you don't want there to be really significant structural disemployment where people cannot compete in the labor market, but they want to, then there has to be some kind of subsidization for retraining. UBI, retraining, something. And before you get to full UBI, there's obviously, you don't have to go as far as that to begin with, but that is the direction of travel over a 20-year period.

One of the great things is we create new jobs when all jobs get retired, and it really is the pace at which that happens. It's kind of sad that cashiers have lost their jobs over the last 10 years, but I remember when they went on strike and McDonald's cashiers were like, we need to make 20 bucks an hour to make this job work, And then McDonald's was like, that's interesting because we have a company that wants to build registers that are touch screens and they're getting cheaper.

And the cost curve at some point, Panera bread and McDonald's were like, why do we need cashierers? Put one. And then everything else is going to be ordering on a chaos. And that job has been eliminated. Those people can go find other jobs. Podcasting's a job now. It's the speed and how we manage the transition because we will create new work. There will be new demand.

People will have new income because of this productivity boost. And so people will have money to spend and people will be more efficient so they could deliver the same output with less work. So the question is how you manage the transition for this period of the next couple decades where people who get pushed out of the workforce have to somehow retrain and adapt. I mean, even, you know, it's pretty clear that there's a retraining and adaptation requirement and that many people are just not going to be able to keep up. We start with factories.

We start with coal workers. You know, if you're a co-worker, and that's all you've done for 20 years, the idea that at 45 years old, you're going to just magically learn code or become a blogger. Kind of hard to think. But then again, with AI tutoring, maybe there's an opportunity that the AI tutoring will get so good that people can actually learn skills faster with customized education, yeah? Right, right. I mean, people, this is the incredible thing, is that it will be a very meritocratic moment because a lot of people who have had safe and steady families for two, three, four generations

have inherited peace and stability in their life. And that has turbocharged their education. It's given them confidence, has given them emotional support. It's given them, you know, access to education, access to opportunities. What's going to happen now is that those people who have been on a comfortable trajectory are going to face the competition by people who are hungrier and who now have access to personalized AI tutors that are going to teach you anything that you're obsessed by,

anything that you want to go deep on. It's infinitely patient. It's infinitely smart. It knows exactly how you like to learn. And it's free. And it's going to basically be free. Free or close to free. Close to free.

I mean, compared to a college education, it's going to be free for sure. I mean, compared, and it's just basically getting an internet. that connection. And, you know, then you are going towards the third rail, which is motivation and drive. And this is a very hard conversation for people to have. But it might be the case that there's somebody in Sri Lanka, Pakistan, San Paulo, who wants it more than somebody in San Diego or Brooklyn, and they're just going to work harder and they're going to spend more time on that AI.

And now it's a global, that AI tutor. And it's a global marketplace. And that's, I think, going to be very scary for people is, oh my God, I'm competing against, you know, the top 5% on a global basis who now have Starlink, have an internet high speed connection, and they've got the AI tutor in the cloud, the Khan Academy teacher that is infinitely patient. And yeah, that's your privilege in the West that you were born in London or New York means nothing in that scenario, right? Yeah, I mean, I have a whole section about that in the book, which I really enjoyed writing. I mean, it's about exactly that story because the costs of production are going through the floor

and everything is now going to be zero marginal cost. So knowledge is widely available, right? And now not just knowledge, but intelligence, right? Intelligence being the mode of synthesizing knowledge and turning it into new strategies or insights or action plans. If that goes to zero marginal cost, then why shouldn't anybody be able to be super creative? and it really is going to be about how hungry and dynamic you are as an individual, which I think is going to really displace, it's going to undermine or put some pressure on the complacency class

that has kind of like taken over us a little bit in the West. It's the group of elites who get into their college because they're a legacy. And if you're a legacy person and you get into Harvard, guess what? It may not mean as much as the person who's motivated and becomes a neurosurgeon or a developer. And they're from, like I said, you know, Bangalore, and they just wanted it more than you.

And now they're going to be, society's going to be super useful for them. Listen, this has been great. Thank you for giving you over an hour of your time. Got to have you come back. Everybody should try. It's Pi, right, is the name of the personal assistant. PAI is the short.

P-I. Pi. Pi. Pi. Pi. Pi. Pi. So Pi stands for personal intelligence, yeah, pi.AI. And the book's called The Coming Wave, which is available now. I didn't realize you had the book. I'm going to read it this weekend.

I'm going to order the audio book now. When did the book come out? It's actually available for pre-order now comes out September the 5th. Oh, fantastic. So perfect. Well, after I read, I'll have to have you come back on and we'll talk all about it. And hopefully we have a book party or something for you here in the valley. If you need the world's greatest moderator to interview you at any book parties for something,

let me know, I'm available. Apparently if I fancy getting married anytime soon, you're available for that too, right? Apparently the world's greatest officiant is available. If you can find a woman who will marry you, Mustafa, but you got a startup. Oh, you already got that accomplished? No, no, I'm struggling with that. I'm very much single. So if you want to marry me to my startup inflection.

You are married to your startup. You raise a billion dollars. I can tell you who you're married to for the next 10 years. Absolutely. Absolutely. Absolutely. Infliction AI and you're 40 people over there. Also, you're hiring. So if you want to join Infliction, go to Infliction AI.

And listen, it's an elite group. They're in the Bay Area, Palo Alto, London. You believe in people working out of an office or you think remote work is fine? What's your take on all this? I have an interesting, we have an interesting balance, actually. So I think that you need the best of both worlds. So the way that we operate is that we run the intelligence. entire company on a six-week cycle, right? So when you join the company, you sign up to traveling

to be in person for a full week, wherever you are in the world, for our seventh-week meetups. And that's a key part of the schedule, because then for that one week in our seventh-week meetup, we have a very intense hackathon-style meetup where it's, you know, the classic 14, 16 hours a day in the same room really going out of hardcore. And the rest of the six weeks, we recognize that people need to work in a flexible way. So I am personally in every day. And so is probably about a third of the company. I would say another third come in Tuesday, Wednesday, Thursday. And then some people are actually fully remote. So that's the right hybrid structure, I think. I agree with you. You know, if you have, my belief is a third of people are more productive

as remote workers. And over the last two years, I figured out who they are. And then there's another two-thirds that do better work when they're in an office with other people, just like some people are better runners alone. And then other people, when they run with the group, the majority of people, when you run with a group, you will perform better when you're running with runners who are faster than you. It's that simple. Or you play with poker players.

You're better than you're going to get better quicker. And so I think there might be on the margins, 25% of third who are a better remote, but I think two-thirds are better in person. That's for sure true. And there's no way that those people can stay completely remote forever. I personally am not a believer in these fully remote environments. So that's why we do this 6-1 rhythm. I love it.

I think it's the right amount, basically. It's the right amount of sacrifice. It creates a certain esprit of corpse, you know, like I could see it being super motivating to like get together. And then, yeah, some people got kids, they got family. You're hiring people who are Uber successful and have many options. So it's not like you can always dictate. You know, you might have somebody who's just a genius who wants to live at Lake Top.

And you may be able to break her off for a week to come, but you might not get her for the seven weeks. So you don't want to lose that person, right? I think that's the weird standoff we now have. Or maybe it's a settlement amongst workers and corporations, right? Because you don't want to lose a high performer. Right, right. Exactly. So, I mean, getting the flexibility is the right way to do it.

And having the kind of peace during the cycle that some people need, not everyone, but some people need to do their own things. and then have the super intense meetup, which I think is a good rhythm. Yeah, I mean, it's also sounds sustainable. I was thinking about the early days of our industry and just everybody at work, six days a week, 12 hours a day, 14 hour days. It led to a lot of incredible outcomes,

so I don't think anybody who does it is making a mistake necessarily. But it can break people. It can exclude certain people from the team that might be high performers. So it's really the job of management to just figure out a cadence. Sounds like you found the cadence that works for you.

Sounds kind of exciting, actually. It's working right now and we're having a great time. So, yeah, if any of your listeners want to come and get stuck in, we're having a great time. I mean, that's the other thing is people have choice amongst elite folks. You've got to make it fun, and it's got to be purpose, right? And it sounds like a lot of fun to go to these remote locations and do a week. So, all right, listen, great job. Look forward to reading the book.

It comes out on September 5th. Everybody pre-order it. Tell me the name one more time with the book. It's The Coming Wave. The Coming Wave. So go look for that on Amazon or Audible and pre-order right now. If you hear my voice, please, pre-order. So he gets that big first week bump.

You need $10,000 in order to make the New York Times bestseller list. That's true. And I'll see you all next week on This Week Startups. Bye-bye.