Jonathan Ross, Founder of Groq

24:21But the determiner of the most beautiful model is not who's most beautiful, it's who has the most money put on them. And that model, like pro parata, to how much money you put in, you get all of the money that's been bet on all the models. And if your model doesn't win, you get nothing, you lose all your money and it goes to the winner. Well, if that's the case, if someone has a lot of money, they can put it down on any model that they want, whether they're beautiful or not. And a bunch of people made other bets. It doesn't matter. They just come in and they make a big bet. Now, when you're watching some of these wagers that are happening in Silicon Valley with people putting in big bets, it's because that used to be what won.

24:59What's changed, though, is that unlike the Keynesian Beauty Contest, where the winner is the one with the most money, in reality, there's a point at which you get enough money and you don't need more. And for the first time in history, the startups are not starved for cash. They have all they need and more. So now everyone's getting funded to the level that they need. And putting more money in is not an advantage. But people are still acting as if putting more money in gives that startup an advantage. Okay. So can you tell the story that you told when we were together at InvitaGTC about the drastic change in Grok's fortune and how fast that happened?

25:34Yeah. Yeah, so going back to what you asked about almost running out of money, we were about three weeks from running out of money at one point. But that was many years ago. That was many years ago. So you weren't close to running out of money this time. No, no, no. We were fine towards the end. You were fine. But the last valuation you raised at compared to what you did this agreement at was drastically different. It was really only a little over 2x. So that wasn't a huge jump. And we also had the ability to raise at the amount that we did the licensing for. Well, let's just go back to what we talked about on stage, where it's just like how fast you guys had this insight and then how fast the trajectory of Grok changed.

26:17Well, it was that three-week period from the time when we presented and asked to buy GPUs until not only the deal was done, but money had been wired. But how many months before were you working on this? It was probably three or four, maybe a little bit longer. But what had happened was I didn't initially think that it was going to be that big of a deal. So I didn't suggest doing it. In fact, it was Sonny, my COO. This goes back to the whole autonomy thing. This is the story I want. Yeah. So he had the idea of trying to put our chips together. And he didn't have like a - Explain what you mean by putting our chips together.

26:59So the LPU and GPU, as mentioned, they're better at different parts of what's called the decoder layer of an LLM. The GPU is better at the attention portion, and the LPU is better at sort of applying the weights, which is the thing that gets trained as opposed to the memory. And what we realized was, and this is what most people get wrong, When they're trying to do this themselves, they'll take the reading of tokens or what's called pre-fill, and they'll do that on one piece of hardware. And then they'll put the generation of tokens on another piece of hardware. But the generation of tokens is the hard part.

27:44That's the thinking. You know, reading is easier than writing, right? And that's true for AI as well. And so what we figured out was if – and again, this was all a bunch of people. It wasn't any one person, and it was a group that all innovated. Once he brought the idea of why don't we put them together because they're a different bottleneck, then the team figured out, oh, this part goes here, this part goes there. We implemented it, and it worked. And so we also weren't afraid to show it to NVIDIA because we wanted to become a customer of NVIDIA. We wanted to buy GPUs. And so what ended up happening was we went, we presented.

28:21It made a lot of sense, and the deal happened. Okay, so from Jensen's point, once he sees this, right, why does he decide to act so quickly? This is the nature of a successful entrepreneur. You move quickly. You don't wait, right? There's opportunity cost to waiting. Technology is not a business where you can wait a year. What I'm getting at is why is that so important to his business? Right now, when you go to use AI, it's a little, it may feel somewhat fast, but that's because you're not used to using it much faster. Sort of like when you first used the internet, it felt fast compared to mailing things around.

29:00But when you got broadband, you realized, oh my gosh, like this is so much better. I'm never going to go back. The difference is broadband actually needed people to make their websites faster to make it usable. If the servers are slow, you don't get a benefit. So it took a while to roll it out and make it good and get video that could stream and all that. The difference is you put these LPUs into a system and all of a sudden the generation of tokens gets faster. It's like getting broadband instantly on these existing models. And so now rather than having to wait a minute to get an answer, you can get an answer in 10 seconds.

29:36And that really starts to compound. So let me walk through an example of why it's not just speed, but it's also quality. The other thing that I did was I created Google TPU. and at Google, there was this time where I'd already moved over to Google X. I was no longer working on the TPU at that point. I'd already done it. And someone else from the TPU team was at Google X, came by, showed me this email from DeepMind saying, hey, you know, we've got this competition. We think we're going to lose. There's a prize purse. Is your chip as fast as we've heard? And we're like, yes. Like, you know, it's like Ghostbusters.

30:16You know, when someone asks you, are you a god, you say yes. Someone asks you, is your chip as fast as I've heard? You're like, yes. So we reply back, yes. And they're like, great. Competition's in 30 days. We're going to play the world champion and go. And we played our test games and we lost. We need to win. So they had no choice but to port over to that TPU chip. So we did it. And a bunch of interesting things happened. And I think, do you know what an ELO score is? Yeah. Okay. So for those who don't know what an ELO score is, it's sort of your ranking in Chass or Go. And something like a 200-point advantage is insurmountable.

30:56Like the probability of you winning is basically zero. AlphaGo running on GPUs had an ELO score of about 3 ,200. Lease it all was about 3 ,550. And I might be getting the first digit wrong. It might be 2 ,000 instead of 3 ,000. But like, you know, it was more than 200. And then when put on TPUs, it actually went to like 3 ,900 or something ridiculous or 2 ,900, whatever the first digit was. It jumped dramatically. And so I think he didn't expect he was going to lose, but he lost badly. But it was the exact same model. So what changed was the ability to compute more made the results smarter. The way that these models work, Thinking Fast, Thinking Slow from Daniel Kahneman.

31:49I read that book. What AI does is if I have 270 possible moves, which is what you have on a Go board, the AI is going to rank those moves and say, this is the best move, this is the next best move, and so on. What happens is you virtually play that best move, and then you virtually play the counter move, and then you virtually play the next one. And then you see how the game unfolds. What you can also do is try that second best move. And occasionally, that second best move, when you play it out, actually turns out to be the right move in this context. It's not the one that you would normally do, but in the context, it's better, and you can see that as you play it out.

32:32In the second game, there was this famous move called Move 37, which was creative. It was original. It actually wasn't completely original. It was a one in 10 ,000 game move. It had been in the canon of games that we're trained on. But when we went back and played it on GPUs, it never found that move because it was too deep in the chain. So over time, these GPUs have, you know, gotten, you know, as good and better than TPUs. As the creator of the TPU, I have to admit, GPUs are now better. This is the benefit of just having an entire industry behind you and ecosystem and everything. But at the time, TPUs had some novel innovations, right?

33:14Now bring in the LPU and you can go deeper, faster, you can search faster. And so you can actually make a model smarter by making it faster. And so the realization is, and now that you've got this ability to reflect, think deeply and change the outcome based on your thinking, being able to think faster makes you think smarter. And so that's the advantage of pairing the LPU and the GPU. I found one of my all-time favorite quotes when I was reading the book Zero to One. The quote says, the single most powerful pattern I have noticed is that successful people find value in unexpected places. And they do this by thinking about business from first principles instead of formulas.

33:55That is exactly what Applovin has done with their advertising platform. Applovin connects you with over a billion potential new customers inside mobile games. Applovin allows you to capture undivided attention. Applovin ads are full screen video ads that are watched for an average of 35 seconds. That is retention that blows other ad platforms out of the water. And you can launch on Applovin in minutes. You set the goal and Applovin achieves it. There's no complex setup, no expertise needed, and Applovin scales quickly. They can put your ads in front of over a billion potential customers. Other businesses have seen immediate results, have scaled to hundreds of thousands of dollars of spend per day, and increased their revenue by millions.

34:41So you want to get started quickly before all of your competitors are on Applovin. And you can do that by going to Applovin.com. That's Applovin.com. Let's go into a couple of your ideas that you've discovered in like the 10 years that you were running Grok. What is your idea about reality quotient? So one of the things that we hired for at Grok was reality quotient, which is different from intelligence quotient. And the way to think of it is there are plenty of really smart people who wouldn't recognize reality if it tapped them on the shoulder. You've got to say more about that. So, like, you just know these people who will construct these very elaborate stories in their minds that are completely disconnected from reality, right?

35:25And then there are some people who are just incredibly street smart, but couldn't do basic arithmetic or anything like that. Reality quotient, oftentimes it starts off as being able to recognize reality. But in the most extreme form, it's the ability to choose the dominant game that's being played. So what most really successful founders and entrepreneurs do is everyone else is playing this game and they realize that if you play this higher level game, you win. Simple example, MySpace was focused on number of accounts signed up. Facebook focused on monthly active users. It was the dominant game, right?

36:06If you have monthly active, that's more important than account signed up. And if you maximize the monthly active, you're going to beat someone who's maximizing account signed up. You're playing a better game. And so as a founder, you're often able to do this better than other people. And your job leading those people is to try and help them connect their activities to that dominant game. So when running Grok, I said our goal was to get to 25 million tokens per second of capacity in our data centers. That meant everyone had a different way to contribute to that. It meant they could make the chip faster.

36:44It meant they could make the software faster. It meant we could deploy more. It meant we could get our power costs down so that we could deploy more chips for lower OPEX. It meant get more data centers. It meant fab more chips. It meant find ways to optimize the supply chain so we could put orders in faster to fill things faster for customers when we got it. So everyone could connect what they were doing to that one dominant game. Where being a founder is challenging is you often see that and you tell it to people and everyone wants to stick with the old way of doing it or they care more about the process.

37:21So moving from being an engineer to being a founder, the thing that finally clicked for me was if I was going to do something disruptive, my job was full-time change management. And the first principle of change management is to make it feel like it isn't a change. Why is that so important? People do not like change. No human being likes change. The difference between people who appear to like change and people who don't like change is often they're looking at different things. And for the one that is fine with the change, nothing changed. So if I'm playing this dominant strategy here and that doesn't change, I'm just trying to maximize the number of tokens per second that I've deployed.

38:06When my approach changes, nothing really changed. But if you're down here thinking, how do I make this chip faster and not thinking about the software, then you might view something that changes the chip as a change. And so your change management duty is to give people enough context so that they see that their job hasn't really changed. What they're trying to accomplish is the same thing, and there is no change. Tell me about Return on Luck. I read this book by Jim Collins a while ago, and it had this chapter in it, and it really resonated with me. And the thesis is the most successful companies don't have more lucky events.

38:47They just seize on that luck better than other companies. And I read this very early on as a founder, and I started to notice it was true. And there was a really good example of this. When LLMs first started to become a thing, I remember getting a phone call from the CEO of GitHub basically saying, I need a bunch of GPUs. We've now gotten LLMs to be able to do code completion. Even though we're part of Microsoft and everything, we just can't get GPUs. Could we use your chips, your LPUs? And I went to the team and I'm like, we got an opportunity. We can do this. And they're all like, nope, not going to work.

39:31Can't run it on these chips. I'm like, no, it looks like it's the ideal thing to run on our chips. It looks almost perfect. They're like, no, it doesn't work. There's all these things that are in GPUs that we don't have. And I'm like, yeah, but none of those are important for LLMs. And they were looking at the wrong things. I let them convince me that we shouldn't pursue it, even though in my bones, I kind of knew that we should. it. This happened another time where there was another opportunity to deploy an LLM and I let them talk me out of it. The third time, like, no, I'm going to do it myself.

40:06And so I just went through the whole thing. I did the arithmetic and determined the performance and everyone disagreed with me that it was possible. I'm like, no, no, no, look at this. And in the end, we ended up hitting exactly those performance numbers. The thing was they were looking at the wrong, they We're looking at all the reasons why it couldn't be done than why it could be done. And we talked ourselves out of it. I had multiple lucky opportunities that I didn't seize. I mean, how much better off would we have been had we been the original thing running LLMs at Microsoft for OpenAI? That would have been a very different outcome.

40:40So we lost a little bit. But we were still ahead of the curve when we realized that fast inference was going to be a thing. And I remember very early on, we would talk to potential customers. We even had a video where we sped up like what it looked like. And everyone who looked at it was like, why do I need an LLM to be faster than I can read? And it hadn't occurred to them yet that you're not going to be doing the reading. You're not going to be doing the reading. But also, that's not how the internet works. Like, do you, are you okay with a webpage showing up? You know, I hate that. Yeah, because eyes don't move that way.

41:17Eyes move all over. You need the entire thing there. You're going to look at it. And even before you read it, oftentimes you'll have a sense of this isn't what I needed. And you'll start typing your question without reading everything that came out. So I realized that fast inference was going to matter. No one else did. What do you mean no one else did? Even within Grok, there was a lot of pushback on, like, we had a lot of turnover at this point. A lot of people were leaving. How many years ago was this? This was probably three or four years ago. And so a lot of people were leaving saying there was no point to fast inference.

41:52It wasn't going to add any value to the ecosystem. And even though you draw very simple parallels like dial-up versus broadband, no one could connect it. I'm going to interrupt you real quick. Can you say more about this? Because now everybody's just talking about fast inference. Yeah, yeah. It's like everything. And I wasn't paying attention to this four years ago. I had other stuff. Like I just wasn't paying attention to this. Can you talk about the difference in, I think this is one of the most important parts of your company story, is just how contrarian, maybe not even the right fucking word, but it was out of favor.

42:22Your idea, your main idea, people are like, no, it's not important. Well, everything was out of favor. Everything was considered a bad idea that we did. But if you don't do things differently, you have no advantage, right? Why do you think so many, four years ago, people just didn't understand? When people don't understand the first principle of something and they're getting involved in it because it's, you know, hype, they don't understand enough to understand why what you're doing is different. So that had to be a disorienting experience to you. Yeah, I mean. To keep saying this. Well, so what eventually worked was, so, and this goes to a little bit of marketing that we came up with.

43:02We realized that there was no possible way, no matter what we showed people, for them to accept that fast inference was going to be helpful unless we let them try it. And I remember this example from Eric Schmidt was involved in this thing, SCSP or whatever. and they showed off Anthropic, an LLM from Anthropic, about three months before the chat GPT moment. And I remember sitting there seeing it, seeing demos of this AI answering questions in the audience and no one reacting. And I'm like, how is it no one is reacting to this? Now compare that to the chat GPT moment where everyone reacted. What was the difference?

43:42The difference was when people asked their question and they got an answer to their question that was specific to them, That was magical. Seeing text show up for someone else's answer wasn't magical. So I realized that. And I'm like, the only way we're going to get people to understand the value of speed is if we just implement this, put it on the internet. So we did. And what ended up happening was we put it online. And I remember I was doing a little bit of a world tour trying to find customers. And I was in Norway. And I was doing a presentation. And I noticed, because we had it working. and I noticed that the presentation was like when I was doing queries using some of the open source models, I remember it just felt a little slow to me.

44:29Not that slow, but a little slower than usual. I'm like, what's going on here? Because like I'd been, you know, Norway's further away than the servers, but I tested earlier and it was fine. I checked in, our usage had skyrocketed. Someone had posted on X a video of an LLM running on Grok that was just running super fast. And it was viral. All of a sudden, everyone started creating applications using it, posting those. And it was just such eye candy when people saw it that everyone started creating their own and we just went viral. Say more about this experience that you had. I'm really like, you're coming at it from first principles.

45:07You're saying these people are getting involved in it because it's hype and it's like the thing that's, you know, being spread around at the moment. Like, what was that experience like for that several years where you're just going through this? of like, I'm trying to explain why this is going to be important. And you're just hitting blank stare or brick wall after brick wall. Well, there's this common theme where a lot of really good innovators are innovators because they experienced the problem before others did. Remember my experience of AlphaGo on, you know, TPUs and being able to outperform the world's best Go player only because of the hardware that we switched to.

45:47Yes, I was able to sort of get this return on luck more than others. I'm more able to like say, okay, there's an opportunity. I'm going to go for it. But I was also exposed to the opportunities first and you need both. So you have to be in a position to see the future ahead of – and this is the common saying, right? The future is already here. It's just not evenly distributed. because I was in a situation where I got to see the future, because I was willing to like seize luck and double down on it when everyone else was like, no, let's not pursue this opportunity. And I'm like, yes, this is the opportunity.

46:23Those two things are what work together. When you're saying, yes, this is the opportunity, would you describe the response you're getting as opposition or indifference? One of the biggest shifts in my leadership, another book, Turn the Ship Around by David Marquette. I read that too. Okay. I technically read books for a living. I don't know. So I adopted that very heavily in my leadership once I read that because it worked really well with my sort of autonomous leadership style. And the basic idea in intentional leadership is if I ask someone, should I do something? Oh, they have opinions. Most people will be pessimistic and give you negative opinions.

47:01On the other hand, if you express intentional leadership, you say, I intend to do this. people don't tend to offer their opinion but if it's very wrong and there's a reason they will push back and the example is the submarine commander took over um i think it was the u.s santa fe i think it was worst in nuclear readiness in in the nuclear submarine fleet and in a year or two he got it to number one in readiness and all he did was shift from command and control to intentional leadership the quintessential example being saying be to say They dived the boat, and there had been incidents where a submarine had dived where the hatch was open.

47:44And no one wanted to push back because the commander was command and controlling, and they were used to doing whatever the commander said. But when people would use this intentional leadership and say, I intend to move the boat down to 500 feet, then all of a sudden someone would say, wait, the hatch is open. They're involved in it now. They're involved in it. And everyone says, I intend to do this. I intend to do this. It gives everyone an opportunity to say what it is that, you know, they're doing so people can hear it. But you're not asking for an opinion. The issue was over and over again earlier on, I was getting opinions from people and they were stopping me.

48:22Going back to the three examples and the return on luck. Yes. Okay. And if I had just said, I intend to do this. Is that what you did on the third example? Yeah, I literally said, I put a presentation together. I said, we're going to get to this particular speed per chip. And rather than people going, we can't do this, they all sort of jumped in and said, this is how we do it. It's a very small change in phrase, but it has all the difference in your ability to move forward. You're not inviting friction, but yet people would still give feedback when it was really important. When there was a real problem, they would raise it.

48:58How does the intentional leadership tie to the question is if you were getting opposition or indifference? Where were you going with that? I was inviting pessimism by asking for people's opinion. Were you asking potential customers? These are teammates? No, teammates. Okay. So almost everything that is difficult is difficult because you can't go to one extreme or the other. You actually have to, in the context, decide whether you're going this way or that way. One of the difficult things is getting feedback. You hear this from leaders all the time. Early in their career, they get way too much pushback.

49:34Later in their career, they don't get enough feedback. How do you balance it so that you're getting real feedback versus just getting unnecessary pushback? And part of that is just the subtlety on the phrasing of I intend to do this as opposed to asking for an opinion. Deal is how the best founders turn the world into their talent pool. I've been studying how history's greatest founders operate for a decade. And one thing they all have in common is they understand that recruiting and hiring the very best talent is your most important priority. A players recognize other A players, which is why top companies like Ramp, Shopify, Eleven Labs, Uber, and DoorDash all use Deal.

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50:42With Deal enabling us to hire and support exceptional talent anywhere, We can accelerate our innovation and bring more voices, stories, and ideas to every corner of the world. Deal is trusted by over 40 ,000 businesses. Learn how they can help your business today by going to deal.com forward slash Senra. That is deal.com forward slash Senra. Let's go back to this time where you were three weeks away from running out of money. Yeah. And you came up with this idea of... Grok Bonds. Of Grok Bonds. Before I even get there, something that just popped in my mind as you were speaking earlier about kind of like leadership styles.

51:20You know, basically, I'm going to tell them what we want to do. You have this organizing principle, but you're not going to tell them how to do it. You're going to let them surprise you. This Phil Knight in Shoe Dog says that over and over and over again. And then I'm reading about Grok Bonds, and it sounds very similar to some of the things that Phil Knight had to do because Nike was so close before the IPO. They had to IPO out of necessity because they were just kept running out of money or very close to it. And he actually converted some of the loans that he got from his employees into equity and wound up, you know, doing very well for them.

51:50So explain this idea that you had for crock bonds. We were going to run out of money. And the leadership team that I had at the time was starting to put together a list of layoffs, of who we were going to lay off. And when I started reviewing the list, it became very clear to me that if we did that layoff, we were dead. Why? We were already struggling to keep up with what we needed to implement. Because this wasn't even pre-product market fit. This was pre-the product working. We had to write a very special compiler that had never been written before that didn't require human beings to write what are called kernels.

52:34It had never succeeded before. No one had ever done this. And our architecture didn't work with kernels the way everything else worked. So we had to get to this point, this sort of critical mass point, before a product would even work. We hadn't done that yet. And we were talking about cutting people who were critical for that. So when I realized that layoffs weren't going to solve the problem, and it was just a simple bit of burn math, like, you know, we were going to run out of money, but we just weren't going to have the talent we needed to succeed. And we were going to have these other costs.

53:08I realized we had to reduce our burn without reducing our people. And the only answer was to get people to take a salary cut. So we had an all hands and we put up, you know, World War II looking pictures of war bonds and we called it Grok bonds. It wasn't technically a bond. It was an exchange of salary for equity. And we expected that we were going to have pretty high attrition. And we actually didn't. 80 % of the employees participated. And I think about half went to the statutory minimum salary by law. Remember, engineers get paid hundreds of thousands of dollars. These folks cut their salary down to like$50 ,000,$60 ,000, whatever the statutory minimum was, like real pain.

53:57And we saved more than three weeks worth of runway. I think it was closer to two months. We had three weeks of money left when we raised. So had we not done this, we would have gone out of business. The thing that was interesting, I go back to this all the time because we've had a lot of close misses where we had to keep the team together. And there's a phrase I have, which is put everyone's hands on the steering wheel. When people are passengers in a car, they're more nervous about a windy road or a scary road. But when they're the driver, they feel more in control. They're more willing to take a risk.

54:35By doing this, we put everyone's hand on the steering wheel. They were participating in saving our runway. And we had less than 10 % attrition. It might have been closer to 5 % when we announced Grok Bonds, which was actually probably better than our attrition rate before then. I love this idea. So the other side, you went from, you know, looking for other ways to not fire people. The other side of firing is hiring. You also have some interesting, like, I think, lessons you learned in the decade you were building Grok about hiring that are also pretty counterintuitive. Yeah. Which a lot of the people that appear on the show have very counterintuitive ideas about hiring.

55:12I was very good at hiring people who were incredibly smart and talented. But a lot of the people that we brought in caused organizational problems. I've already kind of alluded to that. And the reason was I'm pretty clever. And so when I meet someone, I can come up with a reason why I should hire them. I think a lot of people do this. They will convince themselves I should hire this person. They're great because of this. They have this experience. They have this attribute. I'm going to hire this person. So we have this thing we call the people spec or did it grok. And very much like you have a product spec, we had a people spec.

55:49It had version numbers. We would change it. If you don't write down what you're looking for in people, you're not going to hire that. You're not going to be consistent. And so we framed the people spec in positives, things that you look for like return on luck. Give me a couple more examples of what were the positives on that people spec. Poetic design. So poetry is semantic density. It's when you say so much in so few words. Which is actually really important to you. Hugely important. You have this phrase, I think it's on the Grok blog where it was. It was like, make every word count. I think you'd repeat it over again.

56:23Every word matters. Or every word matters. So yes. Yeah. It's the smallest possible, most minimal expression of the thing you're trying to achieve is the most poetic. And that's not just in words. It's also in design, right? You know something is poetic, even if it's not words. And so that's another one. But each of these has a negative version, right? So the opposite of return on luck would be squanders luck. The opposite of poetic design would be, you know, maximalist design. Like, just throw every feature in. Like, you know some of these products where it's like, where am I supposed to click?

57:00And so it's really easy to spot people who fit some of the positives and not realize they have some of the negatives. And what you're really hiring for is to avoid those negatives because if one person comes in with that negative, they're bringing that into the whole team. The biggest flip in my hiring was when I went from looking for positives, which is what you do when you're trying to grow talent, to looking for negatives, which is what you do when you're trying to select talent. Explain that. When I'm trying to help someone grow and improve, I want to show them the path, right? There's a famous example of how do you increase the amount of money given to a charity?

57:37It's not making people realize how great the charity is. It's not making them feel good about giving to charity. It's about telling them where to send the money. If you tell them how, if you give them a skill or a technique, then they can very often learn it and do it. So when you're trying to grow people, show them the positive. Don't say, hey, don't squander luck. Show them what return on luck looks like, which is there was this opportunity once that everyone said no to and we said yes and it made us successful, right? When you're hiring, you're really looking to vet people and you're trying to say no to things.

58:11And that's a very different motion. And some people are really good at growing. Some people are really good at hiring. But you have to separate those two into very different mental modes. The reason I noticed this at Grok was we hired a head of HR who was very good at noticing problems with people and getting them out. And as I observed her doing that, I realized I'd been hiring all wrong. I think the way you described this to me was you essentially inverted it. And now you're hiring for loss bias is, I think, the term that you put on there. This is one of the attributes. And I think it's an important one.

58:49So humans have a natural loss bias, which is people attach a mathematical number to it, which is a loss is six times more painful than a gain. Like you see this where someone will invest money, lose 20 percent, and it'll be very painful. but they didn't invest in something that grew 100 % and that hurts them less than losing 20 % of the money, even though not getting the gain, the opportunity cost is much higher. There's a personality trait in people where I call it sort of booking the win early. And you'll see like we would be in an architecture meeting and someone would say, well, if we do this, the chip will be twice as fast.

59:31And I look around the room and no one seemed that excited about doing it. What's going on here? And I started to realize everyone was hearing, if we do this, the chip will be twice as fast. Let's put that in the next chip. And I'm hearing, if we don't do that in this chip, the chip's going to be half as fast as it could be. As soon as I heard that something could be done, I would book it. I would immediately sort of just assume that if I don't do it, I've lost this thing. So as I started to hire, I would look for other people who had the same sort of book the win early attitude. The moment they hear something's possible, they book it and they're like, I don't want to lose that thing.

1:00:08A lot of the most successful entrepreneurs, they sort of manufacture their own sort of discontent. I want to get there in one second. Yeah, yeah. But I think this hiring for lost bias and applying it to not only the talent, but also these meetings you're having in product, in product design and things that would make your product better is actually really important. You mentioned before that you learned from an episode of Founders on Michael Jordan, One Way to Do This, where he would challenge his teammates to bets. Why was that an interesting idea to you? When I heard your episode on Michael Jordan, I was thinking, like, yeah, so he is very intentionally throwing his keys over the fence so he has to go fetch them.

1:00:44What he's doing is Michael Jordan, he's a very sort of aggressive competitor where he would make bets on everything. Like, could you throw a quarter and hit a target closer or something like that? Just weird stuff like that. And he would just nonstop do it. Most people are afraid to sort of taunt someone else, a competitor, because if they lose, they're going to feel really, really bad. Remember, that loss bias is heavy. Like, if I'm like, we're going to go play basketball. I'm like, I'm going to wipe the floor with you. And then I lose. That's humiliating, right? What I suspect Michael Jordan was doing was he was very intentionally taunting the other players so that a loss would be humiliating to force himself to perform at superhuman levels.

1:01:29He was just doing it over and over again. Most people are so afraid of putting themselves out there and suffering the negative outcome that they won't get their hopes up. They will actually keep their sights much lower. But entrepreneurs, they start a company and they're like, of course I'm going to be successful. I'm going to tell everyone. I'm going to go raise money. I'm going to put my reputation on the line and I'm going to be forced to perform. Michael Jordan's trainer is the one that wrote the book that I did that episode on. And the way he describes this is what Michael would do is exactly what you're saying.

1:02:03He's like, well, once you tell somebody how bad you're going to fuck them up, you have to actually go and do that. Yeah. And what he realizes when Tim Grover was studying Jordan's career, what he realizes is like, He intentionally heaped more pressure on him because the more pressure he put on himself, the higher he wrote, like the better he performed and the higher he rose throughout like his career. I think this is also tied to something that you and I have talked about, which you called manufactured discontent. There's a book on the counter. We were talking in the kitchen earlier before we started recording that I have out there on David Ogilvie.

1:02:36He's one of my heroes. and he calls this divine discontent that you'll find the best entrepreneurs, the best athletes, anybody reaches the top of their profession, right? They don't rest on laurels. They don't sleep on wins. There's another book right next to that, the new biography of Steve Jobs just came out and Steve Jobs demonstrated this concept perfectly when he's just like, well, you made this great product. Now what? He's like, well, I believe that if you make something wonderful, the only thing to do is to do it again. It's to like not think about it, just the next day now I'm going to go on and make another great product.

1:03:08I'm going to keep doing this. Like essentially they're telling you like the journey is a reward. So talk about your idea of manufactured discontent. So I was having a conversation with a bunch of entrepreneurs and a bunch of people in other fields who, you know, some of them had made a lot of money, some of them hadn't. And the entrepreneurs were the ones who were the least happy with their wealth, even though they had more money. Everyone in the discussion was incredibly successful. But what we started to realize was the, like, even though some of these entrepreneurs had made hundreds of millions of dollars, they were comparing themselves to others.

1:03:44And they never had to work again in their life. But because they were unhappy with their wealth, they had a reason to continue and start another company and do more. Meanwhile, the other folks who were very successful, they were quite happy with their wealth. But what they were unhappy with was their previous work product, a previous piece of writing they had written or something like that. And because everyone in this room was successful, what we identified was everyone had something that they were discontent about that drove them. And so I started looking at my own life and, you know, there were a lot of periods where there was genuine discontent because we hadn't had product market fit.

1:04:26But then once we had product market fit at Grok, I was unhappy with the scale. and then I was unhappy with other elements and I just kept finding things to be unhappy with. Most people can be quite content with the status quo and they're not going to keep pushing to innovate. You have to have a personality where you are constantly discontent if you're going to keep pushing things forward. What are you discontent about today? At the moment, I'm discontent with the lack of compute in the world. AI is revolutionary. It's going to change everything for people. You know, there are pros and there's cons, but the pros are massive.

1:05:08You know, there are going to be medical discoveries. And if it takes us an extra year to cure cancer because we don't have enough compute, that's my fault. Every single person who dies from cancer, every single person who becomes old and, you know, infirm and dies, like there could come a point. We don't know. There could come a point where AI comes up with, you know, ways to slow aging, right? All of that, I feel, is kind of on my shoulders. And I need to perform. I need to make sure that the world has more compute. I love that idea of saying, you know, every day that we miss out on this mission, there's a real cost to it.

1:05:49Edwin Land, founder of Polaroid, Steve Jobs Hero, this guy, one of my favorite entrepreneurs of all time I won't shut up about. but way before he invented the Polaroid camera, he was actually trying to invent new ways to reduce headlight glare because in the early days of the automobile, there was so many people dying because the oncoming headlights of the car. And what he did is very similar to what you did. He had like this organizing principle. When you said, you know, we have 25, we need to get to 25 million tokens. He would put on the whiteboard, you know, 300 people died today because of this.

1:06:20And if it takes us an extra week, that's 2 ,100 extra people. I don't know what the number is, but it's something like that. I do think you're in a perfect position. This show is a love letter to capitalism. I think we should end on optimism. I think the best entrepreneurs in the world are default optimistic and default aggressive at the same time. Can you just give us, like give me actually, like just an overview of what you actually think is coming with AI and as a result of AI? Like some of the most optimistic things that you could say. Because you see what's going on right now. Like everybody's, it's super unpopular.

1:06:57People are, you know, they want to blow up data centers. They want to attack certain people inventing the technology. Like I don't think we've done a good enough job of telling like a more positive story. Well, I think that goes back to people perceiving a change, right? I had a recent post that got a lot of negative feedback, which was, I said that there's effectively been code rationing, right? As a software engineer, the default is code is expensive to write. And so I'm going to be very careful about what code I write. I'm not going to create a feature unless I'm absolutely sure that it's the right feature to create.

1:07:35I'm not going to implement something until I've got it figured out. And what we've seen from agile software development is when you take the risk and you sort of implement something, you get feedback, you end up getting better results. But there's still just generally a very natural predilection to say no to things. And so the concept of sort of a no engineer, it's someone's job to say no to things, right? They're the ones in the meeting who say, we can't do this, we shouldn't do this. What I'm seeing is that code is becoming almost free. The marginal cost is approaching zero. and it's shifting the way that things are done for professional engineers where you just implement the thing, you experience it, and you say re-implement it in this different way based on my experience.

1:08:24The other shift is the accessibility. It's very much like literature and literacy, right? There was a time when scribes were the only people who could read and write and they sort of controlled access to the written word. And then we got much simpler reading and writing, you know, alphabets as opposed to these sort of ideographs and hieroglyphs and so on. And all of a sudden, many people could learn to read. And then we got education system and everyone could learn to read and everyone could read and write. All of a sudden, it became about the quality of the written word, not just the written word.

1:09:00But everyone had access. My EA creates software applications now. Like when I go on a trip, she creates a little app, which I can click through and it tells me what the weather is going to be and it updates live and pulls it from sources and, you know, gives me all my phone numbers and all sorts of extra information. That would have been impossible for an individual who didn't know how to write code before. So what I think is going to happen is a lot of people are going to get access to being able to create software to solve problems who would have never had the technical capabilities before, but who would have had good taste and know what good is like.

1:09:37And there's just going to be an enormous number of founders, unlike in the past, where you just didn't have access to the capital and you didn't have access to the talent. I think you're going to see individual founders without large teams creating very valuable companies that solve real problems for people. And I love that framing. We'll end on this, one of my favorite quotes of yours, which you said, looking forward to a year of massive up leveling for anyone who wants it. Anyone who wants to learn can now learn a subject. You just ask questions. The problem with traditional education is it was force fed to you.

1:10:13It wasn't interesting. And if you're going to learn something, it needs to be interesting. The ability to ask questions in the moment when you want to learn something is going to fundamentally change education. And this goes back to what I said earlier, that the AI age is going to be about asking questions. I think a lot of people do ask me, what are they going to do for their kids? And my answer is, stop teaching them to answer questions and start teaching them to ask questions. Curriculums should be revamped around, here's a problem. it actually matters for the community. Maybe, you know, you need to fix the way that permitting is done in the city.

1:10:51Maybe you need a way to improve the way that you get word out of, you know, some sort of events that are occurring, things like that. Have the students write actual applications that are useful for the community that they're in and solve real problems and then have them ask questions. when you create homework or a test for kids, if they can look up the answer online or if they can ask AI to solve it, you haven't taught them what they need for the next stage. But if you give them a problem where they have to ask the questions and get AI to solve it, then you have. Thanks for the time, man. Glad you did this.

1:11:26Thanks. I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review. And make sure you listen to my other podcast founders. For almost a decade, I have obsessively read over 400 biographies of history's greatest entrepreneurs searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through Founders.