0:02
Let's start with this $20 billion rumored $20 billion partnership that you have with Nvidia.
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0:06
Can you talk about the structure of the deal and how it came about?
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>> So the most interesting part about it is the call um where the idea was first floated was about 3 weeks before money was in the bank.
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>> Oh, so Johnson moves fast.
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>> Of course, that's how you stay ahead. >> Yeah. So how'd it come about?
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So, we had been working on uh integrating GPU and LPUs together.
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The best way to describe why this helps is if you were building out a logistics network for the United States and I told you you could have either 18-wheelers or uh you know vans for last mile delivery, which one would you pick?
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And the answer is both, right?
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And so GPUs and LPUs combined ended up giving better performance across the performance curves.
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We had implemented it and we had gone to Jensen asking could we buy about a 100,000 GPUs because we were going to deploy them ourselves and Jensen saw what we had done and thought maybe it would be better to make this available to all of their customers.
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>> You and I had this conversation um at NVIDIA GTC and you were talking about the fact that these technologies are very complimentary.
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Can you explain a little bit more about that?
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When you're processing an LLM token, what's happening is you're doing all these different matrix multiplies.
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And some of them are more compute constrained and some of them are more memory throughput constrained.
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And the ones that are more compute constrained, we put on the GPU and the ones that are more memory throughput constrained, we put on the LPU.
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And the bottlenecks are all over the place.
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There's all sorts of different bottlenecks.
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There is no one strategy.
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There is no one perfect architecture.
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So the realization was you put these two things together and you defeat the bottlenecks across all of the different maples.
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>> There's another thing that I love that you said when we had this conversation that when AI is talking to other AI, speed is becoming more and more important. >> Yeah.
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I mean a human can wait a second or two to get a response when they type a command into a computer.
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AI is just sitting there waiting because it produces these tokens so much f it thinks so much faster.
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Now you bring in LPUs and the speed is so much faster that it just becomes all about how do you move as fast as you can.
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AI is really good at using AI.
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That's really what Agentic is, right?
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So humans benefit from using AI, so does AI.
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Just like you would do research about me before I show up on your show, AI is going to kick off a job doing research on different tools it's going to use while it's using this other tool.
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And so kicks it off to another AI.
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And so you get this exponential growth.
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And >> I'm going to go on a tangent just for a second and then we'll come back to this.
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I talked to a lot of founders about this recently, but there the whole point is like when agents are making payments, the amount of payments that are being going to be made is going to skyrocket.
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Do you have any like insight into that?
2:48
>> Yeah, I I think it's still early and one of the limiters is payments aren't really built for this yet, but if you can make micro payments, the number of payments is going to skyrocket.
2:56
Like I did a little hobby project and for the hobby project I needed a couple of different phone numbers so I could have a bunch of different agents on with me on um Signal and WhatsApp and I had to go to Twilio and I had to like I had to like prove I was a human in order to get the number and all this stuff and it was just this big pain to get it done.
3:17
If on the other hand I could have just done if I had allocated a budget to the AI and it could have just you know used that budget they would have spent it and I wouldn't have never known and it would just been within the budget.
3:30
>> Tell me more about the hobbies and the side projects because this has come up in a couple of our previous conversations that you and I have had.
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>> I like to do um cutting edge stuff on my personal computer.
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So I don't have access to the the work codebase like things where there's risk.
3:45
you know, I'll spin up a a server in GCP or AWS and I'll just start building stuff.
3:50
I built everything from um you know, apps that tell you like how long it like what airplanes to take if you're traveling and and what routes and which routes have the best seats and all this stuff all the way to apps that do certain mathematical things like a daily brief and all of this stuff. Very simple stuff.
4:09
At work, I use it very extensively, but I always start with a hobby project before I bring it to work. What's the daily brief?
4:16
Every morning, I get an email to me that says what's going on in the world based on what I'm interested in and how I interact with it and um just a whole bunch of research.
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It's very much like the presidential daily brief except personalized for me.
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>> And is this in text form? >> Uh yes, you read it.
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>> It's text with links that I can click through to learn more.
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The the big shift I did on the daily brief though was I started off by getting a whole bunch of text and I would read it and then I realized, oh, it's AI.
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It's done a whole bunch of research. It has the context.
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Why don't I have it just summarize everything?
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Just give me a bunch of headlines and I can ask follow-up questions.
4:52
I just spent time and recorded an episode with Gustaf, who's the uh coco of Spotify right now, and he did something very similar because he tries to avoid any kind of feeds.
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the whole thesis behind like the organizing principle of Spotify is like time well spent.
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And he's like, "Well, you know, me just scrolling and getting like rage baited at on X isn't beneficial, but there is information I want to know."
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And so there's a couple different places that his agents go out and and essentially summarize uh stuff he might be interested in.
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He's like, you know, he's like, "Hey, don't include rage bait. Don't include politics."
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But then he also uh heavily emphasizes these are the people that I'm interested in.
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What are the people that I'm interested in speaking about?
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And if the same people that I'm interested in are speaking about the same topic, I want to know about that.
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And then it he can read it, but now he turned it into this like personal podcast and he actually listens to it in Spotify.
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He's like essentially his daily briefing, but he listens to it in like 5, 10 or 15 minutes. >> Yeah.
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I mean, for me, the interactivity of it is important because um it's sort of a You ever play the game 20 questions as a kid? Yeah.
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So, you can figure out within 20 questions what's in someone's mind.
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like, you know, questions allow you to sort of distill down into what you actually care about.
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If you have a podcast, it's a static form.
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You hear it, but if you can interact with it, then you can just get to the information you most want.
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So, as I'm learning through AI, I'm not reading a static piece of content. I'm interacting with it.
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I'm glad you mentioned the thing about questions because I have a series of quotes that I've saved from you, and I love your tweets, by the way.
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So, you said, "Success in the information age was about being able to answer questions.
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success in the AI age will be about being able to ask the right questions. Can you expound on that?
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>> Yeah, and and this also goes towards a shift of going people are moving from being IC's or individual contributors to all being leaders but leaders of AI.
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And what really good leaders do is they don't do the work themselves.
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They don't have the answer themselves.
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They're just asking the question.
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They're just cons, you know, considering everything they're hearing and then they ask the question that no one else asked or everyone is thinking but is afraid to ask.
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So with AI because it can go off and solve all of these problems for you.
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It can do the research report.
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It like the question that you ask determines what you get and that determines the output.
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From information age like we were all trained to just answer questions.
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Like that's what school's about.
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Remember this, remember that, remember this.
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With AI, you just ask AI. It knows.
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You just have to think of the right question.
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It's a fundamental shift.
7:18
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8:23
>> I want to go to something you text me about your views on leadership, which is what is your description of leadership.
8:29
>> So, I I got this from a a John Levy book, but it didn't detail it much.
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And the first principle of leadership is you have followers. Duh, right? It's that simple.
8:38
You're not a leader unless you have followers.
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But when you think about leadership as having followers, it's also like thinking of investing as making money.
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There's a lot of ways to be an investor.
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You can be a venture investor.
8:50
You can give debt where you can take equity. You can be seed stage. You can be series A. You can be growth. You can be crossover.
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Uh you can do convertible notes.
9:01
Like there's, you know, private equity.
9:03
There's so many different ways to be an investor. Public market, right? Same with leadership.
9:06
And so with leadership, the the mistake I often see with new founders is they're like, "How do I be a leader?"
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And the problem is they don't realize that there's an infinite number of ways to be a leader.
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And so they they go off and they listen to someone and they get all this advice and they try and execute on it. It's not true to them.
9:25
So one of the things that was very different for me as a leader, most of the people that I hear on your podcast, they are control freaks.
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They want things done their way.
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and >> on Founders Podcast or this one >> on Well, both actually.
9:40
[laughter] >> And so, had I tried to be an absolute control freak, that wouldn't have worked for me.
9:47
I'm one of these weird people where I can go to a restaurant and I can tell the waiter, "Just bring me whatever you think is best."
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Or I don't even have a driver's license.
9:56
I haven't had a driver's license in since, you know, I was uh 18.
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And I've >> Why don't you have a driver's license?
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>> Cuz I don't feel the need to drive. I'd rather think.
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I don't need to control driving.
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I want to control thinking. I want to focus.
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I want to be on my phone.
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I want to be doing something useful.
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And that's been the case since I was, you know, 19 years old.
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And so, I'm happy to delegate things in a way that others are not.
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So, one of the things that's different about my leadership is when I hire people, I hire very autonomous people who often go off and execute on their own.
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And they would be terrible in most corporate environments.
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but also I can't hire the same kinds of people. And that was true to me.
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Had I, you know, gone the other way, I wouldn't have been very successful.
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If other people go this way, they're not going to be successful.
10:43
Then again, there are a lot of things that are very similar, which is most of the best Silicon Valley leaders, they lead from a place of inspiring their people as opposed to um trying to to get people to be afraid.
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But that is a form of leadership.
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There are plenty of leaders out there who are successful because they cause people to become afraid.
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And so you just have to pick the form of leadership that works for you.
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But also, when you're picking where you're going to work early in your career, you should probably work somewhere where you're going to learn the lessons that are good for you.
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If you're more of a a kind of person who can show appreciation and gratitude, you probably should not work for someone who inspires fear because you're not going to learn any lessons that you can use yourself.
11:25
>> Man, this is so important.
11:25
This this actually like fires me up because did have you ever spent any time with Toby Luke? >> I haven't. >> Okay.
11:31
So, the conversation that I had with him I don't know on the show like 5 months ago.
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I still think about like every few days and in fact we go through the past episodes and like mine and I constantly finding new insights from that and like uh so that's why you see all these like clips that we're putting out on X from from things that might be you know we did 6 months ago.
11:47
And Toby one of the things I love that he said in the conversation was just like man there's not like one right way to do things.
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There's probably a hundred ways that could accomplish your goal.
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You have to do the one that's based on you.
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This is why you see all these different tech companies that are so different from each other.
12:01
Like Apple, complete silos.
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Google, everyone has access to the codebase.
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They're just completely different ways taken to the absolute extreme.
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We just had Dana White on the show and his whole thing was like the first thing to do is like know yourself.
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So, I'm going to ask you a question.
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When you figured out your leadership style, wait one second.
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And then once you have to really know who you are, right, and what fits you.
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And then second thing is like what you actually want to do in life.
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And then once you those are your two biggest questions then you just wake up once you figure that out and you just get after and focus on accomplishing whatever that once you have step one taken care of you just wake up and attack step two.
12:34
So when you figured out that you needed you you couldn't work your leadership style wasn't like the typical way and you needed like these autonomous people which by the way if you ever started another company again then I would have to imagine it's going to be just be you and a bunch of agents.
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>> It would probably be AI.
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So I think this is going to be a shortcut for founders in the future and we can get into this but like the first thing that you have to do as a founder is you have to go from the technical thing that you know how to do and that you can add value with to learning how to manage people and for me that probably cost Grock 3 to four years. >> Say more about this.
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>> I was a terrible leader.
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I was one of the world's worst leader when I started.
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Um, I had a lot of I I I gave people a little too much latitude because I'm more of a delegator, but I I entrusted people who probably shouldn't have been entrusted with that level of autonomy.
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I didn't hire people who could operate autonomously, but I was naturally someone who would delegate and give autonomy.
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So, what ended up happening was things would just grind to a halt because they wouldn't know what to do and I wasn't telling them what to do and they were used to being told what to do.
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And then finally I would get so frustrated I would go in there and tell them what to do but it was so unnatural to me that they didn't accept it.
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>> Why is telling people what to do unnatural to you?
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>> I work through questions.
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I like to I like to set highle direction.
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So for me my leadership style was come up with a goal that was so it took me a while to get there but come up with a goal that was so simple that I could put it on a challenge coin and give it to everyone.
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So, everyone at Grock had a challenge coin that said 25 million tokens per second and had a little graph of it going up and everyone knew that was the thing to do.
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And it's sort of like when you ask um an agent, an AI agent what you know to do something.
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The fewer constraints you give it, the more freedom it has to solve your problem.
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And so I liked to work with incredibly creative people who would come back with surprises. Say that part again. Say that part again. This is important.
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the fewer constraints the fewer constraints that you give someone the more freedom they have to solve the problem and the more freedom they have to surprise you with the solution.
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So if you want to run a highly highly creative and innovative uh organization then what you really want to do is minimize the number of constraints but you also have to give them the things that matter.
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If you aren't able to very crisply distill what you're trying to accomplish, then either you're going to over constrain or under constrain the people.
15:03
When you're trying to do something as a founder, you are inherently trying to disrupt an old industry. There's a moat.
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You're trying to do something differently.
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If you're not doing something differently, what's the point?
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They're already these wellestablished, well-funded companies.
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If you can bring a team together that is also disrupting and innovative and it's not just you, then it goes from, you know, being Superman to being the Avengers.
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And so that was just my natural leadership instinct.
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Had I been more of a command and control person, I should have doubled down on command and control.
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You have to you have to just do what's natural.
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>> Have you spent any time studying Kelly Johnson, the guy that did uh Skunk Works at Lockheed? >> Not too much. >> Okay.
15:43
He has this great quote where he said it reminded me of what you just said when you did the challenge co coin for Grock.
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He says extreme performance often comes from one brutally clear priority. Yes. Yes.
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And you see this because I if you don't give people a clear enough but under constrained enough objective that they can't surprise you in a good way with the result then you are not giving them the ability to innovate.
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I I want I want to double down on that for a second.
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The only way for your team to innovate, right, without you being the innovator is they must be able to surprise you in a good way, which means you must not over constrain the goal.
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>> And that you saying that took you 3 to four years into the founding after the founding of Grock to figure out >> that in basic management dealing with people like little things like one of the the great things that I've learned at NVIDIA.
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Actually, let let's take a lesson from Nvidia because now I'm there. Jensen is world class.
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And one of the things that I observed because I've worked at other tech companies. There's no politics.
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It's the least political large organization you will ever see.
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And I learned this lesson at Grock, but I didn't take it to the extreme.
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So seeing it in the extreme shows me just how valuable it is.
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There is no circumstance at NVIDIA where there where Jensen has one-on- ones with people and tells them one thing.
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I learned this at Grock because what would happen is I would have a conversation with one person and then I would have a conversation with another person and then what would happen is both of them heard very different things and would talk to each other and come to very different conclusions.
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But when I had a room full of people and I said something to them, it's amazing how they all heard the same thing.
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And so when you are leading groups of people if you want to reduce the amount of politics and people going off and and you know forming side clicks and all this, stop having one-on- ones.
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Have big meetings with everyone who you want to tell something and tell them all at once.
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And don't allow anyone to send you an email.
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Like copy everyone on the email.
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Just like the moment someone sends you something.
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Like if someone says, "Hey, this person's screwing up on this thing."
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Copy that person on the email. let them jump in.
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Otherwise, you were allowing politics to happen.
18:01
>> What else have you learned from Jensen?
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>> I got way too cute on trying to play 3D chess.
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Whereas Jensen is very much just like what does the customer need?
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Like I I want to develop trust with the customer.
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I want to always tell the customer things that are true and I believe and I can support.
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And if I have a a thing that isn't what the customer wants, I'm not going to sell it to them.
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I'm going to sell things to customers that they actually need and that I believe they need.
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I'm not going to think like, you know, how do I, you know, build notes? How do I do any of this?
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It's just like, what does the customer need?
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Just build that for them and everything else follows.
18:41
I've done a few episodes of Founders Podcast on Jensen.
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One of them was How Jensen Works, which is essentially stripped away all the biographical information that's in that book, the NVIDIA way.
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And just like I think there's like 19 main ideas that I cover in that podcast.
18:53
But one thing we just mentioned earlier like, okay, well, if you're a founder today, what does founding a company look like going into the future like might just be, you know, you and a co-founder and, you know, 10,000 AI agents.
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And Jensen has this great line where people are like scared of managing AI agents that might be smarter than them.
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He's like, I already do that.
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He has, I don't know, like 60 direct reports.
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He's like, "Every single one of them is smarter in their domain about their domain than I am, and I have no problem orchestrating them and managing them."
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And I thought that was a great metaphor.
19:21
One of the things that really good founders do is it goes back to the asking questions.
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And what a a really good founder is able to do is even though it isn't their domain, someone comes to them, says something, and they ask a question, and that person's like, "Oh crap, I didn't think of that." Right?
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Just over and over and over again. It's a skill. you can hone it.
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>> This is in every book on Bezos. >> Yeah.
19:46
And and but but I think this is universal.
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I think any good founder is able to do this.
19:50
And again, going back to those early stages of going from a non-founder to a founder, you're going to end up hiring people and they're going to be like, "No, no, I'm the expert in this area." Like, just trust me.
20:00
You're you're a kid, you know, just trust me.
20:02
And you have to learn confidence.
20:05
So, one of the things that happened for me that was very helpful early on in being a founder was I actually got to um back when we were maybe 35 people.
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I got to shadow someone who was running an organization of 2,000 people.
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And it was funny because like there's no NDA in place or anything, but every meeting we went into, he's like, "Oh, this guy's got an NDA.
20:27
Don't worry, you can say anything in front of me."
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And I was just like, "Okay."
20:30
Um, but we would go into every meeting and I was sitting there silent and I would think, what would I do?
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And at the end of it, each time when he would make his decision, it's exactly what I would have done.
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What I hadn't realized before that moment was I didn't have the confidence.
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Part of the problem with with being a leader was I needed to have the confidence in a decision so that other people would have the confidence to execute.
20:57
Many people have too much confidence.
20:59
Some people have too little confidence.
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And the question is, if you have too little confidence, you're probably the kind of person who thinks through things a lot more, but you need to still get to a point where you act with confidence.
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And when I realized I was making the same decisions as this very experienced founder or CEO, I started acting with confidence and people started following my direction much more.
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I didn't change my decisions, but it changed my leadership.
21:24
>> How many employees did you have at Grock when you did the partnership with Nvidia?
21:27
about 450, but it was more difficult to manage than a typical group of 450.
21:32
So, in the military, depending on your rank, you are allowed to um to have a certain number of people reporting into you.
21:40
And the higher your rank, the more you can have as an officer.
21:44
But when you're when you have scientists reporting to you, the number is actually much smaller, like dramatically smaller.
21:50
Because I had such a creative organization, it was much harder to manage them.
21:55
the the problems manifested.
21:57
And so it probably was more like managing a group of 5,000 people than it was 450 in many ways.
22:01
In other ways, it was like managing an even smaller group because innovations would just happen on their own.
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But like the better the people, the harder they are to manage.
22:16
>> Can you explain the state where Grock was? Yeah.
22:19
>> I think you were at one point, you know, close to running out of money. Is this not accurate?
22:23
not accurate? So early on at Grock um and this is a lesson for found because if you're doing a capital intensive business you're going to need to raise money and one of the things that we went through was we had raised money from some VCs who fell out of favor with
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other VCs and others didn't want to co-invest and so every time we would try to raise we just struggled and there's also a little bit of biodality in how >> can you say more about Well, the way I like to put it is typical typical not all, but typical West Coast VCs are more like lemmings. And typical East Coast VCs all think
23:01
And typical East Coast VCs all think that they're smarter than each other.
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So, when you try and raise from the West Coast, if one VC puts money in, all the others want to put money in.
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In New York, one VC investing means nothing.
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They're going to run their own analysis.
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They really do not care what other VCs are doing.
23:19
The flip of that is if you're on the west coast and one VC passes, they're going to go tell every other VC and like lemmings, they're all going to pass as well.
23:27
So, we had this problem where all all the VCs on the West Coast didn't want to invest in us.
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We had very few of the typical VCs invested in us at the end.
23:37
We had like a bunch of crossover funds from the East Coast invested. >> Hold on.
23:42
That's actually kind of hilarious.
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the biggest deal Nvidia ever does by like almost 3x and the West Coast VCs missed it.
23:48
They all chose other things that were safer.
23:51
So, so there's this thing called the Keynesian beauty contest. Have you heard of it? >> No, I don't think so. >> Okay.
23:57
Um John Maynard Kees, the economist.
23:59
So, the idea is and it it runs in parallel to VC and and when you see this, you can start to understand some of the behavior, some of the lemming behavior because it's actually a good idea to follow other investors.
24:11
So, in the Keynesian beauty contest, imagine I give you a magazine with a bunch of models in it.
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And your job is to bet on models and say which one's the most beautiful.
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But the determiner of the the most beautiful model is not who's most beautiful.
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It's who has the most money put on them.
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And that model like pro para to how much money you put in, you get all of the money that's been bet on all the models.
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And and if your model doesn't win, you get nothing.
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You lose all your money and it goes to the winner.
24:38
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.
24:47
And a bunch of people made other bets. It doesn't matter.
24:48
They just come in and they make a big bet.
24:49
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.
24:56
What'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.
25:07
And for the first time in history, the the startups are not starved for cash.
25:13
They have all they need and more.
25:16
So now everyone's getting funded to the level that they need.
25:18
And putting more money in is not an advantage.
25:20
But people are still acting as if putting more money in gives that startup an advantage. >> Okay.
25:26
So can you tell the story that you told when we were together invaded GTC about the drastic change in Grock's fortune and how fast that happened? >> Yeah.
25:34
So going going back to what you asked about almost running out of money.
25:38
We were about 3 weeks from running out of money at one point.
25:40
>> But that was many years ago.
25:41
>> That was many years ago.
25:41
And >> you weren't so you weren't close to running out of money this time. >> No, no, no, no, no.
25:45
We were we were fine towards that.
25:47
>> You were fine, but the value the last valuation you raised at compared to what you did this agreement at was drastically different.
25:54
>> It was really only a little over 2x.
25:54
Um so that wasn't a huge jump.
25:58
Um, and we also had the ability to raise at the amount that we we did the licensing for.
26:07
>> Well, let's just go back to what we talked about on stage where it's just like how fast like the how fast you guys had this insight and then how fast the the trajectory of Grock changed.
26:18
>> Well, it it was that 3-week period from the time when we presented and asked to buy GPUs until um not only the the deal was done, but money had been wired.
26:26
But how many months before were you working on this?
26:32
>> It was probably three or four, maybe a little bit longer.
26:34
But but what had happened was I didn't initially think that it was going to be that big of a deal.
26:42
So I didn't suggest doing it.
26:42
In fact, it was Sunny, my COO.
26:45
This goes back to the whole autonomy thing.
26:48
>> This is the story I want. >> Yeah.
26:49
So he had the idea of trying to put our our chips together and he didn't have like a >> Explain what you mean by putting our chips together.
27:00
>> So the the LPU and GPU as mentioned they they're better at different parts of what's what's called the decoder layer of an LLM.
27:08
Um the GPU is better at the attention portion and uh the LPU is better at sort of applying the weights which is the the thing that gets trained as opposed to the memory.
27:19
And what we realized was and and this is what most people get wrong when they're trying to to do this themselves.
27:27
They'll they'll take the reading of tokens or what's called um um prefill and they'll do that on one piece of hardware and then they'll put the generation of tokens on another piece of hardware.
27:39
But the generation of tokens is the hard part. That's the thinking.
27:44
You know, reading is easier than writing, right?
27:47
And that's true for AI as well.
27:47
And so what we figured out was if and and again this was all a bunch of people.
27:54
It wasn't any one person and it was a group that all innovated.
27:57
Once he brought the idea of why don't we put them together because they're different bottleneck then the team figured out oh this part goes here this part goes there.
28:04
We implemented it and it worked.
28:06
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.
28:14
And so what ended up happening was we we went we presented it made a lot of sense and the deal happened. >> Okay.
28:25
So from Jensen's point once he sees this. >> Yeah. >> Right.
28:28
Why does he decide to act so quickly?
28:31
>> This is the nature of a successful entrepreneur. You move quickly. You don't wait. Right.
28:35
There's opportunity cost to waiting.
28:37
Technology is not a business where you can wait a year.
28:40
What I'm getting at is why is that so important to his business?
28:44
>> 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.
28:53
Sort of like when you first use the internet, it felt fast compared to mailing things around.
28:58
But when you got broadband, you realized, oh my gosh, like this is so much better.
29:04
I'm never going to go back.
29:04
The difference is broadband actually needed people to make their websites faster to make it usable.
29:11
If the servers are slow, you don't get a benefit.
29:13
So, it took a while to roll it out and make it good and get video that could stream and all that.
29:18
The difference is you you put, you know, you put these LPUs into a system and all of a sudden the generation of tokens gets faster.
29:24
It's like getting broadband instantly on these existing models.
29:28
And so now rather than having like you know having to wait you know a minute to get an answer you can get an answer in 10 seconds and that really starts to compound.
29:37
So let me let me walk through an example of why it's not just speed but it's also quality.
29:42
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.
29:52
I was no longer working on the TPU at that point.
29:53
had already done it and someone else from the TPU team was at Google X came by, showed me this email uh from Deep Mind saying, "Hey, you know, we've got this competition.
30:04
We think we're going to lose. There's a prize purse.
30:06
Um, is your chip as fast as as we've heard?" And we're like, "Yes."
30:13
Like, you know, it's like Ghostbusters, you know, when uh um someone asks you, "Are you a god?" You say, "Yes."
30:19
Someone asks you, "Is your chip as fast as I've heard?" You're like, "Yes."
30:22
So we reply back yes and they're like great competition's in 30 days.
30:26
We're going to play the world champion and go and we played our test games and we lost. We need to win.
30:30
So they had no choice but to port over to to that TPU chip.
30:36
So we did it and a bunch of interesting things happened.
30:44
I think Do you know what an ELO score is? >> Yeah. >> Okay.
30:47
So for those who don't know what an ELO score is, it's sort of your ranking in chess or go and something like a 200 point advantage is insurmountable.
30:55
Like the probability of you winning is basically zero.
30:59
Alph Go running on GPUs had an ELO score of about 3,200.
31:06
Lease at all was about 35 uh 3550.
31:13
Um and I might be getting the first digit wrong.
31:14
It might be 2,000 instead of 3,000, but like you know it was it was more than 200.
31:19
>> And then um when put on L on TPUs, it actually went to like 3,900 or something ridiculous or 2,900, whatever the the first digit was. It jumped dramatically.
31:31
And so I think he didn't expect he was going to lose, but he lost badly, but it was the exact same model.
31:36
So what changed was the ability to compute more made the results smarter.
31:45
The way that these models work, thinking fast, thinking slow from Daniel Conaman.
31:49
>> Yeah, I read that book.
31:50
>> 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 most this is the best move, this is the next best move, and so on.
32:04
What happens is you you virtually play that best move, and then you virtually play the counter move, and then you virtually play the next one.
32:14
and then you see how the game unfolds.
32:17
What you can also do is try that second best move.
32:19
And occasionally that second best move when you play it out actually turns out to be the right move in this context.
32:26
It's not the one that you would normally do, but in the context it's better.
32:30
And you can see that as you play it out.
32:31
In the second game, there was this famous move called move 37, which was creative. It was original.
32:36
It actually wasn't completely original.
32:38
It was a 1 in 10,000 um game move.
32:42
It had been in the cannon 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.
32:51
So over time, these GPUs have have, you know, gotten, you know, as good and better than than TPUs.
33:00
You know, as the creator of the TPU, I have to admit GPUs are now better.
33:02
This is the benefit of like just having an entire industry behind you and and ecosystem and everything.
33:09
But at the time TPUs had some novel innovations. Right?
33:14
Now bring in the LPU and you can go deeper faster. You can search faster.
33:21
And so you can actually make a model smarter by making it faster.
33:22
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.
33:35
And so that's the advantage of pairing the LPU and the GPU.
33:39
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34:52
Let's go into a couple of your ideas that you've discovered in like the 10 years that you were running Grock.
34:55
What is your idea about reality quotient?
34:57
So, one of the things that we hired for um at Grock was reality quotient, which is different from intelligence quotient.
35:06
And the 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.
35:12
[laughter] You got to say more about that. >> Yeah.
35:15
So, so like you just know these people who will construct these very elaborate stories in their mind that are completely disconnected from reality, right?
35:24
And then there are some people who are just incredibly street smart but couldn't do basic arithmetic or or anything like that.
35:31
Reality [snorts] quotient often times it starts off as being able to to recognize reality but but in the most extreme form it's the ability to choose the dominant game that's being played.
35:42
So [snorts] 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.
35:54
Simple example, MySpace was focused on number of accounts signed up.
36:01
Facebook focused on monthly active users.
36:03
It was the dominant game, right?
36:06
If you have monthly active, that's more important than account signed up.
36:09
And if you maximize the the monthly active, you're going to beat someone who's maximizing account signed up.
36:16
You're playing a better game.
36:17
And so, as a founder, you're often able to do this better than other people.
36:23
And your job leading those people is to try and help them connect their activities to that dominant game.
36:31
So, when running Grock, I I said our goal was to get to 25 million tokens per second of capacity in our data centers.
36:39
That meant everyone had a different way to contribute to that.
36:43
Meant they could make the chip faster.
36:44
Meant they could make the software faster.
36:46
It meant we could deploy more.
36:47
It meant we could get our power costs down so that we could deploy more chips for lower opex.
36:53
It meant get more data centers. It meant fab more chips.
36:55
It meant find ways to optimize the supply chain so we could um put orders in faster to fill things faster for customers when we got a so everyone could connect what they were doing to that one dominant game.
37:11
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.
37:19
So moving from being an engineer to being a founder, the thing that that finally clicked for me was if I was going to do something disruptive, my job was full-time change management.
37:32
And the first principle of change management is to make it feel like it isn't a change.
37:39
Why is that so important?
37:41
People do not like change.
37:41
No human being likes change.
37:44
The difference between people who appear to like change and people who don't like change is often they're looking at different things.
37:54
And for the the one that is fine with the change, nothing changed.
37:57
So if I'm playing this dominant strategy here and that doesn't change.
38:01
I'm just trying to maximize the number of tokens per second that I've deployed.
38:04
When my approach changes, nothing really changed.
38:09
But if you're down here thinking how do I make this uh chip faster and not thinking about the software, then you might view something that changes the chip as a change.
38:20
And so your change management duty is to give people enough context so that they see that their job hasn't really changed.
38:30
What they're trying to accomplish is the same thing and there is no change.
38:32
Tell me about return on luck.
38:35
I read this book by uh 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.
38:45
They just seize on that luck better than other companies.
38:51
And I read this very early on as a founder and I started to notice it was true.
38:54
And there was a really good example of this when LLMs first started to become a thing.
38:59
I remember getting a phone call from um the the CEO of GitHub basically saying, "I need a bunch of GPUs.
39:08
We've we've now gotten LLMs to be able to do code completion even though, you know, we're part of Microsoft and everything.
39:15
Um you know, we we just can't get GPUs.
39:22
Could we use your your chips, your LPUs?"
39:25
And I went to the team and I'm like, "We got an opportunity. We can do this."
39:27
And they're all like, "Nope, not going to work.
39:31
Can't run it on these chips."
39:31
I'm like, "No, it looks like it's the ideal thing to run on our ch.
39:35
It looks almost perfect."
39:36
They're like, "No, it doesn't work.
39:38
There's all these things that are in GPUs that we don't have."
39:40
And I'm like, "Yeah, but none of those are important for LLMs."
39:42
And they were looking at the wrong things.
39:43
I let them convince me that we shouldn't pursue it, even though in my bones I kind of knew that that we should.
39:53
This happened another time where there was another opportunity to deploy an LLM and and I let them talk me out of it.
40:03
The third time like no, I'm going to do it myself.
40:06
And so I just went through the whole thing.
40:07
I I did the arithmetic and and determined the performance and everyone disagreed with me that it was possible.
40:13
And I'm like, no, no, look at this.
40:14
And in the end, we ended up hitting exactly those performance numbers.
40:18
The thing was they were looking at the wrong they were looking at all the reasons why it couldn't be done.
40:23
than why it could be done.
40:23
And we talked ourselves out of it.
40:26
I had multiple lucky opportunities that I didn't seize.
40:32
I mean, how much better off would we have been had we been the original thing running LLM at Microsoft for OpenAI?
40:39
That would have been a very different outcome.
40:40
So, we 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.
40:46
And I remember very early on, we would talk to potential customers.
40:50
We even had a video where we sped up um 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.
41:05
>> You're not going to be doing the reading but also that's not how the internet works.
41:08
Like do you are you okay with a web page showing up? You know >> I hate that.
41:15
>> Yeah, because eyes don't move that way. Eyes move all over.
41:17
You need the entire thing there.
41:19
you're gonna 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.
41:26
So I realized that fast inference was going to matter. >> No one else did.
41:34
>> We had >> What do you mean no one else did?
41:35
>> Even within Grock, >> okay, >> there was a lot of push back on like we had a lot of turnover at this point.
41:40
A lot of people were leaving because >> how many years ago was this?
41:44
>> This was probably three or four years ago. >> Okay.
41:46
And so a lot of people were leaving saying there was no point to fast inference.
41:51
It wasn't going to add any value to the ecosystem.
41:53
And even though you draw very simple parallels like dialup versus broadband, no one could connect it.
41:59
I'm going to interrupt you real quick.
42:00
C can you say more about this because >> now everybody's just talking about fast inference.
42:05
It's like everything but what and I wasn't paying attention to this four years ago.
42:08
I had the other stuff like I was I just wasn't paying attention.
42:12
attention. Can you talk about the the difference in I think this is one of the most important parts of your company's story is just how contrarian maybe not even the right word but it was out of favor your your idea your main idea of people like no it's not important well everything was out of
42:28
favor everything was considered a bad idea that we did but if you don't do things differently that 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 a it's you know hype. They don't
42:44
They don't understand enough to understand why what you're doing is different.
42:49
>> So that had a disorienting experience to you. >> Yeah. >> To keep saying this.
42:54
>> Well, so what eventually worked was uh so and and this goes to a little bit of marketing that we we came up with.
43:00
We 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.
43:11
And I remember this example from um Eric Schmidt does was involved in this thing, SCSP or whatever, and they they showed off Anthropic um an LLM from Enthropic about 3 months before the chat GPT moment.
43:25
And I remember sitting there seeing it, seeing demos of this AI answering questions in the audience and no one reacting.
43:32
I'm like, how is it no one is reacting to this?
43:37
Now compare that to the chat GPT moment where everyone reacted. What was the difference?
43:41
The difference was when people asked their question and they got an answer to their question that was specific to them. That was magical.
43:51
Seeing text show up for someone else's answer wasn't magical.
43:55
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.
44:03
So, we did and what ended up happening was we 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 cuz we had it working and I noticed that the presentation was like when I was doing um queries um using some of the open source models, I remember it just felt a little slow to me.
44:28
Not not that slow but a little slower than usual.
44:30
I'm like what's going on here?
44:32
because like I'd been, you know, Norway's further away than the servers, but I tested earlier and it's fine.
44:36
I checked in, our usage had skyrocketed.
44:39
Someone had posted on um on X uh a video of an LLM running on Grock that was just running super fast and it was viral.
44:51
All of a sudden, everyone started creating applications using it, posting those, and it was just such eye candy when when people saw it that everyone started creating their own, and we just went viral.
45:01
Say more about this experience that you had, though.
45:05
I'm really like, you're coming at it from first principles.
45:06
You're saying these people are getting involved in it because it's hype and it's like the the thing that's, you know, being spread around at the moment.
45:12
Like what was that experience like for that that several years where you're just going through this like I'm trying to explain why this is going to be important and you're just hitting blank stair or brick wall after brick wall.
45:23
Well, there's this common theme where a lot of really good innovators are innovators because they experienced the problem before others did.
45:34
remember my experience of Alph Go on, you know, TPUs and and being able to outperform the world's best Go player only because of the hardware that we switched to.
45:46
Yes, I I was able to to sort of get this return on luck more than others.
45:51
I'm more able to like say, okay, there's an opportunity, I'm going to go for it.
45:56
But I was also exposed to the opportunities first. And you need both.
46:00
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's already here it's just not evenly distributed because I was in a situation where I got to see the future because I was really willing to like seize luck and double down on it when everyone else was like
46:18
no let's not pursue this opportunity and I'm like yes this is the opportunity those 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. >> I read that too. >> Okay.
46:37
So, >> I technically read books for a living. >> I don't know.
46:40
[laughter] >> 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.
46:48
And the basic idea in intentional leadership is if I ask someone, should I do something? Oh, they have opinions.
46:56
Most people will be pessimistic and give you negative opinions.
47:00
On the other hand, if you express intentional leadership, you say, "I intend to do this."
47:08
People don't tend to offer their opinion, but if it's very wrong and there's a reason, they will push back.
47:14
And the example is the submarine commander took over um I think it was the USS Santa Fe.
47:18
I think it was worst in nuclear readiness in in the nuclear submarine fleet.
47:22
And in a year or two, he got it to number one in readiness.
47:26
And all he did was shift from command and control to intentional leadership.
47:33
The quintessential example being saying be to say dive the boat and there had been incidents where a submarine had dived where the hatch was open [snorts] and no one wanted to push back because the commander was command and controlling and they were used to doing whatever the commander said.
47:49
But when people would use this intentional leadership and say, "I intend to move the boat down to 500 ft."
47:56
Then all of a sudden someone would say, "Wait, the hatch is open."
48:00
>> They're involved in it now.
48:01
>> They're involved in it.
48:01
And everyone says, "I intend to do this. I intend to do this."
48:05
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.
48:14
The issue was over and over again earlier on I was I was getting opinions from people and they were stopping me.
48:22
>> Going back to the three examples in the return on luck. >> Yes. >> Okay.
48:27
>> And if I had just said I intend to do this.
48:30
>> Is that what you did on the the third example? >> Yeah.
48:33
I I literally said I I I put a presentation together.
48:36
I said we're going to get to this particular speed per chip.
48:39
And rather than people going, "We can't do this," they all sort of jumped in and said, "This is how we do it."
48:45
It's a very small change in phrase, but it has all the difference in your ability to move forward.
48:49
You're not inviting friction, but yet people would still give uh feedback when it was really important.
48:55
When there was a real problem, they would raise it.
48:58
>> How does the intentional leadership tie to the question is if you were getting opposition or indifference, where were you going with that?
49:05
I was inviting pessimism by asking for people's opinion.
49:12
>> Were you asking potential customers? These are teammates. >> Teammates. Teammates. >> Okay.
49:16
>> So, almost everything that is difficult is difficult because you can't go to one extreme or the other.
49:21
You actually have to in the context decide whether you're going this way or that way.
49:26
One of the difficult things is getting feedback.
49:29
You hear this from leaders all the time.
49:31
Early in their career, they get way too much push back.
49:33
later in their career they don't get enough feedback.
49:35
How do you balance it so that you're getting real feedback versus just getting unnecessary push back?
49:43
And part of that is just this subtlety on the phrasing of I intend to do this as opposed to asking for an opinion.
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51:04
>> Let's go back to this time where you you were 3 weeks away from running out of money. Yeah.
51:08
>> And you came up with this idea of bonds. of grock bonds there.
51:11
Before I even get there, something that just popped in my mind as you were speaking earlier about your kind of like leadership styles, you know, basically I'm going to tell them what we want to do.
51:21
You have this organizing principle, but you're not going to tell them how to do it.
51:24
You're going to let them surprise you. >> Yeah.
51:26
>> That's Phil Knight in Shoe Dog says that over and over and over again.
51:30
>> And then I'm reading about Grock Bonds and it sounds very similar to some of the things that Phil Knight had to do because Nike was so close before they IPOed.
51:38
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 his he got from his employees into equity and wind up you know doing very well for them.
51:50
So explain this idea that you had for croc bonds.
51:53
>> We were going to run out of money and the leadership team that I had at the time uh was starting to put together a list of layoffs of who we were going to lay off.
52:03
And when I started reviewing the list, it became very clear to me that if we did that layoff, we were dead. >> Why?
52:15
>> We were already struggling to keep up with what we needed to implement cuz this was pre this wasn't even pre-product market fit.
52:22
This was pre the product working.
52:24
We had to write a a very special compiler in that had never been written before that didn't require human beings to write what are called kernels.
52:33
It had never succeeded before.
52:36
No one had ever done this.
52:36
And our architecture didn't work with kernels the way everything else worked.
52:39
So we had to get to this point, the sort of critical mass point before our product would even work. We hadn't done that yet.
52:48
And we were talking about cutting people who were critical for that.
52:54
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.
53:07
I I realized we had to to reduce our burn without reducing our people and the only answer was to get people to take a salary cut.
53:19
So we had an all hands and we put up uh you know World War II looking pictures of of war bonds and we called it Grock bonds.
53:27
Wasn't technically a bond.
53:27
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.
53:38
80% of the employees participated and I think uh about half uh went to the statutory minimum salary by law and remember engineers get paid hundreds of thousands of dollars.
53:48
These folks like cut their salary down to like $50 $60,000 whatever the statutory minimum was like real pain.
53:57
And we saved more than 3 weeks worth of runway.
54:02
I think it was I think it was closer to 2 months.
54:04
We had 3 weeks of money left when we raised.
54:06
So, had we not done this, we would have gone out of business.
54:10
The thing that was interesting, I 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.
54:17
And there's a phrase I have, which is put everyone's hands on the steering wheel.
54:24
When people are passengers in a car, they're more nervous about a windy road or a scary road.
54:28
But when they're the driver, they they feel more in control.
54:32
They they're more willing to take a risk.
54:34
By doing this, we put everyone's hand on the steering wheel.
54:37
They were participating in saving our runway.
54:38
And we had less than 10% attrition.
54:42
It might have been closer to 5% when we announced Grock Bonds, which was actually uh probably better than our attrition rate before then. I love this idea.
54:51
So, the other side, you went from, you know, looking for other ways to not fire people.
54:58
The other side of firing is hiring.
55:00
You also have some interesting uh like I think lessons you learned in the decade you were building Grock about hiring that are also pretty counterintuitive which a lot of the people that appear on the show have very counterintuitive ideas about hiring.
55:12
>> I 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.
55:21
I' I've already kind of alluded to that.
55:25
And the reason was I'm pretty clever.
55:28
And so when I meet someone, I can come up with a reason why I should hire them.
55:33
I think a lot of people do this.
55:35
They will convince themselves I should hire this person.
55:38
They're great because of this.
55:38
They have this experience.
55:40
They they have this attribute.
55:42
I'm going to hire this person.
55:43
So we have this thing we call a people spec or or data rock.
55:45
And very much like you have a product spec, we had a people spec. It had version numbers. We would change it.
55:50
If you don't write down what you're looking for in people, you're not going to hire that.
55:54
You're not going to be consistent.
55:56
And so we framed the people spec in positives, things that you look for like return on luck.
56:03
Give me a couple more examples of what were the positives on that people spec. >> Poetic design.
56:07
So poetry is semantic density.
56:10
It's when you say so much in so few words.
56:12
That's >> which is actually really important to you. >> Hugely important. >> Yeah.
56:16
>> Yeah. this this phrase it's like I think it's on the Grock blog where it was like make every word count I think you'd repeat over and over again >> every word matters >> or every word matters there >> yeah it it's the smallest possible the most minimal expression of the thing
56:29
you're trying to achieve is the most poetic and that 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 a negative version right so the the opposite of return on luck would be squanders luck. The opposite of
56:47
The opposite of poetic design would be, you know, maximalist design, like just throw every feature in.
56:54
Like you, you know, some of these products where it's like, where am I supposed to click? >> Mhm.
57:00
>> And so, it's really easy to spot people who fit some of the positives and not realize they have some of the negatives.
57:08
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.
57:14
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.
57:27
>> When I'm trying to help someone grow and improve, I want to show them the path. Right?
57:31
This there's a famous example of how do you increase the amount of money given to a charity?
57:35
It's not making people, you know, realize how great the charity is.
57:39
It's not making them feel good about giving to charity.
57:41
It's about telling them where to send the money.
57:42
If you tell them how, if you give them a skill or a technique, then they can very often learn it and do it.
57:50
So, when you're trying to grow people, show them the positive.
57:53
Don't say, "Hey, don't squander luck."
57:55
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?
58:02
When you're hiring, you're really looking to to vet people and you're you're trying to say no to things.
58:10
And that's a very different motion.
58:12
And some people are really good at growing, some people are really good at hiring.
58:15
But you have to you have to separate those two into very different mental modes.
58:23
The reason I noticed this at Grock was we hired a head of HR who was very good at noticing problems with people and getting them out.
58:30
And as I observed her doing that, I realized I'd been hiring all wrong.
58:35
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 >> one of the this is one of the attributes and I think it's an important one.
58:48
>> So 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.
58:58
Like you see this where someone will invest money, lose 20% 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.
59:10
Even though not getting the gain, the opportunity cost is much higher.
59:17
There's a personality trait in people where I call it sort of booking the win early.
59:24
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."
59:30
And I look around the room and no one seemed that excited about doing it. What's going on here?
59:35
And and I I started to realize everyone was hearing, "If we do this, the chip will be twice as fast.
59:40
Let's put that in the next chip."
59:41
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."
59:46
As soon as I heard that something could be done, I would book it.
59:48
I would immediately sort of just assume that if I don't do it, I've lost this thing.
59:52
So, as I started to hire, I would look for other people who had the same sort of book the win early attitude.
1:00:00
The moment they hear something's possible, they book it and they're like, I don't want to lose that thing.
1:00:06
A lot of the most successful entrepreneurs, they sort of manufacture their own sort of discontent.
1:00:14
>> I want to get there in one second.
1:00:14
Yeah, >> but I I I think this hiring for lost bias and applying it to not only the talent, but also these meetings you're having in product uh in product design and things that would make your product better is actually really important.
1:00:25
You mentioned before that you learned from an episode of Founders on Michael Jordan uh one way to do this where he would challenge his teammates to bets.
1:00:33
Why was that an interesting idea to you?
1:00:37
>> 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.
1:00:44
What he's doing is Michael Jordan, he he's a very sort of aggressive competitor where he would make bets on everything like could you throw a a quarter and and hit a target closer or something like just weird stuff like that and he would just non-stop do it.
1:01:00
Most people are afraid to sort of taunt someone else, a competitor, because if they lose, they're going to feel really, really bad.
1:01:08
Remember that loss bias is heavy.
1:01:10
Like if I'm like, "We're going to go play basketball."
1:01:11
I'm like, I'm going to wipe the floor with you and then I lose.
1:01:16
That's humiliating, right?
1:01:16
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.
1:01:28
He was just doing it over and over again.
1:01:30
Most people are so afraid of putting themselves out there and suffering the negative outcome that they won't get their hopes up.
1:01:37
they will actually um keep keep their sights much lower.
1:01:43
But entrepreneurs, they start a company and they're like, "Of course, I'm going to be successful.
1:01:49
I'm going to tell everyone, I'm going to go raise money.
1:01:50
I'm going to put my reputation on the line and I'm going to be forced to perform."
1:01:54
Michael Jordan's uh trainer is the one that wrote the book that I did that episode on.
1:01:58
And he the way he describes this is what Michael would do is exactly what you're saying.
1:02:03
He's like, "Well, once you tell somebody how bad you're going to them up, you have to actually go and do that."
1:02:07
putting and what he realizes when Tim Grover was studying Jordan's career, what he realized is like he intentionally heaped more pressure on him because the more pressure he put on himself, the higher he wrote like the the better he performed and the higher he rose uh throughout like his career.
1:02:22
I think this is also tied to something that you and I have talked about which you called manufactured discontent.
1:02:30
There's a book on the counter.
1:02:30
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 Oggovy who's one of my heroes and he calls this divine discontent that you'll find the best entrepreneurs the best athletes anybody reach the top of their profession right they don't rest
1:02:46
on laurels they don't sleep on wins there's another book right next to that the new biography of uh Steve Jobs just came out and Steve Jobs demonstrated this this um 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
1:03:00
wonderful the only thing to is to do it again is to like not think about it just the next day now I'm going to go on and make another great product or I'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 um a
1:03:17
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
1:03:32
But what we started to realize was um the like even though some of these entrepreneurs had made hundreds of millions of dollars, they were comparing themselves to others and 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 and do more.
1:03:53
Meanwhile, the other folks who were very successful, they were quite happy with their wealth, but what they were unhappy with was their their previous work product, a a previous piece of writing they had written or something like that.
1:04:07
And because everyone in this room was successful, what we identified was everyone had something that they were discontent about that drove them.
1:04:15
And so I started looking at at my own life and and you know there were a lot of periods where there was genuine discontent because we hadn't had product market fit.
1:04:25
But then once we had product market fit at at Grock I was unhappy with the scale and then I was unhappy with you know other elements and I just kept finding things to be unhappy with.
1:04:40
Most people can be quite content with the status quo and they're not going to keep pushing to innovate.
1:04:44
You have to have a personality where you are constantly discontent if you're going to keep pushing things forward.
1:04:52
>> What are you discontent about today?
1:04:53
>> At the moment, I'm I'm um I'm discontent with the lack of compute in the world. AI is revolutionary.
1:05:02
It's going to change everything for people.
1:05:04
You know, there are pros and there's cons, but the pros are massive.
1:05:07
I'm you know they're 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.
1:05:18
that's my fault. every single person who dies from cancer, every single person who becomes old and and you know infirm and and dies like there could come a point we don't know there could come a point where AI um comes up with you know
1:05:33
slow 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 there's a real cost to it. Edwin Land, founder of Polaroid,
1:05:49
Edwin Land, founder of Polaroid, Steve Jobs hero, this guy, this one of my favorite entrepreneurs of all time.
1:05:53
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.
1:06:06
And he what he did 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.
1:06:14
uh he would put on the on the whiteboard, you know, 300 people died today. >> Yeah. >> Because of this.
1:06:19
And if it takes us an extra week, you know, that's 21,00 extra people.
1:06:25
I don't know what the number is, but it's something like that.
1:06:26
Uh I do think you're in a perfect position.
1:06:28
This show is a love le love letter to capitalism.
1:06:33
I think we should uh end on optimism.
1:06:35
Uh I think the best entrepreneurs in the world are default optimistic and default aggressive at the same time.
1:06:41
same time. Can you just like 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 cuz you see what's going on right now like everybody's it's super unpopular people are you know they want to blow up data centers they want
1:06:59
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 I think that goes back to people perceiving a change right Um, I 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,
1:07:20
As a software engineer, the the default is code is expensive to write.
1:07:25
And so, I'm going to be very careful about what code I write.
1:07:28
I'm I'm not going to create a feature unless I'm absolutely sure that it's the right feature to create.
1:07:34
I'm I'm not going to implement something until I've got it figured out.
1:07:38
And what we've seen from agile software development is when you take the risk and you sort of implement something and you get feedback, you end up getting better results.
1:07:47
But there's still just generally a very natural prediliction to to say no to things.
1:07:52
And so concept of sort of a no engineer.
1:07:57
It's someone's job to to say no to things, right?
1:08:00
They they're they're the ones in the meeting who say we can't do this. We shouldn't do this.
1:08:03
What I'm seeing is that um code is becoming almost free.
1:08:09
It's it 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 in this different way based on my experience.
1:08:24
The other shift is the accessibility.
1:08:24
The other shift is the accessibility. is very much like lit uh you know um literature and 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
1:08:39
word and then we got much simpler reading and writing you know alphabets as opposed to these these sort of um uh idioraphs and 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
1:08:54
All of a sudden, it became about the quality of the written word, not just the written word. But everyone had access.
1:09:02
My EA creates software applications now.
1:09:05
Like when I go on a trip, she creates a little app which I can click through and it tells me what the weather's going to be and it updates live and and pulls it from sources and, you know, gives me all my phone numbers and and all sorts of extra information.
1:09:17
That would have been impossible for an individual who didn't know how to write code before.
1:09:21
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 uh like and 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.
1:09:44
I think you're going to see individual founders um without large teams creating very valuable companies that solve real problems for people. And I love that framing.
1:09:56
Uh we'll end on this one of my favorite quotes of yours which you said looking forward to a year of massive upleveling for anyone who wants it.
1:10:04
>> Anyone who wants to learn can now learn a subject. You just ask questions.
1:10:06
The problem with traditional education is it was force-fed to you. It wasn't interesting.
1:10:14
And if you're going to learn something, it needs to be interesting.
1:10:16
The ability to ask questions in the moment when you want to learn something is going to fundamentally change education.
1:10:20
This goes back to what I said earlier that the AI age is going to be about asking questions.
1:10:26
I think a lot of people do ask me what are they going to do for their kids.
1:10:31
And my answer is stop teaching them to answer questions and start teaching them to ask questions.
1:10:38
Curriculums should be revamped around here's a problem.
1:10:41
it actually matters for the community.
1:10:43
Maybe, you know, you need to you need to fix the way that permitting is done in the city.
1:10:49
Maybe you need um a way to improve the the way that you get word out of, you know, some sort of events that are occurring, things 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.
1:11:08
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.
1:11:18
But if you give them a problem where they have to ask the questions and get AI to solve it, then you have.
1:11:25
>> Thanks for the time, man. Glad you did this. >> Thanks.
1:11:27
>> I hope you enjoyed this episode.
1:11:27
Please remember to subscribe wherever you're listening and leave a review.
1:11:30
And make sure you listen to my other podcast, Founders.
1:11:34
For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs, searching for ideas that you can use in your work.
1:11:41
Most of the guests you hear on this show first found me through Founders.