Chasing Trillion-Dollar Companies, Founder Ambition, Token Budgets, & Regulatory Capture

1. 0:00 – 0:31

   Cold Open Trailer

   1. EGElad Gil0:00

      Seventy percent of France is still nuclear in terms of its power generation. Seventy percent. Where are all the accidents, and where are all the kerfuffles and like, you know, nothing. Nothing's happened. US is eighteen percent, and we haven't built a reactor in forty years. We had a safety lobby in the '70s basically kill abundant clean energy for us. There are real outcomes where safety has hurt us, and the question is: Where do we want the spectrum to be on AI for this stuff? And there's many worlds, many scenarios, many outcomes.

   2. SPSpeaker0:26

      [Instrumental music playing]

2. 0:31 – 1:44

   Episode Introduction

   1. SGSarah Guo0:31

      Hi, listeners. Welcome back to No Priors. Today, it's just me and Elad talking about risk management, RSI, how many trillion-dollar companies there can really be, and the ills of regulatory capture. For any new founders out there, it's also time to apply to Embed, Conviction's low-overhead, high-signal grant program for ten exceptional startups building at the frontier. We hold this program twice a year, and it's two hundred and fifty thousand dollars in cash on an uncapped note, as well as compute and services from our partners, OpenAI, Anthropic, Base10, and others. Most importantly, it's about the company you keep. Our first handful of cohorts have included companies like Cognition, Chai Discovery, Listen Labs, Physical Intelligence, and Flappy Airplanes, people advancing the frontier and diffusing AI into every corner of the economy. Find the app online at embed.conviction.com. Okay, let's get started.

   2. EGElad Gil1:24

      Sarah G, how are you doing?

   3. SGSarah Guo1:27

      Elad, it's good to see you. It's been a while since we just get to hang out with each other.

   4. EGElad Gil1:32

      I know. It's been too long. What happened? Where you been?

   5. SGSarah Guo1:34

      You know, working on companies in DC, trying to take a day off. You?

   6. EGElad Gil1:39

      Uh, there's just so much going on right now in AI. There's so much going on. It's nonstop. It's very exciting

3. 1:44 – 3:12

   The Next Trillion-Dollar Company

   1. EGElad Gil1:44

      times.

   2. SGSarah Guo1:44

      Are you traci- chasing the next trillion-dollar company?

   3. EGElad Gil1:48

      Yeah, it's a, it's a really interesting point because basically what we had is over the last five years or so, we had three companies roughly go from close to zero to a trillion dollars in market cap, right? Anthropic basically didn't exist five years ago. OpenAI, um, was still quite early. I think GPT-3 had just come out, and SpaceX was trading at eighty, a hundred, something like that. And so suddenly we had this massive inflection in terms of valuations of these companies, and I think a lot of people now are assuming that there's a bunch of other trillion-dollar companies that will be formed in three to five years. And, you know, that's unprecedented in human history. Usually, it takes twenty years, right? SpaceX actually took since the early 2000s, and Google took since the '90s. And, you know, these are usually fifteen, twenty-year arcs, and then we had this weird five-year inflection. And so I feel like a lot of people now are looking at different areas that are very exciting, very promising areas, robotics, materials. And everything in everybody's mind is gonna be a trillion-dollar company. And maybe some of these will over the next decade, but it's unlikely that we'll see that many more in the next three to five years. I mean, there's one I can think of that could maybe get there, but not, not multiple. So yeah.

   4. SGSarah Guo2:53

      What's the one?

   5. EGElad Gil2:54

      I'm not gonna say. [laughing]

   6. SGSarah Guo2:57

      Elad, where am I gonna put my money?

   7. EGElad Gil2:59

      I don't know. It's like throwing darts.

   8. SGSarah Guo3:02

      Yeah. That's why we have the dartboard here. So you think it's actually just, like, a very good special point-in-time vintage versus, you know, the ecosystem always gets bigger.

4. 3:12 – 4:42

   Tech Waves as Punctuated Equilibria

   1. EGElad Gil3:12

      Well, it's more like a punctuated equilibrium, right? If you look at, like, theories of evolution, one of them is punctuated equilibrium, where you have, like, a Cambridge explosion, and then you have consolidation, and things are kind of steady state for a while, and then you have an explosion, and then consi... And so that's kind of, like, the history of technology, right? If you think about it, we had a big social wave, but there isn't, like, a dozen new social companies all the time right now. And we had a SaaS wave, and then, you know, they kind of settled down. And so we just had a giant AI wave. And there's still more to come, right? Like, one could argue the internet had, like, four or five periods to it, right? It had the internet of the '90s. It had social of the early to, like, 2010, 2012-ish kind of era. You had SaaS. You had cloud. You had, you know, big security companies. So you had kind of like... You had crypto as a wave. So you had, like, all these waves happening, and sometimes they had two pieces, right? Bitcoin had two-- a couple different cycles, and, um, you know, other technologies will have that. AI, undoubtedly, there'll be some giant breakthrough in model capability, and we'll see another step in suddenly all these startups again, right? But we see, um, these moments in time where things go from zero to a lot, and then those things become consolidators. And then the question is what comes after that. And so I think we've now seen at least some of the consolidators emerge. And the question is: How many more giant companies are coming in the next handful of years? And that's different from saying what happens over the next twenty years. Of course, there's gonna be tons of interesting stuff over twenty years. Over two years, three years, there's still things that will grow a lot. You know, there's still a lot of hundred billion dollar companies to be built, but multi-trillion-dollar companies are kind of hard to

5. 4:42 – 7:14

   TAM vs. Revenue Reality

   1. EGElad Gil4:42

      get to.

   2. SGSarah Guo4:42

      I wanna talk to the investors you're talking to because I feel like I run more into, um, a failure of imagination of how much bigger or better something can be than the closest proxy market from a previous era. Um, and, like, I, I think, like, being able to rethink market size is just still, like, a key underpriced investor skill right now at any stage, right? If you think about-

   3. EGElad Gil5:07

      Sure

   4. SGSarah Guo5:08

      ... take some of the, like, application companies that, you know, we have in common or that other people have invested in. I think there are a lot of investors who have intellectually recognized this idea of AI companies delivering services value, but they don't act like they believe it. They look at everything a little bit more n-linearly, right? So think of, um, if you're looking at Harvey or Abridge or something, then they think about a per seat or per, yeah, per, like, lawyer or per doctor TAM, and they're not actually, you know, asking the question of, like, what does the company look like if they can charge for outcomes, um, and actually thinking about, like, what's happening in the, the coding domain, which is consumption and value, you know, a hundred X from here.

   5. EGElad Gil5:48

      Coding, I think, is, like, a much, much bigger market than anyone thought. And, you know, I think both of us were saying that a year or two ago.

   6. SGSarah Guo5:53

      But now the evidence is out there. You don't have to be a genius to, like, take that to domains.

   7. EGElad Gil5:57

      The evidence is out there, but it's also what is a, what is a trillion-dollar market and what is a hundred billion dollar market? Both of those are big numbers, right? I actually wrote a blog post, like, in 2010 or something talking about how hard it was to get to ten billion in market cap, right? Which is now like a seed round for some of these neo labs. You know, don't get me wrong. I think that- The reality is that there's a lot of these things that could be a hundred, but I don't think there's that many that could be a trillion. Those are just different orders of magnitude. And so then the question is, what are these things that could actually be a trillion-dollar market? 'Cause you just think of the revenue basis that's needed for that, right? You need a hundred billion of revenue or fifty to a hundred billion pretty easily. And so then the question-- with good margin, right? So then the question is, where are the fifty to a hundred billion dollar revenue streams for single companies? That's a different question than is the TAM really, really big, right? [chuckles] That's huge TAM, right? That's a very small number of markets in the world. Um, there's, there's a lot of them, you know. There's like, you know, a dozen-plus companies that, that are there-ish. But, um, how many more will there be in the next five years? That's my question. It's not what in the next twenty years. It's what in the next five years will be able to get to fifty to a hundred billion of revenue? And that changes how you think about this, right? There's tons that can get to five or ten billion of revenue, and they'll be a hundred billion dollar company.

6. 7:14 – 10:32

   Market Size vs. Speed

   1. SGSarah Guo7:14

      I think I'm looking at more, uh, both a little further out, and then I'd say, like, I don't know that there are that many markets that are gonna get to a hundred billion of revenue in the next couple years that aren't, like, inference, right? Tell me what else, what else you think in that timeline. Perhaps some supply chain, like, energy-type technologies.

   2. EGElad Gil7:34

      Maybe, yeah. Yeah, there, there's, there's, like, a list. You can make a list of, like, five or six areas that seem promising. And then part of it too is do you actually... If it's a physically-- if it's a physical goods company, energy, robotics, et cetera, do you actually have the footprint to get there that fast? Again, I'm not, I'm not doubting the size of some of these markets. I'm doubting the speed at which you can get there. Yeah, a hundred percent, and that's the issue. And people, um, are, at least in my experience, uh, collectively at least investing against the fact that they believe the speed is there, which is different from the market size. People are conflating the two things right now, in my opinion. The other phenomena that I think is happening is almost the opposite of it, which is I see some really, really excellent founders going after niche markets because they're now scared of the neo-labs. And I think there's much less head-to-head competition. If you look at the markets that Harvey or, um, OpenEvidence or Decagon or any of these folks at Sierra entered, you know, four or five years ago, three or four years ago even, uh, cognition two-ish years ago, it was big, big markets that could be in the roadmaps of these labs. But I feel like the, the, the two things are happening at the same time. One is for the mid to late stage technology markets, people are continuing to invest in-- a-as if there's velocity to get to a trillion for many companies where I don't think there's a velocity. Again, I think the-- some of them will get to twenty, some will get to a hundred, some of course will go to zero. And then there's a separate thread of all the new stuff that's coming. How aggressive and ambitious are the founders relative to what the labs are doing? And I think that's why you're seeing a flight to hardware companies. "Oh, the labs will never do this hardware thing, and so we'll do that." And American dynamism and, um, niche applications of AI and something that should be provided by an inference cloud and et cetera, et cetera, right? So there's a lot of these types of companies that I think are gonna be potentially a bit more derivative. And don't get me wrong, there's people doing huge, amazing things simultaneously, right? It's not every startup. But there's more and more, at least in my perception, of people doing smaller niche things out of fear of the labs. And that's also, I think, a negative.

   3. SGSarah Guo9:37

      And you feel like they're being too meek, like they should just take on the head-on competition because you can create a much better experience and go just compete on the product, on the distribution, any of it.

   4. EGElad Gil9:46

      I think so, yeah. For certain markets. Of course, there's certain markets where the labs will just eat it naturally, but there's a bunch of markets where they won't. But I think people are staying away from both.

   5. SGSarah Guo9:54

      Well, we have companies in the portfolio that are going against, like, pretty central premises, so I, I don't think all the founders are being too meek.

   6. EGElad Gil10:00

      Oh, I don't think it's all. I think there's more. My point is it's more a trend line, and it's the newest stuff. I'm not saying a thing that's a year old or two years old or... You know, I feel like it's a trend line that's shifting. And again, I'm-- it's not all of them. It's just a s- it's just enough of a subset. In a sense, it's a subset. It's a subset of the good founders. I'm not concerned about the median founder. I'm concerned about the best founders, what are they doing?

   7. SGSarah Guo10:22

      I am, uh, more often disappointed right now that founders are being, like, l-less ambitious than they could be. So maybe that's the trend line you're

7. 10:32 – 14:04

   When Founders Should Sell

   1. SGSarah Guo10:32

      talking about. We were talking about when companies, when founders should sell their companies. What is your thinking on it at this point in time or your framework for it?

   2. EGElad Gil10:40

      There's a handful of companies that should never, ever sell, at least any time in the near term. If you're Anthropic, you shouldn't sell. If you're OpenAI, you shouldn't sell. If, you know, there's a handful of these things that should never sell. Um, most companies in any given era should at least consider it, and there's usually a time-maximizing window where your best outcome is a sale within that window. It's like a twelve to eighteen-month period, usually where the company's worth the most it'll ever be worth. And, um, I, I think we saw one major exit where that was probably the case, uh, reasonably recently. I think there's other companies that, you know, um, should really actively think about it. And from a hygiene perspective, maybe what companies should do... I think Ben Horowitz wrote about this once, you know, basically do a pre-planned once-a-year board meeting where the discussion topic is, in a non-emotional way, should we consider exiting this next six months period? And it's pre-scheduled, so it's not the founders pushing for it. It's not the investors pushing it. It's just a rational conversation. And the answer to the conversation may be, "No, we should keep going. We still think we have X, Y, Z ahead of us." Amazing. But I think it's very useful for people to have that sort of conversation because I feel like in this cycle, every year of AI time is like three to four years of normal cycle time. And so three years is like a decade, right? Like, if you think of what existed in AI three years ago from a model capability perspective, from a vertical app perspective, from AI roll-ups, from you name it, any, any of this stuff, like infrastructure, whatever, radically different world three years ago. And so we're on an accelerated timeline right now where everything is moving faster. And that means that you should double-check your thinking more frequently because the underlying fact set is changing faster than it ever has. I don't know, what do you think? What's your, what's your approach to exits or not exits?

   3. SGSarah Guo12:23

      I agree with you that, um, there, uh, there are a set of companies that should never sell unless they cannot finance their future, right? Um, if I think about... Maybe one principle that is, like, new for this point in time is, um, I might ask at that board meeting or at that meeting once a quarter or once a year, whatever you think is the right pacing today, and it's more often than it was a, a few years ago.

   4. EGElad Gil12:49

      Yeah, it's every six months.

   5. SGSarah Guo12:50

      Okay. Every six months. Great. Uh, are you capturing value as costs fall and capabilities increase? Because if you're the, on the wrong side of this secular change, and you can't get to the other side of it, you should in fact, like, sell. You don't have good ideas about how to be on the right side of history. Um, so I, I think that's a question people should ask them, ask themselves. And then if you think about our, our friends, um, at Cursor, uh, is the way you want to compete capital compute access, and is it perhaps a maximally valuable point in time? That's an interesting question. Um, but I think that like, you know, more broadly, it's a, it's a, I feel like it's a very personal and very interesting risk management question. I do think people should ask themselves, right? Like, the idea that there is pride around, like, never considering this is nonsense. The situational awareness situation is a good reminder that everyone has to stay alive to profit as well. Hedge funds are different than companies. They have to survive to compound. But I, I think even just the premise of, like, you need to match your financing structure to your thesis horizon and then be able to, like, continually finance the company to the promised land of whatever

8. 14:04 – 17:57

   Financing and Time Cost

   1. SGSarah Guo14:04

      you're trying to do.

   2. EGElad Gil14:04

      Yeah. I think the financing part though is gonna be there because basically what's happening because of this rapid rise of three trillion dollar plus companies in a short timeframe, an enormous amount of venture capital is starting to get returned, and that means people are raising bigger and bigger funds, and they need to put it somewhere, and they're gonna put it against trillion dollar companies of the future. And so I do think we're gonna see a ongoing rise in valuations most likely over the next year or two, much more than we've seen to date. And obviously, there'll be some great things in there, and there'll be a bunch of stuff that doesn't de- deserve it, but I actually think financing is gonna get easier, not harder. And so I'd view it less as financing and more, um, what do you think is the true likely expected outcome of your company? Not what investors are telling you, not what the press is telling you, not what Twitter is telling you. Like, just sit down and run the math, and then remember that at some point you'll probably trade it, trade it, like, ten x or something. You know? [chuckles] Maybe fifteen x. And so then the question is: What is your thing gonna be worth, right? And remember, eventually things slow down in terms of compounding too, and you can decide where that slowdown happens. But you kinda do that math. You do future dilution. You look at your potential outcome. You look at years of work it'll take to get there. And you can come to a conclusion because there's two types of opportunity costs that... or risk management. There's risk management against the value of the thing you're doing, but the biggest opportunity cost is your time. Your most productive years of your life are on the line right now, and you can either walk away with a good amount of money and go do the next giant thing, now having done it before, they're going to work with you again, et cetera, et cetera, or you can roll the dice. And you can decide to roll the dice. That may be the right answer. And again, for some companies, absolutely you should do that. But for others, it may be, "Hey, actually now is maybe the time to go." Secondary is an intermediate option, which I actually don't think is always that great because it solves for some short-term needs, but it doesn't actually, um, create a solution. And you see a lot of people from 2020, 2021 still running companies five years later that aren't working. And think of that five or six-year period where they've been locked up when all the AI change happened. What is the cost of that to a great founder? So I think, I think there's that kind of cost that people don't really talk about as much, which I think is the real cost. It's your lifetime cost, right? And you only live once, and it's a short life. And so do you wanna eventually be working on something that's gonna continue to struggle, that's overcapitalized, that has runway for the next ten years or not? And that's where you end up. That's what happened with the 2020, 2021 cohort. There's tons of people running these companies that aren't working still, and we forgot about them 'cause we're talking about AI all the time.

   3. SGSarah Guo16:34

      That is a huge waste. Um, my, uh... I think my point was really that even if there are lots of dollars still rotating into venture or being produced by these huge outcomes over, you know, now and over the next year or two, private markets don't have to be rational or right for long periods of time, right? And so being smart about your ability to finance a company is the equivalent of avoiding margin calls, right? And I, I, I... You know, some founders who are working on something that requires a, like, a technical point of view, for example, or even a, a structural point of view about how the market resolves can get very frustrated because investors will believe something that they think is wrong or stupid for a long time, and it's just the job of the founders to go navigate that narrative or that set of beliefs. And if, if they think it's untenable or if they think they're, like, down some, um, wasteful path of their time, as you describe, then they should sell the company. But it could be worse. I mean, founders, they have the concentration risk, but they could be hedge fund managers facing retail, um, irrational acts in the market and reductions next quarter. So it's just different, um, uh, different environment. But I don't think it's as simple as, like, financing is now free. I do think it is gonna skew, as you said, toward scale of opportunity naturally.

   4. EGElad Gil17:53

      Or perceived scale.

   5. SGSarah Guo17:54

      Perceived scale. Yeah.

   6. EGElad Gil17:55

      Perceived scale is important, I think.

9. 17:57 – 21:49

   RSI and the Looming Promise of ASI

   1. EGElad Gil17:57

      Um, so the other thing a lot of people out here are working on or talking about is, um, if you talk to people at the labs, there's this enormous manic energy right now. We're six months-ish or towards the end of the year to be completely done with code, like, as a solved problem. And then we'll probably hit some form of, you know, light RSI by end of next year. And at that point, you'll have models Training big chunks of the models themselves. I think it, it's probably more post-training initially. Maybe it could impact pre-training over time more quickly as well. And because of that, many people believe, "Hey, you know, if I have a year, year and a half left of productive work in my career, I should be working sixteen hours a day because every week is, you know, two percent of all the time I have left to be productive before I get displaced by AI." Um, what do you think of that?

   2. SGSarah Guo18:45

      Like, do I believe it, or what happens if it's true?

   3. EGElad Gil18:48

      Do you believe it?

   4. SGSarah Guo18:49

      I think the idea that the models can improve their own training if the leading scientists working on this believe it, and it's an extension of what we are already seeing in code and math, of course you should believe it, right?

   5. EGElad Gil19:07

      Yeah, but on that timeline.

   6. SGSarah Guo19:08

      The data I have is that a number of very smart and even very self-aware research scientists have, uh, felt that, you know, there was some knee in the curve on recursive self-improvement or ASI eighteen months away every eighteen months for the last five years. So how good of it is a predictor? It's not clear. I think the, like, the extension from code to training code to data pipeline work, um, is way easier to believe. The question of, like, how you are going to go gather that data for less verifiable, more complex domains, or do you run into the actual constraints on the, like, physical compute accessibility side, like, I think that's probably more of a limiter than, um, this being algorithmically possible.

   7. EGElad Gil20:00

      Yeah, I mean, the physical compute basically, um, reinforces an oligopoly market because what it does is it creates a ceiling on the rate of progress any single lab can get if effectively you assume the compute is roughly pro rata across the ecosystem to the big labs. And so in the absence of a lack of compute constraints, you almost have an enforced oligopoly market up to a point, or at least you force closer competition between the players than would exist otherwise, which I think is an interesting artifact of this moment in time. And the question is when does that lift, and what does that look like? So yeah, it's kind of... it's a very exciting time. I mean, I always wonder about second-order effects of that belief of it's eighteen months away because that does suggest there could be a burnout cycle in eighteen months. Like, I know some people at one of the major labs who for a while, um, brought up with me should they get married. Like, do people want to get married? Should they get married because they don't know what happens in eighteen months to the world. It's like, uh, you should get married. [laughing] You should go ahead. It'll be okay. And so I do think we're living through this very manic, very exciting, very intense work period. Um, so yeah, it's really fun stuff.

   8. SGSarah Guo21:01

      I think it's kind of tragic, man.

   9. EGElad Gil21:03

      Really? Why?

   10. SGSarah Guo21:04

       I think the reactions of some really extraordinary research friends to it, it, it feels a little bit tragic. I think it like, I, I feel like it's psychologically most similar to if people think they're gonna die, right? Like, how would you spend the last two years of your life? Would you spend it the way you are today, or would you spend it in a very different way? Is a, like a, a, a not unrelated philosophical question. And so you do have people who are like, "Ah, like my contribution is a bit irrelevant given RSI in the next eighteen months. So should I get married? Should I bother to work? Should I travel? Should I only work?" Um, and you know, I, I, I just actually think it's a much more stable and satisfying state if people act as if they have, but maybe you think that's blind.

10. 21:49 – 28:12

    Compute Power Laws

    1. EGElad Gil21:49

       No, I just think there's a lot of, um, second-order effects that are happening or gonna happen, and part of them are driven by this belief system and potential burnout over time. Uh, part of it is gonna be, um... you know, one thing that I've noticed that is happening at some of the labs is that, you know, as compute becomes really the scarce resource, it turns out that there's, say, a few dozen researchers that drive a lot of, like, eighty percent of the results at any given place, which is a, a really interesting human power law, right? If you actually look at it, in any field, there's at most a few dozen people who drive the field. You look at breast cancer research, you look at certain subfields of mathematics, you look at subfields of physics, you look at the entrepreneurial ecosystem and founders, like, there's a handful of people, dozens of people who drive most progress. Um, and that also happens in AI research. And, you know, increasingly compute is differentially provided to those people, right? And so I know some labs have slowed down on their hiring of researchers unless they're above a very, very high bar because the cost isn't the researcher, it's the compute associated with the person. That's real- where the real bottleneck is. I think there's this broader concept of, like, return on invested tokens, like an ROIT kinda metric, which is if you have a certain token budget, who do you give it to, and why? And this is kind of like engineering back in the day, right? The internal tools teams at companies were always starved for resources 'cause many, at least tech companies, would rather use the same engineers to build product than to build internal tools that would make other functions more productive. That's why I think the death of SaaS is a little bit overstated because why would you use tokens on a bunch of SaaS stuff that you're not actually paying that much for per year relative to the outcome of those same tokens being invested against a core product or against some massive margin lift or some other thing, right? And so I think increasingly we've shifted from a world where people said, "Hey, everybody use AI and do whatever you want," to, "Hey, we have to, like, measure spend and move more things to open source." And then I think the next wave is what are the projects and people that should actually get outsized pieces of a token budget, and what is that return on investment? It's the sort of next shift that's coming. It'll take some time, though. I mean, many people are still at, like, "Hey, everybody, try AI or whatever," you know, at big enterprises.

    2. SGSarah Guo24:02

       What do you think is the appropriate compute, like, token budget for a business or a human being three to five years from now? Should I look at it like rent?

    3. EGElad Gil24:14

       No, I mean, it depends on what the budget is for what. I mean, people forget too, Minecraft was like, what was it? Five people, 10 people when it was bought for billions of dollars by Microsoft. People keep talking about someday there will be like a multi-billion dollar single-person company. That was basically Minecraft, roughly. It already happened like fifteen years ago or whenever that was. So there are always people who can take outsize advantages of technology, and AI has accelerated that radically. And so at some point, it's like, why give tokens to people who can't do that on a relative basis, unless you just run out of those people. And you may run out of them. This is back to like, well, all the engineers get laid off. Probably not anytime soon, but you could argue that at some companies, even before AI, there was a bunch of engineers that weren't that productive that could be let go, especially some of the big tech companies. And I think a lot of those folks will be very coveted by GE or PG&E or Hershey's or... So even if there is some displacement of engineers at some point in the future, I don't know when that is or if it happens, but if it does happen, there's lots and lots of homes for them because there's tons of enterprises that never had the capability set or ability to recruit these people, and they want the capabilities they bring. Even if they're mediocre in the context of a Google or Meta or whatever, they may be exceptional in the context of a, of a certain subset of old-school enterprises. And so, you know, I do think there's gonna be this permeation through, um, the enterprise landscape of engineering talent in an unexpected way. This is probably many years away. I'm just saying I think that's probably a likely outcome.

    4. SGSarah Guo25:45

       I think relatedly, if, you know, if you are a researcher, eight hundred, and you have not been allocated an outsized number of tokens to work with at one of the major labs-

    5. EGElad Gil25:57

       Yeah

    6. SGSarah Guo25:57

       ... I think the opportunity to go spend your energy on, you know, something where you have like comparative advantage and understanding and should benefit from all of this, the supply chain bottlenecks, um, domains that should accelerate like bio, diffusion into other valuable fields. Like, to me, that, uh, that seems a lot more exciting than being concerned about the downfall of mathematics and going on, you know, vacation until the world ends.

    7. EGElad Gil26:25

       Oh, yeah, lots of places to go do stuff. And I, I do think that's where a subset of the research community will end up over time, right? And that, that's an interesting question is how many researchers do you need if you're a top AI lab? And what-- how does that number relate to the number that you have now, and is it you have the right number? Is it you need five times as many people? Is it need, you need half as many, but they all need to be above a certain bar 'cause it's compute constrained? Um, and you wanna map it against the best ideas, and the best ideas come from a subset of people on average. Not always, but on average. And so it's a really interesting question of like, what is-- how does all this stuff fall out, and then where does the N plus one person go? And there's lots and lots and lots of places for the N plus one person to go. They're still exceptional. They're still top of the bell curve. You know, again, I don't wanna have that misinterpreted as the person is not being amazing. It's just at some point, people will do some cutoff on their power law.

    8. SGSarah Guo27:18

       I think you will appreciate this. Maybe you've heard it 'cause it's an old, uh, ex-Googler joke. But, um, when Google was like, I don't know, fifty thousand people or something, the question was, how many people does it take to run Google? And if you ask somebody within search and ads, they're like, "Oh, like twenty percent of the people in search and ads." And if you ask somebody outside of, you know, search and ads, they'd say like fifty thousand people or whatever Google is. Um, so I think this is probably some varied perspectives on the, uh, concentration of contribution. I don't know, having never worked at Google.

    9. EGElad Gil27:54

       Yeah, I mean, we definitely know that, uh, yeah, I mean, I, I worked at Google. Um, I thought it was a wonderful place.

    10. SGSarah Guo28:00

        In search. [chuckles]

    11. EGElad Gil28:01

        I worked on mobile search a bit, and I worked on, um, I worked on ads a bit. Uh, I mean, I, I worked on a bunch of mobile stuff, and then I worked on a bunch of like AI ads related

11. 28:12 – 33:06

    Regulations and Disruption

    1. EGElad Gil28:12

       stuff.

    2. SGSarah Guo28:12

       May I ask you, um, a very different question, which is like, can you think of anything that could disrupt this all right now? You could have like investors, like a number of different players collapse in their commitment on the CapEx side because the markets hate it.

    3. EGElad Gil28:26

       Mm-hmm.

    4. SGSarah Guo28:27

       There's some sort of freak out about the debt, um, and the returns profile.

    5. EGElad Gil28:31

       Mm-hmm.

    6. SGSarah Guo28:31

       You've seen like minor indication of that, but not, not real pressure yet.

    7. EGElad Gil28:36

       Yeah.

    8. SGSarah Guo28:36

       Um, and the last one is, uh, do you think there's a technological disruption that's possible? Like alternatives to transformers, does that still matter at all? Is there anything that would make the landscape look really different technically? I think there's always technology unknowns, and then I, I think the idea of attempting to restrict model usage of models we already have or open source to dramatically constrain like pace of progress, I think is the other.

    9. EGElad Gil29:03

       Yeah, and I agree with the regulatory angle. What do you think is gonna happen in California? So they passed the billionaire tax, and then, I mean, the Democratic Party in California came out in, in favor of it. Um, you're a founder of one of these companies that you've backed that's now worth ten billion plus. Is the founder gonna have a forced asset sale now next year, assuming it passes? Will dozens of founders have to sell big chunks of their companies?

    10. SGSarah Guo29:33

        It's not clear the regulators have thought through the execution and compliance of this, but I, I think the immediate effect is that, um, a huge number of people that are attempting to create value or do new things in California choose to leave. It's alread-- that's already happening. It's hard to move an entire ecosystem very quickly. This is the fastest way I can think of to chase the entire ecosystem out. What do you think happens?

    11. EGElad Gil30:03

        Yeah, I mean, the way the, that law is written is, um, my sense is it's reasonably broad in terms of once it passes, they can re-implement it, they can lower the bar in future years, et cetera. And my sense is in '28, there's increasing talk about also trying to, um, add a exit tax in California. Uh, so if you actually try and leave, they'll, they'll try and take a big chunk as sort of a penalty for that.

    12. SGSarah Guo30:26

        So is your prediction mass migration in '27 to Miami? Miami finally happens?

    13. EGElad Gil30:32

        [laughs] I think it will, um, take some time. Um, I think the people who wrote the bill want the flight to happen. Um, I think they want people to leave, and I think the two really negative signs for California were is-- were this bill and then, um, the sort of ballot harving-- harvesting initiatives. I think those are the two things that kinda make it a potentially worse future for the state in different ways. So I'm hopeful, like, as usual, that California figures it out, but I think if there's any alternative that was, um, easy to do, a lot of people would... even more people would be leaving. I do think a lot of people are leaving. Like, I know quite a few who are starting to go now or planning to go, uh, by, you know, the fall, next month or so.

    14. SGSarah Guo31:17

        What is your second-choice ecosystem?

    15. EGElad Gil31:19

        I think that there's a few different places that a lot of people are considering, and the question is, um, like, what does critical mass look like in two years at each one of those spots? So I think, I think a lot of these things kinda self-assemble, and people talk about weather, and they talk about all these other things, but the reality is, um, you know, Boston used to be one of the main startup hubs. It still is for biotech, right? Um, they sort of lost competitively in the early '90s, right? In the '80s, Boston was sort of the counterweight to Silicon Valley, um, and the weather there is awful, you know? [laughs] And so I think, um, it's more about where do you have enough smart people aggregated working on common things, and then that's where these renaissances tend to happen.

    16. SGSarah Guo32:02

        I think it's been really exciting to see the amount of, um, like, great technology migration and innovation in, um, in Texas around energy.

    17. EGElad Gil32:14

        Mm-hmm. Mm-hmm.

    18. SGSarah Guo32:15

        Because that is really, like, a reaction to regulatory environment and demand where, um, I've seen a lot of people either move from Silicon Valley or move from other places because it is a place where you can experiment, um, and, uh, the, there is actually an ecosystem now that's super exciting.

    19. EGElad Gil32:33

        Energy and hardware, actually. There's a really growing hardware corridor there as well, which is, you know, it was originally all around El Segundo 'cause that's where SpaceX was, and then Anduril, and now, you know, SpaceX and I think part of Tesla and stuff moved to Texas, and so there's, like, this new ecosystem kind of emerging around sort of, uh, a, a, a part of Texas as well in addition to Austin. So I do think we are seeing these shifts, and these shifts are purely driven by regulation. They're not driven by is Texas a better or worse place to live. I mean, it impacts things, right, but, but it's regulatory shifts driving

12. 33:06 – 34:26

    Beyond Transformers

    1. EGElad Gil33:06

       people out.

    2. SGSarah Guo33:06

       You didn't take my bait on, um, architecture and, like, technology.

    3. EGElad Gil33:11

       What do you think about architectures?

    4. SGSarah Guo33:13

       I think we are going to, like, as an industry, consume all of the compute and power available, whatever the, uh, underlying architectures. Uh, so the idea that you are going to have a lot of pressure to find more memory or power-efficient, um, architectures is, like, more interesting than ever, uh, but catching up to transformers in scale and match for hardware remains pretty tough. But I think people make that-- they will make that bet as they get more desperate in terms of more experimentation. I don't think it changes the direction of the industry.

    5. EGElad Gil33:51

       I think whatever it is gets copied, and then the labs do it, and they have all the compute anyway. You know, that's, that's the high-probability outcome. It's not the only outcome. There could be some lower-probability thing where some neo-lab comes up with something. They keep it super, super secret. They scale on it, and suddenly their model is better than anyone's by far, and then they can afford all the extra compute and everything else, and everybody rallies around them. You know, you could always imagine a scenario like that, but you could also imagine a scenario where just one person from that team leaves for Anthropic or OpenAI, and the knowledge spreads, and the next thing you know, everybody has it, which is what's been happening so far in terms of these models.

13. 34:26 – 39:11

    Tradeoffs - Safety vs. Progress

    1. SGSarah Guo34:26

       Do you think if the dominant thing is access to compute then, and it's an oligopoly because of it, um, do you see the labs using that, uh, access to compute to control other verticals that they want to be in?

    2. EGElad Gil34:42

       Or, or what is the safety that's really needed that's actually protective of people, right? What is the risk? What is the outcome? Um, that's kind of the, you know, Jensen from Jensen Pharmaceuticals. You know, uh, he's, he's considered one of the best drug developers of all times. He has these great videos on YouTube where he's interviewed thirty, forty years ago talking about regulatory capture in pharma, and the reason things got so expensive and so slow is, number one, uh, regulatory capture, and the second is risk-reward scenarios where the FDA, in his mind, I'm not saying this is correct or incorrect, in his mind, the FDA, um, focuses too much on safety and risk and not enough on benefit. And so there's no risk-reward. There's only risk. So that slows everything down 'cause you're only looking at one side of the equation. One could imagine a scenario where in the labs, a version of that is created as well, right? Where the safety burden is so high, even if the outcome is even higher, even if the positive outcome is dramatically higher relative to the risk. And so this is back to if you only focus on one side of the equation, you'll always constrain things. And if you constrain things but then push progress forward internally on an exponent, and a year is worth three or four years in normal time, then you're a year ahead internally. That's a massive advantage. And so it's this very interesting question of, like, where do we as a society feel comfortable on the risk-reward spectrum for different things? Like, if my email gets hacked, is that so terrible relative to better healthcare through AI models sooner, right? And so that's kind of the trade-off. So yeah, these are all things we'll have to work through from a societal perspective.

    3. SGSarah Guo36:18

       I think part of the challenge here is, um, it's not a comfortable stance for the regulators to... m- for many policymakers to hear from technologists that you have to see what happens with the technology versus control.

    4. EGElad Gil36:32

       We've always said that. We've al-- That's always been a tech thing. It's always been throughout history, "Hey," like, "of course, this technology could be used in negative ways," right? Every technology Has both positive and negative applications. Biotech, you could create a virus, but you can cure cancer. Nuclear, you could have free, cheap, abundant energy. You can also create weapons. And if you actually look at it, you know, seventy percent of France is still nuclear in terms of its power generation, right? Seventy percent. Where are all the accidents, and where are all the kerfuffles and what? Nothing. Nothing's happened. US is eighteen percent, and we haven't built a reactor in forty years. Japan is twenty-five percent. Very safe, very abundant, but we had a safety lobby in the '70s basically kill abundant clean energy for us, right?

    5. SGSarah Guo37:19

       Well, we're making them now. We just need to make a lot of them.

    6. EGElad Gil37:23

       We're not making much. We're not making much. So I think, um, we've see-- there, there are real outcomes where safety has hurt us, and that's hurt us in power and energy production. It's hurt us in aspects of medicine. It's hurt us in lots of places. And the question is: Where do we want the spectrum to be on AI for this stuff? And there's many worlds, many scenarios, many outcomes, and societally, we kind of get to choose where do we wanna, where do we wanna place that needle on that, on the wheel of safety versus risk versus outcome.

    7. SGSarah Guo37:55

       Elad, before we go, um, what is something that you're just excited about that is on the positive end of that wheel?

    8. EGElad Gil38:06

       I mean, there's so much stuff I'm excited about there. Like, I think there's so much we can do from a human productivity perspective, from an education perspective, from a healthcare perspective, from a daily life and benefit to life perspective, self-driving and elderly, everything, you know? Like, there's so much good that can come of all this. So I'm optimistic about a lot of applications, and that's why I'm cautious about where we should end up on that spectrum because, um, I do think it's always good to make sure that we have the proper safeguards societally. But I think that historically, for big industries, we've gone too far, and the reason tech has been so successful so quickly and has had so much human impact is because it's been lightly regulated. And I think it's better to keep it that way than not, and we'll lose optimism, we'll lose momentum, we'll lose progress, and that's what happened in biotech, and that's what's happened in a variety of areas over time. That's what happened in energy for a long time.

    9. SGSarah Guo39:05

       Call to arms against regulatory capture. All right, we'll see you guys. [upbeat music]

14. 39:11 – 39:27

    Conclusion

    1. SGSarah Guo39:11

       Find us on Twitter at @NoPriorsPod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.

Episode duration: 39:29