No Priors: Artificial Intelligence | Technology | Startups - Chasing Trillion-Dollar Companies, Founder Ambition, Token Budgets, and Regulatory Capture with Sarah & Elad

Episode Date: August 6, 2026

Is the tech industry moving too quickly, or are founders letting fear of AI labs stunt their ambitions? Sarah and Elad explore the current landscape of artificial intelligence, venture capital, and startup dynamics. They discuss the realities of building multi-trillion-dollar companies, shifting market sizes and outcome-based pricing models, and how founders are reacting to the rise of major AI labs. They also talk about what the framework for startup exits should look like, the potential for researcher burnouts in the next eighteen months as ASI looms on the horizon, bottlenecks for compute, and the impact of regulatory capture and shifting ecosystems from California to Texas. Apply for Embed - Conviction’s Catalyst for AI-Native Startups Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Chapters: 00:00 – Cold Open Trailer 00:31 – Episode Introduction 01:44 – The Next Trillion-Dollar Company 03:12 – Tech Waves as Punctuated Equilibria  04:42 – TAM vs. Revenue Reality 07:14 – Market Size vs. Speed 10:32 – When Founders Should Sell 14:04 – Financing and Time Cost 17:57 – RSI and the Looming Promise of ASI 21:49 – Compute Power Laws 28:12 – Regulations and Disruption 33:06 – Beyond Transformers 34:26 – Tradeoffs - Safety vs. Progress 39:11 – Conclusion

70% of France is still nuclear in terms of its power generation. 70%. Where are all the accidents and where are all the kerfuffles and nothing? Nothing's happened. US is 18% and we haven't built a reactor in 40 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. Hi, listeners. Welcome back to no fires. Today is just me and a lot talking about risk management, RSI, how many trillion-dollar companies there can really be in the ills of regulatory capture. For any new founders out there, it's also time to apply to embed convictions low, overhead, high-signal grant program for 10 exceptional startups building at the frontier. We hold this program twice a year, and it's $250,000 in cash on an uncapped note, as well as compute and services from our partners, OpenAI, Anthropic, Base 10, 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. Sarah G. How are you doing? A lot is good to see you. It's been a while since we just get to hang out with each other. I know. It's been too long. What happened? Where you been? You know, working out companies in D.C. Trying to take a day off.

You? There's just so much going on right now on AI. There's so much going on. It's a nonstop. It's very exciting times. You're chasing the next trillion-dollar company. Yeah, 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. Open AI was still quite early. I think GPT3 just come out, and SpaceX was trading at 80, 100, 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 20 years, right?

SpaceX actually took since the early 2000s and Google took since the 90s. You know, these are usually 15, 20-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 going to be a $20 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 multiple.

So, yeah. What's the one? I'm not going to say. A lot. Where am I going to put my money? I don't know. It's like to run darts. Yeah. So we have the darkboard 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.

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. problem. And then they have an explosion. 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 that internet had like four or five periods to it, right? I 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 a couple different cycles. And, you know, other technologies will have that. AI undoubtedly, there'll be some giant breakthrough and model capability, and we'll see another step and suddenly all these startups again, right? But we see 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 in the next handful of years? And that's different from saying what happens over the next 20 years. Of course,

there's going to be tons of interesting stuff over 20 years. Over two years, three years, there's still things that will grow a lot. You know, there's still a lot of $100 billion companies to be built. But multi-tillion dollar companies are kind of hard to get to. I want to talk to the investors you're talking to because I feel like I run more into a failure of imagination of how much bigger or better something can be than the closest proxy market from a previous era. And I think 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, take some of the like application companies that, you know, we have in common or that other

people have invested in. There are a lot of investors who have intellectually recognized. recognize this idea of AI companies delivering services value. They don't act like they believe it. They look at everything a little bit more linearly. Right. So think of, if you're looking at Harvey or a bridge 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? And actually thinking about like what's happening in the coding domain, which is consumption and value, you know, 100x from here. Coding, I think is like a much, much bigger market than anyone thought.

And, you know, think both of us were saying that a year or two ago. But now the evidence is out there. You don't have to be a genius to, like, take that to domains. The evidence is out there, but it's also what is a trillion dollar market and one is a $100 billion 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 the $10 billion at market cap, right? Which is now like a seed round for some of these neolabs. you know, don't get me wrong. I think that the reality is that there's a lot of these things that

could be 100, 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 market? Because you just think of the revenue basis as needed for that, right? You need 100 billion of revenue or 50 to 100 billion pretty easily. And so then the question, with good margin, right? So then the question is, where are the 50 to $100 billion revenue streams for single companies? That's a different question, then is the tam really, really big? Right? That's huge tam, right? That's a very small number of markets in the world. There's a lot of them, you know, there's like, you know, a dozen plus companies that are thereish. But how many more will there be in the next five years? That's my

question. It's not what in the next 20 years. It's what in the next five years will be able to get to 50 to 100 billion of revenue. And that changes how you think about this, right? There's tons that can get to five or 10 billion of revenue and there'll be a hundred billion dollar company. I think I'm looking at more, both a little further out. And then I'd say, like, I don't know that there are that many markets that are going to get to a hundred billion of revenue in the next couple years that aren't, like in France, right? Tell me what else you think in that timeline?

Perhaps some supply chain, like energy type technologies. Maybe, yeah. Yeah, 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 you actually, if it's a physical goods company, energy, robotics, etc., do you actually have the footprint to get there that fast? Again, I'm not doubting the size of some of these markets. I'm doubting the speed at which you can get there, yeah, 100%.

And that's the issue. And people are, at least in my experience, 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 neolabs. And I think there's much less head-to-head competition. If you look at the markets that Harvey or Open Evidence or Decagon or any of these folks at Sierra entered, you know, four or five years ago, three or four years ago, even, cognition, two-ish years ago, it was big, big market. that could be in the roadmaps of these labs.

But I feel like the two things are happening at the same time. One is for the mid to late stage technology markets, people are continuing to invest 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 some will get to 20, some will get to 100, some of course will go to zero. And then there's a separate threat of all the new stuff that's coming, how aggressive and ambitious of 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 in American dynamism and niche applications of AI and something that should be provided by an inference cloud and et cetera. So there's a lot of these types of companies that I think are going to be potentially a bit more derivative. And don't know why there's people doing huge amazing things simultaneously, right? It's not every startup. But there's more and more, at least to my perception, of people doing smaller niche things out of fear of the labs. And that's also, I think, a negative. 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? I think so, yeah. For certain markets, of course, there's where markets will allows, we'll just eat it naturally, but there's a bunch of markets where they won't. But I think people are staying away from both. Well, we have companies in the portfolio that are going against, like, pretty central premises. So I don't think all the founders are being too me.

Oh, I don't think it's all. I think there's more. My point is it's more 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 trendline that's shifting. And again, it's not all of them. It's just enough of a subset. It's not just 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? I am more often disappointed right now that founders are being like less ambitious than they could be. So maybe that's the trend line I'm talking about. We were talking about when founders should sell their companies. What is your thinking on it at this point in time or your framework?

for it. There's a handful of companies that should never ever sell, at least any time in the near term. If you're entropic, you shouldn't sell. If you're opening out, you shouldn't sell. There's a handful of these things that should never sell. 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 12 to 18 month period. Usually where the company's worth the most it'll ever be worth. And I think we saw one major exit where that was probably the case. Recently recently, I think there's other companies that, you know, 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's just a rational conversation. And the answer to the conversation, maybe no, we should keep going. We still think we have XYZ ahead of us. amazing. But I think it's very useful for people to have that sort of conversation because I feel like in the 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 rollups, from you name it, any of the 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 approach to exits or not exits? I agree with you that there are a set of companies that should never sell unless they cannot finance their future, right? 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 or whatever you think is the right pacing today. And it's more often than it was a few years ago. Yeah, it's every six months. Okay. Every six months, great. Uh,

are you capturing value as costs, fall, and capabilities increase? Because if you're on the wrong side of this secular change and you can't get to the other side of it, you should in fact, sell. You don't have good ideas about how to be on the right set of history. So I think that's a question people should ask themselves. And then if you think about our friends and cursor 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. But I think, like, you know,

More broadly, 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 as 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 think even just the premise of like you need to match your financing structure to your thesis horizon and then be able to like continue. really finance the company to the promised land of whatever you're trying to do.

Yeah, I think the financing part, though, is going to be there because basically what's happening, because of this rapid rise of three trillion dollar plus companies in a short time frame, 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 going to put it against trillion dollar companies of the future. And so I do think we're going to 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 deserve it. But I actually think financing is going to get easier and not harder.

And so I'd view it less as financing and more 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 like 10x or something. You know, maybe 15x. And so then the question is, what is your thing going to be worth? right? And remember, eventually things slow down in terms of compounding too. And you can decide where that slowdown happens. But you kind of 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 are 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, go to 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 and maybe, 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 create a solution. And you see a lot of people from 2020, 2020, 2021, store 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 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 want to eventually be working on something that's going to continue to struggle that's overcapitalized that has runway for the next 10 years or not? And that's where you end up. That's what happened with the 2020, 2020, 2021 cohort. There's tons of people running these companies that aren't working still when we forgot about them because we're talking about AI all the time. That is a huge waste. 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 now and over the next year 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 the company is the equivalent of avoiding margin calls, right? And I, you know, some founders who are working on something that requires a like a technical point of view, for example, or even 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. that set of beliefs. And if they think it's untenable or if they think they're like down some wasteful path of their time, as you describe, and 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, irrational acts in the market and redemptions next quarter. So it's just different environment. But I don't think it's as simple as like financing is now free. I do think it is going to skew, as you said, toward scale of opportunity naturally. Perceived scale. Perceived scale, yeah. Perceived scale is the important angle. So the other thing a lot of people out here are working on or talking about is if you talk to people with the labs, there's this enormous manic energy right now. We're six months-ish or towards the end of a year to be completely done with code. Like it's 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'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 16 hours a day

because every week is, you know, 2% of all the time I have left to be productive before I get displaced by AI. What do you think of that? Like, do I believe it or what happens if it's true? Do you believe it? 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? Yeah, but on that timeline. The data I have is that a number of very smart and even very self-aware research scientists

have felt that, you know, there was some knee in the curve on recursive self-improvement or ASI, 18 months away, every 18 months for the last five years. So how good of it is a predictor? It's not clear. I think the extension from code to training code to data pipeline work 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,

the physical compute accessibility side. I think that's probably more of a limiter than this being algorithmically possible. Yeah, I mean, the physical compute basically 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 odd effect 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 was wondering about second order effects of that belief of it's 18 months away because

that does suggest there could be a burnout cycle in 18 months. Like I know some people at one of the major labs who for a while brought up with me should they get married? Like, do people want to get married? Should they get married? Because I don't know what happens in 18 months to the world. It's like, you should get married. 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. So yeah, it's really fun stuff. I think it's kind of tragic, man. Really? Why?

I think the reactions of some really extraordinary research friends to it, it feels a little bit tragic. I feel like it's psychologically most similar to if people think they're going to 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? It's a like 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 18 months. So should I get married? Should I bother to work? Should I travel? Should I only work? And, you know, 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. Yeah, I just think there's a lot. lot of second order effects that are happening are going to happen. And part of them are driven by this belief system and potential burnout over time. Part of it is going to be, 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, 80% of the results at any given place, which is 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. And that also happens in AI research. And, you know, increasingly compute is differentially provided to those people. 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 research or is the compute associated with the person. That's where the real bottleneck is. I think there's this broader concept of like return on invested tokens, like an R-O-I-T kind of metric, which is if you have a certain token budget,

who do you give it to and why? This is kind of like engineering back in the day, right? The internal tools teams that companies were always starved for resources because 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 measure or 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. I'll take some time, though. I mean, many people are still at like, hey, everybody try AI or whatever, you know, at big enterprises. 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?

No, I mean, it depends on what the budget is for what. I mean, people forget to 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 15 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 very important?

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 at 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. 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 certain subset of old school enterprises. And so, you know, I do think there's going to be this permeation through 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. I think relatedly, if you are a researcher 800 and you have not been allocated, an outsized number of tokens to work with at one of the major labs, I think the opportunity to go spend your energy on something where you have compared advantage and understanding and should benefit from all of this, the supply chain bottlenecks, domains that should accelerate like bio, diffusion into other valuable fields, Like, to me, that seems a lot more exciting than being concerned about the downfall of mathematics and going on vacation until the world ends. I have lots of places to go do stuff.

And I do think that's where a subset of the research community will end up over time, right? And that's an interesting question is, how many researchers do you need if you're a top AI lab? And how does that number relate to the number that you have now? And is that you have the right number? Is that you need five times as many people? need you need half as many of it, they only need to be above a certain bar because it's compute constrained. And you want to 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, 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 want to have that misinterpreted as the person is not being amazing. It's just at some point, people will do some cut off on their power law. I think you will appreciate this. Maybe you've heard it because it's an old ex-Gougalor joke. But when Google's like, I don't know, 50,000 people or something, the question was, how many

people do they take to run Google? And if you ask somebody within search and ads, they're like, oh, like 20% of people in search and ads. And if you ask somebody outside of, you know, search and ads, they'd say like 50,000 people or whatever Google is. So I think this is probably some very. perspectives on the concentration of contribution. I don't know, having never worked at Google.

Yeah, I mean, we definitely know that, yeah, I mean, I worked at Google. I thought it was a wonderful place. In search. I had worked on mobile search a bit, and I worked on ads a bit. I mean, I worked on a bunch of mobile stuff, and then I worked on a bunch of AI ads-related stuff. I ask you 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. There's some sort of freak out about the debt and the returns profile. You seem like minor indication of that, but not real pressure yet. And the last one is 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 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.

Yeah, and I agree with the regulatory angle. What do you think is going to happen in California? So they passed a billionaire tax. And then, I mean, the Democratic Party in California came out in favor of it. you're a founder of one of these companies that you've backed that's now worth $10 billion plus. Is the founder going to have a forced asset sale now next year? Assuming it passes?

Well, dozens of founders have to sell big chunks of their companies. It's not clear the regulators have thought through the execution and compliance of this. But I think the immediate effect is that a huge, number of people that are attempting to create value or do new things in California choose to leave. 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? Yeah, I mean, the way that law is written is 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, etc. And my sense is

in 28, there's increasing talk about also trying to add a exit tax in California. So if you actually try and leave, they'll try and take a big chunk as sort of a penalty for that. So is your prediction mass migration in 27 to Miami? Miami finally happens? I think it will take some time. I think the people who wrote the bill want the flight to happen. I think they want people to leave. And I think the two really negative signs for California were this bill and then this were to ballot harvesting initiatives. I think those are the two things that kind of make 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 easy to do, a lot of people would, even more people would be leaving. I do think a lot of people are leaving. I know quite a few who are starting to go now or planning to go by, you know, the fall in the next month or so. What is your second choice ecosystem? I think that there's a few different places that a lot of people are considering. And the question is, like, what does critical mass look like in two years at each one of those spots? So I think a lot of these things kind of self-assemble and people talk about weather and they talk about all these other things. But the reality is, you know, Boston used to be one of the main startup hubs, it still is for biotech, right? They sort of lost competitively in the early

90s, right? In the 80s, Boston was sort of the counterweight to Silicon Valley. And the weather there's awful, you know. And so I think it's more about where do you have enough smart people aggregated working on common things. And then that's where these renaissancees tend to happen. I think it's been really exciting to see the amount of like great technology, migration and innovation in Texas around energy. Because that is really like a reaction to regulatory environment and demand where 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. And there is actually an ecosystem now. That's super exciting.

Energy and hardware, actually. There's a really growing hardware corridor there as well, which is, you. It was originally all around El Segundo because that's where SpaceX was and then Anderol. 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 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 this Texas is a better or worse place to live. I mean, it impacts things, right?

But it's regulatory shifts driving people out. You didn't take my bait on architecture and technology. What do you think about architectures? I think we are going to, like, as an industry, consume all of the compute and power available, whatever the underlying architectures. So the idea that you are going to have a lot of pressure to find more memory or power-efficient. architectures is more interesting than ever, 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. I think whatever it is gets copied and then it laps to it and they have all the computer you know, that's the high probability outcome. It's not the only outcome. There could be some lower probability thing where some NeoLab comes up with something, they keep it super, super secret, they scale on it, and suddenly their model is better than anyone wants by far. And then they can afford all that 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 or just one person from that team leaves for Anthropical

or opening eye 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. Do you think if the dominant thing is access to compute then, and it's an oligopoly because of it, do you see the labs using that access to compute to control other verticals that they want to be in? Or what is the safety that's really needed that's actually protective of people, right? What is the risk? What is the outcome? That's kind of the, you know, Jansen from Jansen pharmaceuticals. you know, he's considered one of the best drug developers of all times.

He has these great videos on YouTube where he's interviewed 30, 40 years ago talking about regulatory capture and pharma. And the reason things got so expensive and so slow is number one. 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 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 because 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 will always constrain things. And if you constrain things but then push progress forward internally on an exponent in 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 society feel comfortable on the risk-reward spectrum for different things? Like, if my email gets hacked, is that so terrible relative to better health care 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. I think part of the challenge here is it's not a comfortable stance for the regulators to, for many policymakers to hear from technologists that you have to see what happens with the technology versus control. We've always said that. It'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, 70% of France is still nuclear in terms of its power generation, right? 70%. Where are all the accidents and where are all the kerfuffles and nothing? Nothing's happened. US is 18% and we haven't built a reactor in 40 years. Japan is 25%. Very safe, very abundant, but we had a safety lobby in the 70s basically kill.

abundantly in energy for us, right? Well, we're making them now. We just need to make a lot of them. We're not making much. We're not making much. So I think 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 want to place that needle on the wheel of safety versus risk versus outcome. A lot. Before we go, what is something that you're just excited about that is on the positive end of that wheel? 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 health care 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 I do think it's always good to make sure that we have the proper safeguard 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. Call to arms against regulatory capture. All right. We'll see you guys. Find us on Twitter at No Prior's Pod.

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