Chasing Trillion-Dollar Companies, Founder Ambition, Token Budgets, & Regulatory Capture Transcript, AI Summary & Key Points

00:00 70% of France is still nuclear in terms of its power generation. 70%. Where are all the accidents and where are all the curtles and you 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?

00:19 And there's many worlds, many scenarios, many outcomes. Hi listeners, welcome back to No Fires. Today is just me and Alad 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 Conviction's low overhead, high signal grant program for 10 exceptional startups building at the frontier.

00:53 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 Enthropic 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.

01:14 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. It's 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 at companies in DC, trying to take a day off.

01:37 You >> uh there's just so much going on right now in AI. There's so much going on. It's non-stop. It's very exciting times. >> You're tracing chasing the next trillion dollar company? 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?

01:57 Enthropic basically didn't exist five years ago. Open AAI um 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.

02:18 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 '9s. And you know these are usually 15 20 year arcs and then we had this weird 5year inflection. And so I feel like a lot of people now are looking at different areas that are very exciting, very promising areas.

02:36 Robotics, materials, and everything in everybody's mind is going to 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. >> What's the one >> am I going to say?

02:56 A lot. Where am I going to put my money? >> I don't know. It's like throwing darts. >> Yeah. That's why we have the dart board 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 you have consolidation and things are kind of steady state and then you have an

03:22 explosion and then 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 SAS 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?

03:38 that had the internet of the '9s. You had social of the early to like 2010 2012ish kind of era. You had SAS. 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 um you know other technologies will have that.

04:02 AI undoubtedly there'll be some giant breakthrough in model capability and we'll see another step and 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

04:30 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 will grow a lot you 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 get to. >> I want to 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.

04:56 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 >> sure >> take some of the like application companies that you know we have in common or that other people have invested in I there are a lot of investors who have >> intellectually recognize 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 linearly right so think of um if

05:24 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 um and actually thinking about like what's happening in the 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 I think both of us were saying that a year or two

05:52 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 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 10 billion at market cap, right?

06:09 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 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?

06:27 Because you just think of the revenue basis that's needed for that, right? Need a 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 hundred billion dollar revenue streams for single companies? That's a different question than is the TAM really really big right that's huge TAM right that's a very small number of markets in the world.

06:48 Um there's there's a lot of them you know there's like you know a dozen plus companies that that are thereish but um how many more will there be in the next 5 years? That's my question. It's not what in the next 20 years, it's what in the next 5 years we'll be able to get to 50 to 100 billion of revenue. And that changes how you think about this, right?

07:10 There's tons that can get to five or 10 billion of revenue and they'll be a$100 billion dollar company. >> 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 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 what else you think in that timeline.

07:29 Perhaps some supply chain like energy type technologies >> 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 it's a physical goods company, energy, robotics, etc. Do you actually have the footprint to get there that fast?

07:49 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, 100%. 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.

08:10 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 not scared of the Neolabs. And I think there's much less head-to-head competition if you look at the markets that Harvey or um 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 uh cognition two-ish years ago.

08:36 It was big big markets that could be in the road maps 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 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 of them will get to 20 some will get to 100 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

09:02 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 etc. Right? 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 get wrong there's people doing huge amazing things simultaneously right it's not every

09:27 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 markets where the labs will just eat it naturally way, but there's a bunch of markets where they

09:51 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 I don't think all the founders are being too meek. >> 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.

10:10 And again, I'm it's not all of them. It's just it's just enough of a subset and 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 uh more often disappointed right now that founders are being like less ambitious than they could be.

10:28 So maybe that's the trend line you're 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? >> There's a handful of companies that should never ever sell at least anytime in the near term. If you're anthropic, you shouldn't sell. If you're open AI, you shouldn't sell.

10:46 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 12 to 18month 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.

11:07 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?

11:27 And it's pres-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 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 this cycle every year of AI time is like 3 to four years of normal cycle time.

11:47 And so 3 years is like a decade, right? Like if you think of what existed in AI 3 years ago from a model capability perspective, from a vertical app perspective, from AI rollups, from you name it, any any of the stuff like infrastructure, whatever, radically different world 3 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.

12:20 I don't know. What do you think? What's your what's your approach to exits or not exits? >> 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 or whatever you think is the right pacing today and it's more often than it was a few years ago.

12:49 >> 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 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 themselves.

13:10 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 like, you know, more broadly, it's a it's a I feel like it's a very personal and very interesting riskmanagement question. I do think people should ask themselves, right?

13:39 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 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 you're trying to do.

14:04 >> 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.

14:22 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 today. 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 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?

14:43 not what investors are telling you, not what the press is telling you, not what Twitter's 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.

15:01 And you can decide where that slowdown happens. But you kind of do that math. You defeature dilution. You look at your potential outcome. You look at years of work it'll take to get there. And then 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.

15:19 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 to go do the next giant thing now having done it before wanting to work with you again, etc., etc., or you can roll the dice. And you can decide to roll the dice. That may be the right answer.

15:37 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 5 years later that aren't working.

15:57 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 want to eventually be working on something that's going to continue to struggle, that's over capitalized, that has runway for the next 10 years or not?

16:23 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 because we're talking about AI all the time. >> 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 over the next year or two private markets don't have to be rational or right for long periods of time right and so

16:56 being smart about your ability to finance a company is the equivalent of avoiding margin calls right and I I you know some founders ers who are working on something that requires 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

17:25 untenable or if they think they're like down some um 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 um irrational acts in the market and redemptions 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.

17:47 I do think it is going to skew as you said toward scale of opportunity naturally >> or perceived scale. >> Perceived scale. Yeah, >> perceived scale is the important angle. 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 6 monthsish or towards the end of the year to be completely done with code.

18:10 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

18:38 time I have left to be productive before I get displaced by AI. Um, 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

19:13 uh felt that you know there was um knee in the curve on recursive self-improvement or ASI 18 months away every 18 months for the last 5 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

19:56 more of a limiter than um this being algorithmically possible. Yeah, I mean the physical compute basically um reinforces an igopoly market because what it does is it creates a ceiling on the rate of progress any single lab can get if effectively assume the comput is roughly parata across the ecosystem to the big labs and so in the absence of a lack of compute constraints you almost have an enforced alleg market up to a point or at least you force closer competition between the players than would exist otherwise which I

20:21 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 always wonder 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 um brought up with me, should they get married?

20:41 Like do people want to get married? Should they get married? Because they 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. Um, so yeah, it's really fun stuff. >> I think it's kind of tragic, man.

21:03 >> Really? Why? >> 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 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 is a like a a not unrelated philosophical question.

21:24 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? 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.

21:48 Yeah, I just think there's a lot of um 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. uh part of it is going to 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 80% of the results at any given place which is a really interesting human power law right if

22:18 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 sub fields of mathematics you Look at sub fields at 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 computually provided to those people, right?

22:44 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 ROI 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 at companies were always starved

23:12 for resources cuz 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 SAS is a little bit overstated because why would you use tokens on a bunch of SAS 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?

23:34 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?

23:54 is this 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 big enterprises. >> What do you think is the appropriate compute like token budget for a business or a human being 3 to 5 years from now? Should I look at it like rent? >> No. I mean it depends on what the budget is for what.

24:16 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 15 years ago or whenever that was. So there are always people who can take outsiz advantages of technology and AI has accelerated that radically.

24:42 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 of probably not anytime soon but you could argue that at some companies even before AI there's 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

25:06 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 that 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 going

25:35 to 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 >> I think relatedly if you know 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.

25:57 >> Yeah. >> I think the opportunity to go spend your energy on, you know, something where you have like compared 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.

26:25 >> 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 that you need five times as many people as it need you need half as many but they only need to be above a certain bar because it's compute constrained um and you want to map it

26:50 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 plus1 person go and there's lots and lots and lots of places for the N plus1 person to go there's 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 is just at some point people will do some cut off on

27:16 their power law. >> I think you will appreciate this. Maybe you've heard it cuz it's an old uh ex googler joke, but um when Google's like I don't know 50,000 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 20% of the 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.

27:41 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. >> Yeah, I mean we definitely know that uh yeah I mean I worked at Google um I thought it was a wonderful place >> in search. >> 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 stuff.

28:12 >> Can 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. >> There's some sort of freak out about the debt >> um and the returns profile. You seem like minor indication of that but not not real pressure yet.

28:34 Um, 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 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.

29:03 >> Yeah. And I agree with the regulator 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 10 billion plus. Is the founder going to have a forced asset sale now next year?

29:25 Assuming it passes, will 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 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 that's already happening.

29:53 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 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, etc. And my sense is in 28 there's increasing talk about also trying to um add a exit tax in California.

30:21 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. >> So is your prediction mass migration in 27 to Miami? Miami finally happens. >> I 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 was this bill and then um the sort of ballot haring harvesting initiatives.

30:51 I think those are the two things that kind of 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 are starting to go now or planning to go uh by you know the fall in the next month or so.

31:17 >> 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 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 kind of 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.

31:40 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, 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.

32:01 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 >> 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's 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

32:38 you know it was originally all around Elsagundo because that's where SpaceX was and then 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 Why is Texas a better or worse place to live?

33:01 I mean, it impacts things, right? But but it's regulatory shifts driving people out. >> You didn't take my bait on um architecture and like 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 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.

33:34 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. think whatever it is gets copied and then the labs do it and they hand all the comput you know that's that's the high probability outcome.

33:57 It's not the only outcome. There could be some lower probability thing where some neol comes up with something they keep it super super secret they scale on it and suddenly their model is better than anyone else 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 open AI and the knowledge spreads and the next

34:20 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 igopoly 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 >> 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 Jansen from Jansen Pharmaceuticals you know uh he's he's

34:52 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 uh regulatory capture and the second is riskreward 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 riskreward worth there's only risk so that

35:22 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'll always constrain things.

35:42 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 a society feel comfortable on the riskreward spectrum for different things like if my email gets hacked is that so terrible relative to better healthcare through AI models sooner, right?

36:12 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 um it's not a comfortable stance for the regulators to for many policy makers to hear from technologists that you have to see what happens with the technology versus control. >> We've always said that.

36:32 We've this 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.

36:57 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 curles and you 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 abundant clean energy for us, right?

37:19 >> 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 um we've se 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?

37:40 And there's many worlds, many scenarios, many outcomes. And society, we kind of get to choose where do we want to where do we want to place that needle on that on the wheel of safety versus risk versus outcome? >> Glad before we go, um, 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.

38:07 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 safeguard society, but I think that

38:38 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.

39:01 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 pod. Subscribe to our YouTube channel if you want to 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-priers.com.