Martin Shkreli: Big Pharma's Blind Spot, Betting Against Quantum & What Prison Taught Him

(0:00) Martin Shkreli joins the Besties! (0:55) Why quantum computing is a one-trick business and why he's betting against it (7:42) The drugs big pharma sells too cheap and his journey from hedge fund trader to pharma (11:37) AI, drug discovery, and who actually wins (21:32) Brilliance and rule-breaking, the gifted-kid trap & what prison taught him Thanks to our partners for making this possible! IREN is a vertically integrated AI Cloud platform, delivering data centers, compute and software for AI training and inference. https://iren.com/ Oracle connects the data, applications, and infrastructure that turn AI into business outcomes—with the flexibility, choice, and control to optimize as AI evolves. http://oracle.com/ai EY helps tech innovators scale from startup to exit to megacap. You build the future. We'll handle the rest. http://www.ey.com Meta believes the future is for everyone. We're focused on giving every person the tools to reach their full potential and making sure the benefits of technology are distributed to all. http://www.meta.com Keel Infrastructure owns the power, land, and connectivity that HPC and AI run on - backed by secured energy assets and established grid interconnections across North America. https://keelinfra.com/ Airwallex - Agentic Global Business Accounts. Open local accounts in 70+ countries to accept payments, earn yield, pay globally, and manage spend. http://airwallex.com PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. For more information, visit https://www.paypal.com Google for Startups connects founders with the right people, products, and best practices to help startups build faster and go further. https://startup.google.com/ Explore ideas, industries, and technologies worth understanding with Chamath every week on Learn with Me: https://research.socialcapital.com/allin Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg #allin #tech #news

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  He's been called the most hated man in America because he jacked up the cost of a lifesaving drug.

• 0:05Speaker 01

  Raised the price of a lifesaving drug 5,000%.

• 0:09Speaker 02

  He became the poster child for capitalist greed.

• 0:11Speaker 03

  Martin Shkreli, seven years in prison, we definitely planned on raising the price, that's for sure. You can be prosecuted for not maximizing profit. And you have to do everything in your power to make as much money as possible. And if I'm the enemy for you, good, I'm happy, man. I'm fulfilling some need in your life.

• 0:27Speaker 02

  Purchased the only copy of a legendary Wu-Tang Clan album with no immediate plans to play it.

• 0:32Speaker 03

  It's not a good record. Ego is the fountainhead of all progress.

• 0:36Jason Calacanis?

  Please welcome Martin Shkreli.

• 0:43Speaker 04

  Welcome, Martin. Thanks for coming out.

• 0:45Speaker 03

  Nice to meet you.

• 0:46Speaker 04

  You wore Brad's outfit today.

• 0:48Speaker 03

  Exactly.

• 0:48Speaker 04

  All right. Welcome, Martin. We've got so many topics to go into with you, but I want to start with one.

• 0:56Speaker 04

  You've basically taken down quantum computing. You said you think that this is a fraud, that people might be going to jail. So two-part question. Number one, maybe you could give us your thesis on why this is a fraud or fugazi, a fugizi, whatever it is.

• 1:17Speaker 03

  Disgracead.

• 1:18Speaker 04

  And a disgracead. And then two, your best advice for those CEOs when they do go to jail, you've got a little experience.

• 1:28Speaker 04

  Best advice in terms of, you don't seem to be the kind of guy who would thrive in prison, but from what I understand, Martin.

• 1:36Speaker 03

  You're like a shock collar.

• 1:37Speaker 04

  Hold on. You turned into Lex Luthor and you were running the yard, I understand. So break us through the quantum trade and, you know, best practices when you go to the huskow.

• 1:49Speaker 03

  Yes, I have to shout out my wife, Chelsea. So Chelsea, very few people know her, but she's, I think, a big deal in technology. She was one of the first people at OpenAI. And her father, believe it or not, was one of the first people in Nvidia. In my family, I was one of the first people in prison. So

• 2:07Speaker 03

  I thought I'd switch the gene pool, you know, shifted over a little bit. But Chelsea studied MIT and taught quantum computing with Scott Aaronson, the preeminent guy in quantum. You know, she got to teach me over many hours, you know, penetrated my thick head, you know, what exactly quantum computing even is. And I think a lot of people sort of synonymize it with faster computing, which sounds great. That's really not what it is at all. And I think the basic point of quantum computing is that there's one algorithm. In fact, the whole field was invented because of Peter Schorr's thought experiment. Peter considered this idea that there are kinds of algorithms that you can run faster in different media. That was a revolutionary idea in computing, but it's not really a revolutionary idea for business.

• 2:51Speaker 03

  Because, and this is my thesis, of course, because there's one algorithm that runs faster in a quantum computer. And so if you ask somebody, which computer can sort a deck of cards? If anybody knows the famous algorithms book, CLRS, you know, the first five pages are about sorting algorithms like bubble sort and merge sort and things like that. And I asked quantum fans, well, which one can sort it faster? And they said, well, of course, the quantum computer. And I said, of course not. That's not the case. The modern quantum computers run at a clock speed of hundreds of hertz or thousands of hertz, not at the gigahertz that we're used to. They're single core. And they're single-threaded when it comes to doing a job like a merge sort or something like that. So the only thing it's really good at, which indeed is a magic trick,

• 3:32Speaker 03

  is going from exponential time algorithm, which is integer factorization or elliptic curve discrete log problem, which I want to mention a little later, shout out to the ECDLP problem. The quantum computers can do those in polynomial time, which is remarkable. And to a nerd, that's the greatest, coolest magic trick ever. But to a business person, unless you're in the business of factoring numbers, there's not a whole lot to be excited about here. So the other algorithms that quantum could do are not much faster. So they'll never beat an Nvidia machine. They'll never beat a million Nvidia machines. And quantum doesn't have storage. There's no such thing as quantum RAM. So you have to design a problem that has a really, really small input. You cannot put the world's compute into it.

• 4:12Speaker 03

  Every time somebody pitches OpenAI, from what I've heard, about a quantum computer, they say, no, we're not interested.

• 4:18Speaker 04

  I think I'm closer to you in my perspectives on quantum, but let me take the other side. Let's just take the other side. The other side would say, look, we're in this very early primordial phase of actually, you know, constructing the transistor. And from the transistor, what we're going to do is like put a bunch of these things together. We're going to figure out gates. We're going to figure out logic. We'll then be able to build some reasonable operating system on top of it. We'll have some reasonable, you know, like mechanisms to write software to these things. And none of that really exists. So do you buy that we're just at an early phase? And this is why we're frustrated that there's a computational limitation, except to a very narrow use case? Like, do you believe that that could be possible?

• 4:59Speaker 03

  Yeah, no, of course. I think the issue, though, is do you buy, would you go long punch cards and vacuum tubes, right? Mercury delay lines, core memory, these are all things that disappeared and went bankrupt. Those were stocks at one point. CTR, you know, computer tabulator machine, got incorporated to IBM.

• 5:15Speaker 04

  But, okay, so but your critique is business model and timing, or is it technological?

• 5:20Speaker 03

  I think it's both. You know, I think that if you, when you talk to Google or IBM or Microsoft and ask them, why are you spending hundreds of millions of dollars a year on this? They say, well, someday there might be something that comes out of it. That's not a great business to me.

• 5:31Speaker 04

  Well, it is very shocking. I mean, you can probably tell the story, but how many machines actually, how many machines does IBM and Google actually have?

• 5:37Speaker 03

  Very few. So Google has a few prototypes. IBM has a little cloud. Chelsea and I have tried every single quantum computer in the world. We cannot multiply 300 by 200 on any of these.

• 5:47Speaker 04

  And you're shorting them?

• 5:49Speaker 03

  Yeah, I've been shorting them. Unfortunately, we lost millions of dollars shorting quantum computing companies, but I told my wife that we gained billions in insight.

• 5:58Speaker 04

  Right. It's not easy to time the markets. Yes, definitely. So where is the natural computational edge? I mean, some people would say it's going to be towards encryption and defeating encryption because now we can actually use what it's actually good at to essentially brute force solutions to things that we can't solve today.

• 6:17Speaker 03

  I think it's crazy to even think about it because we have a computational problem that we're trying to solve. And every time somebody uses the word AI, I actually think of the word matmul. These are matmul machines. And I think that as we

• 6:31Speaker 04

  Matrix multiplication is what he's referring to.

• 6:32Speaker 03

  Right. So multiplying two lists of numbers is what a GPU spends 99.9% of its time doing. But it does it in N cubed time. Now, what's funny, I think things like complexity algorithms are going to be parlance, you know, in the next five to 10 years as we move towards a society. So N cubed means if you want to multiply 1,000-element square matrix versus another 1,000-element square matrix on an Nvidia machine, it takes a billion operations. That's a lot of operations. As we learned about this computing stuff with, through my lovely wife, I was flipping through Nature, which is what we do as a hobby. And I saw a paper from Microsoft that was called AOC, Analog Optical Computing. In New York, that means something different, but AOC in this case is.

• 7:15Speaker 03

  And what I saw in the corner of my eye was that matmulls in light are free. They happen in instant time, which is a really remarkable thing. So we're investigating, maybe we'll start a company or something like that. But there's a couple of startups like Neurofas, Opticor, Lumai, that want to make these photonic matmul cores, which would run circles around what Nvidia does. Now, of course, that's not the whole story. There's a lot of details and nonlinearity and storage.

• 7:42Speaker 04

  Beyond the hatred for quantum and your raging short that may or may not be winning. You were a hedge fund manager, correct?

• 7:50Speaker 03

  Yeah.

• 7:50Speaker 04

  And the famous episode, just to catch everybody up, was you bought a drug and then you inflated the price of the drug. Is that what happened? Can you just give us the

• 8:01Speaker 03

  Sure.

• 8:03Speaker 04

  The history and what takes you now to

• 8:06Speaker 03

  Yeah, I started at 16, Jim Cramer's hedge fund. Cramer was actually a tremendously effective and skilled trader. But Cramer's idea of investing was to, you know, buy and sell all day, every day, everything. And he somehow squeezed profits out of it. That was the way you do it sort of in the 80s and 90s. And he actually made 23% a year net of fees.

• 8:26Speaker 04

  And so you worked for him as a 16-year-old?

• 8:27Speaker 03

  I worked for him at 16.

• 8:28Speaker 04

  How did you get that job?

• 8:30Speaker 03

  Just being in Manhattan, having a big mouth, and, you know, it's a great city. And loving stocks, loving investing. Bill Gates was my hero. And I found a way to, you know, get into that. And Cramer one day pointed at me and said, genomics, you're going to look at genomics. And I said, okay. And it was a lot of fun. And I went to a Tiger Cub. I started my own fund. I realized I was a much better entrepreneur and private equity guy than a trader. We found that the marketplace for molecules had huge amounts of alpha, whereas the marketplace at public companies, we could buy a drug from, I bought a drug from Bristol Myers for 2 million. And that same company is public today. It's worth 6 billion. So that was my first startup. And it was a really inefficient market. Pfizer would sell you a drug for a million bucks that was worth a billion dollars. They didn't care.

• 9:15Speaker 04

  Sell you the rights to the drug. You could productize it, price it.

• 9:18Speaker 03

  Yeah, absolutely.

• 9:19Speaker 04

  Sorry, what do you mean? You just knock on Pfizer's door and they're like, take what you'd like for a million dollars?

• 9:23Speaker 03

  We saw this and we built two very similar companies that our biggest constraint was finding people to vet these assets. So I started a small

• 9:34Speaker 04

  Clinically?

• 9:35Speaker 03

  Yeah, just reading papers, reading patents, going to universities and small drug companies and big drug companies, making relationships. It's called business development in our field. And our biggest bandwidth was, problem was, was reading these papers. So I started a small NLP company on the side in 2015. We had Watson. It was great. And we tried to read every, it wasn't great. We tried to read every paper that was published in medicine. It was 40 million papers. Having humans do that is a really tough thing.

• 10:02Speaker 04

  How do you take a molecule and flip it from 25 million to 6 billion? What are you doing in that six or nine months that you own it?

• 10:08Speaker 03

  I think so what I did with Sparsentan, a drug that I named and is now FDA approved, now a blockbuster drug, is Bristol discovered and developed it for hypertension, which is sort of a dead sleepy market that nobody really needs.

• 10:22Speaker 04

  Hypertension, high blood pressure?

• 10:23Speaker 03

  Yeah. And so we moved into a disease called FSGS, which is a disease that never had a drug approved for it. So you sort of try to angle it in a way that makes sense. And in that case, the drug got FDA approved. 30,000 people who have focal segmental glomerulus sclerosis and never had a drug before now have a drug. I can be a good guy, too. And I think the, you know, that, that was, you know, it's the art of that field. It's a great way to make a billion dollars. It's a

• 10:49Speaker 04

  Is this what royalty pharma does?

• 10:51Speaker 03

  Roy Van.

• 10:52Speaker 04

  But is this also what royalty pharma does? They'll come in?

• 10:54Speaker 03

  Yeah, royalty is a bit of a different player. Genius guys, obviously. Their big claim to fame was they went to NYU. It was getting this big royalty from Remicade. And they bought it for like one times earnings.

• 11:04Speaker 04

  Yeah.

• 11:04Speaker 03

  And then that royalty 20xed.

• 11:06Speaker 04

  They buy the cash flows at a bigger discount than how they're being

• 11:09Speaker 05

  Brad, I know you want to get in here, so jump in here. Martin, take this framework now. You know, you know, kind of the big levers in the market today, right? What's expected in terms of compute? What's expected in terms of this capex build out? Are you spending much time looking at that? And do you see any boogeyman there? What are you worried about? What would you be shorting? What would you be long in that complex?

• 11:29Speaker 03

  Yeah, I mentioned photonic computing before because I do think in 20 years, these H100s are in the scrap heap, B100s are in the scrap heap, B200s are in the scrap heap. There's, we're going to find ways to do this better. I think it's insane that we're thinking about space. I think it's insane we're thinking about nuclear. Sam Altman finally said publicly that he thinks photonic computing is on a rival podcast. That photonic computing could be the future. So I'm just wondering why we think this is a sane idea to cover the earth with computers when we can make a faster one. I think that that doesn't mean Nvidia is not along and along semis right now. I think Jevon always wins in the end. Everyone, Jevon's paradoxes, you know, the crap out of everything. Alex Wang told me Jevon's gonna, you know, take care of all this. It'll be a million X compute improvement.

• 12:14Speaker 03

  There are guys in the valley talking about better transformers that are a thousand times better. If there's a thousand times more efficient transformer, that's bad for Nvidia. There's no doubt about that. So, you know, we're not going to have a million X demand. Now, we might in another way, but there aren't a million X more people. You know, whether we have a million X more compute needs, you know, we'll have to see. For now, I think there's, you know, my guess, like anybody else's is long, but I probably would have guessed in 2000 that Yahoo and Cisco longs too. So it's a tricky place.

• 12:45Speaker 05

  So how do you, do you run a hedge fund still?

• 12:47Speaker 03

  I don't.

• 12:47Speaker 05

  Okay, so you're managing your own money. And when you think about portfolio allocation, are you thinking about it in the way you did when you ran your fund? Are you looking for specific high alpha events or startup or disruption? And really going long behind those and not really thinking about competing with the market?

• 13:04Speaker 03

  Yeah, I'm sort of nimble. I mean, I think this, this Dario trade was like a great reason to go long. So I sort of piled in, you know, onto the sphere, but I really, I don't

• 13:14Speaker 04

  Meaning the sell off yesterday was sort of like just a head

• 13:17Speaker 03

  Yeah, I think it was ridiculous. Yeah.

• 13:18Speaker 04

  What did you think about his essay?

• 13:20Speaker 03

  I think, I think that I want to pay deference to him creating a trillion dollar company, but I kind of hate the guy. And I think that he, you know, it's not easy to make a trillion dollar company.

• 13:30Speaker 04

  Why do you hate it?

• 13:31Speaker 03

  I think he's a fear monger. I mean, I also think that he, you know, imagine you and I started a business and I said, I'm going to show you Pandora's box. Here it is. And you say, wow, that's amazing. And you said, thanks a lot, Martin. Going down the street, I'm going to start Pandora's box too. What are you doing? You know, it would, I would probably sue you for trade secrets or something. This is exactly what happened.

• 13:51Speaker 04

  So you're saying because he was at OpenAI and then he and his five or six co-founders leave, you're saying that like

• 14:01Speaker 03

  Would you be, would you be friends with that guy? Would you like that?

• 14:04Speaker 04

  Well, I mean, I think the thing with Silicon Valley is that it kind of happens a lot. And, you know, it's sort of hard to pinpoint what is a patent versus what is, you know, retained knowledge. I'm not excusing it or I'm not saying I know anything that happened, but

• 14:19Speaker 03

  The Waymo guy went to jail for it.

• 14:22Speaker 04

  Well, I think that that was because he was found to have actually stolen physical documents that were. Okay, so you have, you have an issue with sort of the origin story of the product.

• 14:34Speaker 03

  The fear mongering, the ridiculous, you know, hyperbole.

• 14:36Speaker 04

  Right.

• 14:37Speaker 03

  You know, he says the AI is going to help drug development.

• 14:39Speaker 04

  Well, let me ask you more.

• 14:40Speaker 03

  Total bull

• 14:41Speaker 05

  What about effective altruism?

• 14:43Speaker 03

  Yeah, even worse.

• 14:45Speaker 04

  Why? Why is, what do you find so loathsome about this group of people? Let's unpack it.

• 14:50Speaker 03

  Yeah, so I helped popularize and I even think, I would say start the EA movement, which was this counter insurgency. I think that

• 14:59Speaker 04

  Sorry, there's EA?

• 15:00Speaker 03

  Yes.

• 15:01Speaker 04

  And then there's EA.

• 15:02Speaker 03

  Which was intentionally named

• 15:03Speaker 04

  EA is effective altruism. EA is?

• 15:06Speaker 03

  Effective accelerationism.

• 15:08Speaker 04

  Effective accelerationism.

• 15:09Speaker 03

  Which means?

• 15:10Speaker 04

  Which means?

• 15:11Speaker 03

  That's a great question. It does mean sort of move fast and break things. And, you know, it's no secret that Zuckerberg came out the other day and said, you know, we should move fast in AI. I think the fear mongering is a little ridiculous and there'll be time to panic. It's just not now. Physical AI presents those opportunities. You know, for now, I mean, you can turn off the computer. It's not that big of a deal. I think nerds get carried away. You know, if you ever were a nerd or a jock, I was, believe it or not, a little bit of both. The jocks know what to do when the spotlight's on them. You know, the nerds don't and they clam up. And some of them, instead of saying everything's fine, they want to draw attention to themselves. They want to say, well, the thing I'm working on is really amazing. And the reality is that, you know, this is just software. It's not the end of the world.

• 15:54Speaker 03

  It is the most important kind of software ever made, as you said earlier. But it's not going to kill us all. Dario also said that we'd be fully unemployed within, you know, one to five years, about two years ago. Well, he's got three years left. He also said that we'd cure all diseases. I don't think there'll be a single AI relevant drug in the next 10 years. AI does not help drug discovery. If you've ever built drugs, then raise your hand if you've made a drug, gotten FDA approval for a drug. Okay, I'm pretty sure that's it. You know, it's pretty hard. It's pretty hard to do. And we've had computers for a long time. I built a Cray cluster to analyze drugs. Now I can use, you know, GPUs, it's great. But there isn't much AI can help us with in drug discovery.

• 16:34Speaker 04

  What is, what is the revenue cycle that's driving valuation? So just unpack it from a hedge funder. You're sitting there, you're like, well, you see these run rates, right? I think Jensen mentioned it yesterday. $400 billion have gone into venture that has been redirected into AI. So do you interpret that as VCs funneling LP money to buy tokens effectively?

• 16:57Speaker 03

  Yeah, I think so. And

• 16:58Speaker 04

  That's your argument.

• 16:59Speaker 03

  Yeah. And I think you're going to have

• 17:00Speaker 04

  So there's a valuation trap in there, do you think?

• 17:03Speaker 03

  I don't think so. I think that what you mentioned earlier, productivity, I mean, there is infinite demand for intelligence and infinite demand for work. You know, when you said the workforce, I said, I think Brad's making a mistake here because if you take the TAM and limit it to knowledge workers, you're really undercutting the TAM.

• 17:17Speaker 05

  Yeah, for sure.

• 17:17Speaker 03

  And, you know, there's

• 17:19Speaker 05

  Trying to be conservative.

• 17:20Speaker 03

  Yeah, there's, there's no limit towards intelligence. One example I used to give and my buddy started, left OpenAI and started a company called Core Automation, which we're really excited about it, I mentioned the other night. He invented the reasoning model at OpenAI and he's got a somewhat under the radar startup. I think that you're going to have AI starting businesses. And that's, I think, part of his plan is to, is to have within, you know, OpenAI, OpenAI Pharma, OpenAI Motors, OpenAI Aviation, OpenAI, et cetera. And, you know, he will have his versions of those. And you're going to have a whole S and P 500 within OpenAI or Anthropic or wherever. So I think that the opportunities are boundless.

• 18:01Speaker 03

  And one last thing is that McDonald's can afford to spend $10 billion and say, OpenAI, give me 10 million GPUs. I want to know what the best next sandwich we should launch is. And that's an NPV positive. You know, if the intelligence is worth it, if it's not asymptotic, at some point, you know, more tokens doesn't get you a better answer. But, you know, when that day comes where, where you say, you know, instead of hiring McKinsey or a focus group, I need 10 million GPU minds to think about what is, what should McDonald's do right now?

• 18:27Speaker 04

  What is a good analog for the OpenAI, oh, sorry, for the AI marketplace? Meaning you see a handful of providers of a critical resource. You see incredible valuations that obviously attracts a lot of incremental capital. That breeds a lot of competition. There's various different solutions. Jensen made the analogy yesterday of like, it's kind of like water. You still have $8 water, but you still have the free water. And there's a, there's a world in which, you know, all flowers bloom. What's your mental model? How do you equate this to previous markets that we would understand? Oil or other

• 18:58Speaker 03

  It has to be like the internet. You know, at least part of it, you know, has to be like the internet where the only winners

• 19:03Speaker 04

  What do you mean specifically?

• 19:03Speaker 03

  Yeah, the only winners can be consumers and businesses. They get this tax-free kind of internet. Cisco and some other guys will get a little bit out of it. But the main highway that we're all going to benefit from, which we already are, is we have free intelligence in our pocket.

• 19:16Speaker 04

  So, but that, I would interpret what you're saying then as like, we have to now, right now, be guessing around, A, who's going to overearn and B, the duration of that over earning?

• 19:24Speaker 03

  Sure. And I think it's Meta.

• 19:25Speaker 04

  Timing.

• 19:27Speaker 03

  I think it's Meta.

• 19:27Speaker 04

  Meaning the company Meta?

• 19:28Speaker 03

  Yeah, they have, they have

• 19:29Speaker 04

  Why is that?

• 19:30Speaker 03

  They have the distribution with Muse. They showed that they still have product chops. I think we're still in day one of AI. I actually think that, you know, the chatbots are, we're going to look back and laugh and say, these are the early

• 19:41Speaker 05

  Would you own, would you go out today and buy OpenAI and Anthropic?

• 19:45Speaker 03

  Yeah. Yeah. So we have OpenAI, obviously, through a few different ways. And I think that Sam's hedge with hardware is really brilliant because the, you know, he'll always have the best model, I think. But being able to deliver that model is probably be more valuable than, you know, and that's what we saw with Muse, you know, this really creative product work that Alex did shows a, you know, a new avenue for them.

• 20:09Speaker 05

  What did you think about Elon's suggestion of competitor peer review? As kind of this third way, this middle ground pragmatic solution to balancing safety and speed?

• 20:22Speaker 03

  You know, they said at the Wright Brothers, if Warren Buffett could go back in time, he'd go back and shoot the Wright Brothers and stop the aviation industry from ruining capitalism. I think that Nick Bostrom wrote this book, Superintelligence, that scared the hell out of Elon. And, you know, maybe, I'm not advocating for violence, but you know, it would have been better if some of the EA kind of folks never sort of had this like narcissistic kind of self-importance that is driving some of these fear mongering. And I think OpenAI doesn't believe it. I think they're in this as wanting to not be too different from Anthropic. Thankfully, you know, some other guys have the spine. Meta has the spine to call it out and say, this is nonsense. The president has the spine to do the same. Jensen has the spine to do the same. It's just computer science. It's not that big of a deal.

• 21:06Speaker 05

  Some of these EA guys are pretty brilliant. Dario's no dummy.

• 21:11David Friedberg?

  Dustin Moskovitz, Sam Bankman-Fried.

• 21:14Speaker 03

  Sometimes you can be really smart at one thing and really dumb at another.

• 21:17David Friedberg?

  Let me ask you this question. And I actually, yeah.

• 21:21Speaker 03

  I have a good example.

• 21:23David Friedberg?

  Well, so this is actually my point. I feel like some of the smartest people are unconventional thinkers. And you're clearly a brilliant person. People that are brilliant are generally brilliant because they're not conventional. They're heterodox in some way, but in the same vein, if you have that brilliance, perhaps it's also hard for you to be conventional in the ways that are perhaps called it law-abiding.

• 21:47Speaker 03

  I have a problem with that.

• 21:49David Friedberg?

  Yeah. By the way, I just want to... Hold on one second. So I just like, so I want to just ask your opinion on Sam Bankman-Fried.

• 21:57Speaker 03

  Yeah.

• 21:58David Friedberg?

  So you've met the guy. You were, I think, right?

• 22:00Speaker 03

  I give him advice, yeah.

• 22:02David Friedberg?

  You give him advice on how to tough it out. But can you just give us your sense on the guy? You know, he was early in anthropic at the beginning. You look at the portfolio of investments he made. You look at the portfolio of investments he made. Across the board, it was crazy in 2021, 2022. It looked like [BLEEP].

• 22:21Speaker 03

  Genius, yeah.

• 22:22David Friedberg?

  Genius. Can you just tell us a little bit about what got that guy off track? Is it that he was so out of the bounds as a thinker? That's what got him all the alpha, but in the same way, it also got him in trouble?

• 22:33Jason Calacanis?

  Or was it speed?

• 22:34Speaker 03

  Yeah, I think it's both. You know, I think that one of the problems, if you grow up as an intellectually gifted person, you have this big albatross. I think this happened as well to Elizabeth Holmes. You know, Rajarajanan was another example of a hedge fund manager that was a decent investor. You know, it was much more that he, you know, I think, you know, to keep it short, you have this self-conscious, you have to live up to something. And sometimes wanting to live up to that big thing can be very detrimental and just failing and admitting that you are not that great at that is not that bad. I had a hard time admitting I was not the best hedge fund manager in the world, but I was a wonderful entrepreneur. That simple refusal to admit that put me in prison.

• 23:14David Friedberg?

  Have you changed your views on our behavior? Like, what has changed about you from prison? Like, what's the change?

• 23:20Speaker 03

  I think that, you know, psychology is a really powerful force. You know, we all do things. All of our actions are motivated by some innate drives that we don't always understand. You know, I didn't realize how petrified I was of not being seen as a great trader. What a stupid thing. You know, what a big deal. You know, you can be a great long-term investor, value investor, entrepreneur, you know, startup guy, whatever.

• 23:41David Friedberg?

  Dad.

• 23:42Speaker 03

  What's that?

• 23:43David Friedberg?

  Dad.

• 23:44Speaker 03

  Yeah, a great dad. A great scientist, whatever. You know, but if you really like fixate your own mind on, I have to be this, then that can be really detrimental.

• 23:53Jason Calacanis?

  Putting aside your case, valid or not, you know, if you were targeted for whatever reasons, what did you learn during your time in the big house? And how did it change you as a person? Because you're very different. You know, you seem to have gotten a lot sharper and a little bit more methodical in your approach to life. So take us through what you learned in that time.

• 24:19Speaker 03

  Well, thanks a lot. I think that the, one of the interesting things about prison is no matter where anybody's from or what they did or whatever, everyone sort of has a lot of these same real common features that bind us as humans. Everyone's sort of upset that their friend didn't stay in touch or longing for that person that, you know, they miss or whatever the case is. And it's really refreshing, you know, to remember that, you know, we're, you know, no matter what heights we achieve, that at bottom, you know, like you said earlier, we all want to be the best dad we can or whatever the case is.

• 24:50David Friedberg?

  Martin, you're, look, I think you're just a very independent thinker and you're also, you're willing to say the things and communicate in a way that other people don't. That's why we wanted to have you here. And I think, obviously, this was a great showcase of that. We really appreciate you taking the time to be with us.

• 25:06Speaker 03

  Thank you so much.

• 25:07David Friedberg?

  Yeah, it's great.

• 25:08Jason Calacanis?

  Thanks, Martin. Nice to meet you.

• 25:10Speaker 03

  Nice to meet you.

• 25:11Jason Calacanis?

  Hey, man, come back anytime.

• 25:12David Friedberg?

  Thanks, Mark.

• 25:13Jason Calacanis?

  Thank you.