All-In with Chamath, Jason, Sacks & Friedberg - Martin Shkreli: Big Pharma's Blind Spot, Betting Against Quantum & What Prison Taught Him

Starting point is 00:00:00 He's been called the most hated man in America because he jacked up the cost of a life-saving drug. Raise the price of a life-saving drug 5,000%. He became the poster child for capitalistic greed. Martin Schrelli, seven years in prison. We definitely plan 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.

Starting point is 00:00:25 I'm fulfilling some need in your life. Purchase the only copy of a legendary Woutain clan album. with no immediate plans to play it. It's not a good record. Ego is the fountainhead of all progress. Please welcome Martin Schrelli. Welcome, Martin. Thanks for coming out.

Starting point is 00:00:45 Nice to meet you. You wore Brad's outfit today. Exactly. All right. Welcome, Martin. We've got so many topics to go into with you. But I want to start with one. You've basically taken down quantum computing.

Starting point is 00:01:00 You said you'd 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. Disgratziad. And a disgraciad.

Starting point is 00:01:19 And then two, your best advice for those CEOs when they do go to jail, you've got a little experience. 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, you basically... You're like a shock caller. 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

Starting point is 00:01:48 the who scow. 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 a video. In my family, I was one of the first people in prison. So 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 Aronson, the preeminent guy in quantum. And, 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.

Starting point is 00:02:30 That's really not what it is at all. No. 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 Schorer'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, because, and this is my thesis,

Starting point is 00:02:53 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 data, deck of cards. If anybody know 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 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 sword

Starting point is 00:03:28 or something like that. So the only thing it's really good at, which indeed is a magic trick is going from exponential time algorithm, which is integer factorization or elliptic-curved 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 ruin 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 an Nvidia machine. They'll never beat a million of

Starting point is 00:04:02 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. Every time somebody pitches open AI, from what I've heard, about a quantum computer, they say no, we're not interested.

Starting point is 00:04:18 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 constructing the transistor. And from the transistor, what we're going to do is put a bunch of these things together.

Starting point is 00:04:35 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.

Starting point is 00:04:48 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? Yeah, no, of course. I think the issue, though, is do you buy, would you go along, 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 to corporate IBM. But, okay, so, but your critique is, is business model and timing, or is it technological? I think it's both. You know, I think that if you, when you talk to Google or IBM or Microsoft, NASA, why are you spending hundreds of millions a year on this? They say, well, someday there might be something that comes out of it.

Starting point is 00:05:29 That's not a great business to me. Well, it is very shocking. I mean, you can probably tell the story, but how many machines actually, how many machines is IBM and Google actually have? 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.

Starting point is 00:05:44 We cannot multiply 300 by 200 on any of these. And you're shorting them? Yeah, I've been shorted them. Unfortunately, we lost millions of dollars shorting quantum computing companies, but I told my wife that we gained billions in insight. Right. It's not easy to time the markets, yes. Definitely.

Starting point is 00:06:04 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. 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 matmull. These are matamol machines.

Starting point is 00:06:28 and I think that as we... Matrix multiplication is what he's referring. Right, so multiplying two lists of numbers is what a GPU spends 99.9% of its time doing. But it does it in N-Cube time. Now, what's funny, I think things like complexity algorithms are going to be parlance, you know, in the next five to ten years as we move towards a society.

Starting point is 00:06:49 So N-Cube means if you want to multiply a thousand-element square matrix versus another thousand-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 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.

Starting point is 00:07:11 In New York, that means something different, but AOC in this case is. And what I saw in the corner of my eye was that mat moles 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 Neurofoss, Optiore, Lumi, that want to make these photonic matmole cores, which would run circles around what Nvidia does. Now, of course, that's not the whole story.

Starting point is 00:07:38 There's a lot of details and non-linearity and storage. Beyond the hatred for quantum and you're raging short that may or may not be winning, you were a hedge fund manager, correct? Yeah, yeah. And the famous episode, just to catch everybody up, was you bought a drug, and then you inflated the price of the drug?

Starting point is 00:08:00 Is that what happened? Can you just give us the history and what takes you now to? Yeah, I started at 16 at Jim Kramer's hedge fund. Kramer was actually a tremendously effective and skilled trader. But Kramer's idea of investing was to buy and sell all day, every day, everything, and he somehow squeezed profits out of it. That was the way you do it in the 80s and 90s. And they actually made 23% a year, net of fees.

Starting point is 00:08:25 And so you worked for him as a 16. I worked for him at 16. How did you get that job? 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 Kramer one day pointed at me and said, genomics, you're going to look at genomics.

Starting point is 00:08:44 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 there 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

Starting point is 00:09:05 $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. Sell you the rights to the drugs. You can productize it, price it. Yeah, absolutely. Sorry, what do you mean? You just knock on Pfizer's door, and they're like, take what you'd like for a million We saw this and we built two very similar companies that our biggest constrained was finding people to vet these assets. So I started a small... Clinically. Yeah, just reading papers, reading patents, going to universities and small drug companies and big drug companies, making relationships.

Starting point is 00:09:42 It's called business development in our field. And our biggest bandwidth was, problem 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.

Starting point is 00:09:59 And having humans do that is a really tough thing. How do you take a molecule and flip it from 25 million to six, but what are you doing in that six or nine months that you own it? I think, so what I did with Sparsantan, 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. Hypertension, high blood pressure. Yeah. And so we moved into a disease called FSGS, which is a disease that never has 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 glomerilus sclerosis, never had a drug before. Now have a drug. I can be a good guy too. And I think the, you know, that was, you know, it's the art of that field. It's a great way to make a billion dollars. Is this what royalty pharma does?

Starting point is 00:10:51 Royvan. But is this also what royalty farmer does? They'll come in. Yeah, royalty is a bit of a different player. Genius guys, obviously. Their big claim to fame was they went to NYU, but was getting this big royalty from Remikade, and they bought it for like one-time's earnings.

Starting point is 00:11:04 And then that royalty, 20xed. They buy the cash flows at a bigger discount than how they're being proud. I know you want to get in here, so jump in you. Martin, take this framework now. You know, you know kind of the big levers in the market today, right?

Starting point is 00:11:17 What's expected in terms of compute? What's expected in terms of this KappaX build up? 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? Yeah, I mentioned photonic computing before because I do think in 20 years these H-100s are in the scrap heap. B-100s are in the scrap heap, B-200s and scrap heap.

Starting point is 00:11:37 We're going to find ways to do this better. I think it's insane that we're thinking that space. I think it's insane we're thinking about nuclear. Sam Aldman finally said publicly that he thinks photonic computing is on a rival podcast. that Potonic 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.

Starting point is 00:11:58 I think that that doesn't mean Nvidia's not along, and among semis right now, I think Jevin always wins in the end. Everyone, Jevin's paradoxes, you know, the crap out of everything. Alex Wang told me Jevin's going to, you know, take care of all this. This will be a million-x computer improvement.

Starting point is 00:12:14 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, what we'll have to see.

Starting point is 00:12:35 For now, I think there's, you know, my guess like anybody else's is as long, but I probably would have guessed in 2000 that Yahoo and Cisco longs, too. So it's a tricky place. So how do you man, do you run a hedge fund still? I don't. 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

Starting point is 00:13:00 behind those and not really thinking about competing with the market? Yeah, I'm certain nimble. I mean, I think this Dario trade was like a great reason to go along. So I sort of piled in, you know, onto the sphere. But I really, I don't... Meaning the sell off yesterday was sort of like just a headfin. I think it was ridiculous, yeah. What did you think about his essay?

Starting point is 00:13:20 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. I think he's a fearmonger. 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.

Starting point is 00:13:41 And he said, thanks a lot, Martin, going down the street. I'm going to start Pandora's Box, too. I said, what are you doing? You know, I would probably sue you for trade secrets or something. This is exactly what happened. 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... Would you be friends with that guy?

Starting point is 00:14:03 Would you like that? 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, retain knowledge. I'm not excusing it or I'm not saying I know anything that happened but The waitmo guy went to jail for it Well I think that that was because He was found to

Starting point is 00:14:25 I have actually stolen physical documents That were Okay so you have You have an issue with sort of the origin Story of the fear mongering, the ridiculous You know hyperbole You know he says the AI is going to help drug development Well let me ask you more

Starting point is 00:14:40 What about effective altruism Yeah even worse Why? Why is, what do you find so lonesome about this group of people? Let's unpack it. Yeah, so I help popularize and I even think, I would say, start the EAC movement, which was this counter-insurgency. I think that... Sorry, there's E-A. Yes. And then there's E-AC.

Starting point is 00:15:02 Which was intentionally named to... E-A is effective altruism. E-AC is? Effective accelerationism. Effective accelerationism. Which means? Which means? That's a great question.

Starting point is 00:15:11 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 fearmongering is a little ridiculous, and there'll be time to panic. It's just not now. Physically, 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.

Starting point is 00:15:35 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 spotlights 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. It is the most important kind of software ever made, as you said earlier.

Starting point is 00:15:58 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, and raise your hand, if you've made a drug, got an 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.

Starting point is 00:16:27 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. 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? Yeah, I think so.

Starting point is 00:16:58 That's your argument. Yeah. And I think you're going to have one. So there's a valuation trap in there, do you think? I don't think so. I think that what you mentioned earlier with productivity, I mean, there is infinite demand for intelligence and infinite demand for work. 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.

Starting point is 00:17:17 For sure. And, you know, there's... Trying to be conservative. Yeah. There's no limit towards intelligence. One example I used to give, and my buddy started left Open AI 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 OpenA.I.

Starting point is 00:17:33 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 have within, you know, Open AI, Open AI pharma, Open AI motors, Open AI aviation, Open AI, etc. And, you know, he will have his versions of those. And you're going to have a whole S&P 500 within OpenAI or Anthropic or wherever. So I think that the opportunities are boundless. 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.

Starting point is 00:18:11 And that's an NPV positive. You know, if the intelligence is worth it, if it's not asymptotic, at some point, more tokens doesn't get you a better answer. But, you know, when that day comes, where you say, you know, instead of hiring McKinsey or a focus group, I need 10 million GPU mines to think about what should McDonald's do right now. What is a good analog for the open A, oh, sorry, for the AI marketplace, meaning you see a handful of providers of a critical resource,

Starting point is 00:18:34 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 world in which all flowers bloom. What's your mental model?

Starting point is 00:18:54 How do you equate this to previous markets that we would understand? Oil or other things. 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. What would mean specifically? Yeah, the only winner is going to 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, as we have free intelligence in our pocket.

Starting point is 00:19:16 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 over-earn and be the duration of that over-earning? Sure. And I think it's meta. Timing. I think it's meta. Meaning the company meta. Yeah. They have. Why is that?

Starting point is 00:19:30 They have the distribution with Muse. They showed that they still product chops. I think we're still in day one of AI. I actually think that the chatbots are we're going to look back and laugh and say these are the early. Would you own, would you go out today and buy OpenAI and Anthropic? Yeah. Yeah, so we have Open AI, obviously, through a few different ways. And I think that Sam's hedge with hardware is really brilliant because he'll always have the best model, I think.

Starting point is 00:19:58 But being able to deliver that model is probably going to be more valuable than, you know, and that's where we saw with Muse. is this really creative product work that Alex did shows a new avenue for them. 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? 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 Boschrom wrote this book, Super Intelligence that scared the hell out of

Starting point is 00:20:33 Elon and, you know, maybe, I don't know, 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 Open AI 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. Some of these EA guys are pretty brilliant.

Starting point is 00:21:08 Dario's No Dummy. Dustin Moskowitz, Sam Bankman-Fried. Sometimes you can be really smart at one thing and really dumb at another. Let me ask you this question. And I actually, yeah. I'm a good example. Well, so this is actually my point.

Starting point is 00:21:25 I feel like some of the smartest people are unconventional thinkers, and you're clearly a brilliant person. and 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 call it law-abiding. And by the way, I just want to just ask your opinion on Sam Bankman-Fried. Yeah.

Starting point is 00:21:57 So you've met the guy. You were, I think, right? I give him advice. 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 and anthropic at the beginning. You look at the portfolio of investments he made. You look at the portfolio of investments he made.

Starting point is 00:22:17 Across the board, it was crazy in 2021, 2021, it looked like shit. Genius, yeah. 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 out. but in the same way it also got him in trouble. Or was it speed?

Starting point is 00:22:35 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, Raj Rajah Rzotnam was another example of, you know, head for manager that was a decent investor. You know, it's much more that he, you know, I think, you know, to keep it short, you have this self-conscious.

Starting point is 00:22:56 You have to live up to something. And sometimes wanting to live up to that big thing can be very done. 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 heteroom manager in the world, but I was a wonderful entrepreneur. That simple refusal to admit that put me in prison. Have you changed your views on our behavior? Like what has changed about you from prison? Like what's the change? I think that you know, psychology is a really powerful force. You know, we all do things. All 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

Starting point is 00:23:33 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, you know. Dad. What's up? Dad. 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. 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.

Starting point is 00:24:19 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 cases. 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 cases. 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.

Starting point is 00:24:59 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. Thank you so much. Yeah, it's great. Thanks, Lord. Nice to meet you. Hey, man, come back anytime.

Starting point is 00:25:12 Thanks, sir. Thank you.

All-In with Chamath, Jason, Sacks & Friedberg - Martin Shkreli: Big Pharma's Blind Spot, Betting Against Quantum & What Prison Taught Him

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