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

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. Aren't sterly seven years in prison, we definitely plan on raising the price, that's for sure. But 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. I've purchased the only copy of a legendary routine plan out of them with no immediate plans to play it. It's not a good record. You go as the found head of all progress. Please welcome Martin Schreley. Welcome, Martin. You're like, thanks for coming out. Nice to meet you. You wore brads outfit today. Exactly. All right. Welcome, Martin. We got so many topics to go into with you, but I want to start with one. You've basically taken down quantum computing. 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 a fugacy, a fugacy, whatever it is. 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'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 best practices when you go to the who scout. Yes, I have to shout out my wife, Chelsea. So Chelsea, very few people know her, but 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, shift it over a little bit. But Chelsea studied MIT and taught quantum computing with Scott Irons, and the preeminent guy in quantum. And you know, she got to teach me over many hours, you know, a penetrating 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 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. 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, you know, the famous algorithm's 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 they 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. And they're single-threaded when it comes to doing a job like a merge sort or something like that. So the only thing that's really good at, which indeed is a magic trick, 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 were 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. Every time somebody pitches open AI from what I've heard about quantum computer, they say, no, we're not interested. 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. 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 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. Do you believe that that could be possible? Yeah, 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, computer, tabulator machine, got a corporate IBM. But your critique is business model and timing, or is it technological? I think it's both. I think when you talk to Google or IBM or Microsoft NAS and why are you spending hundreds of millions 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. Well, it is very shocking. 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. We cannot multiply 300 by 200 on any of these. And you're shorting them? Yeah, I've been short 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. 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 "matmal." These are matmal machines. And I think that as we matrix multiplication is what he's referring to. Right. So multiplying two lists of numbers is what a GPU spends 99.9% of its time doing. But it does it in end cube time. Now, it'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 the society. So, end cube 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, 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. What I saw in the corner of my eye was that matmoles 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, Opticore, Lumai, 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. There's a lot of details and non-linearity and storage. Beyond the hatred for quantum and your raging short, that may or may not be winning. You were a hedge fund manager, correct? 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 history and what takes you now to? Yeah, I started at 16 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 sort of in the 80s and 90s. And he actually made 23% a year, not a fees. 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, real gates is my hero. And I found a way to get into that. And Kramer, one day, pointed at me and said, "genomics, you're going to look at genomics." 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 a 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 just the Myers for $2 million. And that same company is public today with $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 could productize it, price it. Yeah, absolutely. I'm 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." 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-- Minically. 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 and 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. 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? - I think so what I did with Sparsantan, a drug that I named and is now FDA approved, now Blockbuster Drug, is it Bristol discovered and developed it for hypertension, which is sort of a dead sleepy market that nobody really needs. - High-pertention, 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 focussing metal, glomerulus, glorosis and never had a drug before. Now I have a drug, I can be a good guy too. And I think that was, it's the art of that field. It's a great way to make the billion dollars. - Is this what royalty farmer does? - Royvan. - 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, it was getting this big royalty from Medicaid. And they bought it for like one time's earnings. - Yeah. - And then that royalty 20x'd. - They buy the cash flow at a bigger discount than how they're being produced. - Brad, I know you want to get in here. So you jump in here. - Martin, take this framework now. 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 cap X build out? Are you spending much time looking at that? And do you see any boogie men 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 H100s are in the scrap heap. B100s are in the scrap heap, B200s are in the scrap heap. There's, we're gonna find ways to do this better. I think it's insane that we're thinking of space. I think it's insane, we're thinking about nuclear. Sam Aldman finally said publicly that he thinks photonic computing is on arrival podcast. The fact that photonic computing could be the future. So I'm just wondering why we think this is a same idea to cover the earth with computers. One we can make a faster one. I think that that doesn't mean Nvidia's not along. And I'm long semi's right now. I think Jevin always wins in the end. Everyone Jevin's paradox is the crap out of everything. Alex Wang told me Jevin's gonna take care of all this. He'll be a million X compute improvement. They're 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 we're not gonna have a million X demand. Now we might in another way, but there aren't a million X more people. Whether we have a million X more compute needs, we'll have to see. For now, I think there's my guess like anybody else's is long, but I probably would have guessed in 2000 that Yahoo and Cisco belongs to. So it's a tricky place. - So how 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 behind those and not really thinking about competing with the market? - Yeah, I'm certain, Emble. I mean, I think 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 mean- - I mean, the sell-off yesterday was sort of like, just ahead of me. - Yeah, I think it was ridiculous, yeah. - What did you think about his essay? - I think that, I want to pay difference 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 sure that he had it. - I think it's a fear-monger. I mean, I also think that he, you know, imagined you and I started a business. And I said, I'm gonna show you Pandora's Box. Here it is. And you say, wow, that's amazing. And he said, thanks a lot, Martin. - I'm going down the street. I'm gonna start Pandora's Box too. So what are you doing? You know, it would, 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, would you be friends with that guy? 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, and I'm not saying, I know it's anything that happened, but - The Waymo guy went to jail for it. - 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 property. - That's the fear mongering, the ridiculous, you know, hyperbole, you know, he says the AI is gonna help drug development, we'll see. - Well, let me ask you more. - A total of, what about, what about effective altruism? - Yeah, even worse. - Why? - Well, what do you find so most about this group of people? Let's unpack it. - Yeah, so I hope popularizing, and I even think, I would say start the EAC movement, which was this counter insurgency. I think that - Sorry, there's EA. - Yes. - And then there's EAC, which was intentionally named. - EA is effective altruism. - EAC is - Effective accelerationism. - Effective accelerationism. - Which means? - Which means? - That's a great question. It does mean sort of move fast and break things. And it's no secret that Zuckerberg came out the other day and said, we shouldn't move fast. And now, I think the fear mongering is a little ridiculous. And there'll be time to panic. It's just not now. Physically, I presents those opportunities. For now, I mean, you can turn off the computer. It's not that big of a deal. I think nerds get carried away. If you ever were a nerd or a jock, I was, I believe they're not a little bit of both, the jocks know what to do when they're the spotlights on them. 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, but it's not gonna kill us all. Daria also said that we'd be fully unemployed with them, 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. 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, although-- What is the revenue cycle that's driving valuations? 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, and 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, 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 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 called core automation, which we're really excited about. I think 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 have within OpenAI, OpenAI Farma, OpenAI Motors, OpenAI Aviation, OpenAI, et cetera. And 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. And that's an NPV positive. If the intelligence is worth it, if it's not asymptotic, at some point, more tokens doesn't get you a better answer. But when that day comes, where you say, instead of hiring we can see your focus group, I need 10 million GPU minds to think about what should McDonald's do right now. What is a good analog 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 world in which all flowers bloom. What's your mental model? How do you equate this to previous markets that we would understand? Oil or other things? It has to be like the internet. At least part of it has to be like the internet, where the only winners. The only winners you 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. So 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 a meta. Meaning the company meta. Yeah, they have-- Why is that? They have the distribution. With Muse, they showed that they stole product chops. I think we're still in day one of AI. I actually think that the chat bots are this-- we're going to look back and laugh and say, these are the early-- Would you go out today and buy OpenAI and Anthropic? 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 he'll always have the best model, I think, but being able to deliver that model probably be more valuable than, you know, and that's where we solved the muse, you know, this really creative product work that Alex did shows a, you know, 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 that 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 Bosch from wrote this book, Super Intelligence that scared the hell out of Elon, and, you know, maybe I'm not advocating for pilots, 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 opening eye doesn't believe it. I think they're in this as wanting to not be too different from a 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. Some of these EA guys are pretty brilliant. Dario's no dummy, Dustin Moskovitz, Sam Beckman Freed. Sometimes you can be really smart at one thing and really dumb at another. Let me ask you this question. Do you, and I actually, yeah, that is a good example. 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 call it law abiding. And by the way, I just want to just ask your opinion on. Sam Beckman Freed. Yeah. So you've met the guy, you were, I think, right? I give him advice. Yeah. 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 investment made. You look at the portfolio of investments he made across the board. It was crazy in 2021, 2021, you look like. And genius. 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. Or was it speed? 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 it's happened as well to Elizabeth Holmes. You know, Roger Jordan was another example of your head for 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 head for a 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 of our actions are motivated by some in eight 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. Dad. What's up? Dad. Yeah, a great dad. 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. Well, thanks a lot. I think that 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. Martin, you're, 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. Thank you so much. It's great. Thanks, Martin. Nice to meet you. Nice to meet you. Hey, man. Come back anytime. [BLANK_AUDIO]