He's been called the most hated man in America because he jacked up the cost of a life-saving drug, raised the price of a life-saving drug 5,000%. He became the poster child for capitalistic greed.
Martin Shkreli, 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. I'm fulfilling some need in your life.
Purchased the only copy of a legendary Wu-Tang 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 Shkreli.
Welcome, Martin. Thanks for coming out.
You wore Brad's outfit today.
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 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 fugazi, 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 shot, 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.
Yeah. So, 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 at NVIDIA. 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 at MIT and taught quantum computing with Scott Aaronson, 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. 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 Shor'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, 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, 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 OpenAI, from what I've heard about quantum computers, 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, 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, as 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?
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 Tabulating Machine, got incorporated into IBM.
But okay, so but your critique 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 and ask them why are you spending hundreds of millions 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. I mean, you can probably tell the story, but how many machines actually, how many machines does 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.
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 matmul. These are matmul 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 n-cubed time.
Now, what's funny, I think things like complexity log algorithms are going to be parlance, you know, in the next five to ten years as we move towards a society. So n-cubed 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.
I was flipping through a paper from Microsoft that was called AOC, analog optical computing. 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 matmuls 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 Neurophos, Opticore, Lumi 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.
Just 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 at 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.
And so you worked for him as a 16-year-old.
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 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 Myers for two million. And that same company is public today. It's worth six 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.
I'm sorry. What do you mean? You just knock on Pfizer's door. 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...
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 problem was reading these papers. So I started a small NLP company on the side in 2015. We had Watson. It was great. 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 bill? What are you doing in that six or nine months that you own it?
I think so. What I did with Sparatan, 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 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 glomerulosclerosis and never had a drug before now have a drug. I can be a good guy, too. And I think 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? Is this also what Royalty Pharma 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, was getting this big royalty from Remicade, and they bought it for like one times earnings.
And then that royalty 20x'd.
They buy the cash flows at a bigger discount than how they're being...
Brad, I know you want to get in here, so jump in.
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 capex buildout. 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 H100s are in the scrap heap. B100s are in the scrap heap, B200s in the scrap heap. 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 doesn't mean Nvidia is not long, and I'm long semis right now. I think Jevons always wins in the end. Everyone, Jevons' paradox is, you know, the crap out of everything. Alex Wang told me Jevons is going to, you know, take care of all this. We'll be a million-x computer improvement. 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 belonged, too. So it's a tricky place.
So how do you manage? Do you run a hedge fund still?
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, or 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 sort of nimble. 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...
You're thinking the selloff yesterday was sort of like just a head.
Yeah, I think it was ridiculous. Yeah.
What did you think about his essay?
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... I think he's a fear-mongerer. 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 he said, 'Thanks a lot, Martin. I'm going down the street. I'm going to start Pandora's box 2.' 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? 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, retained knowledge. I'm not excusing it, and I'm not saying I know anything that happened, but...
The Waymo guy went to jail for it.
