All-In Podcast
Martin Shkreli: Big Pharma’s Blind Spot, Betting Against Quantum & What Prison Taught Him
Ask the tutor about this video
Sign in with a verified email and ask the tutor anything about this video.
TL;DR
Research summary
▶ Quantum computing thesis
The introduction describes Shkreli as widely hated after raising the price of a life-saving drug by 5,000%, recalls his prison sentence, and mentions his purchase of the sole copy of a Wu-Tang Clan album. Asked to explain his criticism of quantum computing, he says his wife Chelsea, whom he describes as an early OpenAI employee and a quantum computing teacher, helped him understand the field. He argues quantum computing is not simply faster general-purpose computing: he says its most striking advantage is solving integer factorization and elliptic curve discrete logarithm problems in polynomial time, while other algorithms are not much faster. He contrasts quantum machines’ reported hundreds or thousands of hertz, single-core and single-threaded operation with conventional computing, and says they lack quantum RAM and require problems with small inputs. In his view, they will not outperform Nvidia machines on ordinary computing tasks, and he says OpenAI has declined quantum computer pitches.
▶ Quantum investment debate and photonic alternatives
The interviewer offers the counterargument that quantum computing is in an early hardware and software stage, and asks whether its current limits might be temporary. Shkreli agrees that the technology is early, but compares investing in it to backing older computing technologies such as punch cards, vacuum tubes, mercury delay lines, and core memory, which he says disappeared or went bankrupt. He says his criticism is both technological and business-related: major companies spend hundreds of millions annually based on the possibility of future results, which he does not consider a compelling business. He says Google has a few prototypes and IBM a small cloud offering, and that he and Chelsea tried the available machines but could not multiply 300 by 200 on them. He reports losing millions of dollars shorting quantum companies, while saying the experience gave him insight. On encryption, he describes quantum’s relevance as limited to particular computational problems. The conversation turns to matrix multiplication and analog optical computing: Shkreli says light-based matrix multiplication could be fast, mentions startups working on photonic cores, and says he is investigating the area, possibly including starting a company. He notes unresolved details including nonlinearity and storage.
▶ Investing in drug assets and development
Asked about his history as a hedge fund manager and drug pricing, Shkreli says he began working for Jim Cramer at age 16. He describes Cramer as an effective trader who made 23% annually net of fees, and says he later realized he was better at entrepreneurship and private equity than trading. He says drug assets could be acquired cheaply from pharmaceutical companies and cites buying a drug from Myers for $2 million; he says the company that developed from that first startup is now worth $6 billion. He describes building companies around finding and evaluating drug assets, with research papers and patents creating a bottleneck, and says he started a small natural language processing company in 2015 to help review medical literature, which he says comprised 40 million papers. He explains that a drug he named Sparantan had been developed by Bristol for hypertension, then was redirected toward FSGS, a disease he says had no approved treatment. He says the drug received FDA approval and that 30,000 people with FSGS now have a drug. He describes this kind of repositioning as a way to create substantial value. In response to a question about Royalty Pharma, he distinguishes its model, describing its purchase of a Remicade royalty at about one times earnings and the subsequent increase in that royalty’s value.
▶ Semiconductors, compute demand, and disruption
Asked about risks in the current computing buildout and possible positions, Shkreli says he expects current Nvidia accelerators such as H100s, B100s, and B200s to eventually become obsolete, and argues photonic computing could offer a better approach. He criticizes plans to cover the earth with computers, including proposals involving space or nuclear power, if faster computing methods emerge. He says this does not mean Nvidia is not a long and states that he is currently long semiconductors. He invokes Jevons paradox and describes possible improvements such as million-fold computer improvements or transformers said to be a thousand times more efficient. He says substantially greater efficiency could be bad for Nvidia, and questions whether demand will grow a million-fold, while allowing that it might. He calls the outlook difficult and compares the uncertainty to how an investor in 2000 might have incorrectly expected Yahoo and Cisco to remain winners.
▶ AI portfolio views and market economics
Shkreli says he no longer runs a hedge fund and manages his own money. He describes himself as nimble and says the Dario trade gave him reason to go long; he calls a recent selloff ridiculous. He criticizes Dario as a fear-monger and objects to the circumstances around a group leaving OpenAI to start a competing company, while acknowledging that Silicon Valley departures and the boundary between patents and retained knowledge can be hard to assess. He says he does not know all the facts and notes that the person in question was found to have taken physical documents. On AI’s economic prospects, Shkreli argues that demand for intelligence and work is effectively unlimited, and that restricting the market to knowledge workers understates its potential. He imagines AI systems starting businesses across sectors and says a company such as McDonald’s could rationally spend $10 billion on GPUs if the resulting intelligence justified the cost, subject to a limit where additional tokens stop improving answers. He agrees with the interviewer’s framing that some venture capital may be redirected into AI token purchases, after the interviewer cites Jensen’s statement that $400 billion had gone into venture and been redirected into AI, but rejects the interviewer’s suggestion that this necessarily creates a valuation trap. He compares AI partly to the internet, where consumers and businesses may capture much of the benefit while infrastructure providers capture less. He says it is necessary to judge which companies will over-earn and for how long, favors Meta because of distribution and product capability, and says chatbots may later look primitive. He says he has exposure to OpenAI through several routes and sees its hardware efforts as a way to deliver its models.
▶ AI safety, effective altruism, and drug discovery claims
Asked about Elon Musk’s competitor peer-review proposal as a balance between safety and speed, Shkreli rejects much of the AI safety rhetoric as fear-mongering. He criticizes effective altruism figures for what he characterizes as self-importance and says he helped popularize effective accelerationism, which he describes as moving fast and breaking things. He argues that it is too early to panic, says current AI is software that can be turned off, and distinguishes its importance from claims that it will end the world. He disputes predictions attributed to Dario, including that people would be fully unemployed within one to five years and that AI would cure all diseases, and says he does not expect a single AI-relevant drug in the next 10 years. Drawing on his own work with computing for drug analysis, he says AI has little to contribute to drug discovery and emphasizes how difficult drug development is. The discussion then turns to Sam Bankman-Fried. Shkreli says Bankman-Fried’s investments looked brilliant in 2021 and 2022, and suggests that intellectual gifts and pressure to live up to a reputation can contribute to poor decisions. He says he himself had difficulty admitting he was not the world’s best hedge fund manager and was instead a strong entrepreneur, and says that refusal put him in prison.
▶ Prison, ambition, and personal perspective
Shkreli says prison helped him recognize the psychological drives behind his actions, including his fear of not being regarded as a great trader. He says people can pursue other identities, such as long-term investor, value investor, entrepreneur, or startup founder, and that fixating on one required identity can be damaging. Asked what prison taught him more broadly, he says people from different backgrounds share common human concerns, such as missing friends and loved ones, and that achievement does not change those basic needs. The interviewers thank him for his independent thinking and candor, and the conversation ends.
Full transcript
[0:31] play it. >> It's not a good record. Ego is [music] the fountain head of All Progress. >> Please welcome Martinrelli. >> Good. >> Welcome, Martin. Thanks for coming out. Nice to meet you. >> You wore Brad's outfit today. >> Exactly. [laughter] >> All right. Welcome, Martin. Um, we got so many topics to go into with you, but I want to start with one. um you've basically taken down quantum computing. You said you'd think that this is a
[1:02] fraud uh that people might be going to jail. So twopart question. Number one, uh maybe you could give us your thesis on on why this is a fraud or fugazi, a fugizi, whatever it is. Um >> discretiad >> and a discard. And then two, your best advice for those CEOs when they do go to jail. You've got a little experience. >> Yeah. [laughter] >> Best advice in terms of you don't seem
[1:32] to be the kind of guy who would thrive in prison, but from what I understand, Martin, >> you shot, you turned into Lex Luthther 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 I have to shout out my my wife Chelsea. So, Chelsea um very few people know her, but she's uh 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
[2:03] family, I was one of the first people in prison. So, [laughter] I thought I'd switch the gene pool, you know, shift it over a little bit. But [laughter] Chelsea t um studied MIT and taught uh quantum computing with Scott Aronson, the the pre-minent guy in quantum and you know, we we uh she she got to teach me over many hours, you know, uh 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. >> No. >> Um and I think the the basic point of
[2:34] quantum computing is that there's one algorithm. In fact, the whole field was invented because of Peter Shore's thought experiment. Peter uh 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 uh 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 know the famous algorithms
[3:04] 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 sword 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
[3:34] 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 polinomial 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 there's not a whole lot uh 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
[4:04] doesn't have storage. There's no such thing as quantum RAM. Uh so you have to design a problem that has a really really small input. uh you cannot put the world's compute into it. Every time somebody pitches open AI from what I've heard about quantum computer, they they say no, we're not interested. >> I 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
[4:34] 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 uh 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 a computational limitation except to a very narrow use case? Like do you believe that that could be possible? >> Yeah. No, of course. Uh I think the issue though is do you buy would you go long punch cards and vacuum tubes,
[5:04] right? uh mercury delay lines, core memory, these are all things that disappeared and went bankrupt. Those are those were stocks at one point. Uh CTR, you know, uh computer tabulator machine got incorporated into 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 and ask them why 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 you can probably tell the story, but how many machines actually how how many
[5:35] machines does IBM and Google actually have? >> Uh, very few. So, Google has a few prototypes. IBM has a little cloud. Chelsea and I have tried every single quantum computing uh 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. Uh, unfortunately, we lost millions of dollars shorting quantum computing companies, but I told my wife that we gained billions in insight. [laughter] >> Right. It's uh it's it's not easy to time the markets. Yes, definitely. >> So, where where is the natural
[6:06] 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 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 matmo. These are matt mole machines and I think that as we >> matrix multiplication is what he's referring >> right so so multiplying two lists of numbers is what a GPU spends 99.9% of
[6:37] 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 uh you know in the next 5 to 10 years as we m 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
[7:08] AOC, analog optical computing. In New York, that means something different, but AOC in this case is >> Yeah. >> Uh, and what I saw in the corner of my eye was that Matt Moles in light are free. >> Yeah. >> They happen in instant time, which is a really remarkable thing. So we're investigating maybe we'll start a company uh or something like that but the there's a couple of startups like Neurofas Opticore Lumi that want to make these photonic matal cores which would run circles around what Nvidia does. Now of course that's not the whole story
[7:39] there's a lot of details and nonlinearity and storage >> just be beyond the 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 um just to catch everybody up was uh you bought a drug and then you inflated the price of the drug. Is that what happened? Can you just give us the just the >> Yes. the history and what takes you now to >> Yeah. I started at 16 at Jim Kramer's hedge fund. Kramer was actually a
[8:10] tremendously uh effective and skilled trader. Uh but Kramer'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, uh net net of fees. >> And so you worked for him as a 16-y old. >> I worked for him at 16. >> How did you get that job? >> Uh just being in Manhattan, having a big mouth, and you know, it's a great city. Uh and uh loving stocks, loving investing. Bill Gates was my hero. >> Uh and I found a way to you know, get
[8:40] into that. And Kramer one day pointed at me and said, "Genomics, you're going to look at genomics." And I uh 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 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 2 million. And that same company is public today. It's worth 6 billion. So that was my first startup. And um it was a really inefficient
[9:11] market. Fiser 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 you can productize it, price it. >> Yeah, absolutely. >> I'm sorry. What do you mean? You just knock on Fizer'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 uh our biggest constraint was uh finding people to vet these assets. So I started a small >> NL clinically. >> Yeah. just reading papers, reading uh uh patents, going to universities and small drug companies and big drug companies,
[9:41] 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. Uh 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 what are you doing in that 6 or 9 months that you own it? >> I think so. What I did with with Sparantan, a drug that I named and and
[10:13] 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 we moved into a disease called FSGS, uh 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 FD approved. 30,000 people who have focusal glamial sclerosis and never never had a drug before now have a drug. I can be a good guy too. Uh and I think the you know that that was you know it's
[10:45] it's the art of that field. It's a great way to make a billion dollars. It's uh >> is this what royalty pharma does? >> Roy Van but is is this also what Royalty Pharma does? They'll come in. >> Yeah. Royalty is a bit of a different player. Genius guys obviously they they their big claim to fame was they went to NYU was getting this big royalty from from Remicade and they bought it for like one times earnings. Yeah. >> And then that royalty 20xed >> 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 you know kind of the big levers in the
[11:16] 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 in 20 years these H100s are in the scrap heap. B100s are in the scrap heap, B200s in the scrap heap. There's we're 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 Alman finally said publicly that he thinks
[11:46] photonic computing is on a rival podcast. uh the that batonic computing could be the future. So I'm 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 I'm long semis right now. Um I think Jeban always wins in the end. Everyone Jeban's paradox is you know uh the crap out of everything. Alex Wang told me Jeban's going to you know take care of all this will be a millionx computer improvement. There are guys in the valley talking about better transformers that are a thousand times
[12:17] 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. Uh now, we we we might in another way. Uh 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 belongs too. So it's it's a tricky place. >> So how do you man do do you run a hedge fund still?
[12:47] >> 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 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 I I 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, uh, onto the sphere, but I really I >> thinking the selloff yesterday was sort of like just a head.
[13:17] >> Yeah, I think it was ridiculous. Yeah. >> What did you think about his essay? >> Uh, I think 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. >> I 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 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, it would I
[13:48] would probably sue you for trade secrets or something. This is exactly what happened. Um, >> 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. Um, and you know, it's it's sort of hard to pinpoint what is a patent versus what is, you know, retained knowledge. I'm I'm not excusing it and I'm not saying I I know ex
[14:18] anything that happened, but um >> the way guy went to jail for it. >> Well, I think that that was because he was found to have actually stolen physical documents that were um Okay. So, you have you have a an issue with sort of the origin story of >> the fear-mongering, the ridiculous, you know, hyperbole, right? You know, he says the AI is going to help drug development will kill. >> Let me ask you to what about effective altruism? >> Yeah, even [snorts] worse. >> Why? Why is What do you find so bosome about this group of people? Let's unpack
[14:49] it. >> Yeah. So, so I helped popularize and I even think I would say start the EAC movement which was this counter insurgency. I 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. Uh it may 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.
[15:20] I think the fear-mongering is is a little ridiculous and there'll be time to panic. It's just not now. Um physical AI presents those opportunities. You know for now I mean uh you can turn off the computer. It's it's it's not that big of a deal. I think nerds get carried away. Um, 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 they're 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
[15:51] is just software. It's it's it's not uh the end of the world. It is the most important kind of software ever made, as you said earlier, but it's not going to kill us all. Uh 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. Uh 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. Um AI does not help drug discovery. If you've ever built drugs, and raise your hand if you've made a drug, gotten FDA approval for a drug. Okay, I'm pretty sure that's it. Um you
[16:21] know, it's pretty hard. Uh it's pretty hard to do. And and 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. Other >> what is what is the revenue cycle that's driving valuation? So just unpack it from a hedge funer. You're sitting there, you're like, well, >> you see these run rates, right? Um I think Jensen mentioned it yesterday, $400 billion have gone into venture that has been redirected into AI. So, do you
[16:51] interpret that as VCs funneling LP money to buy tokens effectively? >> Yeah, I think so. And 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 [snorts] 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. You know, when you said the workforce, I said I think Brad's making a mistake here because if if you take the TAM and limit it to knowledge workers, you're really undercutting the TAM for sure. And you know the there's >> trying to be conservative. >> Yeah. There there's there's no limit
[17:22] towards intelligence. One example I used to give and um my my buddy started left a open AI started a company called core automation which we're really excited about. I think I mentioned the other night u he invented the reasoning model at openai and he's got a somewhat under the radar startup. Um, 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, Open AI, Open AI Pharma, Open AI Motors, Open AI Aviation, Open AI, etc. And, you know, he he'll have his versions of those. And you're going to
[17:53] have a whole S&P 500 within Open AI or anthropic or or wherever. Um, so I think that the opportunities are are boundless. And and one last thing is that McDonald's can afford to spend $10 billion and say, "Open open AAI, 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, uh if the intelligence is worth it, if it's not asmmptotic, 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 Mackenzie or focus group, I need 10 million GPU minds to
[18:24] think about what is what should McDonald's do right now. What is a good analog for the open a 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 it 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
[18:55] previous markets that that we would understand oil or other >> has to be like the internet I you know at least part of it you know has to be like the internet where the only winners >> specifically >> yeah the only winners are going to be consumers and 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 the the main highway that we're all going to benefit from which we already are as we have free intelligence in our pocket >> 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 >> sure and I think it's meta
[19:25] >> timing [laughter] I think >> I think it's meta >> meaning the company meta. >> Yeah, they they have they have >> Why is that? >> They they have the distribution with Muse. They showed that they still have product chops. Um I think we're still in day one of of AI. I actually think that you know the chat bots are this we're going to look back and laugh and say these are the early >> Would you own would you go out today and buy open AI and anthropic? >> Yeah. Uh yeah. So we have open AI obviously through through through a few different ways. And um I think that Sam's hedge with hardware is is really brilliant because the you know he'll
[19:56] 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 where we saw with Muse you know this really creative product work that that that Alex did um 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 uh uh balancing safety and and speed? You know, they said that the Wright brothers uh if Warren Buffet could go back in time, he'd go back and
[20:26] shoot the Wright brothers and stop the aviation industry from ruining capitalism. I think that Nick Boston wrote this book, Super Intelligence, that scared the hell out of Elon. And you know, maybe I'm not advocating for violence, but you know, it would 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 uh anthropic. Uh thankfully, you know, some other guys have the spine.
[20:57] 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. Uh Daario's no dummy. Dustin Mosovitz. Um Sam Bankman 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. I'm a good example. >> Well, well, so this is this is actually my point. I I feel like some of the smartest people are unconventional
[21:28] thinkers and you you're clearly a brilliant person. People that are brilliant are generally brilliant because they're not conventional. They're they're they're 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 um I've [laughter] Yeah. And by the way, I just >> Hold on one sec. Hold on one sec. So I just like So I I want to just ask your opinion on Sam Bankman Freed. >> Yeah. >> So you've met the guy you you were uh I
[22:00] think right? >> I give him advice. 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 you look at the portfolio of investments made, >> you look at the portfolio of investments he made across the board, it was crazy in 2021, 2022, looked like >> and >> 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
[22:31] the alpha but in the same way it also got him in trouble >> or was it speed? >> Yeah, I think I think it's both you know I think that one of the problems if you grow up as a intellectually gifted person you have this big albatross. I think this happened as well to Elizabeth Holmes. You know, uh Raj Rodney was another example of you know head for manager that was was a decent investor. Uh you know it was much more that he you know I think you 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 um very detrimental and
[23:02] 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 hetero 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 or behavior? Like what has changed about you from prison? Like what's what's the what's the change? >> I I think that you know, psychology is a really powerful force. You know, we all do things. Our all of our actions are motivated by some innate drives that that we don't always understand. You know, I didn't realize how petrified I
[23:32] was of not being seen as a great trader. What a stupid thing. you know what a big deal, you know, uh 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 guy, great a great scientist, whatever. Uh, you know, but if you really like fixate your own mind on I have to be this, then that can be really detrimental. Uh, putting aside your case, valid or not, you know, if you were um targeted uh for whatever reasons, what did you learn during your
[24:04] uh time in the in the big house? And how did it change you as a person? Because you're very different, you know, you 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. >> Oh, thanks a lot. Um, I think that the one of the interesting things about prison is no matter where anybody's from or 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 uh upset that their friend didn't stay in touch or
[24:34] longing for that person that that you know they miss or whatever the case is. And it's really uh refreshing, you know, uh to remember that, you know, we're, you know, no matter what heights we achieve that at 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 uh 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. Um and I think obviously this was a great showcase of
[25:04] that. We really appreciate you taking the time to be with us. Thank you. >> Thank you so much. That's great. >> Thanks, B. Nice to meet you. Nice to meet you. Hey, man. Come back anytime. [music] Thank you.