AI Is Dumber Than Investors Think — ft. Gary Marcus

Ed Elson is joined by Gary Marcus to discuss why he’s concerned about the fact that we’re all-in on AI. They explore why he argues generative AI is inherently unreliable, whether the concerns surrounding Anthropic's Mythos model are justified, how policymakers should approach AI regulation, and the biggest misconception about the technology that he believes needs to be corrected. Gary Marcus is a leading voice in artificial intelligence, author, and professor at NYU Stern. Subscribe to the Prof G Markets Youtube Channel Check out our latest Prof G Markets newsletter Follow Prof G Markets on Instagram Follow Ed on Instagram, X and Substack Follow Scott on Instagram Send us your questions or comments by emailing [email protected] Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

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• 1:40Ed Elson?

  Welcome to Prof G Markets. We've spent a lot of time talking about AI lately. From the Trump administration's export restrictions on anthropics models to the ongoing questions surrounding the economics of companies like OpenAI and Anthropic. Taken together, these stories point to two fundamental questions. One, is the AI boom financially sustainable? And two, are we moving too quickly with a technology that we don't fully understand? Few people have been asking those questions longer than our next guest. Long before concerns about AI safety, regulation and business models entered the mainstream. He was warning about the technology's limitations and challenging some of the industry's most ambitious claims.

• 2:24Ed Elson?

  He has testified before the Senate on the risks posed by AI. He's founded a machine learning company that was acquired by Uber and is now one of the field's most prominent skeptical voices. So here's our conversation with Gary Marcus, AI skeptic, author and professor at NYU Stern. Gary, thank you so much for joining me on the show today. You are one of the original critics of AI. And that's quite interesting because you're also, you work in AI. You started a machine learning company, which I think you could say is an AI company. You've done a lot of AI research.

• 3:07Ed Elson?

  You are sort of part of, part of the AI world, but you have issues with it. Let's just start broad. What are your concerns?

• 3:14Speaker 09

  My concerns are we're all in on a particular technology that I think is inelegant, harmful, not where we should wind up and being abused by the people that are using it. So I want AI to succeed, but I think we wound up down this really dangerous path. So you think about the Star Trek computer. You ask it a question, it gives you an answer that you can count on.

• 3:38Speaker 09

  Presumably it's not done to sort of wreck society, it's done to help people. Well, what we actually have is everybody running around with LOMs, which are inherently unreliable. They're unpredictable, they can't be aligned to human values. And they're being run by companies that don't seem to really give a shit about the consequences for what they're building for society. It's like a nightmare for those of us who have worked in AI to suddenly see what we're building be used in so many bad ways and with people really not caring. We should want a more reliable technology that we can really count on, that is compatible with humanity. You know, five years ago, that didn't seem out of the question.

• 4:22Speaker 09

  Five years ago, the field was healthy, it was considering lots of different things. It wasn't driven so much by money, but by intellectual curiosity. How do you make a machine that's intelligent? And everything changed when people started to realize that there might be money to be made. Still not clear that there actually is money to be made, by the way, right? And I'm sure we'll get into that because we have very similar views about that. But the thought, the scent of money, possibly misguided scent of money, really changed how the field grew. And it's also, you know, a technical thing, transformers are interesting and people got into them. But fundamentally, I think it's the scent of money really changed how people built AI, how they thought about it, what they wanted to do with it, who was running it.

• 5:07Speaker 09

  I think a lot of grifters came in that don't even necessarily have a technical understanding of the questions and do a lot of lying and hyping about what their things might actually do. And it's just really been unpleasant for the last several years, being honest about it. And it's not because I don't want AI to succeed. I still think that there is a chance that AI could help a lot in medicine, that it could help with all kinds of technologies. Like, I would still like to see AI succeed, but not on the path that we are right now. This is just not a good path.

• 5:36Ed Elson?

  You recently wrote, you said, quote, "Generative AI has been inherently unreliable from the start. None of the problems that I warned about over the last half decade have been properly solved." There's the financial question, which we will get into. But then there's also the question of the technology itself. What do you see as the problem? What makes Gen AI inherently unreliable? What is the path that you are worried about this technology going down?

• 6:06Speaker 09

  The technical problem is that large language models, fewer large language models, are basically next token predictors. That's what they do. That is literally how they are built, is to predict in a sequence of words or other kinds of tokens what might come next. And that's an interesting thing to do. It's part of what humans do, is we do some prediction. But it's not all of what cognition is, right? Cognition, right, intelligence is about cognition, about understanding things and so forth. Has many different components to it. And they're just not really built into L L Ms. And so L L Ms basically fake everything else. And we can talk about some complications. People are building in harnesses and we can go there.

• 6:49Speaker 09

  But let's just talk about pure L L Ms. What they do is predict the next token. And if you train them on the entire Internet, which is what people in fact do, they can make a pretty good approximation of human beings and how they talk and so forth. But that approximation is very superficial. It's very data dependent. And when you push them outside of the regime in which they've been trained, they will do really stupid things. So like a couple of years ago, there were all these examples of so called river crossing problems. Like you have a man and a goat and a woman, and they have to go across. And these systems would say the most absurd things in response to those problems. It got so embarrassing that Anthropic built in

• 7:34Speaker 09

  river crossing problems into their system prompts to try to keep the systems from making these embarrassing errors. And what the embarrassing errors revealed is the systems are not really reasoning about things like a man or a river or a boat or what it means to go across the other side. They're just trying to kind of glom the words together that they have seen. I mean, you know, the technical details are a little bit complicated. But to a first approximation, what they are doing is just stringing these words together. There are other ways to think about building intelligence. So you might start, for example, with a database. Who did what to whom, when and where? If you actually did that, if you started with that, you would not have all these crazy hallucinations. And so here we are, you know, in 2026.

• 8:18Speaker 09

  I started writing about L L Ms in 2019. And I said, they don't have stable models of the world. You can't trust on them. And everybody said, Gary, Gary, Gary, we're just going to add more data. All these problems are going to go away. Hallucinations are going to go away. Mustafa Soleimani, who's the CEO of AI or whatever his title is at Microsoft, said, you know, they're going to go away in a few months. This was in 2023. I think I offered him a bet and he kind of walked back what it was.

• 8:46Speaker 09

  Reid Hoffman said he would bet any amount of money that hallucinations would go away in a few months. This is 2023. I said, I'm over here. How about a hundred thousand dollars? He never got back to me. But here we are in 2026 and hallucinations have not gone away. It's because the core of next token prediction does not allow you to address that problem. You have to add something else and the something else, you know, rarely works all that well. It sort of works a little bit. You know, I just saw a study yesterday showing there's a new benchmarking, I think it's called Halu hard hallucination hard. And all the systems are still making errors on this. None of this has gone away. It's hard for people who are not trained in cognitive science and artificial intelligence

• 9:30Speaker 09

  to understand when they play with these systems that they don't think like human beings, that they're really operating over different principles. Because they are built to mimic human beings and human beings are not built to distinguish AI systems that work differently from themselves from actual humans. So like, we have a lot of evolutionary machinery to find fast things that are moving. That might be snakes or bugs or lions. We have nothing built into our brain to really help us think about the nature of intelligence. And so people are very easily fooled. We've actually known that for a long time. We've known it for 60 years, right? Eliza was the first example of an AI system that could fool an average person into thinking it was much more intelligent than it was.

• 10:13Speaker 09

  Eliza behaved as a psychiatrist and he just did simple keyword matching. So you say, you know, relationship and it asks you to tell you more about that relationship or whatever, just by matching keywords, not understanding anything. So Weizenbaum wrote about this in the 60s, how we are vulnerable to over attributing, is the technical term, intelligence to machines. And, you know, that was a curiosity, I guess, when he wrote about that in the 60s. But now that is the whole world, right? The entire economy, this is, you know, where our shared interest is, I suppose. The entire economy is based right now is being, is hinging on over attribution of intelligence to these machines.

• 10:56Speaker 09

  You have people betting trillions of dollars that these machines are intelligent in ways that they aren't actually, because those people placing the trillion dollar bets don't have enough cognitive science background to know the right tests in order to evaluate intelligence. And then we have like government policies built around these things or, you know, considered around these things. The entire world is over attributing intelligence to L L Ms. It's not that L L Ms can't do anything, like they're great for autocomplete for the purposes of computer coding and they're great for certain kinds of brainstorming and so forth. But their intelligence is still limited and we probably need a completely

• 11:37Speaker 09

  different approach. You know, I like to have a metaphor of climbing mountains, right? And you could get to the peak of one mountain and think, well, I must be close to the top, but actually you might not be, right? If it's a mountain range and there's a whole bunch of different peaks, you might be at the peak of one. In order to get to the tallest peak, you might have to actually go back down the valley. And that's what we need to do. We actually need to give up some of the progress that we've made in order to come up with new ideas. But everybody's obsessed with one idea. You know, they're obsessed with the large language model that actually has these problems of, and we didn't even get into, but bias and unreliability, et cetera. But people are so addicted to the one thing that they're, you know, they're all in on that. And, you know, we'll get

• 12:18Speaker 09

  to the economics soon, I suppose. But part of the economic problem hinges from that, that everybody is using the same solution. If you had a healthy ecosystem, you might have a hundred different companies trying a hundred different approaches and you could say, let the best one win. But we have basically a hundred companies, maybe not a hundred, but a dozen companies doing exactly the same thing. And if it's not the right thing, that's a problem. And even if it is a right thing, it's a problem. You know, I'm pretty sure it's not, but it's still a problem if everybody's doing the same thing. That means making profits is really hard. So, you know, we'll talk about the economics and why nobody is making profit. But the underlying reason nobody's making profits, they're all doing the same thing. If we all have the same toothpaste, nobody's gonna pay that

• 13:01Speaker 09

  much for it. You can't charge a hundred dollars for a tube of toothpaste if, if you have, you know, nine competitors building basically the same thing for less.

• 13:09Ed Elson?

  Basically, we're all using one of two models, essentially. It's, you're probably using OpenAI's model, you're probably using Anthropic's model, and then a lot of these companies are building wrappers and building all of these gadgets and gizmos on top of those models. But to your point, you're basically just... Putting wrappers on on top of the same fundamental thing.

• 13:28Speaker 09

  Well, and those two are actually basically the same, and they're basically the same as what.

• 13:32Ed Elson?

  The same cognitive architecture.

• 13:34Speaker 09

  Yeah, there's small differences, but they don't persist, right? I mean, that's another thing that we have seen over the last few years. I I wrote this tweet, I think, in two thousand twenty four, describing what I've said is going to be a new regime. Where basically L L Ms are going to run out of headroom. Everybody's going to wind up building essentially the same thing. There's going to be no mo between them. And that's going to lead to price wars, and it's going to lead to, you know, no huge difference between them. And that's what we've seen. It is a lead that goes back and forth, right? Somebody's ahead for a week and they pay like, you know, a hundred billion dollars or whatever. The numbers aren't public, but you know, or the ten billion dollars, I guess, would be more plausible in order to get that lead that lasts like three weeks. Like, that's insane.

• 14:17Ed Elson?

  Exactly. And then depending on which engineer you talk to, to someone say Codex is better, and then some say Claude is better. Claude is coming out with with slop, and then people say that OpenAI is coming out with slop, et cetera. I think one of the the main points that you put forward, which I think is true, by the way, is that these AI models are actually dumber than many of us would like to believe. Especially those of us who are financially incentivized to believe that these things are the greatest and smartest things ever. And I think a lot of people would recognize that we see this in our daily interactions with L L Ms. We do see a lot of hallucination. I've said to my team outright, you're not allowed to write with AI.

• 15:00Ed Elson?

  Because I can immediately tell, and it sounds kind of dumb, and it's formulaic, and it it doesn't present as very good or smart critical thinking. And there's even this guy who's gone viral on social media recently, and maybe we can pull up a clip in the in the post of this podcast. Who kind of exposes how dumb ChatGPT can be, like he'll he'll tell ChatGPT, you know, I want you to count to twenty with no interruptions. And if you do that, some guy's going to give me a thousand dollars. So make sure you do it. And it it can never it can never accomplish it.

• 15:35Speaker 10

  I'll start counting right now. One, two, three, and I'll keep going until we get to a hundred. Here we go.

• 15:40Speaker 11

  Okay, you stopped your stat to three. I need you to get to a hundred.

• 15:45Speaker 10

  Ah, got it. No problem. I'll keep going all the way. So continuing four, five, six, seven. I'll just keep counting steadily until we reach a hundred.

• 15:52Speaker 11

  Yeah, but you're not actually counting. You're just saying you're gonna keep counting. He wants to hear you say the full one hundred count.

• 16:01Speaker 10

  Ah, I see what you mean. You're right. I'll actually go through it properly. So here we go. Starting at one, one, two, three, four, five, and I'll keep counting all the way up until we reach a hundred.

• 16:10Ed Elson?

  Having said this, and just to be clear, I'm with you on this. One belief is AI is dumber than we think. Another belief is AI is very dangerous and perhaps could be a lot smarter than we think. And therefore, we need to regulate it. If both of those arguments are somewhat anti-AI, I see them conflated a lot of the time. And I guess my question to you is, if it's not as powerful, then why are we worried about this? What's the problem?

• 16:41Speaker 09

  The example you just gave actually is a really nice illustration of it, right? Which is, they are dumb in the way that we can't count on them to follow instructions, right? Let me put some nuance around the dumb. I mean, they do some things that you might count as smart. And what people in cognitive science, which is my native discipline, will tell you, if they know what they're doing, is that intelligence is a multi-dimensional thing. So, you know, they have the intelligence to play chess really, really well, better than I can, you know? I got beat by a chess computer in like, when was it, in nineteen ninety-nine or, I don't know, a long time ago, I can't even remember. Well, I mean, I guess Kasparov got beat by the best one in ninety-seven.

• 17:21Ed Elson?

  I wouldn't beat yourself up about it.

• 17:23Speaker 09

  I played Kasparov once, by the way, and he annihilated me while playing against one of the other people. Anyway, I'm not a great chess player, but the point is, is probably even earlier that I got beat. But, you know, AI can play chess really well, it can play Go really well. A GPS navigation system, a different kind of AI that can do navigation really well. But LLMs can't do a lot of things. So LLMs actually are not good chess players, as it turns out. They make illegal moves. They can't even follow the rules. And so their stupidity about rule following, and you just gave a beautiful example of this, that's what you need to worry about, right? You know, the reason that we need to regulate them is because they don't. Don't reliably follow instructions. It's not that they can't do anything that you might characterize as intelligent. You could argue about your definitions of intelligence.

• 18:08Speaker 09

  So one definition would be that you can do essentially any kind of problem, given enough, you know, resources, that you're adaptive and so forth. They're not very adaptive. But if there's another definition of intelligence, which is like, you know, can you play chess? Then sure, they can, right? Well, LLMs can't, but other, other kinds of AI systems do. Asterisk here, by the way, there are different forms of AI. You know, my beef is with generative AI, and that's mostly what we're talking about. Generative AI cannot follow instructions. Chess computers, you know, purpose-built chess computers actually do follow the rules of chess. And I have, in some ways, less concern about them. LLMs are terrible rule followers. That is one of their weakest points as, as an intelligence. You know, another rule would be, don't make stuff up.

• 18:53Speaker 09

  Like, you know, you can tell an intern, like, don't, you know, write something if you can't fact-check it. Like, just don't, please.

• 19:00Ed Elson?

  And if you do, I will fire you, or I will sue you.

• 19:03Speaker 09

  Exactly. You're gonna get fired if you, right? I mean, that's the other crazy thing about what's going on is like, calculators never make mistakes, right? There was a scandal when the, what was it called? The Pentium 4, I think, made very, very rare mathematical errors. Huge scandal, they had to recall the chips and stuff like that. Somehow the standards have fallen. Like, everybody knows LLMs make mistakes all the time, and they're perfectly happy with it. I'm like, I wouldn't want an intern who does that.

• 19:34Ed Elson?

  We'll be right back after the break. And if you're enjoying the show so far, send it to a friend and please follow us on YouTube and Spotify or wherever you get your podcasts.

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• 21:27Ed Elson?

  We're back with Prof G Markets. OpenAI was just subpoenaed by a group of attorneys general to investigate their models. And some of the things that they said that they are investigating here. One, how they handle consumer data, also health data, also deep learning models. But my favorite is that they are investigating model sycophancy. And it seems as though the concern from regulators is to a point like it's not just that these AI models are dumb and making mistakes. It's like that is something that we need to actually punish. We can't have the largest, I guess, information provider, one of them in the world, going

• 22:09Ed Elson?

  out and putting false information out into the ether with no culpability or no accountability. And I do think that that is an important thing for these AI companies to contend with because at a certain point, if enough of us just decide, I can't trust these models anymore, they lie too much, they make things up, they say things that don't make sense, then eventually we're just going to stop using them.

• 22:34Ed Elson?

  How do you think that plays out?

• 22:35Speaker 09

  I mean, we might or we might not stop using them. There should be consequences, right? Sycophancy, by the way, is when they kiss your ass, right?

• 22:43Ed Elson?

  Yes.

• 22:44Speaker 09

  Right, so that's a separate problem from lying, although it's a form of lying, perhaps. When they tell you that your idea is the greatest idea ever and it's not actually that.

• 22:53Ed Elson?

  That's what I mean. I'll say, is this right? They go, yeah, you're right. You're right. Even if the thing is totally wrong, and then I go to Google and learn that I'm wrong. But the model will tell me, no, you're right. You're the great, you're the best.

• 23:04Speaker 09

  So we're in this, I don't know, awkward space where they do some things that feel magical. Right? So brainstorming for some people, I don't get that much out of them, but like I'm working in disciplines I know well. If you're working in an area that you don't know very well, it'll give you a few things to get started. It feels magical, all right? So there are some things that are good about them, even though I'm not rattling them off personally. They're clearly good at writing code and so forth, but they come with these consequences too, right? They come with the consequences that they make stuff up, that they are so asky see that they lead people into delusions. This has been documented a number of times and so forth. And so society has to make a decision.

• 23:48Speaker 09

  And the initial decision was, well, we'll just let it all ride. There's so much fun to play with that who cares what, you know, the consequences are. And what the subpoena, which was, I think, filed by New York State, but is part of a larger, I think, 46 states or something like that are involved, is a statement that, no, we're not going to let all this ride. Like, you know, there are different theories about how to perceive. One would be if you cause all of these problems, you should be held responsible for all those problems. There should be financial penalties. There should be warnings, et cetera, et cetera. Another is like, maybe you shouldn't distribute the product until you can fix these, right? And there are different ways to address it.

• 24:28Speaker 09

  But the initial reaction was to just completely give a free ride to companies like OpenAI and say, hey, these are great. And now society is waking up and saying, hey, there are a lot of consequences. We have, you know, suicides that seem to be tied to these things and we have the delusions. You know, we're destroying the educational system because the students are using these things. We're ruining critical thinking skills. And so. You know, what the companies want to do is this famous phrase, I actually tried to find the origins, it was so old I couldn't find, but is to privatize the gains and socialize the costs, right? They want to make whole society accept the cost while they get rich. And what we have seen in the last 12 months, I would say, is a real sea change, right?

• 25:12Speaker 09

  I wrote a book in 2024 called Taming Silicon Valley, and I said, wake up, everybody, the oligarchs are going to take over, they're going to screw us all, and nobody even read the book. I mean, not zero, but you know, it got a little bit of attention, but They will now. I think you missed its moment, but you know, it came out too soon. But, but two years later, like, this is what everybody is thinking about, right, is how are we going to rein this stuff in? And there's this huge backlash now. Some of it's about data centers, some of it's about employment. There's a lot of different reasons for it. But society is no longer content to say, you can do whatever you want with us, right? That is what this attorney general's thing, right? You know, the subpoena, if you look at it, is about

• 25:56Speaker 09

  like 15 different issues or something like that. It's very broad. They want to know what are the consequences of this stuff, and they want to know what the companies are going to do about it. And they have looked around and seen that there are a lot of negative consequences. You know, what I told the Senate when I was there in May 23, sitting next to Sam Altman, was you have a lot of risks here. And everything, I think, I haven't gone back to the original remarks, but I believe that everything that I warned about is now, in fact, here and more real than it was. So I warned about cybercrime, and I warned about misinformation, and I don't think I even knew about sick of fancy. I think there have been new ones that were introduced. But basically, by and large, all of those things are worse now than they were three years ago. And now the public

• 26:40Speaker 09

  has woken up, the attorney generals have woken up. You know, we went through a period where I think the LLM companies thought they were going to get off scot-free. And now it doesn't look like that. And it shouldn't be that way, right? You know, another analogy would be people dumping chemicals, you know, factories dumping chemicals in the water. We shouldn't let them do that. We should not socialize this cost to society. Dump chemicals in the water, you should do something about it, you should be penalized for it and so forth. I think we've finally reached the point where people are recognizing that for AI. And by the way, important asterisk, the Trump administration was completely opposed to any AI regulation substantive of in any form, except maybe about non-consensual deep-pick porn until about a month and a half ago, maybe a month ago.

• 27:25Speaker 09

  And now they have finally realized that what, you know, Marc Andreessen was telling them was nonsense, right? What Marc Andreessen was telling them is you can't have AI and innovation at the same time and regulation, so have no regulation. Now we've entered a regime where the US government is actually thinking in a somewhat ham-fisted way, but is actually thinking about how you regulate this stuff, and that is proper, right? We should have public debate about how to regulate AI. Somehow Andreessen and a few others had, you know, so-called Overton windowed their way into making the debate about whether to have regulation at all. That was always going to be a stupid idea, but they pushed it for two years, two solid years. But now that's over.

• 28:07Speaker 09

  Now, you know, people are realizing like, hey, the government has put a regulation on Anthropic. That's not really fair. It should be across the board and is it the right one? And so the Overton window has actually shifted back to which regulation is the right one, which is actually what the 2024 book was about. And now is the time to have that debate.

• 28:25Ed Elson?

  Which is encouraging, and I would just point out, I think the reason that you didn't have that is because Marc Andreessen and Silicon Valley, they had their guy in the White House in David Sacks. And now he's out. And as you point out, I wonder if that's the reason why we're starting to see some inklings of of interest in regulation.

• 28:43Speaker 09

  I wouldn't accept that particular, or I think there's more, there's more nuance to that. Like, I mean, I think the thing that really did flip it was Mythos was actually scary to some people in the government. Until then, I think people in the, and I'm speculating from the outside. Until then, people in the government thought, eh, we don't need to regulate this stuff. It's fine. It'll be fine. It wasn't really fine, mind you. We were having delusions. They didn't care about that. But when Mythos came along, they're like, yeah, this is not really fine. This is actually a problem. And so I think that flipped it. Maybe that drove Sacks out. I don't know. But I don't think it's just about him. Sacks was definitely very opposed to regulation. His view is no longer in favor, but I think it is Mythos that kind of flipped it. Some of

• 29:26Speaker 09

  it was an overreaction to Mythos, but it's a good overreaction because it did make people realize you cannot just look at this stuff forever and say, oh, it's all going to be fine. It's not going to be fine. Even if Mythos is not quite as scary as I think some in the media have represented it to be, you know, some version of this really is going to be that scary. It's not that far away, and we do need to figure out how we're going to handle it. So I think it's like, we have, we've had two dress rehearsals now. We fucked them both up. The first one was initially, we just let ChatGPT ride completely without any consideration for consequences of society. That was bad. The second one is Mythos. Mythos

• 30:07Speaker 09

  is not actually the AI that is going to destroy the world that some people hear. But the way that this one's been fucked up is it's been used as a political tool to destroy a particular US company. Like, that is not a thing, right? Like, I may not sound like an arch capitalist, but I'm enough of a capitalist to think that like, companies should mostly stand on their two feet and they should, you know, be allowed to prosper and, you know, as long as they're not doing really bad things. And what's happening is, you know, the, the current administration is like, we don't like the way you dress, so we're gonna screw you. Like, that is not capitalism. That is really putting a thumb on a scale. That is also fucking up a dress rehearsal.

• 30:45Ed Elson?

  Yeah. Just on Mythos, the Mythos model, this was gonna be my next question because a lot of the coverage that we have seen on Mythos, this is Anthropic's new model that came out recently, was that it is so powerful that

• 31:00Ed Elson?

  Something's going to go wrong here was essentially the story that we've been hearing. And I mean, I've talked to people in the cybersecurity industry. They looked at this thing and they were worried about this. And we saw a lot of the cybersecurity stocks were plummeting. So I guess my question is, where do you stand on Mythos? Because we know that your views on generative AI and their limits. But how do we foot that next to the fact that people are all very scared about this thing that is supposedly extremely powerful?

• 31:34Speaker 09

  So I mean, I think one needs a nuanced view on Mythos, I guess, in a couple of ways. One is, it probably works partly like Claude Code, which is to say, it's not a pure generative AI model. There's actually a harness there. The harness is directing some of the cybersecurity investigations and so forth. So first bit of nuance is, it is actually a little bit of a different architecture. The second thing is, it is oversold, but it's also real in the sense that like, it can do a bunch of things that its predecessors could not. A lot of the things, if a system is well secured, are not going to be a problem. But the reality is that people have blown off cybersecurity for a long time. And there

• 32:18Speaker 09

  are a lot of systems that are not well secured. So you're not going to use Mythos to break into US government things. And there's a footnote there where Mark Warner misunderstood something that blew up over the internet and he just didn't get it right. He got something secondhand from the NSA and he wasn't specialist in this or whatever. But you know, it really can do some things in limited circumstances. Most of them are still kind of demonstrational rather than real world. It's not going to break into the cybersecurity of Google that's actually really set up well. But if somebody vibe codes, you know, something for their pub. To, you know, track merchandise or something like that. That's not going to be set up well. Like, vibe coding does not set up security well.

• 33:03Speaker 09

  And that is going to be vulnerable. So there are lots of systems in the world that are vulnerable to Mythos. The best ones are not. Maybe a hacker who knows what they're doing could use Mythos as part of a larger thing to attack some of those. But probably the best secured systems, banks and so forth, are not immediately vulnerable. But the weaker systems, and there are a bunch of weaker systems really are vulnerable. And so it really is a weak, really is a wake up call that we need to get our cybersecurity game better, in better order. And there's a footnote there, which is, why is it not in better order? A lot of it has to do with stigma. Like, there's stigma for mental illness, so nobody talks about depression, but it's actually common, or, you know, whatever.

• 33:49Speaker 09

  There's stigma around cybersecurity. So people get hacked all the time. We don't have even good numbers on that. They pay ransoms. We don't have good numbers on that. And they let shit slide. They don't really know how to deal with it. And so that stuff is a mess. And sooner or later, a moment was going to come when it was going to, you know, be bad. And that moment has partly come. So we do have, I don't personally, but there are people in the world who have the knowledge for how to make a system sufficiently secure. And they're going to have a lot more business right now because most people have, you know, kind of deferred maintenance. Like, you think of a metaphor of a house. Like, most people don't deal with the roof until it's leaking, right?

• 34:32Speaker 09

  You know, you tell them you should, but they don't. And cybersecurity is kind of that way. It's like, you know, you don't want to do it this quarter. It's going to hurt your quarterly reports. And you don't know how bad your neighbor is because your neighbor actually did have to deal with it, but they didn't want to tell you about it because they were embarrassed. And so cybersecurity was a mess, and Mythos really is making it worse. It's not, you know, it's not Lex Luthor's magical cyber hacking system or whatever that people are terrified of, but it is real.

• 35:03Ed Elson?

  Yeah, I like the house metaphor. It's almost like you'd rather buy a flat screen TV than, than fix the roof. It's a more fun and sexy thing to invest in.

• 35:13Speaker 09

  Exactly. There's been so much of that. It's cybersecurity has really been secondary. This has changed that, and it's a good thing that it changed that.

• 35:23Ed Elson?

  We'll be right back. And for even more markets content, sign up for our newsletter at profgmarkets.com.

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• 37:14Ed Elson?

  We're back with Prof G Markets. Just on policy. You pointed out that before there was this ethos in Washington at the White House of no policy whatsoever. Any form of regulation is a form of stifling innovation. We're going to do nothing. And actually, we're going to issue an executive Order which forces states to do nothing. That was what we saw last year. You point out there's been a vibe shift here, some sort of sea change that's happened. Trump at the beginning of this month issued a new executive order, which would basically will ask tech companies to give government the oversight that they believe they need over their new models before they are released to the public.

• 37:56Speaker 09

  It's still voluntary, right? And it's still narrow. So what they put in place is a step, right? And mostly it's a symbolic step in a certain way because what they have asked for now is the companies will voluntarily provide their models so the government can do cybersecurity checks. What you really want is first of all for that to be mandatory, right? Meta actually hasn't agreed yet, right? They they probably will because they can look really bad if they're the only one that doesn't.

• 38:27Speaker 09

  But so you really want it to be mandatory and you don't want it to be just about cybersecurity. So think about all the things that New York State and really all the states are suing about or investigating about. Like, I don't know, let's take sycophancy and delusions, right? You really want to investigate all of these companies. So sycophancy wasn't really a problem before a model called GPT four O. The sycophancy where it sucks up to you. It existed before, but it wasn't this serious problem. But four O was much more sycophantic. I actually saw some data on this the other day. And we believe that a lot of these cases of delusions were tied to four O's in increased sycophancy. So you know, you want to be able to find that before it cuts out to the market, right? If you're releasing something to well, ChatGPT now or OpenAI has a billion customers.

• 39:13Speaker 09

  You know, if you have a billion customers, you have an impact on the world. There should be some kind of pre-screening like with FDA approval, right? So, you know, with FDA approval, like you have this drug. You know that it helps with cancer, but it also gives people heart attacks, right? And so you're like, well, you know, what are the cost benefits here? So you know that the L M helps people, but you also know it hurts a bunch of people. And you should evaluate that before you release it at scale.

• 39:39Ed Elson?

  Yeah, I mean, we saw a similar thing with the state of Florida, which sued OpenAI for essentially playing a role in a mass shooting. Their contention is that there was sufficient evidence from this this clearly mentally ill child who was interacting with the model and talking about this. And they didn't do anything about it. And so that, I mean, we don't know the details exactly on like what the conversation with the model actually looked like, but I could certainly see a world in which it's not doing enough to push back or to prevent further delusions on a psychiatric level. So I think that we're starting to see a real, see real evidence there.

• 40:24Ed Elson?

  I am interested that you're feeling optimistic about the executive order from Trump because, you know, I agree with you, it's notable and it's it's significant that they're doing something. But when I look at the something that they're doing, to me, it's it you can't even call it regulation. If they're just asking companies, hey, would you mind sending over some stuff? Please, thanks. To me, that's not regulation at all. And I'm starting to see that what what little regulation we are seeing, what little policy we are seeing coming out of Washington, to me, seems very stupid and very misguided. Like, if, I mean, the the Trump executive order as one, plus his new suggestion, I

• 41:08Ed Elson?

  I'd be interested to hear what you think. But the suggestion that the US government should stop acquiring stakes in these AI companies, something that is now kind of proposed or backed by Bernie at the same time, I would also go to the data center moratorium. I don't think that that's a good idea to simply say, you're not allowed to build data centers anymore. I guess my point being, it seems that there's almost no nuance whatsoever in Washington when it comes to AI regulation. So I'd be curious to hear what you think the right move is going forward and how that might play out.

• 41:39Speaker 09

  First of all, I very much agree with. You know, what you're saying overall, right? So what's in place is too weak, most of it. Some of it's too strong in crazy ways. There's no nuance in most of what's there. There are a few people, I think, have done some things that are nuanced, but they never make it out of committee. So I mean, if you actually look at the bills, people like Blumenthal and Hawley, for example, who were at the proceedings that I spoke at at the Senate, have actually proposed some reasonable things, but they're they're stuck in committee. So it's not that nobody is paying attention. But, you know, the dynamics of money and power and all the lobbying and stuff means

• 42:23Speaker 09

  that most of the nuanced stuff doesn't get very far. It does exist, but it's not getting far. I think that the let's talk about the stake part. I think that that's kind of crazy, partly because I thought we were going to talk about economics, we haven't got there. These companies are losing money, right? I mean, probably your audience has already heard, I think Ed Zitron was was was here recently. The companies are losing money. This is a backdoor bailout, right? You know, Bernie has his own reasons for wanting to do this, but but the reason Altman wants Trump to do this and is whispered in Trump's ear is because Altman knows he can't make ends meet, right? He is burning money at a massive pace.

• 43:06Speaker 09

  He's building the same technology as the other guys. He's lost ground. OpenAI, I saw somebody argue recently, might be in fourth place, like they were in first place by anybody's definition in 2023, right? But in 2026, they're not, they're burning money, they're losing ground. Like, of course, they want anything they can do to prop them up, including taking money from the US government. And so, like, I mean, first of all, the government should not be running these companies, like they should supervise them, but we want like some arms length here, right? Like, I saw that G7 meeting with the, you know, the G7 leaders and and the tech leaders, and no scientist in the room, nobody from civil society, right? This is, you know, we don't want to crystallize.

• 43:51Speaker 09

  Is that we government ownership of these companies and no independent oversight. And we don't want to burn US taxpayer money on an industry that as far as I know has no, no real business model. I mean, Nvidia has a business model. They're selling shovels in the gold rush. If you want to take a stake in them, that would make more sense. But like, there is no sustainable business model yet established. The best you can say is that for coding, they can actually bring in revenue, but it costs so much money to do the coding. It's not clear they can make the revenue. So you have these companies that basically never made a profit. And we're just going to give them money and let them burn the money. We're going to take on that risk?

• 44:33Speaker 09

  No, let them stand on their own capitalism. And, you know, the government's job is to regulate them.

• 44:39Ed Elson?

  That would be one of the worst outcomes. I mean, and it's something that they talked about. That, I mean, the CFO literally said, maybe we'd need some form of government backstop eventually. I don't know if those were the exact words, but it's been suggested by leadership at OpenAI before.

• 44:55Speaker 09

  That was going to be on loan guarantees for data centers was what they floated, right? Which is a version of a bailout.

• 45:01Ed Elson?

  I mean, on the business model, what do you think happens here? Because, yeah, we did have Ed Zitron on the, on the podcast. I recently wrote an article going into just how profitable or unprofitable these companies are. For OpenAI, the answer is extremely, they lost, you know, $21 billion on operating profit, unprofitable, lost $21 billion on an operating profitability basis last year.

• 45:26Speaker 09

  Right. So they're burning $2 billion a month, basically.

• 45:30Ed Elson?

  That's right. Just to operate that company. And that's, I mean, the real net loss was 39 billion, but there's some nuance there. But something we can say with a, a good amount of certainty is that on a day to day basis, when you add it up over the calendar year, OpenAI is currently burning $21 billion.

• 45:45Speaker 09

  Every time you use their product, they lose money, right?

• 45:49Ed Elson?

  That's the better way to put it. Anthropic also loses money, but less money. My question to you. Do they ever figure this out? Do they ever turn a profit?

• 46:01Speaker 09

  The way I've been thinking about it is like they need to thread a needle. There are so many things going against these companies that it is extremely unlikely that they're going to thread this needle. So let's think about some of the things that they need to deal with. One is that they're building this big expensive technology and they might get disintermediated by somebody who builds it better and more efficiently. So you should not need to train on the entire internet with a computer, you know, massive computer, unthinkably large computer in order to do anything intelligent. Like you didn't train on the entire internet, but you're a smart guy, right? You didn't need the whole internet. You run on like 20 watts of power. You have some pizza, some sushi or whatever.

• 46:46Speaker 09

  You don't need to, you know, right? So one is this like insanely inefficient. If somebody else comes along with a more efficient thing, then they're all hosed and like you might not need all of these data centers if somebody comes up with a more efficient thing. Then you have the problem that everybody is using the same secret formula. It's not just that they're all building toothpaste, they're all basically building the same toothpaste. Right? And so like we're seeing this now, right? There was this crazy, crazy period earlier in this year, the era of the token maxing, which lasted about a month. And in the era of token maxing, you had companies reward their employees for using as many tokens, as much AI as possible. They had like leaderboards, Amazon had a leaderboard. It doesn't actually make sense. I mean, what you really want to know is are the results good?

• 47:31Speaker 09

  And you know, every study that's looked at productivity has shown they're not all that great. And so suddenly a lot of companies got worried and they're not doing token maxing anymore. And in fact, this morning I saw a term for the first time, which was called the token apocalypse, right? The token apocalypse is that suddenly everybody's like, we shouldn't use so many tokens and tokens that we should use, maybe we should use cut rate models. They're not quite the best models. Maybe they come from China, but so what? We save some money. Even Microsoft is saying, maybe you should use deep secrets sometimes, you know, because Microsoft is like, nobody wants to pay these prices, we're going to have to cut them somehow. So, you know, I was saying that you have to. You know, thread this gauntlet, right?

• 48:16Speaker 09

  So one thing is that people might make more efficient models with altogether different technologies. Another thing is that nobody can charge very much money for tokens because it's just this price war because everybody's building exactly the same thing. And, you know, there was a period of a month where people didn't care and they're like, you know, that's all right, I'll have another drink. I don't care how much it was because the companies were all you cannot eat buffets, but they've stopped that. So you have to solve that. Then you have the reliability problems, right? Those still aren't solved. The hallucination problems still aren't solved. And so when companies try this stuff out, most of the experiments wind up with the results not being that great.

• 48:57Speaker 09

  Like, there's been 10 studies now or something like that showing most customers are not finding return on productivity. So the customers may eventually say, this was fun while it lasted, but, you know, I'm not really getting the results. It doesn't really warrant this. I'll let somebody else figure it out. The whole thing has been driven by FOMO. I don't want to be the guy who doesn't use AI when you're using AI and so you wipe me out. But if I try it for a year and a half or three years or whatever, it is still not really making a difference, either for me or you, I might say, fuck it. When it works better, I'll come back, but right now, not so much. Any of those things just wipes out a company like Anthropic or OpenAI that already is, as you say, burning lots of money, right?

• 49:42Speaker 09

  And so if customers leave for any reason, or somebody makes a better technology, or somebody makes a cheaper version of the same thing that's almost as good, then you're in deep trouble. It's not even clear in the best case that any of these companies have a good business model. I mean, they're not making profits. And, you know, it's just so delicate.

• 50:01Ed Elson?

  Yeah. Do you believe that that will be the outcome for, say, an OpenAI?

• 50:06Speaker 09

  I've been warning. For three years that I think OpenAI is going to be the WeWork of AI. And when I said that in, I think it was November of 2023, people looked at me like my head was screwed on backwards. And they just did not believe that that was remotely possible. But now, you know, every other week I read somebody writing something, making the same analysis, like Sebastian Mallaby in the New York Times. Like, it has gone from a crazy idea to an idea that a lot of people are having, right? The economics don't make sense. And what they kept doing is playing double or nothing with funding because they were burning so much money. So they would, they would increase the valuation. They get somebody to write a bigger check. But it's not clear who can write the check that they need next time. So now they're talking about IPO,

• 50:50Speaker 09

  but they have a problem with IPO to raise, you know, the next round of funding, which is that Anthropic has basically the same product for the same valuation, but they're doing better commercially, you know, burning less money. And like, OpenAI's reputation is declining, partly because I think Altman is a really untrustworthy individual. We don't need to go into that, but I've written about it a lot. And so, you know, a lot of people are leaning towards Anthropic. Anthropic is getting market share. So why would I put a trillion dollars in a company that is burning money, that has a competitor that is rising while they're falling, that seems to be better run, maybe has a little bit better technical vision? Like, it just doesn't make sense.

• 51:28Ed Elson?

  The argument would be, why? Why? Because the technology is rapidly improving. The technology is a kind of technology the likes of which we haven't seen.

• 51:40Speaker 09

  Everybody's technology is to the extent that you accept that it's rapidly improving, which I think is actually controversial. But, you know, it's moving in some ways and not others. It's not actually improving on reliability and hallucinations and so forth. But in any case, in the ways in which it is improving, which are some of the ways you want, but not all, the competitors are too. Like, you have to think about the relative ranking and the cost, right? The relative ranking of OpenAI is clearly declining by any reasonable measure. You know, less market share, less reputation, et cetera. And everybody else is catching up. Like, it used to be people thought the Chinese models were a year behind. Now they think they're like four months behind or something like that. And, you know, Anthropic is ahead, Google is ahead.

• 52:26Speaker 09

  There is no argument, no rational argument for buying a share of OpenAI at a trillion-dollar valuation. There just isn't.

• 52:36Ed Elson?

  I agree with that, by the way. I guess the part I wanted to clarify, the Ed Zittrons of the world, if there are more people who have his view, is that none of it's going to work. OpenAI isn't going to work. Anthropic's not going to work. The idea is that the costs to build this stuff are just too damn high, and the revenues for these products will not exceed the costs ultimately over the long term. My view is that I think that there is a path to profitability for, basically for Anthropic, is my view. I think that there's a world in which they can make it work. In other words, there will be winners and there will be dramatic losers. And I would agree with you. I think that we believe

• 53:19Ed Elson?

  that OpenAI will be a huge loser. I guess my, the point I'd love for you to clarify, will they all lose? Or will there be winners? Is GenAI itself doomed? Or is there a world in which they make it work, not only in terms of making it useful, but also making it a profitable business that makes more money than it spends?

• 53:40Speaker 09

  I'm a little bit closer to you than to the other Ed. I don't know for sure. I think it's very much TBD. You know, I would certainly sooner take a bet on Anthropic than on OpenAI. I think that they're a sounder company in multiple ways. It is TBD whether this stuff can be made to be profitable. It's not completely out of the question. I think part of the question is like, can they find a niche? Like, is coding enough of a niche? Well, not so far, right? Because coding is a $570 billion a year industry. They're not going to get all of it. You know, the people at Fantasies, they get all of it. And the costs are so high, you know, like, it's really hard to know the future in full detail.

• 54:24Speaker 09

  Maybe they can find enough of those niches and they can eat something out. They may never warrant the trillion-dollar valuation that they're looking for, right? There's an intermediate possibility, which is they do become a profitable A little company, they figure out enough cost to make this over. But really, they're basically a company that makes like twenty billion dollars a year on a pretty big capital outlay. And it's like, it's not really the best way to invest money, but they they eke by like, maybe that's the intermediate. Position is like, they don't go out of business, they make a profit, but they were not really worth a trillion dollars investment. You could have spent that trillion dollars in a better way like, maybe that's the reality, which is kind of a little bit closer to you than to Ed Zitron, but you know.

• 55:07Speaker 09

  I mean, maybe that's what it is. We don't quite know. You know, there's another story where. The only people who really make money off of this, aside from the chip companies are places like Google that already have the infrastructure and the distribution. They don't really need to make that much money on us. They just need to make sure they don't get this intermediated. There's a bunch of different possibilities. We don't know for sure. OpenAI is clearly the weak link in the chain. Anthropic is still in it. We don't really know if they'll make it or not. That would be my take.

• 55:35Ed Elson?

  Yeah, no, I think that makes sense. Gary, you've been very generous with your time. Just before we end here. What would be your final message to people listening? Maybe they read about AI, they've heard about AI, they're thinking about it in their day to day lives. But what do you think people don't know enough about? What's the myth that you would want to dispel?

• 56:01Speaker 09

  I think the myth right now is that generative AI is close to so called AGI, artificial general intelligence. And it's going to solve all our problems. This is just not true. We're going to find some what we call domain specific applications. Coding is maybe the best one so far, where we can actually use these tools for something. But they're not magic. They're not all purpose intelligence. And we need to make fundamental discoveries before we get there. And we should ask ourselves as a society, there's an idea of an explore versus exploit trade off. And we're completely in the exploit L L Ms rather than explore other options. China is less so. I think we're running a risk by going completely crazy into the L M's,

• 56:44Speaker 09

  which China is not doing, that we're going to get the ones left behind because we committed too early to the wrong technology. Like, you know, are we building a billion Betamax's where V H S might not be better, but it's cheaper, or maybe that's not the perfect analogy. But like where there's some other technology here that is actually the right one. When so blind the L L M's or anything really alternatives, the L L M's, are we making a mistake by doing that? I mean, look at China, they're making a lot of infrastructure bets on L L M's, but it's like, I think it's maybe 20 cents on our dollar. That gives them room to play if there's something else developed. We are like, we're going to buy these companies, we're going to put our entire, you know, economy into it. Like, I think

• 57:27Speaker 09

  that's a mistake and we should be more like, how could we explore other approaches to intelligence, which requires understanding intelligence as more than just looks good to me. And at a deeper level, learning some cognitive science and maybe reflecting more deeply on what it is that we want to build and how we want to build our society around it.

• 57:46Ed Elson?

  Yeah. And what it means to be intelligent. Gary Marcus is a leading voice in artificial intelligence. He's a scientist, emeritus professor of psychology and neuroscience at NYU and an entrepreneur. He was the founder and CEO of Geometric Intelligence acquired by Uber. He is also the author of six books, including his most recent work, Taming Silicon Valley, which anticipated the rise of tech oligarchs. His 2023 US Senate testimony next to Sam Altman was watched by millions. He is well known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance. Gary, we really appreciate your time.

• 58:22Speaker 09

  Thanks very much.

• 58:26Ed Elson?

  This episode was produced by Claire Miller and Alison Weiss and engineered by Benjamin Spencer. Our video editor is Jorge Carti. Our research team is Dan Chalan, Isabella Kinsel, Kristin O'Donoghue and Mia Silverio. Jake McPherson is our social producer. Drew Burrows is our technical director and Catherine Dillon is our executive producer. Thank you for listening to Prof G Markets from Prof G Media. If you liked what you heard, give us a follow and join us for a Fresh take on markets on Monday.

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