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Chapter 1: What is the main topic discussed in this episode?
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Chapter 2: What concerns does Gary Marcus have about AI technology?
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Welcome to ProfgMarkets. We've spent a lot of time talking about AI lately, from the Trump administration's export restrictions on Anthropix models to the ongoing questions surrounding the economics of companies like OpenAI and Anthropix. 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.
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. You are sort of part of the AI world, but you have issues with it. Let's just start broad. What are your concerns?
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.
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 LLMs, 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 about You know, 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. Five years ago, the field was healthy.
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Chapter 3: Why does Gary Marcus believe generative AI is inherently unreliable?
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. Both of those arguments are somewhat anti-AI, and 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?
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 multidimensional 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 1999 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 97. Yeah, I wouldn't beat yourself up about it. I played Kasparov once, by the way, and he annihilated me while playing with one of the other people. Anyway, I'm not a great chess player, but the point is, it was 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 LLMs them is because they 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. So one definition would be that you can do essentially any kind of problem given enough resources. You're adaptive and so forth.
They're not very adaptive. But 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 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 an intelligence. You know, another rule would be don't make stuff up. Like, you know, you can tell an intern, like, don't, you know, write something if you can't fact check it.
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Chapter 4: What are the implications of AI regulation according to Gary Marcus?
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 and we're ruining education. critical thinking skills. And so, you know, what the companies want to do is this famous phrase, I actually tried to find the origins, but it's 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? 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.
You know, it got a little bit of attention.
They will now.
I think it missed its moment, but, you know, it came out too soon. 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 thing, you know, the subpoena, if you look at it, is about 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 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 sycophancy. I think there have been new ones that were But basically, by and large, all of those things are worse now than they were three years ago. And now the public has woken up. The attorney generals have woken up.
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Chapter 5: How does the AI industry handle financial sustainability?
One is it probably works partly like cloud 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. 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 are a lot of systems that are not well secured.
So you're not going to use mythos to break into U.S. 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. You know, he got something secondhand from the NSA and he wasn't a specialist in this or whatever. But, you know, you 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 website, to track merchandise or something like that, that's not going to be set up well.
Like, vibe coding does not set up security well, 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 wake-up call that we need to get our cybersecurity game going.
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 um there's stigma around uh cyber security 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 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 kind of deferred maintenance. Like you think of a metaphor of a house. Like most people don't deal with their roof until it's leaking, right? 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.
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Chapter 6: What misconceptions about AI does Gary Marcus aim to correct?
But 4.0 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 4.0's increased sycophancy. So, you know, you want to be able to find that before it gets out to the market, right? If you're releasing something to, well, ChatGPT now, OpenAI has a billion customers.
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 this LLM helps people, but you also know that it hurts a bunch of people. And you should evaluate that before you release it at scale.
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 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 real evidence there. 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 significant that they're doing something. But when I look at the something that they're doing, to me, 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 little regulation we are seeing, what little policy we are seeing coming out of Washington, to me seems very stupid and very misguided.
Like, I mean, the Trump executive order as one, plus his new suggestion, I'd be interested to hear what you think, but the suggestion that the US government should start 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.
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Chapter 7: How does the AI sector's reliance on large language models impact its future?
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 U.S. 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 arm's length here, right? Like, I saw that G7 meeting with, you know, the G7 leaders and the tech leaders, and no scientists in the room, nobody from civil society, right? This is, you know...
We don't want to crystallize that with government ownership of these companies and no independent oversight. And we don't want to burn U.S. taxpayer money on an industry that, as far as I know, has 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, and we're going to take on that risk?
No, let them stand on their own capitalism. And, you know, the government's job is to regulate them.
That would be one of the worst outcomes. I mean, and it's something that they talked about. 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.
That was going to be on loan guarantees for data centers was what they floated, right? Which is a version of a bailout.
I mean, on the business model, What do you think happens here? Because, yeah, we did have Ed Zittrain 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.
Right, so they're burning $2 billion a month, basically. 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 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.
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Chapter 8: What role do policymakers play in AI regulation?
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. 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... 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? 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. Yeah.
Do you believe that that will be the outcome for, say, an open AI?
I've been warning customers
three years that i think open ai is going to be the we work 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 um making the same analysis like sebastian malaby um in the new york times like it has gone from a crazy idea to an an idea that a lot of people are having right the 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.
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