AI Is Dumber Than Investors Think — ft. Gary Marcus

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Try ODU for free at ODU.com. That's ODOO.com. Welcome to ProxyMarkets. 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. Taking together these stories point to two fundamental questions. One is the AI boom financially sustainable, and two are we moving

to 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 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. Here is 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 start at a machine-learning company, which I think you could say is an AI company. You've”

reached 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, you know, again, harmful, not where we should wind up and being abused by the people they're using it. I want AI to succeed, but I think we wound up down this really dangerous path. You think about the Star Trek computer. You ask a question, it gives you an answer

that you can count on. Presumably, it's not done to sort of wrecks society. It's done to help people. 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 us 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. 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

be money to be made. It's 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 fields grow. 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. 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 piping 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. 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

GNI inherently unreliable? What is the path that you are worried about this technology going down?

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 there's many different components to it. And they're just not really built into LOMs. And so LOMs basically fake everything else. And we can talk about some complications, people are building

in harnesses, and we can go there. But let's just talk about pure LOMs. What they do is predict the next token. And if you train them on the entire internet, which is what people will go back to, they can make a pretty good approximation of human beings in 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 the Earth. And the systems would say the most absurd things in response to those problems. They'd be got so embarrassing that anthropic built in river crossing problems into their system problems to try to keep the systems from making these embarrassing errors. And what the embarrassing errors revealed is that

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 glam the words together that they have

seen. I mean, 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 went 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 2020, six, I started writing about LMS 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, we're just going to add more data. All these problems are going to go away. hallucinations are going to go away. Mustafa Soleimann, who's the CEO of AI or whatever's title is. And 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 when it was. Read Hoffman said he would bet any amount of money that hallucinations will go away. In a few months, this was 2023. I said, I'm over

here. How about $100,000? I never got back to me. But here we are in 2020, six, and hallucinations

have not gone away. And it's because the core of next token prediction does not allow you to address

“that problems. You have to add something else. And something else, you know, rarely works all”

that well. It sort of works little bit. And I just saw it study yesterday showing there's a new benchmarking. I think it's called Hallu Hard, hallucination hard. And all the systems are still making errors on this. None of this is going away. It's hard for people who are not trained in cognitive science and artificial intelligence to understand when they play with these systems that they don't

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 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. You've known it for 60 years,

right? Leiza was the first example of an AI system that could fool an average person into thinking

it was much more intelligent than it was. Leiza 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 why isn't Ben wrote about this in the 60s? How we are vulnerable to overtributing 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 overattribute attribution of intelligence to these machines, right? You have people betting trillions of dollars that these machines are intelligent and 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, consider around they say, the entire world is overtributing intelligence to LLMs. It's not that LLMs can't do anything like they're great for auto-complete 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 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, wow, 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 and it may 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 it, but bias and unreliability, et cetera. But people are so addicted to the one thing that they're all in on that. And we'll get to the economics soon and I suppose the 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 and 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 with 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 are all doing the same thing. If we all have the same toothpaste, nobody's going to pay that

much for it. You can't charge a hundred dollars for a tube of toothpaste if you have nine competitors building basically the same thing for less. Basically we're all using one of two models. Essentially, it's you're probably using open-aliice model, you're probably using anthropic model. And then a lot of these companies are building rappers and building all of these gadgets and gismos on top of those models. But to your point, you're basically just putting rappers on

top of the same fundamental thing. Well, and those people are actually basically the same. And they're basically the same was was the same cognitive architecture. Yeah, it's small differences, but they don't persist, right? And that's another thing that we have seen over the last few years. I wrote this tweet, I think, in 2024, describing what I've said is going to be a new regime. We're basically LOMs are going to run out of headroom. Everybody's going to wind up building essentially the same

thing. There's going to be no mode 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. 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 are public. But you know, the ten billion dollars, I guess,

would be more plausible in order to get that lead that last like three weeks. Like that's insane. Exactly. And then depending on which engineer you talk to, it's as somewhat say codex is better. And then as I'm saying, Clawd is better, Clawd is coming out with, with Slop. And then people say that OpenAI is coming out with Slop. It's such a, I think one of the main points that you perform, which I think is true, by the way, is that these AI models are actually Duma,

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 LOMs. We do see a lot of hallucination. I've said to my team outright, you're not allowed to write with AI because I can immediately tell and it sounds kind of done at its formulaic. And 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, chat GPT can be like he'll he'll tell chat GPT, you know, I want you to count to 20 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 can never, it can never accomplish it. I'll start counting right now, one, two, three, and I'll keep going until we get to 100. Here we go. Okay, you stopped your stats at three. I need you to get

to 100. Ah, got it. No problem. I'll keep going all the way. So continuing four, five, six, seven, and I'll just keep counting steadily until we reach 100. Yeah, but you're not actually counting. You're just saying you're going to keep counting. He wants to hear you say the full 100 count.

I see, which, I mean, you're right. I'll actually go through a 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 100. Having said this. And strictly, I'm, I'm with you on this. One belief is AI is done more 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, 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? I mean, 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, and like when was it in 1999 or I had a long time ago, I can't remember. Well, I mean, I guess Kasparov got beat by the best one in '97. Yeah, don't, I wouldn't beat yourself up about it. I played Kasparov once, by the way, he annihilated me while playing through my other game. Anyway, I'm not a great chess player. But the point is, it's probably even earlier

that I get 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 the illegal moves. They can't even follow the rules. And so, they're 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 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 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 kinds of AI systems do. Asterisk here, by the way, there are different forms of AI. 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. 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. Like, just don't, please. And if you do, I will fire you, or I will sue you. Exactly. You're getting fired at you. Right? I mean, that's the other crazy thing about what's going on.

It's like, calculators never make mistakes, right? Do as a scandal when the, uh,

“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. Some have the standards of following. 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. We'll be right back off for the break. And if you're enjoying the show so false,

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$250 credit for the next one. Just go to linkedin.com/scot. That's linkedin.com/scot. Terms and conditions apply. We're back with ProfG Markets. Open AI was just subpoenaed by a group of attorneys general to investigate their models. 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 models, sick of density. It seems as though the concern from

regulators is to your point. It's not just that these AI models are done 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 out and putting false information out into

“the ether with no culpability or no accountability. 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. How do you think that plays out? We might not stop using them. There should be consequences. Sick of density by the

crap. I'm going to tell you that your idea is the greatest idea ever, and it's not actually. That's what I mean. I'll say, is this right? 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, now you're right. You're the great you're the best. We're in this awkward space where they do some things that feel magical. 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. 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. They come with the consequences that they make stuff up. They are so asky see that they lead people into delusions. There's been documented a number of times and so forth.

And society has to make a decision. 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 the consequences are. And what the subpoena, which is I think filed by New York State, but as part of a larger, I think 46 states or something like letter 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 proceed. One would be, if you cause all of these problems,

“you should be held responsible for all of 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. But the initial reaction was to just completely give a free ride to companies like OpenAI and say, hey, these are great. And now as a 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

that 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, but it's so old I couldn't find. But it is to privatize the gains and socialize the costs, right? They want to make a 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'm not zero, but, you know, you know, it got a little bit

“of attention, but they will now. I think it's the 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 rain 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, right? You know, this 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 seeing that there are a lot of negative consequences. You know, what I told the Senate when I was there made 23 sitting next to Sam Alman 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. I warned about cyber crime and I warned about misinformation. And I don't think I even knew about sick ofancy. 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 has woken up, the 30 generals have woken up. You know, we went through a period where I think the LM companies thought they were going to get us got 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 those costs to the society.

“If you're going to dump chemicals in the water, you should do something about it. You should be penalized”

for it. And so for that, I think we finally reach the point where people are recognizing that for AI. And by the way, important asteris, the Trump administration was completely opposed to any AI regulation substantive in any form, except maybe about non-consensual depick born until about

a month and a half ago. Maybe a month ago. And now they have finally realized that what, you know,

mark on Dreson was telling them was nonsense, right? When mark on Dreson was telling them, "You can't have AI in innovation at the same time." And regulations have no regulation. Now we've entered a regime where the U.S. government is actually thinking,

And that is proper, right? We should have public debate about how to regulate AI. Somehow, on Dreson 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. 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 U.S. and Window has actually shifted back to which regulation is the right one, which is actually with the 2024 book was about. And now is the time to have that debate. Which is encouraging. I would just point

“out. I think the reason that you didn't have that is because Mark and Dreson instead of convalley,”

they had that guy in the White House in David Sachs. And now he's out. And I was, you point out, I wonder if that's the reason why I was starting to see some inklings of interest in regulation. I wouldn't accept that particular, or I think there's more nuance to that. 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 and I'm speculating from the outside. Until then, people in the

government thought, "Yeah, 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. We were having other problems. 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 Sachs out. I don't know. But I don't think it's just about him. Sachs 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 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, "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 release 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've had two

dress rehearsals now. We fucked them up both up. The first one was initially we just let chatGBT ride completely without any consideration for consequence of the society. That was bad. The second one is mythos. Mythos is not actually the AI that is going to destroy the world of some people here. 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. That is not a thing. I may not sound like an archcapitalist,

but I'm enough of a capitalist to think that companies should mostly stand on their two feet and they should be allowed to prosper and as long as they're not doing really bad things. And what's happening is the current administration is like we don't like the way you dress so we're going to screw you. Like that is not a capitalist and that is really putting a thumb on a scale. That is also fucking up a dress rehearsal. Yeah. Just on mythos. The mythos model. This was going to be my

next question because a lot of the covers that we have seen on mythos. This is anthropics. New

model that came out recently was that it is so powerful that something's going to go wrong here.

It was essentially the story that we've been hearing. I mean, I'll talk to people in this cybersecurity industry. They looked at this thing and they were worried about this. We saw a lot of the cybersecurity stocks 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 put that next to the fact

“that people all very scared about this thing that is supposedly extremely powerful? 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 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 nuances, 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 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 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 second hand from the NSA and he wasn't specialist in this, whatever. But you know, it really can do some things in limited circumstances.

this versatility of Google. This actually really set up well. But if somebody vibe codes, you know,

“something for their pub 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 the, 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 cyber security 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 from mental illness, so nobody talks about depression, but it's actually

common or, you know, whatever, there's stigma around 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 in a common 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?

“You know, you tell them you should, but they don't. And cyber security is kind of that way.”

It's like, you know, you don't want to do with 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 cyber security was a mess. And mythos really is making it worse. It's not, you know, it's not Lex Luther's magical cyber hacking system or whatever that people are terrified of, but it is real.

Yeah, I like the house metaphor. It's almost like you'd rather buy a flat screen TV than, then fix the roof. It's a more fun and sexy thing to invest in. Exactly. It's been so much of that. It's cyber security has really been secondary. And this has changed that. And it's a good thing that it changed that. We'll be right back. And for even more markets content, sign up for our newsletter at profgmarkits.com.

“More foyer, more intrigue, the dragon kern zurück.”

Stream the new Staffel, House of the Dragons, at 22nd June with wow. Further from the staffel one and two of the previous series, and further highlights.

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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,

over their new models before they're released to the public.

It's still voluntary, right? And it's still narrow. So what they've 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 probably will because they're going to look really bad if they're only one that doesn't. But so you really want it to be mandatory. You don't want it to be just about cybersecurity. So thing 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 sick of fancy and delusions, right?

You really want to invest in all of these companies. So sick of fancy wasn't really a problem before model called GPT 40. The sick of fancy where it sucks up to you. It existed before, but it wasn't this series problem before, it was much more sick of antics. 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 rows

in increased sick fancy. 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, chat to PT now,

open AI is a billion customers. 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 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 Santa Florida, which suit open AI for essentially playing a role in a mass shooting, that 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 was 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, 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, the Trump executive order, as one plus his new suggestion, I, I'm, I'm, I'm be interested to hear what you think. But the suggestion that the U.S. government should sort of

acquiring stakes in these AI companies, something that is now kind of proposed or, 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.

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 Holly, for example, who were at the proceedings that I spoke at the center, 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 that most of the nuance stuff doesn't get very far. It does exist, but it's not getting far. I think that 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 to. These companies are losing money, right? I mean, probably your audience is already heard. I think it's, uh, Zetron was, was, was here recently. The, the companies are losing money.

but, but the reason allman wants Trump to do this and his whispered in Trump's ear is because allman knows he can make any's meet, right? He is burning money. It's massive pace. He's building the same technology as the other guys. He's lost ground. Open AI, 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 2020, six, 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 lengthier, 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 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

real business model. I mean, Nvidia has a business model. They're selling shovels in the gold rush. I mean, 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

“that you may not remember. 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. 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, I mean, I mean, 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 there's the exact words, but it's been suggested by leadership at

Open AI before. That was going to be on loan guarantees for data centers was what they floated, right, which is a version of the bailout. I mean, on the business model, what do you think happens here? Because, yeah, we did have Ed Zittron on the podcast. I recently wrote an article going into

just how profitable or unprofitable these companies are. For Open AI, the answer is extremely

they lost $21 billion on an operating profit. Unprofitable lost $21 billion on an operating profitability basis last year. So they've burned in $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 with the calendar year, Open AI is currently buying $21 billion.

Every time you use their product, they lose money, right? Yeah, that's the better way to put it. Unthropic also loses money, but less money. My question to you, do they ever figure this out? Do they ever turn a profit? The way I've been thinking about it is like they need to thread a needle. There's so many things going against these companies that it is extremely unprofit, 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 are building this big expensive technology, and they might

“get disintermediate 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 smart guy, right? You didn't need to be whole internet. You run on like 20 watts of power. You have some pizza, some sushi or whatever. You don't need to, you know, right? So one is just like insanely inefficient. If somebody else comes along with a more efficient thing, then they're all host. And like you might

not need all of these data centers. If somebody comes up with a more efficient thing, 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 then 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 leader. It doesn't actually make sense. I mean, what you really want to know, is are the results good. 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 is like we shouldn't use

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-sweet 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? So one thing is that people might make more efficient models with all together 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 out on that. Care on what it was because the companies were all you cannot eat buffet, 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 foam all. I don't want to be the guy who doesn't use AI when you're using AI and so you'll 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 open AI 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, and 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. 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 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 2000 to 23, people looked at me like my head was screwed on backwards. I mean, they just did not believe that that was really possible. But now, you know, every other week, I read somebody writing something, making the same analysis, like Sebastian Maliby in the New York Times, like, it has gone from a crazy idea to an AI, 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 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, but they have a problem with the IPO to raise, you know,

the next round of funding, which is the anthropic, because basically the same product for the

same valuation, but they're doing better commercially, you know, burning less money. And, like, opening AI'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 marketing. 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. The argument would be why, why, because the technology is rapidly improving, the technology is the kind of technology that likes which we haven't seen. And everybody's technology is to the extent they 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. But in any case, in the ways in which it is improving, which are some of the ways you want but not all, the competitor is all

“R2. Like, you have to think about the relative ranking at the cost. Right? The relative”

ranking of OpenAI is clearly declining, like any reasonable measure. You know, less market share, less reputation, etc. And everybody else is catching up. Like, it used to be people thought that Chinese models were year behind. Now they think they're like four months behind or something like that. And, you know, Anthropic as they had Google is at, there is no argument, no rational argument for buying a share of OpenAI at a trillion dollar value ratio. There just isn't. I agree with that

by the way. I just, I guess the pot I wanted to clarify, the edge entrance 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. Anthropics are not going to work. The idea is that the costs to build this stuff are just too

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 that OpenAI will be a huge loser. I guess the point I'd love to view to clarify, will they all lose or will that be winners? Is, is, is Gen AI 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 that spends. 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. I would certainly sooner take a bad on Anthropic than on OpenAI. I think that there is sound or company in multiple ways. 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 get all of it. And the costs are so high, you know, like it's really hard to know the future and full detail. 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. There's an intermediate possibility, which is they do become a profitable company. They figure out enough cost making so far. But really, they're basically a company that makes like $20 billion 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 eat by, like maybe that's the intermediate position. It's like they don't go out of business. They make a profit. But they were not really worth a trillion dollars.

In 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 exit from them. But, you know,

“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 or places like Google that already have the infrastructure and the distribution, they don't really need to make a that much money on them. They just need to make sure they don't get this intermediate into. There's a bunch of different possibilities. We don't know for sure. Open the eyes clearly, the weekly can change. And for Epic is still in it, we don't really

know if they'll make it or not. They'll be my take. Yeah, no, I think that makes sense. God, 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 data data lives. But what do you think people don't know enough about what's the myth that you would want to dispel?

“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 their problems. This is just not true. We're going to find some what we call domains, 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 LLMs, rather than explore other options. China is less so. I think we're running a risk by going completely crazy into the LLMs, which China is not doing. That we're going to get the ones left behind because we committed too early to the wrong

technology. Are we building a billion data maxes where VHS might not be better, but it's cheaper

or maybe that's not the perfect analogy. But like whether some other technology here, there's actually the right one. And we're in so blind, the LLMs, or anything, we're like that. Alternatives, the LLMs, are we making a mistake by doing that? I mean, look at China. They're making a lot of infrastructure bets on LLMs, 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're like, we're going to buy these companies when they put our entire economy into it. Like, I think that's a mistake, and we should be more like how could we explore other approaches to intelligence. But it requires understanding intelligence as more than just what's good to me. And in 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.

Yeah. And what it means to be intelligent. Gary Marcus is a leading voice in artificial intelligence. He's a scientist, a meritist professor of psychology and

acquired by Uber. He is also the author of six books, including his most recent work,

“Timing Silicon Valley, which anticipated the rise of tech all regards his 2023 US Senate”

testimony next to San Alpman 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. Thanks very much.”

This episode was produced by Claire Miller and Alison Weiss and engineered by Benjamin Spencer.

Our video editor is Jorge Colty. Our research team is Dan Chalon, Isabella Kinsel,

“Kristen Adana Hugh and Mia Savario. Jake McPherson is our social producer, Drew Borrow's”

is our technical director and Catherine Dylan is our executive producer. Thank you for listening to "Proft You Markets" from "Proft You Media." If you liked what you heard, give us a follow and join us for a fresh take on markets on Monday. [Music] [BLANK_AUDIO]