Is AI Scaling Dead? — With Gary Marcus

Gary Marcus is a cognitive scientist, author, and longtime AI skeptic. Marcus joins Big Technology to discuss whether large‑language‑model scaling is running into a wall. Tune in to hear a frank debate on the limits of “just add GPUs" and what that means for the next wave of AI. We also cover data‑privacy fallout from ad‑driven assistants, open‑source bio‑risk fears, and the quest for interpretability. Hit play for a reality check on AI’s future — and the insight you need to follow where the industry heads next. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

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

• 0:00Alex Kantrowitz?

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Alex Kantrowitz?

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• 1:03Alex Kantrowitz?

  What's going to happen now that AI research houses are coming up against a scaling wall? We'll find out with the leading AI skeptic, author, and founder Gary Marcus. And I am thrilled to be joined by Gary here in studio today. Gary, great to see you. Great to see you. Welcome to the show. Thanks for having me. So the genesis of this episode is that I did an episode with Mark Chen from OpenAI about GPT-4.5. And you come into my DMs and you say, listen, I want to give a rebuttal. Scaling is basically over and it's not exactly what OpenAI has said. Now, for those who don't know about the scaling laws, basically, the idea is that the more compute and data you put into these large language models, the better they're going to get, basically predictably, linearly. Well, exponentially was the idea.

• 1:48Alex Kantrowitz?

  Right. And so the context here is now we've seen almost every research house all but admit that That has hit the point of diminishing returns. I think Mustafa Suleyman was here. He pretty much admitted it. Thomas Kurian, CEO of Google Cloud, said that their diminishing returns are happening. Yann LeCun has also talked about the fact that you're just not going to see as many returns from AI scaling as you would beforehand. So just describe the context of what we're seeing right now. How big of a deal is it? And then what are the implications for the AI industry? Because this is the big question. I mean, how much better can these things get? Right? That is the big question with AI today.

• 2:27Speaker 01

  Well, I mean, I have to laugh because I wrote a paper in 2022 called Deep Learning Is Hitting a Wall. And the whole point of that paper is that scaling was going to run out, that we were going to hit diminishing returns. And everybody in the field went after me. A lot of the people you mentioned, I mean, LeCun did, Elon Musk went after me by name, Altman did, and they all, like Altman said, give me the strength of the, of a mediocre deep learning skeptic. So the people were really pissed when I said that deep learning was going to run out. So it's amazing to me that a bunch of people have conceded that these scaling laws are not working the way they used to be. And they're also doing a bit of backpedaling. I think that that Mark Chen interview, I can't quite remember the details, but I think it was a version of backpedaling and redefining things.

• 3:09Speaker 01

  So if you go back to 2022, there were these papers by Jared Kaplan and others at OpenAI. And they said, look, we can just mathematically predict how good a model is going to be from how much data there is. And then there were the so-called Chinchilla scaling laws. And everybody was super excited. And basically, people invested half a trillion dollars, assuming that these things were true. You know, they, they made arguments to their investors or whatever. They said, if we put in this much data, we're going to get here. And they all thought that here in particular was going to mean AGI eventually. And what happened last year is everybody was disappointed by their results.

• 3:48Speaker 01

  So we got one more iteration of scaling after 2002, after 2022 that worked really well. And we call that GPT-4 and all of these models that are sort of like that. So I wrote that paper around GPT-3. We got another iteration of scaling. So right, three was scaling compared to two, it was much better. Two was scaling compared to one, it was much better. So much better. Sorry, much more data meant much better.

• 4:12Alex Kantrowitz?

  But what is what is much better?

• 4:14Speaker 01

  Well, I mean, one way to think about it is you didn't need a magnifying glass to see the difference between GPT two and what was that? We didn't call it GPT one, but the original GPT. And you didn't need a magnifying glass for GPT four as opposed to GPT three. It was just obviously better. A lot of people thought is that we would pretty quickly see GPT five. And a lot of people raced to build it. So OpenAI tried to build GPT five, and they had a thing called Project Orion, and it actually failed. And eventually got released as GPT four and a half. So what they thought was going to be GPT five just didn't meet expectations. Now, they could slap any name on any model they want. And in fact, lately, nobody understands how they're naming their models. But they haven't felt like any of

• 4:58Speaker 01

  the models that they've worked on since GPT four actually deserve the name GPT five. And it didn't meet the performance that these so-called mathematical laws required. And what I said in that paper is they're not really mathematical laws. They're not physical laws of the universe like gravity. They're just generalizations that held for a little while. Like, a baby may double in weight every couple of months early in its life. That doesn't mean that by the time you're 18 years old, that you're going to be 30,000 pounds. And so we had this doubling for a while. And then it stopped. And we can talk about why. But the reality is, it's not really operative anymore. So there's been efforts to kind of misdirect and shift direction. So I think everybody in the industry,

• 5:41Speaker 01

  quietly or otherwise, acknowledged that, hey, we're not getting the returns that we thought anymore. And nobody's been able to build a so-called GPT five level model. That's a big deal, right? I'm a scientist. And as a scientist, originally scientist, as a scientist, we have to pay attention to negative results as well as positive results. So when 30 people try the same experiment and doesn't work, nature is telling you something. And everybody tried the experiment of building models that were 10x the size of GPT four, hoping to get to something they could call GPT five. And it was like a quantum leap better than GPT four. They didn't get there. So now they're talking about scaling inference time compute. That's a different thing.

• 6:22Alex Kantrowitz?

  Yeah, but before we get there, I just want to talk to you about, I want to test your your theory here. So it's not that scaling is over, right? I don't think anyone that we're talking about say scaling is over. Basically, what they're saying is if you want to make the model better, and I think that means more intelligent, more conversational, even more personable, you can still do it by scaling. I think what they admit, the thing that they admit, though, is that it takes much, much more compute and much more data to get the same results that you would in the previous loops.

• 6:58Speaker 01

  So let's clarify two things. One is that what people talked about, about scaling originally, was a mathematically predictable relationship between performance and amount of data. You can go back and look at the Chinchilla paper, the Jared Kaplan paper, and lots of things that were posted on the internet. There were papers that saying, or T-shirts saying, "Scale is all you need." You looked at that T-shirt, and it had equations from the Jared Kaplan paper. And it said, you know, here's the exponent, you can fit the equation. If you have this much data, this is the performance you're going to get. And there were a bunch of papers, a bunch of models that actually seemed to fit that curve. But it was an exponential curve. And what's happening now is, yeah, you add more data, you get a little bit better, but you're not fitting that curve anymore. We've fallen off the curve.

• 7:42Speaker 01

  That's what it really means, to say that scaling isn't working anymore. Is you don't, you know, if I drew a curve for you, it was going up and up and up really fast. And it's not going up as a function of how much data you had. So, or how much compute you had. So you added a bunch of compute, and you got this much better performance. And this is how people justified running these experiments that cost a billion dollars. Is they're like, "I know what I'm going to get for the billion dollars." And then they ran the billion-dollar experiments, and they didn't get what they thought they would. Yeah, you get a little bit better, but that's what diminishing returns means. Diminishing returns means you're not getting the same bang for your buck as you used to. And that's where we are now. So anytime you add a little piece of data, the model's going to do better.

• 8:24Speaker 01

  On that piece of data, but the question is, is it generalize and give you significant gains across the board? And we were seeing that and we just aren't anymore.

• 8:33Alex Kantrowitz?

  So is there still a path for these models to become much more performant? I mean, let's say you do supersize these clusters to the point that is insanely, they are insanely bigger than they were previously. Let's talk about like Elon Musk's 1 million GPU cluster.

• 8:51Speaker 01

  Well, let's look at what Elon got for his money, right? So he built Grok 3 and by his own testimony, it was 10 times the size of Grok 2. It's a little better, but it's not night and day, right? Grok 2 was night and day better than the original Grok. GPT-4 was night and day better than GPT-3. GPT-3 was night and day better than GPT-2. Grok 3 is like, yeah, you can measure it. You can see that there's some performance, but for 10x, the investment of data, compute, and not, not to mention costs of energy to the environment, it's not 10 times smarter by any reasonable measure. It just isn't.

• 9:28Alex Kantrowitz?

  Okay. And so this would be the point where I say, well, then this entire AI moment is done. However.

• 9:37Speaker 01

  Well, it's this moment. There will be other AI moments, but this one.

• 9:40Alex Kantrowitz?

  I'm setting it up to say that it's not because, because like you mentioned, you're talking about test time compute. That's another way to say reasoning, I think, which is these models.

• 9:49Speaker 01

  Well, I'm going to give you a hard time about that, but I mean, people do do that.

• 9:53Alex Kantrowitz?

  But with, with reasoning or test time compute, you'll help me figure out the finer details. What these models are doing is they're coming to try to find an answer and they're checking their progress and deciding whether it's a good step or not, and then taking another step and another step. And we've seen that they have been able to perform much better when you put that reasoning capabilities on top of these large models, which has enabled these research houses to continue the progress.

• 10:19Speaker 01

  To give you, but it's not really you. It's, it's these companies that push back on that. So it is true that you can build a model that will do better if you put more compute on it, but it's only true to some degree. So now I'll get to whether it's actually reasoning or not, but it turns out that on some problems, you can generate a lot of data in advance. And for those problems, adding more test time compute seems helpful. There was a paper this weekend that's. And calling some of this into question.

• 10:52Alex Kantrowitz?

  By the way, just to explain to folks, test time is when the model is giving an answer. That's what test time is.

• 10:58Speaker 01

  That's right. So you have these models now like oh, three and oh, four, that will sometimes take like thirty seconds or five minutes or whatever to to answer a question. And sometimes it's absurd because you ask it like, what's thirty seven times eleven and it takes, you know, thirty seconds. You're like, my calculator could have done it faster, but we'll put aside that absurdity. In some cases, it seems like time was spent, sometimes not. But if you look carefully, the best results for these models are always almost are almost always on the same things, which are math and programming. And so when you look at math and programming, you're looking at domains where it's possible to generate what we call synthetic data. And to generate synthetic data that you know are correct.

• 11:40Speaker 01

  So for example, on multiplication, you can train the model on a bunch of multiplication problems. And you can figure out the answer in advance. You can train the model, what it is that you should predict. And so on these problems in what I would call closed domains, where we can do verification as we create the synthetic data. We verify that the answer we're teaching the model is correct. The models do better. But if you go back and you look at the oh, three, sorry, the oh, one paper. Even then, you could already see that the gains were there and not across the board. They reported that on some problems, oh, one was not better than GPT four. It's only on other problems, these cut and dry problems with the synthetic data.

• 12:24Speaker 01

  That you actually got better performance. And I've now seen like ten models and always seems to be that way.

• 12:31Speaker 01

  We're still waiting for all the empirical data to come in, but it looks to me like it's a narrow trick that works in some cases. The amazing thing about GPT four is that it was just better than GPT three on almost anything you could imagine. And GPT-3, the amazing thing is it was better than GPT-2 on almost anything you can imagine. Models like O1 are not systematically better than GPT-4. They're better in certain use cases, especially ones where you can create data in advance. Now, the reason I wouldn't call them reasoning models, though you're right that many people do, is what I think they're doing is basically copying patterns of human reasoning. They're getting data about how humans reason certain things. But the depth of reasoning there is not that great. They still make lots of stupid mistakes all the time.

• 13:14Speaker 01

  I don't think that they have the abstractions that we think, for example, a logician has when they're reasoning. So it has the appearance of reasoning, but it's really just mimicry. And there's limits to how far that mimicry goes. I'll give you just one more example, is O3 apparently hallucinates more than the models that came before it.

• 13:34Alex Kantrowitz?

  Which is stunning, like, how does that happen?

• 13:37Speaker 01

  I mean, that's a good broader question, which is, our understanding of these models is still remarkably limited. So the technical term, or one technical term.

• 13:46Alex Kantrowitz?

  Interprability.

• 13:47Speaker 01

  Well, I was gonna give you a different one, which is, which is black box.

• 13:50Alex Kantrowitz?

  Okay.

• 13:51Speaker 01

  But they're closely related, those two terms.

• 13:53Alex Kantrowitz?

  You need interprability to get, figure out what's going on in the black box.

• 13:56Speaker 01

  If you can at all. I mean, I'd almost put it another way, which is.

• 13:59Alex Kantrowitz?

  But isn't the black box the thing in the plane that tells you what actually happened?

• 14:03Speaker 01

  Well, that's a different thing, right? So a black box in a plane is actually a flight recorder that records a lot of data. But what we mean in machine learning by black box is you have a model where you have the inputs, and you have the outputs, you know how you calculate them, but you don't really understand how the system gets there. So in this case, you're doing all this matrix multiplication, nobody really understands it. And so nobody can actually give you a straightforward answer for why O3 hallucinates more than GPT-4. We can just observe it. That's what happens with black boxes, is you empirically observe things. And you say, well, it does that, but you don't really know why, and you don't really know how to fix it either. Another example, just in the last couple of days, is apparently Sam Altman reported, I forget, the new model is

• 14:48Speaker 01

  is stubborn, or what was it? I forget.

• 14:50Alex Kantrowitz?

  No, it's not stubborn, it's a bro.

• 14:52Speaker 01

  It's a bro.

• 14:53Alex Kantrowitz?

  But that's GPT four O, it's just like, it became very fratty.

• 14:57Speaker 01

  Became very fratty, okay.

• 14:58Alex Kantrowitz?

  And like, you like, right, you would be like, what's going, like, help me with this, and it's like, yo, that's a hella good question, bro. And they're like, we don't know why this happened, and they rolled it back completely.

• 15:08Speaker 01

  Yeah, exactly, or I thought they were partly rolled it, or whatever.

• 15:11Alex Kantrowitz?

  No, no, Sam said it's now the latest, um, iteration's been completely rolled back.

• 15:14Speaker 01

  Completely rolled back, so, right, that was what I would call, again, empirical, like, they tried it out, and it didn't work, or it worked in a way that irritated people, right? And so, we don't know in advance, like, there's a lot of just, like, try it, because that's how black boxes work, and we have some things, but those things are not very strong, so the scaling, quote, laws were empirical guesses about how these models work, and they were true for a little while, which was amazing, and they're not true anymore, which is also amazing in a way. Um, so we don't know what's going to happen from the black boxes.

• 15:48Alex Kantrowitz?

  Right, okay, so, but let me now, sort of.

• 15:51Speaker 01

  And sorry, let me come back to one other thing quick, which is interpretability, so that's a very closely related notion, so, um, let's say you look at a GPS navigation system, that's a piece of AI that's very interpretable, so, you can say, it is plotting this route, it says, you know, you can go this way, you can go that way, this is the function that it's maximizing, this is the database it's using, this is how it looks up the data, we don't have any of that in these so-called black box models. We don't really know what the database is that it's consulting, it isn't exactly consulting a database at all, and we don't know how to fix it. And so, you know, um, Dario Modi, uh, who's a CEO.

• 16:26Alex Kantrowitz?

  We just talked about this on the show, you actually praised his interpretability post.

• 16:31Speaker 01

  That's right.

• 16:31Alex Kantrowitz?

  Call for interpretability.

• 16:32Speaker 01

  I'll be honest, I haven't read the paper yet, I just read the title, so, so, bad on me, but the title of this paper was something like, on the desperate need for interpretability, or urgent need.

• 16:39Alex Kantrowitz?

  That captures it.

• 16:40Speaker 01

  And I think he's right, I've said this too, myself, like, in my last book, I talked about interpretability being really important. The only difference between Dario and me on this point is. We both think that we're screwed as a society if we stick with uninterpretable models. He just thinks that L L M's will eventually be interpretable. And his company, to be fair, has done the best work on interpretability of L L M's that I'm aware of. Chris Ola, I think, is brilliant. But they haven't got that far. They've gotten further than anybody else, but I don't think we're ever going to get very far into the black box. And so I think we need to start over and find different approaches to A I altogether.

• 17:19Alex Kantrowitz?

  Right. So Gary, if I'm listening to what you're saying on this show so far, it is basically after G P T four, we haven't made a lot of progress. However.

• 17:28Speaker 01

  A little bit.

• 17:29Alex Kantrowitz?

  But let me just do the pushback here, which is, I mean, if you think about what it's like using these these models after G P T four, they are significantly better. I'll give you one example. I was using O three, this new reasoning model or test time model, whatever you want to call it. And I just, I'm, I'm in it and I'm doing crazy things and it's exceptionally helpful. So I put a photo of myself on a rock climbing wall and said, what's going on? And it like was able to look at the form, where my body was, where my, what my posture was, and like analyze all these things and give actually helpful coaching tips, which you never would have had with, with G P T four. Then you think about what Claude is doing, the anthropic bot. I was with some friends last night, and this is what we do for fun.

• 18:13Alex Kantrowitz?

  I vibe coded a retirement calculator directly in Claude. It took like 10 minutes. We went from, we took a bank statement, we got a line graph of the person's balances, a bar graph of their expenses, financial plan, and then we coded a retirement calculator based off of the data that we had there. And then you also, you also have P H D's that are now adding their unique, unique insights into these models for training. They just basically are sitting and writing down what they know and the model is absorbing it. So we are seeing, I would call it vast improvement over the G P T four models.

• 18:50Speaker 01

  So, I mean, there's a couple of different ways to think about that. So one is on a lot of benchmarks, there is improvements, but there's also issues of data contamination. Alex Reisner wrote an excellent piece in The Atlantic about the issues of data contamination. And we've seen a lot of studies where people are like, well, we tried it in my company. It's not really that much better. So they're better on the benchmarks. Are they better in general? Not so clear. It was a new benchmark. Released by a company called Val AI or something like that. Val's AI, the Washington Post talked about yesterday, where they looked at things like, can you pull out a chart based on a series of financial statements, SEC statements, from a bunch of companies? And these systems all claimed to do it, but accuracy was under 10%.

• 19:34Speaker 01

  And overall, on this new benchmark, accuracy was at 50%. Would these be, new models be better than GPT-4? Maybe, but they weren't that good. So I think people tend to notice when they do well. They don't notice as much when they do poorly. And although I think there's been some improvement, there has not been the quantum leap that people are expecting. We have not moved past hallucinations. We have not moved past stupid reasoning errors. If you go back to my 22, uh, 2022 paper, Deep Learning is Hitting a Wall. I didn't say there'd be no progress at all. What I said is, we're gonna have problems with hallucinations. We're gonna have problems with reasoning, with planning, until we have a different architecture in some sense. And I think that that's still true. We're still stuck on the same kinds of things.

• 20:19Speaker 01

  So if you have, you know, deep research write you a paper, it's gonna make up preferences.

• 20:24Alex Kantrowitz?

  Okay.

• 20:24Speaker 01

  It's probably gonna make up numbers, like, you know, did you actually go back and check? So, for example, with, I think it's called, they all have similar names now. Whatever Grok's version is, Deep Search, Deep Research.

• 20:35Alex Kantrowitz?

  Yeah, it's had some, I don't know.

• 20:37Speaker 01

  Deep Research.

• 20:37Alex Kantrowitz?

  I won't be convinced that we have AGI until these companies learn how to call deep research something other than deep research. They all use the same exact name. It's really bizarre.

• 20:45Speaker 01

  So whichever version Grok has, I asked it, for example, to list all of the major cities that were west of Denver. And to somebody who wasn't paying attention, it'd be super impressive, but, because I really wanted to know how well it was working. I checked, and it left out Billings, Montana. Right, so you got a list that looks really good, and then there are errors. This often happens. And then I had a crazy conversation with it after that. I said, what happened to Billings? And it said, well, there was an earthquake there on February 10th or whatever. And I looked up in the, you know, the seismological data. I use Google, because I wanna have a real source or DuckDuckGo. And there was no earthquake then. And I pushed it on and said, well, I'm sorry for the error or whatever.

• 21:29Speaker 01

  So we're still seeing those kinds of things. We may see them less. But they are still there. We still have those kinds of problems. So I don't doubt that there's been some improvement, but the quantum across the board that people were hoping for is not there. The reliability is still not there. And there's still lots of subtle errors that people don't notice. And then, you know, if you want to talk to me about retirement calculators, there are a lot of those on the web. So the easy cases for these systems are the ones where the source code is actually already there on the web. Like Kevin Roos talked about this example of having, he quote vibe coded a system to look in a refrigerator and tell him what recipe to make. But it turns out that app is already there on the web and there are demos

• 22:13Speaker 01

  of that with source code. And so like, if you ask a system to do something that's already been done, that's always been true with all of these systems. That's their sweet spot is regurgitation. And so, yeah, they can build the stuff that's out there. But if you want to code things in the real world, you usually want to code something that's new. And the systems have a lot of problems with that. Another recent study, excuse me, showed that they're good at coding, but they're not good at debugging. And like coding is just the tiniest part of the battle, right? The real battle is debugging things and maintaining the code over time. And these systems don't really do that yet.

• 22:45Alex Kantrowitz?

  But you know, search has made them more reliable. When these bots are able to search the web and they are now starting to give you lots of links in the actual answers.

• 22:55Speaker 01

  I still like get daily people sending me examples of, you know, it hallucinated these references.

• 23:00Alex Kantrowitz?

  I'm not saying hallucinations have been solved, but for me, like I will use it. It's an incredible research assistant. And then when it links out to things and I'm not sure of those figures, I'll then go to the primary sources and start reading.

• 23:15Speaker 01

  I mean, good on you that you go to the primary sources. I worry the most about people who don't. And we've seen countless lawyers, for example, get in trouble using these.

• 23:24Alex Kantrowitz?

  Has it been countless? I just heard of one.

• 23:27Speaker 01

  Oh, no, no, no. There's many more than that. There's some in the US, there's some in Canada. I think there was just one in Europe. I mean, it's not really countless one could sit there and do it, but it's got to be at least a dozen by now.

• 23:38Alex Kantrowitz?

  And whether this is gonna be, alright, I think we can both agree on this, that whether this is the end of progress or towards the end of progress, or whether there's a lot more progress, there's a real problem of people outsourcing their thinking to these bots.

• 23:50Speaker 01

  Well, Microsoft did a study, in fact, suggesting that critical thinking was was getting worse as a function of them, and that wouldn't be too surprising. We have a whole generation of kids who basically rely on these bots and who don't really know how to look at them critically. You know, in previous years, we were starting to get too many kids relying on whatever garbage they found on the web, basically. And I mean, chatbots are basically synthesizing the garbage that they find on the web. And so, we're not really teaching kids critical thinking skills. And nowadays, like, the idea for many kids of writing a term paper is, I typed in a prompt in ChatGPT, and then maybe I've made a couple edits, and I turn it in. You're obviously not learning how to actually think or write in that fashion. A lot of these tools, I think, are best used in the hands

• 24:35Speaker 01

  of sophisticated people who understand their limits. So, you know, coding has actually been, I think, one of the biggest applications. And that's because coders understand how to debug code. And so, they can take the system, basically, it's just typing for them and looking stuff up. And if it doesn't work, then they can fix it, right? The really dangerous applications are like when somebody asks for medical advice, and they can't debug it themselves, and, you know, something goes wrong.

• 25:01Alex Kantrowitz?

  Okay. So, I'm gonna take into consideration all the things that you've said so far, and see if I can get a sense as to where you think we're heading. It seems like there was a push to just make these models better, based off of scale. That could be things like the 300,000 GPU cluster, I think Meta used for Llama 4, or it could be the million cluster GPU center that Elon's built for Grok. And what you're saying is, that's been maxed out, pretty much. Like, no one's-

• 25:31Speaker 01

  I'd be more careful. It's not maxed out, but it's-

• 25:33Alex Kantrowitz?

  There's diminishing returns.

• 25:34Speaker 01

  There's diminishing returns, exactly.

• 25:36Alex Kantrowitz?

  So, the point that I'm trying to make here is, you don't believe that there's gonna be anyone that's gonna build a bigger- GPU data center than that because if you're seeing diminishing returns from something that costs billions of dollars, it doesn't make sense to invest.

• 25:50Speaker 01

  Well, wait a second. I'm not saying people are rational. I think that people will probably try at least one more time. They'll build things, you know, probably Elon will build something that's 10 times the size of Grok 3, which will be huge and it will, you know, it will have a serious impact on the environment and so forth. I just don't.

• 26:07Alex Kantrowitz?

  It's not just GPUs, also it's data, right? Like how much more data is there?

• 26:10Speaker 01

  Well, let's come to data separately in a second. And so I think people will actually try.

• 26:14Alex Kantrowitz?

  Right.

• 26:15Speaker 01

  I think Masa has just bankrolled Sam to try. I just don't think they're going to get that much for it. I don't think they'll get zero. I mean, there will be tangibly better performance on certain benchmarks and so forth, but I don't think that it's going to be wildly impressive. And I don't think it's going to knock down the problems of hallucinations, boneheaded errors.

• 26:33Alex Kantrowitz?

  So here's what I'm getting at. That's not going to feel much better than what we have today. It doesn't seem like you believe that reasoning is going to make the bot feel much better than we have today.

• 26:41Speaker 01

  Not, not the kind of reasoning we're doing.

• 26:43Alex Kantrowitz?

  And there's no emergent, there's no emergent coding. So are you basically saying that what we have in AI today, this is it? Like this regenerative AI?

• 26:53Speaker 01

  For a while, I guess. I mean, look, I put out some predictions last year in March that people can look up that had on Twitter. And those predictions included, I said there'd be no GPT-5 this year, or if it came out, it would be disappointing.

• 27:05Alex Kantrowitz?

  It's supposed to come in summer.

• 27:07Speaker 01

  Well, this is last year. So, so I said, in 2024, we won't see this. And that was a very contrarian prediction at that point, right? This was a few weeks after people had said, oh, I bet GPT-4 is going to drop off to the Super Bowl, like right after the Super Bowl. Won't that be amazing? So people really thought it was going to come last year. If you go back and look at, you know, what they said on Twitter, et cetera. And it didn't. And I correctly anticipated that it wouldn't. And I said, we're going to have a kind of pileup where we're going to have a lot of similar models from a lot of companies. I think I said seven to 10, which was sort of roughly right. And I said, we were going to have no moat because everybody's doing the same thing. And the prices were going to go down. We'd have a price war. All of that stuff happened. Now, maybe we get to so-called GPT-5 level this year. Keeps getting pushed back.

• 27:53Speaker 01

  I don't know if we'll get much further than that without some kind of genuine innovation. And I think genuine innovation will come, but what I think is we're going down the wrong path. Yan Le Cun used this notion of, you know, we're on the exit ramp or how do you say it? Large language models are the off ramp to AGI. You know, they're not really the right path to AGI. And I agree with him, or you could argue he agrees with me because I said it, you know, for years before he did, but we won't go there. The broader notion is sometimes we make mistakes in science. I think one of the most interesting ones was people thought that genes were made of protein for a long time. So the early 20th century, lots of people tried to figure out what protein is a gene made of. It turns out it's not made of a protein.

• 28:38Speaker 01

  It's made of a sticky acid that everybody now knows called DNA. So people spent 15 years or 20 years like really looking at the wrong hypothesis. I think the giant black box L L Ms are the wrong hypothesis, but science is self correcting. In the end, people put another $300 billion into this and it doesn't get the results they want. They'll eventually do something different.

• 29:01Alex Kantrowitz?

  Right, but what you're forecasting is a basically an enormous financial collapse because.

• 29:06Speaker 01

  That's right. I don't think L L Ms will disappear. I think they're useful, but the valuations don't make sense. I mean, I don't see OpenAI being worth $300 billion. And you have to remember that venture capitalists have to like 10x to be happy or whatever. Like, I don't see them, you know, IPOing at $3 trillion. I just don't.

• 29:26Alex Kantrowitz?

  No, it's interesting because I almost see the OpenAI valuation as the one that makes the most sense because they have a consumer app. The place that I start to get, if what you're saying is correct, that we're not going to see any more, if we're seeing real diminishing results from scaling, and this is basically where we are, then there's real worry for companies like Nvidia, which has basically risen on the idea of scaling.

• 29:50Speaker 01

  They're down a third, a third this year.

• 29:52Alex Kantrowitz?

  A little 2. Something 2.5 trillion last year.

• 29:55Speaker 01

  They're a genuinely good company. They have a wonderful ecosystem. They're worth a lot of money. I mean, I don't, I don't want to put an exact figure, but I'm not surprised that they fell and I'm not surprised that they're still worth a lot.

• 30:07Alex Kantrowitz?

  But this is a thing. If we end up seeing the fact that this next iteration, the $10 billion that Sam is going to spend seemingly on the next set of GPUs, if that doesn't produce serious results.

• 30:21Speaker 01

  That's gonna hurt Nvidia.

• 30:22Alex Kantrowitz?

  That will cause a crash in Nvidia because so much of the company's demand is coming based off of this idea that scaling is gonna work.

• 30:29Speaker 01

  So they have multiple problems, both OpenAI and Nvidia. So one is, it does look to me like we're hitting diminishing returns. It does not look to me like this inference time compute trick is really a general solution. It doesn't look like hallucinations are going away. And it does look like everybody has the same magic formula. So everybody is basically doing the same thing. They're building bigger and bigger LLMs. And what happens when everybody's doing the same thing? You get a price war. So DeepSeek came out and OpenAI dropped its prices quite a bit.

• 30:59Alex Kantrowitz?

  Right.

• 30:59Speaker 01

  And so every, because everybody, I mean, not literally everybody, but you know, 10, 20 different companies all basically have the same idea or trying the same thing, you have to have a price war. Nobody has a technical mode. OpenAI has a user mode. They have more users and that's.

• 31:16Alex Kantrowitz?

  That's the most valuable thing they have. Like for them, the API, I would say the API is close to worth, I don't know, worthless is the right word, but it's worth, it's not worth very much. It is the, ChatGPT is the thing that, that really has that.

• 31:29Speaker 01

  It's the brand name that is most valuable.

• 31:31Alex Kantrowitz?

  I also think it's the best bot right now.

• 31:34Speaker 01

  I, it might be. I mean, I think people go back and forth. Some people someday say it's Claude.

• 31:38Alex Kantrowitz?

  I've, I've been on the Claude train for a long time.

• 31:41Speaker 01

  And now you're on the ChatGPT.

• 31:41Alex Kantrowitz?

  And I'm on ChatGPT, I think.

• 31:43Speaker 01

  What I think is gonna happen is you have leapfrog, but the leaps aren't gonna be as big as they were. So four was a huge leap. I mean, this is a different way of saying what I said before. It was a huge leap over three. You know, let's say, I can't even keep up with the naming scheme, GPT 4.1.

• 31:59Alex Kantrowitz?

  Right.

• 31:59Speaker 01

  Let's say it's better than Grok 3.0, or Claude 3.7, let's just say hypothetically. And so people run to this side of the room and then, you know, Claude, whatever, 3.8.1 or whatever, will be a little better than some people will run to that side of the room. But nobody's gonna be able to charge that much money because the, the advances are gonna be smaller. And people start to say, well, you know, I use this one for coding and this one for brainstorming and whatever. But nobody anymore says, this is just like dominant. Like. GPT four was just dominant. When it came out, there was nothing as good as it for anything. If you wanted this kind of system, you used it, right? I mean, that's my memory of it.

• 32:42Speaker 01

  I don't hear any of the the chat GPT or whatever. I can't even keep up with the names anymore. Any of those products, any of the open AI products being referred to in the same kind of hushed tones like they're just better. And like, you know, Google's still in this race and they may undercut on price. Meta is giving stuff away. People are building on it. Deep seek I hear has something new that's gonna, you know, be better than chat GPT. Um, and, you know, maybe it's true, maybe it's not, but we we were we're in this era where the the differences between the models are just getting really small.

• 33:18Alex Kantrowitz?

  I I was, I want to ask you when you're gonna admit that you were wrong about things or if you ever will.

• 33:23Speaker 01

  Which things, which things?

• 33:25Alex Kantrowitz?

  I think that I so so, but I I also realize that the question doesn't really hit. Because I just want to say, we we spoke the last time you were, I think you've been on the show two times, once with Blake Lemoine, once one on one.

• 33:38Speaker 01

  Yeah.

• 33:38Alex Kantrowitz?

  And we because it's interesting. I think you're the one of the the most outspoken AI critics. And you say a lot of the things that we say here on the show, which is that AGI is marketing. And even if we don't hit AGI, there's still a lot to be concerned about, whether that's the BS that people are talking about. Or being able to use these models for, um, you know, for nefarious purposes by churning out like content. Like, I don't know if you saw, there was this study of this University of Zurich tried to fool people on Reddit or tried to convince people on Reddit based off of answers by GPT, and it's still convinced more people than

• 34:12Speaker 01

  This is the new persuasion study? I'm aware of it, but I'm ready.

• 34:16Alex Kantrowitz?

  So I I guess like to me, it's it. It does seem like it's kind of tough to be a critic of LLMs right now because they have been getting so much better, but. I don't know, just sort of like.

• 34:28Speaker 01

  I mean, people say, Gary, you're wrong. And I say, well, here are the predictions I actually made. Like, I've actually reviewed them in print and I asked people who say that I'm wrong to like, point. What did I say that was wrong? I think that sometimes people confuse my skepticism with other people's skepticism. But I think if you look at the things that I have said in print, they're mostly right. And it, you know, like Tyler Cowen said, you're wrong about everything. You're always wrong. And I said, Tyler, can you point to something? And he said, well, you've written too much. I can't do it.

• 34:59Alex Kantrowitz?

  Well, I looked through some of your stuff and I do think that sometimes it seems like you might have put like this enormous burden of proof for the AI industry. Like, you do pick out sometimes like everyone that says like AGI is coming this year and you're like, these people are liars. But that being said, like, I think your core arguments about scaling.

• 35:17Speaker 01

  I've offered to put up money. I offered Elon Musk a million dollars. And I offered Criterion, I'll tell you about that. In 2022, in May, I offered him a hundred thousand dollar bet. Later, I upped it to a million dollars. And I put out criteria on Twitter. I said, I'm gonna offer these. Do these make sense to you? And everybody on Twitter, not everybody, nearly everybody on Twitter at the time, said those were fine. Like, people accuse me of goal posting shifting, but my goalposts are the same, right? The 2014 paper in the New Yorker, article in the New Yorker, where I talk about a comprehension challenge. I've stuck by that. That is part of my AGI criteria. I made a bet with Miles Brundage on the same criteria, which he actually took the bet to his credit.

• 35:59Speaker 01

  But when I put them out in 2022, this is the important part. Everybody was more or less in agreement that those were reasonable criteria. And I said, if you could beat my comprehension challenge, which is say, you know, watch movies, know when to laugh, understand what's going on. If you could do the same thing for novel, if you could translate math from English into stuff you could formally verify. If you could go into a random kitchen, you know, tail operating a robot and, you know, make a dinner. If you could, what was the other criterion? Oh, you write, I think it was 10,000 lines of bug-free code. I mean, you could do debugging to get there, whatever. You know, okay, if you could do like three out of five, we'll call that AGI. And at the time, everybody said that's fine.

• 36:42Speaker 01

  Now people are backtracking. Like Tyler Cowen said, oh, three is AGI. By what measure?

• 36:48Alex Kantrowitz?

  I felt that that was kind of a stretch.

• 36:50Speaker 01

  It was cheesy and he said, he said the measure was him. It looked like AGI to him. He invoked the, you know, classic line about pornography. I know when I see it. But people have pointed out lots of problems with O three. I think it's absurd to call O three AGI.

• 37:03Alex Kantrowitz?

  I wouldn't call it AGI.

• 37:04Speaker 01

  So, you know, you, you a minute ago said, Gary, you're wrong, but then you ticked off a bunch of things I'm actually right about.

• 37:10Alex Kantrowitz?

  I didn't say, Gary, you're wrong. I said, is there a point you'll admit you're wrong? Like, but what I'm trying to say is.

• 37:16Speaker 01

  It's the point at which I'm wrong. So, so let me clarify one other thing.

• 37:19Alex Kantrowitz?

  But let me just say, I didn't say that you're, you're wrong. I just said, like, it, what is the point of advance that you would say, okay, I've been wrong about this stuff? Because I have listened to some of your.

• 37:31Speaker 01

  Let me clarify something.

• 37:32Alex Kantrowitz?

  But I also, right after I said that, I was like, you know, it's kind of like a tough question. And then I explained where I agreed with you.

• 37:38Speaker 01

  Yeah, yeah, that's what happened. Um, so.

• 37:43Speaker 01

  Some people take me as saying that AI is impossible. And that's not me, right? I actually love AI. I want it to work. I just want us to take a different approach. Right? I want us to take a neuro symbolic approach where we have some classical elements of classical AI, like explicit knowledge, formal reasoning and so forth. That people like Kinton have kind of thumbed their nose at, but they say Demis Hassabis is used very effectively in AlphaFold. So we get into that if you want. If we get to AI, the question about whether I'm right or not depends on how we get there. So I've made some pretty particular guesses about it. And I have guessed that pure LLM will not get us there, pure large language model. So will I concede I'm wrong when we get to AI that actually works? Depends on how it works.

• 38:25Alex Kantrowitz?

  Okay. Yeah. And I think it's clear that, I mean, I don't know, we could watch this back in a couple of years.

• 38:31Speaker 01

  If we get to pure LLMs, if another round of scaling, you know, gets us to AGI by the criteria that I laid out, then I will have to concede that I was wrong.

• 38:41Alex Kantrowitz?

  Okay, all right, I'm gonna take a quick break and then let's come back and talk a little bit more about the current risks. And maybe read some of your tweets and have you expand upon them. We'll be back right after this. And we're back here on Big Technology Podcast with AI skeptic Gary Marcus. Gary, let me ask you this. So, you know, one of the things we talked about last time you were here was that AI doesn't have to reach the AGI threshold to be something that we should be concerned about.

• 39:09Speaker 01

  Absolutely not.

• 39:10Alex Kantrowitz?

  And a lot of the focus was on hallucinations. You and I both, I think we have a little bit of a diverging opinion on hallucinations. I think they've gotten much better. You think it's still a big problem.

• 39:19Speaker 01

  Those could both be true, by the way.

• 39:21Alex Kantrowitz?

  That could both be true. All right, so let's, let's put a pin in that for now. I think where I'm seeing the most concern is virology. Or we just had a study that came out that showed that AI is now in PhD, on PhD level in terms of virology. We had Dan Hendricks from the Center for AI Safety who was here, who talked about the fact that like AI can now walk virologists through how to create or enhance the function of viruses. And we're starting to see some of these AI programs, like you mentioned DeepSeek, be available to everybody, be pretty smart, and be released without guardrails. Or not enough guardrails, especially if they're open source.

• 40:05Alex Kantrowitz?

  So what are you worried about here? Is that the core concern or is there other stuff?

• 40:09Speaker 01

  I think there's actually multiple worries. And the different worries from different architectures and architectures used in different ways and so forth. So dumb AI can be dangerous. So if dumb AI is empowered to control things like the electrical grid, and it makes a bad decision, that's a risk, right? If you put a bad driverless car system in, you know, a million cars, a lot of people would die, right? The main thing that has saved a lot of people from dying in driverless cars is there aren't that many of them. And so, you know, even though they're not actually super safe at the moment, you know, restrict where we use them and so forth, we don't put them in situations where they wouldn't be very bright. So dumb AI can cause problems.

• 40:53Speaker 01

  Super smart AI could, you know, maybe lock us all in cages if it wanted to. I mean, we have to talk about the likelihood of it wanting to, but there are definitely worries there and we need to take them seriously. And then you have things that are in between. So for example, the virology stuff is AI that's not generally all that smart, but it it can do certain things. And in the hands of bad actors, it can do those things. And I think it is true either now or will be soon enough that these tools can be used to help bad actors create viruses that cause problems. And so I think that's a legitimate worry, even if we don't get to AGI. So we have dumb AI right now is a problem. Smarter AI, even if it's not AGI, can cause a different set of problems.

• 41:37Speaker 01

  And, you know, if we ever got to super intelligent, that that might open a different can of worms. I mean, you can think like, you know, human beings of different degrees of brightness and with different skills. If they choose to do bad things can, you know, cause different kinds of harm.

• 41:51Alex Kantrowitz?

  And so what's your view on open source then?

• 41:54Speaker 01

  I worry about it. I do worry about it because bad actors are using these things already. They're mostly using them for misinformation. Not sure how much biology they're doing, but they will, and they're going to be interested in that. You know, state actors that want to do terrorist kinds of things will do that. I am worried about open sourcing at all, and I think the fact that Meta could basically make that decision for the whole world is not good. Like, I think there should have been much more government oversight. Scientists should have contributed more to the discussion. But now those kinds of models are open source. They've been released. We can't put that genie back in the bottle. And over time, just like people, I should have said this earlier.

• 42:39Speaker 01

  Even if the models don't get any better, we will still find new uses for them. And some of those new uses will be positive and some of them will be negative. Right? We're still exploring what these technologies can do. And people are finding, you know, ways to make money in dubious ways and to cause harm for various reasons and so forth. And so, you know, giving those tools very broadly has problems. On the other hand, I think what we've learned in the last three years is that the closed companies are not the ethical actors that they once were. So, you know, Google famously said, don't do evil, and they took that out of their platform. You know, Microsoft was all about AI ethics and then. You know, when Sydney came out, they're like, we're not taking this away. We're going to stick with it.

• 43:21Alex Kantrowitz?

  Oh, they did kill Sydney. Sydney was this

• 43:23Speaker 01

  Well, they

• 43:24Alex Kantrowitz?

  Very, I don't know, raunchy AI that tried to steal Kevin Roos's wife.

• 43:29Speaker 01

  Yeah, I mean, they reduced what it could do, but um, but they stuck with it in some sense, but you know, and like OpenAI said that we're, you know, nonprofit for public benefit. Now they're desperately trying to become a for-profit. That is really not particularly interested in public benefit. It's interested in in money and they may become a surveillance company, which I don't think is

• 43:51Alex Kantrowitz?

  Because what you're talking about with the advertising side?

• 43:53Speaker 01

  Yeah. So basically they have a lot of private data because they have a lot of users and people type in all kinds of stuff. And they may have no choice but to monetize that. And, you know, they've been showing signs of that. They hired Nakasone, who used to be at the NSA. They bought a share in a webcam company. And they recently announced they're trying to build a social media company. They want, you know, they look like they're on a a path to sell your data, your very private data to, you know, whoever they care.

• 44:21Alex Kantrowitz?

  It's concerning because whatever data I gave to Facebook, like I always used to think that this conversation around Facebook data was a little ridiculous because I didn't think I was giving that much information to Facebook, but I am giving OpenAI a lot of information.

• 44:35Speaker 01

  I mean, there are a lot of people that treat it as a

• 44:38Alex Kantrowitz?

  Well, that's the number one use is therapist companion. I don't use it as a therapist, but I'm like putting a lot of my work

• 44:43Speaker 01

  I read a great book called uh, Privacy and Power, I'm blanking slightly on the title by Carissa Velez. And she had examples in there, like people were taking data from Grindr and extorting people, right? Grindr is an app for gay people, if you don't know. And um, you know, that's still in our society and like in some places it's acceptable and other places. Um, you know, people don't necessarily want to come out if they're gay or whatever. And so people have been extorting people with data from Grindr. Imagine what they're going to do. You know, people type into ChatGPT like their very specific sexual desires. Maybe crimes they've committed, like people are typing a lot of

• 45:26Alex Kantrowitz?

  Crimes they want to commit?

• 45:27Speaker 01

  Crimes they want to commit. You know, we have a political climate where You know, conspiracy crime or conspiracy might be treated in a different way than it once was. And so just typing it into chat GPT might, you know, get somebody deported. Who knows?

• 45:42Alex Kantrowitz?

  Now I'm freaked out.

• 45:43Speaker 01

  It's I I wouldn't personally use the system because the writing is on the wall. And I think that they they make some promises to their business customers, but not to their, you know, consumer customers. And that stuff is available for them to do what they want with it. And they probably will because that's how they're going to make money. Here here's another way to put it is suppose I'm right about the things I've been arguing, and they can't really get to, you know, the GPT seven level model that everybody dreamed of. It can't really build AGI, but they're sitting on this incredible treasure chest of data. What are they gonna do? Well, if they can't make AGI, they're gonna sell that data.

• 46:25Alex Kantrowitz?

  This is why I always thought like when you take in a lot of money, it's always you always have to pay that money back in some way, and that changes the way you operate.

• 46:33Speaker 01

  That's right. I mean, look at 23andme. They're out of business and now that data is for sale. And like, who knows what's gonna happen with the 23andme data?

• 46:40Alex Kantrowitz?

  I hope you're wrong about this one, but well, the history of the internet is.

• 46:44Speaker 01

  Exactly.

• 46:45Alex Kantrowitz?

  I'm not saying you are. I'm just saying I hope you are because that would be bad.

• 46:47Speaker 01

  I hope I'm wrong, too.

• 46:48Alex Kantrowitz?

  But there is a level of.

• 46:49Speaker 01

  A lot of things I hope I'm wrong.

• 46:51Alex Kantrowitz?

  Gary, if the if people got freaked out about what Facebook was doing with your data, if they overstep, there's gonna be a major societal backlash.

• 47:01Speaker 01

  Maybe. I mean, sometimes people just accommodate to these things. I've been amazed at how willing people are to, you know, give away all that information to Facebook. I don't use it anymore, but.

• 47:11Alex Kantrowitz?

  Let me ask you this. You quote tweeted one of these. So we'll get into a tweet here. You quote tweeted one of these tweets, is the push to optimize AI for user engagement just metric chasing Silicon Valley brain or an actual pivot in business model from create a post scarcity society, God, to create a worse TikTok? This is what basically we're talking about is that that might be the pivot.

• 47:34Speaker 01

  Yeah, that's right. I think that was someone else's tweet that I.

• 47:36Alex Kantrowitz?

  Yeah, Daniel Lit, and you said, I've been basically telling you about this.

• 47:39Speaker 01

  Yeah, exactly.

• 47:40Alex Kantrowitz?

  So that's what it is.

• 47:46Alex Kantrowitz?

  You also wrote this, saying the quiet part out loud, the business model of Gen AI will be surveillance and hyper-targeted ads, just like it has been for social media.

• 47:53Speaker 01

  That's right. We were just talking about that. And what I was quote cheating there was something from Aravind Srinivas, if I pronounce his name correctly, who's the CEO of Perplexity. And he basically, I said he's saying quiet part out loud, he basically said, we're going to use this stuff to hyper-target.

• 48:08Alex Kantrowitz?

  You also said that companies like Johnson & Johnson will finally realize that Gen AI was not going to deliver on its promises. Have there been companies that have pulled back? Like, are you just using Johnson & Johnson as an example?

• 48:21Speaker 01

  That was based on a Wall Street Journal thing, and I may have failed to include the link because of Elon Musk's crazy notions around links.

• 48:28Alex Kantrowitz?

  Elon, you gotta put the links in the...

• 48:30Speaker 01

  Elon, you gotta put the links in whatever else you do. That's right. So, anyway, I was alluding to a Wall Street Journal report that had just come out, which showed that J&J had basically said, in so many words, I'll paraphrase it, they tried Gen AI in a lot of different things, generative AI, and a few of them worked and a lot of them didn't, and they were gonna like stick to the ones that did, like customer service, and maybe not do some of the others. I mean, you have to go back, you know, a year and a half in history to when people thought Gen AI was going to do everything that an employee was able to do, basically. And I think what J&J and a bunch of companies have found out is that's not really true. You know, they can do a bunch of things that employees do, but they can't typically do everything that a single employee does. And, you know, they're

• 49:14Speaker 01

  reasonably good at triaging customer service, and they're not necessarily good at creating, say, a careful financial protection.

• 49:22Alex Kantrowitz?

  Okay. So, Gary, you have like five minutes left. I want, you said something in the, I think in the first half, about the path that you think needs to be taken to AGI. Can you explain what that is in like, as basic of a way as you can to like... You know, make it as simple to understand for anyone who's not caught up with the systems that you spoke about.

• 49:45Speaker 01

  Sure. So a lot of people will have read Danny Kahneman's book, Thinking Fast and Slow. And there he talked about System One and System Two cognition. So System One was fast and automatic, reflexive. System Two was more deliberate, more like reasoning. I would argue that the neural networks that power generative AI are basically like System One cognition. They're fast, they're automatic, they're statistically driven, but they're also error prone. They're not really deliberative. They can't sanity check their own work. And I would say we've done that pretty well. But System Two is more like classical AI, where you can explicitly represent knowledge, reason over it. It looks more like computer programming. And these two schools have both been around since the 1940s.

• 50:30Speaker 01

  But they've been very separate for what I think is sociological and economic reasons. Either you work on one or you work on the other, people argue, or fight for graduate students and fight for grants and stuff like that. So there's been a great deal of hostility between the two. But the reality is they kind of complement each other. Neither of them has worked on its own. So the classical AI failed, right? People build all these expert systems, but there were always these exceptions and they weren't really robust. You'd pay graduate students to patch up the exceptions. Now we have these new systems, they're not really robust either, which is why OpenAI is paying Kenyans and PhD students and so forth to kind of fix the errors. The advantage of System One is it learns very well from data.

• 51:15Speaker 01

  The disadvantage is not very accurate, sorry, very abstract. So the, no, I should have said that slightly differently. The large language models and that kind of approach, transformers, are very good at learning, but they're not very good at abstraction. You can give them billions of examples and they still never really understand what multiplication is. And they certainly never get any other abstract concept well. The classical approach is great at things like multiplication. You write a calculator and it never makes a mistake. But it doesn't have the same broad coverage and it can't learn new things. You can wire multiplication in. But how do you learn something new? The classical approaches have had trouble with that. And so I think we need to bring them together. And this is what I call neuro-symbolic AI.

• 52:00Speaker 01

  And it's really what I've been lobbying for for decades. And I think it was hard to raise money to do that in the last few years because everybody was obsessed with generative AI. But now that they're seeing the diminishing returns, I think investors are more open to trying alternatives. And also, AlphaFold is actually a neuro-symbolic model, and it's probably the best thing that AI ever did. And so

• 52:23Alex Kantrowitz?

  Decoding proteins, protein folding.

• 52:25Speaker 01

  Yeah, figuring out the three dimensional structure of a protein from a list of its nucleotides.

• 52:31Speaker 01

  And so

• 52:31Alex Kantrowitz?

  Are you going to raise money to try to do this?

• 52:33Speaker 01

  I'm very interested in that. Let's put it that way.

• 52:35Alex Kantrowitz?

  Masa Sun, if you want to make use of your money.

• 52:40Alex Kantrowitz?

  No kidding. You talking to him?

• 52:42Speaker 01

  Uh, not at this particular moment.

• 52:44Alex Kantrowitz?

  Okay. Masa, if you're watching.

• 52:48Alex Kantrowitz?

  I don't know. Trying to help. Okay, great. Well, Gary, can you shout out where to find your Substack? So if anybody wants to read your longer work on the state of AI, where should they go?

• 52:59Speaker 01

  Sure. So people might want to read my last two books, by the way, Taming Silicon Valley, which is really about how to regulate AI, and Rebooting AI, which was 2019, is a little bit old, but still, I think, anticipates a lot of the problems around common sense and world models that we're still facing today. And then for kind of almost daily updates, I write a Substack, which is free, although you can pay if you like to support me. And that's at garymarcus.substack.com.

• 53:25Alex Kantrowitz?

  Okay, well, I'm a subscriber, Gary. Great to have you on the program. Thanks so much for coming.

• 53:29Speaker 01

  Thanks a lot for having me again, yet again.

• 53:31Alex Kantrowitz?

  Yet again, yet again. Well, we'll keep doing it. It's always nice to hear your perspective on the world of AI. So

• 53:35Speaker 01

  I always enjoy our conversations. Thanks for having me.

• 53:37Alex Kantrowitz?

  Yes, same here. All right, everybody, thank you for listening. We'll be back on Friday, breaking down the week's news. Until then, we'll see you next time on Big Technology Podcast.

• 54:00Speaker 02

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' health care to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, theHouseMajorityPact.com. Not authorized by any candidate or candidate's committee.