Why Can't AI Make Its Own Discoveries? — With Yann LeCun

Yann LeCun is the chief AI scientist at Meta. He joins Big Technology Podcast to discuss the strengths and limitations of current AI models, weighing in on why they've been unable to invent new things despite possessing almost all the world's written knowledge. LeCun digs deep into AI science, explaining why AI systems must build an abstract knowledge of the way the world operates to truly advance. We also cover whether AI research will hit a wall, whether investors in AI will be disappointed, and the value of open source after DeepSeek. Tune in for a fascinating conversation with one of the world's leading AI pioneers. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology 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?

  Why has generative AI ingested all the world's knowledge, but not been able to come up with scientific discoveries of its own? And is it finally starting to understand the physical world? We'll discuss it with Meta Chief AI Scientist and Turing Award winner Jan Le Cun. 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,

• 0:44Alex Kantrowitz?

  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? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:20Alex Kantrowitz?

  Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. I'm Alex Kantrowitz, and I am thrilled to welcome Jan Le Cun, the Chief AI Scientist, Turing Award winner, and a man known as the godfather of AI to Big Technology Podcast. Jan, great to see you again. Welcome to the show.

• 1:39Speaker 01

  Pleasure to be here.

• 1:40Alex Kantrowitz?

  Let's start with a question about scientific discovery and why AI has not been able to come up with it until this point. This is coming from Dwar Keshe Patel. He asked it a couple of months ago. Why do you make of the fact that AIs, generative AI, basically have the entire corpus of human knowledge memorized, and they haven't been able to make a single new connection that has led to discovery? Whereas if even a moderately intelligent person had this much stuff memorized, they would notice, oh, this thing causes this symptom. This other thing causes this symptom. There might be a medical cure here. So shouldn't we be expecting that type of stuff from AI?

• 2:16Speaker 01

  Well, from AI, yes. From large language models, no. You know, there's several types of AI architectures, right?

• 2:25Speaker 01

  All of a sudden, when we talk about AI, we imagine chatbots. Chatbots, LLMs, are trained on an enormous amount of knowledge, which is purely text. And they're trained to basically regurgitate, to retrieve, to essentially produce answers that are conformed to the statistics of whatever text they've been trained on. And it's amazing what you can do with them. It's very useful, there's no question about it. We also know that they can hallucinate facts that aren't true. But they're really, in their purest form, they are incapable of inventing new things.

• 2:58Alex Kantrowitz?

  Let me throw out this perspective that Tom Wolfe from Hugging Face shared on LinkedIn over the past week. I know you were involved in the discussion about it. It's very interesting. He says, "To create an Einstein in a data center, we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought or dared to ask. One that writes, 'What if everyone is wrong about this when all textbooks, experts, and common knowledge suggest otherwise?'" Is it possible to teach LLM to do that?

• 3:31Speaker 01

  No. No, not in their current form. I mean, and whatever form of AI would be able to do that will not be LLMs. They might use LLM as one component. LLMs are useful to turn, you know, to produce text, okay? So we might, in the future AI systems, we might use them to turn abstract thoughts into language. In the human brain, there's a tiny little brain area right here called the Broca area, it's about this big. That's our LLM, okay? But we don't think in language.

• 4:10Speaker 01

  We think in, you know, mental representations of a situation, we have mental models of everything we think about. We can think if we can speak. And that takes place here. That's like, you know, where real intelligence is. And that's the part that we haven't reproduced, certainly with LLM. So the question is...

• 4:31Speaker 01

  You know, is, are we going to have eventually AI architectures, AI systems that are capable of not just answering questions that are already there, but solving, giving new solutions to problems that we specify? The answer is yes, eventually, not with current telethems. And then the next question is, are they going to be able to ask their own questions? Like, figure out what are the good questions to answer? And the answer is, eventually, yes, but that's going to take a while before we get machines that are capable of this. Like, you know, in, in humans, we, we have all the characteristics. We have people who are, who have extremely good memory, they can, you know, retrieve

• 5:15Speaker 01

  a lot of things, they have a lot of accumulated knowledge. We have people who are problem solvers, right? You give them a problem, they'll solve it. And I think Thomas was actually talking about this kind of stuff. He said, like, you know, if you're good at school, you're a good problem solver, we give you a problem, you can solve it. And you score well in math or physics or whatever it is. But then, in research, the, the most difficult thing is to actually ask the right, is to ask the, the, the good questions. What are the important questions? It's not just solving the problem, it's also asking the right questions, kind of framing a problem, you know, in the right way, so, so you have kind of new insight.

• 5:59Speaker 01

  And, and then after that comes, okay, I need to turn this into equations or into something, you know, practical, a model. And that may be a different skill from the one that asks the, the, the right questions. It might be a different skill also to solve equations. The people who write the equations are not necessarily the people who write them, who, who solve them. And

• 6:23Speaker 01

  Or the people who remember that there is, you know, some textbook from a hundred years ago where similar equations were solved, right? Those are three different skills. So LLMs are really good at retrieval. They're not good at solving new problems, you know, finding new solutions to new problems. They can retrieve existing solutions. And they're certainly not good at all at asking the right questions.

• 6:43Alex Kantrowitz?

  And for those tuning in and learning about this for the first time, LLMs is the technology behind things like the GPT model that's baked within ChatGPT. But let me ask you this, Jan. So the AI field does seem to have moved from standard LLMs to LLMs that can reason and go step by step. And I'm curious, can you program this, this sort of counterintuitive or this heretical thinking by imbuing a reasoning model with an instruction to question its directives?

• 7:15Speaker 01

  Well, so we have to figure out what reasoning really mean. Okay. And there are, you know, obviously everyone is trying to get LLMs to reason to some extent to perhaps be able to check whether the answers they produce are correct. And the way people are approaching the problem at the moment is that they basically are trying to do this by modifying the current paradigm without completely changing it. Okay. So can you, you know, bolt a couple of words on top of LLMs so that you kind of have some primitive reasoning function?

• 7:57Speaker 01

  And that's essentially what a lot of reasoning systems are doing. You know, one simple way of getting LLMs to kind of appear to reason is chain of thought, right? So you basically tell them to generate more tokens than they really need to in the hope that in the process of generating those tokens, they're going to devote more computation to answering your question. And to some extent, that works surprisingly, but it's very limited. You don't actually get real reasoning out of this. Reasoning, at least in classical AI and in many domain, involves a search through a space of potential solutions. Okay. So you have a problem to solve, you can characterize whether the problem is solved or not.

• 8:42Speaker 01

  So you have some way of telling whether the problem is solved. And then you search through a space of solutions for one that actually satisfies the constraints or, you know, is identified as being a solution.

• 8:56Speaker 01

  And that, you know, that's how, that's kind of the most general form of reasoning you can imagine. There is no mechanism at all in L L Ms for this search mechanism. What you have is you have to kind of bolt this on top of it, right? So one way to do this is you get an L M to produce lots and lots and lots of sequences of answers, right? Sequences of tokens which, you know, represent answers. And then you have a separate system that picks which one is good. Okay, this is a bit like writing a program by sort of randomly more or less generating instructions, you know, while maybe respecting the grammar of the, of the language. And then checking all of those programs for one that actually works.

• 9:39Speaker 01

  It not a good way, not a very efficient way of producing correct pieces of code. It's not a good way of reasoning either. So a big issue there is, is that when, when humans or animals reason, we don't do it in token space. In other words, when we reason, we don't have to, you know, generate a text that expresses our solution and then generate another one and then generate another one. And then among all the ones we, we produce, pick the one that is good. We reason internally, right? We have a mental model of the situation and we manipulate it in our head. And we find kind of a good solution when we plan a sequence of actions to, I don't know, you know, build a table or something.

• 10:24Speaker 01

  We, we plan the sequence of action, you know, we have a mental model of that in our head. If I, if I tell you, and this has nothing to do with language. Okay, so if I tell you, imagine a cube floating in front, in front of us right now. Now rotate that cube 90 degrees along a vertical axis. Okay, you can imagine this, this thing taking place. And you can readily observe that it's a cube. If I rotate it 90 degrees, it's going to look just like the cube that I started with. Okay. Cause you have this mental model of a, of a cube.

• 10:58Speaker 01

  And that reasoning is in some abstract continuous space. It's not in text. It's not related to language or anything like that. And humans do this all the time. Animals do this all the time. And this is what we yet cannot reproduce with with machines.

• 11:15Alex Kantrowitz?

  Yeah, it reminds me, you're talking through a chain of thought and how it doesn't produce much novel insights. And when DeepSeek came out, one of the big screenshots that was going around was someone asking DeepSeek for a novel insight on the human condition. And as you read it, it's another one of these very clever tricks the AI pulls because it does seem like it's running through all these different, very interesting observations about humans, that we take our hate, like our violent side, and we channel it towards cooperation instead of competition, and that helps us build more. And then you're like, as you read the chain of thought, you're like, this is kind of just like you read Sapiens and maybe some other books, and that's your chain of thought.

• 11:54Speaker 01

  Pretty much, yeah. I mean, yeah, a lot of it is negotiation.

• 12:00Alex Kantrowitz?

  I'm now going to move a part of the conversation I had later closer up, which is the wall. Effectively, is training standard large language models coming close to hitting a wall, whereas before there was somewhat predictable returns if you put a number, a certain amount of data and a certain amount of compute towards training these models, you can make them predictably better. As we're talking, it seems to me like you believe that that is eventually not going to be true.

• 12:33Speaker 01

  Well, I don't know if I would call it a wall, but it's certainly diminishing return in the sense that, you know, we've kind of run out of natural text data to train those LLMs, where they're already trained with, you know, on the order of, you know, 10 to the 13 or 10 to the 14 tokens.

• 12:50Alex Kantrowitz?

  That's a lot.

• 12:51Speaker 01

  That's a lot, and that's the

• 12:52Alex Kantrowitz?

  It's like the whole internet.

• 12:53Speaker 01

  That's all the publicly available internet, and then, you know, some companies license content that is not publicly available. And then there is talks about, like, you know, generating artificial data and then hiring thousands of people to kind of, you know, generate more data

• 13:09Alex Kantrowitz?

  Find other knowledge, PhDs and professors.

• 13:11Speaker 01

  Yeah, but in fact, it could be even simpler than this because most of the systems actually don't understand basic logic, for example, right? So. So to some extent, you know, there's going to be slow progress along those lines with synthetic data, with, you know, hiring more people to, you know, plug the holes in the in the sort of, you know, knowledge background of of of those systems. But but it's diminishing return, right? The the costs are ballooning of generating that data and the the returns are are not not that great. So we need a new paradigm, okay? We need a new kind of

• 13:51Speaker 01

  architecture of systems that at the, you know, at the core are capable of those search and, you know, searching for a good solution, checking whether that solution is good, planning for a sequence of actions to arrive at a particular goal, which is what you would need for an agentic system to really work. Everybody is talking about agentic system. Nobody has any idea how to build them, other than basically regurgitating plans that have the system has already been trained on. Okay. So, you know, it's like it's like everything in computer science. You you can you can engineer a solution which is limited

• 14:30Speaker 01

  for for in the context of AI. You can make a system that is, you know, based on on learning or retrieval with enormous amounts of data. But really, the complex things, the complex thing is how you build a system that can solve new problems without being trained to solve those problems. We are capable of doing this. Animals are capable of doing this. Facing a new situation, we can either solve it zero shot without without training ourselves to handle that situation, just the first time we encounter it, or we can learn to solve it extremely quickly. So, for example, you know, we we we can learn

• 15:12Speaker 01

  to drive in, you know, a couple dozen hours of practice.

• 15:18Speaker 01

  And to the point that after twenty, thirty hours, it becomes kind of second nature, where this become kind of subconscious.

• 15:26Alex Kantrowitz?

  You don't even think about it.

• 15:27Speaker 01

  You don't even think about it.

• 15:28Alex Kantrowitz?

  Speaking of system one, system two, right?

• 15:30Speaker 01

  That's right. So, you know, recalls the discussion we had with with Danny Kahneman a few years ago. So, you know, the the first time you drive, your system two is all present. You have to use it. You imagine all kind of catastrophic scenarios and stuff like that, right? Your full attention is devoted to to driving. But then after a number of hours, you know, you can talk to someone at the same time. Like, you don't need to think about it. It's become sort of subconscious and more or less automatic. It's become system one. And pretty much every task that we, you know, learn, that we accomplish the first time, we have to use the full power of our of our minds. And then eventually, if we repeat them sufficiently many times, they get they get kind of subconscious.

• 16:14Speaker 01

  I have this vivid memory of once being in a workshop where one of the participants was a chess grandmaster and he played a simultaneous game against like fifty of us, right? You know, going from from one person to another. You know, I got wiped out in ten turns or something. I'm terrible at chess, right? But so he would come, you know, come to my my table and, you know, I had time to think about this because he, you know, he was playing the other fifty tables or something. So I make my move in front of it. He goes like, what? And then immediately plays. So he doesn't have to think about it. I was not a a challenging enough opponent that he had to actually call his system two. His system one was sufficient to beat me.

• 16:59Speaker 01

  And what that tells you is that when you become familiar with the task and you you train you train yourself, you know, it it kind of becomes subconscious. But the but the essential ability of humans and many animals is that when you face a new situation, you you can. Think about it, figure out a sequence of actions, a course of action to accomplish a goal, and you don't need to know much about about the situation, other than your common knowledge of how the world works, basically. That's what we're missing, okay, with the AI systems.

• 17:32Alex Kantrowitz?

  And it's, okay, now, now I really have to blow up the order here, because you've said some very interesting things that we have to talk about. You talked about how, basically, LLMs have hit the point of diminishing returns, large language models, the things that have gotten us here, and we need a new paradigm. But it also seems to me that that new paradigm is, isn't here yet. And I know you're working on the research for it, and we're going to talk about that, what the next new paradigm might be. But there's a real timeline issue, don't you think? Because I'm just thinking about the money that's been raised and put into this.

• 18:05Speaker 01

  Yes.

• 18:05Alex Kantrowitz?

  Last year, six point six billion to OpenAI, last week or a couple of weeks ago, another three and a half billion to Anthropic after they raised four billion last year. Elon Musk is putting, you know, another small fortune into, into building Grok. These are all LLM-first companies. They're not searching out the neck, I mean, maybe OpenAI is, but that six point six billion that they got was because of ChatGPT. So where's this field going to go? Because if that money is being invested into something that is at the point of diminishing returns, requiring a new paradigm to progress, that sounds like a real problem.

• 18:45Speaker 01

  Well, I mean, we have some ideas about what this paradigm is, the, the difficulty that, I mean, what we're working on is trying to make it work. And it's, you know, it's not simple that they take, that they take, that they take years. And so the question is, is the, the, are the capabilities we're talking about, perhaps through this new paradigm that we're thinking of, that we're working on, is it going to come quickly enough to justify all of this investment? If it doesn't come quickly enough, is the investment still justified? Okay, so the first thing you can say is, we are not going to get to human-level AI by just scaling up LLMs. This is just not going to happen, okay?

• 19:30Alex Kantrowitz?

  That's your perspective.

• 19:32Speaker 01

  There's no way, okay? Absolutely no way. And whatever you can hear from some of my more adventurous colleagues, it's not going to happen within the next two years. There's absolutely no way in hell to, you know, pardon my French. You know, the idea that we're going to have, you know, a country of genius in a data center, that's complete BS, right? There's absolutely no way. What we're going to have maybe is systems that are trained on sufficiently large amounts of data that any question that any reasonable person may ask will find an answer through the systems. And it would feel like you have, you know, a PhD sitting next to you. But it's not a PhD you have next to you.

• 20:12Speaker 01

  It's, you know, a system with gigantic memory and retrieval ability, not a system that can invent solutions to new problems, which is really what a PhD is, okay? This is actually

• 20:26Alex Kantrowitz?

  That's the whole deal.

• 20:27Speaker 01

  It's, you know, connected to this post that Thomas Wolf made that you inventing new things, you know, requires a type of skill and abilities that you're not going to get from LLMs. So this big question, which is the investment that is being done now is not done for tomorrow. It's not, it's done for, you know, the next few years. And most of the investment, at least from the meta side, is investment in infrastructure for inference.

• 21:09Speaker 01

  Okay, so let's imagine that by the end of the year, which is really the planet meta, we have 1 billion users of Meta AI through smart glasses, you know, standalone app and whatever. You're going to serve those people and that's a lot of computation. So that's why you need, you know, a lot of investment in infrastructure to be able to scale this up and, you know, build it up over months or years.

• 21:36Speaker 01

  And so that, you know, that's where most of the money is going. Um, uh, at least on, on, you know, on the side of companies like, like, like Meta and Microsoft and, and, and Google and potentially Amazon.

• 21:50Speaker 01

  Then there is, so this is just operations, essentially. Now, is there going to be the, the market for, um, you know, one billion people using those things regularly, even if there is no change of paradigm? And the answer is probably yes. So, you know, even if the revolution or new paradigm doesn't come, you know, within three years, this infrastructure is going to be used. There's very little question about that. Okay. So it's a good investment and it takes so long to set up, you know, data centers and all that stuff that you need to, to get started now and plan for, you know, progress to be continuous, uh, so that, uh, you know, eventually the investment is, is justified. But you can't afford not to do it, right?

• 22:35Speaker 01

  Because, um, because that would be too much of a, of a risk to take if you have the cash.

• 22:40Alex Kantrowitz?

  But let's go back to what you said. The stuff today is still deeply thawed and there have been questions about whether it's going to be used. Now, Meta is making this consumer bet, right? The consumers want to use the AI. That makes sense. OpenAI has 400 million users of ChatGPT. Meta has three, four billion. I mean, basically, if you have a phone on earth.

• 23:00Speaker 01

  Well, you have three, three point something billion, uh, users, uh, 600 million users of Meta AI.

• 23:07Alex Kantrowitz?

  Right. Okay. So more than ChatGPT.

• 23:09Speaker 01

  Yeah, but they, but it's not used as much as ChatGPT. So the users are not as intense.

• 23:14Alex Kantrowitz?

  As active. But basically, the idea that, that Meta can get to a billion consumer users.

• 23:18Speaker 01

  Yeah.

• 23:18Alex Kantrowitz?

  That seems reasonable. But the thing is, a lot of this investment has been made with the idea that this will be useful to enterprises, uh, not just a consumer app. And there's a problem because, like, we've been talking about, it's not good enough yet. Uh, you look at deep research. This is something Benedict Devens has brought up. Deep research is pretty good, but it might only get you 95% of the way there and maybe 5% of it hallucinates. So if you have a hundred page research report and 5% of it is wrong and you don't know what 5%, that's, that's a problem. And similarly, in, in enterprises today.

• 23:56Alex Kantrowitz?

  All every enterprise is trying to figure out how to make AI useful to them. Generative AI useful to them and other types of AI. But only 10% or 20% maybe of proof of concepts make it out the door into production because there it's either too expensive or it's fallible. So if this is if we are getting to the top here.

• 24:18Alex Kantrowitz?

  What do you anticipate is going to happen with with everything that's that that has been pushed in the anticipation that it is going to get even better from here?

• 24:30Speaker 01

  Well, so again, it's a question of timeline, right? When when are those systems going to become sufficiently reliable and intelligent so that the deployment is made easier? But but you know, I mean, the the situation you're describing that.

• 24:47Speaker 01

  You know, beyond the impressive demos, actually deploying systems that are reliable is where things tend to falter in in the use of computers and technologies and particularly AI. This is not you. It's it's basically. You know, why we we had super impressive, you know, autonomous driving demos 10 years ago.

• 25:15Speaker 01

  But we still don't have level five self driving cars, right? It's the last mile that's really difficult, so to speak, for cars. You know, it's, you know, the last.

• 25:26Alex Kantrowitz?

  I see what you did there.

• 25:27Speaker 01

  The last few.

• 25:29Speaker 01

  That was not deliberate. The, you know, the last few few percent of reliability, which makes a system practical

• 25:38Speaker 01

  and how you integrate it with sort of existing systems and and blah, blah, blah, and you know, how it makes users of it more efficient, if you want, or more reliable or or whatever.

• 25:52Speaker 01

  That's where that's where that's where it's difficult. And, you know, this is why if we take if we go back several several years and we look what happened with IBM Watson. Okay, so Watson was going to be the thing that. You know, IBM was was going to push and generate tons of revenue by by having Watson, you know, learn about medicine and then be deployed in every every hospital.

• 26:18Speaker 01

  And it was basically a complete failure and was sold for parts, right?

• 26:24Speaker 01

  And cost a lot of money to IBM, including the CEO. And the... What happens is that actually deploying those systems in situations where they are reliable and actually help people and don't like hurt the natural conservatism of the labor force. This is where things become complicated. We're seeing the same, you know, the process we're seeing now with the difficulty of deploying AI system is not new. It's happened absolutely at all times. This is also why, you know, some of your listeners perhaps are too young to remember this, but there was a big wave of interest in AI in the 1980s, early 1980s, around expert systems.

• 27:06Speaker 01

  And, you know, the hottest job in the 1980s was going to be knowledge engineer and your job was going to be to sit next to an expert and then, you know, turn the knowledge of the expert into rules and facts that would then be fed to an inference engine that would be able to kind of derive new facts and answer questions and blah, blah, blah.

• 27:29Speaker 01

  Big wave of interest. The Japanese government started a big program called fifth generation computer. The hardware was going to be designed to actually take care of that and blah, blah, blah. You know, mostly a failure. There was kind of a...

• 27:44Speaker 01

  You know, the wave of interest kind of died in the mid 90s about this and, you know, a few companies were successful, but basically for a narrow set of applications for which you could actually reduce human knowledge to a bunch of rules and for which it was economically feasible to do so. But the wide-ranging impact on all of society and industry was just not there. And so that's the danger of AI all the time. I mean, the signals are clear that, you know, still LLMs with all the bells and whistles actually play an important role, if nothing else, for information retrieval.

• 28:28Speaker 01

  You know, most companies want to have some sort of internal experts that know all the internal documents so that any employee can ask any question. We have one at Meta, it's called Meta Mate. It's really cool. It's very useful.

• 28:41Alex Kantrowitz?

  Yeah, and I'm not suggesting that AI is gonna, that modern AI is not, or modern generative AI is not useful, or I'm, I'm asking purely that there's been a lot of money that's been invested into expecting this stuff to effectively achieve god-level capabilities. And we both are talking about how like there's, you know, potentially diminishing returns here, and then what happens if there's that timeline mismatch, like you mentioned?

• 29:08Speaker 01

  Yeah.

• 29:09Alex Kantrowitz?

  And this is the last question I'll ask about it, because I feel like we have so much else to cover, but I feel like timeline mismatches, that might be personal to you. You and I first spoke nine years ago, which is crazy now, nine years ago, and, you know, about how in the early days you had an idea for how AI should be structured, and you couldn't even get a seat at the conferences. And then eventually with the right amount of, when, when the right amount of compute came around, those ideas started working, and then the entire AI field took off based off of your idea that you, you worked on with Benjio and Hinton.

• 29:47Speaker 01

  And a bunch of others.

• 29:49Alex Kantrowitz?

  And many others. And, but, for the sake of efficiency, say, go look it up. But just talking about those mismatched timelines, when there have been overhyped moments in the AI field, maybe with expert systems that you were just talking about, and they don't pan out the way that people expect, the AI field goes into what's called AI winter.

• 30:09Speaker 01

  Well, there's backlash, yeah.

• 30:10Alex Kantrowitz?

  Correct. And so if we're gonna, if we are potentially approaching this moment of mismatched timelines, do you fear that there could be another winter now, given the amount of investment, given the fact that there's going to be potentially diminishing returns with the main way of training these things, and maybe we'll add in the fact that the market is, is, the stock market looks like it's going through a bit of a downturn right now? Now, that's a variable, probably the third most important variable of what we're talking about, but it has to factor.

• 30:39Speaker 01

  So, yeah, I think, I mean, there's certainly a question of timing there, but I think if we try to dig a little bit deeper, as I said before, if you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake. So if you're, if you're an investor, and you invest in a company that told you, we're going to get to human-level AI and PhD-level by just, you know, training on more data and with a few tricks. Yeah.

• 31:08Speaker 01

  I don't know if you're gonna use your shirt, but that was probably not a good idea. However, there are ideas about how to go forward and have systems that are capable of doing what what every intelligent animal and human are capable of doing and that currently our systems are not capable of doing. And I'm talking about understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that that, you know, need to be there. And that requires systems that, you know, can acquire common sense, that can learn from natural sensors like video as opposed to just text, just human-produced data. And that's a big challenge.

• 31:52Speaker 01

  I mean, I've been talking about this for many years now and and saying this is this is what the challenge is, this is what we have to to figure out. And my group and I have, or people working with me and others who have listened to me, are making progress along along this line of systems that can be trained to understand how the world works on video, for example, systems that can use mental models of how the world, the physical world works to plan sequences of actions to arrive at a particular goal. So we have kind of early results of this kind of systems. And there are people at DeepMind working on similar things, and there are, you know, people in various universities working on this.

• 32:31Speaker 01

  So the question is, you know, when is this going to go from interesting research papers demonstrating a new capability with a new architecture to, you know, architectures at scale that, you know, are practical for a lot of applications and can find solutions to new problems without being trained to do it, et cetera? And, you know, it it's not gonna happen within the next three years, but it may happen with, you know, between three to five years, something like that. And that's kind of corresponds to, you know, the the sort of ramp-up that we see in in investment. Now, whether other

• 33:13Speaker 01

  So that's the first thing. Now, the second thing that's important is that there's not going to be one secret magic bullet that one company or one group of people is going to invent that is going to just solve the problem. It's going to be a lot of different ideas, a lot of efforts, some principles around which to base this, that some people may not subscribe to and will go in a direction that is, you know, will turn out to be a dead end. So there's not going to be like a day before which there is no AGI and after which we have AGI. This is not going to be an

• 33:51Speaker 01

  event. It's going to be continuous conceptual ideas that as time goes by are going to be made bigger and to scale and going to work better. And it's not going to come from a single entity, it's going to come from the entire research community across the world. And the people who share their research are going to move faster than the ones that don't. And so if you think that there is some startup somewhere with five people who has discovered the secret of AGI, and you should invest five billion in them, you're making a huge mistake.

• 34:24Alex Kantrowitz?

  You know, Jan, first of all, I always enjoy our conversations because we start to get some real answers. And I remember even from our last conversation, I was just always looking back to that conversation, saying, okay, this is what Jan says, this is what everybody else is saying. I'm pretty sure that this is the grounding point, and that's been correct. And I know we're going to do that with this one as well. And now you've set me up for two interesting threads that we're going to pull out as we go on with our conversation. First is the understanding of physics and the real world, and the second is open source. So we'll do that when we come back right after this. This episode is brought to you by AppPoint. Everyone's racing to roll out AI right now: copilots, chatbots, agents doing real work. But here's the part nobody loves talking about:

• 35:06Alex Kantrowitz?

  all that AI runs on your data. And most teams have no single way to see it, secure it, and prove it's under control. That's exactly what AppPoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended Across your entire AI estate, your data, your cloud, and the agents acting on your behalf. It's how more than 28,000 organizations deploy AI with confidence. So innovation scales without scaling risk. AV Point, the unifying trust layer for AI. Head to avpoint.com to see how enterprises deploy AI with confidence. Learn more at avpt.co/bigtechnologypodcast.

• 35:51Alex Kantrowitz?

  That's avpt.co/bigtechnologypodcast. Violent threats against executives are growing at an alarming rate. When a company experiences backlash, executives are the first people blamed. When their address and other personal details are sitting online, this backlash can lead right to their front door. The team at Ironwell knows this better than anyone. They've protected some of the most targeted executives and individuals on the planet for almost two decades. As a wide-glove enterprise security service, they protect your people with continuous personal data removal, proactive prevention tools, and human-led emergency support. So when someone goes looking for your executives, Ironwell ensures they hit a dead end. So here's what to do. Go to ironwell.com/bigtechnology,

• 36:35Alex Kantrowitz?

  fill in the quick form, and request your free risk assessment. Their team will show you just how exposed your executives are and how to lock it down before a threat reaches their front door. That's ironwell.com/bigtechnology. Learn how to stop online threats before they become real-world attacks. The link's in the show notes.

• 36:56Alex Kantrowitz?

  And we're back here with Jan LaKoon. He is the chief AI scientist at Meta and the Turing Award winner that we're thrilled to have on our show, luckily, for the third time. I want to talk to you about physics, Jan, because there's sort of this famous moment in Big Technology Podcast history, and I say famous with our listeners. I don't know if it really extended beyond, but you had me. Uh, write to ChatGPT, if I hold a paper horizontally with both hands and let it, let it go, let go of the paper with my left hand, uh, what will happen? And, uh, I write it and it convincingly says, like it writes, the physics will, will happen and the paper will float towards your left hand. And I read it out loud convinced and you're like, that thing just hallucinated and you believed it. That is what happened. So, listen, it's been two years.

• 37:41Alex Kantrowitz?

  I put the test to ChatGPT today. Uh, it says, um, when you let go of the paper with your left hand, gra- gravity will cause the left side of the paper to drop while the right side still held up, but your right hand remains in place. This creates a pivot effect where the paper rotates around the point where your right hand is holding it. So now it gets it right.

• 38:02Speaker 01

  It learned the lesson. You know, it's quite possible that this, uh, um, you know, some, someone hired by OpenAI to solve the problem was fed that question and sort of the answer and the system was fine too with the answer. I mean, you know, obviously you can imagine an infinite number of such questions and, and this is where, you know, uh, the, the, the so-called post-training of NLM becomes expensive, um, which is that, you know, how much coverage of all those style of questions do you have to do for the system to basically cover ninety percent of, or ninety-five percent or whatever percentage of all the questions that people may ask it? Um, but there, you know, it's, there is a long tail and there is no way you can train

• 38:47Speaker 01

  the system to answer all possible questions because there is an essentially infinite number of them. And, and there is way more questions the system cannot answer that, um, than questions it can, it can answer. You cannot cover the set of all possible training, uh, you know, questions in the training set, the training set.

• 39:04Alex Kantrowitz?

  Right. So because I think our conversation last time was saying, you, you said that because these actions of like what's happening with the paper, if you let go of it with your hand, has not been covered widely in text, the model won't really know how to handle it because unless it's been covered in text, the model won't have that understanding, won't have that inherent understanding of the real world. And I've kind of gone with that for a while. Uh, then I said, you know what? Let's, let's try to generate some AI videos. And one of the interesting things that I've seen with the AI videos is there is some understanding of how the physical world works there. In a way that, in our first meeting nine years ago, you said, um, one of the hardest things to do is you ask an AI, what happens if you hold a pen vertically on a table and let go?

• 39:49Alex Kantrowitz?

  Uh, will it fall? And there's like a unbelievable amount of permutations, uh, that can occur and it's very, very difficult for the AI to figure that out. Because it just doesn't inherently understand physics. But now you go to something like Sora, uh, and you say, um, show me a, a video of a man sitting on a chair kicking his legs. And you can get that video. And the person sits on the chair and they kick their legs and the legs, you know, don't fall out of their sockets or stuff, they bend at the joints.

• 40:20Speaker 01

  And they don't have three legs.

• 40:22Alex Kantrowitz?

  And they don't have three legs. So wouldn't that suggest an improvement of the capabilities here with these large, large models?

• 40:28Speaker 01

  No.

• 40:29Alex Kantrowitz?

  Why?

• 40:30Speaker 01

  Because you still have those videos produced by those, uh, video generation system where, you know, you spill a, a glass of wine and, and the wine like floats in the air or like flies off or disappears or whatever. And, um, so, you know, of course, for every specific situation, you can always collect more data for that situation and then train your model to handle it. But that's not really understanding the underlying, uh, reality. This is just, you know, compensating, uh, the, the lack of understanding by, uh, uh, increasingly large amounts of data.

• 41:07Speaker 01

  Um, you know, children understand, uh, you know, simple concepts like, like gravity, um, with a surprisingly small amount of data. Um, so in fact, there is an interesting calculation you, you can do, which I've talked about publicly before, but, um, if you take, um, LLM, typical LLM train on thirty trillion tokens, something like that, right? Three ten to the thirteen tokens. A token is about three bytes. So that's point nine ten to the fourteen tokens. Let's say ten to the fourteen tokens to, to round this up.

• 41:46Speaker 01

  That text would take any of us probably on the order of 400,000 years to read.

• 41:52Alex Kantrowitz?

  No problem.

• 41:54Speaker 01

  At 12 hours a day.

• 41:58Speaker 01

  Now, a four-year-old has been awake a total of 16,000 hours.

• 42:05Speaker 01

  You can multiply by 3,600 to give the number of seconds. And then you can put a number on how much data has gotten to your visual cortex through the optic nerve. Optic nerve, each optic nerve, we have two of them, carries about one megabyte per second roughly. So it's two megabytes per second times 3,600 times 16,000. And that's just about 10 to the 14 bytes. Okay, so in four years, a child has seen through vision, or touch for that matter, as much data as the biggest data limbs. And it tells you clearly that we're not going to get to human level, I bet, just training on text. It's just not a rich enough source of information. And by the way, 16,000 hours is not that much video. It's 30 minutes of YouTube uploads.

• 42:50Speaker 01

  Okay, we can get that pretty easily. Now, in nine months, baby has seen, you know, let's say 10 to the 13 bytes or something, which is not much, again. And in that, in that time, baby has learned basically all of intuitive physics that we know about. You know, conservation of momentum, gravity, conservation of momentum, the fact that objects don't spontaneously disappear, the fact that they still exist even if you hide them. I mean, there's all kinds of stuff, you know, really basic stuff that we learn about the world in the first few months of life.

• 43:30Speaker 01

  And this is what we need to reproduce with machines, this type of learning of, you know, figuring out what is possible and impossible in the world, what will result from an action you take,

• 43:41Speaker 01

  so that you can plan a sequence of actions to arrive at a particular goal. That's the idea of a word model. And now, connected with the question about video generation systems, is the right way to approach this problem to train better and better video generation systems? And my answer to this is absolutely no.

• 44:03Speaker 01

  The problem of understanding the world does not go through the solution to generating video at the pixel level. Okay, I don't need to know. If I take this glass of, this cup of water and I spill it, I cannot entirely predict, you know, the exact path of that the water will follow on the table and what shape it's going to take and all that stuff, what noise it's going to make. But at a certain level of abstraction, I can make a prediction that the water will spill. Okay, and it'll probably make my phone wet and everything.

• 44:44Speaker 01

  So I can't predict all the details, but I can predict at some level of abstraction. And I think that's really a critical concept. The fact that if you want a system to be able to learn, to comprehend the world and understand how the world works, it needs to be able to learn an abstract representation of the world that allows it to make those predictions. And what that means is that those architectures will not be generative.

• 45:17Alex Kantrowitz?

  Right, and I want to get to your solution here in a moment, but I just wanted to also, like, what would a conversation between us be without a demo? So I want to just show you, I'm going to put this on the screen when we do the video, but there's, this is a video I was pretty proud of. I got this guy sitting on a chair, kicking his legs out, and the legs stay attached to his body. And I was like, all right, this stuff is making real progress. And then I said, can I get a car going into a haystack? And so it's two bales of haystacks, and then a haystack magically emerges from the hood of a car that's stationary. And I just said to myself, okay, Jan wins again.

• 45:54Speaker 01

  It's a nice car, though, yeah. I mean, the thing is, those systems have been fine-tuned with a huge amount of data for humans because, you know, that's that's what people are asking. Most videos, they ask, so there is a lot of data of humans doing various things to train those systems. So that's why it works for humans, but not for a situation that... The the people training that system had not anticipated.

• 46:21Alex Kantrowitz?

  So you said that the model can't be generative to be able to understand the real world.

• 46:25Speaker 01

  That's right.

• 46:26Alex Kantrowitz?

  You are working on something called V Jeppa.

• 46:29Speaker 01

  Jeppa.

• 46:30Alex Kantrowitz?

  Jeppa, right. V is the video. You also have I Jeppa for images.

• 46:34Speaker 01

  Right.

• 46:34Alex Kantrowitz?

  That is.

• 46:35Speaker 01

  We have Jeppas for all kinds of stuff, text also.

• 46:37Alex Kantrowitz?

  And text. So explain how that will solve the problem of being able to allow a machine to abstractly represent what is going on in the real world.

• 46:46Speaker 01

  Okay, so what has made the success of AI and particularly

• 46:54Speaker 01

  natural language understanding and chatbot in the last few years, but also to some extent computer vision is self-supervised learning. So what is self-supervised learning? It's take an input, be it an image, a video, a piece of text, whatever, corrupt it in some way and train a big neural net to reconstruct it, basically recover the uncorrupted version of it, or the undistorted version of it, or a transformed version of it that would result from taking an action. Okay.

• 47:28Speaker 01

  And, you know, that that would mean, for example, in the context of text, take a piece of text, remove some of the words, and then train some big neural net to predict the words that are missing. Take an image, remove some pieces of it, and then train a big neural net to recover the full image. Take a video, remove a piece of it, train a neural net to predict what's missing. Okay. So LLMs are a special case of this, where you you you take a text and you train the system to just reproduce the text, and you don't need to corrupt the text because the system is designed in such a way that to predict one particular word or token in the text, it can only look at the tokens that are to the left of it. Okay, so so in effect.

• 48:11Speaker 01

  The system has hardwired into its architecture the fact that it cannot look at the present and the future to predict the present, it can only look at the past. Okay. So, but basically you train that system to just reproduce its input on its output. Okay. So this kind of architecture is called a causal architecture. And this is what an LLM is, a large language model. That's what, you know, all the chatbots in the world are based on.

• 48:37Speaker 01

  Take a piece of text and train the system to just reproduce that piece of text on its output. And to predict a particular word, it can only look at the word to the, to the left of it. And so now what you have is a system that, given a piece of text, can predict the word that, that follows that text. And you can take that, that word that is predicted, shift it into the input, and then predict the second word. Shift that into the input, predict the third word, that's called auto-regressive prediction. It's not a new concept, very old.

• 49:08Speaker 01

  So, you know, self-supervised learning does not train to do a particular, does not train a system to accomplish a particular task other than capture the internal structure of the, of the data. It doesn't require any labeling by, by a human. Okay. So, apply this to images. Take an image, mask a chunk of it, like a bunch of patches from it, if you want, and then train a beginner on that to reconstruct that, what is missing.

• 49:40Speaker 01

  And now use the internal representation of the image, learned by the system, as input to a subsequent downstream task for, I don't know, image recognition, segmentation, whatever it is. It works to some extent, but not great. So there was a big project like this, to do this at FAIR, it's called MAE, masked autoencoder. It's a special case of denoising autoencoder, which itself is, you know, the sort of general framework from which I, I derived this, this idea of self-supervised learning. So, it doesn't work so well.

• 50:14Speaker 01

  And there's various ways to, you know, if you apply this to video also, I've been working on this for almost 20 years now. Take a video, show just a piece of the video, and then train the system to predict what's going to happen next in the video. So same idea as for text, but just for, for video.

• 50:31Speaker 01

  And that doesn't work very well either. And the reason it doesn't work, why does it work for text and not for video? For example. And the answer is it's easy to predict a word that comes after a text. You cannot exactly predict which word follows a particular text, but you can produce something like a probability distribution over all the possible words in your dictionary, all the possible tokens. There's only about, you know, 100,000 possible tokens. So you just produce a big vector with, you know, 100,000 different numbers that are positive and some to one. Okay.

• 51:09Speaker 01

  Now, what are you going to do to represent a probability distribution over all possible frames in a video or all possible missing parts of an image? We don't know how to do this properly. In fact, it's mathematically intractable to represent distributions in high dimensional continuous spaces. Okay? We don't know how to do this in a kind of useful way, if you want. And so, and I've tried to, you know, do this for video for a long time. And so that is the reason why those ideas of self-supervised learning using generative models have failed so far.

• 51:50Speaker 01

  And this is why, you know, using, you know, trying to train a video generation system as a way to understand, to get a system to understand how it works. That's why it can't succeed. So what's the alternative? The alternative is something that is not a generative architecture, which we call JPA, so that means joint embedding predictive architecture. And we know this works much better than attempting to reconstruct. So we've had experimental results on learning good representations of images going back many years, where instead of taking an image, corrupting it and attempting to reconstruct

• 52:32Speaker 01

  this image, we take the original full image and the corrupted version, we run them both through neural nets. Those neural nets produce representations of those, of those two images, the initial one and the corrupted one. And we train another neural, a predictor, to predict the representation of the full image from the representation of the corrupted one. Corrupted one. Okay. And if you train a system, if you successfully train a system of this type, this is not trained to reconstruct anything. It's just trained to learn a representation so that you can make prediction within the representation layer. And you have to make sure that the representation contains as much information as possible about the input, which is where it's difficult, actually.

• 53:17Speaker 01

  That's the difficult part of training those systems. So that's called a JEP, Joint Embedding Predictive Architecture. And to train a system to learn good representations of images, those joint embedding architectures work much better than the ones that are generative, that are trained by reconstruction. And now we have a version that works for video too. So we take a video, we corrupt it by masking a big chunk of it. We run the full video and the corrupted one through encoders that are identical. And then in some contingency, we train a predictor to predict the representation of the full video from the partial one.

• 53:56Speaker 01

  And the representation that the system learns of videos, when you feed it to a system that you train to tell you, for example, what action is taking place in the video, or whether the video is possible or impossible, or things like that, it actually works quite well.

• 54:13Alex Kantrowitz?

  That's cool. So it gives that abstract thinking, in a way.

• 54:17Speaker 01

  Yeah, right. And we have experimental result that shows that this joint embedding training, we have several methods for doing this. There's one that's called DINO, another one that's called VCREG, another one that's called VicREG, another one that's called IJEP, which is sort of a distillation method. And so we have several different ways to approach this, but one of those is going to lead to a recipe that basically gives us a general way of training those JEP architectures. Okay, so it's not generative, because the system is not trained to regenerate the part of the input. It's trained to generate a representation, an abstract representation of the input. And what that allows it to do is to ignore all the details about the input that are really not predictable.

• 55:02Speaker 01

  Like, you know, the pen that you put on the table vertically, and when you let it go, you cannot predict in which direction it's going to fall, but at some abstract level, you can say that the pen is going to fall.

• 55:12Alex Kantrowitz?

  It's falling.

• 55:12Speaker 01

  Right? Without representing the direction. So that's the idea of JEP. And we're starting to have, you know, good results on sort of...

• 55:24Speaker 01

  Having systems, so the VJAP system, for example, is trained on natural, lots of natural videos. And then you can show it a video that's impossible, like a video where, for example, an object disappears or changes shape, okay, you can generate this with a game engine, something. Or a situation where you have a ball rolling and it rolls and it stops behind a screen. And then the screen comes down and the ball is not there anymore.

• 55:49Alex Kantrowitz?

  Right.

• 55:49Speaker 01

  Okay, so things like this. And you measure the prediction error of the system, so the system is trained to predict, right? And not necessarily in time, but but like basically to predict, you know, the coherence of the video. And so you you measure the prediction error as you show the the video to the system. And when something impossible occurs, the prediction error goes goes through the roof. And so you can detect if the system has integrated some idea of what, you know, is possible physically or what's not possible, but just being trained with physically possible natural videos. So that that's really interesting, that's sort of the first hint that a system has quite some level of common sense.

• 56:32Alex Kantrowitz?

  And Yes.

• 56:33Speaker 01

  We have versions of those systems also that are so-called action conditions. So basically, we have things where we have a chunk of video or an image of, you know, the state of the world at time T, and then an action is being taken, like, you know, robot arm is being moved or whatever. And then, of course, we can observe the the result resulting from this action. So now what we have, when we train a JAP with this,

• 57:00Speaker 01

  the the model basically can say, here is the state of the world at time T, here is an action you might take. I can predict the state of the world at time T plus one in this abstract representation space.

• 57:09Alex Kantrowitz?

  There's this learning of of how the world works.

• 57:11Speaker 01

  Of how the world works. And and the cool thing about this is that now you can Imagine, you can have the system imagine what would be the outcome of a sequence of actions. And if you give it a goal saying like, I want the world to look like this at the end. Can you figure out a sequence of actions to get me to that point? It can actually figure out by search for a sequence of actions that will actually produce that result. That's planning, that's reasoning, that's actual reasoning and actual planning.

• 57:37Alex Kantrowitz?

  Okay, Jan, I have to get you out of here where we are over time. But can you give me like 60 seconds, your reaction to DeepSeek? And sort of has open source overtaken the proprietary models at this point? And we've got to limit to 60 seconds, otherwise I'm going to get killed by your team here, so.

• 57:55Speaker 01

  Overtaken is a, is a strong word. I think progress is faster in the open source world, that's for sure. But of course, you know, the proprietary shops are profiting from the progress of the open source world, right? They get access to that information like everybody else. So what's clear is that there is many more interesting ideas coming out of the open source world that any single shop as big as it can be cannot come up with. You know, nobody has a monopoly on good ideas. And so the magic efficiency of the open source world is that it recruits talents from all over the world. And so what we've seen with DeepSeek is that if you set up a small team with a relatively long leash

• 58:39Speaker 01

  and few constraints on coming up with just the next generation of LLMs, they can actually come up with new ideas that nobody else has come up with, right? They can sort of reinvent a little bit what, you know, how you do things. And then if they share that with the rest of the world, then the entire world progresses. Okay, and so it clearly shows that, you know, open source progress is faster. And, you know, a lot more innovation can, can take place in the open source world, which the proprietary world may have a hard time catching up with. It's cheaper to run.

• 59:18Speaker 01

  What we see is for, you know, partners who we, we talk to, they say, well, our clients, when they prototype something, they may use a proprietary API. But when it comes time to actually deploy the product, they actually use Llama or other open source engines because it's cheaper and it's more.

• 59:36Speaker 01

  Secure, you know, it's more controllable, you can run it on premise, you know, there's all kinds of advantages. So we've seen also a big evolution in the thinking of some people who are initially worried that open source efforts were going to, I don't know, for example, you know, help the Chinese or something, if you have like some geopolitical reason to think it's a bad idea. But what DeepSeek has shown is that the Chinese don't need us. I mean, they can come up with really good ideas, right? I mean, we all know that there are really, really good scientists in China. And one thing that is not well known is that the single most cited paper in all of science

• 1:00:21Speaker 01

  is a paper on deep learning from 10 years ago, from 2015, and it came out of Beijing.

• 1:00:26Alex Kantrowitz?

  Oh.

• 1:00:27Speaker 01

  Okay. It's, the paper is called ResNet. So it's a particular type of architecture of neural net where basically by default, every stage in a deep learning system computes the identity function. You just copy its input on its output. And what the neural net does is compute the deviation from this identity. Okay. So that allows to train extremely deep neural net with, you know, dozens of layers, perhaps a hundred layers. And it was, the first author of that paper is a gentleman called Camming He, at the time he was working at Microsoft Research Beijing. Soon thereafter, the publication of that paper, he joined FAIR in California. So I hired him.

• 1:01:11Speaker 01

  And worked at FAIR for eight years or so, and recently left and is now a professor at MIT. Okay. So there are really, really good scientists everywhere in the world. Nobody has a monopoly on good ideas. Certainly Silicon Valley does not have a monopoly on good ideas. Or another example of that is actually the first Lama came out of Paris. It came out of the FAIR labs in Paris, a small team of twelve people. So you have to take advantage of the diversity of

• 1:01:49Speaker 01

  Ideas, backgrounds, creative juices of the entire world if you want science and technology to progress fast, and that's enabled by by open source.

• 1:02:00Alex Kantrowitz?

  Yeah, and it is always great to speak with you. Appreciate this is our, I think, fourth or fifth time speaking. Again, going back nine years ago, you always helped me see through all the hype and the buzz and actually figure out what's happening. And I'm sure that's going to be the case for our listeners and viewers as well. So yeah, thank you so much for coming on. Hope we do it again soon.

• 1:02:18Speaker 01

  Thank you, Alex.

• 1:02:19Alex Kantrowitz?

  All right, everybody. Thank you for watching. We'll be back on Friday to break down the week's news. Until then, we'll see you next time on Big Technology Podcast.

• 1:02:26Speaker 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, the HouseMajorityPac.com, not authorized by any candidate or candidate's committee.

• 1:02:56Speaker 03

  In this life, you can't win.

• 1:02:57Speaker 04

  When the world gets you down.

• 1:02:59Speaker 03

  No!

• 1:03:00Speaker 04

  Escape to Pluto TV.

• 1:03:01Speaker 03

  This is objectively fantastic.

• 1:03:03Speaker 04

  Relax into the movies and shows you already know and love, all for free.

• 1:03:07Speaker 03

  Shwing!

• 1:03:08Speaker 04

  Even our news is uplifting.

• 1:03:09Alex Kantrowitz?

  You stay classy, San Diego.

• 1:03:10Speaker 04

  Watch School of Rock, Crazy Ex Girlfriend, Anchorman: The Legend of Ron Burgundy, The X-Files, Wayne's World, and so much more.

• 1:03:16Alex Kantrowitz?

  We're not worthy!

• 1:03:18Speaker 04

  The best things on TV really are free. You just have to know where to look. Escape to Pluto TV. Stream now, pay never.