‘Hard Fork’ Live, Part 3: Differing Visions of an A.I. Future

24:37And, you know, people who are experts on this, like Michael Horowitz, have used our frame to argue that military air, at least today, is a normal technology in its view as well. But frankly, the actions that are being taken by countries worldwide, by nation states, are pretty, pretty, pretty darn alarming. I mean, I think we shouldn't take it for granted that companies or countries can use kill bots. And that is not something that requires further technological investment either. It's not something where we have any technical bottlenecks we can use, like off-the-shelf computer vision libraries to basically build killer robots today.

25:09It is actually something where we need to exercise a lot of agency. and I'm not really positive about where things are going right now on that front. Well, I truly believe that whatever is about to happen to us lies somewhere in between the views of these two people. So we will continue to pay very close attention to your work. Thank you so much, Daniel and Sayaj. Thank you for joining us. Thanks, guys. That was fun. Thank you. Thank you. Thank you. Thanks, Ash.

25:40We'll be back with more HardFork Live after these messages.

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27:20One thing we know for sure is that no matter what happens with the future of AI, it will be extremely fun to talk about robots. Yes. So we have already shown you, I think, more than 10 robots tonight, including members of our robot choir. But we have one more very special robot guest tonight. We are about to bring on George Iekis. He is the director of engineering at Toberlife AI, a robotics company in Silicon Valley that is one of the leading distributors of humanoid robots, specifically these unit-free robots from China. And we are going to be joined by George and Toby the robot. George and Toby, come on out.

28:02Thanks for having me. Good to see you. George. You're a very convincing humanoid. Oh, no, wait. That's Toby.

28:14Do we shake hands? Okay. Try it here. Hi. Short King.

28:23It's great. I appreciate the weak grip strength. It gives me comfort. Yeah, it's sort of like a dead fish handshake. Yeah. Now, he is advancing on me. All right. Oh, okay. Wow. Now, we're going to talk about all the things that Toby and his brethren can do, but we heard that Toby can actually dance. Is that true? That is the case. Okay. Can we see that? Toby, can you dance for us? Dan, will you help us out? Hit it, DJ.

29:05Oh, Jesus Christ.

29:11Listen, we've all been there. Sometimes you just dance to the drop. This robot left it all on the dance floor, ladies and gentlemen. Could have been an operator. Thank you. Thank you, Toby, for your sacrifice. You will not be forgotten. We'll add you to the In Memoriam next year. Now, is Toby capable of standing up? Is he okay? Yeah, probably just a misclick on the controller end. He's not autonomous right now. We're so back. He's absolutely fine. They're quite durable. Yeah. Oh, my God. That was not in the script. Yeah. No.

29:50I'm sorry. We've traumatized our audience here tonight. I'm so sorry.

29:58Now, George, were you the choreographer on that? Nope. Okay. Well, it was great choreography. So, George, what is the use case for these other than doing dance demos and sometimes following over? Who is buying and renting these humanoid robots from your company, and what are they doing with them? Well, right now, the early market for the humanoids is the research market. People want to collect a lot of data. You guys had the Neo folks on specifically Burnt, right? And they're deploying the humanoids into households to try to collect a lot of data in households. People with the Unitree robots are also targeting different use cases.

30:33Different companies are pursuing different verticals with them and trying to get big data sets and train models on these humanoids. There are also a set of robots that we also sell, which are more reliable, more industrial right now, called Quadrupeds. and probably easier just to remember them as the dog robots. You can put LiDAR on them. Put different sensors. But a mask goes Mark Zuckerberg or Elon Musk on them. We saw that earlier tonight, yes. I forgot about that. Somehow, somehow I forgot about that. But they are practical for like inspection use cases or security patrols. So those are kind of being pushed out into industry and applications more.

31:11And these are on the edge of research and acquiring data to build policies. How much does one of these cost? uh they range in cost if you want one to just dance around uh i don't remember the exact figure on the low level dancing ones but um they're less than the ones that you could put dexterous hands on and then go and collect manipulation data uh with on tasks so you collect data from doing tasks with them so like more or less than ten thousand dollars more more more okay that's a great question The ones I was getting to are like in the 50 to 70 range, the ones with the hands. So like a mid-range sports car?

31:56Yes. Yeah. All right. I have to say, it did not inspire a lot of confidence in me to learn that the primary use case for these robots is data collection. I mean, I think the vision is that these things, as we saw when we talked with Bert from 1X about their robot, as we're hearing about these unitry robots, the dream is that these things will just be in your house and will be doing chores for you, folding laundry, doing the dishes, cleaning the house. What is the timeline for that? Do you think that is realistic? Should people be pre-ordering now in hopes of automating their chores forever? Where are we on the chore spectrum?

32:29I think Bernd's very optimistic. I'd put it a few more years out than he would on terms of being in your house, but in terms of maybe operating in an industrial setting where they can maybe load up a fabricator or something with a material or a part, I think that's in the next couple of years. And there's actually early implementations of that by like Figure and Unitree and Unitree in their factory, Figure and the BMW factory. So people are doing that with these. But the widespread adoption, I believe, in the next couple of years will happen in those settings. Let me ask one question about the data collection.

33:03Some security researchers have claimed that Unitree robots might have a backdoor that could allow remote users to control or monitor what they're seeing. Can Tobii send the data to China? so they do send logging data to china just like every other chinese thing that you can own like a computer or um any other computer chip based thing that connects to the internet that sends logging data they send that but they don't actually like there hasn't been an established thing that sends camera data or telemetry data of the joints to China. So there are things that people will be like, oh, it sends data to China.

33:46It's like, yeah, and your computer sends data to Microsoft, and it's because your computer crashed and it needs to send data to Microsoft. Right. I think the difference is in this case that Unitry is a Chinese company, and some members of Congress have become very worried about the fact that these are now being sold in the United States. Some have even proposed banning the importation of these specific unitary robots. How likely do you think that is? And would that be a big hit to your business? What's your plan if they ban these? It'd certainly be problematic. There's not a lot of American alternatives.

34:17I wouldn't like that. Yeah, if they're going to ban all Chinese humanoid robots, I wouldn't be too stoked on that. So I don't know how much more to say. Well, much to consider. Before we let you go, does Toby maybe have, you know, one more cool routine he could show us? Yes, he does. Take it easy. All right. DJ Dan, will you help us out again?

34:54It's great.

34:58this is like what happened the last time casey had a long island iced tea at the club all right okay fascinating george and toby thank you for joining us thank you ah

35:17you're so good i believe in you

35:25Any AI developer tool can create a brand new project from scratch. That part's easy. The hard part is working with the code your business already runs on. IBM Bob is a new AI development partner that helps you do the hard job. Moving your technology into the AI age without losing the legacy code your business was built on. Let's create smarter business. IBM. I gave my brother a New York Times subscription. She sent me a year-long subscription so I have access to all the games. We'll do Word O, Mini, Spelling Bee. It has given us a personal connection. We exchange articles. And so having read the same article, we can discuss it.

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36:51All right, gang. We are in the homestretch, but we had one more friend of the pod who we just wanted to bring on and have a little bit of fun with before the end of the show. Yes, our next guest is friend of the pod and YouTuber and podcast sensation, Dwarkesh Patel. Dwarkesh, come on out.

37:14What's up, guys? Good to see you. Hello. All right. How am I supposed to follow a robot dancer? You could fall over. Yeah. You could just face plant. That would be great. Marquesh, it's been a hell of a year for you. You are firing on all cylinders, doing interviews with Jensen Huang and other tech luminaries. You've got a new Blackboard series that teaches people, you know, extremely dense and esoteric concepts in AI. You also got profiled in the New York Times in April. and they made a big deal of you and your media empire that you are building here. I don't really have a question about that.

37:56I'm just kind of like in awe of what you have managed to build. I'm curious like what you hear when you hear the conversation about AI 2027 versus AI and normal technology. Where are you on the spectrum of like everything is changing? The scaling laws are holding to maybe things are slowing down and we don't quite have the breakthrough ideas yet to get to AGI. I think fundamentally the scary thing is we realize just how far we are from human intelligence, yet these models are so powerful. And so that raises the obvious question, is when they not only have the current advantages that they do, that they can think thousands of times faster, they have greater ability to absorb knowledge across a wide variety of domains.

38:40If anybody's used these models, like coding work or any sort of computer use work, you must have experienced this. And then you think, well, there's this huge overhang where humans are able to learn about new things literally a million times faster. If you think about how much information you see from birth to adulthood versus what these models see, we're capable of retaining information across sessions. We're learning on the job. We're not just like first day on the job the way these models are experiencing things. And so I think that the really scary thing really is that like we know that there's a big difference between where these models are currently and where human intelligence lies.

39:12We're making really fast progress towards human intelligence. Already, these things are so capable. What happens when they not only have their inherent advantages because they're digital minds, but also have all our advantages? You've written and spoken before about how you've tried and failed to automate parts of your own production process with your podcast and your YouTube show. And how hard it's been to sort of get rid of some of the sort of sticky human processes there. Are you having better luck with newer models? Like, is your operation more AI than it was six months ago? So most of the tokens I see in a given day are produced by AI.

39:47And so I can't really come here and say, no, AI is not making me more productive or I'm not using it in a significant way. I think people underrate how hard it is to automate jobs. People underrate how much it takes to do every single thing a human, even white-collar worker might be doing. At the same time, you guys must be fine in this as well, just the ability to tree out user amounts of information, which is a large part of my job, has just gotten way better. Yeah, how have you guys been finding these models? I mean, sort of the same. I do feel like with each of the big leaps in model capability, it becomes better at tasks that are quite useful.

40:24And for example, like the preparing for a podcast, right? If we're sitting down with a guest that I'm not that familiar with saying, hey, go out and prepare a briefing document for me about this person and give me some interesting directions to maybe take the conversation based on things they've said in public in the last three months. I mean, that's absolutely a job that I could have hired for. And now, you know, I can get in about like four minutes on my computer. So that's really useful. Does it make me more productive? Yes. But do I like work less or use the computer less? No. I'm finding something similar.

40:55I like, I want to use these models to automate a lot of my life. And I've been very successful at doing some pieces of it. But there are just things that, now the primary feeling I had, like I got access to Claude Fable yesterday. day. And the primary feeling I had was like, I am too dumb to use this thing. Like, I actually don't know what I would prompt it to do that a previous model would not have been able to do. But I'm not building RL environments. I'm not overseeing training runs. So like, what is the use for you as a media figure and podcaster? Like, what is the thing that you wish the models could do that they can't currently?

41:31I think because we're so, first of all, every time I say something viewership of the models, I put it in the context that we're living in an absurd timeline. And I am reacting to my close friends who are just like, well, you just had some of them on. And we're talking about the singularity in two years. But I feel like we're so used to what these models are capable of currently that we ask these questions like, well, what is it that they can't do? Aren't they clearly already AGI? It's like, no, we all have jobs. That wouldn't happen in the world with HCI, right? Like, just to get them to do something pretty, OK.

42:08So for example, I'm negotiating with a sponsor for next season or something. And they ask for, you do the back and forth there with the relevant context about how we think about our business and stuff. It's probably a one hour horizon task for me or my general manager. The models couldn't do it at all. Or let's say book a show in another city, but book an event like this, right? There's a lot of people who are involved in this. What part of it could the models do reliably? It's like, anyways, all this to say, I think people really underrate what the range of human, even white collar work is. I mean, it seems to me like it might be very helpful in a negotiation though.

42:44Like particularly, I mean, you know, you're not in this position, but maybe you're just, you know, starting a new podcast and you have some interest from a sponsor and you say, go tell me something about this market. And what's sort of the best place to get started? Like I could see it compressing that into a much smaller problem. But to your point, somebody still has to do the rest of the job. Yeah, that's right. I mean, they can't do something on a computer you might want them to do, right? And it's actually quite interesting. Why are they so bad at computer use, given that it's an extremely verifiable domain?

43:10And I think that actually goes to show you that it's not just about verifiability. It's about the ability to... The environment has to be one which allows you to deterministically run many parallel rollouts at the same time. And if you try to do that on Amazon, Andy Jassy will just shut your ass down. And so, you know, they have to build clones every single website because it takes a ton of data in the relevant domain in order for these models to become competent in, like, learning how Amazon works or Slack works. So you've got to build clones of those things. That's very labor-intensive. Yeah, so I think we'll make progress on that as well.

43:40But, yeah. One of the issues that you really brought to the forefront of the industry's conversation, I would say, over the past year has been the failure of these models when it comes to continuous learning, right? So, you know, it's often observed that, like, a good LLM might be better on day one than an intern, but the intern is almost always better after two weeks because they've been able to learn. Are you still as convinced that this is going to be a major hiccup to getting us all the way to AGI? Or have recent developments, maybe any new models, changed the way you think about that? So there's a big crux in how people think about how these models will evolve.

44:17And one side of the discussion says you need some way in which between sessions for a given user, the weights themselves are updating. Because if you think about the way humans learn, there's not like, you know, you're way better at your job than you were the first day you were on your job. Like people often say an employee is not net productive until six months on the job. What is happening to that time? It's not like you're building up this intensely accurate episodic recall of every single thing that has happened to you over the six months, which is what in-context learning is like, that just grows linearly in size as you spent more time on the job.

44:52It's like, you know, there's some distillation back in like a higher level abstraction that's happening over time. And so does there need to be an updating that happens back in the way it's a real question? Some people say, well, no, you just, basically you'll get to a point where these models are spending six months on the job and that six months is happening in context and we're going to train them in such a big variety of RRL environments that they'll learn how to adapt to any given situation you put them in. My question with something like this is, I think that might be enough to get these labs to like a trillion dollars in revenue or something.

45:21Like truly ludicrous outcomes. I'm concerned about or also interested in, well, do we get to super intelligence or something like that? And one question you ask is, how would you build something that is as good as Henry Kissinger in politics? There's no relevant training environment for that you can run in a data center. And so you do need something that can learn that on the fly. And maybe just by doing enough RLBR, or you build something that can just pick up whatever Kissinger picked up through his life that through interacting with the world. Maybe not. You know, the headline coming out of this talk is going to be, Dworka says, Henry Kissinger is good at politics.

45:59So I'm just preparing you for that. LBJ or whatever, the example doesn't matter. You know what I'm saying. Interesting. You have a very old soul. All your references are to mid-20th century. You live in San Francisco with Sholto Douglas, a researcher in Anthropic, and Dylan Patel of Semi-Analysis, a very influential semiconductor newsletter. Have you seen The Rent Man? I've got to split it. Well, that's my question. Semi-Analysis is reportedly making something like$100 million a year in revenue. Anthropic is obviously very valuable. At what point are you guys rich enough to not need roommates?

46:39The problem is everybody else in SF is also getting so rich. And so the housing is increasing at the same rate that our net worth is increasing. We're never escaping this. One knock that I sometimes hear on the sort of San Francisco AI scene is that it's all very clubby and insular, that there aren't a lot of people who are doing the work of holding people to account or being appropriately skeptical. you you know one detail in the New York Times profile of you is that you sometimes invest in companies whose CEOs or leaders you interview do you think that journalists and other sort of more conventional media people have the wrong sort of framework for thinking about conflicts of interest or do you just think you're doing something different I totally see the rationale for journalistic policies that say you're not allowed to have any sort of financial entanglement with the company that you're covering or whatever.

47:35I think at the end of the day, I hope the product speaks for itself. And that if you watch an interview I do with a CEO or an executive, you hopefully feel like I ask the relevant questions. Look, I also don't try to steel man some objection that I don't have. But when I do think that they're not making sense, I try to say so. And I hope that that in and of itself speaks for the interview. Who's your white whale? Who's the guest that you wish you could book that has not agreed to come on? Can you make this happen? Robert Caro? Okay, Robert, if you're out there, go on Dorkesh. Come on hard fork first.

48:09Yeah, I will say that Robert Caro was also famously Conan O 'Brien's white whale and Conan O 'Brien never got him on the show. No, he got him on. Did he? Yeah, and Conan O 'Brien needs a friend. All right, he just fact-checked my ass. Yeah. Well, Dorkesh, the podcast and the show is amazing. I learned so much from it. I listen to every episode and I understand about 80 % of it now, which is up from a, you know, 20, 25, about 20%. So I'm learning along with your audience and we thank you for all the work you do. It's a great show. Thank you, Drogas. Great to see you guys. Good to see you. Thank you.

48:45All right. Okay, well, friends, we are almost there at the finish line, but before we go, we wanted to take some questions. If any of you have questions for us, we will spend a few minutes answering them. We have mic runners upstairs and downstairs. So raise your hand. Someone will approach you with a mic. Anything, we're an open book. You can ask us about it all. It's like a YouTube comment section, but in real life. Right here. Hi, my name is Dallin. I'm here with my brother from Utah. What happened to the Fediverse? Great question. The Forkiverse, I should say. The Forkiverse was, of course, our effort to build a social network in a federated way, sort of show people what it would be like to be part of a social network that wasn't owned by a giant corporation.

49:32And I think it just ran into the challenge that any social product does, which is that if you're not constantly bringing in new users, it's like default state is to just kind of shrink. And so, you know, we've been in discussions recently about like, what is the future of it? I think it was a fun experiment. But, you know, we didn't really have that strong of an idea of what was going to happen after we started it. And so we're now sort of living with the consequences of that. Balcony. Do we have anyone in the balcony? Yes. Hi, Kevin and Casey. I was wondering why we're not hearing more from executives like Satya and other tech leaders who are restructuring their companies around the premise of AI.

50:13They just don't seem to want to engage with that premise when you ask them. What do you think that's about? I mean, I think there's a lot of conflicting incentives here, right? There are some companies that really want you to know how much they are using AI and how much more productive they are getting and how many workers they are laying off. And sometimes that's real, and sometimes it might just be covering for some overhiring they did a couple years ago. I think that's going to flip at some point where companies will not want to advertise the fact that they are restructuring around AI. Right now, there is still sort of this weird market premium for that.

50:50And so I think that will continue for as long as the market premium lasts. And then it'll be like, we're just going to sort of sweep it under the rug and hide it. And if we're going to lay people off to replace them with AI, we're going to call it something else. Because we don't want to deal with the backlash. But I think that really hasn't happened yet, which has been a surprise to me. What about you? No, I agree with that. And in the interest of answering as many questions as possible, I think we should move on to the next one. One right here. Hi, my name is Ina. I work at Quizlet. If you've gone to school in the last 20 years, you've heard of Quizlet.

51:19If you haven't, what? Anyways, education is being obviously radically changed, but what people need to learn and the fact that you need to learn doesn't really change. So I'm curious, if Quizlet were to just start everything from the ground up tomorrow, what do you think we should build? I mean, that is really challenging. I mean, Kevin and I get a chance to go speak in schools from time to time. And I think what we find is people who are like doing their absolute best to introduce like fairly incremental change and kind of see what happens. There's just tremendous uncertainty right now. You know, school is typically trying to educate you for like a fixed target.

51:59You know, like when I went to journalism school, it was like, well, if I get these skills, then, you know, I can have this kind of job. I think, you know, like we're not able to ask any guests on this stage about anything longer than a two year timeline because just none of them have credibly anything to say about that. So, you know, how do you like educate a five-year-old so they'll be prepared for the world when they're 18? Like, you know, good luck.

52:22What an inspiring message. Thank you.

52:28All right, let's take a couple more. Yes, up there in the balcony. Okay. Can you hear me? Okay, great. Please introduce yourselves. Oh, hi, I'm Liz. Hi, Liz. Okay, so two real legitimate questions. Number one, what are we wearing now that All Birds is under? Okay. And two, so I work as a regulator. I work for the state of California. I do privacy regulation. And so my question is on, so if you were to take a stab at what would be in the AI, the new world for privacy, like how are you going to protect your digital cells, either your sons or your friends? Like, what are we going to do when it's all owned in one walled universe?

53:11Yeah, I mean, my hope is just that that is not the case. You know, we sort of asked Cindy about that tonight. Like, I think there is a lot of logic in having some kind of privilege-like system that protects certain kinds of conversations that you would have with a chatbot the same way, you know, that a conversation with a lawyer might be protected. But I also think there's a lot of wisdom about what she said is, you know, what systems can we build that would ensure that that sort of data never makes it into the hands of a big corporation? I think we should outlaw data brokers. Next question. Oh, yeah.

53:40Outlaw data brokers. That's a good one. What's that? Oh, yeah. And where do you get your shoes, Kev? These are from Quintz. Yeah. That was not sponsored content. They just are. Yeah. Yours are better, though. I got these from, like, online unspecified. I honestly don't remember, but I can look into it. I'll figure it out by the reception. How's that? All right. Just a couple more. So I'm a software engineer, so take this for what it's worth. There's been some talk about, you know, like lots of people are afraid of jobs going away. And then you hear other people saying, oh, there's tons of hiring going on.

54:18That's what I see. I see a lot of hiring going on. But it's all for senior engineers for people who know how to fact check the models or how to architect and combine the things that they can do really fast. What's happening with the entry level folks? It seems like that is a real problem. Yeah. So I've talked to a couple of labor economists about this within the past couple of weeks. And they have sort of said, like, believe it or not, things were actually just like much worse during the great financial crisis. And that like the circumstances that we're seeing today, like don't approach that at all.

54:51Now, maybe they will eventually. But one labor economist I talked to, Catherine Ann Edwards, was telling me like some people sometimes forget that like your first job just sucks and has nothing to do with what you actually want to do. And so she's sort of like encouraging younger folks to manage their expectations, which is also not a very inspiring message. I think we can do one more question. So let's have the last question. Yes. Hey there. My name is Kevin. Oh. Great name. We're good. Yes, my name is Kevin. And what is your optimistic view over here in the middle, if you're looking out? What is your optimistic view on AI for about three years out, two to three years out?

55:34Just curious to get y 'all's take. Yeah. My optimism is around the acceleration of science and medicine. This is really a place I care a lot about. I don't know if any of you saw the cheering at the conference the other week where they announced that they had created a new breakthrough therapy for pancreatic cancer. I want there to be many, many more of those very soon. And I want the... Yeah, thank you. So that is my case for optimism, is that we sort of muddle through the transition from the old jobs to the new jobs. We deal with the safety risks that are really extreme. And then we just accelerate the hell out of the things that make people's lives healthier and longer and allow us to flourish.

56:25Yeah, I mean, that's my number one. But two more I would throw in there is like AI is amazing for learning and AI is amazing for building. And it's fun to learn and it is fun to build. Like if I were in school right now, like I frothed the mouth thinking of what it would have been like to take like my AP exams in a world where I could have, you know, chat GPT, generate infinite quizzes for me to do. And, you know, like Kevin and I have talked a lot on the show about vibe coding in the past year. I've been like making new projects this week and annoying my fiance and making him come see them, even though they're just pure slop.

56:54But it is fun to make things. It is fun to annoy your partner with random AI stuff that you build. All right, we're going to stop it there so that we can get to the reception. We'll see you all at the reception. Thank you so much for coming. Thank you. We love you. We love you.

57:32Thank you.

58:02Moving your technology into the AI age without losing the legacy code your business was built on. Let's create smarter business. IBM. I'm opening up cross play. I've been playing against Dan, my colleague at the New York Times. I'm going to play stoop, S-T-U-P-E, across the triple word multiplier square. Kat's played another move. Ugh, and she did have an S. She played stoop for 36 points. I've got a Z, which is 10 points. If I can put my X over there, I can make box. I have two A's, N's, and T's. I'm guessing tanga is not a word. Let's see. Tanga is a word. Oh. Don't know what tanga means, so I'm going to press down on the word.

58:43And oh, definition popped up. Former monetary unit of Tajikistan. Learn something every time I play this game. Even though I'm about 50 points ahead, one thing I've learned in crossplay is that the game is never over. I just got a notification and Dan played his last turn. Let's see who won. It's so close. But I did win. New York Times game subscribers get full access to Crossplay, our first two-player word game. Subscribe now for a special offer on all of our games.

59:31Video production by Sawyer Roquet, Jake Nickel, and Chris Schaap. Special thanks to the New York Times Live event team who helped us put on Hard Fork Live this year. Hilary Kuhn, Beth Weinstein, Caitlin Roper, Chantal Regnier, Melissa Tripoli, Natalie Green, Kirsten Birmingham, Marissa Farina, Jennifer Feeney, Morgan Singer, Dana Praskowski, Hayley Duffy, Yanwei Liu, Matt Kaiser, Sarah Cheever, Johnny Marola, Victoria Kim, and SV Productions. Thanks also to everyone at the Yerba Buena Center for the Arts and the Blue Shield of California Theater where we held the event. They were so fantastic to work with.

1:00:11And a special thanks to Paula Schumann, Pui Wing Tam, and Dalia Haddad. You can email us, as always, at hardfork at nytimes.com.

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