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Chapter 1: What is the main topic discussed in this episode?
This is Maurice Shema, the host of a new podcast from Serial Productions, The Marshall Project, and The New York Times. Last year, I spent three months embedded with a Capitol defense team. Their client had been on death row for more than 30 years, and now his execution date had been set.
I followed along as the lawyers tried to prove something nobody had successfully done in three decades, that one of Texas' most notorious serial killers was actually innocent. The last 12 weeks. Listen wherever you get your podcasts.
This episode of Hard Fork is brought to you by our Hard Fork Live 2026 sponsors. Premier sponsor, IBM. Associate sponsors, Everpure, Pure Leaf, and the University of Notre Dame. And supporting sponsor, Atlassian.
Well, Casey, we are still on our annual summer vacation. And can you believe there is yet more amazing stuff from Hard Fork Live that we have not shared with our podcast listeners? There is. In particular, we had a really fun discussion at the event between Daniel Coccatello and Sayesh Kapoor, who have somewhat different views of how fast the AI conversation is going to go.
We've heard them debate before. We wanted to sort of have an updated discussion with them now that it's been, you know, get in close to a year since the last time they had it. So I think you'll really enjoy hearing what they have to say about that. We also had the great podcaster Dwarkesh Patel stop by and hang out with us a bit, tell us a little bit about what is on his mind.
And just to round it out, we took some live Q&A and heard what was on the minds of our audience after a spectacular Hard Fork Live 2.
So these are all conversations that I would classify in sort of the same bucket of like insider sense-making, people who are deeply enmeshed in the AI scene in San Francisco, trying to understand and explain what is going on, the pace of progress, the trajectory of these models to the outside world.
And Sayash, Daniel, and Dwarkash are among the three most gifted people I have ever heard try to explain this stuff to an outside world that doesn't always know exactly what's going on. It's a great set of conversations. We think you'll really enjoy it. This is our final installment of our episodes from Hard Fork Live 2.
We will be back in two weeks to our regularly scheduled Hard Fork programming. In the meantime, enjoy your summer. Wear sunscreen. So this next segment I am so excited for because we're going to have a conversation with two people who have very different views about how AI is going right now. Yes, we have Daniel Coccatello with us tonight.
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Chapter 2: What are Sayash Kapoor's views on the diffusion of A.I. technology?
So to give you one example of a bottleneck, the other day I was talking to a lawyer friend of mine, and he uses these tools, he's very bullish about them, But what has turned out to be the case is as he started using these tools for bigger and bigger tasks, the rate of hallucinations, the rate of unreliable outputs has sort of remained the same, right?
It's not because the AI systems haven't gotten better. They indeed have. They are so much better today than they were just a year ago. But the fact is that the tasks that you can do with these systems actually are bounded by the rate of hallucinations or the reliability. And that's one place where AI systems continue to struggle.
And in a domain like software engineering, where you have this instant feedback loop, where you can actually run the code and see what the output would be, it's a much easier bottleneck to address as opposed to something like the law, where even the right answer is not obvious to a domain expert. Domain experts can reasonably differ in the approach that they take.
So this is just one example of a bottleneck in a domain where the right answer can be a bit more subjective than encoding.
Daniel, I think when AI 2027 first came out, there were some people who dismissed it as sort of speculation or scary science fiction was a term that some people were throwing around a lot. I reported on this. I talked to you and your co-authors then. I know that you grounded this in real forecasting work, like months of trying to figure out what would happen as the technology got better.
And I will say a lot of that has come true already. So you predicted in your AI 2027 that we would start to see large parts of coding become automated. That much has come true.
I was reading today, someone was copying and pasting something that you had written about Frontier Labs sort of restricting the use of their models for Frontier LLM development, something that has happened this week with Claude Fable. So what are the things that you think will happen if your scenario continues to mostly hold for, let's call it the rest of 2026? What are we going to see this year?
So we're not going to see an intelligence explosion this year in the scenario that happens next year. That's close. So that's nice.
Intelligence explosion being recursive self-improvement leading to sort of out-of-control, runaway, superhuman AI.
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Chapter 3: How does Daniel Kokotajlo predict the future of A.I.?
I guess the entire skepticism about neural networks. So from the 1990s to the 2010s, the entire AI community has dismissed neural networks as a joke. Basically, you could count the number of researchers who took you seriously if you worked on neural networks on two hands.
It was only through the persistence of a few people like Fei-Fei Li, who released this big data set that led to the deep learning revolution, and Joshua and Jan and Jeffrey Hinton, who later went on to win the Turing Award for their work on deep learning, that this sort of subfield persisted and eventually was able to disprove claims of skeptics.
And in the same way, I think the AI community might be hurting too much around, let's say, transformer-based models right now, and perhaps at the expense of other transformative improvements that are breakthrough improvements that are sort of being sidelined because of this community's single-minded focus on it.
I think an experience that you both have in common and that Casey and I also share is writing things that we think are very measured and careful and precise, and then just having people interpret them in the wildest possible ways. You both published your sort of breakout essays, scenarios, And it was immediately both of them were sort of seized on by these polarized camps.
You know, David Sachs, the former White House adviser, was was, you know, posting things about AI being a normal technology and sort of agreeing with you and taking issue with you for changing your forecast. And oh, my God, the doomers are are, you know, are backed into a corner now.
Gary Marcus and JD Vance and Bernie Sanders and all kinds of people have used your arguments in support of kind of whatever they already believed. How has that been to watch your work ripple out in maybe these ways that aren't what you expected?
Well, I guess I'll go first. It's been a leap of faith, faith in humanity. At OpenAI, I was doing scenario forecasts like this too, much smaller, low effort versions. But they were just for internal use only. I wouldn't be allowed to publish them. And it seemed to me that the world really needs to wake up to AI and what's coming and start thinking more seriously about it.
And, you know, the discourse is not necessarily so great. And there's lots of terrible people and lots of terrible takes. And, you know, it's very chaotic and confusing. But we at AI Futures Project are sort of making a bet that, like, Well, we should say what we think is coming. We should be clear. We should be articulate. We should explain our reasoning. The discourse will get rolling.
Lots of people will say lots of things. Hopefully in the end, it will converge towards the truth. Hopefully in the end, it'll converge towards better decision-making on average. And we'll see what happens. I have faith.
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Chapter 4: What are the main disagreements between Sayash and Daniel regarding A.I.?
Main one would be loss of control. Number two would be concentration of power. There's a whole bunch of other ones besides that, but I'll stop there. I can elaborate if you like.
Those seem pretty bad. Sayash, what about you? Actually, this is another thing we were just talking about backstage. I mean, I was surprised to hear that we disagree far more, or like I'm far more concerned about military uses of AI than Daniel is. I mean, it's on the list. It's just... Perhaps, yeah.
It's a couple notches down.
But I mean, like, as you both know, in the essay, we explicitly carved out military air because we felt like we weren't the right people to comment on it. And, 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 his view as well.
But frankly, the actions that are being taken by countries worldwide, by nation states, are pretty, pretty, pretty 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. It 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 Sayash. Thank you for joining us. Thanks, guys. That was fun. Thank you.
Thank you.
Thank you. We'll be back with more Hartford Live after these messages.
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Chapter 5: What are the bottlenecks to A.I. advancement discussed in the episode?
Even though I'm about 50 points ahead, one thing I've learned in cross-play 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.
One 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 Ekus.
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 Unitree robots from China. And we are going to be joined by George and Toby the robot. George and Toby, come on out. Thanks for having me. Good to see you. George. You're a very convincing humanoid. Oh, no, wait. That's Toby.
Do we shake hands? Okay. Let's try it there. Hi. Short King. It'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. Oh, Jesus Christ! Listen, 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 end memoriam next year. Now, is Toby capable of standing up?
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Chapter 6: How do Sayash and Daniel view the potential for recursive self-improvement in A.I.?
Yeah, so I think we'll make progress on that as well. But 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 it's often observed that 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 would evolve. And 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're way better at your job than you were the first day you were on your job.
People often say an employee is not net productive until six months on the job. What is happening at 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 spend more time on the job.
It's like there's some distillation back in 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. Because some people say, well, no. 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 RL 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, like truly ludicrous outcomes.
I'm concerned about or also interested in, well, do we get to superintelligence 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 RLVR, 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, Dwarka says Henry Kissinger is good at politics. So I'm just preparing you for that. LBJ or whatever, the example doesn't matter.
You know what I'm saying.
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Chapter 7: What are the implications of A.I. on job markets and employment?
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. Yeah. I mean, that's my number one.
But two more I would throw in there is AI is amazing for learning and AI is amazing for building. And it's fun to learn and it is fun to build. If I were in school right now, I froth at the mouth thinking of what it would have been like to take my AP exams in a world where I could have ChatGPT 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. But 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.
Thank you.
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 Wordle, Mini, Spelling Bee. It has given us a personal connection. We exchange articles. And so having read the same article, we can discuss it. The coverage, the options, it's not just news.
Such a diversified desk. I was really excited to give him a New York Times cooking subscription so that we could share recipes. And we even just shared a recipe the other day. The New York Times contributes to our quality time together.
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