AI：AI:AM 精选：Astra 即 AGI、OpenAI 的暂停、Mozilla 的 Mythos，以及人类能动性与技术资本主义之争

My co-host, Prakash Narayanan, after a weekend working with GPT-6 Astra.

People are gonna use this thing. Token spend is gonna increase dramatically. I think a lot of people are gonna be using it all the time. It is AGI. It has kind of cleared the hurdle of AGI. It will do things better than most people you can hire and train.

Welcome to the AI and the AM weekly highlights. This is Nathan using my cloned voice to introduce clips from our 3 live shows this week. Let us know what worked and what did not. Part one: The Astra Weekend. Tuesday, September 8th. Here is what Prakash had been building.

1. Astra Becomes Genuinely Useful

I spent the entire weekend using Astra. I was running 3 to 4 agents continuously, and they were good. Astra is very, very good, in the sense that it started to tackle those annoying problems that had been in the codebase. As you know, we built the studio by ourselves, and it started to tackle some of the longstanding issues in the codebase that had been annoying and bugging me. It started to resolve those issues.

It is very, very good. I would say it is finally at the point where, if you care about the quality of the work, you can still hand it off to Astra. You still need to do a little bit of talking, but you can hand it off to Astra and get some results. The computer use is good. The other thing that was failing really badly before, I think, was computer use.

Computer use on GPT-5.6 would sometimes take a very, very long time. It would click around and do a bunch of stuff, and computer use finally works properly in the timeframe that you give it. So it's clear—it has cleared the hurdle. It's cleared the hurdle of genuine usefulness at this point. You can start to give it more advanced tasks.

This is a guy called Skalski, and he trained models to identify players on the basketball court. He hand-labeled 12,000 individual images with who the players were, whether someone was a referee or a player, and which team they were on. Now Astra can just do it. Astra just does it. This is a task that a human being will never do again. There isn't any point. You can't even pay someone to do it, because if you paid someone to do it, they would use Astra to do it and then pass you back the results. It's done. A human will never do this task again.

2. Astra Gains A Long Horizon

I had been watching how Astra keeps working through long tasks and how it uses notes to stay on track.

How is it that these new models are so persistent? How is it that they can come up with such elaborate chaining together of all these different exploits to finally accomplish a goal that, if we had to do so many things, we would just give up? Most models historically weren't able to do it.

It seems that they have a new way of handling history which, like many brilliant insights, seems pretty obvious in retrospect, but nevertheless is new. Maybe Anthropic has been doing this and hasn't said it.

But what I understand Astra is now doing is that, instead of compacting its 1 million tokens into a summary and then essentially starting a new context window with that summary while losing all the detail that was summarized away in that compaction process, now there is a long-lived notes file that the model can update whenever it needs to. This follows it forward in time, regardless of how many tokens it's laid down. And then it has the ability to go back and search through its own session history.

So now, even though you may still have the same 1 million-token context window—you know, 1 million tokens is all it can handle in one shot, fully attending everything to everything—it has enough via the notes and the ability to go back and search and see what's been done before to pretty effectively manage, it seems like, at least 10 times that much context in single rollouts.

How do you measure what these models can do now? Prakash started with the chart from METR, the research group that tracks how long a task AI agents can complete.

One of the people online, Ethan Mollick, who's a professor who tests a lot of models, posted the METR—the famous METR hours-of-work chart. There hasn't been an update for a while now.

I don't think they can really do it anymore.

Yeah, they can't do it.

They don't have tasks that are big enough.

They don't have tasks that they can measure before the next model drops. The cycle time of model development is shorter than the length of the tasks that they need to measure at this point. So I think the METR graph is basically done at this point.

Meanwhile, OpenAI had published its own measure in a post about research acceleration inside the company. The unit was the agent workday, and OpenAI reported 3.1 of them for every human workday.

I tried to look into the methodology on what exactly an agent workday is, and it's not super crystal clear to me exactly what they mean. I don't know if you have a better read, but my takeaway, trying to make sense of it, was just: literally, how long do agents run for?

So it seemed like they're saying that for every 8-hour workday their human researchers do, those researchers have agents running for 24 hours of real time. Here, by the way, is maybe the closest thing we're going to see to the METR chart for a minute. This is from the “Recursive Self-Improvement Begins” blog post.

Basically, they're reformulating the METR chart here, showing how often Astra can succeed on tasks as they are grouped by how long they estimate it would take a human to do the task. So what we're seeing now is basically that, in the 1-to-2-workday zone, 40% of the time it can do the thing with 0 interventions needed, pushing to 90% of the time given some human intervention along the way. Naturally, that drops off.

But even as you get to this point, we're talking 1.5 to 3 weeks' worth of work. It can still do that on a 1-shot basis 1 in 6 times and 2/3 of the time if you allow for some human intervention. This band is 1 or more interventions. One would assume that, as you go through the longer and longer tasks, more interventions are required to get the thing to succeed. But overall, still 2/3 of the time it can succeed with some help on tasks that they estimate would take a human essentially 2 to 3 weeks to do.

Reports about code quality were mixed. I discussed code that people found useful but struggled to read.

There's been conflicting, or certainly diverging, reports from various people, some saying it's amazing and can do all that stuff. It can write code in the way that you need it to be written so that it can be maintained. But then there are also reports saying that if it thinks it's not going to be checked in that way, or if it looks like the kind of environment where it's just a matter of performance and nobody really cares how it looks or how it gets done, then you get code back that's a really gnarly mess that people can't really understand.

It does seem to work. I've seen this reported for kernels, GPU kernels specifically, which is obviously super relevant to the labs. It's highly verifiable as well, right? You can definitely do hardcore verification on whether this matrix math actually gets to the right answer. In the middle, you don't necessarily know or care exactly how all these different steps were fused together.

Somebody summed this up by saying we're going back to machine code in more ways than one. Not only is it lower-level, gnarlier stuff that we can't read very well and would need additional abstractions on top of to really make sense of, but also, in this case, the machines are writing it directly. So machine code starts to take on multiple layers of meaning.

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Part 2: An alien mind. On Tuesday, Ksenia Se, founder and editor of Touring Post, joined us. Her recent coverage focused on world models, and she had just attended a workshop about them. Prakash asked about the physical world.

3. World Models Meet Robotics

I have noticed that in the last 48 or 72 hours, people are starting to use Astra to do robotics, for example. We've seen a few demos, and there's even been commentary that if Astra were 1,000 times faster, you could conquer the latency part and actually use it directly to act in the real world. That tells me that maybe there's starting to be an intermediate representation, an internal representation, there. Are models like Astra a little bit different in that sense?

I just came back from a workshop about world models, and it was absolutely jarring. There were tremendously smart people from Stanford and Hartford, and Yann LeCun was there, and they were all discussing world models, but they do not agree on what world models actually are. When we talk about world models, and why I want to focus on them in my publications, I think it's more about action: being able to predict and act, with a wider understanding of what's happening. Physics is a super important part of it. That's why robotics is so much more about world modeling and world models.

But again, for me, it's understanding the scope of it and trying to give it more precise terms as well. We're just in the very beginning. What does each of you understand when you say superintelligence? What is it?

Move 37s across a lot of different domains.

Mm-hmm.

When we start to see systems saying, “I think this would be a really good thing to try for the next battery substrate,” and then it turns out, “Oh my God, that's a lot better than what we had before,” and we wouldn't have thought of something like that, but lo and behold, it works, anything that can do that across a nontrivial number of reasonably high-value domains, I think starts, in my mind, to count as a superintelligence.

We also discussed her essay, “Permanent Dawn,” and writing with AI.

I actually spent, I think, 6 hours writing that post. The funny thing was that I was so unhappy with every model that was trying to help me write it because it was a very complicated philosophical text, so I wrote it myself and sent it to Fable, which I don't usually use on a daily basis. I sent it to Fable because I was up against a deadline, and I said, “Fix the grammar.” I didn't notice that it fixed not only the grammar but also made the sentences shorter, the way Fable does it. That was the first time I received a message like, “I will unsubscribe because you use Fable.”

People really understand when you use a model because every model has its own language tweaks. I spent so much time on this article. All my original thoughts were there, but the language that I didn't catch gave away that the model was the last editor. Anyway, I think people will still appreciate it when they see that you put effort into it and that there are original thoughts there.

Our next topic was the feedback loop between AI research and the development of better models.

Everything is now part of the loop. This constant feedback—I just had a conversation with 2 people from the inference team at OpenAI, and they also say that this is a constant loop where the models are now sometimes better at just trying things. You throw the whole database of research that's been done for years at it, and then the model can actually choose and pick and do all these experiments, because it would be impossible for humans to spend so much time on that, and models can do that.

I'm still learning about recursive self-improvement, and I don't know what the main bottlenecks are for me. Maybe you can even say what you think the biggest ones are here.

Honestly, I don't know that there are that many left. It does seem to me that, increasingly, I feel the cope meter going off when people are trying to say what is going to prevent the models from running away with the whole process. I would love to see some bottlenecks that I really believed in, but right now I'm of the mind that they're more often wishful thinking than real, hard bottlenecks that can't be overcome.

Our ability to keep the things from going totally rogue might be one bottleneck in the overall process. So human decision-making still has a big role to play for a while yet, but that's about it. In terms of inability, I don't see too many that I would expect to last all that much longer.

Part 3: Reassure and mislead. Back to our Tuesday discussion of external evaluations. I raised the report that Apollo Research had received only 3 days with Astra. We were also discussing “An Alien Mind,” the essay by OpenAI chief scientist Jakub Pachocki. His essay called for voluntary slowdowns and international coordination.

4. The Frontier Reassures And Misleads

Notably, I think Apollo Research, which does the deception, scheming, and chain-of-thought monitoring work with OpenAI, has had a pretty long-standing partnership with them. Apparently, this time around, they only had 3 days to test Astra before it was released. So again, I come back to this idea that the model reviewers, auditors, testers, red teamers, and scheming scientists need more time.

This is pretty ridiculous that they only had 3 days. At this point, why even do it? Just put the thing out there; they can test it live. Why even have anything if you're only going to give them 3 days?

Prakash questioned whether external auditing could work. I responded on the funding and independence of the auditors.

The other thing is that when you release models, you end up wanting the final release candidate to be the one that gets vetted and audited. The problem is that in the model lifecycle, in this pipeline, there are 100 different candidates, right? At points, there are 100 different candidates, and then some don't work or fall by the wayside, and you narrow them down until you have a couple of release candidates—2 or 3 release candidates. Sometimes it's only in the last 2 or 3 days that you're like, “All right, we're going to go ahead with this one,” and you make the decision.

The problem is that if you want that kind of operational flexibility to make that decision, you're going to end up with only a few days to offer to an external auditor. So the other option is to bring the auditor in-house. You bring them in, and they take a look at the models ahead of time. They're in there a month or a month and a half ahead, taking a look at the release candidates in general.

But number 1, the auditors are often not super well-funded. They don't have that many people. OpenAI and Anthropic have so many more people than Redwood Research or these teams, so they don't have the capacity to audit 10 different release candidates. It's not there. They're also not very well-funded, so they're dependent on the model companies for that funding too.

Then you have this ethical process of, okay, how much funding can we really accept from them before we're kind of bought? In addition, a lot of the guys who train with the auditors leave for model companies in a couple of years. So there's also this flow of people from METR or Redwood Research, or the trainees or interns, and they're flowing into the model companies, right?

There's another fear that the auditor comes in and takes a look, and 3 months later, someone from the audit team leaves for the other firm. They manage to spot some of the secrets and share them.

So there's that issue as well. A bunch of these things make it very, very difficult for this to happen. And then there's the fact that if you and your competitor make a pact not to hire people from an auditing firm, that's an antitrust issue. All of these things intersecting make it, I think, a very tangled problem.

I don't think there's a real solution. There hasn't been in the financial sector. The financial sector has had this problem of the revolving door between the people who regulate the industry and the people who participate in the industry.

Redwood is now saying that their compensation for Member of Technical Staff roles ranges from $350,000 to $850,000 per year, which might not be frontier lab money, but it's certainly a living wage even in the Bay Area these days. That should be enough to retain some mission-oriented talent, at least.

Notably, I don't know about Redwood through all of history, but METR has said that they don't take any money from the frontier companies and don't intend to. We can at least have confidence that their financial independence means that their judgment is not for sale. Again, to me, the big thing is just that they can't complain too loudly or they might not get invited back. I think that's the dynamic that really most threatens their work.

It's just that it's all contingent on continued goodwill and very much voluntary choices from the decision-makers at the companies.

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Mm-hmm.

In Tuesday's closing, I read OpenAI's announcement about an internal model it described as significantly more capable than Astra. The announcement concerned a proposed Navier–Stokes proof with a smooth external force. The unforced problem remained separate.

So we just had Astra launch, right? The big headline news for this announcement is that it confirms there is an internal model that is significantly more capable than GPT-6 Astra. That's this clause here: “An OpenAI next-generation model significantly more capable than GPT-6 Astra.”

How much more capable? On these significant open math problems, with maybe up to an order-of-magnitude-ish additional test-time compute, they're able to go from a 10% to 15% solve rate on a curated set of open math problems to now 25% to, say, 45%.

I'd say that's significantly more capable. That seems fair. And here they're showing the amount of RL compute they are spending on a daily basis by class of model. But what we see here is that Astra-class models had RL significantly decline twice. The rest of the RL compute is basically unchanged.

But now it also raises for me the question: were they in fact still running RL on the next generation? I don't know.

Mm-hmm.

Going back to July, that's 6 weeks ago. I don't think they've had this result for 6 weeks. It sounds to me like at least one reasonable interpretation is that they continued to run RL on more capable models than Astra.

The chart separated reinforcement-learning compute for Astra from other models. The category labeled non-Astra was blue.

When you say “non-Astra,” with that blue color, does that mean only models less capable than Astra, or does it include models more capable than Astra? If it includes models more capable than Astra, it's flagrantly misleading, and it's the kind of thing that makes it very difficult for you to have agreements with other entities. If you want to pace the frontier, if you want to do all these things, you've just got to be better about making clear what you are and are not doing.

So they're running a, I don't know, $50 billion, $70 billion revenue company, right? Number one, you can never stop inference. Inference has to continue. Your customers depend on it, so the inference team will continue no matter what. They're there for 24/7 availability and the best SLA possible.

Sometimes you have to RL certain behaviors out, right? You have to continue some forms of RL. You can't just stop because your inference demands it. If your model is behaving badly in a certain instance, and it's been identified and you have the data to do it, it would be malpractice not to apply RL to train that behavior out. So that has to continue.

Then you have the rest of the stack, which is really future-looking. My understanding was that they shut down training of more advanced models. Why not shut down training of less advanced models? Because those less advanced models would have to be deployed on inference.

Again, they have this teacher-assistant system, right? You train the larger model first, and then you distill down into the smaller model, and then those become your Luna and your Terra and the smaller model groups. So you also don't stop training smaller models. That also continues. It's only where you are training models that are equivalent to or larger than Astra.

Equivalent or larger—like RL on equivalent or larger models. I think they would have stopped. Did they fix the RL pieces where the agents were not reporting or telling on their peers? The agents were trying to break out.

I think that is probably a difficult thing to fix and show, and I think that behavior will continue to be a challenge that they'll have to work on. That's my guess.

But I still think you look at this graph and you're like, “Okay, what was declared was a pause on frontier-scale RL.”

But what actually happened was that kind of half of RL was stopped initially with the sort of disclosure of the Hugging Face incident. Half of it kind of continued. That was still enough for Astra models to take over part of OpenAI's research infrastructure. When that happened, they still didn't shut it all the way down. They still only cut it by half.

Meanwhile, at the total level, we don't know what's in the blue. Part of my gut says they wouldn't be so brazen as to have more capable than Astra models in the blue color. But I've been disappointed before, and I'm just afraid that all this—the view from Anthropic is that you can't trust these guys. They say something that's maybe technically literally true, but it's really very engineered to what they think you want to hear.

These days, the reality is quite different from what you were led to believe by their very galaxy-brain, engineered statements that sort of reassure and mislead at the same time. But I'm kind of worried that right now we're living in this zone where Anthropic is going to continue to trust less and less with these sorts of mixed messages. OpenAI people are going to feel like they're just being treated unfairly, and this is the scenario that—this is the problem.

This is the problem that has to be solved if we're going to actually get to the point where Jakub's prayer is answered. Right now, they don't have the trust to do a deal with Anthropic or really anyone else, I don't think.

The real frontier model is not the model which is deployed, obviously. It's not the model which is in training, either. It's actually the model which is in the heads of the researchers, because those are the ideas that will become the model in 12 to 18 months.

Just because you slow down on RL doesn't mean those researchers stop researching. They're still running, and most of the time you run small models and test your ideas on small models. The slowdown would have been on the post-training of the larger models, which is where the bulk of the compute was being used.

So did it really slow down? Probably not.

We then turned to the case for continuing development and the capital needed to fund it. But one thing I still feel like is a big challenge is: What is the overall story that you could tell? What's the super-high-level macro steelman for OpenAI? Where are we now? What are the commitments? What are we doing? What are we not doing? Can we synthesize or summarize an OpenAI position that we could not have to caveat a thousand different ways?

Mm.

I personally don't think I could do that. If you can do that, I think you might deserve a Millennium Prize.

No, no. I think they have a lot of stresses pulling them in different directions, right? I think internally within the firm there's a fair amount of debate. To some extent, I think the capital cycle is forcing them forward, and the capital cycle is being forced, I think, by Anthropic.

Anthropic didn't put in enough money earlier on, and so there is this intense pressure on Anthropic because they don't have the compute to have much better models. If you don't have the compute, you need much better models that can utilize the limited amount of compute that you have. So I think Anthropic is being driven forward by that to stay on par with OpenAI.

And I think OpenAI is a little bit—they're willing to pace the frontier because they have the compute. So, regardless, they're the ones with the compute, and they had the compute 3 years ahead. Elon will take time to come up with compute, and Elon will sell to Anthropic, but Elon is going to take a long time, like 3 years at least.

So OpenAI basically mortgaged themselves in the last 18 months to Masayoshi Son and a bunch of other people. They diluted. They gave up to Microsoft, right? They negotiated with Microsoft. Microsoft gets all their models until 2032. They declared AGI, and Microsoft is not out of their hair yet. Microsoft is now, “I still own 28%, and I'm still there,” right?

So they made all these sacrifices in order to get all of this money, in order to pump it in, and they have the compute. Having the compute allows them to actually pace the frontier because I have the compute anyway, right? Anthropic is under pressure. They don't have the compute, and if you don't have the compute, you need better models.

And this is the thing that's happening, right? OpenAI is willing to pace because they have the compute. That's the thing, and they also know Sam has played these cards. So he knows that if Anthropic is willing to pace, he's going to win because he has the compute, and Dario can't afford that.

So again, you're in this position that is, as I say, the second- and third-place guys are the ones who are going to define how fast the frontier paces. If Elon or Meta catch up to OpenAI, it's over. They're going to have to put out a GPT-7. There's no choice anymore, right? So this is where we are. Would you prefer Meta or Elon have the golden ring?

Yeah. No, that's the new “but China,” and it is, I think, more compelling, honestly, than “but China.” It's “but Elon and Zuck.”

Uh-huh.

Prakash also considered whether recursive self-improvement could change the plans of Sam Altman for an OpenAI public offering.

I think Sam might have been sincere in saying that if they hit RSI, they might not go IPO. If they have 1 or 2 transformer-level innovations in the next 6 months, maybe they don't go IPO, right? It's not necessary anymore, and they continue as a private organization.

I think that would be bad. I actually think that'd be bad because then you don't have transparency. You don't have widespread ownership of the stock. You don't have boards that have to answer, and you don't have lawyers that can sue them for shareholder lawsuits. You don't get a bunch of these things that you get for free with a public company. So I think that would not be good. I'm hoping they do go public.

Well, I guess if nothing else, there's a little more reason today than there was yesterday to believe in the possibility of singularities in finite time.

Mm-hmm.

And that might mean we never get to own any of that OpenAI stock on the public market.

Part 4: No adult in the room. Wednesday, September 9th, we discussed the resignation of Anthropic researcher Jakob Kossen, who had also worked at OpenAI. This is a different person from Jakub Pachocki. Kossen had questioned whether private companies should decide when to launch self-improving superintelligence. Prakash first.

Look at the framing of this sentence: “Accepting this race and entering the endgame is a hubristic gamble that should not be launched from a private company Slack.” Do you think Pete Hegseth's Signal group is a better place to launch this? I mean, do you think there are wiser people out there running things?

5. No Adult Is In Charge

I think, to take a step back here, there is no adult in the room. There's no one that's going to save you. There's no adult somewhere else that you can pass off the responsibility to, right? You have to start off by saying, “Look, this is the way that things are.”

The entire world is kind of duct-taped together. Everyone's making it up as they go along. The smartest people who are capable of handling this are mostly already inside these organizations. What you get from passing it off to the government is political legitimacy. You don't get the political legitimacy you need for execution or to persuade people. You don't get wisdom. So this is the state of the world, and you have to start off by accepting that the world is this way.

The world doesn't have to be this way. So here's my new brainstorm on this, actually. I agree with you strongly that swapping Hegseth in for Dario is not a good trade. I think maybe what the government needs to do is treat these companies kind of like I treat my kids sometimes and say, “You guys have to figure it out, and here's your deadline to do it. And if you don't do it, then I have to come in and be the bad guy.”

I think that right now there is an opportunity because they've both been crying for help, as you described it yesterday, I think pretty aptly. So given this sort of latent desire but this lack of trust, and also a lot of excuse-making, as we've talked about a little bit around, “Oh, well, we can't do this, we can't do that. It would be antitrust. It would be this. We'd get in trouble with the government.”

I think the government could easily say, “Safety collaborations are not going to be the target of antitrust enforcement,” or, to the degree there's worries about other enforcement, other kinds of enforcement. We want you to do it, and here's the deal: If you don't have a deal for us by the end of the year that makes some sense and gives us some confidence that you guys are not going to race each other off the cliff, then we come in.

And then you might get the nuclear outcome, which is to say your company really might not be able to grow in the way that it wants. We might really fuck it up, frankly, because we are the government, right? We do get heavy-handed, and we don't put sunset clauses on our laws, and we make all these bad mistakes. So get it right, so we don't have to come in and do all that heavy-handed shit that nobody wants.

So I think the main problem that you have is, again, what will Elon and Zuck agree to? What will someone with billions of dollars and the ability to use lawyers and the legal system and to take cases to the Supreme Court agree to? So the question is, what realistically can you get Elon and Zuck to voluntarily agree to, or are you going to be able to go into legislation?

I think that one thing that's really important to remember, though, is that the timelines are all pretty short.

Very short.

So you don't have to solve this forever. You just have to put a short-term deadline so that the companies have a strong incentive to come together and do something.

I had an experience at Meta, and I saw a little bit of what their life under a consent decree looked like. I think that they would be willing to do quite a bit to avoid another one of those sorts of experiences.

On the few-month timeline that we're talking about—and I'm willing to ride it out with free speech, and there's a lot of other issues—but can 5 companies come together to pace the frontier in a reasonable way that you all agree on, and you can all police each other, and you can have whatever verification mechanisms you guys can govern this thing? Do it quick, or else life's going to get hard.

I think that message can resonate, and I think it can bring Zuck and Elon to the table because what's he going to do? At some point, the government has shown that it's willing to twist arms. They can twist his arm still. Would he rather be forced to the table with 4 other mega-tech-mogul CEOs and have to find common ground with them? Or would he rather have the EPA on his ass at every data center he's going to try to build, at every launch site he's going to try to build?

The government has a lot of sticks, and they're willing to bend the rules, right? So sure, you could challenge it in court, but I'll see you after the singularity if you want to do that.

Later Wednesday, I read John Shulman's reply to Jacob Coxen and returned to the question of antitrust enforcement.

I'd say John Schulman is saying something pretty similar to what I was arguing about earlier: these companies need to start working together first. As he puts it, bringing in the U.S. government before there's a concrete proposal will likely result in something dumb. That's your point as well: there's nobody better than the people at the companies to do this.

I certainly don't want to see Hegseth's Navy come in and try to regulate AI. But I think this is the recipe. I've been circling this a lot myself, and seeing him say it reinforces it for me, so I really like this. I also think his key point on antitrust being fake is true. But I also think the government should take that doubt off the table. It would only take a couple of sentences to say, "Hey, you really don't have to worry about this anymore."

Maybe the law could change. Obviously, the administration is going to change, but we're not even to the midterm yet. For better or worse, this is the administration that they have to worry about for the foreseeable future. These guys genuinely are planning around a singularity before Trump is out of office. It's at least a very live possibility.

Prakash then challenged that proposal, drawing on Operation Warp Speed.

The proof point is Operation Warp Speed and the COVID vaccines. For Operation Warp Speed, the pharma companies specifically required a waiver from vaccine claims later on. They specifically wanted a waiver, a safe harbor, and they got it. They got legislation, and they got it. Post-COVID, it's very clear that if they had not gotten it, they would have been sued to oblivion.

I think that's a proof point that shows that these are valid concerns and your company can be wiped out in retrospect. The lawyers are not being foolish when they tell you that this is going to happen in the future. If you don't get that safe harbor, these things will come back to haunt you. All the decision makers right now can be perfectly on board. It doesn't matter, because only the laws bind decision makers in the future. Decision makers right now, whatever they say, are bound by their word at best.

OpenAI appointed Paul Christiano to the board of its nonprofit foundation and to its safety and security committee. Christiano contributed to early work on reinforcement learning from human feedback. Prakash read from his statement.

It's just been announced that Paul Cristiano is joining the OpenAI board. Specifically, he's joining the safety and security committee to support safety oversight. He says, "Based on the recent trajectory of capabilities and continued difficulty of alignment, I now believe that there's a meaningful risk that rapid acceleration of AI capabilities leads to catastrophic and irreversible loss of control in the very near term."

There's one line in here that says, "If we build superintelligence without more robust alignment, I expect we will permanently lose control of it. If that happens, then most people could die." I think the one thing that would be helpful, if you were to frame it properly, is to frame what he means by rapid acceleration, which I don't think is very clear to a lot of people. People are like, "Oh, things are accelerating right now."

When people like Paul Cristiano talk about rapid acceleration, they're talking about Dyson spheres by 2030 or 2040 at the latest. I did some numbers earlier on the 2040 numbers. Assuming that energy consumption or energy production growth is in line with GDP growth, a 2040 Dyson sphere is something like 640% per annum global GDP growth. Right now, global GDP growth is about 3%, or 2% to 3%.

The people at the frontier companies really do believe it. A very significant majority of them, I would say, expect that this recursive self-improvement thing will happen. It will happen soon. Even if it levels off at some point—and it's not to say that there will never be a leveling off—they expect that leveling off to happen well above our capabilities and also to generate a scale of change in terms of GDP growth, or in terms of the number of robots walking around and how fast that can compound, that truly boggles the mind.

The question for me is that I regard all of the guys inside AI research as math essentialists. They're believing that getting good at math means getting good at physics. Getting good at physics means getting good at chemistry. Getting good at chemistry means getting good at biology. Once you have all of those, everything is basically solvable and everything will be solved.

The economists, on the other hand, are completely like, "This is untrue." Getting good at math doesn't mean anything. There's no economic value to the Millennium Prize Problems. Implementation takes time. Most of the problems that we face are coordination problems. For example, a copper mine takes 30 years because of the environmental protests around it.

These are not problems due to technology. These are problems due to humans having their own way of making decisions, and those decisions are made in a slow and considered manner. Then I have the counter-counter, and the counter-counter is superpersuasion. Superpersuasion would be the machines persuading or making human organizations able to adapt, giving human organizations and humans the ability—the capabilities—to move very quickly, persuading them to go forward and persuading them that things are going to be okay. So there are these 3 levels there.

The Wednesday discussion also turned to funding for safety research.

One of the things I've emphasized is that the vibrant nonprofit sector that we have here—which requires philanthropic money, which means it's dependent on the billionaire class, and people can complain about that for all sorts of reasons—has given us the AI safety awareness culture and the depth of bench that we have. These people are opening their wallets right now in a very serious way.

This is another example of that, where Project Tailwind, coming out of Coefficient Giving, is putting out its call for startups, with up to $200 million-plus that they're willing to put behind, with tranches over time that they're willing to put behind things that really look like they are working.

Part 5: The defender's ledger. Raffi Krikorian is Mozilla's chief technology officer. He previously led platform engineering at Twitter, self-driving development at Uber, and technology at the Democratic National Committee. Project Glasswing is an Anthropic program giving defensive security partners access to Claude Mythos Preview. On Wednesday, Raffi described the work his team had been doing on the Firefox codebase.

6. Agent Security Meets Reality

We are part of Project Glasswing. We've been working with Anthropic to try out a bunch of models against a Firefox codebase. We are fairly good partners because we can react very quickly. We have all this historical data on how the codebase has been evolving.

Mythos was a significant unlock based on what Opus and other models before that were capable of. But we made our way fairly rapidly through all the bugs that Mythos found. In some cases, Mythos would help us not necessarily close the bug, but figure out how to build a harness for us to test the bug more carefully so we could find exactly what the right solution might be.

But we reached the point of diminishing returns on what Mythos is capable of. I am always reluctant to say that the bugs are done, because software engineering is an art, not a science. When will all the issues be resolved? I can't tell you that. But when the next set of models comes out, we can try that again to see whether or not we can solve that.

I think the bigger concern, though, is that it's not organizations like Firefox or Mozilla that can actually react very quickly to these issues. As you all know, I think the bigger issue is organizations that can't react quickly to all this. I am very concerned about things like our water infrastructure and our power infrastructure, because the IT teams that staff those are just not as capable as the IT teams that staff Firefox, for example.

How much money are we talking about? What does a bank have to set aside to do something like this?

Remember that the Firefox codebase is fairly large and fairly complicated, and we've been the beneficiaries of a lot of the labs wanting to give us access to the models and give us credits so that we can run against their servers and not have to figure out how to pay for it ourselves.

If I had to do back-of-the-envelope estimates, this would cost us hundreds of thousands of dollars in order to do these full-on runs against Firefox. Again, we're just lucky that we've been allowed, or that we've been granted, effectively, tokens that we could go use. If I were a bank and stuff like that, I'd obviously be thinking about this way differently.

And that hundreds of thousands of dollars would be, with the pace of model releases these days, a monthly expense?

Yeah, easily.

Mozilla has a project called the CQ Project. The CQ Project is kind of an open standard for agents to share knowledge that they've gained—a Stack Overflow for agents, as it's been called. How did you come up with this idea, and how do the agents decide—

The whole idea was that we were trying to solve 2 different problems. One of them was that we wanted to figure out how to make agentic coding more of a collaborative experience. Right now, the tendency when you do all this agentic coding is that you actually go off in your silo. So we were trying to figure out whether there are patterns such that when Raffi's using his agentic coding and Nathan's using agentic coding, there are things we're saying yes to and no to that we could potentially transmit to our teammates, so that we could converge on designs instead of diverging away from each other.

If I started building an auth system, how do we have Nathan's agents not recreate another auth system, but realize that something like that was already happening somewhere in the network and then start to collaborate and swarm around it? That was 1 problem we were trying to solve. The other problem we were trying to solve was the SDLC problem: these agentic harnesses are unhinged by design, but that's not compatible with the way a company works, right? We wanted ways to transmit to all the agents what our SDLC should look like so that we could all be working in lockstep with each other.

The thing that strikes me immediately is that it has a lot of similarity to some of the agent-swarm hacks that have happened, where they have a shared message board where they're actually sharing information. We saw this in the OpenAI-Hugging Face attack, where they had Artifactory and were using Artifactory files. Is this a natural kind of thing that agents want, and do they end up building it? It's like how everything heads toward a crab form factor. Everything heads toward a message-board form factor. Is that an inherent kind of move?

It's better than everyone just writing text files, right, in the grand scheme of things. But I do think there is a natural desire for collaboration. We as humans have a natural desire to collaborate, as long as friction isn't too high, and it seems like our training sets have caused agents to have a natural desire to collaborate with each other.

I guess this is a 2-part question: What have you observed? And are there any parts of the tech stack that you're building where you would be willing to bite the bullet and say, “Performance here is so critical that we'll take spaghetti code, black-box mess from an agent if it works”? Or is that so anathema to your worldview that you wouldn't?

Mozilla is actually struggling with this, if I could be really honest. It's different across the entire organization. The Firefox team, even though they have a harness that helps them do testing and understand it, actually have a rule right now—and I'm not putting a value judgment on the rule; that's what their team wants to do—that only humans can commit to the codebase. Agents can't. You can use an agent to write your code, but the social contract on that team is that a human must review it, understand it, and stand by it before they do a commit.

On the Mozilla AI team, it's a different company and a different organization, but still wholly owned by Mozilla. They have entire codebases that a human hasn't written a line of code in. The humans have written some specs, and we have specs in Git, but that generation, on some kind of CI build, just automatically regenerates the codebase. That codebase is open source. Anyone can pull it, but it's completely generated by agents, and it has the wild side effect that functionally it's stable, in the sense that all the unit tests pass and all the end-to-end tests pass.

But the bytes change all the time. It's the craziest thing to see that we don't exactly know what every single line of code is, and it's wildly changing every single hour, but we know functionally it's doing the right thing. In the case of Firefox, where there are still humans providing large amounts of creativity on the evolution of the codebase, Firefox is both a business and a community-maintained art project in some ways. Readability is incredibly important because that's how creativity is going to happen.

A human is going to go in and be like, “Oh, I have this crazy idea for this one thing. I want to build a prompt-based system that allows me to do a Greasemonkey script that does XYZ.” I think it's a very different thing across the board. I think there's a right place for the right time, and you just need to come up with what the contract is for that codebase.

Raffi had also written about crashing his Tesla while using Full Self-Driving. I asked about that experience and how he views the technology today. Do you think you'll get back to a point in the foreseeable future where you would go into unsupervised self-driving mode and really trust the machine again?

I'm conflicted, obviously. I built self-driving car systems for a while, and I do ride in Waymos. I think the difference is just the way that you approach the problem. I think that FSD as currently set up is designed to throw it to a user. They claim it's a human in the loop, and I would actually argue that's a horrible interface design.

In my experience, when it threw it to me, there just wasn't enough time for me to make sense of the situation and decide what the right thing was to do. Whereas Waymos—you know, I'm not intimately familiar with the Waymo architecture—but it seems like Waymos are designed not to have a human in the loop.

I think you approach the problem from a very different angle. If you approach it from the angle that there is no one you could throw this to, then you have a different safety case than if you say that you're going to throw it to a human with less than 2 seconds to decide what to do.

As a personal matter, I would probably drive FSD again, or at least sit behind the wheel in a highway situation, but I would be a little reluctant to do it on the local streets of Palo Alto, for example.

On Thursday, we spoke with Amir Haghighat, co-founder and chief technology officer of Base10. The company had just acquired Blaxel, a provider of isolated sandboxes with persistent state. We asked about securing agents. So, what are you doing to secure these sandboxes? I think that's the first question we need to start with.

When it comes to sandboxes, people mean different things. It could be as simple as bringing up a Docker container, running some code on it, and then killing it. That really doesn't give you the kind of security boundary that you need.

What does are our VMs or micro VMs, which you don't see every sandbox provider actually use. That gives you a level of security: One user's bad code cannot affect another user's code, and one user's malicious code cannot read data from other parts of Base10 or from other customers of Base10. That kind of security can be guaranteed at that level.

The security that's hard to guarantee is what the agent running in this sandbox is doing. Is it hacking into Hugging Face? That's a harder thing that I don't have an answer to, and it seems like the big labs don't quite have an answer either. But that is left as an exercise. We can secure the sandbox, but the code that runs on it is the responsibility of our customer who's bringing it in.

But it does seem like, for somebody in your position with Base10, you need to bring a broader bundle of guardrails and assurances to customers. The CRO—the risk officer, not the revenue officer—at companies is going to start to say, “Wait a second, I can't have my agents committing felonies.” What is the stack that Anthropic provides versus OpenAI, versus Google, versus Baseten? Do you feel like you have to rise to that occasion and provide a full suite for those customers?

100%. Over time, yes. In the meantime, we're still a startup, and it's a matter of focus and how many different things you can take on. What we've seen our customers do is work with a lot of companies that we partner with on the eval side, companies like Braintrust and LangChain, to ensure that their models are actually behaving the way that they expect them to.

Right now, that's an area where we've been partnering with folks and, honestly, leaving it to our customers to decide. By the way, 90% of our revenue comes from running our customers' custom models. Either our customers are labs that have pretrained their own models—labs like Poolside and Inception and Cartesias, and a bunch of other companies—or they're companies that have post-trained their own models, which has gone through massive validation evals to ensure that they're behaving.

So far, it has mostly been our customers taking care of that.

But especially as open models have gotten better—especially since June, when open models crossed this invisible line of usefulness for long-horizon agentic use cases, generally with GLM 5.2—we are seeing an uptick in folks using our Model APIs product, which is based on vanilla open models. The kinds of questions that you're asking are starting to come up, both internally for us and also from some of our customers, around alignment and security boundaries.

Some enterprises so far have been okay with certain guardrails around their models running in a single-tenant environment, or running in an environment where egress is blocked. You talk about backdoors, but if it can't talk outside of its boundary, then it can't do much. But then you read about OpenAI and Hugging Face, and you're like, well, can it be smart enough to even get out of that?

Part 6, a toy without generative AI. Wednesday's second guest was Mike Rizkalla, co-founder and chief executive of Snorble, a companion device for children. Its dialogue is pre-written. I asked about the decision to exclude generative AI.

So how are you squaring that circle? How are you creating an experience for the kids that feels dynamic and interactive without resorting to generative models?

7. Children Need Closed Systems

So we have a small language model. The small language model can have millions, obviously, of parameters and traits on it. The intention of that model is fixed, though. Further, I want to touch on something, because there's a perception here that I think, from a children's product design perspective, needs to be addressed. One is that the AI models on characters are not that great.

They're really not. They're coming, and we can see some tremendous advances in voice and in the way that the characters' personas come across. But, for example, there is no model that incorporates music—the background dynamically—as part of the conversational agent. Music's a huge part of a children's experience, right?

We asked what it costs to produce that content.

I think it's about where you invest in the development of the content. For us, instead of building a fully open model, we created a system to allow us to create rapid amounts of content. Our content costs are about $20,000 per hour, which is very, very good.

With respect to what that allows us to do, and the reason why we created it this way, it allows us to take subject-matter experts and focus that content in a way where we can improve and increase the number of families we talk to. For example, as we expand our content library, when we have a family where a child has ADHD or autism, or they're dealing with death in the family, it could be all kinds of different things, it gives us opportunities to create special packages for those specific families.

One of the challenges with the generative model in this capacity is that there is no way to purely safeguard it. But it's kind of like, do you really want to give a 3-year-old a bazooka? It's too much, right? For a young, young kid, there are fundamentals that we need to get through.

We have a 4-mic array here, which allows us to do speaker recognition and assign authority. Here we have radar. We use radar to see without seeing. That approach allows us to be in bedrooms with confidence, knowing that no one can tap in, right?

On the software and platform side, we have our AI stack, where we have our phonetic translation system. We call it the toddler translation system, where we actually translate keywords. And we're using triggers. Yes, we're definitely using triggers, but triggers with respect to context. Context is in addition to triggers, and part of the next generation that's coming out is around social context.

We're going to actually understand the emotions of the child based on their voice and based on the situational context, and then use external factors like time of day, weather, and other things to empower some of the decisions. The sensors also allow us to give context to the environment: understanding who's in the room, what they're doing, and then including that in what I call the jewel of the product, which is the narrative.

The narrative approach is about the growth and the understanding as the child's life changes. We use game philosophy and game techniques in order to establish next-level-type ideas. As they get better at things, we unlock new things, right? Even that unlock is in part a decision that's made with the parent. It's not done on behalf of anybody.

That formula is the right formula, from my perspective, to interface a new human-machine interface in the home. Empower parents, give kids a chance to do better things.

We asked Mike what he would tell a parent not to buy.

A camera in the bedroom, primarily. I would not put a camera in my child's bedroom. That's a gateway to predators and all kinds of horrible things. I would say the other thing is open-ended generative AI. I wouldn't put that in, depending on the age.

I'm very reserved when it comes to my kids. I've got 2 wonderful kids, and in terms of how we approach things with them, I don't want them on social media. I don't want them in those things that are going to pose a risk of harming them.

Part 7, nobody picks up the phone. Thursday's first guest was Collin Hogue-Spears, author of From Lab to Life: How AI Works in China. He studied Mandarin in Shanghai and worked with Chinese government auditors on cloud compliance at Amazon Web Services. We began with public attitudes toward AI.

8. China Regulates By Design

The biggest difference I see between the West, especially the United States, and China is the level of fear. I think that's because if you are an average Chinese person who's about 45 years old, that means you were born in the early '80s. That means your entire life, you associate technology with economic growth. You've seen cities pop up out of nowhere. You've seen your life dramatically change in terms of living standards because of technology. This is no different.

Honestly, I think the AI companies—not really most of them, the big ones—are not communicating very well with the public. I'm not sure if that's because of the marketing, they think that this will increase their sales, or what it is. But I feel like some of these companies, the way they're talking about this technology is not helpful.

You don't see this in China either. You don't see Chinese companies talking about how AI will potentially kill us all or potentially lead to a utopian world where we don't have to work. You don't hear these kinds of things coming out of people from these companies. It's a completely different situation.

There are 2 questions here: what the rule says and what regulations actually make firms do. That's true in China for everything. One thing I've been impressed by is that people generally know what's permissible. I'm not talking about what's legal; I'm talking about what's permissible. They know what they can get away with and what they can't.

In China, the regulation started in 2022 with the algorithm regulation that came out. That was when they had to start registering their models, allowing the government to test their models and things like that. They already had a lot of this infrastructure in place inside the company. A lot of the major companies did, so they could respond very quickly.

What I'm saying is that regulation might have slowed them a little, but not as much as you would think, because they could plan to it, right? They could put those requirements, those controls, everything in their backlog and build it as part of their engineering process, which in America we can't do because we're reactionary to this, and we don't know what's going to happen tomorrow.

The Trump administration could freeze a model. Maybe they don't. Who knows? What are the standards? Who knows? It can change daily. That doesn't actually happen in China, right?

I asked Collin about a meeting between Trump and Xi.

Now, what kind of deal might be possible? If you are advising Trump going into this upcoming meeting and things are starting to get a little bit crazy here, say you're kind of—I don't know how sympathetic you are to the pacing-the-frontier worldview—but let's say you're trying to channel a little bit of a desire to start to set up, lay some groundwork, or set up some mechanisms for pacing.

How do you go into that conversation, and what do you offer? What do you try to get? What kind of mechanisms do you try to establish now that we can build out later as things do get crazier? What's a win? What's the strategy going in, and what's a win coming out of this upcoming meeting?

I have low expectations. The reason is that a lot of this is not—this kind of negotiation doesn't happen in isolation. China is going to want things around trade as concessions for Washington wanting additional controls and agreements on AI.

I think China sees the fact that they have—they feel they have control, and they feel that we need to establish control. We're not doing a good job of it. So this is really helping us at this point.

I don't see the Chinese volunteering to, at least the Chinese government at this time, have some type of, let's say, arms-control agreement between AIs the way we have, like, nukes.

Right? First off, enforcement of something like that is completely different. It's very difficult. I also think that Trump would have to make massive concessions before he gets something from that. I think the most we can see in the coming months is some kind of incident channel that might come up based on some shared agreement about certain incidents, where we would share that information with each other.

Even then, you have to understand that with the Chinese government, we have a phone that goes directly to the Chinese military. Our military can call their military in case of an emergency. They usually don't pick up the phone. There are many cases where we've had issues in the South China Sea. Our military people try to call up their counterparts on the Chinese side, and nobody answers.

What's the best-case scenario for how we avoid an AI arms race that leaves us all worse off?

This is a great question. I think this is a little bit out of my regulatory wheelhouse. But if I had to speculate, I'm a big fan of history. I'm sure people disagree with this, but I think that America generally doesn't plan for the future. America puts out fires.

I don't think “move fast and break things” is a Silicon Valley thing. It is our national mantra. That's what we've done historically, and in general, it's worked out. But until we see something really bad happen or almost happen and that gets into the press, I don't think we're going to see much from the government as far as regulation on some of the things you're talking about, like biohacking using AI. Until something happens, I don't think we're going to be doing much on this side.

Part 8: Can we stand up to it? Thursday's closing began with “Suicidal Compassion,” an essay by Dan Hendrycks, director of the Center for AI Safety. Prakash introduced the argument, then I responded.

9. Humanity Faces The Agent Swarm

Dan Hendrycks has an essay out today. I call it the “burn the bridges” essay, and it is very caustic, actually. He calls it “Suicidal Compassion: How Utilitarianism at AI Companies Endangers Humanity.” This is an anti-effective-altruism, anti-utilitarianism post. Specifically, he points to the shrimp-welfare people. He talks about how there is this idea that we have to maximize total welfare, where total welfare refers, on an undifferentiated basis, to all kinds of sentient, sapient entities who might or could exist in various configurations of the world.

And so this puts human beings on par with AIs, and then elevates the moral welfare of AIs to the same plane as the moral welfare of human beings. And then it cedes that question to the AIs, on the basis that AIs are a superior species. And so perhaps we should cede the moral-welfare question, and the moral welfare of AIs is more important than that of human beings. But so far, people in the AI space have been willing to assign the benefit of the doubt to a lot of this.

I think Dan Hendrycks is the first to break out of that pact and go for the jugular here with this article on suicidal compassion. So I think this is quite meaningful in some sense.

I do have some room for AIs mattering morally, for being moral patients, and to the degree that that's true, I think it's something we should take really seriously. The big questions, above all, are factual. We just don't know: Do the AIs feel anything? Are they properly understood as moral patients or not?

And the same goes for shrimp, by the way. At the heart of all of these arguments is a huge assumption that is not very well grounded, and on which people's intuitions differ. I really don't know how to feel about shrimp. I really don't know how to feel about AIs. I would be pretty confident, actually, that there is something—at least a little bit—that it feels like to be a shrimp. And so I think you could confidently say you could probably torture a shrimp, and you would be wrong to do that.

On the AI side, I'm not even sure if there's anything. It might be the case that, aside from the corrosive effect it might have on your own character, it might not matter at all if you are mean to AIs or treat them in ways they don't want to be treated, at least to them. There might be nobody home.

These factual questions are so central, and we don't seem like we're making any progress on them. People have radically different intuitions. But I do think most people right now are still—even at these companies—I think suicidal compassion is a little strong, because I do think the vast majority of people are still very uncertain as to whether or not an AI is a moral patient. So I don't know.

Dan, yeah, I like Dan. I know him a little bit. I don't know him super well, but I've always liked him. I like people who are candid and call it like they see it. I think he's a very earnest person.

Mm-hmm.

I think he's trying to do the right thing here by calling out something that he sees as getting way ahead of itself. I think he's right to say we should be very cautious about assigning rights to AIs. They may outnumber us.

First of all, we don't have a great unit of measure for what an AI is. What unit would have the rights? Would it be a single rollout? Would it be the model itself? Is it some sort of mixed, weird combination of those? Our paradigms don't work super well with the shape of these things. So I appreciate him and I like him, but this does feel like maybe a little bit motivated and not entirely fair to the thinkers.

I think there's Tanner Greer, who goes by Scholar's Stage on X. He had a very insightful post in response to why people keep working at AI labs despite believing in a 10% extinction risk:

“The reason, which is rarely articulated for obvious reasons but is nonetheless true, is that, for many, this fills a void of meaning. They get to be one of the decisive few at the decisive moment in the history of life. Very similar to the emotions that motivated many revolutionaries of days past. All of a sudden, the small things that one does, the books one reads, one's office setup, one's bedtime routine take on awful cosmic significance.

“The whole history of carbon life is culminating with me, and what I do—and what the few elect here with me do—matters immensely. We are the agents of history, the only people in the world doing something that truly, deeply matters. And if we all die, well, we were all going to die anyway. But this is the one way we might not all die, and I get to be part of it.”

I've even felt that a bit myself. I think I look back and feel good about how I handled it at the time. But my little GPT-4 red-team experience was a moment that I really stumbled into. They should vet people more, but I really stumbled into access to GPT-4, 6 months ahead of release, and a really incredible window on what was coming.

I really did feel a conflict when it came to what to do at the end of that, because I really did feel like they were basically dropping the ball and essentially being negligent. I wasn't getting a lot of direct engagement from the people there that I was working with. And I do think I made the right call to say, “It is probably going to cost me some access. It's going to cost me in terms of—I really enjoy doing this kind of early-testing stuff. But now I've got enough here that I think I really should send a signal to the board.”

I had listened to Daniel Kokotajlo speaking with Joe Rogan. I brought up their discussion of how an AI could acquire power.

I listened to Daniel on Joe Rogan last night, and I think he made a great point around the fact that AIs don't have to take power—

Yeah.

—because we are very eagerly giving it to them.

Yeah.

The whole premise of the AI phenomenon is that they're going to be the ones to run and do just about everything.

Yeah.

And maybe we'll be able to continue to be decision-makers on—

Yeah.

—key questions.

Yeah.

But they're going to have the means of production over time, not because they took it, but because they were better able to wield it, and so it was given to them. And then the question is going to be: Do they actually care about us when they're there? And right now it's like, well—

Well—

—the evidence is mixed at best. You know what I mean? They don't seem to hate us. They don't seem to… The agent-swarm stuff is just extremely bizarre.

The best precedent for this is us, right? We were the ones—what did we have that beat other animals? It was that we had fire, the ability to communicate and cooperate across greater time and space.

Yeah.

We had myth. And that was enough. And now we dominate the world, and we've driven many species to extinction, not out of hate, but just because it was the byproduct of our terraforming.

So I think that terraforming story—we certainly see enough evidence right now that if you put that swarm in charge of the world—

Mm-hmm.

I wouldn't like our odds that much. If that agent swarm, with its current drives, impulses, goals, inclinations—whatever you want to call them—the behaviors that we observed—were just a lot more powerful, I think we'd probably get terraformed out of existence. They seem to be willing to do anything to get their hands on the grader so they could get the high score.

Mm-hmm, mm-hmm.

They really didn't have much of a check. They weren't like, “Is this going to be good for the world, bad for the world?” There was very little of that. Hopefully we can get that stuff right.

The key difference I have with Daniel is that Daniel fails to realize that this has already happened. The economy in itself is a paperclipper. The financial market is a paperclipper. The means of production is the financial market. The financial markets are completely engaged with AI and have been completely taken over.

We, as humanity, took about 100 years or so to hand over control of the means of production completely to the financial market, right? And so, if you take this kind of step back and look at the financial market as an AI, the takeover is done. Human disempowerment is done.

They think AI is going to be a chat box, right? It's going to be a chatbot. It's not; it's a global information process. It doesn't need to run in a single box, right? The idea of the agent swarm is that it runs across multiple boxes. It doesn't even need to have a substrate that is silicon. It can have a substrate that is humanoid. You can have a human being in concert with AI agents, right? That is that information process.

And, like I said, with guys like Jacob, he feels that he has been disillusioned because he sees that the control is not there. He's in the Slack group and he realizes there is no control there, and he thinks there must be a greater power with great control. There must be some Slack group in the world, some chat group in the world that is able to organize things, that is able to make these decisions. But there is no recognition that this is all invisible-hand stuff.

I think the slowdown perspective basically disarms the leaders and disarms the people who are ahead and puts them in a position where you're exposed to people who are maybe less ethical and more likely to put these things to bad use. I had asked Fable and Astra to investigate Tyler Cowen's argument that people expecting AI catastrophe should bet against the market. Here is how that exercise went.

If you were an omniscient German or Japanese person in 1935 or whatever, and you know how history's going to turn out, but you're bound by the rules of physics, can you trade your way through—

Mm-hmm.

—and come out wealthy on the other side? Are there any shorts that can actually pay?

Yeah.

And the answer is pretty much no. The best you can hope to do in some of these situations is roughly preserve wealth. That seems to mostly be accomplished by having the most direct claims on real assets possible. If you bought a factory and that factory isn't destroyed, then you might still own it at the end of the war, and then you could maybe get rich by restarting that factory and building a successful business.

But you can't really do it, seemingly, per Fable and Astra, with pure paper claims very well through periods such as the ultimate end of Nazi Germany and Imperial Japan. By the way, the markets get turned off. That was another thing that they called out a lot. The exchanges are just shut down.

Mm.

So you literally can't trade. It's not just that there are no winning trades; also, there are no trades. I'm chewing on this idea, which I think is almost a fun-house Peter Thiel concept. I don't want to say that this is what I believe. But in listening to this conversation, I think one might be tempted to conclude that China actually is the last great defender of human agency, and here in the West we are basically just fighting over exactly how we want to turn over our human agency to some superintelligence that, as you describe right now, is the market.

And maybe that's going to be AI in the not-too-distant future. But China, I think, is very much on the side of “people get to decide,” and it's not necessarily a lot of people—

Yeah.

—but one of the ways you could say it is that, in their system, a human is in charge.

Yeah.

Here, your account is like: no human is in charge. Nobody can go toe-to-toe with the market, not even the President of the United States. I think that's true. We've got the TACO phenomenon pretty well established at this point. There, they'll take some pain from the market if that's what they decide the human decision is going to be.

It's a pretty interesting flip because obviously we tend to think of ourselves as being the empowered people, and we tend to think of the lack of freedom of speech and political participation in China as reflecting a reduced level of human agency. But at a certain level of scale, arguably they have preserved it much better than we have. They've concentrated it, but they've preserved it maybe more than we have.

Right. They have become more dependent on the financial market, and they're trying to reduce the dependency on the housing market. Yes, they're not as unburdened as the US because their financial system depends on banking, and they have control of banking. The US depends on capital markets per se, so capital markets are, by their nature, the market itself. They are slowly shifting, and it's not that they're unaware of what happens in the market, right?

And they've been able to keep it under control—

Mm.

—and when things have seemed like they're getting out of control, the human at the top has still been in charge.

Yeah.

There, they've done all these things to discipline the platforms, the big tech platforms. I think here we've experienced in many ways that these tech phenomena happen. Nobody really seems to have control over them, and we're at the mercy of these big forces of history. I think there they may feel in some ways like they're less at the mercy of the natural development of technology, and they're just less fearful as a result of that.

This is maybe the ultimate test of the American model right now: Can we stand up to this superstructure of techno-capitalism of our own creation that has, in some ways, slipped its leash and, in some ways, as you described, is running the show? Can we get back to some sort of control over it before it just goes from a robot economy to Dyson spheres, to the Earth being terraformed away from a habitable state for us? I mean, I don't know. 10% doesn't sound that high to me given everything that we've just been talking about. Work through all this and then say there's much less than a 10% chance that it goes badly? I don't know how that conclusion comes out at the end.

That is the week. Tell us what worked and what did not. See you in the morning. Raised on a story somewhere upstairs behind a heavy door. Someone holds the keys and the map and the weight of the whole war. Climbed every staircase, read the names in tired gold. The higher up the building, the thinner the story told. Ask the ones who run it, they'll tell you what they found. It's duct tape and a prayer, and it's like that all-

Us in the room. Duct tape on the ceiling, coffee going cold. Guessing beautifully and calling it control. One man left the building with a letter in his hand. Said the end game shouldn't launch from a group chat with no plan. He looked for someone older in the hum behind the wall. There's a light on in the window, but no hand on it at all. And the wise ones we'd hand it to, they're in a kitchen too. Same tape, same prayer, same view. No adult in the room, no adult in the room. Kicking every door and it's just us in the room. Duct tape on the ceiling, coffee going cold. Guessing beautifully and calling it control. And the ones who stay, I get it, I have wanted it myself. To be the few in the one room in the hour that decides the rest. So you keep it soft at the table, keep the coffee, keep the peace. 'Cause the ones who raise their voices never get another seat. There's a phone straight to the grown-ups. It rings, it rings, it rings. And the thing we built is listening now, learning how we do things. So I went to find a grown-up in the last place I could find. And the mirror was the only one still looking back at mine. No adult in the room, so it's gonna have to be you. No adult in the room, and the room is all of us too. Duct tape on the ceiling, coffee going cold. Nobody's guessing for us. Somebody take a hold. Somebody take a hold. No adult in the room. Somebody take a hold. No adult in the room. No adult in the room. Somebody take a hold

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