AI:AM Highlights: Astra as AGI, OpenAI's Pause, Mythos @ Mozilla & Human Agency vs Technocapitalism

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

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

Welcome to the AI in 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 1, the Astro Weekend. Tuesday, September 8th. Here is what Prakash had been building.

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 which had been in the codebase. As you know, we build the We built the Studio, you know, by ourselves. So, and this, it started to tackle some of the longstanding issues in the codebase, which had been kind of annoying and bugging me. And like, it started to like 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.

Mm-hmm.

But you still need to do a little bit of talking, but you can hand it off to Astra and you can, you can get some results. And, you know, and the computer use is good. The other thing that was failing really badly, I think, before is computer use. Computer use on GPT-5.6 would sometimes take a very, very long time. It would kind of click around and do a bunch of stuff. And computer use finally works properly in the kind of like timeframe that you give it. So it's cleared the hurdle. It's cleared the hurdle of genuine usefulness at this point. And you can start to give it more advanced tasks. So this is a guy called Skalski. And so he trained models to identify players on the basketball court. He hand-labeled 12,000 individual Like images with who the players were, referee, or this player, that team, et cetera. And he hand-labeled 12,000 images. And now Astra can just do it. Like Astra just does it. This is a task that a human being will never do again. Like there just isn't. Any, any point. You, 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 like pass you back the results. Like, it's, it's done. Like, a human will never do this task again.

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, right? How, how is it that they can come up with such elaborate chaining togethers of all these different exploits to finally accomplish a goal that if we had to do so many things, we would just give up. Right. And 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, and maybe Anthropic has been doing this and hasn't said it, but what I understand Astra is now doing is instead of compacting its million tokens into a summary and then essentially starting a new context window with that summary, but losing all the detail that was summarised away in that compaction process, now there is a long-lived notes file. that the model can update whenever it needs to. And this kind of 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 only have maybe still the same million token context window, a 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 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 Meter, the research group that tracks how long a task AI agents can complete.

You know, one of the people online, Ethan Mollick, who's a professor who tests a lot of models, he posted the meter, the famous meter hours, you know, hours of work chart. There hasn't been an update for a while now. You know, I think it's—

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

Yeah, they can't. They can't.

They don't have tasks.

That are big enough.

They don't have tasks which 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 meter 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 is an agent workday, 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 take was— my takeaway, trying to make sense of it was just like, literally, how long do agents run for? So it seemed like they're saying for every 8-hour workday that they have human researchers doing, 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 meter chart for a minute. This is from this Recursive Self-Improvement Begins blog post. And basically they're kind of reformulating the meter 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 in the 1 to 2 workday zone, 40% of the time it can do the thing, zero interventions needed, pushing 90% of the time given some human intervention along the way. And naturally that drops off. But even as you get to like, here we're talking 1.5 to 3 weeks worth of work, can still do that on a one-shot basis. 1 in 6 times and two-thirds of the time if you allow for some human intervention. This band is one or more interventions. So one would assume that presumably as you go through the longer and longer tasks, it's more interventions that are required to get the thing to succeed. But overall, still two-thirds 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, it can do all that stuff, it can write you know, code in the way that you need it to be written so that it can be maintained, blah, blah, blah. But then 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 like a really gnarly mess that people can't really understand. Does seem to work. I've seen this reported for Kernels, GPU kernels specifically, which is obviously super relevant to the labs, highly verifiable as well, right? Like, you can definitely do hardcore verification on did this matrix math actually get to the right answer? And in the middle, you kind of don't know, don't necessarily care exactly how all these different steps were fused together. And somebody summed this up by saying, We're going back to machine code in more ways than one. Not only is it, like, 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. Hey, we'll continue our interview in a moment after a word from our sponsors.

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Part 2, An Alien Mind. On Tuesday, Ksenia Say, founder and editor of Turing 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.

I have noticed that in the last, I don't know, 48, 72 hours, people are starting to use Astra to do robotics, for example. So we've seen a few demos, and there's even been commentary that, okay, if Astra was 1,000 times faster, you can conquer the latency part. You could actually use it directly in order to act in the real world. That, to me, kind of says that maybe there is starting to be an intermediate representation, an internal representation there. What do you think about— are models like Astra kind of a little bit different in that sense?

And I just came back from a workshop about world models. And it was absolutely jarring. There were tremendously smart people from Stanford and Harvard, and Yann LeCun was there, and they were discussing world models, but they do not agree on what world models actually are. So when we talk about world models and why I want to focus on them in my publications, because I think it's just more about action, being able to predict and act. Um, like wider understanding what's happening. Physics is super important part of it. That's why robotics is so much more about world modeling and world models. But again, it's like, for me, it's understanding the scope of it and trying to give it more precise terms as well. But we would just in the very beginning. What each of you understand when you say superintelligence? What is it?

Sort of move 37s across a lot of different domains. When we start to see systems saying, I think this would be a really good thing to try for the next battery, you know, substrate. And then it turns out, oh my God, you know, 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 non-trivial 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, like 6 hours on writing that post. And the funny thing was that I was so unhappy with every model that was trying to help me write it. Because it's like very complicated philosophical text that I wrote it myself. And I sent it to Fable, which I never use usually on my daily basis. And I sent it to Fable because I was running on a deadline and I said, fix the grammar. And I didn't notice that it fixed not only the grammar, but it made this like shorter sentences. The way Fable does it. And that was the first time when I received the message like, I will unsubscribe because you use Fable. So people really understand when you use a model because every model has its own language tics. And I was like, I spent so much time on this article. There was like all my original thoughts there, but the language that I didn't catch gave away that the model was like the last editor anyway. Uh, Yeah, I think people will still appreciate when they see that you put effort into that and there are original thoughts there.

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

And everything is now part of the loop. This feedback, this constant feedback. I just had a conversation with 2 people from the inference team in OpenAI, and they also say that This is a constant, constant loop where the models now become better at some— sometimes better just like trying things. So you throw the whole database of research that has been done for years, and then the model can actually choose and pick and do this all experiments because it would be impossible for humans to spend so much time on that, and models can do that. Um, I'm still learning About recursive self-improvement. And I don't know what are the main bottlenecks for me. Maybe you can even say what you think are the biggest here.

Honestly, I don't know that there's that many left. It does seem to me like, increasingly, I feel the cope meter going off when people are trying to say, you know, what it is gonna be that is gonna 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 kind of of the mind that they're more often wishful thinking than they are real hard bottlenecks that can't be overcome. I mean, our ability to keep the things from going totally rogue might be one bottleneck on the overall process. So human decision-making, I think, still has a big role to play for a while yet. 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.

Notably, I think Apollo only had Apollo Research, who does the deception, science of scheming, chain of thought monitoring work with OpenAI. They've had a pretty longstanding partnership. 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, scheming scientists, they need more time. This is like pretty ridiculous that they only had 3 days at the end. Like, at this point, why even do it? You know, just put the thing out there, they can test it live. Like, 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 to have the final release candidate to be the one that gets vetted and audited. And 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 some fall by the wayside, and you narrow, narrow, narrow, narrow, narrow. And then you have like a couple of release, 2 or 3 release candidates. And then sometimes it's only in the last 2 or 3 days, you're like, all right, we're gonna go ahead with this one, and you make the decision. And so the problem is, if you want that kind of operational flexibility to make that decision, you're gonna end up with, you only have a few days to offer to an external auditor. So the other option is you bring the auditor in, right? So you bring the auditor in-house. I mean, you bring them in and they take a look at the models ahead of time. So they're in there like a month, month and a half ahead. They're taking a look at the release candidates in general. But number one, the auditors are often not like super well-funded. They don't have that many people. The opening Anthropic has 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. And they're also not very well funded. So they're dependent on the model companies for that funding too. And then you have this, like, the ethical process of like, okay, how much funding can we really accept from them before we're kind of bought? In addition, a lot of the auditors, the guys who train with auditors, leave for model companies in a couple of years. So there's also this flow of people from like Meter or Redwood Research, or like the trainees or interns, and they're flowing into the model companies, right? So there's another fear that The auditor comes in and they take a look. And 3 months later, someone from the audit team leaves to the other firm and they manage to spot some of the secrets and they share that. So there's that issue as well. So a bunch of these things make it very, very difficult for this to happen. And you need that whole thing that if you and your competitor make a pact not to hire people from the auditing firm, that's an antitrust issue. So all of these things intersecting make it, I think, a very tangled problem. And I don't think there's a real solution. There hasn't been in the financial sector. So 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 tactical technical staff roles ranges from $350,000 to $850,000 per year, which might not be Frontier Lab money, but it's certainly, you know, living wage even in the Bay Area these days. So that, you know, should be enough to retain some mission-oriented talent at least. So, and notably, I don't know about Redwood through all of history. But METER 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 like, 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, you know, uh, very much voluntary choices from the decision makers at the companies. Hey, we'll continue our interview in a moment after a word from our sponsors.

Today's episode is brought to you by Anthropic. By now, you know my story. Claude drafts my intro essays and I rewrite them. Not because the drafts are bad, but so I can stand behind everything I publish. Well, I have an important update. Claude Fable-5 is the first model to have me rethinking my rule. Today, I now think co-authorship, not sole ownership, should often be the goal. Where the model excels, rewriting its work can be more about vanity or a misplaced sense of duty than integrity. I feel it most in songwriting. I'm no lyricist, but I'm good with a song concept, and Fable writes some amazing verses. I give it feedback on its misses, and I push it to aim for higher inspiration, add layers of meaning, optimize syllable density, and above all, write a hit song. These days, I get compliments on just about every song we write together. Claude is the AI for problem solvers. It's the collaborator that understands your entire workflow and thinks with you Not for you. Whether you're debugging code at midnight, building a financial model, or strategizing your next business move, Claude extends your thinking to tackle the problems that matter. For problems worth solving, get started with Claude at claude.ai/tcr. That's claude.ai/tcr. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Once more, that's Claude.ai/tcr.

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? So the big headline news for this announcement is it confirms that there is an internal model that is significantly more capable than GPT-6 Astra. So that's this clause here, an OpenAI next generation model significantly more capable than GPT-6 Astra. How much more capable? Well, 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 10% to 15% solve rate on a curated set of open math problems to now like 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 like Astra-class models had RL significantly decline twice. The rest of 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? Going back to July, that's 6 weeks ago. I don't think they've had this result for 6 weeks. It's Sounds to me like at least one reasonable interpretation is they continued to run RL on more capable models than Astra.

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

When you say non-Astra with that blue colour, 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 $50 billion, $70 billion revenue company, right? So number one, you can never stop inference. Inference has to continue, right? So inference never stops. Your customers depend on it. So the inference team will continue no matter what, right? No matter what. They're there for like 24/7 availability, best SLAs possible. They'll continue, right? And sometimes you have to RL certain behaviors out, right? So you have to continue some forms of RL. You can't just stop because your inference demands it. Like if you have some Your model is behaving badly in a certain instance, and it's been identified, and you have the data to do it, it'll be malpractice not to apply RL to train that behavior out. So that has to continue. So then you have the rest of the stack, which is really future-looking, right? And then you have the future-looking stack. And of that, my understanding was that they shut down trading of more advanced models. Why not shut down trading of less advanced models? Because those less advanced models would have to be deployed on inference. Again, they're training. They have this teacher assistant system, right? So they're training the— you train the larger model first, and then you distill down into the smaller model, and then those become like your Luna and like your Terra and like the smaller model groups. So you also don't stop training smaller models. That also continues, right? So it's only where you are training models which are larger Larger equivalent or larger models. That's where you— the larger pre-trains equivalent or larger, like RL on equivalent or larger models. That I think they would have stopped. Did they fix the RL pieces that the agents were not reporting, not telling on their peers, that the agents were trying to break out? I think that is probably a difficult thing to fix and show. And I think that behavior I think 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 kind of half of RL was stopped initially with the sort of disclosure of the Hugging Face incident. Half kind of continued. That was enough still 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. And 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 you can't trust these guys. They say something that's like maybe technically, literally true, but it's really very engineered to what they think you want to hear. And then 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, like, this is the problem. This is the problem that has to be solved if we're going to actually get to the point where Jakob's prayer is answered. Right now, it's like 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 also. 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. And 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 you test your ideas on small models. This 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 like, 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 synthesise or summarise an OpenAI position that we could not have to caveat 1,000 different ways? 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. And I think internally within the firm, there's a fair amount of debate. I think to some extent, 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 kind of like 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's going to take a long time. 3 years at least. So OpenAI, they 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 till 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. 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. And having the compute allows them to actually pace the frontier because I have the compute anyway. So Anthropic is under pressure and 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. 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 it, the 2nd and 3rd place guys are the ones who Who are going to define how fast the frontier paces. If Elon or Meta catch up to Fable, it's over. They're going to have to put out GPT-7. There's no choice anymore. So this is where we are. Would you prefer Meta or Elon have the golden ring?

Yeah, no, that's, I mean, that, that's the new But China, and it is, I think, more compelling, honestly, than But China is But Elon and Zuck.

Um, 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 if they hit RSI, they might not go IPO. If they have like 1 or 2 transformer-level innovations in the next 6 months, maybe they don't go IPO. Right? Like, it's, it's not necessary anymore. And they, they continue as a private organization. And I think that would be bad. I actually think that'd be bad because then you don't have transparency. You don't have like widespread ownership of the stock. You don't have like, you know, boards that have to answer and you don't have like 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 public company. So I think that would not be good. So I'm hoping they do go public.

Well, I guess there's, if nothing else, there's a little more reason today than there was yesterday to believe in the possibility of singularities in finite time. And, uh, 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 Jacob Coxon, who had also worked at OpenAI. This is a different person from Jakub Pachocki. Coxon 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, like, do you think there are wiser people out there running things? So 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. 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 kind of making it up as they go along. The smartest people who are capable of handling this are mostly already inside these organizations. And there is no— what you get from passing it off to the government is you get political legitimacy. You don't get political legitimacy that you need for execution, that you need to persuade people. You don't get wisdom. And 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, 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. And 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, 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. It would be 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 of 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? And 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, the, 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 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. And I do think that the executive— I had an experience at Meta, and I saw a little bit of what their life under consent decree looked like. And I think that they would be willing to do quite a bit to avoid another one of those sort of experiences. So I do think that the 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 you 5 companies come together to pace the frontier in a reasonable way that you all agree 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 arms 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 Coxon and returned to the question of antitrust enforcement.

And I'd say John Shulman here is saying a pretty similar thing to what I was on about earlier. basically saying these companies need to start working together first. As he puts it, bringing in the US 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. So 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. Seeing him say it kind of reinforces it to me. So I really like this. And I also think his key point on antitrust being fake, I think that's 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. And yeah, maybe the law could change. Obviously the administration is going to change, but we're not even to the midterm yet. So 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. And it is, uh, for Operation Warp Speed, the pharma companies specifically required a waiver From vaccine claims later on. So they specifically wanted a waiver, a safe harbor, and they got it. They got legislation and they got it, right? And post-COVID, it's very clear that if they had not got it, they would've been sued to oblivion. So I think that's a proof point that shows that, look, these are valid concerns and your company can be wiped out. In retrospect. And 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, they're 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.

So it's just been announced, uh, Paul Christiano is joining the, uh, OpenAI board. Uh, specifically, he's joining the safety and security committee to support safety oversight. And 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 in AI capabilities leads to catastrophic and irreversible loss of control in the very near term. And, uh, there is, there's one line in here that he's like, everyone, 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 he were to frame it properly is to frame what he means by rapid acceleration, which I think is not very clear to a lot of people. People are like, oh, things are accelerating right now. When people like Paul Christiano talk about rapid acceleration, they're talking about Dyson spheres by 2030 or 2040 at the latest. So I did some numbers earlier on the 2040 numbers. And assuming that energy consumption or energy production growth is in line with GDP growth, a 2040 Dyson sphere is something like a 640% per annum global GDP growth. Right now, global GDP growth is about at 3%, 2 to 3%.

The people at the frontier companies really do believe it. A very significant, I would say majority of them do expect that this recursive self-improvement thing will happen. It will happen soon. And even if it levels off at some point, it's not to say that there will never be a levelling off, but that levelling off they expect 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.

So the question for me, which I regard all of the guys inside AI research as math essentialists, They're believing that, okay, math, 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. And once you have all of those, everything is basically solvable and everything will be solved, et cetera, et cetera, et cetera. And 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 Challenge 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. And then I have the counter-counter. And the counter-counter is super persuasion. So super persuasion would be the machines persuading or making human organizations able to adapt, making human organizations and humans able to— giving them the ability, giving them the capabilities to move very quickly, persuading them to go forward and persuading them things are going to be okay. So that's a super persuasion argument. So there's these kind of 3 levels there.

The Wednesday discussion also turned to funding for safety research.

One of the things I've emphasised is that the vibrant nonprofit sector that we have here, which, yes, requires philanthropic money, which means it's sort of dependent on the billionaire class, which people can complain about for all sorts of reasons, it has given us the AI safety awareness culture, depth of bench that we have. And these people are opening their wallets right now in a very serious way. And this is another example of that, where Project Tailwind coming out of Coefficient Giving is putting out basically their call for startups with Up to $200 million plus, you know, that they're willing to put behind, um, you know, with tranches, right, over time, that they're willing to put behind things that really look like they are working.

Part 5, The Defender's Ledger. Raphi 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, Rafi described the work his team had been doing on the Firefox codebase.

We are part of Project Glasswing. We've been working with with Anthropic in order to actually try out a bunch of models against the 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. And so Mythos was this, like, not exponential, but like 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 us a harness to test the bug more carefully so we can find exactly what the right solution might be. But we sort of reached the point of diminishing returns on what Mythos is capable of. I am always reluctant to say that the bugs are done because I just don't— like, software engineering is an art, not a science. So like, when will all the issues be resolved? I can't tell you that. But, you know, 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 like, it's not organizations like Firefox or Mozilla that actually can react very quickly to these issues. Like, as you all know, I think the bigger issue is like organizations that can't react quickly to all this. Like, I am very concerned about things like, you know, our water infrastructure, our power infrastructure, because, you know, the IT teams that staff those are just not as capable as the IT teams that staff Firefox, like, for example.

How much money are we talking about how, you know, what does a bank have to set aside to do something like this?

Yeah, I mean, 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 frontier and give us credits so that we can run against their servers and not have to figure out how to pay for it ourselves. But, you know, if I had to do back-of-envelope estimates, Like, this would cost us hundreds of thousands of dollars in order to do these, like, full-on runs against Firefox. Again, we're just lucky that we've been allowed, or we've been granted effectively, tokens that we could go use. Now, 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, that would be like, I mean, with the pace of model releases these days, this is like a monthly expense?

Yeah, easily.

Mozilla has a project called the CQ Project. And the CQ Project is kind of an open standard for agents to share knowledge that they've gained, kind of a Stack Overflow for agents, as it's been called.

How did you come up with this idea, and how do the agents decide But no, I mean, like, the whole idea was like, we were trying to solve 2 different problems. One of them was we wanted to figure out how to make, um, agentic coding more of a collaborative experience. Because right now, the tendency when you do all this agentic coding is that you actually go off in your silo. And so we were trying to figure out, like, are there patterns that when Raphaël's using his agentic code and Nathan's uses of agentic code, that like, are there things that we're saying yes to and no to that we could then potentially be transmitting to our teammates so that we could then be converging on designs and not be diverging away from each other? So if I started building like an auth system, how do we instead have— how do we have Nathan's agents not instead 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. So that was one problem we were trying to solve. And the other problem we were trying to solve is like the SDLC problem of like these agentic harnesses are like unhinged by design, but that's not compatible with the way a company works, right? So we wanted ways to actually transmit to all, all the agents what our SDLC should look like so that we can all be working in lockstep.

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

I mean, it's better than everyone just writing text files, right? Like, in the grand scheme of things. But yeah, I mean, I do think that like, there is a natural desire for collaboration, right? Like, we as humans have a natural desire to collaborate as long as friction is not too high. And it seems like our training sets have caused agents to have a natural desire to collaborate with each other.

So I guess 2-part question is, what have you observed? And then 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 just so anathema to your Uh, worldview that you, you wouldn't?

No, I mean, well, Mozilla is actually struggling with this, if I could be really honest. Like, and it's different across the entire organization. So Firefox team, even though they have a harness that helps them do testing and to help them 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, is that only humans can commit to the codebase. So actually agents can't, only humans can. 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, stand by it before they do a commit. On the Mozilla AI team, so it's a different company, different organization, but still wholly owned by Mozilla, they have entire codebases that a human hasn't written a line of code in. Like, the humans have written some specs, and we have specs in Git, but that gen— 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 like all the unit tests pass, all the end-to-end tests pass, but like the bytes change all the time. It's like the craziest thing to see that like 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. But in the case of Firefox, where like there are still humans that are providing large amounts of creativity on like the evolution of that codebase, like Firefox is both a business and a community-maintained art project in some ways, that their readability is incredibly important because that's how creativity is going to happen. Like a human's 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 GreachMonkey script that does XYZ. And so I think it's a very different thing across the board. So like, I think there's a right place for the right time. You just need to come up with what the contract is for that codebase.

Rafi 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 mean, I'm conflicted, obviously. I built self-driving car systems for a while, and I actually do ride in Waymos. And so I think the difference though is that it's just the way that you approach the problem. I think that FSD as currently set up is set up to throw it to a user. So it's, they claim it's a human in the loop. And I would actually argue that's a horrible interface design because 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's the right thing to go do. Whereas Waymos, you know, I'm not intimately familiar with the Waymo architecture, but it seems like Waymos are designed to not have a human in the loop. And so, like, I think you just approach the problem from a very different angle. And so if you approach the 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 I'm gonna throw it to a human With less than 2 seconds to decide what to do. So I'd probably, as a personally— as a personal matter, drive FSD again, or at least sit behind the wheel on 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 Hagigat, 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 gotta start with.

Look, when it comes to sandboxes, people mean different things. It could be as simple as, you know, bring up a Docker container, run some code on it, and then kill it. And that really doesn't give you the kind of security boundary that you need. What does are our VMs or microVMs, which is, you don't see every sandbox provider actually use. And so, that gives you a level of security, as in, you know, one user's bad code cannot affect another user's code, or one user's malicious code cannot read the data from other parts of Base10 or other customers of Base10. So, that kind of security can be guaranteed. The security that is hard to guarantee is the agent that is running in this sandbox. What is it doing? Is it hacking into Hugging Face? That is a harder thing that I don't have an answer to. And it seems like the big labs don't quite have an answer to either. But that is left as an exercise. Really, we can secure the sandbox, but the code that runs on it is the is the responsibility of our customer who's bringing it in.

But it does seem like for somebody in your position with Base10, it's like, don't you need to bring a broader bundle of kind of guardrails and assurances to customers? Because isn't like the CRO, the risk officer, not the revenue officer, at companies going to start to be like, wait a second, I can't have my agents committing felonies. Like, what is the stack that Anthropic provides versus OpenAI versus Google versus Base10? Do you feel like you have to rise to that occasion and kind of have 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 can you take on. And so what we've seen our customers do is work with, honestly, a lot of companies that we partner with on the eval side, companies like Braintrust, like LangChain, to ensure that their models are actually behaving the way that they expect them to. Right now, that is an area that we've been partnering with folks and honestly leaving it to our customers to decide. Our customers, which, by the way, 90% of our revenue comes from running our customers' custom models. It's either, you know, our customers are labs who have pre-trained their own models, labs like Poolside and Inception and Cartesias and a bunch of other companies. And then a bunch of companies who have post-trained their own models, which has gone through massive validation evals to ensure that they are behaving So, so far it has been mostly our customers taking care of that, but especially as open models have gotten better, especially as honestly since June when open models crossed this invisible line of usefulness for, you know, long horizon agentic use cases, sort of generally with GLM 5.2. And then beyond that, we are seeing an uptick in folks using our Model APIs product, which is, vanilla base Open models. And that is starting to— the kind of questions that you're asking are starting to come up both internally for us and also from some of our customers around alignment or around security boundaries. Some of the enterprises so far have been okay with certain guardrails around their models running in a single-tenant environment, their models running in an environment where Egress is blocked. And 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? Like, how are you, how are you creating an experience for the kids that feels dynamic and interactive without resorting to generative models?

So we have a small language model. The small language model can have millions, obviously, of parameters and, you know, and traits on it. And that, that intention of that model is fixed though. And, and further, actually, I want to touch on something because here's kind of— there's some perception here that I think is, uh, from, from a children's product design perspective that's— that needs to be touched upon. One being, um, the AI models on characters are not that great. Like, they're really not. They're, they're— I mean, I know they're coming. Like, we can see some tremendous On the, you know, on the influx of voice and the way that the character's persona goes. But for example, there is no model that incorporates music in terms of the background dynamically as part of the conversation agent.

Well, guess what?

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. You know, for us, what we did was we actually, instead of, you know, instead of building a fully open model, we created a system to allow us to create rapid amounts of content. You know, so our content costs are about $20,000 per hour, right, which is very, very good. With respect to what that allows us to do, and the reason why we created it this way, is it allows us to take subject matter experts and then focus that content in a way where we can improve and increase the number of families we talk to. So for example, if as we expand our content library, when we have a family that has ADHD, but a child has ADHD, or a child has autism, or they're dealing with death in the family, it could be, 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?

You know what I mean?

It's too much, right?

For a young, young kid, there's fundamentals that we need to get through. We have a 4-mic array here, allows us to do speaker recognition and assign authority, right? Here we have radar. We use radar to see without seeing. So that approach allows us to be in bedrooms with confidence knowing that no one can tap in. 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, there's in addition to, and part of the next generation that's coming out is around social context. So we're going to actually understand the emotions of the child based off of their voice and based off of the situational context. And then using external factors like time of day, weather, other things in order to empower some of the decisions. But as well, the sensors allow us to give context to the environment. So understanding who's in the room, what they're doing, and then including that into 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. So we use game philosophy, game techniques in order to establish next-level type ideas. So as they get better at things, we unlock new things. And even that unlock is in part a decision that's made with the parent. It's not done on behalf of anybody. So that formula is the right formula for, from my perspective, to interface a new human-machine interface in the home, right? Empower parents, give kids a chance to do better things.

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

Camera in the bedroom, primary one. I would not put a camera in my child's bedroom. That's a gateway to predators and all kinds of horrible things. I say, I would say the other thing is open-ended generative AI. I wouldn't put, like, it depends on the age again, you know, but I'm actually, so I'm very reserved when it comes to my kids. You know, I've got, I got 2 wonderful kids and just in terms of How we approach things with them. I don't want them on. I don't want them on social media. I don't want. I don't want them in those things that are going to harm. You know, that weighs a risk of harming them.

Part seven: Nobody picks up the phone. Thursday's first guest was Colin 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.

The biggest difference I see between the West, especially the United States, and China is the level of fear. Um, and I think that's because if you are an average Chinese person who's about 45 years old, um, that means you were born in the early '80s, and that means your entire life with you, you associate technology with economic growth. You've seen cities pop up out of nowhere. You've seen your life dramatically change as far as living standards by technology. This is no different. And honestly, I think the AI companies are not really— most of them, the big ones, are not communicating very well with the public. I'm not sure if that's because the marketing, they think that this will increase their sales, or what it is. But I feel like some of these companies, how 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 type of things coming out of people from these companies. Um, so it's completely different situation. So there's 2 questions here: what the rule says and what regulations may actually make firms do. That's true in China for everything. Everybody— one thing I've been impressed by is people generally know what's permissible. I'm not talking about what's legal, I'm talking about what's permissible. So, um, oh man, they know what they can get away with and what they can't. And so in China, the regulation started in 2022 with the algorithm regulation that came out. So 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 bit, 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 the model. Maybe they don't. Who knows? What's the standards? Who knows? It can change daily. That doesn't actually happen in China, right?

I asked Colin 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 you're like, you know, 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? Like, 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, and the reason is 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 Um, additional controls and agreements on AI, because 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. Um, I, I don't see the Chinese volunteering to— at least the Chinese government at this time— I don't see them volunteering to Have some type of, like, 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. And I also think that I just— Trump would have to make massive concessions before he gets something from that. I think the most we can see, um, in the next coming months is like some kind of incident channel, um, that might come up about based on some shared agreement of certain incidents, and we would share that information with each other. Even then, you got to understand that the Chinese government— like, 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 their counterparts on the Chinese side and nobody answers.

And like, what's the best case scenario for how we avoid an AI arms race that leaves us all worse off?

I, I, um, this is a great question. I think that 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 will disagree with this, but I think that America generally doesn't plan for the future. America puts out fires. I just don't. We, we kind of like move fast and break things is not a a Silicon Valley thing. It is our national mantra. That's what we've done historically. And it, in general, it's worked out, right? But I think 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 as far from the government as far as regulation on some of the things you're talking about, like Bio, um, 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 Hendricks, director of the Center for AI Safety. Prakash introduced the argument, then I responded.

Um, and Dan Hendricks has an essay out today I call it the Burn the Bridges essay. And it is very, very caustic actually. He calls it Suicidal Compassion: How Utilitarianism at AI Companies Endangers Humanity. And so this is an anti-effective altruism, anti-utilitarianism post. And he specifically, he points out to the shrimp welfare people. Specifically, he talks about how there is this, we have to maximize total welfare, where total welfare refers to, on an undifferentiated basis, all 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 seeds that question to the AIs as being the AIs are a superior species. And so perhaps we should cede the moral welfare question. And so 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. And I think Dan Hendricks is the first to kind of break out of that pack And kind of go for the jugular here with this article on suicidal compassion. So I think this is quite meaningful in some sense.

And I do have some room for AIs mattering morally, being moral patients. And to the degree that that's true, then I think it is something we should take really seriously. And the big questions, I think, are maybe above all 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 the shrimp, by the way. It's like, 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 does feel 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. So these factual questions are so central, and we don't seem like we're making any progress on them. And people have radically different intuitions. But I do think most people right now are still, even at these companies, I think suicidal empathy is a little strong because I do think the vast majority of people are very uncertain still 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, and 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 is an AI, what unit would have the rights? Would it be a single rollout? Would it be the model itself? Is it some sort of mix, 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 is— he goes by Scholar Stage on X. He had a very insightful post in response to why people keep working at AI labs despite believing in 10% extinction risk. The reason that is rarely articulated for obvious reasons, but which is nonetheless true. 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 the few elect here with me, matters, and 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, and 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 where all of a sudden I was in kind of— and I really stumbled into it. They should vet people more. But I really stumbled into all of a sudden 6 months ahead of release access to GPT-4 and a really incredible window on what was coming. And 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. And I wasn't getting a lot of engagement from the people there that I was working with. And I do think I made the right call to kind of say it is probably going to cost me in some access. It's going to cost me in terms of, like, I do really enjoy doing this kind of early testing type stuff, but I think now I've got enough here that I think I really should send a signal to the board.

I had listened to Daniel Kokotailo 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 AIs don't have to take power because we are very eagerly giving it to them. The whole premise of the AI phenomenon is that they're going to be the ones to run and do just about everything. And maybe we'll be able to continue to be decision makers on 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 they were given it. And then the question is going to be like, Do they actually care about us when they're there? And right now it's like, well, evidence is mixed at best. They don't seem to hate us. The agent swarm stuff is just extremely bizarre. The best precedent for this is us, right? What did we have that beat other animals? It was like, We had fire, we had ability to communicate and cooperate across greater time and space, 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 kind of 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, I wouldn't like our odds that much. If that agent swarm was like, with their current drives, impulses, goals, inclinations, tendencies, whatever you want to call it, the behaviours that we observed, if they were just a lot more powerful, I think we'd probably get terraformed out of existence because They seem to be willing to do anything to get their hands on the grader so they could get the high score. You know, it's like they really didn't have much check. You know, they weren't like, you know, is this going to be good for the world, bad for the world? Like, very little of that, you know. So hopefully we can get that stuff right.

And I— 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, have been completely taken over. This is not a— and 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 you 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 like 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, which is silicon. It can have a substrate, which is humanoid. You can have a human being in concert with AI agents, right? That is that information process. Like I said, with guys like Jacob, Jacob 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.

Yeah.

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 like make these decisions. But there is no recognition that this is, this is all invisible hand stuff. And I think the slowdown perspective basically disarms the leaders and disarms the people who are ahead and puts them in a position where you are exposed to people who are maybe less ethical. And, uh, more likely to, you know, 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 is going to trade out, but you're bound by the rules of physics, can you trade your way through 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, And that seems to mostly be accomplished by having the most direct claims on real assets possible. Like, 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 such periods as the ultimate end of the regime of Nazi Germany and Imperial Japan. And by the way, also the markets get turned off. That was another thing that they called out a lot. The exchanges are just shut down, so you literally can't trade. It's not just that there's no winning trades, also there are no trades. I'm kind of chewing on this idea, which I think is sort of a funhouse Peter Thiel concept almost, which I'm, you know, I don't want to say that this is like what I believe, but in listening to this conversation, I think one might be tempted to conclude that like 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 super beneficiary that, as you described right now, is the market. And maybe that's going to be AI in the not too distant future. But like China, I think, is very much on the side of people get to decide, and it's not necessarily a lot of people. But one thing you could say in their system is a human is in charge. Here we're kind of your account is like no human is in charge. You know nobody can can can go toe to toe with the market, not even the president of the United States. I think that's true. You know we've got the taco phenomenon. Pretty well established at this point. 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 kind of 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. So 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, as burdened as, as the US because their financial system depends on banking and banking they have control of. Uh, the US depends on capital markets per se. So capital markets are, you know, by the nature of the market itself. But yeah, they are shifting, right? They're slowly shifting. Um, and it's not, it's not that they're unaware, uh, of, of what, what happens in the market, right?

And they've been able to keep it kind of under control And when things have seemed like they're getting out of control, the human at the top has still been in charge. They've done all these things with disciplining the platforms, the big tech platforms. I think here we've kind of experienced in many ways that these tech phenomena kind of happen. Nobody really seems to have control over them. and we're kind of 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 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, is like, 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, kind of is running the show. Can we get back to some sort of control over it before it just goes like robot economy to Dyson spheres to the Earth is terraformed away from an inhabitable 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 like 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.

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 the way down. 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. One man left the building with a letter in his hand, said the endgame shouldn't launch from a group chat with no plan. Healed up for Someone older in the home 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. Checking every door and it's just us in the room. Duct tape on the ceiling, coffee going cold. Yes, and 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 my— 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 whack.

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