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

The Astra Weekend

0:00My co-host, Prakash Narayanan, after a weekend working with GPT-6 Astra.

0:08People 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 and train. Welcome to the AI and the AM Weekly Highlights. This is Nathan, using my cloned voice to introduce clips from our three live shows this week. Let us know what worked and what did not.

0:42Part 1. The Astra Weekend. Tuesday, September 8th. Here is what Prakash had been building. I spent the entire weekend using Astra. I was running three to four agents continuously, and they were good. Astra is very, very good. In the sense that it started to tackle those annoying

1:17problems, which had been in the code base. As you know, we built the studio by ourselves.

1:26It started to tackle some of the longstanding issues in the code base, which had been kind of 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. But you still need to do a little bit of talking, but you can hand it off to Astra, and you can get some results. And the computer use is good. The other thing that was failing really badly,

2:01I think, before is computers. 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 time frame 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. So he trained models to identify players on the basketball court. He hand labeled

2:3812,000 individual images with who the players were, referee or this player, that team, etc. 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 point. You can't even pay someone

3:10to 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.

3:22I had been watching how Astra keeps working through long tasks and how it uses notes to stay on track.

3:30How 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, you know, if we had to do so many things, we would just give up. Right. Um, 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, uh, insights seems pretty obvious in retrospect, but nevertheless is new. And maybe

4:06I'm trying to, cause I'm doing this and hasn't said it, but what I understand and, uh, 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 summarized away in that compaction process. Now there is a long lived notes file that the model can update whenever it needs to. And this

4:36kind 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, uh, context window, you know, million tokens, all that 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, you know, it seems like at

5:09least 10 times that much context in, 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, Ethan Malik, who is a professor, um, who tests a lot of models, he, uh, posted the meter, the famous meter hours, you know, hours of

5:43work 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, the, the, the, the time, the cycle time of like 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

6:21its 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.

6:33I 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 take away, 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 eight 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

7:10for 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 like basically in the one to two workday zone, 40% of the time it can do the thing, zero

7:44interventions 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 one and a half to three weeks worth of work can still do that on a one shot basis, one in six times and two thirds of the time. If you allow for some human intervention, this is, this band is one or more interventions.

8:14So 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. Um, but overall still two thirds of the time it can succeed with some help on tasks that they estimate would take a human, essentially two to three 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 like diverging reports

8:50from various people. Some saying it's amazing. It can do all that stuff. You know, 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, 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

9:23kernels, 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 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.

9:54Not only is it like lower level, gnarlier stuff that we can't read very well and, you know, 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.

10:15Hey, we'll continue our interview in a moment after a word from our sponsors. Today's episode is brought to you by Athena, the executive assistant company on a mission to improve how people work and live. If you want to increase your impact, you have to free up your time. And that's what Athena does best. They match you with a dedicated full-time top 1% executive assistant who can take over your inbox, calendar, travel, and everything else that's quietly eating

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An Alien Mind

13:51Part 2. An Alien Mind On Tuesday, Ksenia Say, 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. I have noticed that in the last 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 a thousand times faster, you can conquer the latency part. You could actually use it

14:26directly for, 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. Like, what do you think about, is our 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 all

14:58discussing 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, 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

15:34terms as well. But we were just in the very beginning. What each of you understand when you say super intelligence, 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.

16:07Anything that can do that across like a non trivial number of reasonably high value domains, I think starts in my mind to count as a super intelligence.

16:22We also discussed her essay, permanent dawn and writing with AI. I actually spent, I think, like six 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,

16:57fix the grammar. And I didn't notice that it fixed not on 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 fix. And I was like, I spent so much time in this article, I was like, all my original thoughts there. But the language that I didn't catch, gave away that

17:28the model was like the last editor. Anyway, yeah, I think people will still appreciate when you when they see that they put effort into that. And there are original thoughts there.

17:41Our next topic was the feedback loop between AI research and the development of better models. And everything is now the part of the loop. This feedback, this constant feedback, I just had a conversation with two people from 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

18:13actually 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. I'm still learning about self about recursive self improvement. And I don't, 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

18:45increasingly, I feel the cope meter going off when people are trying to say, you know, what it is going to be that 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 kind of, of the mind that they're more often wishful thinking than they are, like, real hard bottlenecks that can't be overcome.

19:23I mean, our ability to like, 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 not it. In terms of inability, I don't see too many that I would expect to last all that much

Reassure and Mislead

19:41longer. Part three, reassure and mislead. Back to our Tuesday discussion of external evaluations. I raised the report that Apollo research had received only three days with Astra. We were also discussing an alien mind, the essay by OpenAI chief scientist, Jakob 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

20:16OpenAI. They've had a pretty longstanding partnership. Apparently this time around, they only had three 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 pretty ridiculous that they only had three days at this point. Why even do it? Just put the thing out

20:48there, they can test it live. Like why even have anything if you're only going to give them three 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 reddit and audited. And the problem is that in the model lifecycle in this pipeline, there are a hundred different candidates, right? At points,

21:23there are a hundred different candidates and then some don't work or some fall by the wayside and you're narrow, narrow, narrow, narrow, narrow. And then you have like a couple of release, two or three of these candidates. And then sometimes it's only in the last two or three days, you're like, all right, we're going to 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 going to 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

21:58at 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 like that many people. The opening anthropic is so many more people than Redwood Research or these teams. So they don't have the capacity to, you know, audit like, you know, 10 different, you know, release candidates. It's not there. And they're also not very well-funded.

22:31So 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, you know, the auditor comes in and they take a look.

23:03And three months later, someone from the audit team leaves to the other firm and they take, they manage to spot some of the secrets and they're, you know, they share that. So there's that issue as well. So a bunch of these things make it like very, very difficult for this to happen. And you need that whole thing that like, like if you and your competitor, like make a pact not to hire people from the auditing firm, that's an antitrust issue. So all of these things intersecting, like make

23:33it like, I think a very tangled problem. And I don't think there's a real solution that there hasn't been in the financial sector. So the financial sector has had this problem of the revolving door, between, you know, 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 to $850,000 per year, which might not be frontier lab money, but it's

24:07certainly, you know, living wage, even in the Bay area these days. So that, you know, should, uh, be enough to retain some mission oriented, uh, talent at least. So, and, and notably, I don't know about Redwood through all of history, but meter has said that, you know, 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, you know, means that their, uh, their judgment is not for sale. Again, to me,

24:41the 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 is 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

25:13essays 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,

25:47and 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

26:18business move, Claude extends your thinking to tackle the problems that matter. For problems worth solving, get started with Claude at claude.ai slash tcr. That's claude.ai slash 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 slash 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

26:52proof with a smooth external force. The unforced problem remained separate. So we just had Astra launch, right? So I'd say 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, you know, maybe up to an

27:28order 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%. So 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

28:05significantly declined 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?

28:19I don't know. Going back to July, that's six weeks ago? I don't think they've had this result for six weeks. It sounds to me like at least one reasonable interpretation is they continue to run RL on more capable models than Astra.

28:34The chart separated reinforcement learning compute for Astra from other models. The category labeled non-Astra was blue.

28:43When 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 like flagrantly misleading. And it's the kind of thing that makes it very difficult for you to have agreements with other entities. You know, if you want to pace the frontier, if you want to do all these things, like you've just got to be better about making clear what you are and are not doing.

29:13So they're running a, I don't know, $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. So the inference team will continue no matter what, right? No matter what. They're there for like 24-7 availability, the best SLEs possible. We'll continue, right? And sometimes you will have to RL certain behaviors out, right? So you have to continue some forms of RL. You can't just stop because your inference

29:44demands it. Like if you have some, your, you know, model is behaving badly in a certain instance and it's been identified and you have the data to do it, it'll be malpracticed not to apply RL to train that behavior out. So that, 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 like training of like 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,

30:17they're training, they have this teacher assistant system, right? So they're training the, 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, that's where you, the, the larger pre-trains equivalent or larger, like RL on equivalent or larger models. That I think they would have stopped.

30:52Did they fix the RL pieces that, you know, the agents were like not reporting, not telling on their peers, that the agents are 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, 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

31:25half of RL was stopped initially with the sort of disclosure of the hugging face incident half kind of continued. The, that was enough still for Astra models to take over part of open AI'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, you know, we don't know what's in the blue part of my, my gut says like they wouldn't be so brazen as to have more capable than Astra models in the blue

32:00color. But you know, I've been disappointed before and I'm just afraid that like all this, you know, the, 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 like quite different from what you were led to believe by

32:31their like very galaxy brain engineered statements that sort of reassure and mislead at the same time. But I'm kind of worried that like right now we're living in this zone where Anthropoc is going to continue to trust less and less with these sorts of mixed messages. Open AI 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

33:02get to the point where Jakob's prayer is answered, you know, right now it's like they don't have the trust to, 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, you know, 12 to 18 months. And just because you slow down like

33:37on RL doesn't mean those researchers stop researching, right? 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, you know, 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.

34:00We then turned to the case for continuing development and the capital needed to fund it. But what, one thing I still feel like is a big challenge is like, what is the overall story that you could tell? What's the, what's the super high level macro steel man for open AI? Where are we now? What are the commitments? What are we doing? What are we not doing? Can we, can we synthesize or summarize an open AI position that we could like not have to caveat

34:34a thousand different ways? I personally don't think I could do that. If you can do that, you know, I think you might deserve a millennium prize. No, no. I, I, I think, I think they have a lot of stresses pulling them in different directions, right? So, and I think internally within the firm, there's a fair amount of debate. I think, 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

35:08have 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, 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, you know, regardless, you know, they're the ones with the compute and they have the compute three years ahead. Elon will take time to come up with compute and Elon will sell to Anthropic, but Elon is going to take

35:38a long time, like three 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 had Microsoft, they negotiated Microsoft. Microsoft gets all their models to 2032. They declared AGI and Microsoft is not out of their hair yet. You know, Microsoft is now, I still own 28% and, you know, I'm still there, right? So they made all these sacrifices in order

36:11to 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, right? 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, right? Like 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

36:43if 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, you know, as I say, the second and third place guys are the ones who are going to define how fast the frontier pace is. Like if Elon or Meta catch up to Fable, it's over. They're going to have to put out, you know, GPT-7. There's no choice anymore, right? So this is where we are. Would you prefer Meta or Elon have, you know, the golden ring?

37:24Yeah, no, that's, I mean, that's the new but China. And it is, I think, more compelling, honestly, than but China is. But Elon and suck.

37:37Prakash also considered whether recursive self-improvement could change the plans of Sam Altman for an OpenAI public offering.

37:44I think Sam might have been sincere in saying if they hit RSI, they might not go IPO. If they have like one or two transformer level innovations in the next six months, maybe they don't go IPO, right? Like it's, it's not necessary anymore. And they, they, they continue as a private organization. And I think that would be bad. I actually think that would 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

38:15shareholder 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 that might mean we never get to own any of that open AI stock on the public market. Part four, no adult in the room. Wednesday,

No Adult in the Room

38:48September 9th, we discussed the resignation of anthropic researcher, Jacob Coxon, who had also worked at open AI. This is a different person from Jakub Pachocki. Coxon had questioned whether private companies should decide when to launch self-improving super intelligence. Prakash first. Look at the framing of this sentence. Accepting this race and entering the end game 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, I mean, like, do you think there are wiser people

39:25out there running things? So I think, I think to take a step back here, there, 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 like, look, this is, 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 in, who are, you know, capable of handling this are mostly

39:57already inside these organizations. And there is no, like the, what you get from passing it off to like the government is you get political legitimacy. You don't get, you know, 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, 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

40:28strongly that like 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

40:59for help. As you described it yesterday, I think you're 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, you know, 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, you know, to the degree there's worries of other enforcement, other kinds of enforcement. We want you to do it.

41:31And 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, 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

42:03that 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

42:33to 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, you know, 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

43:05would 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, you know, write it out with free speech and there's a lot of other issues, but like, can you five 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

43:38life's going to get hard. I think that message can resonate. And I think it can bring Zuck and Elon to the table because, you know, what's he going to do? Like at some point the government has shown that it's willing to twist arms. They can twist his arms still, you know, would he rather be forced to the table with four 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, you know, that the government has a lot of sticks and

44:12like they're willing to bend the rules, right? So sure you could challenge it in court, but I'll see 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 saying a pretty similar thing to what I was thought about earlier, basically saying like these companies need to start working together first. As he puts it, bringing in the U.S. government before

44:43there's a concrete proposal will likely result in something dumb. That's, you know, your point as well. Like there's nobody better than the people at the companies to do this. So we, I certainly don't want to see, uh, headsets, uh, you know, Navy come in and try to regulate AI, but I think this is the, the recipe I've been circling this a lot myself, seeing him say it, you know, kind of reinforces it to me. So I really like this. And I also think his key point on antitrust being fake. Um, I think

45:19that'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 this, you know, 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. You know, it's at least a very live possibility.

45:56Prakash 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, uh, a waiver from vaccine claims later on. So they specifically wanted a waiver, a safe harbor and they got it. They got legislation

46:27and they got it. Right. And post COVID it's very clear that if they had not got it, they would have been sued to, you know, oblivion. So, um, 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, you know, being foolish when they tell you that this is, this is going to happen in the future. Like if you don't get that safe harbor, like these things will

46:59come back to haunt you. You can like every, all the decision makers right now can be perfectly like, you know, on board. It doesn't matter because only the laws bind decision makers in the future. Decision makers right now, whatever they say, they bound by their word at best. Right. OpenAI appointed Paul Cristiano to the board of its nonprofit foundation and to its safety and security committee. Cristiano contributed to early work on reinforcement learning from human feedback. Prakash read from his statement.

47:32So it's just been announced, uh, Paul Cristiano 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 AI capabilities leads to catastrophic and irreversible loss of control in the very near term. And, um, there is, there's one line in here that he's

48:04like, everyone, uh, if we build super intelligence without more robust alignment, I expect we will permanently lose control of it. If that happens, then most people could die. Um, I think, I think to one, 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, um, is not, is not very clear to a lot of people like, Oh, things are accelerating right now. When people like Paul Cristiano talk about rapid acceleration, they're

48:34talking about Dyson spheres by 2030 or 2040 at the latest. So, uh, I did, I did some numbers earlier on the 2040, on the 2040 numbers. Um, and, you know, assuming that energy consumption or energy production, uh, growth is in line with GDP growth at 2040 Dyson sphere is something like a 640% per annum global GDP growth. Uh, right now global GDP growth is about at 3% to the 3%. The people at the frontier

49:08companies really do believe it. You know, they, they, the very significant, I would say majority of them, um, do expect that this recursive self-improvement thing will happen. It will happen soon. And even if it levels off at some point, you know, it's not to say that there will never be a leveling off, but that leveling 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, you know, the number of

49:42robots walking around and how fast that can compound that truly boggles the mind. Uh, so the, like the question for me, which I, I regard all of the guys inside AI, AI researchers, math essentialists, they're believing that, okay, math, uh, you know, 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, like everything is basically, you know, solvable and, you know, everything will be solved, et cetera, et cetera, et cetera. And, uh, the economists on the

50:16other hand are like completely like, this is untrue. Like getting good at math doesn't mean anything. There's no economic value to the millennium challenge problems. Uh, implementation takes time. Uh, most of the problems that we face are coordination problems. For example, you know, a copper mine takes 30 years because of the, you know, environmental protests around it. These are not, these are not problems due to, you know, technology. These are problems due to, you know, humans having their own way of making decisions and those decisions are made in a slow and considered manner. Uh, and

50:48then I have the counter counter and the counter counter is super persuasion. Like, so super persuasion would be, uh, the machines persuading or, uh, making human organizations able to adapt, uh, making human organizations, you know, and humans able to, you know, giving them the ability, giving the capabilities to move very quickly, uh, persuading them to go forward and persuading them and things are going to be okay. So that's a super persuasion argument. So there's like these kind of three levels there. The Wednesday discussion also turned to funding for safety

51:19research. One of the things I've emphasized is that the vibrant nonprofit sector that we have here, which yes, requires philanthropic money, which means, you know, it's sort of dependent on the billionaire class, which people can complain about for all sorts of reasons. Um, you know, it has given us the AI safety awareness culture, you know, depth of bench that we have. And it, and these people are

51:53opening their wallets, you know, 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 trotches right over time that they're willing to put behind things that really look like they are working. Part five, the defender's ledger. Rafi Krikorian is Mozilla's chief technology officer.

The Defender Ledger

52:27He previously led platform engineering at Twitter, self-driving development at Uber and technology at the democratic national committee. Project glass wing 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 code base. We were part of project glass wing. We've been working with, with anthropic in order to actually try out a bunch of, um, models against the Firefox code base. We are fairly good partners because we can react very quickly. We have all this historical data on

53:03how the code base has been evolving. And so mythos was this like X, 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 will help us not necessarily close the bug, but figure out how to build us a harness to test the bug more correct, more carefully. So we can find exactly what the right solution might be, but we sort of reached the point of the mission returns on what mythos is capable. I am always reluctant to say that the bugs are

53:36done because I just like software engineering is an art, not a science. So like when, 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, 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 of 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

54:09infrastructure, 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 code base is fairly large and fairly complicated. And we've been the, the, the beneficiaries of a lot of the labs wanting to give us access to the models and give us front 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,

54:42if 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, um, against Firefox. Again, we're just lucky that we've been, we've been allowed, uh, or we've been granted effectively, uh, 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. Yeah. Easily Mozilla has a project called the CQ project and the CQ project is kind of a,

55:22uh, open standard for agents to, uh, share knowledge that they've gained kind of a stack overflow for agents as it's, as it's been called. Um, how did you come up with this idea and how do the agents decide? But no, I mean like the whole idea was like two, we're trying to solve two 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 these agenda coding is

55:56that you actually go off in your silo. And so we were trying to figure out like, are there patterns that when Rafi is using his agentic code and Nathan's uses agentic code that like, are there things that we're saying yes to and no to that we could 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 off system, how do we instead have, how do we have Nathan's agents not instead recreate another off system, but realize that something like that was already happening

56:29somewhere in the network and then start to collaborate and swarm around it. So that was one problem we're 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 the agents, what our SDLC should look like so that we can all be working in lockstep with each other. So the thing that strikes me immediately is that that has a lot of similarity to I think some of

57:03the agents form 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 opening eye hugging face attack where they had, you know, artifactory where they're 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, it's like how every, everything, everything heads towards the crab form factor, like everything heads towards kind of like a message board form factor. Is that, 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

57:37things. 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 two 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 like, you know, performance here is so critical that we'll take spaghetti code black box mess from an agent

58:10if it works? Or is that just like so anathema to your worldview that you wouldn't? I mean, well, Mozilla is actually struggling with this, if I have to be really honest, like, and it's different across the entire organization. So Firefox team, even though they have a harness to help 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 code base. So actually agents can't only humans can you can use an agent to write your

58:41code. 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 code bases that a human hasn't written a line of code in. Like the humans have written some specs, and we have specs and Git, but that on some kind of CI build just automatically regenerates the code base. That code base is open source. Anyone can pull it, but it's completely generated by agents. And it has the wild side

59:13effect 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 that, the evolution of that code

59:43base, like Firefox is both a business and a community-maintained art project in some ways, but 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 Greechmonkey script that does like me 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 and you just need to come up with what the contract is for that code base.

1:00:16Rafi 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, you know, go into unsupervised self-driving mode and really trust the machine again?

1:00:39I mean, I'm conflicted, obviously. I mean, I built self-driving car systems for a while and I actually do write in Waymos. And so like, 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, it's, they, they claim it's a human 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

1:01:14for 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 going to throw it to a human with less than two seconds to decide what to do. So I probably as a

1:01:47personally, 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.

1:02:00On 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.

1:02:14So what are you doing to secure these sandboxes? I think that's the first question we got to start with. Look, when it comes to sandboxes, people mean different things as it could be as simple as 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 VMs or micro VMs, which is you don't see every sandbox provider actually use. And so that gives you a level of security as in, you know,

1:02:49one 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 at that level. The security that is hard to guarantee is the agent that is running in this sandbox. What is it doing? Like, is it hacking into a hacking place? 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

1:03:24left as an exercise, really, like, 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, 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, you know, CRO, the risk officer, not the revenue officer at companies going to start to be

1:03:56like, wait a second, I can't have my agents committing felonies. Like, what is the stack, you know, that Anthropic provides versus OpenAI versus Google versus Base10? Do you feel like you have to rise to that occasion and kind of have of a full suite for those customers? 100%. Over time, yes. In the meantime, you know, we're still a startup and it's a matter of focus and like, you know, how many different things can you take on? And so what we've seen our customers do

1:04:26is work with, honestly, a lot of companies that we partner with on the e-mail side, you know, 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, like 90% of our revenue comes from running our customers' custom models. It's either, you know, our customers are labs

1:05:01who have pre-trained their own models, labs like Poolside and Inception and Cartesia's and a bunch of other companies. And then a bunch of companies who have post-trained their own models, which has gone through, you know, 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 OpenModels have gotten better, especially as, honestly, since June, when OpenModels crossed this invisible line of usefulness for,

1:05:33you 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, you know, vanilla-based OpenModels. And that is starting to, you know, 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

1:06:06their models running in a single-tenant environment, their models running in an environment where egress is blocked. And, you know, you talk about backdoors, but like, you know, if you can't talk outside of its boundary, then it can't do much. But then you read about, you know, OpenAI and Hugging Face, and you're like, well, you know, can it be smart enough to even get out of

A Toy Without Generative AI

1:06:26that? Part six, a toy without generative AI. Wednesday's second guest was Mike Rizkala, co-founder and chief executive of Snorvel, a companion device for children. Its dialogue is pre-written. I asked about the decision to exclude generative AI.

1:06:46So how are you squaring that circle? Like, 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 intention of that model is fixed, though. And further, actually, I want to touch on something, because here's kind of, there's some perception here that I think is, from a children's product design perspective, that needs to be touched upon.

1:07:21One being, the AI models on characters are not that great. Like, they're really not. 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 characters' persona go. But for example, there is no model that incorporates music in terms of the, 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?

1:07:49We 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,

1:08:20and 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

1:08:56capacity is that there is no way to purely safeguard it, but it's kind of like, do you really want to give a three-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 four microarray 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 without, with confidence, knowing that no one can tap in, right? On the software and platform side, we have our AI stack where we have

1:09:32our 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, 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,

1:10:06the sensors allow us to give context to the environment. So understanding, you know, 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, type ideas. So as they get better things, we unlock new things, right? And, and even

1:10:38that unlock is in part a decision that's made with the parent. It's not, 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.

1:11:05Camera 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 two 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

1:11:36harm, you know, that poise a risk of harming them.

Nobody Picks Up the Phone

1:11:41Part 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.

1:12:00The biggest difference I see between the West, especially the United States, and China is the level of fear. And I think that's because if you were an average Chinese person who's about 45 years old, 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.

1:12:32And 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

1:12:50some 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.

1:13:15It's a completely different situation. So there's two questions here. What the rule says and what regulations actually make firms do. That's true in China for everything. Everybody. One thing I've impressed by is people generally know what's permissible. I'm not talking about what's legal. I'm talking about what's permissible.

1:13:40Man, 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

1:14:11them a little bit, but not as much as you would think because they could plan to it. 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,

1:14:42right? I asked Colin about a meeting between Trump and Xi.

1:14:50Now, 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. You know, 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, you know, 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 you know, what do you offer? What do you try to get? What kind of mechanisms do you

1:15:27try 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

1:15:44this 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. 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. I don't see the Chinese volunteering to, at least the Chinese government at this time, I don't see them

1:16:18volunteering 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 Trump would have to make massive concessions before he gets something from that. I think the most we can see in the next coming months is like some kind of

1:16:50incident channel. 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

1:17:23people try to call both 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?

1:17:38This is a great question. I think that this is a little bit out of my regulatory wheelhouse, but if I had to speculate,

1:17:50I'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.

1:18:02I just don't. We kind of like move fast and break things. It's not a Silicon Valley thing. It is our national mantra. That's what we've done historically. 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

1:18:34about like bio, biohacking using AI. Until something happens, I don't think we're going to be doing much on this side.

Can We Stand Up to It?

1:18:43Part 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. 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

1:19:18endangers humanity. And so this is an anti-effective altruism, anti-utilitarianism post. And specifically, he points out to the shrimp welfare people. Specifically, he talks about how how there is this, we have to maximize total welfare, where total welfare is kind of like refers to,

1:19:50you know, on an undifferentiated basis, all kind of like, you know, 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, you know, plane as the moral welfare of human beings. And then seeds that question to the AIs as being the AIs are superior species.

1:20:26And so perhaps we should seed, you know, 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 pact and kind of go for the jugular here with this article on suicidal compassion.

1:20:58So I think this is, this is quite, um, quite meaningful in some sense. And I do have some room for AIs mattering morally, being moral patients and like 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 like factual, like we just don't know, you know, do the AIs feel anything?

1:21:30Are they properly understood as moral patients or not? And the same goes for the shrimp, by the way, you know, 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'm, 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 like

1:22:03confidently say you could probably torture a shrimp and you would be wrong to do that. Um, on the AI side, I'm like, not even sure if there's anything, you know, it might be the case that like, you know, aside from the corrosive effect it might have on your own, uh, character, like it might not matter at all if you are mean to AIs or, you know, treat them in ways they don't want to be treated. Um, at least to them, like there might be nobody home. Um, so these like factual questions are so central and we don't seem like we're making any progress on them and people

1:22:36have radically different intuitions. And I, but I do think most people right now are still, even at these companies, I think suicidal empathy is a little strong. Um, because I do think the vast majority of people are like very uncertain still as to whether or not an AI is a moral patient. So I don't know, Dan, I like Dan. Um, I know him a little bit, I don't know him super well, but I, I've always liked him. Um, I like people who are candid and, you know, call it like they see

1:23:10it. Uh, I think he's a very earnest person. Um, and I think he's like trying to do the right thing here by calling out something that he sees as like getting way ahead of itself. Um, I think he's right to say we should be very cautious about assigning rights to AIs. Um, they may outnumber us. First of one, we don't have like a great unit of measure for like, what is an AI, what unit would have the rights, you know, would it be a single rollout? Would it be the model itself? Is it some,

1:23:41you know, sort of mixed weird, you know, combination of those that our, our paradigms don't like work super well with the shape of these things. Um, so, you know, I, 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, uh, there's Tanner Greer who is, uh, he goes by scholar stage on, uh, X. He had a, uh,

1:24:14very, very insightful, very insightful post in, in, in response to, uh, 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 the sudden, all of a sudden, the small things

1:24:47that 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.

1:25:21I'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 like 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, you know, they should vet people more. Uh, but they, you know, you know, I really stumbled into all of a sudden six months ahead of release access to GPT-4 and a, a really incredible window on what was coming. And I really did feel a conflict when it came to

1:26:01what to do at the end of that, you know, cause I really did feel like they were basically dropping the ball and essentially being negligent. Um, and I wasn't getting a lot of front of engagement from the people there that I was working with. And I do think I made the right call to, to kind of say, you know, 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, you know, early testing type stuff. But I think now, you know, I've got enough here that I think I really should like send a signal to the board.

1:26:37I had listened to Daniel Kokotilo 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. Yeah. The whole premise of the AI phenomenon is that they're going to be the ones to run and do

1:27:09just about everything. Yeah. And like, maybe we'll be able to continue to be decision makers on key questions. Yeah. Um, but like, they're going to have the means of the, 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. You know what I mean? They're not,

1:27:44they don't seem to hate us. They don't seem to, you know, the, the agent swarm stuff is just extremely bizarre. The best precedent for this is us, right? We were the one, what did we, what did we have that beat, you know, other animals? It was like, we had fire, you know, we had ability to communicate and cooperate across greater time and space. We had myth. Um, and that was enough, you know, and now we like dominate the world and we've like driven many species to extinction, not out of hate, but just because it was kind of the byproduct of our terraforming.

1:28:21So I think that terraforming story that, you know, that 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. You know, if that agent swarm was like with their current drives, impulses, goals, inclinations, you know, tendencies, whatever you want to call it, the behaviors 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

1:28:52willing to do anything to get their hands on the greater 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, uh, you know, is this going to be good for the world, bad for the world? Like very little of that, you know? Um, so hopefully we can get that stuff right. And I, the, the, the key difference I have with Daniel is that Daniel fails to realize that this has already happened. The economy in itself is a paper clipper. The

1:29:23financial market is a paper clipper. The means of production is the financial market. The financial markets are completely engaged with AI. I have been completely taken over. This is not a, this is not a, and, and we, you know, as humanity took about a hundred years or so to hand over control of, you know, 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 has gotten, you know, it's the takeover is done. Like human disempowerment is

1:29:57done. They don't recognize, they think AI is going to be a chat box, right? It's going to be a chat bot. It's not, it's like a, it's like a global information process. It doesn't need to run in a single box, right? The idea of the agent's form 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 human, humanoid. You can have a human being in concert with, you know, with AI, you know, agents, right? That, that is that information process. And like, like I said with guys like Jacob, Jacob feels that,

1:30:31you know, 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 like make these decisions. But there is no recognition that this is, this is, this is all invisible hand stuff. And I think the slowdown perspective basically disarms the leaders and disarms the people who are

1:31:01ahead and puts them in a position where you are exposed to people who are maybe less ethical and 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. You were an omniscient German or Japanese person in 1935 or whatever. And you know how history is

1:31:37going to trade out, but you're bound by the rules of like physics. Can you trade your way through and come out wealthy on the other side? You know, are there any, are there any shorts, you know, that can actually pay? Yeah. And the answer is pretty much no. You, the best you can hope to do in some of these situations is like roughly preserve wealth. And that seems to mostly be accomplished by having like the most direct claims on real assets possible. Like if you bought

1:32:09a 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 like, you know, restarting that factory and, and building a successful business, but you can't really do it seemingly per Fable and Astra with pure paper claims very well, uh, through such periods as, you know, the ultimate, like end of the regime, you know, of Nazi Germany and Imperial Japan. So, and, and by the way, also the, the,

1:32:42the markets get turned off. That was another thing that they called out a lot. You know, the exchanges are just shut down. So you literally can't trade, you know, it's not just that there's no winning trades. Like also there are no trades. I'm kind of chewing on this idea, which I think is sort of a fun house, 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

1:33:15be 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, uh, in the not too distant future, but like China, I think

1:33:49is very much on the side of people get to decide and it's not necessarily a lot of people, but when the only thing you could say in their system is a human is in charge here, we're kind of your, your account is like no human is in charge. You know, nobody can, can, uh, 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, um, pretty well established at this point there. They'll take

1:34:20some 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, you know, 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,

1:34:50but they've preserved it maybe more than we have. Right. So, so they are, 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 on, as burdened as, as the U S because their financial system depends on banking and banking. They have control of, uh, the U S depends on capital markets per se. So capital markets are, you know, by the nature of the market itself, but yeah, they are, they are shifting, right? They're, they're slowly shifting. Um, and it's not, it's not that they're unaware, uh, of, of what,

1:35:25what 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. You know, we, we have this kind of like there, they've done all these things with, um, disciplining the platforms, you know, the big tech platforms. I mean, here we've kind of experienced in many ways that like these tech phenomena kind of happen. Nobody really seems to have control over them. And we're kind of at the mercy of these like big forces of history. I think there, they may

1:36:01feel in some ways like they're less at the mercy of, you know, natural development of technology and they're just less fearful as a result of that. This is maybe the, the ultimate test of like 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 like slipped its, you know, its, uh,

1:36:34a 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, you know, robot economy to Dyson spheres to, you know, the earth is terraformed away from, uh, an inhabitable, uh, state for us. I mean, I don't know. I 10% doesn't sound that high to me given everything that we've

1:37:07just been talking about, you know, work through all this and then, you know, 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. raised on a story somewhere upstairs behind a heavy door. Someone holds the keys in the map and the

1:37:46weight of the whole wall. Climbed every staircase, read the name's entire goal. 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.

1:38:18One 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

1:38:55calling 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 the grown-up in the last place I could find. And the mirror was the only

1:39:25one 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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