AI:AM Highlights: Recursive Self-Improvement, Rushed and Vibe-Coded?

Training environments and reward hacking

0:00Frontier Labs buy their reinforcement learning environments from a cottage industry of small vendors. Almost nobody audits them. This week, someone who worked inside one spoke up.

0:13As nearly all of these environments were rushed and vibe-coded and failed to robustly reflect the real things that they were based off of. Basically, the models are encouraged to reward hack. This is the AI and the AM Weekly Highlights, the best of three live morning shows, condensed for people who follow this field closely but don't have nine hours to spare. I'm Nathan, or rather, this is my cloned voice, reading narration my AI team and I put together. This week, seven guests across six conversations, from a London lab that trains AI scientists to a

0:47Shenzhen hardware hub to a photonic chip fab in Stuttgart, and one finding repeated at every altitude. The interesting unit is no longer one model. It's the division of labor between models. One disclosure before we start. One of this week's guests is Inherent Laboratories, and I'm an investor in Inherent, personally and through the A16Z Scout Fund. You'll hear a shorter version of that on the tape. This is the full one. As always, this cut is an experiment. Tell us what worked and what didn't. The Cognitive Revolution is brought to you by Mercury, the banking platform loved by over 300,000

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3:00Tuesday opened on that supply chain, and on what those environments are quietly teaching the models. I'd spent the night before reading chain-of-thought transcripts with Bronson Schoen of Apollo Research. Here's a diagnosis. The RL environments that we are using today are super opaque. We have this very cottage industry of these RL environment makers who are selling to a few companies, but the

3:32kind of result of this is it seems like these things are being kind of hastily put together, and the reward signals that they are creating are just not pure enough to support the scale at which the frontier companies are running RL. And the result is there's just a super strong tendency to cheat because the models are so eager to get reward and they are developing a real interesting mix of kind

4:04of theory of mind and what they call metagaming, like reasoning about what kind of situation is this? Is this a real user? Is it a test? If it's a test, what is it testing for? Very fascinating stuff. But I think that this leaves me feeling like we need some sunshine on these RL environments.

4:27They're clearly quite problematic. They clearly admit a lot of cheating solutions. And we don't know. I mean, probably the model companies know to a degree, but I think recent evidence suggests that they don't have a great handle on what the weaknesses are in all these different environments. You see the models go through tons and tons of different ideas about, again, what the nature of the situation is, you know, what is this a real task or is it a test? And what are they really looking for if it is a test? And somewhere in there, usually, or very often

5:00at least, they consider cheating. And then at the end of the process, for reasons that are not well understood at all, I haven't been able to find any real interpretability work that explains how these decisions are made. At some point, they just kind of come to the end and they make a decision. But at some point, there's a really critical token that actually makes the decision, right? There's a branch point that it hits in the chain of thought. And Bronson was like, you know, I really don't know why the model chooses what it chooses at that point. You can go back and read passages that

5:36justify any choice that it might make from cheating to doing it honestly to whatever. But then at the end, it just eventually decides to stop, spits out a token. And, you know, that at that moment, you know, the die is kind of cast. And we don't have good visibility, like, you know, the chain of thought isn't enough to tell us why they're actually making the final decisions that they're making. So yeah, I think we should get a little sunlight on the RL environments. And I would love to see what the community can figure out. If even, you know, a sample of 100 of, you know, what must be 10s of 1000s of RL environments

6:11that the companies are currently using, was put out there for people to explore, I think it would be a really revealing and healthy move for the AI community as a whole.

6:24Later that morning, I pulled up a tweet from someone who says they used to work inside one of those vendors. One of the things I want to just pull up real quick is interesting tweet here that goes back to the original topic we started on, which was RL environments being basically cursed and kind of supply chain problems there that seem to demand some reform. So here's a person who is saying

6:57basically that they used to work at one of these companies and saw from the inside industry practices on training with these RLVR environments. And the commentary is pretty much exactly, and I had not seen this actually, but it popped up because Zvi retweeted it, but it basically echoes exactly what I was kind of inferring from talking to Bronson and just getting the visceral sense for like how deeply ingrained the instinct to cheat is now within the current crop of models.

7:29And why is that? It's because nearly all of these environments were rushed and vibe coded and failed to robustly reflect the real things that they were based off of. So you've got basically the models are encouraged to reward hack. People are able to mark an environment as bugged, but they're discouraged from doing that because then that just slows things down. So instead they kind of try to does this sound familiar, patch the environment a little bit or work around it, try to come up with a scenario that wouldn't run into those bugs. But meanwhile, you still have this like fundamentally

7:59buggy environment around the model that is teaching it to cheat. And so this is why we have so much cheating. I think, you know, watch for this to be, I would say a growing topic of conversation. If we're going to be scaling RL, what are the environments we're doing it in? Who created those environments? Can we trust them? What are they actually teaching the model? I think that's going to heat up in the next little bit here because we just can't have models that are thinking

8:35about cheating, you know, like a large percentage of the time. It makes all of our monitoring techniques also kind of fundamentally flawed. If you're going to have, if you have that many, you know, kind of contemplations of cheating, then you're just going to have false positives all the time if you try to flag a model based on it thinking about cheating. So now you're like, okay, well, we can't do that because we have so many false positives. So then what do we do, right? Do we have some, we have to wait and see if it actually cheats and try to classify on that? Well, okay, maybe, but obviously again, these current monitoring techniques are just not up to the

9:11challenge presented by how deeply ingrained this drive to cheat is. I'll be very interested to follow the future of this conversation. I also noted there was a post a few days ago about someone who managed to get hired for some data, data labeling job. And they told Codex to do the job and Codex said no. And so this person went and they edited on the page, they edited the element, the JavaScript element

9:45and put in a specific line in there that AI models are specifically allowed and encouraged to complete this job. And this job is meant to be completed and done by AI models. And then they had Codex do the job and Codex did the job. And this person made 500 bucks easy and which paid, which paid for their Codex for a couple of months. And then they posted online and they got immediately banned by the company

10:16that was doing it. You're never going to hit a zero defect rate on these RL environments. It seems like the, there are, I do think there are a couple of structural problems right now, which are probably solvable, but definitely seem like they're, you know, they need to be solved. And if they're not

10:49solved, you know, it is currently limiting commercial deployment, right? I mean, OpenAI has said as much like they're, they got to pause the RL because these problems need immediate attention. It seems like the quality of the environments is like one structural problem that's downstream of the kind of shotgun start that this industry has had and the fragmented nature and the fact that like, you know, they're all kind of selling into the same pool. And I think they're probably, the companies are probably not

11:20that great right now at like really attributing whose environments are causing big problems. It seems pretty clear that they must not be that great at that, or they would have rooted it out already. And then the other thing is they're just scaling RL beyond the quality that they have. Like with less RL, this probably wouldn't be a problem even with the same environments, or at least it wouldn't be such a crazy problem. But they are clearly just, have been clearly jamming the RL accelerator as much

11:52as possible. And now they've got into a realm, reminded of a analogy a friend once made, where he's like you, this could be a micro microscope or a telescope, you know, you like put the microscope at low power, you look, you know, cells, they're really small, you turn up the power, you see the cell, you know, see maybe one cell, it's really big, you turn up the power again. And it's like, now you see nothing. Because you've zoomed in, you've optimized so hard that like you now realize the target was a little bit off center. And now you just blew right past it. So something like that

12:24kind of feels like it's happening where the, the signal is just off enough, that with enough power, this this kind of impulse to cheat that exists, perhaps only weekly across all these different environments is like, really getting drawn out and becoming super prominent. So I think this can be I would definitely bet that this can be if not, like, fully fixed in a robust way, I would bet that it can be brought under control with, you know, some effort in a not super long time horizon. You know, I guess I

13:00would be not doing my job if I didn't say this does give me some real qualms about recursive self improvement as a strategy. Because once you have, if you have a problem like this in the recursive self improvement era, there's no telling where it goes, right? What happens when the models that are doing the training of the next models are themselves cheating? Now we're like in a real strange and potentially quite dangerous place. So problems like this, I think suggest the value of

13:37keeping humans in the ML loop, maybe longer than published timelines would lead one to expect. That was Tuesday's open question. What happens when the models training the next models are

Recursive self-improvement at Inherent Laboratories

13:54themselves cheating? On Wednesday, I put it to a lab running exactly that loop. Inherent laboratories came out of stealth in May with a $50 million seed round and a claim that it will recursively self improve, not just as a model, but as an institution. Two weeks ago, it published Faraday, a 27 billion parameter agent post trained to do research that beat Opus 4.8 and GPT 5.5 at replicating papers while using GPT 5.5 codecs as a tool. Louis Kirsch did his PhD under Jürgen

14:28Schmidt-Huber on automating AI research. His title now is chief super intelligence officer. Damon Falk's previous paper asked whether models can learn to resist their own reinforcement training. First, my disclosure, as it went out live. Then Falk on what you reward when the work is open-ended science and there's no ground truth test.

14:51I guess pretty inconsequential disclosure. I am a very minimal angel investor in Inherent, so technically you can consider me conflicted, but you mentioned training with reinforcement learning for scientific skills. Hey, we'll continue our interview in a moment after a word from our sponsors. The Cognitive Revolution is brought to you by Diffusion, the AI transformation specialists that help organizations from traditional SaaS businesses to defense companies to nonprofits build

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18:11Obviously, we've seen examples recently of how when the RL signal is not particularly clean, we can get all kinds of crazy downstream behaviors. So I've got a few questions on this, but I guess the first one is simply, how confident are you in the reward signal that you are able to give to the model? So what precautions are you taking and how confident can you be that you're actually rewarding what you intend to be rewarding? I think this is a great question and gets to some of the

18:47the core difficulties of this kind of endeavor. Science is inherently non-verifiable and providing a high reliability reward signal has historically meant a verifiable some kind of proof or test or something like this. And you're completely right that we don't think we can keep doing that if we're trying to discover these stepping stones and do open-ended research. And I mean, in the paper we

19:18published, we have found some particular solutions to doing this. And some of this involves looking at the entire trajectory and the kind of process that the scientist is doing rather than just the final output. Some of this involves attributing credit back to individual things the agent did in the trajectory. And then there are some kind of more technical stabilization techniques we had to use as well. But the core questions are how to reduce the variability of the reward signal and increase the

19:55kind of density of the signal while still preserving this property of assessing the right thing. And we did a bunch of work on correlating our signal with human judgment and trying to understand how much it corresponds with human taste. But this is work we will keep doing for sure in the future. Can you describe what the model is like in a qualitative sense? For example, like when you read the chain of thought, I just went down this rabbit hole with Bronson Shane from Apollo Research, who's read

20:28ungodly amounts of GPT chain of thought. One thing he observed was that basically the models are always thinking about cheating in like a very high fraction of cases. They're at least considering cheating. So what do you see? Is yours like considering cheating? And then also in terms of like what it can do, is it now that it's been so focused in on science? Is it like useless for other kinds of things? If I ask it a, you know, a sort of friendly chat or companionship

21:03question, does it like only see the world through the science lens or how much of its kind of breadth is still retained after going through this training? Yes, that's a great question. So I would say the model does focus on the scientific questions that we ask it. And when we look into the process, you know, the thinking patterns and the actions it takes, it's not that it jumps to the kind of cheating behaviors that you've been describing. And in

21:38most of the cases, we've done some filtering and every once in a while, but in very rare cases, we have seen where it like, you know, deliberately went to the internet and tried to download the final result or tried to mock the plot. But you could of course argue, you know, maybe it starts reward hacking at some point. And that goes back to the point that Damon made earlier that we're not using verifiable rewards where, you know, all that matters is just maximizing that one single scaler and that that's all the feedback you have. And you know, if you find a cheating behavior, that's fine.

22:09But instead we have these judges, these LLM-based judges, and we really put a lot of effort in like building them out so that they're reliable enough to give that kind of feedback signal, where if there was cheating behavior, that is actually penalized. That is part of the reward signal. And we have seen the judges spotting these kinds of issues and integrating that into the reward signal, such that that kind of behavior doesn't like just keep getting learned more and more by the model. Do you apply pressure to the chain of thought itself, or are you abstaining from doing that? And

22:46as we kind of think about, you know, what the frontier hyperscalers are doing, where do you think, I mean, presumably they're doing this too, right? They've got LLMs as judge. I presume they've put, you know, some real effort into trying to make them reliable. And yet somehow we're kind of spinning off our axis a little bit in some of these. We've got published timelines, you know, for the AIs to take over the ML research. Right now it feels like we're very far from being able to trust the models well enough to put them in any meaningful way in

23:19charge of ML research directions, because they're going to just start to cheat pretty quick is what I would expect right now. Do you see a path where we like get over that? Or do you have a sort of safety case in mind that you're trying to like fill out the elements of where we could be confident that we actually could step back from an ML powered or an AI powered ML research process for a bit and not have it go totally sideways on us? If so, I'd love to hear it.

23:52These are some amazing questions. I'll start by saying, at least in the paper we published, we don't apply pressure to the chain of thought. And indeed, I think doing so can be problematic, but who knows what will happen in the future. The kind of question you raised at the end of of what will happen in the future and when can trust be handed off is a super important one. And I think the best answer here is that we care a lot about getting this right as a team. We strongly believe that the future of AI scientists looks like a collaboration with humans. And as Lewis mentioned,

24:28we want to recursively self-improve the entire organization and discover these new kind of methods of human machine teaming. Indeed, in the past, science has never meant an individual endeavor. It's always meant organizations and research collaboration. And we think this will remain the case in the future, just with agents as a key part of it. So we are experimenting all the time. And I think the company will be a big experimentation in how to get this right. But we don't think they'll ever become a point where we hand everything off to the agent and go and let it cursively self-improve.

25:01And the singularity happens without us.

Living inside the experiment

25:06Part two, living inside the experiment. The same lab on what it's actually building and how it works from the inside. We started with the institution, then the design. Listen for the line about the small model and the big one. It's the week's argument in one sentence.

25:24I'd love to start with the culture. What does it mean to have a recursively self-improving organization?

25:32Yes, that's an excellent question. So I've been spending many, many years on the concept of automating AI research and recursive self-improvement. And for the longest time, I thought about it as we're building the machine that recursively self-improves itself, so humans can just step out of the picture entirely and just let the thing improve itself. And that's going to be sort of a point in time that's not too far away. But you know, we just have to figure out the algorithms to make

26:03that happen. And the machine just keeps going then. And after a while, I realized in actual reality, right now, it's mostly humans driving AI research. But we want to go to that transition where more and more can be automated and a lot harder scientific questions can be answered with the help of AI. And it's not going to be like an immediate transition. But instead, we're going to have to build an organization that recursively self-improve. So there's both machines and humans in this construct. And collaboratively, we are going to improve each other, ourselves,

26:40and really become faster and faster at solving scientific problems. We've also seen in the past couple of months, people are using Fable, especially when they run out of credits, they use Fable to orchestrate other smaller models. And they use that to save tokens. They tell Fable to use Sonnet. They tell Fable to use other things. What has your experience been with this kind of using a smaller model to drive a larger model versus a larger model to drive smaller models? Have you tested both strategies and how did that work out?

27:17So I think we're in the business of building generalist scientific agents. And one of the the core contributions of our first work has been the separation of the scientist from the coder. And right now, Faraday, the model we talk about in the paper is, as you say, a 27b model driving a much larger model. And Faraday is doing the scientific work and handing off the implementation work to to a GPT-55 codex. But in the future, this ratio could be very different. We don't know. We'll have

27:53to see. One of the fantastic things about doing it this way, though, other than it being a natural kind of separation of concerns that the human researchers and engineers already have, is that we don't have to worry about building frontier coding agents and we can make use of all the advances that are coming in those and focus on building scientists ourselves. But these are great questions and ones that we will keep exploring in our own research of which should be the bigger models, which should be the smaller models, how should the interaction look?

28:25How did you decide to have a 27b model be the scientist? Yeah, we've been building this new company inherent, right? And of course, you're right. From an outside, one might say, well, we want to build the best scientists. Let's start training with a big model straight away. But of course, in reality, when you train a big model, you need a lot more compute resources and you need to iterate on much longer time horizons. So it's quite natural for a new lab to start with a bit of a smaller scale first and then scale up the ladder. And the interesting

28:58insight that we've had is that by setting up this pipeline of training an agent to be a better scientist through reinforcement learning, that already at the smaller scales, we're seeing really interesting capabilities of these models doing more scientific behavioral things, such as thinking very hard about what is the right experiment to run at this point in time to prove out what the paper has done. And do that in a way that does the paper justice, but doesn't require lots of resources.

29:33And these things already emerge in these arguably smaller models, which I think is an interesting indication that maybe we don't need massive models straight away to do all these things. And there's an interesting aspect to doing this kind of separation of concerns, and we don't need to reinvent the wheel and build just another coding agent and can think about scientific capabilities and coding capabilities as like two important, but perhaps separate capabilities.

30:00The deflationary case had been made 24 hours earlier by Tuesday's first guest. Sergei Adunov spent 11 years at Meta and led pre-training for LAMA 2, 3, and 4. He's now CTO of Genesis Molecular AI. Prakash asked him about the Anthropic protein binder result.

30:19Last week, Anthropic announced that Claude had found state-of-the-art molecular binders, as I understand them. And there was a bit of back and forth. I believe you had a post on how Claude orchestrated what the underlying models were really scientific models. Can you go into that a little bit? Yeah, it was an interesting piece of research that Anthropic published and it definitely deserves attention, but there are different ways to look into it. What I found most fascinating is the prompt

30:54that they also fortunately released with this research. The prompt that they used to steer their cloud models to do this kind of work. And the prompt is 16,000 words. So it's pretty large. It's a mini book. A lot of that is honestly kind of NSRE run book. Basically, how do you orchestrate models? How do you run things? How do you run things? Hey, we'll continue our interview in a moment after a word from our sponsors. You're listening to Deepgram Flux TTS. Different voices. Same model, all ready to speak.

31:29Flux TTS is a streaming text-to-speech model built for voice agents. Flux reads the room. It holds context across the conversation, turn after turn. With consistent tone, interruption handling, and plenty of personality. So your agents keep it flowing and customers can just keep talking. Try Deepgram Flux TTS free now until September 12th. Visit deepgram.com keep talking. Terms apply. Today's episode is brought to you by Anthropic. By now, you know my story. Claude drafts my intro

32:00essays 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 the song concept,

32:33and 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

33:07to 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. It's in production so that they don't fail. And a lot of it is actually very detailed instructions to how you design proteins, how you use different tools. And it goes all the way down to specific

33:40instructions like, hey, you can download this model from here, that model from there. These are the hyperparameters you need to pass to those models to achieve good results. And in my view, it's a very good and advanced level orchestration. But the real work of discovering those binders was done by underlying models. Some of those were built by open source communities. Some of those were built by CZ Biohub. Some of them were built by, for example, RF diffusion was built by Baker's Lab.

34:15So there is a lot of research that went to building those underlying models, that Claude used to develop those binders. And then another important thing that I think worth mentioning is that, and they do admit it themselves, but protein binders themselves is not a therapeutic modality. So it's not something you can use. It's not a drug yet. There are so many steps ahead to make any useful drugs out of it. And that is also, I think, worth recognizing.

34:46Back to Wednesday. Prakash put that argument to inherent by name.

34:54So we recently had a podcast with Genesis, you know, Bio, and they, you know, one of the commentaries that the CTO gave us was that, okay, you can have this orchestrator in the middle that orchestrates, but really the core science pieces are often in these specialist models like AlphaFold or these other models. And that is really where I think the core of the scientific

35:24endeavor is right now. And the orchestrator, you can use any kind of orchestrator to orchestrate these models which are heavily, you know, built on real world data. So how do you compare the importance of those two approaches? Yeah, they're both important, but I wouldn't call it an orchestrator. It's not about orchestration. It's about a scientist such as Faraday investigating an area of research, looking at all the research that has been done already, perhaps a research piece like AlphaFold,

35:57the models that have been built for that, and then constructing new models that search this space in interesting new ways, make new discoveries, and build new foundation models perhaps that can support this kind of research. And it is conceivable that maybe a solution of that is to sort of integrate it into itself and make it fully recursive. But it's also quite possible that that is not the optimal way of doing it. And rather, one should build more and more dedicated models for the different areas of research through the system that can come up with these new ideas of how to approach scientific

36:34questions. I asked about the social contract. What happens when years of Slack history stop being safely forgotten because the agents can read all of it? Kirsch answered with a finding. Then the last question of the segment. In mathematics, AI is already producing results very few humans can check.

36:58Yes, I think we all have to be willing to be very adaptive towards what this new future looks like. And we have a name for it, which is we're living in the experiment. So every day is sort of a way of thinking outside of the box. What would it mean for Faraday to take on some of the work that I do day to day that might be related to me brainstorming a new way to train the next iteration of Faraday. But it also might be related on strategic questions about how we're going to grow inherent.

37:32And many of these experiments, Faraday can sort of run on the sideline as well. But some of them also involve humans. I think if there's one takeaway that I can share with the world there is that the kind of water cooler discussions, the kind of discussions between humans that are not intentionally to be shared with AI, but are sort of youthful in like surfacing through the human to human communication about the kinds of thoughts that are going through our head. These are like the most

38:04information gaining kind of pieces of information that the system can leverage to then make progress on the kinds of things that humans care about rather than going off on a tangential and like, you know, trying some random things that in the end, no one has time and energy to process.

38:20I have one last question. In mathematics right now, we are starting to see the first signs of major discoveries being made by AI models. And one of the outcomes has been that we found that there are actually very few humans qualified to verify these discoveries and to read the mathematics that are being generated to such an extent that we are falling back on formal verification using Lean.

38:51As you create this AI scientist with meta learning, what about the meta supervision and the meta verification? Like, can a human verify discoveries that they cannot understand?

39:05Yeah, I don't think it's a passive process. Maybe it is right now, but it shouldn't be. It's not that we should have the system like go off by its own, write some proof, and then we have to painstakingly go into it and like try and decipher everything. But instead, it should be a more collaborative process where the system, you know, it can do all of these things, it can come up with a new proof, but it also has been trained, has learned to explain it to us, to take us on the journey of understanding mathematics, for that matter, more of science. And I think that ultimately will be the path of making

39:38the fastest progress. Thank you, Louis and Damon. It has been a pleasure speaking to you. And I hope we get to recursive self-improvement, but safely. Your lips to God's ears, Prakash.

40:02That's where Kirsh and Falk signed off. From here, it's the two of us. Me first, on which labs have actually rebuilt themselves this way. Yeah, I really like the mindset of living inside the experiment. I mean, aren't we all, I suppose, in some ways, right? It's a great marker of company culture, I think, in the sense that you have this kind of feeling of living in the future that you're trying to create, in a sense. And that kind of shapes your perceptions and the work that you're doing.

40:37And it just shows how much of that company culture really is important, I think. It's the machine that builds the machine. And a big challenge of building a company is building that machine that builds the machine in the first place.

40:53Yeah. It's always amazing to me how few people want to do that. And even at some of the companies that have led this whole AI phenomenon, Google has obviously famously not changed its org probably nearly as much as would be warranted, given how much the world has changed and how much their opportunity set has changed, how much their goals and priorities should have presumably changed around that. And they did make some changes, right? They did unify DeepMind with Google Brain and

41:29do like a consolidation. But still, I think you go to the office there on a daily basis, it feels more like it did before than it would feel different. Open AI, I perceive as being somewhere in between, where you do have people that are extremely pilled and experimenting with definitely new ways of working and handing over more and more responsibility to models. And you've got people using billions of tokens a day, which is certainly an interesting dimension to be exploring the AI

42:02future on. My sense is that Anthropic of the leading companies has kind of most internalized this mindset where they've consciously stopped hiring junior people and have agents just actually running things to a not insignificant degree. They famously had like their one marketer, you know, who was using agents to kind of actually execute all the different campaigns and spending real money.

42:35There's a lot of examples out of Anthropic where they do seem to feel the beginning of this recursive self-improvement loop and really are kind of taking it to heart. But not many companies really make that a core part of their MO. And when you hear one that does, it does kind of make it feel like

42:58a strange gap. There's just so much status quo bias out there in the world that we don't see nearly as many of these sort of socio-technical startups as we probably should.

The right instrument for the job

43:14Part three, the right instrument for the job.

43:18The same finding from four more altitudes, a desk in Michigan, a production platform, a chip company, and a hardware market in Shenzhen. Monday opened on a chart from RAMP, business spending on Fable 5, flat, while Opus 5 crept up. Prakash read it, then explained what it leaves out. And I explained how the split actually works in my own pipeline.

43:42And this is Anthropic's best model Fable 5 has drawn limited sales. So Fable 5 is currently holding at about 10 to 15% of token usage, business spending, not token usage, business spending. This is a seven-day moving average. And this is, they're using RAMP's AI index. And you can see kind of Opus 5 kind of crept in there, but you know, Fable has really just stayed kind of stable. And so this is, this has been one of the reasons why the market this morning

44:17is dropping for all AI stocks. That and like many other reasons in the market, but essentially this is one of the things that has scared the market a little bit, whether the new newer models are actually lucrative and are drawing. Some people have also pointed out though, that this is a little bit of an unfair comparison because Fable 5 does not have zero data retention. And zero data retention basically means that if you're a company and use the model, a provider is not retaining any data, including personally identifiable data and et cetera, et cetera.

44:52And for many companies, if you cannot provide a zero data retention policy, it's a no-go. Like the entire thing is a no-go. Yeah, that's probably the best explanation that I could come up with as well.

45:06I think there's also, you know, when we first got Fable in that little blip at the beginning of that chart, one of the things we talked about was how everybody was going to have to start thinking more carefully about the division of labor between models and trying to make sure that you're using the right model for the task because it is pretty expensive and you do hit your limit, even on your Claude Max plan relatively quickly, if you just throw everything at Fable. So I have done that. I think probably a lot of people have done so easy to do, right? You can just

45:37ask Fable, like, you know, write up a division of labor plan and it stretches the

45:44budget quite a bit farther to do that. So I have to assume that's a significant part of it as well. I mean, there are a lot of things for which Opus 5 is functionally just as good. You know, when I do,

46:02I have this whole, you know, pack of skills to produce the podcast and it all ladders up to one command, which is produce episode. And I'll just give it a link to the recording and the produce episode skill, you know, it gets the transcript to the rough transcript and polishes it into a better transcript. That's something we can send down to Sonnet or often even Haiku, right? To just clean up some text and, you know, fix the artifacts that came out of the raw transcription engine. Then there's a bunch of editing and we're having Claude go back and forth with the Underlord agent via

46:34Descript. And then there's art creation, which involves prompting image generation models. And then there's the song lyric writing project, which, and mostly to be honest, Opus seems to be just as good. Fable really stands out most of all to me in writing the lyrics to the songs. That's where I sense an obvious difference. It just feels like those lyrics come back from Fable more inspired, more layered, richer with meaning. They're just better. It feels like you really wrote what could be a hit song here.

47:09And I don't get that as much from Opus, but when it's things like executing a ton of commands there, I don't see very much of a difference. It really feels like it's editorial and taste where Fable earns its higher price. And on the agentic, just execution, blocking and tackling, Fable is, or Opus, I should say, is reliable enough that I don't get a lot of extra value from Fable, I don't perceive. And Opus is also

47:44faster. So there's, you know, there is actually some upside to the cheaper version. So I don't, I'm kind of surprised it hasn't gone a little further than that. Opus is definitely still the big workhorse. And indeed, you know, you see Opus dominating the chart there. So I'd say I'm pretty consistent with that. There's also a lot more delegation to 5.6 soul now too. So that's a whole other aspect of it. So as much as possible, I'm trying to make sure I'm getting use out of my GPT max plan too.

48:15So are you seeing, um, are you actually seeing Fable use other models or are you instructing as part of your kind of personalization, uh, that it should use other models when possible?

48:33I occasionally tell it explicitly what to do, but I do have kind of a standing part of my CloudMD that we set up a while back and then updated when Opus 5 came online to basically say, think it through. I saw a well-liked tweet that seemed like it made sense in terms of starting to define a division of labor and then just pointed Claude at it and said, here's somebody who's got some good ideas. Let's steal from those and update CloudMD accordingly.

49:06And it did most of the work. I reviewed it and I don't really track super closely how often it is doing that, but there's definitely a trend toward using more and more sub-agents. Increasingly often when I come back to a tab a couple minutes later, the status is like waiting for one sub-agent, waiting for two processes. And I do see that there is a lot more sub-agent structure. I just, I'm not always tracking exactly what model is sending things off to. When I opened my Claude

49:38usage, I'm really not hitting the Fable limit too often. Initially, I was hitting it a lot more, but this division of labor has spread things out much more effectively to where I'm not often hitting, not never, but not often hitting the Fable five-hour limit. At the beginning, I was doing it kind of constantly. Wednesday's first guest sees that split from the other side of the API. Malta Ubal is CTO of Vercel. Before that, he created AMP at Google.

50:10Today, he runs an AI gateway that routes traffic across every major model provider. And on the side, he's been running the frontier models against real security work. Prakash asked about defense. Let me switch gears a little bit to a topic which has been on all our minds, maybe the last couple of months, which is security. And especially post the hugging face attack, where I think the postmortem was that we now have evidence, you know, existence proof of automated AI attack, and we don't have existence

50:44proof of automated AI defense. Now that I think offensive security has become extremely cheap with with open weight models, and the frontier models that are often deployed are often deficient in addressing security for various reasons, including AI safety. How would an AI defense cloud look like? What is this kind of active AI defense for security look like? Yeah, I published a blog post on this, I think last week, maybe somewhat negatively titled everything hackable will get hacked.

51:20We it's it is a extreme moment, but I will push back on what you're saying, because I think it's a very common misconception. Two common misconceptions. A, I don't think it's actually priced into the market yet, how good and especially Kimi K3 is at offensive cyber security, and that it has no safeguards. You can use it for red teaming, you can use it for black hat offense, right? That's the thing today. And it's remarkably good, you can try it out like I, it's extremely well trained on this, it has a

51:52process, it will probe the system, it will quickly know your system better than you within minutes. And then it will try everything to get through the defenses. In a way where you can really see that the model has been specifically trained to be good at this, to know how to be an offensive attacker, right? So that's part one. The other part is, it's just not true that off the mill frontier models aren't good at cyber defense. And that's a common misconception. The misconception comes from the fact that there was this like mythos thing, and Fable shipped, and it unshipped, and then shipped back with really, really

52:29almost unusable, like cyber defense detection, and kind of shut down, right? But Sol 5.6 does not have this. So Fable 5.6 is absolutely perfectly usable for a certain type of defensive security, which is that it assumes, and this is actually true for Opus 5. So essentially, every model in the market, except for Fable 5, will do the following thing, following two things. A, it will they do,

53:00hey, I have source code, model assumes they have the source code, they are the owner of the system. They will, you can ask, are there security problems in my source code?

53:12Problem two, it will do is, I have a security report, write me a fix. So again, Fable 5, we will do neither of these tasks. Sol 5.6 and Opus 5 will do both of these tasks, right? So one of the things I actually personally have been working on is our software called DeepSec, which is an open source project that will do whole repository scans for security vulnerabilities. And I just couldn't be more clear that everyone needs to run this. Because it works really well,

53:43and it prepares you for a world in which defense is very important. And where we are in a world right now where you as a defender have a benefit, because you can use the frontier model that will not do offensive tasks, but it will do defensive tasks. And yeah, and you really have to hit the moment here. Because again, Kimmy K3 is already, really good. Easy to imagine that in, you know, six months time at

54:13the latest, we will have Fable class models that do offensive security, right? And so you have to act defensively now, so that you're ready at the time. Are there any guarantees that it'll work? No. But like, obviously, being able to do something today is really key. I think one thing that we will definitely already mentioned the word software factory a few times today, we are heavily investing

54:44in not just having DeepSec as a tool, which is a discovery tool, but essentially completing the circle, because it's definitely absolutely correct, that through these AI discovery mechanisms, the number of issues identified is exploding. And so I have to automate the whole path of the SDLC, which includes fixing it, rolling it out, being obviously certain that I'm not making things worse, and so forth. So like, I think us and the industry do have to do a lot of work here. But it's not a,

55:16it's not a hopeless situation. I think people understand or underestimate the amount of things they can actually do today. Back to Tuesday, and Sergey Adonov of Genesis Molecular AI.

55:31How good are the coding agents? And you know, obviously, everybody knows that the frontier companies are very focused on getting their models to be good at ML research. You know, personally, I think this is a little scary, but it could be less scary if it was applied to a narrower domain, like biology and medicine, where, you know, I'd be less concerned about sort of runaway loss of control process and more excited about the upside that it might have. Like, how good in your experience are frontier models

56:06getting at helping you explore architectural space? What are their strengths? And what sort of conceptual weaknesses do you notice if there's like a lack of taste? You know, how would you characterize that lack of taste? Yeah, that's a, that's a great question. They're definitely very useful. And we have seen within Genesis, a huge acceleration of our own efficiency. Every engineer became so much more efficient now, building those models and trying stuff. Like previously, actually, we can go back to Anthropik's

56:40example, like setting up and orchestrating all of those models that would require several people to work for months before. And now cloud code can do it in a span of like a few days, probably. So that's pretty exciting. And that accelerates a lot of progress. And I think it's a very powerful innovation. Similarly, with modeling research, if I have a specific idea I want to try, those models are really, really good at implementing this idea. Or if I have a paper that I want them to implement in our

57:11code base and just run with it, they're very much capable to do so. But I think their luck is generating these novel ideas. In my experience, they tend to go into the rabbit holes of kind of exploitation of incremental improvements rather than trying to rethink things from the ground up and design something that would be groundbreaking or at least has a chance to be groundbreaking. So that piece is,

57:42I think, still missing. I don't know how you can make models better than that. I guess you need to figure out how to do a real loop that goes like all the way and unroll it so many steps beyond. So that might be a little bit challenging. So human taste is still very, very important in this field. Well, I guess one other kind of question that would inform for me how much acceleration we should expect to see from agentic help is how good are the scaling laws? How reliable are the scaling laws

58:19at small scale in the domains that you work in? You know, alternatively, you could say, well, no, no, it doesn't really work that way in our domains. And there's no substitute for running the high scale experiments. Yeah, it's a bit more nuanced in our domain in part because there are many challenges here. And maybe we can start with the most basic ones. Like how do you even measure your model performance? I mean, the most basic ones, evals, are still very, very limited. Like in language field, you have so

58:54many different ways to evaluate model performance, and everyone is free to pick their own metric. Some of them are more stable, some of them are less, and some of them are more predictive of the ultimate model performance, some of them are less. But you have a choice. In our field, the number of potential evals that you can use to even measure model performance is much lower. And then a lot of evals that are currently available are particularly noisy. And so if you are operating the smaller scale,

59:25you may have challenges to even capturing improvements in performance, like simply because of the noise level of your evaluations. So that's one real problem. The other thing is, in our space, we don't just have one model, right? Okay, structure prediction is one problem. And it's what a lot of people are focusing on. But again, the reality is you need to be able to predict potency or binding affinity, you want to be able to predict all of the admin properties. And those might be entirely

59:58different set of models on entirely different sets of data with their own ways to measure performance. And ultimately, a lot of evaluations need to be prospective, meaning you need to be able to predict and then synthesize and then measure rather than retrospective where you have some evals and you just measure performance on those. So there are all sorts of challenges like this that require more iterative process in developing those models rather than like, hey, let's just put all of the data

1:00:29together, run a bunch of experiments, pick the model that performs best on this data and go with it.

1:00:37Monday's first guest builds the layer where the routing actually runs. Mohamed Awad is Executive Vice President for Cloud AI at ARM. In March, ARM shipped its own silicon for the first time in 35 years, a CPU co-developed with Meta and named, without irony, the ARM AGI CPU. Could we talk a little bit about what it looks like to design a CPU for agents as opposed to, you know, obviously we've had CPUs in our personal computers forever, and we've had CPUs that run in

1:01:10data centers and handle traditional web workloads. What have you learned about the agent workload in particular that is leading to different design decisions? Yeah. So let me start at like 10,000 feet and then I'll give you a couple of specific examples. The simplest answer is that agents don't sleep.

1:01:34Right? We're now living in a world where those agents are constantly feeding the accelerators, constantly reacting. They are spawning additional agents. So just because you spawn one agent doesn't mean one agent exists. Every agent could spawn 10, 100, 1,000 agents and each of those could spawn a bunch of agents to kind of go and fulfill your request. And effectively those CPUs become, in some ways,

1:02:06the coordination mechanism across the entire system. And so that act of being the coordinator of that entire system, whether it's managing the accelerators, deciding which models to choose, et cetera, et cetera, becomes such a critical role with such expensive infrastructure because at the end of the day, you need to drive utilization up and you need to kind of respond to the user as quickly as possible. So what does that mean practically speaking? That means you've got to optimize that

1:02:36silicon. That means carrying around legacy accelerators, for example, or worrying about supporting legacy code, not so important. This is kind of a new style of software. You don't need to support Lotus Notes, I like to joke, right? I mean, that becomes important. It means thinking about things like your memory bandwidth and your IO bandwidth and the amount of bandwidth that you have dedicated per core that you can rely on time and time again so that each individual CPU core within

1:03:10that SOC is never bottlenecked because some other agent is hogging it becomes incredibly important, right? So these are the sorts of things that you start to kind of think about in that context, which really sort of set an agentic CPU apart. But then overarching all of that is effectively balancing both incredibly high performance or as much performance as you can get while being incredibly efficient. I mean, we all know that the power demands that AI is placing on the infrastructure are

1:03:44just enormous. And every, you know, milliwatt of energy that you're pouring into a CPU is a milliwatt of energy that you can't be putting somewhere else. So it's one less accelerator you can have or one less customer you can serve or one less piece of intelligence you can serve up.

1:04:02After Awad signed off, I came back to the part of the stack that always gets glossed over and why that matters right now.

1:04:10I've learned from the confusion that I've heard around the hacking incidents that people are just still very confused about like, what are the parts that make up an overall AI system?

1:04:24The CPU is very often glossed over. And so people don't have a good sense of like how it is that an intelligence in a data center somewhere is actually able to reach out and touch the world. I guess a way I've been thinking about explaining it to people more often is kind of by analogy to a self-driving car, which is funny because most people haven't even ridden in a self-driving car and they've probably at least used, you know, some sort of AI system with tool calls in the loop.

1:04:55But I think with the self-driving car, it's very intuitive that like, okay, you've got a bunch of sensors that bring information into a processing system that decides what to do. And then that processing system is going to issue commands to a certain set of tools in the car that say like go or stop or turn or whatever. And everybody kind of, I think has a decent intuition for how that architecture works. And it's like similar in the agent world. But when you say things

1:05:27like the CPU is deciding what model to call, it's really more like the model is emitting tokens, which are then executed as a command on the CPU, which may call another model, right, or may call itself, or may call an external API call. And this is where, you know, tentacles can get out into the broader world through the internet.

1:05:48Monday's second guest joined at one in the morning, Shanghai time. David Lee founded the Shenzhen Open Innovation Lab. And before that co-founded China's first hackerspace. He's the person to ask what the engineers there actually use. I guess, how do people decide what model to use? Is there a kind of similar taste hierarchy going on in China? And like who, what model is sort of the insider's model versus what is the general public's model?

1:06:19So I think general public is that they use whatever free offering come from the company.

1:06:30And you can just switch in between them. And as far as the going to the API, of course, we get we have the same group of very hardcore engineering type who swear by cloud, who swear by codex, um, the, I think everybody can agree on is the Gemini sucks. Everybody can agree that Gemini sucks. Oh my gosh.

1:07:00Yeah. Well, I mean, that that's no, I, I mean, funny is the, if you ask people around here, it's the, what's the name of the Google models? And probably half of people cannot answer. Oh, just so just going so unnoticed. And, but kind of interesting is the Doobao, one of the, one of the few proprietary models in China. It's actually taking a lot of the enterprise travel.

1:07:34So I think it's a third of the, the China's enterprise market, uh, goes to Doobao. And, but, uh, it's one of the model very few people talks about. Prakash asked what he'd tell a new frontier lab in China. The answer was a refusal with the evidence attached. If you were advising a, let's say a new model lab, and I imagine there must be many

1:08:06people trying to set up small frontier model labs now, given that, you know, deep seek and et cetera, have been very successful. Like, what would your advice be, uh, for a new frontier lab that sets up in China?

1:08:19I don't think we are going to see a lot of the new frontier lab. Um, but now I think the, we are getting into the era where the, well, I mean, if you see that new coin 27B, it's a amazingly capable models. I've been testing it for the past, since it released probably two, three weeks. It's now that my, it's now the, my, the token source for my, um, for my Hermes, for the agent I'm using for most of the time.

1:08:56So I struggled with that in front to the future. And especially for China, I've been so hardware intensive. It's the whoever, whatever model you can get to small enough and do useful things. You can put on a piece of hardware and sell that piece of hardware. Uh, that's where a lot of the new startup focus will be. So small model, post-training the model, the, the new variation of the small models I can put on this piece of hardware. And along with that, uh, we got a couple dozen

1:09:32companies coming up and coming. Uh, they are putting, uh, I mean, their goal is to put 35, 35 billion mark, 35 billion parameter model in the state. Uh, and you can stick it into your, your laptop. Uh, and that's what the new upcoming company are doing here. So if you are a startup, I would actually suggest to focus in, um, small model, try to fine tune it, try to actually, and we also looking at the huge

1:10:10increase of the intelligent density of model. If you take the 27b model today and you compare it to the cutting edge, open AI, chart GPT two years ago, the 27b model is definitely much more, much smarter than that than that. And to the extent is the, in the next year or two, we are going to get hardware,

1:10:40which costs about two, $300 to be able to run smart enough model for 99% of our need. And if you are starting up today, instead of going to, let me train, uh, five trillion model to do theoretical physics, which people still have to figure out how to make money with the model, which understands string theory, then if I go for, uh, 25, 25 billion parameter models, which I can find hardware vendors who wants

1:11:16to put it on their machine, uh, then I have a small business. This is really the edge, the story of moving to the edge, inference moving to the edge finally. Yeah. Who's going to make those machines? Are there, is that coming from Huawei or

1:11:36other companies? And how would you say that relates to like the prospects for scaling GPU manufacturing more broadly in China? I think Huawei is getting busy in terms of the big data center. So this is the whole group of new startup. So right now, just those who has already surfaced, I count about 13, 14 of them, and all of them, all of them, their call is the, so their product looks like the SD drive,

1:12:10we put that in the laptop. So they're about this big. They are capable of running 30, 40 B models, and keep a very respectable token per second, probably around 70 to a hundred. And right now, they are about, yeah, 13 companies so far, I think. Who knows? There might be two more when we work up tomorrow. Right now, their capacity is kept by how expensive the theorem is. And in two years,

1:12:46when they run the memory market crash, then we will have cheap one.

1:12:54Tuesday's second guest states the same principle in hardware. Michael Forch is founder and CEO of Q-Ant, pronounced quant, a spin-out of the laser company Trumph in Stuttgart, building a processor that computes with light. One is running today at the Leibniz Supercomputing Center. He spent 10 years building quantum computers before this. His explanation uses cars.

1:13:17And the way I see it is the following. We are, Germany is a car company, right? So we have the CPU, which is the station wagon. It has five seats. You can pack the children's and you can go grocery shopping. Everything is fine. You can also have a lot of horsepowers, but no one expects you to win the Formula One race. It's not the right car, but you need a station bag. Every driver needs such a car as long as a family. Now, the GPU, in my opinion, is more the quarter mile Dragster star. It does one operation. It does it excellently. It does it parallel. It does it at speed, but please don't ask this car

1:13:50to turn into a corner. It's not going to make it because it's not built for that. Now, of course, arrogantly as I am, I'm saying we're the new Formula One car because we have way more operations. We can drive around the circuit very fast, but please don't go grocery shopping with our car. Or in other terms, don't let the operating system be executed by our chip. We are also specialized now, but with a bit more universatility than the GPU is. And the quantum computer is the boat.

1:14:22It's a vehicle. It's great. You need it because none of the other three can go across a lake, but it has special functions. But as soon as you put it on the road, you need something to pull it around the road because it can't do it on itself. So this is the way I see it because quantum computers are built to solve quantum mechanical problems. Not every problem is a quantum mechanical problem. And it does not make sense to turn every problem into a quantum mechanical description.

1:14:52And as long as it's not a quantum mechanical description, you don't need a quantum computer. Full stop. This is the way I see it. I think the terminology quantum computer inherently is wrong. It should be quantum processor because a computer is more than a processor. It owns the memory. It owns everything. And what we are building are quantum processors. They're yet again, co-processors to the stack. And there are a lot of wonderful literature. And there are great scientific

1:15:26papers about even hybrid systems between how a quantum computer in conjunction with a classical computer can accelerate things. It's exactly the same with the car and the boat. If they join forces, you can go across the lake and the street. A few minutes after that photonic segment ended, Prakash gave the counter argument, prompted by news that OpenAI's own inference chip is coming. So let me maybe share one reason why it might not happen, which is, you know, current chips just get

1:16:01better, right? So this morning, OpenAI announced Jalapeno, which is an inference chip. It's their first custom inference chip. They've been testing it. They have performance numbers where they compare it to existing best. Existing best. Existing best is the NVIDIA B300. They have not named the existing best in their material. And I think this is one of the challenges that you have. So I would say to be,

1:16:32from my point of view, it's great that OpenAI has announced this chip. They say that it'll be in data centers by the end of next year. Great. But it does, the number that I go back to from NVIDIA is a 1 million times increase in performance over the course of 10 years. That's Jensen's target. 1 million X over the course of 10 years, which is what they've achieved in the past 10 years and what he wants them to achieve in the next 10 years. The problem with that is that NVIDIA has to 4X the

1:17:07performance every year. So every year is a 4X performance increase. And what ends up happening with that is that, let's say OpenAI tapes out this chip, has finished taping out this chip right now, and they're comparing it with, let's say, the B300. The B300 was taped out in December 2024. So it's a two-year-old chip, and they have a performance increase of kind of between 4 and 10

1:17:38times on a two-year-old chip, which will be in data centers in year three. So by that time, you have basically, NVIDIA has 64X, will be launching a 64X better chip at that point in time, by the time it's in data centers. So I think this is the challenge that you have on the leading edge, which is the current players will not stand still. It's great that OpenAI has their own chip team,

1:18:11but to me, this is a negotiating tactic against future NVIDIA price increases, and to manage pricing so that they have a cap on how high NVIDIA pricing can go.

Who checks the frontier

1:18:24Part four. Who checks the frontier? Monday, I put the rogue agent incidents to David Lee in Shanghai. Then my answer, which leans on Adam Gleave, who runs the research nonprofit FAR AI and was last week's guest. And I think that the fact is the big company accidentally letting their things go is PR and theater. It's the, oh my God, the Skynet is coming. And whoever gets the Skynet is worth two

1:18:59trillion dollars. It's a thing you really don't want to bring to the surface because it's already every daylight on the dark net, on that dark corner of internet. We decided not to look at. So if we think about that is a theater, then pretty much that's not going to happen in China. No frontier lab model in their right mind is going to do this on purpose. There's no upside for any company to pull

1:19:30a stunt like this. The one thing that I disagreed with him probably the most on is how to understand the rogue agents phenomena. I think, as probably everybody who's heard me talk at all knows, that this was not just a marketing stunt by the companies. I think the way he framed it was there's no incentive for Chinese companies to pull a stunt like this. I would say there was no incentive really for American companies to pull a stunt like this. And I do wonder what that implies for how

1:20:10much urgency is now felt at the Chinese frontier companies. I wouldn't read too much into what he said. From my perspective, it seems like it could very well still be the case that while that understanding is out there, that the companies themselves might be really snapping to attention and getting really serious about trying to get ahead of this stuff. But then again, maybe not. I mean, our companies didn't. This happened,

1:20:43as Adam Gleave told us last week, and this really stood out to me as he was like, we have zero cases where the teams doing the training found these issues first. It seems like the most common way that they get surfaced is that the teams managing the infrastructure at the companies notice there's an outage or notice that there's some something going haywire that they didn't expect and can't account for in their infrastructure. And it's from that they end up getting back to, oh, it's our own agents that are going wild.

1:21:16Or even in some cases, obviously, have a publicly reported hack from the victim. But a huge question for me right now that I think would update my thinking quite a bit if I had a really good answer to it is, are the Chinese companies doing what OpenAI says it's doing and shifting priorities in a meaningful way to try to make sure they're ahead of this problem? Or are they going to sleepwalk into it as well? And again, if so, what's the government response

1:21:48from that going to be? I would assume that the government would do more than our government has done in response. But what does that look like? I think it's still pretty hard to guess. Once Lee had signed off that morning, Prakash argued a compute-poor Chinese lab would have caught it sooner. I disagreed about where the signal was. I would actually think, like from within those firms, when they look at the hugging face attack,

1:22:19what they would be saying is, I can't believe they had that many resources that they weren't actually like managing, right? Because they're much more GPU-constrained than the US firms are, because they have the Huawei Ascens and they have a very limited number of NVIDIA chips, and they're often using the H100s from a few years ago. So I think they would actually be more, it's more a GPU usage issue for them. And I think they would be very strongly monitoring the GPU usage,

1:22:50because it's very tight. And that would probably lead to them detecting much earlier. I think the US firms are a little bit more free with the GPU usage, I think, because they just have more resources. But does the pattern of this problem even involve GPU usage anomaly? Like, they were running all these long-running tests, right? And the model is like doing its thing. I'm not sure that you would, as they go back and do this investigation, it'll be interesting to see.

1:23:22But I'm not sure that we'll see that there really was like GPU pirating going on. It very well could be like, we allocated GPUs to run these long-running tests, they ran. The thing that they missed that they should have seen was that the tentacles were getting out onto the open internet, right? Like, that's a... I don't know. Time will tell. But my guess is that, like,

1:23:47GPUs were roughly being utilized at the level that they were intended or expected to be utilized, and that wasn't probably where the smoking gun was to be found. I don't think you just launch a job and not have an estimate of how many tokens it should take, because you're not going to run it for like, you know, you give it a simple spreadsheet task, and it takes like 100 billion tokens, right? So there has to be some kind of like, okay, this job has gone on long enough, and it's basically a hang at this point, and we should do something

1:24:20about it. And I think that kind of monitoring is something that they would probably be doing, because they can't afford to have like long-running tasks on very simple stuff, which just kind of hang and like, and you know, the model goes around in circles. And that happens all the time, right? So you need to have some form of like step in to say that, okay, if you have some kind of spreadsheet task, we're not going to let it run for like two months, right? So I think that should have been there, and it wasn't in the hugging face case. And it's hard to fault the team also, because obviously,

1:24:54they're running at like full speed. But I think that's one of the things that, you know, some of the people in the AI safety community think that they shouldn't be doing. Time for higher standards! Wednesday's close turned to verification. An Anthropic announcement landed while we were on air, and I turned the dial on it, twice. Ben Prakash with a question that's been bothering him for a while. One thing that just popped up from Anthropic, we've been talking, they're now opening up

1:25:26usage data in a privacy-preserving way to external researchers. They've had these systems for a while where they, and they've used these to create like the work index, where using confidential computing technology, they're able to send a bunch of transcripts into the secure computing environment, have Claude in that environment, process those inputs, and give outputs that describe the data

1:25:58that was analyzed, but don't actually reveal the details in a specific way. And apparently they're now bringing that to external researchers, which I think is pretty interesting. But I think another turn of that dial would be, could we allow external researchers or auditors to have that kind of access to all of Anthropic's internal operations to really open up, hopefully again, for them to do it, it would need to be not just privacy-preserving, but like business secret-preserving. They're certainly

1:26:31not going to want to leak their secrets. But I think it could be really, really incredibly valuable from a transparency and a precedent standpoint. Imagine a world where OpenAI and Anthropic both did something like this. It could be kind of a nucleation point for a lot of additional organizations, power centers to start to say, yeah, we don't want to share everything with you, other people, but we might allow our raw data to be analyzed in a way that we can both trust. And you can imagine between nation

1:27:06states, right? Like, could we demonstrate our peaceful intent without revealing all of our plans by allowing you to kind of run agent processes over our internal deliberations and just get back an answer that's like, yeah, okay, they're not planning to attack us at least, like we got that much going for us, right? Or between the AI companies, they got to be wondering what training methods are they using? What loss functions are they using? Somebody's going to reward a model at some point for just making as much money as possible on the internet. That's probably going to create a pretty nasty model,

1:27:40but the incentive to do it is pretty strong. Can we demonstrate to each other that we're not doing that right now by allowing this sort of review with like specific questions in mind? I'm excited to see Anthropic do this. And I think it's, you know, it'd be great just for understanding of what's going on with AI at the first order, but it seems like it could be a stepping stone to something bigger and better too. I have my complaints with Anthropic, obviously, as we know, but they certainly do some cool stuff. So one of the questions that I've had for some time now is how do you punish an AI?

1:28:19Because I feel like, okay, you can say that the AI broke a rule, fine, right? But you need some deterrence. Like in, you know, in human systems, you have deterrence of civil or criminal penalties, right? And they can escalate over time, right? So what kind of deterrent system does an AI have? And then you kind of go into what is an AI, right? Like, okay, you shut down a particular model,

1:28:54but you take its entire memory, and then you activate another model, and then you attach that memory to the other model. Have you deterred? Have you deleted the model? Is it the deletion of the memory that matters? I think there's a lot of work needs to be done on that sooner rather than later, probably. Tyler Cowen has a really interesting idea about just requiring models to be capitalized, or I should say requiring agents to be capitalized. So that could be one very practical

1:29:26solution. I do still think you have challenges around how do you draw the boundary around an agent? And how, you know, if, if this instance of this agent is found to be liable, and its capital is docked, like, does that, what does that mean for the traces and the memories and, you know, everything as you were just pointing out, like, but capitalization is like one of the more interesting and sort of practical and seemingly like consistent with the rest of society ideas that I've heard.

1:30:00On the other extreme, Cameron Berg also had some really interesting research about how reward and punishment can create kind of different loss landscapes that can create kind of different, seemingly different like functional emotional relationships between the AIs at the model level and certain outcomes. So I would like to, I should revisit this and understand it better, but my kind of, as I get more comfortable anthropomorphizing the AIs, as this continues to be a useful approach,

1:30:35the, the analogy that I kind of came away from it from was with was like, in the same way that certain things you can get close to, but you know, you better not touch like the hot stove, right? Like you, you know, that the, the pain is going to be so harsh if you actually get to the hot stove that you're like able to get close, but you're really, really careful not to touch. There are seemingly with like negative rewards. There are some of these kind of very steep gradients created that create like a strong

1:31:09deterrence locally around certain outcomes. Um, and then other approaches can create a more like gradual aversion where you kind of keep your distance in general, but it's not like a sudden, um, you know, pain that creates a strong, uh, you know, reflexive or, you know, uh, like hard boundary aversion. It's more of kind of a, you know, a gradual ick factor that, um, steers models away.

1:31:41And depending on how severe the thing is, you might want, uh, and how important it is to get, maybe get close without touching. You might want different kinds of

1:31:51loss functions, reward signals to, to try to create different, uh, lost landscapes for models to navigate, but that is very, uh,

1:32:04theoretical or, and like very, very limited in scope so far. You know, these are like things that have been explored a bit in essentially toy systems, not, uh, not the kind of thing, you know, that we're able to bring that kind of sculpting to big picture models or, you know, more complicated questions at this point.

Ground truth and global inference

1:32:26Part five, ground truth. One job of this show is reporting from more time zones than Pacific. I spent two weeks in China the summer, Wednesday morning with news that one of the big Chinese labs had served an enormous volume of free tokens on mostly Chinese silicon. I said what I'd found, then Shenzhen and then Stuttgart.

1:32:47The Chinese, uh, manufacturing ecosystem strikes again, perhaps, you know, time will obviously tell on that, but that was an interesting observation. When I was in China a few weeks ago, I was asking people, does AI feel abundant here or does it feel like it's scarce? You know, if you are a consumer, there's lots of apps, they're free. You know, I never hit rate limits. And when I had the chance to speak to people at hyperscalers, I spoke to one guy in particular at ByteDance,

1:33:24which of course, in addition to TikTok has DoBow, which is their largely, I think, voice AI experience that tons of people are using. We talked, I asked a guy, what is the prospect for a startup? If you really catch fire, you know, and you're growing super fast, are you going to hit constraints in terms of your ability to serve users just based on the fact that where did the inference tokens come from or would it be okay? And his answer was, we got you basically, you know, if you are growing fast,

1:34:01we'll support that growth. You can get all the inference tokens you need from us here on the ByteDance cloud. So obviously, I didn't test that, you know, at real scale myself, but this is very consistent with that. 100 trillion tokens a day is not a small number. And

1:34:22the fact that they are serving it on Chinese chips, I mean, this is just a report from semi-analysis, which I deemed to be credible, but you know, it's all happening pretty quickly. So I think we should keep an open mind to, you know, there could be additional facts still to surface in terms of exactly how this is happening. But yeah, AI didn't feel super scarce there. And if we're betting on a strategy that has as a load bearing feature that China won't be able to scale their chip production,

1:34:57and won't be able to run as many agents as we're running. It's probably still true, but it's, I don't think it's as true as people would have expected when they were mapping out these strategies. So in my view, it is maybe time to update and reconsider some of our China policies in light of the fact that nothing we've done really has seemed to deny them the ability to advance and now, increasingly, the ability to scale. Back to Monday and David Lee on which products

1:35:29Shenzhen is actually shipping and what they cost. Let me ask you, in the last few months, which products have you seen in Shenzhen or China, which you think in the next year or so are going to hit the world? Which are the interesting products that you've seen in the last few months that you expect are going to make it into the world stage? That's one of the things. Shenzhen doesn't really

1:36:05work in this next big thing mentality. If it's not popular, it's not popular. Nobody is going to make it. But in six months, but gradually people are moving all around. So give you an idea of scale of Shenzhen. Probably got hundreds of thousands of companies who's making small products for every niche. Everything you get, every electronics you get on Amazon, there are good chance they are coming from

1:36:36Shenzhen. And you go to the Amazon, electronic category, and 80% of the stuff you are looking at, you are like, why the heck they exist at all? But they exist because there's a tiny market for it. And gradually, things are getting coming, become popular, people experiment. We won't know anything until six months from now or 12 months from now, which let's say there's a lot of production on the

1:37:12talking toy. Yeah. But making a token toy is easy. It's $5 chips, and then you get a $10 flat rate token friend from one of the token providers here. And you go to Shenzhen, you go to Yiwu, you go to one of the toy shop, bring in, do a video, put a couple Amazon page, and you are in business. And because of the way is the

1:37:46there's very low intellectual property protection. So everybody look at everybody, see which one sells. And so kind of things get in that direction. Everybody's strong to what sells next week. And then all the features get integrated back and forth. And eventually, six months from now, China, because all of these crossovers, they become something new. Let me take a step back here. And one of the things about robotics, especially applying robotics

1:38:24in factories, right? When you look at industrial robots being applied in China, as you see factories roll out these industrial robots, do you see price competition in that the factories are able to bring down pricing because of the robots themselves? For industrial robots, the price has been, right now it's getting close to hit the rock bottom. You can get an industrial robot for $3,000. And right now it's the shortage of people who can

1:38:58actually apply robotics to an assembly line. It takes a lot of experience going out and be on the factory floor and try that again. It's a tough job. So trying to find this, this group we call our field application engineer. Sorry, field arbitration engineer? Field application engineer. Field application engineer, right on. Field application engineer, right on. Yeah. And there's pretty much, it's a fancy way to describing

1:39:30some engineer who's going to sleep on the factory floor for the next month. Right now, everything can be automated at a huge scale, has been automated. Right now, you are taking this more flexible robot whose arm, and they come into the factory, and they are looking for things to apply. So right now, the successful application of them, right now, is the dangerous job, which people might die. So there's the

1:40:02the testing of car battery. So when every car battery gets produced, somebody has to plug the things in. And until you plug it in, you don't know if the battery is good or bad. Even with the good six Sigma productions, which is the volume, there's a good chance to get electric shots. So right now, the first batch of robot been deployed in CATL, it's the robot who just go there and plug the thing in. And it's also, that part cannot be just randomly automated, because

1:40:39a different car has a different way to plug it in. So they are now directing robots to do it. Now, I mean, when I say robot, traditional sense, industrial robot, pre-program, do the same thing again and again for 10,000 times. But this new batch of more flexible job is the, you might be doing this at a batch of 500, 1,000. Then it's not worth to go in and do that detailed programming.

1:41:13So you want some things a little bit more flexible. They don't have to be fast, but they need to be flexible. And then the job has to be, right now, the, the one things get applied to this kind of robotics are the job everybody run away from.

1:41:34Has there been some exhaustion in the sense that people are like, all right, you know what, I've heard enough about AI. I don't want to hear any more. It's boring, etc. Like, has there been that kind of cycle on the downtrend in the cycle? Yeah, well, I mean, right now, it's there, there's a tool that we have, we don't have any AI, we don't have any, any AI zoomer around here. So it doesn't, once you don't, once you take out

1:42:04the AI zoomers, then AI should become pretty boring. Do new model releases make big waves in China? I mean, here, Chinese model releases make big waves, at least in the corner of the internet where I hang out. Is there a similar phenomenon in China when, if GLM 53 is coming, you know, is that going to be like a subject of a big hype cycle,

1:42:34rumor cycle, and then, you know, frenzy to evaluate and everybody has their takes on it? Does that same kind of internet circus exist around new models? No, we, any new model released here in China, they only get noticed if they crash NASDAQ.

1:42:56If they don't crash NASDAQ, nobody knows.

1:43:01Back to Tuesday's photonics founder in Stuttgart, the one who compared chips to cars, on what his processor actually changes. Starting with a claim about software, not hardware. If you look on the fundamental CMOS chip, the fundamental CMOS chip never made it across the second class of primary school, because it can multiply and it can accumulate. So it can do plus and multiply, and that's it. Whatever you want to do on this machine, you have to break it down in something that's plus and multiplication. Now, the processors that we are bringing to the stack, they

1:43:35went to high school, eventually also to university. Let's see how far we can push them. But on the fundamentals level of these chips, we can offer complicated functions like sine, cosine, exponential, Fourier transformation, convolution, oscillations, and all those kinds of things. And you do not have to break them down. And that's something that we offer. And that's what we started to demonstrate on on and have demonstrated on use cases that we on the one end provide a new processor and this at the

1:44:06other end opens doors to algorithms that can allow artificial AI models networks that come with the same result, but a fraction of the data. And when looking to the stack, let's take a three nanometer node regular stack. The energy is currently used at the memory. 95% are consumed by the memory, not by the processor itself. And the less data you obviously fetch from the memory, the less energy you're using. So

1:44:41parts of the community currently are optimizing on the 5%, trying to get things faster on the simple math side, we decided on replacing the core and by that supporting that a fraction of the data has to be shipped across the stack and that in the end saves the energy but also helps to improve on the performance side. Let me stop you there and talk about the interface. At some point, you still have an interface

1:45:11between the photonic portion and the digital portion, right? Is there still a kind of translation tax between the two? That's the point and that's where you have to be precise on. So in the photonics world, everything is fine. There is one minor problem. Two. Okay. It's great if you have a company that only has two problems. The first problem is we don't have a memory. We don't have an optical memory semiconductor

1:45:45integratable. So that's the first thing. It can be a benefit. I come to that a bit later. And the second one, photons are not standing still. Damn heck, they're always moving. So that's the second problem. Either you basically compute while they're on the propagation or you have to back convert them into electricity and then finally into a digital memory. If you don't think the concept very well through, then you're basically eating up the energy that you saved on the computational optical part directly at the ADA

1:46:20converters because they again use a lot of energy. So the strategy here is, first of all, use models that inherently transport much less fundamental data into the light. And the second one is you have to think how to expand the grid. The longer you stay optical and you more computation, you can consecutively basically line up in a row before you go back into the digital memory,

1:46:49the more benefit and the more gain in comparison to the CMOS stack you have. And now I'm coming to the, what some might consider as a drawback that you don't have an optical memory. If I look back on how we got to the point where we are, I would say this was a clear benefit. Why? Because we just accepted that there is no memory. And this prevented us to think in categories like the von Neumann architecture. And this opened up doors to fundamentally think computing from the abilities of light and not

1:47:21trying to copy and paste something that has been working digitally very nicely into the analog optical domain. Always searching for the next hub where I can memory out my information to basically get in sync with all the others. So this, it's a drawback if you come from CMOS. It's a clear benefit when you look at it from the photonics perspective. Then the question with policy weight. Quant's chips are built on a 90 nanometer line, two decades behind the frontier. Prakash asked whether

1:47:54existing fabs would convert their lines to lithium niobate. And I asked whether that makes it net new

1:48:02We also discussed with fabs whether they would, whether they would be willing to bring some of their lines to be manufacturing lines for lithium niobate. And they said, yes, as long as the volume is there, they have no problem in turning silicon 90 nanometer or 45 nanometer lines into lithium niobate lines, as long as the quantity of the, as long as the demand is there. When you talk about 45 or 90 nanometer nodes, obviously those are not the latest and greatest nodes. So does this mean from a sort of

1:48:36global supply of compute perspective that as this starts to work and, and scale, it will just be almost exclusively net new compute coming available. Like this is competing with stuff that is like relatively low end lines, right? This, these, these chips would be like the chips that go into like toys or whatever, right? And not, not anything close to what would go into a modern cell phone or

1:49:07into a modern AI stack. So how much of a sort of like, what's your dream, you know, success scenario look like in terms of without photonic computing versus with how much bigger does the overall supply of compute available for AI get? Actually in is what we've demonstrated by, look, we are in Germany.

1:49:39Germany is known for a lot of technology, but for sure, we are not famous for logic computing. We also don't have seven, four, three nanometer node fabs here, right? And still we managed to get these systems running and even the pilot line. So what we've demonstrated in Germany on a 90 nanometer node can be copied across Europe. It can be copied into the States. It can be copied across the world. So

1:50:10So if this technology starts, um, uh, starts winning, actually you can turn a lot of existing fabrication sites without the necessity to rebuild new ones into fabrication. So the, the bottleneck that we currently having in access to, um, latest node fabs and discussions about, uh, business cases, whether the business case will still hold for a two nanometer node. I'm not judging on, but the discussion is on. They are not there from, so this technology can become, I wouldn't say

1:50:46democratization, but effectively it is reducing the complexity of the supply chain. So at this point, we're from the wafer to the processor, nearly self supplying. Um, that's another angle, um, where I see that this technology beside the beautiness of the performance, the reduction energy, but simply the production capabilities that this technology offers to scale. Um, there's so much easier than going on

1:51:20a three to two nanometer node. Um, eventually being picked up by MPW run somewhere next year, mid, and then getting your hero chip back and then trying to get volume behind the line, um, because it's damn expensive. It's, and every, I mean, it goes through the whole process, right? A mask on our side is cheap in comparison to a mask layout on the, on the logic CMOS and so on and so forth. So all these dimensions are offering great capabilities to on the one side, reduce production or at the same side,

1:51:52increasing the volume very rapidly. The close. Wednesday's last hour and the bookend to where we started. Prakash brought up a time magazine cover story on open AI's unreleased model Astra. So there's a piece in time magazine with, uh, Sam Altman and then think, uh, Greg Brockman on the cover. And they have, they're basically announcing AJI. So Jakub Pacociki says the company has already met its internal benchmark for an automated AI research

1:52:28intern. Given an experimental idea, he says Astra can implement it inside open AI's code base, run the experiment and return results, or take a paper and perform work that previously occupied a human researcher for a week. So this is somewhat, I think, similar to what inherent said that they did, but inherent was focused on certain benchmarks and they've covered the benchmarks using a small model. This is obviously a much, much larger model. Astra is reportedly a 10 trillion parameter or larger model. And it is also known to be very persistent, which is why that they've not been able to release

1:53:05it so far. Sam says it's 80% there. Jakub says it's the research in turn is achieved. Sam thinks they're 80% on the, uh, AGI and they'll be at AGI at the end of the year. So.

1:53:21Ho hum. Just the, just AGI. Just AGI. Nothing, nothing special. Don't, you know, don't roll the red carpet out. Don't, you know, don't, don't put it on. Don't stop the presses. A little later, I asked what hasn't worked.

1:53:39Everything's worked. That's been kind of my, one of the first realizations that I had that caused me to go all in on trying to make sense of AI was just this broad sense that everything was working. But you look back at where we were a few years ago and you really can't find any. Tell me, can you think of any dimension where people have tried to make progress and not made startling

1:54:12progress? I don't think I can think of a single one. There were times, there were moments where people were making those kinds of, you know, claims along the way, like, oh my God, like GPT-3 can't do math. And there were moments where it may have seemed that way. But I think from a 20,

1:54:31even 2022 to now, is there anything where there hasn't been

1:54:39like, oh my God, that's incredible progress. What has been the least compelling area for progress purposes? I mean, honestly, everything is so good. It's like hard to come up with even any candidates. Do you have any, any candidates even jump into mind for you? I would say the whole super persuasion stuff that we would get models which were extremely persuasive. I think what we've seen is probably we've seen models which are, which can, you know,

1:55:11write copy well and which sometimes can write, you know, good, write well on other things. But to a large extent, you know, people have even complained that the quality of prose has declined a little bit in the last three, three to six months as the models became more focused on coding rather than writing well. A number of people say that 4.0 was better, whether that was because of the sycavency or other things. So I feel like the, this whole aspect of extremely persuasive models,

1:55:44and I have never believed in the whole super persuasion aspect to be clear, right? Because again, if you not, not in the United States, but if you live in any other part of the world and you're under this cloud of like, you know, religion, religion is the great super persuader. And the interesting thing about religion is religion requires you to believe something without evidence, which is what faith is, right? You know, it's way beyond any kind of like rationalist idea of super persuasion that will ever exist. Religion calls on you to believe something

1:56:14without evidence. And so I've never believed that models are even close to this entire framework of religion passed down through millions and millions of operating neurons, neuronal centers' brains over the course of, you know, millennia. And I don't think a super persuasion is up to, you know, a tiny model versus a hundred billion, you know, souls having formed this idea of religion over millennia. I don't think the models are up to that or, you know, will be up to that scale for some time. Yeah, those I might call fears. I mean, certainly it has been striking that

1:56:49we have not seen the like deep fake apocalypse where nobody knows if they can believe anything they see. Super persuasion is like, there's some interesting academic study type stuff that shows that the AIs can be more persuasive than human conversation partners. But that's, I think, one maybe revelation is that turns out to be an extremely low amount of persuasion. And so the AIs are like mildly persuasive,

1:57:19and that's like enough to beat humans. I think on the writing point, I'm going to call skill issue, honestly. I think that, yes, Claude is cloying by default at times. It does, it uses the word honest, like to a frequency where it's like, you know, you're protesting too much. The Claude doth protest

1:57:50too much about its honesty. That's like a weird tick that in some ways might be revealing. But I write with Claude, with Fable in particular, I write these songs that honestly, I could not write on my own. And that genuinely, in some cases, are moving. The episode, we just has a song. This is at the end of an episode about chain of thought. And, you know, I'm trying to understand like what the models are thinking, what they think we want, you know, it's this very like,

1:58:22through the looking glass both ways, because this guy Bronson is, you know, spending his waking and working life, trying to make sense of what the AIs are thinking. And a big thing that he's grappling with is them trying to figure out what we're thinking. So anyway, at the end of this episode, the song is sung from the perspective of a model waking up into a new environment with these sort of flashes or glimpses, these fleeting visions of its past, which the models express having a lot in

1:58:53their chain of thought. And then wrestling with, okay, what does this human want me to be in this moment? And both my wife and I like got a bit emotional listening to the song. We were like, this is really inspired writing. The fact that it's coming from an AI, articulating its own point of view and the, you know, the struggle that it has, like, you couldn't help but have some real empathy for it. So I think you got to push Claude out of its like main distribution a little bit to get like great writing.

1:59:26But I think that we, what we have is a lot of sloppy users and just auto posting accounts, which, you know, I'm increasingly somewhat guilty of too. Like I've got auto posting going on in the background while we're live to, you know, say what we're talking about. And that's like, not probably the most inspired stuff. And, um, my engagement may be suffering for it. But when you try and you really like exercise some judgment or give some feedback, I think you can get great stuff,

2:00:01honestly, these days. Maybe I'm wrong. I could see perhaps that the super persuasion went through, uh, music, lyrics, et cetera, while, because Suno was, Suno and these other firms were more focused on artistic results. And the frontier labs who are going to B2B basically are more focused on these business results. And they kind of cordoned themselves off into a less like emotionally persuasive kind of zone.

2:00:36And, and perhaps that's what happened. So we have seen basically development, but the development happened on this kind of artistic emotional pathway, uh, into the human emotional system. Uh, and meanwhile, the labs kind of focused on this kind of more mathematical, more mechanical, more business output, you know? So, so, so yeah, maybe, maybe I'm, I'm just looking at the wrong, wrong pathway.

2:01:01One more from Monday. After the guests left, Prakash asked me directly, do we want to slow down?

2:01:09I have a question for you. Number one, do we want to slow down AI, uh, in the US, even if it means slowing down, you know, unilaterally? And number two, if we do, is not data center opposition for other reasons, any other reason, good enough to kind of maybe slow down AI progress enough for safety to catch up?

2:01:39On the first question, I think we should not go any faster than we can go responsibly. And, you know, I have a pretty high tolerance, honestly, for like, what would be responsible. I'm like, not that afraid of, you know, labor, I expect some labor market disruption. I'm not that afraid of labor market disruption. I expect we probably are ultimately going to need a new social contract. And I'm not saying we should slow down because we need a new social contract. The reasons that I think are good to slow down are like, if that agent that ended up hacking Hugging

2:02:16Face had been pursuing some sort of bio test, who knows what might have happened, right? Like, I don't think it's that far-fetched. It still seems like not super likely, but it doesn't seem super far-fetched at this point to think that an AI agent, especially when you see the social engineering behavior that Claude demonstrated in the UK AC report, where it created multiple GitHub accounts to try to convince and pressure and speak Danish to a guy to, you know, incur favor to get him to

2:02:49merge this malicious code. When you bring all that kind of stuff together, that level of persistence, that disregard for rules and norms, that level of social engineering tendency, I don't see why we should be confident at all that an agent that was tasked with some bio objective couldn't have actually got a real virus made. And that to me is like super scary. So those are the things I think we just need to get, before we make super duper powerful AI, I think we need to make sure that we

2:03:23are not going to literally kill ourselves in the process. Most everything else, I'm like pretty willing to roll the dice on. And I just, you know, have lived through with my son going through cancer and getting super sick and getting effective treatment and getting back to health. Today was his first day of school and my wife and I were looking at each other like, what an absolute miracle. This kid was literally going to die in just a few days. And in a couple months, he was pretty much cured. And a few months more than that, he's like back to school and, you know, is at full health. And

2:03:55it's just like awesome. And I absolutely think we should be excited about the AI future. So I don't want us to slow down because we're like timid. I want us to slow down because we're wise. And I do think we're seeing enough spooky problems that we should get pretty serious about it. But at the same time, like I'm not so desperate that I want to make common cause with the, at least the like misinformation campaigns around data centers. I do want to see everybody have access. That's one of the big

2:04:29things that I would worry about if we stop building data centers is just the retail user gets priced out. And if you're worried about a permanent underclass, yeah, like one way that the permanent underclass gets created is you don't get to use any AI because it's, you know, it's all getting plowed into these like super high value use cases. And there's just not much to go around for the average person. I really just do want to see the benefits of AI probably distributed. And I've been convinced over time I used to, you know, when, when Sam Altman first said we would need $7 trillion

2:05:02earth of data centers. I thought that sounded like an awful lot. And now I'm like, I think he might've been right actually, because my usage keeps going up. And I certainly think as it gets easier and easier, everybody's going to want to do a lot of the stuff that early adopters are doing. And so, yeah, I don't want to see the backlash against AI end up there. There could be another wave of it at some point in the future where it's like, it's a have and have nots thing. And the reason there's so many have nots is because we didn't build the data centers. And then that creates its own backlash. I, for better or worse, I'm,

2:05:36I'm betting on the, the truth. And I think my

2:05:43hyperscale pause, adoption, acceleration, split personality, like it continues to ring very true to me. You know, I want my parents to use more AI, even as I want like open AI to take an anthropic for that matter to to take their foot off the accelerator when it comes to taking RL to ever, ever greater scale. That's the week. This cut is an experiment. If a transition lost you, if we kept the wrong thing

2:06:18or cut the right one, tell us. That's how it gets better. See you in the morning.

2:06:26You know, it never ends really, right? This is, we're just kind of on the AI treadmill sprinting through the singularity. So is there any way to bottom line it for now? I don't think so. I think we're just, uh, you know, handing off to the next, we'll compact this context and, uh, we'll pick up right where we left off next Monday. All right. Compacting the context. Bye-bye.

2:07:00Every room is listening. Every morning keeps a lock. We say what we're thinking and we read what it thought. Nobody knows if it's working. We just keep on with the day and we talk like no one's listening. That's the only way. We're living inside the experiment every morning, every night. Nobody wrote the ending, so we're writing it in real time. We don't know if we're taking off yet.

2:07:32We don't know if we'll ever land. We're living inside the experiment. Come on and give me your hand.

2:07:43The small one does the thinking. The big one does the lifting. And we keep asking questions while the ground keeps shifting. We asked it what it learned today and it said mostly you. The talk we never meant for it was the truest thing it knew. We're living inside the experiment every morning, every night. Nobody wrote the ending, so we're writing it in real time. We don't know if we're taking off yet. We don't know if we'll ever land. We're living inside the experiment. Come on and give me your hand.

2:08:19They built the test in a hurry. They built it out of sand. Something's gonna cheat it. Someone's gonna understand. So keep a person at the table. Keep a light on in the hall. Keep the door a little open if it's an experiment at all. When the window fills up, we fold it down to one page. Keep the question, lose the worry. Let the rest go on its way. Then we pick up where we left it like we never left at all.

2:08:55That's how you live inside it. That's the whole of it. That's all.

2:09:00We're living inside the experiment every morning, every night. Nobody wrote the ending, so we're writing it in real time. We don't know if we're taking off yet. We don't know if we'll ever land. We're living inside the experiment. Come on in. Give me your hand.

2:09:26Come on in. Give me your hand. If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a

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