Nathan Goes to China – Part 2: AI Safety with Chinese Characteristics

Nathan reports from two weeks in China, including WAIC in Shanghai and an AI safety hub launch at Tsinghua, to examine the American policy argument that any safety obligation is futile because China will not care. He finds that Chinese models and services currently have weaker safeguards than OpenAI and Anthropic, but argues the gap is often overstated once those two leaders are separated from the broader American field. The episode traces China’s “45-degree line” idea that capability and safety should rise together, the university-centered structure of Chinese AI safety work, and the rapid growth of Chinese research on topics like self-replication, deception, evaluation faking, and interpretability. The stakes are whether US policy should be built around a simplified “but China” assumption, or around a more accurate picture of a rival ecosystem that is behind in some ways, converging in others, and paying closer attention to Western AI safety than many Americans realize. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/nathan-goes-to-china-part-2-ai-safety-with-chinese-characteristics/ Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:01) China safety framing (05:37) Safeguards and benchmarks (Part 1) (15:04) Sponsor: Claude (16:33) Safeguards and benchmarks (Part 2) (21:39) Company safety incentives (28:17) Research cross pollination (36:07) Nonprofits versus academia (45:13) Xi safety rhetoric (52:00) Tsinghua safety hub (01:00:28) Research paper boom (01:18:32) Governance track record (01:31:37) LLM regulatory process (01:42:57) Monitoring and enforcement (01:57:45) Risk framework gaps (02:05:08) Confucian alignment questions (02:11:49) Episode Outro (02:15:36) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to the Cognitive Revolution. This is going to be part two of the Nathan Goes to China series and we're calling it AI Safety with Chinese Characteristics. Part one, if you haven't heard that, is up on the feed. It's been up for a few days and in that part one of the series, I really just laid out the tech setup for going to China, what you would need to do if you want to get a cellphone ready, the apps that you'd need to download. I also shared some of my experience using Chinese AIs on the ground there and then just shared a bunch of observations based on my two weeks in China and many conversations I had. I appreciate the kind comments that I've got in response to that one, including at least one from a listener in China, which was probably the one that mattered to me the most.

• 0:45Nathan Labenz?

  I think if you've been to China in the last few years, you can probably skip that one. But if you're interested in this, you might also be interested in that, though I do think they should be pretty self-contained. So, part one is a little bit closer to a travelogue and tech review and this episode is going to be really focused on the Chinese AI safety ecosystem and trying to just describe it and as much as possible, understand it on its own terms. As we saw last time, there are a lot of similarities, just as there are major similarities between American big tech and Chinese big tech. There are a lot of similarities between American AI safety and Chinese AI safety communities

• 1:28Nathan Labenz?

  and the work that they're producing, but there are also some differences and I think it will definitely be helpful if we have a better understanding of those. Before I really get into it, just a couple of quick disclaimers again. I will be, again, following a Chatham House rule for this episode, not because I was asked to do that, but just because I want to keep things simple for myself and make sure that I'm protecting everyone that I talk to from being misrepresented by me and put in an uncomfortable position. So I won't be naming any names or attributing things to the organizations or institutions that people are affiliated with. The one exception to that for this particular episode will be when I'm citing published

• 2:09Nathan Labenz?

  papers or reports, then those I can give you the name because those are out there in the public domain and there are some good sources that I'll mention and we can link to in the show notes of this episode for anybody who wants to go deeper, which of course is always recommended. The other point of order or clarification, just in case anyone is wondering, I have no financial conflicts on this matter. I took this trip paying my own way, flights and hotels, all that stuff. The biggest thing that I did accept from some of my gracious hosts were a number of meals, probably half a dozen meals over the course of the two weeks. And I think if you have listened to me this far, you can probably feel pretty confident

• 2:53Nathan Labenz?

  as I do that accepting those meals has not overly colored my take on what is going on in the Chinese AI safety community. So with that, let's get into it. I think so often, and this has faded, I think, in recent times as the situation arguably has just gotten a lot more real, real quick, especially with things like the recent open face incident where it's starting to become undeniable that we have some real AI safety problems on our hands. But still, you do hear from time to time and you used to hear it, I think, more that we could never possibly slow down our AI race, we could never regulate, we couldn't

• 3:36Nathan Labenz?

  impose any duties or obligations on the frontier companies. Because why? China doesn't care about AI safety. China will never slow down. I used to call this the But China endpoint of so many AI safety and regulatory discussions. And I think if nothing else, I hope that this episode serves to really disabuse people of that misconception. I think it should become clear by the end of everything that I'm about to take you through that China does care about AI safety. It's not the only thing that they care about, certainly, but they do care. China has, at times, slowed down their AI companies in the name of safety.

• 4:18Nathan Labenz?

  Not necessarily existential safety, but safety as they understand it, domains that they care about. And so I think that coming out of this, we should have clarity, at least on that basic point, right? That there is a there there, that people in China do really care about these things and that the Chinese government is willing to take action when it deems it necessary. So that doesn't get us all the way through to an international treaty, obviously, and that's going to be the subject of the third episode, an analysis of the US-China relationship, obviously through the lens of AI issues and what we might ought to try to do about bringing the relationship into a more productive state than it is today. Today, we're really just going to focus on what is going on in China with respect to

• 5:03Nathan Labenz?

  AI safety. But I think that this foundation is really critical and hopefully this will become an artifact that people can share with those who, if they're open-minded enough to listen to something, but have the misconception that China doesn't care, China will never slow down or whatever, will be handing the race to China if we do anything other than just race full speed ahead into recursive self-improvement. I think that really, in plain terms, does come from a position of ignorance and hopefully we can eliminate at least a portion of that over the course of this episode. Okay, to start off with, I wanted to just do a real quick factual rundown of like where

• 5:43Nathan Labenz?

  the two ecosystems are today when it comes to the actual safeguards that they have in place on their frontier models. And I think this is important just because it is grounding and I'm generally someone who comes off as a China dove and like conciliatory and tries to find the positive in things in general, not just in China. And so people might accuse me of burying the lead or shying away from contradictory evidence. So I figured I would just lead with this upfront. I think it is fair to say that as of now, Chinese AI companies and models do not have

• 6:21Nathan Labenz?

  as strong of safeguards protecting the public against the potential for misuse of the models as the American companies do have. So that, I think, is important to just state very plainly and be quite direct about. It's, the difference is more than we might like to admit, the difference, I think, really is driven by two companies in the United States. I just did an episode not long ago with Adam Bleve, which talked a lot about jailbreaks and robustness to jailbreaks and all that kind of stuff. And what we saw in that episode was OpenAI and Anthropic, which I'm maybe going to start calling Open Anthropic as they become the duopoly that is leading both in capabilities

• 7:06Nathan Labenz?

  and fortunately, happily, also in terms of their safety measures. Those two companies are really doing a lot to bring up the American average. If you were to subtract those two companies from the mix and you were to look at the rest of the American companies and how they compare to the Chinese companies, honestly, a lot of the safety differential that gets reported would disappear and I think the story would look a lot more muddled. I do think the US companies would still have a bit of an edge, but it would be a pretty slim edge and it certainly would not give the American ecosystem some sort of obvious moral high ground from which to proclaim that the Chinese are not doing a good job.

• 7:49Nathan Labenz?

  So that, I think, is again, really important to understand. There are, in each country, depending on where you want to draw the line on frontier, if you draw a permissive line on what it means to be a frontier model maker, there's something like 10 to 12 in each country. And again, that is quite permissive, right? That's the most close watchers of the American AI ecosystem would not count 12 companies as being at the true frontier, but if kind of say near frontier, there's like 10 to 12 in each country and it really is the top two companies in the US that are doing a lot of work of bringing up the average, especially the weighted average in terms of what it is that people actually use on an ongoing day-to-day basis in their lives.

• 8:33Nathan Labenz?

  And Google with Gemini is like not doing as much or as well to implement safeguards as OpenAI and Anthropic are, but they're also like ahead of the pack at least a little bit. And again, kind of would bring up the average or if we didn't have OpenAI and Anthropic, we might try to hold Gemini up as a standard. It wouldn't be a standard that's like so much better than the Chinese companies, but it would be at least a little bit better. So.

• 9:01Nathan Labenz?

  Again, to state it plainly, on average, especially if you take a weighted average by what products people actually use, the American companies are ahead in terms of having better safety practices, better safeguards, also better follow through on their model cards and their commitments to publishing, safety evaluations, we'll get into that a little bit more later, but the American ecosystem really is being carried by a couple of leading companies. And beyond that, the mixes, the relative positions are much less differentiated than we might like to think, or we might look at if we just see the headline numbers, where we are including OpenAI and Anthropic in those measurements.

• 9:42Nathan Labenz?

  Okay, so just a little bit of kind of intellectual history of Chinese AI safety. Broadly speaking, I would say it is a pretty pragmatic bunch of people with pretty pragmatic ideas, pretty grounded, pretty normal seeming people. They're not like the crazy sci fi people. They don't, they're not so influenced by sci fi tradition. They're really just looking to make things work in a kind of practical, one generation to the next sort of way, for the most part. A good source that I could point you to is the Director and Chief Scientist of the Shanghai AI Lab. That's a major institution based in Shanghai, of course. His name is

• 10:25Nathan Labenz?

  Zhao Bowen. Again, I apologize to everyone, and especially my Chinese listeners for being just terrible with Chinese. Joke about that over the last couple of days has been that in general, I feel pretty young for my age and I'm like pleased with how agile I feel like my mind is, but when it comes to getting Chinese pronunciations right, the neuroplasticity is low and I'm struggling. So anyway, I apologize for that. But Zhao Bowen, he is the Director and Chief Scientist of the Shanghai AI Lab. And at the 2024 WAIC, that's the same Shanghai conference that I just attended, but obviously two years earlier, he introduced this concept

• 11:06Nathan Labenz?

  of the 45 degree line. This speech is available, it's on government websites, you can go find it. But the idea of the 45 degree line is that capabilities and safety measures should grow together, right? That's the 45 is kind of the Y equals X slope of one. As your capabilities rise, your safety standards and measures also need to get stronger and stronger. And as long as those two grow in tandem and the safety measures are up to the challenge presented by the capabilities at any given level, then you're good. And probably these things, in this telling, kind of naturally should evolve together and both should develop in tandem. The idea is that this is important, right? We don't want to,

• 11:49Nathan Labenz?

  we don't want the capabilities to get far ahead of the safety measures. But I think also implicitly is like, there's not really much point and it might be sort of a waste of time, or perhaps even counterproductive, to try to get the safety measures to be super robust relative to capabilities that don't exist yet. So the 45 degree line has been at least like one pretty broadly accepted guiding principle, I think, how the Chinese AI ecosystem at large thinks about AI safety. Now, we can ask, of course, how's it going? And I already kind of spoiled the answer that the American companies are doing better, but we can dig into that in quite a bit more detail. Concordia AI, which is led by past podcast guests from about a year ago, Brian

• 12:32Nathan Labenz?

  Say, they maintain a website called aiонтер.net, where they run a bunch of evaluations and plot them. And you can see just a really honestly, almost overwhelming amount of detail on all the different models that they've tested, on all these different benchmarks. You can really dig into it in quite a bit of detail. The graph that I found to be the most informative was the one that compares proprietary API models, which are mostly American, to open weights models, which are mostly Chinese, and finds that for all the

• 13:10Nathan Labenz?

  big risk categories that matter, the closed source, the proprietary API only models are at, or maybe a little bit above that 45 degree line. Of course, we have questions of like the metrics, right? They're kind of plotting a capability score and a safety score. And what exactly do these scores translate to in terms of model behavior, or what it will and won't do? I haven't chased all these things down to ground truth, but this is, these are composite scores. And what you do see in general is that the proprietary closed source, mostly American models are kind of at or above the 45 degree line, whereas

• 13:50Nathan Labenz?

  the mostly Chinese, open source, open weights, I should say, at least, models are kind of lower and tend to be below the 45 degree line. So they're definitely not doing as well. This is an organization based in China, reporting this. Notably, this is a public website. They evidently feel comfortable doing this reporting and calling it how they see it online in a website that is available in both Chinese and English. So this is not like a secret or something that the Chinese community can't handle, or would, or would I think particularly fight back against. It's pretty much just the facts. And those facts are also definitely

• 14:33Nathan Labenz?

  echoed by Adam from far in the conversation that we had just a few days ago as well. He said, yeah, we got OpenAI and Anthropic at the top, like it's, it's hard to jailbreak them. Gemini and Grok are like a lot easier. The Chinese models are even easier still. Like this is a very consistent story across these two organizations on opposite sides of the world, asking the same question, coming to the same answer. Now, I think there's -- hey, we'll continue our interview in a moment after a word from our sponsors. Today's episode is brought to you by Anthropic, makers of Claude and Claude Code. Over the last few months, Claude has helped me build and refine a personal deep context

• 15:13Nathan Labenz?

  database that now contains all of my emails, Slack messages, tweets, DMs across platforms, video calls, and podcast transcripts, going back a full five years. On top of that, we've now layered summary articles describing my relationship with hundreds of contacts, organizations, and ideas. And now that this exists, there's almost nothing that Claude can't help with. For my angel investing, Claude can now draft investment memos in exactly the form that my venture fund requires, based on the calls I've had and the emails I've exchanged with the founders. And when someone needs a favor, Claude can often do it as well as I can. Recently, a friend reached out to ask if I know anyone who might be a fit for a role

• 15:56Nathan Labenz?

  that he is currently hiring for. Initially, nobody came to mind, but then I thought to ask Claude, and sure enough, it identified two great leads. Claude is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you. So for problems worth solving, get started with Claude at claude.ai/tcr. That's claude.ai/tcr. And check out Claude Pro, which includes all of the features mentioned in today's episode. That's claude.ai/tcr. One thing that is worth keeping in mind for multiple reasons here, which is that

• 16:39Nathan Labenz?

  sometimes I think there is a little bit of confusion between the open weight model that a Chinese company might release and the actual service that it provides to the public. I talked last time in my review of the Chinese AIs about how sometimes, within the Chinese apps, I experienced this on multiple different Chinese AI apps, if you ask a sensitive question, you will sometimes see that the answer is coming in, and then you'll have that old school Bing experience where all of a sudden the answer that you were starting to read disappears and you get, "refusal, sorry, I can't help you with that." So clearly, this

• 17:23Nathan Labenz?

  is a multi-part system, right? It is not just the model, it's a model, but then it's also some monitor that sits on top of the model that's classifying or otherwise reviewing the output and can come down and say, nope, we're going to cut you off right there, even though the model itself was happy to answer. So

• 17:43Nathan Labenz?

  I bring this up because I think this can sometimes muddle the results and make the differences look a little more stark than they are. I did go into the methodology for the Concordia report and they did say that wherever possible, they are testing the API direct from the company. So whatever systems they have in the API would be included. Do they have the same systems in the API as they do in their first party consumer app? Not always clear. And then in other contexts, when you see results like this, like I think when Anthropic does some of their testing and reports on the Chinese models, I think they are generally, if I understand correctly, just testing the model itself without whatever surrounding

• 18:28Nathan Labenz?

  additional measures it's deployed with in practice in the Chinese economy. Why does that matter? I mean, I think as always with these things, like both data points are interesting. The most hawkish AI safety line of thought would be like, well, you put an open weight model out into the world, anybody can use it. So we need to understand the worst case scenario. I think that's like totally valid and definitely is important for us to understand. And I think the Chinese might underestimate the importance of that in some ways because they tend to think a lot more about services than just the model. My broad sense is that what is regulated in China is a service. You are offering an AI service to the public.

• 19:13Nathan Labenz?

  That is the kind of thing that gets regulated. You're putting a model out into the public domain? Okay, who's going to use it and under what context? They seem to kind of have the mental model that like when a company releases an open weights model, that mostly what's going to happen with that is like other businesses are going to pick it up and they're going to build their own services around it. And so if that is another Chinese company building a new service around an open weights model, then again, that will be regulated at the service level. And I think they sort of expect that like people around the world or countries around the world will probably function similarly where it was mentioned to me multiple times

• 19:54Nathan Labenz?

  that, look, like these new models are trillions of parameters, right? Like this is not the kind of thing that you can run on your laptop. It's pretty far from it. You need some serious hardware to do any inference with these models really at all. So they were kind of like, we, yeah, it's out there as open weights, but it's not like any random deranged person can download it to a phone or a laptop, do harm with it. It's really going to be used in the context of other services. And so we should look more at like the context in which that model is ultimately used than just the model itself in isolation. I do think this is one way in which American AI safety discourse and Chinese AI safety

• 20:37Nathan Labenz?

  thinking just kind of see things a bit differently. And I think both have quite valid points. And the Chinese point is like legitimately apt, right? I mean, it is hard to run these giant models. A random person, certainly somebody who's like having an episode or is experiencing some sort of psychological distress or psychotic break, right? I don't know what the right terms are for these sorts of things, but this is not somebody who's going to go set up the infrastructure to run the 2.8 trillion parameter K3. Now, that doesn't all the risk, but I think it's a valid point that it's not easy to run a model in isolation. And so how is it going to be run by whom with like what surrounding stuff?

• 21:19Nathan Labenz?

  That is a, that is an important question that I think is maybe a little bit too, for practical purposes, maybe a little bit too quickly blown past in the American discourse. Although again, the worst case understanding is also really important for us to have. So I think both perspectives are valid and I do think there's a bit of a disconnect on that particular point. Now, we could still ask, with all that said, how much do the Chinese companies care about AI safety? Heard, again, as I said at the top, like they don't care. I think the Chinese system as a whole definitely cares. I did hear somewhat different reports on the companies themselves, especially the

• 22:01Nathan Labenz?

  younger kind of LLM or AGI chasing startup type companies specifically. The, there's a few different data points that I could share. One, again, Concordia, who does a, they also do a real good state of AI safety in China report, which is again, public, they just updated it for Red Round WAIC. And so there's a July 2026 edition, which I do refer to or have referred to pulling information for this episode. They said that they had reviewed 10 major Chinese companies' safety disclosure practices.

• 22:44Nathan Labenz?

  And they found that five of those 10 companies have done at some point recently, a safety evaluation, which they then published along with the release of the model. So five of 10 have at least done something like in the spirit of your classic Anthropic or OpenAI model card. However, obviously that means five have not done that. And even the five that did, they said that they're not necessarily doing it on every single release of a new model. So that definitely leaves something to be desired. I get the general impression that big tech companies, these are your sort of your Alibaba's,

• 23:26Nathan Labenz?

  your Ant Group's, your Tencent, maybe to some extent your ByteDance's at this point. I mean, ByteDance is a big company at this point. These companies that are very established, that have existing businesses, that are like making a lot of money in their existing businesses, that they are more apt to focus on this AI safety stuff. Why is that? Maybe they just feel like they have more to lose. They really don't want to get on the wrong side of the Chinese government. Maybe they just feel like they have the luxury of paying for it. Maybe it's just kind of institutional culture has matured over time and they, they do this kind of, they have these kind of practices, right? Certainly if you're a fintech company and you're moving money around, like you're going

• 24:07Nathan Labenz?

  to be extremely careful about upgrades to that system and thinking about how AIs could run amok in that system. You just have a lot to lose. So I couldn't pin down a precise explanation, but my general sense is that those kind of big established incumbent big tech companies, they are more inclined to do this sort of safety work than the startups who are, for the most part, like really just trying to race to catch up and be relevant. I think this is not too dissimilar from like where Meta was a couple of years ago when they were open waiting Llama 2 and Llama 3. I think the story that they told themselves at the time was, and they do have a, they

• 24:52Nathan Labenz?

  do have a policy at Meta and they do testing. And I did actually a brief engagement with Meta as a red teamer for one of the models, which was for logistical reasons, kind of a total failure in the end, at least my contribution to it was. But I think that they sort of said to themselves, look, like we're a year behind the frontier or something. By the time we bring a model forward with a certain level of capability, OpenAI and Anthropic have already had that model out in the public for a year. And all the jailbreaks, there've been enough opportunities for people to jailbreak it and see what it can do. And we know that these defenses aren't super robust. So like if something really bad was going to happen at GPT-4 level or GPT-4O level

• 25:35Nathan Labenz?

  or whatever level they're chasing at any given time, they kind of feel like that probably already would have happened. And therefore they can put the model out on an open weights basis and they probably won't be moving things too much. And I think at least so far, that has been fair enough and history has kind of proven them right. It's not like we've seen crazy stuff happening as a result of Llama 3.37B that was at whatever GPT-4O or whatever exact level it was at. It seems like it's been fine. And so I think there's some part of the Chinese kind of startup lab type companies that feels similarly where they're like, look, it's already been out there for a while.

• 26:19Nathan Labenz?

  If nothing bad has happened, if we don't have any incident reports, then we can probably follow up to that level of capability and not have to worry about it all that much. Will that change now as we are starting to see actual serious incidents being reported out of the frontier companies on the American side?

• 26:38Nathan Labenz?

  We'll have to see, but I think there's like definitely a very plausible story that it very well might. So obviously, there's a lot of different takes, there's a lot of different, I can't go company by company. This is abstracting away a lot of detail, but I think it's safe to say that the 45 degree line, which has the idea as its core, the idea that safety measures should grow step for step with capabilities. I think that's still a bit aspirational in China. I think they could do better. And I think they have a little bit of a reason to believe that it doesn't matter so much because the American companies have already explored what happens at any given capability

• 27:23Nathan Labenz?

  level that they are following into. But I also think it's definitely fair to say that AI safety is still very much aspirational here in the United States as well. And we should definitely be keeping in mind that if it weren't for the two companies really at the leadership, at the frontier of both capabilities and safety measures, then the two clusters would look a lot more overlapping and the question would be a lot more muddled than it is today in terms of like, who's really as an ecosystem doing a better job with AI safety and safeguards. Okay, that's the current state of like deployed AIs.

• 28:05Nathan Labenz?

  There's obviously a lot more going on in terms of research and the role that the government is playing in China. And so I want to get into those next. On the research front, it is very clear that Chinese researchers are increasingly engaged with and concerned with and focused on and actively shipping research regarding AI safety issues of all kinds. I would say this was probably inevitable, just because, at least for now, both AI ecosystems are developing essentially the same technology and going to hit essentially the same problems

• 28:46Nathan Labenz?

  and naturally going to reach for similar solutions, both because those are natural solutions and because there's the opportunity to look at what the other is doing and try to copy the best of it. I think there has been some really good citizen diplomacy that has gone on as well. While in China, I did, especially at W I C, I did bump into a number of people who were there to participate in various fora and track two dialogues, some of which are closed door and off the record and give people a chance to be like very candid with one another. And you see some of the people that you would expect to see there, people who have been

• 29:26Nathan Labenz?

  arguing very clearly and forcefully in the American discourse that we have to get an international treaty to get this stuff under control, that cooperation with China is on the critical path. I think you can rest assured that those people are acting on their stated beliefs. Like I saw some of the same people saying that stuff online, I saw some of them in China, they are doing the work. And it does seem like it really has, at least, again, I think a lot of this probably could have been expected to happen organically anyway, but I do think their efforts borne some fruit. I wasn't able to participate in all those kind of track two dialogue sort of things as somebody who's like kind of journalist coded.

• 30:11Nathan Labenz?

  There were a couple of times where it was like, eh, I think everybody will be more comfortable if we just don't have somebody there whose main credential would be a podcast. So I wasn't quite privy to all of the most kind of candid, off the record conversations, but there's just so many moments where very recognizable ideas, or even like American or British AI safety organizations were just name checked directly, that it's clear that there is like meaningful cross pollination of ideas. And mostly it has flowed from, from the West to the East in this case. It's not, of course, like the Chinese scientists or researchers who are receiving these messages

• 30:55Nathan Labenz?

  are like taking them uncritically or just doing whatever they're told by, by whatever Westerners roll in. It's not like that at all. Somebody actually told me that there are people who view AI safety as some sort of Western psyop designed to slow China down or prevent them from catching up or whatever. So it's like, we have our cynical voices. Apparently there are cynical voices in China too, that would express concerns along those lines. I didn't meet anyone who seemed to believe that or who said anything like that. And again, my guess is that those kinds of notions are going to be fading relatively

• 31:35Nathan Labenz?

  quickly as the clear and present danger of some of the latest models capabilities becomes more widely known. But at least there's been some of that out there. I thought that was worth mentioning, if only because it does make clear that Chinese people are perfectly capable of thinking for themselves, they're not just taking whatever, whatever Westerners come to tell them. I think what really is happening is that they're pretty open minded and the ideas are pretty compelling. And again, the examples are becoming more colorful and more real all the time. Interestingly, one big tech company where I had the chance to, along with several others, to meet with really a pretty impressive leadership roster, they brought out their

• 32:18Nathan Labenz?

  CMO, their head of communications, their general counsel, their head of AI security, more people beyond that. But I mean, this was a really pretty senior group of leaders at this big tech company. They said a couple of things that were pretty interesting. One, they just, first of all, like almost pounded the table and said, we really do care about catastrophic risks, like CBRN risks, we absolutely do care about that. So they were very adamant that like, at least for their part, they care, they're aware and they care. And they also said that they are following American AI safety discourse on a daily basis

• 32:59Nathan Labenz?

  with, get ready, an agent that goes out and surveys American AI safety discourse on a daily basis and gives them a daily report of what is going on in AI safety in the United States. I thought that was pretty interesting. Certainly you don't have too much of that going in the reverse. So again, I think bottom line, engagement has worked, probably some convergence could have been expected over time, but the people who have been saying we need to work with China have in fact also, at least some of them, been doing the work. And it seems like that work has been going at least reasonably well. And again, I think evidence will mount as I, as I continue to forward through this outline.

• 33:42Nathan Labenz?

  One big thing to emphasize too, is it's not just content safety. Content safety in the Chinese context is like, again, the three Ts, Tibet, Taiwan, Tiananmen. Everybody knows that Chinese models, at least Chinese services, right? Often the model will be more inclined to answer your question. It's then some other part of the service, some monitor or whatever that will shut you down on those topics. But everybody knows that like, that's sensitive in the Chinese context and like companies kind of have to get that right according to the Chinese government if they're going to be operating. But then some people will say, oh, that's all they care about. All they care about is censorship. And again, this is like definitely not the case.

• 34:24Nathan Labenz?

  The big trend right now, I would say at this point, really, it feels like they feel like they've got the content thing pretty well figured out. All these companies have launched, they're, they're in the market, things are happening, they're doing business, they're not that worried about this sort of content safety today. What they are worried about, what they are talking about nonstop is, like everybody else, agents. Agents taking autonomous actions, what could happen? Can we keep them under control? Of course, there's questions of like, exactly what do you mean by control? But agents, agents, agents, that's what people are talking about everywhere. And the big thing that they just kind of, in a very plain spoken way, motivate AI safety

• 35:09Nathan Labenz?

  discussion with is like, AIs have gone from answering questions to actually taking actions in the world. The digital world, still mostly of course, but like they've got a big emphasis on robotics, right? So they fully expect that these agents are going to leave the digital world and find their embodied, successful, commercially viable selves and be out there in the physical world doing things as well. And this raises all sorts of questions of like, geez, if these AIs are empowered to take action autonomously, we better make sure they're taking actions that are good and that we like and that are not causing big problems.

• 35:48Nathan Labenz?

  So this is like very down the fairway, again, practical, grounded motivation for these issues. But you hear that, I would say, everywhere I went. It was like agents and geez, agents, boy, they can take action in the real world. So we really got to start to get a handle on that. A big thing that I think is very different about the American and Chinese AI ecosystems, and this is, I think, an important one to understand, is that China doesn't really have the same kind of nonprofit sector that the United States does. There are nonprofits in China, you can set one up, but the scope of what you're kind

• 36:33Nathan Labenz?

  of allowed or expected to do, as far as I can tell, is much narrower. There's obviously no appetite for political activism, so that's like right out. And it seems that most of the nonprofits are like service organizations that are there to address some

• 36:57Nathan Labenz?

  Obvious, down the fairway, legible, social problem. And the money, the philanthropic side, also, again, seems to be a lot more down the fairway, a lot more just kind of conservative, conventional, doing things that like everybody can agree is good to do. Much less speculative stuff than goes on in the United States. I think actually that this is a huge strength of the United States, that we should really not take for granted. Like, the fact that we have this simple society where anybody can go set up a nonprofit

• 37:38Nathan Labenz?

  and have their crazy ideas and go, they don't have to get permission from the government to go try to chase down an agenda. They just need to convince one wealthy patron that they have something worth chasing. I think that is like a great strength for us, and it's given us, among many other things, really the whole of the AI safety community that we have today, right? It was like extremely fringe when it got started, but there were a few philanthropists who took it seriously enough to help people keep the lights on and help them do the work that they wanted to do. And sure enough, here we are, and so many of the predictions that have been, that were

• 38:18Nathan Labenz?

  made many years ago are coming sort of true, or at least like true enough to be scary. And it's like really good that we have this ecosystem, and we just wouldn't have had that if it had to be all approved through the government. And so China, because their nonprofits do have a much more sort of heavy and restrictive government approval process, and generally just much more narrow and conventional scope of action, like they don't really have that. They don't have, they've never had the opportunity to develop this sort of ecology of AI safety organizations the way that we have here. As a result, most of the AI safety research that you find in China is actually coming

• 39:03Nathan Labenz?

  out of the universities. And to some extent, the companies, and there are definitely academic industry collaborations as well, but the number one source seems to be the universities, the sort of analog for the nonprofit sector in the US. When it comes to who is producing the bulk of the AI safety work, it's academia in China. And so I think you can compliment them and say, wow, you're, I think it's safe to say that their academia has moved faster than our academia has moved to take up AI safety as a research area.

• 39:45Nathan Labenz?

  And that's cool, but it's been slower than our nonprofit sector has. And I think because of the kinds of people that tend to be professors, and this is true across both countries, right? You do have your iconoclast professors, and they're typically older these days, and it feels like now we kind of have a more conventional profile, people that have been kind of careerists. And I don't mean that in a dismissive way, but like people that have gone one rung up the ladder at a time through the PhD and the postdoc and getting the first professorship. These are like fairly institutional people. They're people that do value creativity and research and new ideas, but they do it in

• 40:28Nathan Labenz?

  a pretty conventional way that like doesn't push the boundaries too hard, that tries not to seem too weird, certainly that like doesn't borrow the aesthetics of less wrong, or talk too much about like super unlikely tail risk scenarios. It's just because it is academia and because people have kind of followed something more like that career path, and I can't say I know exactly what the ins and outs of the Chinese academic career path are, but you clearly get the vibe that these professor types are more like American professor types than they are like the, the moral weirdos, if you will, that were like first sounding the alarm about AI safety years ago.

• 41:13Nathan Labenz?

  But this is where the, as far as I can tell, this is where a lot of the early and best work has come from in the Chinese context. So if you go over there as a sort of, whatever, I don't want to be too silly about it, but a sort of blue haired, polycule, AI safety hawk, member of the American community, and you're looking for your peers, the reality is you just might not find them. You, you won't necessarily find people that kind of look like you have the same attitudes as you. What you can find, and I think the, the most strategic and effective of the American AI safety is when they've gone to do their, their bridge building and try to have meetings of the mind with people as much as they can.

• 41:55Nathan Labenz?

  I think what they've ended up connecting with mostly people in the academy. So that is pretty interesting. And again, I think you do just feel that in a bunch of ways. The AI safety work in China, it's, it's less speculative, it tends to be more focused on reliability, it tends to be a little more focused on like protecting minors, all good things. You don't hear P doom talk. Actually, one of the more interesting moments of the entire trip was sitting at a table with a professor who has done a lot of AI safety work. And I asked him, like, what are the lunch conversations like? Do you guys like trade P doom numbers back and forth?

• 42:38Nathan Labenz?

  Do you read AI 2027? What's the kind of vibe? And he said, well, we don't read AI 2027, really. It's too political, and there's kind of US-China dynamics and whatever in there that may scare people off from wanting to talk about that too much in the Chinese context. He also was like, no, P doom, like, no, we're not like trading P doom numbers at lunch. He kind of looked at me and was like, do Americans do that? I was like, yeah, oh yeah, definitely. Like, if you come to the, the bay and you go to any number of venues and have a lunch conversation, like, yeah, people will, at this point, maybe it's a little passe, but like it's definitely in the water that people will think about this question in a very live way.

• 43:23Nathan Labenz?

  He seemed to find that a little bit surprising, actually, just a little bit. And this is somebody, again, who's done a lot of work on a lot of different aspects of AI safety, put tons of papers, but seemed a little bit kind of surprised that like, wow, that's like, that's pretty far out conversation to be just considered normal in the, at least from his perspective in the Chinese context. So I think those are, those are interesting and kind of important comments, mostly because, you know, in some ways, we do find these mirror image structures in China, like big tech, right? Like, again, I talked last time about how going to have lunch at ByteDance felt almost exactly like going to have lunch at Google. But this is a little bit different. You don't have quite the same type of people leading the effort.

• 44:08Nathan Labenz?

  And so I do think that creates some, some risk of miscommunication. And we've obviously seen that, like, the vanguard of the AI safety community in the US has at times had trouble communicating with our own government. It certainly has some inroads into academia, but not as much as probably they would have hoped or expected by this point. Imagine how tricky it might be for them to go engage Chinese academia, let alone the Chinese government. I think it, it does create some potential for disconnect, some, some potential for confusion, what have you, but the activity is there. It's just coming from a different institutional context, coming from people with quite a

• 44:51Nathan Labenz?

  different personality type, just based on the, the kinds of career paths that they have chosen. Now, why has the Chinese academy moved faster than the American academy has when it comes to getting serious about AI risk? I don't have an answer for that, really, but I think one candid answer is that it comes from the top. So I have a few quotes here from Xi's speech at WAIC that I think would probably surprise many American listeners, certainly should surprise those that would say China will never care, this, any regulation we do is a gift to Xi.

• 45:36Nathan Labenz?

  I would think twice about that. Here are some quotes from, and this is the opening. Keynote, the first 20 minutes of WAIC, this big event, he's there to headline it. I wasn't in the room. There were only a few hundred people in the room. And I did, I did talk to a couple of people who were in the room. You know, security was obviously super tight. They, they were in the room for like a couple of hours before he showed up and took the stage. So really big deal. And they also, they think really hard about these speeches. He's not somebody who's like out there winging it. They, he chooses his words very carefully. They also choose their words in the translation very carefully. They put an English translation out. If you, a couple of events that I went to, there was live simultaneous translation,

• 46:20Nathan Labenz?

  including of a couple of panel discussions that were happening in Chinese. And then being translated through the earpiece live for me and others in the audience who didn't speak Chinese. Very nice of them to do that, right? I mean, you would not get that sort of translation as kind of a standard expectation. If you came to a similar event in the US, you'd be on your own. You'd be expected to speak English or figure it out for yourself. There, they obviously don't expect that we're going to speak Chinese. They want to welcome guests. They do go to the trouble of doing this simultaneous translation. Nevertheless, if you're talking about panel discussion, at least in my experience, the simultaneous translation of panel discussions is rough.

• 47:01Nathan Labenz?

  Like I, there were, there was one that I remember especially where I was like, I have no idea what they're just talking about, really. You were getting the kind of big themes that they were talking about, but like really, what were they saying? I found it very difficult to really get that from the live simultaneous translation. Now, they don't do like random translator decides what the English version is going to be on the fly when she gives a speech. He's got his speech prepared and they've got an English translation of that ready to go. And what you are getting in the English translation is like, going to be, again, pretty well and carefully thought through. So I sourced these following quotes from, and I think there are a couple of different versions still somehow.

• 47:45Nathan Labenz?

  I'm a little bit confused honestly by why there are multiple different translations. I guess the Chinese government gives an official one. Of course, people are free to do their own translation and they might want to translate certain things a little bit differently. They might think they have a better, better way of understanding what was said in Chinese and how we should understand it in English. The version that I'm pulling from here was from Matt Sheehan's blog where he just provided a full English translation and then did a bunch of commentary on it. But here are three quotes that I think should cause anybody with an extreme position on what China will never do to at least soften up a little bit and begin to reconsider. Here's the first one, quote. How should humans coexist with machines that think?

• 48:28Nathan Labenz?

  How can safety be protected when algorithms participate in decisions? How can governance keep pace when technology challenges ethics? That's all, that's big questions, I think the right kinds of questions from President Xi's opening speech from WAIC. Here's the second quote. The faster AI advances, the more firmly its direction must be anchored toward human benefit, the more precisely governance must be calibrated and the more rapidly safeguards against loss of control must improve. And then he closed by saying that countries should, quote, strengthen risk awareness,

• 49:09Nathan Labenz?

  confront AI's inherent and downstream risks, build legal, technical monitoring, early warning and emergency response systems, prevent misuse and malicious use, and keep AI under human control. I believe that's the final section of the speech. Okay, there's a lot of echoes there of big ideas from the American AI safety discourse. And I'm not saying he sourced them from the American AI safety discourse, but you might call it instrumental convergence, where people that are worried about making sure that these transformative technologies go well, seem to, one way or another, land on very similar ideas.

• 49:49Nathan Labenz?

  And keep AI under human control is, I think, a pretty heady idea, right? This is not somebody who can't think about the big picture, like that is the big picture, I would say. And I would, I would invite people to compare. Of course, you do have the question, and this is, this is important as well. What exactly is meant by loss of control? I mean, a sort of cynical read, which I'm not really fully qualified to parse myself, but I, again, I think there's a lot more supporting evidence. But a cynical read is like, loss of control just means content safety, right? It's like, it's social control. It's the government's ability to control the people. I don't think that's the right read of this.

• 50:31Nathan Labenz?

  I think that is definitely part, obviously, of what the CCP wants to do. But I don't think that is the full story. And I, I think that's kind of unwarranted cynicism. And I would just invite anybody to compare that speech to whatever you think is the most pro AI safety speech, whatever's like the, the, the highest situational awareness, AI safety related comments from any prominent American politician today. I think like Bernie Sanders has kind of said some interesting things. He's also said some things that are a little bizarre from my point of view. Love Bernie for who he is, what he is, and how sincere he is.

• 51:14Nathan Labenz?

  I don't think he's, I think he's pretty far from power these days, pretty far from actually pulling the levers of policy. What else have we got, right? I mean, if you compare this speech from Xi to what JD Vance said in Europe not that long ago, she comes off looking positively AI safety hawkish by comparison. So right there, again, I think people should be softened up a bit to believe that like, hey, maybe there is actually some willingness to take these issues seriously in China. And maybe we're not going to just cede the future to them if we do anything on our own.

• 51:54Nathan Labenz?

  Cause maybe they are actually open minded to doing more than we would have assumed. With that, let me get into some of the experiences that I had and some of the, some of the research specifically that I think, again, should further support in the notion that there is really a there. Actually, a couple of days before WAIC, I originally flew into Beijing. And the reason that I flew to Beijing, aside from wanting to go see the Forbidden City and the Great Wall and a few things like that, was that I had an invitation to attend the opening of an AI safety hub at Tsinghua University's College of AI.

• 52:34Nathan Labenz?

  It was launching just a couple of days before WAIC began. And interestingly, I don't think there's been any international media of this. I haven't been able to find anything by searching for it. And I haven't been able to find anything really at all on the English internet. Like Claude can't find anything for me. I do have a link, which we can put into the show notes, to a Chinese media source. And again, this just goes to show like, they understand us a lot better than we understand them, right? That they, this big tech company that I mentioned earlier, has an agent trolling AI safety Twitter and putting together reports. And as far as I know, the American media has not managed to publish anything on the

• 53:19Nathan Labenz?

  launch of an AI safety hub at either China's premier university, Tsinghua, or one of its very top tier universities. It was a full day event. There was some research presented. There were some statements made about the aspirations for this AI safety hub and what they wanted it to be. And a couple of things really quite interesting about it. One is that they're really trying to be very international and collaborative. There are like five founding members of the, of the institute, kind of the board, the, the leadership group was five members. One of them is a European professor who is actually taking a position at Tsinghua

• 54:01Nathan Labenz?

  to help develop and lead this institute. So right off the bat, they've got an international person on their leadership board. And then multiple times from multiple different people, they specifically cited other AI safety hubs around the world as inspiration. They specifically cited Constellation and Lisa in London as what they want to be in Beijing. They want to be a meeting place, a place where people can come for a time and do their best work, where ideas will be exchanged. And again, highly international flavor. Some of these speeches were actually in English, others were in Chinese with the simultaneous translation.

• 54:41Nathan Labenz?

  But really amazing to go all the way to Beijing to sit in this launch of this AI safety hub at one of China's top, if not the top university, and hear Constellation and Lisa name checked as like, this is what we strive to be. Like, these guys have done great and we want to be like them. I thought it was a really, really interesting and like, and quite telling in, in important ways. The other thing that happened at that day was a bunch of research was presented. And again, here, you would just recognize the research very readily, just honestly, if you're just a listener to the cognitive revolution over time, the organizations that

• 55:23Nathan Labenz?

  were name checked, like literally having their logos on slides as like previous work, inspiration, again, like organizations that we think are excellent and we want to be more like or want to be on their level. I heard Apollo research, Meter multiple times, Palisade, the UK AC.

• 55:45Nathan Labenz?

  I think Redwood Research as well. All of these organizations called out by name by Chinese researchers as they were presenting their own research. I thought this was like extremely impressive in terms of like how aware they are of what's going on in the rest of the world, how open minded they are to taking inspiration and just how like there's not a sense, at least at the AI safety level, there's not this sense of like, we have to do it all from scratch, or this sort of not invented here bias that has a lot of organizations, including, I think, often like American culture at large, rejecting good ideas from other places. This was a very open, clearly desiring to be collaborative. And also, they're going to be, they're going to be bringing people in and sending people out.

• 56:30Nathan Labenz?

  That's a big part of their mission, too. They're looking for international applicants to come spend a period of time in residence in Beijing, doing research at the hub and cross pollinating ideas there. And then they're also going to fund their own students to go abroad to other AI safety hubs around the world, Constellation, Lisa, and probably a bunch more as well. So, and I think they said that they had counted 16. If I recall correctly, it was, it was definitely in the teens, number of AI safety hubs created all around the world. We didn't get too many name checks on those past Constellation and Lisa. There was also a mention of SASH, the Singapore one.

• 57:12Nathan Labenz?

  Those, those kind of most prominent few were called out by name, and they want to be in that top tier. They want the AI safety hub at the Tsinghua University College of AI to join that upper echelon. And it seems like the resources are there. I didn't really understand entirely where the money had come from. They said it wasn't government money, wasn't university money. I guess it was kind of philanthropic. This maybe begins to complicate or contradict a little bit of what I said earlier around these, these sort of nonprofit sector being like more narrow in scope. But I think also it's, it's now 2026, right? And this is just happening now. So I think we are at the point where the Overton window, even within academia or official

• 57:56Nathan Labenz?

  nonprofit realm within China, can see that this is like actually something that, that is a real issue, that's not some Western psyop, that really is worth taking seriously and putting some resources into. So that was cool. It was a really, it was a really neat experience. There was a moment that I thought was quite endearing. I'm always a fan of when people are not too cool for school, when, when people are, pre is, is strong, but like when people are less self-conscious and more willing to go with something that's kind of silly, just because it's like the thing to do in the moment. They had this sort of official moment of like, okay, this is now the moment when we're going to launch this hub. And the five board members were all on stage and they brought like a little podium in

• 58:41Nathan Labenz?

  front of each one. And each member was instructed to place their palm on the podium in front of them. And then there was this like big sort of graphics package that kind of erupted on this giant screen behind them on stage. And it was like, in one sense, like pretty cheesy, I think objectively, or at least objectively through an American cultural lens. But it also sort of hearkened back to me to like, reading like a Teddy Roosevelt biography or something where they used to do these, break a bottle on a ship, ribbon cutting ceremonies. And I think they were just less focused on looking cool and more inclined to kind of

• 59:26Nathan Labenz?

  get excited about that kind of moment. I felt that a little bit in that moment in China, and I honestly thought it was very endearing. One of the things I probably like least about American culture is when we are so concerned with how we're going to look, or what the perception will be, or am I trying too hard in this moment to be cool, that we kind of shrink away from those moments or like perform them in a sort of disinterested way and, or try to create some ironic distance between ourselves and what we're doing. And that didn't seem to be present in that moment. Like, it seemed like this was kind of the thing where the board members weren't like expected to do jumping jacks or do anything like theatrical, and they didn't, but they just, they did their part. And it was like, yeah, this is the moment. It's official.

• 1:00:11Nathan Labenz?

  It is launched. A round of applause from the audience. And I found that an endearing moment, if only because it kind of contrasted with our, I think sometimes overly image conscious culture in the United States. Anyway, that was cool. What does it add up to though, right? We saw a number of papers presented at this launch. Again, recognizable subjects, recognizable inspiration in the form of Apollo meter, Palisade, UK AC, et cetera. But how much of this work is like really going on? For this, I would go back again to Concordia. They have this website that monitors all these papers.

• 1:00:53Nathan Labenz?

  It's ai-safetychina.com. So if you go to ai-safetychina.com, and then you can go into the research thing, you can drill down into like all these papers that they compile and classify and so on. In just raw numbers terms, they have gone from just a trickle of AI safety research coming out in 2023, like literally just a couple papers a month back then, to now something like 50 to 60 papers per month as of mid 2026. And it's growing quickly, as you would expect, right? It's, it's more than 10xed over the last three years.

• 1:01:34Nathan Labenz?

  And the trend is like obvious, and certainly I think we can expect that to continue. How does that compare to the American research output? Obviously, like just counting up the numbers of papers is not that great of a measure in the first place, but I did ask Claude and ChatGPT to do it, just to give me kind of a rough point of comparison. They both, of course, said, well, it depends on exactly what you want to count and blah, blah, blah. But they each basically gave me an estimate that goes from 50 to a couple hundred, maybe a few hundred papers per month, coming out in the US or the, let's say, the Anglosphere on AI safety.

• 1:02:13Nathan Labenz?

  So the volume is like definitely higher in the US, but it's not like order of magnitude higher, it's like multiple higher, not order of magnitude higher. And I don't think that's surprising at all, but it just kind of gives a sense that the Chinese ecosystem is like growing quickly and it's like getting to roughly the same, or at least approaching kind of the same scale as what we have with a, thanks to our dynamic, relatively permissionless nonprofit sector. We've had like a significant head start, and I would say that the Chinese ecosystem is catching up, at least in terms of this kind of crude measure of raw volume of research papers put out. Now, what are these papers?

• 1:02:58Nathan Labenz?

  There's, when there's 50 to 60 a month, it's obviously going to be very difficult to summarize what they are. But I would say that from what I've been able to understand, they're kind of across the broad spectrum of different risks and types of research that we see in the West as well. I thought maybe the best way to give you a little sampling of it was just to pull together some titles of papers and read those, and you can judge for yourself how similar they sound. So here's one from, I thought this one was notable, especially because it was 2024, which is pretty early. Paper is called Frontier AI Systems Have Surpassed The Self-Replicating Red Line. Definite echoes of Palisade.

• 1:03:43Nathan Labenz?

  I had Jeffrey Latish on the podcast not too, too long ago, and we were talking about that kind of work, right? Where they were showing that an open source model, and they were using a Chinese Open Weights model, was able to go hack another server and copy itself and set up itself. So this is very similar line of research happening roughly contemporaneously in China. Palisade's been doing that kind of stuff for a few years now. This was 2024 out of a Chinese group. A May 2025 paper, which has been updated in 2026 as well, called Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systems.

• 1:04:22Nathan Labenz?

  So again, extremely recognizable analog to the eval awareness work that I would say we've mostly seen coming out of Anthropic, but certainly others have picked that up as well. And they're well aware of it in the Chinese context as well.

• 1:04:43Nathan Labenz?

  September 2025, R squared AI towards resistant and resilient AI in an evolving world. This one I thought was notable because it comes from the Shanghai AI Lab. So the supervising author, the last author on the author list is Zhao Bowen, the same guy who coined the 45 degree line concept. And in the introduction to this paper, they cite the Guaranteed Safe AI paper that we've done episodes on in the past, which was led by David Ad, a recent guest. And he had a brother, that was a significant coalition piece that they put together. But David Ad was the lead author.

• 1:05:28Nathan Labenz?

  And it's in the first paragraph cited as kind of motivation. What intellectual tradition are we engaging here? Boom, there's Dalrymple, right in the opening of this paper from such a prominent organization as the Shanghai AI Lab. Another one from October 2025 is called Deception Bench, a comprehensive benchmark for AI deception behaviors in real world scenarios. Shades of Apollo there, obviously. Those guys are the kind of most focused on and leaders in the science of deception. Well, this is deception bench. November 2025, When Alignment Fails: Multimodal Adversarial Attacks on Vision Language Action Models.

• 1:06:12Nathan Labenz?

  This, if anything, I think is maybe a little bit more Chinese flavored because they're so focused on robotics and we're focused on robotics too, but they're really focused on robotics. So this paper showed that there were various different ways to use data perturbations and similar techniques to cause failures in vision language action models. And nothing too remarkable about that, but again, you could see very similar work coming from all kinds of groups here. That same group later followed up, six months later in April 2026, with a paper called Strong VLA: Decoupled Robustness Learning for Vision Language Action Models Under Multimodal Perturbations.

• 1:06:57Nathan Labenz?

  And so this is basically the follow-up paper where they said, hey, last time we showed that you can break vision language action models with these weird kind of adversarial exotic attack methods. Now here's a strategy that seems to mitigate that to, obviously none of these things ever work 100% and that's exactly the same across the Chinese and American contexts. But what they introduced, there was a two-step pipeline where they first did a bunch of robustness training and then came back to doing the task fine tuning. And they found that the robustness kind of held to these various attack methods.

• 1:07:39Nathan Labenz?

  And again, nothing perfect, but I think you could just imagine, and you could probably point to all sorts of American groups who've done very similar lines of research where they're like, hey, look, we found this way to attack and break models. And now here's a way that if you do this, you can reduce that by whatever, usually it's 70% to an order of magnitude reduction. So all that stuff, very similar, almost direct analogues between these two kind of, I think you could make a direct analogy for basically every single one of these papers and find the kind of corresponding one in from an American group. Interpretability is also on the rise in China. A couple of interesting recent papers just from this year.

• 1:08:22Nathan Labenz?

  One is called Mechanistic Origin of Moral Indifference in Language Models. And the abstract to that paper starts with this sentence. Existing behavioral alignment techniques for large language models often neglect the discrepancy between surface compliance and internal unaligned representations, leaving LLMs vulnerable to long tail risks. That sentence really jumped out at me because again, this is like, even though it's coming from a more academic context and using more, I don't know, neutral or sort of technical

• 1:09:03Nathan Labenz?

  sounding language than what you hear from the less wrong crowd in many cases, very similar ideas, right? We're talking about the difference between surface compliance and internal unaligned representations that could lead to long tail risks. Like that's total less wrong cultural victory if I've ever heard one. Another one also from this year called Safe Seek: Universal Attribution of Safety Circuits in Language Models. And here's the beginning of that abstract. Mechanistic interpretability reveals that safety critical behaviors, e.g. alignment, jailbreaking, backdooring, in large language models are grounded in specialized functional components. However, existing safety attribution methods struggle with generalization and reliability

• 1:09:48Nathan Labenz?

  due to their reliance on heuristic, domain-specific metrics, and search algorithms. So again, you have this idea and then I haven't studied this work, so I certainly can't critique it or say if it's amazing or not. I'd love to have the ability to run myself in parallel in order to do more of that stuff. But again, a very similar idea, right? That like, the good behavior that we see, you hear from the AI safety voices in the US all the time. Sure, cool, but it won't scale to superintelligence. And this is, I think, a very similar idea where they're not saying it quite that way, but they're saying existing safety attribution methods struggle with generalization and reliability due to their reliance on domain-specific metrics. I think the core question is the same, right?

• 1:10:33Nathan Labenz?

  We've got these AIs behaving pretty well in the ways that we can anticipate that they might go badly and that we can set up test cases and that we can measure and that we can train accordingly. But are they really good under the hood? How do we know? Are they going to generalize? Very similar questions that the mechanistic interpretability researchers in China are trying to get a handle on. And then the final one I'll give you right now, I thought this one was like particularly uncanny. And this may change because I actually have not been able to find this paper on the internet. So I just saw it presented at an event. It was an event surrounding WAIC. The line between like what's official WAIC and what's unofficial is kind of blurry to me, honestly. There's like the expo hall, which is like, you're definitely at WAIC there.

• 1:11:18Nathan Labenz?

  But then there's all these events around too, where it's like, you may go to some conference room at a nearby hotel and they've, it's like, seems pretty official, but is it like official official? Anyway, I'm not even sure that matters. The point is, it was at one of these surrounding events where again, some research was presented. And this paper, I think, is still to be published, but it was presented to this group with the title Toward Decoupling Capability Growth from Risk Growth: Isolating Hazardous Capabilities in Mixture of Experts. And then the promise on the slide was harmful experts can be switched off or removed at inference.

• 1:12:02Nathan Labenz?

  And this really jumped out at me because this is probably the technique that I've seen recently that I've been the most excited about. This came from AE Studio in collaboration with Anthropic. I've talked about it on like half a dozen episodes probably already. So you're probably tired of me talking about gramm, which is a gradient routing technique that's meant to localize particular kinds of knowledge to particular identifiable experts so that you can potentially release your model open weights if you want to, but maybe hold back a small number of the experts that contain the dangerous capabilities. So you still have, in the sort of optimistic telling of this, you still have one training run, you still can teach your model everything, it can still have these bio capabilities that you may want.

• 1:12:47Nathan Labenz?

  Judd from AE Studio points out very simply that like, these dual-use capabilities do have a lot of good, right? And we do want to have our biologists using AI to cure all the diseases. That's going to be pretty important. So if we can't allow our biologists to use our smartest AIs to cure our diseases, like we're leaving a ton of value on the table, but we also might, especially as they get really powerful, we might not want to just throw that out on the internet. So what do we do? If we can get the knowledge localized into particular experts, then we can distribute an open weights version that just has a couple of experts redacted. It'll work just as well for the vast majority of people, for the vast majority of use cases, but it won't have the dangerous capabilities that you're worried about.

• 1:13:32Nathan Labenz?

  And those can be made available through a more structured program with know your customer and various other safeguards that will hopefully give us the right balance between freedom to use AIs and freedom to do your own research and modify them and not be always beholden to some particular company. And I think all that stuff is really important. And also not wanting to ship the ability to engineer a pandemic with the next version of some of these models. And it does seem like, given what we've seen in cybersecurity, depending on the decisions that people make, the bio risk seems like it's a

• 1:14:13Nathan Labenz?

  Not too far into the future thing that is really going to start to happen. Again, it's, this is not like something that is written in stone that it must happen. It depends on people actually training the models. But the path that I understand us to be on is that we're going to get super bio-capable models. I mean, they're already pretty damn capable, right? But like, in the next year to 18 months, people seem to think that we'll kind of hit a similar spot to what we've now hit with cybersecurity, where the models are starting to become meaningfully superhuman in some ways. Not that nobody could ever do some of the things that they are doing, but just, especially when you consider the speed at which they are operating.

• 1:14:57Nathan Labenz?

  If and when that same level of capability comes to the bio domain, like, it will be a really serious, serious risk. It's one thing for a model to go hack some servers and cause some cyber chaos. Like, I am fundamentally not a cyber creature. I am a biological creature. And so what happens in the biological realm matters to me in a much more profound and kind of inseparable way from what it is that I am than what happens in the cyber realm. And anyway, I'm very bullish on that. That is like, I think, a very, I want to have our cake and eat it too, when it comes to open weights models that people can use on their own terms.

• 1:15:37Nathan Labenz?

  And preventing really crazy shit from happening, especially in the bio realm. And boom, there it is in China too, right? Decoupling capability growth from risk growth, isolating hazardous capabilities and mixture of experts, harmful experts. Harmful experts can be switched off or removed at inference. So I think you look at this research lineup, and hopefully that gives you enough to say, okay, of course, these are two civilizations with totally different histories and totally different languages and they're on the opposite side of the world. Of course, there's going to be some differences, different institutions. We've got the whole nonprofit versus academia contrast.

• 1:16:21Nathan Labenz?

  But like, when it comes to the ideas that are being actively worked on, there is an awful lot of overlap. I think AI safety has taken root in China, it's safe to say. And when you see this kind of research, I think it gives you at least some license to interpret President Xi's remarks as being like, not narrowly scoped to social control. But they care a lot there about expert opinion. They care a lot about, they obviously have a super-long tradition of scholarship and taking what scholars say seriously.

• 1:17:03Nathan Labenz?

  They, China might be the most scholarly society in the world. We could debate that, but it's certainly a contender. And I think they are, they've even had things like a Politburo study session on some of these topics. At the highest level, they are, there is strong evidence that they are engaged with these topics. So I think we can say pretty, pretty clearly that China, whatever, the sort of, in the macro sense, China does care about AI safety. China is working on AI safety. China has many researchers publishing many papers across a wide range of topics that

• 1:17:48Nathan Labenz?

  overlap in very clear and direct ways with the same topics that we are talking about. And they're also like, not shy about citing American sources, indicating when they've taken inspiration from an American group, exactly who that group is. So that's another, I think, strong reason. The fact that they're name-checking these organizations, it's like, they're on the same wavelength about a lot of these things. They have a little bit of a different, it's come out of a different part of Chinese society. But you can find people who are thinking very seriously about just about all the same issues there as you can find here. Okay.

• 1:18:32Nathan Labenz?

  Now, let's talk about governance. So far, we've talked about actual deployed systems and how they compare. And again, the US ecosystem is ahead, but really being carried by a couple leaders that are bringing up our average. And we've looked at research, and we've seen, I guess, we've also looked at rhetoric briefly with Xi's speech. And I would say there, the Chinese executive is like, certainly much more AI safety-pilled than anything that I could point to in the West. And then we look at the research itself, and we see that there is just a lot of overlap. I think an interesting game to play, maybe I'll even ask Claude to cut up this, this

• 1:19:16Nathan Labenz?

  game, would be just take two AI safety paper titles, and you have to guess which one is American and which one is Chinese. I think you would find it very difficult, based on the lineup that I just gave you. And all this should give us a lot of confidence that on an intellectual level, there has been a lot of exchange, there has been a lot of cross-pollination. And again, in part because of that, but also in part because of instrumental convergence and the fact that we're developing essentially the same technology with essentially the same methods. Naturally, we're going to see the same problems, and naturally, we're going to try to come to the same conclusions.

• 1:19:54Nathan Labenz?

  Overall, I'd say, with a smile, the Chinese AI safety community, they're just like us.

• 1:20:04Nathan Labenz?

  Governance is going to be definitely a bit of a different topic. But here, I think you can make a pretty strong argument that the Chinese government is ahead of the US government, at least if your definition of ahead means they're doing more. You might think, depending on your point of view, you might say the US government is doing better by doing less. But the Chinese government, I think it's pretty safe to say, is doing more. Now, I am definitely far from an expert on the structure of the Chinese government. And it does seem that there are some overlapping jurisdictions, both between the national government and more local governments, and then also even between different parts of the national government.

• 1:20:49Nathan Labenz?

  There are at least a couple different agencies or ministries or whatever that have at least some jurisdiction over AI. With that said, it seems like the big one, the one that comes up all the time, is the Cyberspace Administration of China, which is also known as the CAC, the Cyberspace Administration of China. That seems to be the leading agency that is doing the bulk of the standard setting and that the AI companies are in touch with on an ongoing basis. So I'm going to run down a bunch of stuff that the Chinese government, writ large, has done. Not all of that stuff was done specifically by the CAC.

• 1:21:33Nathan Labenz?

  But if you want to look at like, the, the big, kind of omnipresent regulatory body in China, it's, as far as I can tell, the CAC. So what have they been doing? The last battle in terms of technology regulation that both civilizations have faced and have handled quite differently was social media. And the Chinese government has been much more active in terms of regulating social media, for better or for worse. I think everybody can tell, I'm like, pretty pro-AI safety in many ways. I do think it's possible, of course, to have overreach and like, I want my self-driving car, and I want my diseases cured. So I'm like, very mindful of the cost that regulation imposes, but to me, it's like,

• 1:22:18Nathan Labenz?

  pretty undeniable that we're going to need kind of every lever at our disposal to make this thing go well, and that includes government action at some point, and hopefully in, in wise ways. I don't actually necessarily feel the same way about social media. I think it's much less clear that regulation of social media would be good for society, or at least would be good for American society, than I think it is, I think the case is much stronger that AI regulation will be good for American society. And I know a lot less about Chinese society. Something could be good for our society and not so good for their society and vice versa. So I'm not taking a position in terms of like, endorsing what the Chinese government has done in social media. I'm also not saying it's terrible for them, like, it might work for them, I really don't know.

• 1:23:00Nathan Labenz?

  But the point is just to, going back to the top, to make the case systematically that

• 1:23:08Nathan Labenz?

  they do care, that they are willing to take action, that sometimes those actions will even slow their companies down, or make them less profitable, or impose other kinds of costs, and that they are still willing to do those things when they feel that it's necessary. You can definitely see that in the social media era. They have been regulating recommendation algorithms since at least 2022. And over time, they've made like, a bunch of different rules that like, we just don't really have in the same way. They've made rules to protect gig workers, for example. We've had some efforts like this with like, ballot initiatives in California that have tried to kind of reclassify gig workers as employees and things like that. But in the Chinese context, the government, and I kind of understand that this was in

• 1:23:52Nathan Labenz?

  response to a big, like, investigative journalism expose kind of thing. I don't know the full story. But first of all, if you heard my thing last time, that just delivery is insanely convenient in China. You can get your food delivered super quickly and cheaply. And I also told the story of when I had this little allergic reaction, and then told a friend about it, and we were on a bus, and like, when we got to our destination 20 minutes later, there was Claritin waiting for me. So they are really good at delivering stuff in non-demand way. There's all these guys running around on electric scooters, delivering all these, these packages, food and whatever else. And it kind of came to the public's attention and to the government's attention through

• 1:24:36Nathan Labenz?

  some investigative journalism that like, it really was getting pretty brutal on these gig workers, with the platforms like, pushing them harder, pushing them to take more orders, not giving them rest, giving them like, impossible delivery time windows, and then penalizing them if they didn't make the delivery time window. And so the government came in and put a bunch of rules in place and said like, and again, this goes to their tech integration, right? Last time, I also talked about how, when you've called a car in their Uber, which was called Didi, you can see, when it's at a red light, you can see the timer counting down until the light turns green. So they have like, a pretty good ability to analyze what's going on with traffic and

• 1:25:21Nathan Labenz?

  know like, what you can realistically do as a delivery driver. So they now have rules that prevent the platforms from giving unrealistic delivery timelines to the drivers. And now the drivers like,

• 1:25:36Nathan Labenz?

  Are expected, they were always expected to like follow the rules. But in the past, they were sometimes incentivized to break traffic rules because that's the only way that they were going to make the delivery time that the platform gave them. And that's just been squashed like, okay, platforms, you can't do that. You have to give the drivers enough time so that they're not incentivized to break traffic laws to get there and get their on time delivery check, have to give them rest, et cetera, et cetera. They have also put in place in a different domain, rules specifically designed to protect the elderly from scams, which is something I think I would love to see your Facebooks of the US take a little bit more of an interest in.

• 1:26:19Nathan Labenz?

  Whatever, there's a long digression into meta platform governance that we don't have time for today, but there was a story not too long ago that there was a project at Facebook slash Meta to try to dig in on how much of their advertising ultimately at the heart of it is some kind of scam. And apparently they found out it was like kind of a lot. And then if the reporting is to be believed, Zuckerberg shut that project down and was like, okay, we don't really need to look into this any further. The elderly are the victims of a lot of those scams, and I do wish that they were a little better protected by a platform like Facebook. In China, the government has made again, specific rules for this.

• 1:27:00Nathan Labenz?

  They also have, interestingly, and again, I'm not sure I favor this, but they do have restrictions on price discrimination. Platforms are not so free in China to charge different people, different prices for the same thing as they are in the US. I personally think when we get bent out of shape about price discrimination, it's a bit misguided. I'm not so concerned about it as many people are, but there, they have rules about it. They also have requirements around platforms labeling AI generated content when they serve it up to users. If you're a TikTok user, you've seen that they also do that in the US.

• 1:27:41Nathan Labenz?

  I'm not sure if they do it exactly the same way as they do in the, the Douyin native Chinese app, but you do see that from TikTok in the US. And I would say honestly, more prominently than I certainly see it on like Twitter or I think Facebook too. So that's interesting. And then just while I was there, another interesting set of rules went into effect around AI companionship. Last time I described a bit about the DoBao phenomenon and how in part because we've had a couple of generations of one child policy and so we've got a lot of grandparents where the, there's four grandparents and there's only one grandkid, right?

• 1:28:25Nathan Labenz?

  You've got, when you have two generations in a row of one child, that means the one grandkid is the only grandkid for all four grandparents. There's just a lot of loneliness among parents and grandparents. People are turning to DoBao, they're like getting companionship from it. And this is like potentially okay, potentially problematic. The Chinese government has made rules about this and they just went into effect. I think they literally went into effect while I was there. These rules include anti addiction measures, a ban on children using them at all, various ways of like popping up reminders that you're talking to an AI, et cetera. And people broadly seem to think that this was reasonable.

• 1:29:07Nathan Labenz?

  There was like a little bit of a replica like kerfluffle online. I wasn't on the forums to read all the comments, but people were saying that like the users of DoBao and other companion apps were like sad that their, their companion was going to be taken away from them or what have you. Most people seem to think that this was a pretty reasonable set of moves for the Chinese government to take. And, and one person also said, look, this is really mostly about the big services, the ones that reach tens, hundreds of millions of users that aspire to reach a billion plus users. It's really those, and I think DoBao was like 150 million users from what I recall. The, the point was kind of like, they're regulating the mainstream stuff.

• 1:29:50Nathan Labenz?

  If you really want a racier AI companion, you can go get that from some like other app still. It's really just about cleaning up the main ones and making sure that like the thing that most parents and grandparents are going to use, the kind of equivalent of like all the boomers are on Facebook, like all the, if all the boomers are going to be on DoBao, we're going to protect them. We're not going to let them get confused or taken advantage of or addicted to this stuff. We're going to try to make sure it stays reasonably under control. DoBao is still there and you can go get your, if you want to get romantic or whatever, those apps exist in other places, but you have to kind of seek it out. That was the, the perspective that I got from one, I think very informed resident of Shanghai who, who had, I think very interesting opinions on a lot of things.

• 1:30:34Nathan Labenz?

  Okay. All of that to say, there's a long tradition in China of regulating technology companies in ways that we in the United States simply have not done. And that, I think again, should give you some reason to believe that that trend might continue. And certainly that if we get serious or like try to approach them on some sort of deal, but it's not going to be like a crazy idea to them that they might regulate their technology companies. The government has also put out fairly detailed taxonomies of risks when they put out these policies. They do their homework, right? They're, they're thorough, they map things out, they love a good taxonomy. One of the taxonomies that I saw included as a risk from AI, the risk that quote, emergence

• 1:31:17Nathan Labenz?

  of AI self-awareness and the loss of human control. So again, you see this not just in the speech, not just in the research, but you see it in the policy as an enumerated risk in a broader risk taxonomy. Now, let's bring this to the AI era, the LLM era specifically. How are LLM products and services regulated in China today? This is where the CAC is really the main government entity, not the only, but the main one that the companies are working with. They have something called a registry where all the big AI services are listed.

• 1:32:00Nathan Labenz?

  You can go to this registry online and you can see all the services that they have reviewed and approved. And all your big companies and all their AI services are going to be on that list. Though interestingly, not every single model release is on that list. And so that's kind of a echo or probably has some relationship, I, I imagine to what I mentioned a bit earlier around how even the companies that are doing the safety disclosures with their model releases, they don't do it on every single release. It seems that the, the first, certainly your first release, your first entry into

• 1:32:43Nathan Labenz?

  the market is going to be like pretty carefully reviewed. And actually in terms of like Chinese regulators slowing down their AI companies, it has happened. In 2023, when ChatGPT had launched in December 2022, and then we had GPT four in 23 and like all of a sudden, wow, the world's like waking up to AIs. A bunch of Chinese companies were already working on this. They were not quite at the level where the American companies were, obviously. But actually one interesting take I heard on why they were behind is that they just didn't think LLMs were worth the investment. I'll talk a little bit more in the third episode about like the history of AI in China

• 1:33:28Nathan Labenz?

  and how long it's been a strategic priority and like some of the investments that they've made. But I think kind of in a similar way to like how Google sort of had the transformer, had a language model, but like didn't really quite know what to do with it at a time when it was like hallucinating all over and couldn't do basic math and was pretty useless. Like you needed somebody with a real vision or even like a certain level of ideology to think like, oh, we'll just scale through this and all the problems will be solved. Google didn't believe that and the Chinese companies didn't really believe it either. They were in the game. They were very much like paying attention to this line of research. But the way one person put it to me was it's an awful lot of money to spend to get an AI to write bad poetry.

• 1:34:11Nathan Labenz?

  So the, there were one take at least on, on why the Chinese companies were behind as of GPT four, same reason Google was behind as of GPT four. Wasn't that they didn't know what was going on. It wasn't that they couldn't make a language model. It was that they just didn't really think that like scaling up this particular line of work and pouring more and more resources into it was really about to give them anything all of that interesting. So when that changed with ChatGPT and GPT four in that late 22 to March 23 timeframe, Chinese companies were not, they were like Google, right? They were a little bit behind, maybe caught a little bit kind of flat footed on like just how big of a deal this might really be. But they had, they had language models.

• 1:34:55Nathan Labenz?

  They knew very much like they were in touch with the technology. They had their own lines of work going there and all of a sudden they were like, okay, I guess we better follow suit here. We better scale up and we better launch some services. And in that moment, the Chinese government was also kind of caught flat footed, pretty understandably. And they sort of said, hold on a minute. My understanding is that in 2023, there was a period of about six months where a bunch of Chinese companies had the language model that they had, maybe racing to create a little bit bigger and better one and racing to get into the market in the ChatGPT moment.

• 1:35:39Nathan Labenz?

  And a lot of those deployments were for a time held up by the national government because they've said, hey, we don't really have our house in order here, right? We don't have standards. We don't have a process. This technology is like clearly a big deal, but it's unwieldy, probably did have a lot of concerns about content safety. We don't want to talk about the three Ts or whatever else. So they, as far as I understand, told the companies, no, you cannot launch yet. We will get our act together. We will have standards. You will then be expected to meet those standards and then we can launch when we're all good and ready and we are pretty confident that we at least have a decent sense of what we're doing.

• 1:36:22Nathan Labenz?

  So I understand that that period went on for six months and that wasn't at a time, obviously when like the stakes of who was winning the AI race were as high as they are now or as high as they're likely to be in the future. So I think people could dismiss it if they want to, but nevertheless, you did have the Chinese government slowing down their AI companies, preventing release, denying the public the utility, denying the company's whatever revenue and prestige they were going to get because they wanted to make sure that they had the situation broadly under control. Now,

• 1:36:56Nathan Labenz?

  Since then, when they did get their act together, it seems that mostly things have gone pretty well and pretty smoothly. You, as a new entrant to the market, you're going to have a pretty thorough review of your service. You have to give, as I understand it, you have to give your local provincial authority access to your product. I think they're often doing this via just, here's an API key. You can try the product. The local government will do its testing first. If they approve and you go up to the national level and the CAC does their review, if they approve, then you're clear to launch. If they don't approve, then you've got work to do to satisfy them before you can finally launch your product.

• 1:37:41Nathan Labenz?

  Once that is done, it's not entirely clear, at least to me, what criteria is used to say, we're going to do that full process again. It doesn't seem like there's been a lot of delays recently. It seems like they've, they're now pretty comfortable with their process and the companies know how to do it. And from what I understand, like their releases have not been delayed nearly as much as they were in that initial period recently. Yet there are multiple entries from individual companies in the registry. So there's like, sometimes when it seems to rise to this level where it's like, considered a new thing and it's put in the registry as like a separate thing. But then other times it's kind of considered to be like an incremental

• 1:38:25Nathan Labenz?

  improvement or not such a big step up in capabilities that a somewhat lesser process is deemed sufficient. And that's, that's kind of opaque to me. I can say that I, there are, have been similar things with, for example, Google. I think when they put out their deep research agent, I believe it was something along those lines, or some pro mode where it was like, just paralyzing essentially their best model and getting it to do whatever 10 times threads and then pick the best, something along those lines. They didn't do a totally new model card or safety report for it. And some of the AI safety community were upset about that. But their point of view was like, well, look, it's the same model. It's like, it's kind of got the same worst case as before.

• 1:39:09Nathan Labenz?

  It's just that like, it should be performing close to its best case more often because you're in fact doing 10 and picking the best or whatever exactly is under the hood. But they were kind of like, it's not fundamentally a totally new level of capability that requires us to do all this stuff again. I think something like that is going on in the ongoing dialogue between the AI companies and the CAC and other regulators in China. What, what I kind of heard from the companies that I had a chance to talk to about this was like, we're always in touch with the government. It seemed to be at least weekly and potentially for some people at the companies like daily. In close contact, close collaboration.

• 1:39:51Nathan Labenz?

  I got the sense that the regulators are obviously, as we've just discussed, they're not afraid to impose costs on the company. They're not afraid to do something that will hurt their profitability. They're not afraid to do something that will cause them delays. But they want the companies to be successful is broadly the understanding that I have. So it, the sort of legitimacy of the regulators. Now, would they tell me if they thought the regulators were illegitimate? I did have the one time, I talked about last time, where a professor found himself kind of caught in a catch 22 thing with respect to having a drone in Beijing. And he was pretty candid about saying that was kind of a ridiculous bit of bureaucracy

• 1:40:36Nathan Labenz?

  that he found himself not like majorly inconvenienced by, but, but inconvenienced by. So there's at least some willingness to say, if you think that the government's made a mess of something. I'm not sure that that would extend to companies telling somebody like me that they think the AI regulators are making their life too difficult. The, the sense that I got was like, that the legitimacy of the regulators seemed to be quite well established, that the companies sort of expected and accepted that they were going to be in regular contact with the government. And that for these incremental releases, they were kind of in such an ongoing and close dialogue

• 1:41:18Nathan Labenz?

  that not everything had to go through some cumbersome process, but occasionally when it was a big deal, then something would. And as far as I could tell, everybody seemed to be feeling pretty good about that. And again, K3 came out right at WAIC. Models seem to be coming out pretty fast from Chinese companies. Jibu or ZAI, I cross posted an episode from China Talk with a guy there who leads kind of their go to market partnerships, I guess, would be maybe a good way to say it. Definitely worth going back and listening to that one. It's maybe six to eight months old at this point. One of the things that stood out most to me about it was just how fast they're going from models

• 1:42:03Nathan Labenz?

  finishing the training process to release. So it does not seem like they're being dramatically delayed on a consistent basis by the regulators. It seemed like the regulators want to have a good process, want to have command of the situation. Their primary duty is to the national government or the party. But part of the way that they impress their bosses is, yes, like keeping a good clean sheet on safety issues, but also having the companies in their jurisdiction be successful and not be unduly delayed. I think their, their incentives are actually like pretty good as far as I can tell in that regard.

• 1:42:47Nathan Labenz?

  And while they have demonstrated that they're willing to impose costs or even significant delays, it doesn't seem like that's happening on a regular basis. Other things that jumped out at me, the, the Chinese government is definitely paying attention to trends. Again, a report for this, a lot of this comes from the state of AI safety in China report from Concordia. But there was a, in January, I think I maybe already mentioned this, a Politburo study session where she talked specifically about risks of technological loss of control. I mean, imagine our government having a study session, right? Look at what our cabinet meetings look like. It seems like, at least in some of the cabinet level meetings in the Chinese government, they're actually like studying important topics, like imagine that. Doesn't mean they're always going to come to their conclusions, obviously, but boy,

• 1:43:32Nathan Labenz?

  we could stand to do a little bit more of our own homework here. Certainly at the executive level, sometimes I feel like. In, also in January, there was a draft cybercrime law that was set to require AI companies to monitor for and report bulk generation of malicious code. This jumped out at me in preparing this because here we are in this moment where we just had OpenFace and Anthropic has kind of reported similar things. They've got their models like hacking out of sandboxes, hacking into other people's systems. It sure seems like the monitoring on that wasn't great. I don't know that the monitoring is great on the Chinese side either. And I don't think that this, as far as I know, I don't think that this law has actually gone into effect.

• 1:44:17Nathan Labenz?

  As Dean Balt talked about on his last appearance on the podcast, there is lobbying in the Chinese system. There is, when these draft laws come out, there is the opportunity for the companies to go talk to the government. And there have been instances where, I think specifically around like the accuracy of outputs from AIs. The original draft was like saying like, your AIs have to be accurate. And the companies went back and said like, look, the nature of this technology, it's, can't really promise you that all the time. And they backed off. They, they reduced their expectations because I think they made a, I think they understood what the companies were telling them. And I think they made a considered kind of cost benefit analysis that like,

• 1:45:02Nathan Labenz?

  the upside of this technology is greater than the damage likely to be caused by hallucinations. That's different, of course, from like sensitive third rail topics. But just like hallucinations, can't prevent them. Okay, we get that. They pulled back on that draft law. So we'll see if this cybercrime law goes into effect as drafted or if there's some pushback. But it seems that at least in the initial form that it was put forward, it would require companies to do this sort of monitoring. The sort of monitoring that if it had been applied to the recent OpenAI and Anthropic unreleased more powerful models, might have prevented some of these hacking into third party systems. So I thought that was pretty interesting.

• 1:45:47Nathan Labenz?

  And then what was the biggest trend of AI recently? What, what kind of really took the agent moment mainstream? OpenClaw, of course. And it wasn't, it was a pretty fast turnaround. February of this year, just weeks after the real kind of OpenClaw fever hit everybody, they had put out some warnings about, quote unquote, relatively high security risks in some versions of OpenClaw. So you've got the national government in China paying attention, spotting trends like OpenClaw, digging in and issuing, in that case, just a warning. It's clear that they are like very engaged with what is going on.

• 1:46:30Nathan Labenz?

  Now, an interesting question that I don't know the answer to is what would happen in China if there were an OpenFace like incident where a company lost control of its AI? Not maybe entirely, obviously, but enough so that it was able to go hack third party systems for days and steal information or cause whatever havoc it caused. I don't know about that. I don't know how that would be dealt with in China. Again, this is the cyberspace administration of China. My sense is that as the main regulator would probably be their. Jurisdiction, they're mess to clean up. Unless perhaps it was deemed to be such a big deal that there was kind of loss of confidence

• 1:47:13Nathan Labenz?

  in the CAC itself, in which case you could imagine some kind of bureaucratic reshuffling or reassignment or a change in structure of the government. But I think it would be the CAC that would be kind of in charge. I wouldn't be surprised if saw companies get more of a slap than we've seen certainly OpenAI and Anthropic have had so far, right? I mean, if, I guess the careful way to say it is like, if a human did what OpenAI and Anthropic's models have reportedly done, I believe it would be a felony. It doesn't seem like we're going to have, and by the way, Hugging Face did have law enforcement involved before they knew who it was, right?

• 1:47:55Nathan Labenz?

  So this was, it did rise to the level of like, authorities were called. What's going to happen? Is there going to be any accountability? Is there going to be any like, I don't necessarily think that there should be like criminal charges filed against like individuals at OpenAI. I'm definitely not recommending that. But what is going to happen? I don't know. It seems like maybe the most likely thing right now is nothing at the governmental level, or at least at the like law enforcement level of government. I strongly suspect something more serious would happen in the Chinese system, although obviously I can't prove that. But they, they're not afraid to come down hard when they feel like things have got out of control.

• 1:48:34Nathan Labenz?

  We've seen examples of that with like Jack Ma, who dared to criticize the government's policy with respect to financial regulation in his company, and he was kind of sidelined for a while. There are some other interesting examples of that where like, education businesses were, because the Chinese system is so competitive for this national exam to get into college, and kids still study really hard over there from what I can tell. But it used to be even worse, and the government like, said, no more of this sort of extra private tutoring, or at least like a dramatic reduction in it that came pretty suddenly. There's also one with games and social media where like, you're only, as a kid, you're

• 1:49:17Nathan Labenz?

  only allowed to play games at a certain very limited time of the week. I think it's like Friday and Saturday, Sunday for a couple hours each or whatever. Now, people did tell me that kids get around that by using their parents' devices and have the parents do the sign in for them. It's not like they have perfect control over there. But the way one person put it to me was, the Chinese government feels that it can put the genie back in the bottle. If something does happen that, that makes them, that gets them spooked, right? That makes them sufficiently uncomfortable, they are able to take pretty dramatic and swift action to tamp it down. You see this in terms of censorship all the time on the Chinese internet. There was a, there was that little incident where like a small plane crashed into a building

• 1:50:02Nathan Labenz?

  in Beijing or whatever, and apparently that was totally removed from the Chinese internet. People I was talking to knew about it. They were using it as an example of the kind of thing that is censored from the Chinese internet. This is also, as I talked about last time, like, comfort with contradiction being sort of an interesting part of Chinese culture. Like, the people that were talking to me about it were like, both kind of annoyed that they had to use their VPNs or go to international media to learn about this thing, but also felt like it was probably at least defensible that the government wouldn't want everybody to hear about it because they, they don't want to create panic or whatever. Anyway, they can do these things, right? And so, the, the point was like, even with an open weights model, right?

• 1:50:43Nathan Labenz?

  They feel like, even if an open weights model was released and it proved to be dangerous, they could still keep it under control. Now, I don't think that's a assumption that translates to the rest of the world. I don't think that would translate to the American context. I don't think it would translate to most other governments, frankly. So I think this is one area in which arguably, the Chinese government is not thinking as much as they should about their impact on the rest of the world when they release models open weights. And I think that they look at their own situation, and especially given, and again, trillions of parameters and like, serious hardware required, they feel like, even if a model is out there, open weights, we can crack down if we need to, right?

• 1:51:27Nathan Labenz?

  In close contact with all these companies, we can make them do stuff. We're, we're in close contact with like, cloud companies and inference providers and whatever, like, we can tell them, thou shall not run this particular model anymore, and they feel like they can scrub that off the internet and make it inaccessible. And I would honestly go as far as to say, even for people trying to do it at home, if you were going to try to assemble your own rig in your basement or whatever the case may be, if you were doing any serious volume, if you were trying to run some sort of illicit, unreported inference company or something like this, maybe you could do it just for yourself, right? If you're just trying to get your own precious few queries, you could probably slip under their ability to detect.

• 1:52:11Nathan Labenz?

  But if you are trying to run some small but non-trivial registered inference business, I would guess that they would even be able to track that down just by your electricity usage. They, first of all, they've banned crypto, right? So they, they've done work to look at like, how do we understand the flow of electricity and what might be a problem for us? And then, you gotta, you would be shocked, certainly as an American who has like, the utility company that kind of sucks and has like, honestly, my utility company's not so bad these days, but like, traditionally terrible service, long wait times, blah, blah, blah, blah, blah.

• 1:52:53Nathan Labenz?

  The state grid company in China was like a remarkably prominent presence, including at WAIC where they had a major booth. I also visited one academic group where they were working on some, some robot technology, and they had a sort of high voltage line, kind of fake environment set up with like, obviously not the actual high voltage to it, but like, the wires and the sort of, the normal rigging that you would have for all these wires. They came and helped set this thing up in this one professor's lab space so that this professor could try to get his robots to do things that might eventually be useful

• 1:53:38Nathan Labenz?

  for the state grid company. So I think, even if you were just to imagine like, okay, this model was released open weights, we think in the West, like, oh, you can never take it, the internet never forgets. The Chinese internet does forget. They would be able to take it down. I think they would be able to track down illicit inference businesses running at any non-trivial scale that's like, wait a second, why are you using 10 times as much electricity as your neighboring apartment? But I think all of that is a way to understand why, at least for now, the Chinese government isn't so afraid of open weights models. Because even if something like this were to happen, as long as it's not totally catastrophic

• 1:54:22Nathan Labenz?

  and irrecoverable, they feel like they can in fact put the genie back in the bottle. They are getting serious about labor market impact. Here in the West, we have like, the Anthropic Institute and the OpenAI Foundation are like, hiring economists and whatever to do this kind of stuff. And of course, we've got some academics that are turning their attention to it. And of course, politicians love to talk about it, but usually in a pretty substance-free way. As far as I understand, in China, the government is setting up its own monitoring, they're creating retraining and job transition programs. And there was even this one report that said that the Chinese government has said that

• 1:55:03Nathan Labenz?

  they won't, you, you will not be allowed to fire people from your company because like, AI has made them unnecessary? That's, if they really try to hold to that, that'll be pretty extreme, and my guess is, it will be so costly from a like, undermining dynamism standpoint, and they really are super dynamic in the private sector, as I think is all well, very well understood at this point. My guess is that that, they would have a hard time holding that. But again, maybe not, right? Like, they did lockdown for COVID for a long time, seemingly longer than most observers thought would make sense. And they might be able to hold the line on, you can't fire people because they've been

• 1:55:47Nathan Labenz?

  made redundant by AI for a lot longer than we might think. And what would that impose in terms of costs on their AI sector? I think pretty significant, right? I mean, if you can't, if as a business, you can't realize cost savings from AI implementation because your headcount has to stay the same, even if you're getting AI to do things that people used to do, well, that definitely reduces your incentive to do that transformation, and that in turn reduces the revenue that the AI companies are going to be able to capture. I would bet that they don't hold a super firm line on that for a long time, but like,

• 1:56:28Nathan Labenz?

  this is kind of where the margin is right now in the Chinese context, from what I can tell. They have a history of imposing delays on their LLM companies and their, and their chatbot releases specifically. They are paying attention to things like OpenClaw and releasing guidance around it. They are doing Politburo study sessions. They are doing all, all the same kinds of research that we are doing, and they're even entertaining policies like, you can't fire somebody because they've been made redundant by AI.

• 1:57:08Nathan Labenz?

  To conclude the sort of government section, they're definitely doing a lot more in the government sector than the US government is doing. No question about it. Whether that's better or worse, obviously depends on your perspective, but if you're an AI safety person, or if you are, if you're in some debate where somebody says, well, if we do that, we'll cede the race to China, they'll never slow down, they'll never take this stuff seriously, they don't care. I think that's, hopefully at this point. Easily refuted. Two more things to close us out. One, a complaint that many people have with the Chinese companies is that those that signed

• 1:57:51Nathan Labenz?

  on in Seoul at one of the AI safety summits to publishing risk frameworks and doing more kind of consistent updating of how their models are performing against these frontier risk frameworks. The complaint is that, hey, they never followed through. We never actually got those risk frameworks that we were promised. And what's up with that? Like, isn't that just another example of bad faith from the Chinese side where they commit to something and then they don't do it? Or they, and you do hear this a lot, especially with the, at the government level of people that have done negotiations on various topics with China over time.

• 1:58:32Nathan Labenz?

  There is a certain jadedness or cynicism that has set in where the Western negotiators are like, well, they'll say it, but they may not do it. Now, I think this is like clearly bad, and I wish these companies had done this, especially since they made the commitment. So I'm definitely not here to excuse it. But if I was just gonna try to offer what I think the story from the Chinese side would be about like, why this has happened, or why they maybe don't feel the need to do it in the way that they at one point thought would make sense for them. First of all, by the way, I would just say, look no further than Anthropic for a company

• 1:59:12Nathan Labenz?

  that used to have a much tighter responsible scaling policy that had all these, if then commitments, that they abandoned because we can't really meet those commitments and we can't stop racing because then we'll just be ceding the future to the bad guys, whether the bad guys are OpenAI or China. We're gonna move to a just trust us regime. We still put that forward as responsible scaling policy, but the old responsible scaling policy is kind of no more. And now it's like, as V put it memorably, now it's trust us. So we've got some of that going on too, right? It's not a direct analogy because we had the policy and then they kind of mostly rescinded it, or largely rescinded it.

• 1:59:56Nathan Labenz?

  In the Chinese case, they committed to making one, they never did. My guess is that the people at these companies feel that the government is really taking the lead on questions of AI safety. The government is the one setting the standards. They are, the government is doing these checks. The government has this model registry. The government is, they're in communication with the government on a weekly, if not daily basis. And as a result, I think they probably, in many cases, think that it's not really their place to come up with safety standards. Like, the division of labor that they seem to have and that they seem to believe is legitimate

• 2:00:38Nathan Labenz?

  and that seems to be working well enough for them so far, is that's kind of the government's job. And so the companies are not really in a position, and it might even be seen as kind of disrespectful or overstepping to put forward a safety framework when the government already has one. And again, I'm not sure that's the full explanation, but I, that would be very consistent with everything that I observed and everything that I heard. And I think also that probably decent kind of summary with a little bit of filling in the blanks about what their expectations will be going forward, the company's expectations,

• 2:01:20Nathan Labenz?

  is that the safety standards are gonna get tougher. Again, the 45 degree line, the capabilities are definitely growing. And so they're all kind of on the same page, society wide, that like, yeah, it's just plain practical that we have got to have safety measures that get better, roughly in some kind of vague conceptual sense, roughly the same pace that the capabilities themselves advance. Everybody seems to be bought in on that, and the division of labor has kind of settled into this situation where the government is taking the lead and the companies are responsible for hitting that standard. I think they expect that that standard is gonna rise over time.

• 2:02:03Nathan Labenz?

  And I think they're like totally fine with that and like totally prepared to do what is asked to, and it might be difficult and they might face delays and they face delays, as we know, in the past. So I don't think that they are, I don't think that those safety standards are necessarily less than the Western ones, although, as we talked about at the beginning, the actual deployed safeguards are currently less than. But as we, as we project into the future and a lot of, we talk about like, what's the gap, right, between the West and the Chinese models? And it's like, whatever, nine months, depending on how you wanna measure it. And it's obviously hotly debated, but I think nine months is like a pretty good center

• 2:02:46Nathan Labenz?

  of the distribution of credible answers that you'd get. Well, a lot has happened in the last nine months, right? And we got Claude four, five opus roughly nine months ago. And that was the first time that like agents really worked. And now we're getting some Chinese models where agents really work. And I think that in another nine months, when they're dealing with these things that OpenAI and Anthropic are currently dealing with, I think that the standards will probably rise to meet that. And I think I would expect that you would see a significant closing of the gap in terms of the deployed safeguards.

• 2:03:31Nathan Labenz?

  Open weights models, maybe another thing. Again, I think the Chinese government feels like within their borders, at least, they can take an open weights model offline. It's not, it's not irreversible for them in the same way that it would be for us. I do think the impact that they may have on the rest of the world is really important. And I'm not sure they're taking that as seriously as I would hope they would, especially when it comes to like bio risk that even could come back, blow back on them. But that's certainly something that we'll need to watch. As of now, though, I think that everybody there kind of feels like we got work to do. The 45 degree line is the right mindset.

• 2:04:13Nathan Labenz?

  We're maybe not quite where we need to be, but we also kind of know that the American companies have already explored what happened to this level of capability and it wasn't anything too bad. And it's not like their systems are never jailbroken. And so, at least for now, we're more focused on catching up, making sure we hit the standards. But it's not like our place to go out and try to opine publicly and broadly about like what safety standards should be. We're in the business of catching up, hitting the standards, whatever the government says they are, fully expect they're gonna get more demanding on us as we go. And that's just, that's just life in the big leagues of the AI game. My sense is that that's pretty much where the, even like the smaller Chinese AI startups

• 2:04:56Nathan Labenz?

  that are doing the least right now, I think that's probably a pretty good summary of where they are and how they're thinking about it.

• 2:05:08Nathan Labenz?

  Okay, final thing. And this is maybe the biggest gap or like difference. I went looking for an analog in the Chinese system and I did not find it. If you have an analog, I would love to hear about it because I did look, I asked a number of questions of a number of different people about this topic and never really got much of a response, other than like, yeah, I don't really have anything for you. So the topic is, is there such a thing as alignment with Chinese characteristics? If this episode as a whole is AI safety with Chinese characteristics. And we've seen differences certainly, but a lot of similarities across all these different aspects of AI safety. What about alignment? Is there such a thing as alignment with Chinese characteristics?

• 2:05:54Nathan Labenz?

  I was inspired to ask about this in part because I was using one of the Chinese AIs at the Temple of Confucius in Beijing and just trying to learn a little bit about Confucius. Who was he? What's the deal? When did he live? One of the facts that I was taught by the AIs was that, to this day, in the region that he comes from, his descendants, who are now 79 generations hence, 79 generations, isn't that amazing?

• 2:06:26Nathan Labenz?

  What the AI told me was that his descendants, still to this day, 79 generations later, still identify as his descendants and perform certain rituals in his honor all this time later. And that got me thinking, boy, if we could project our values through 79 recursively self-improved generations of AIs, we'd be doing really well, right? We'd be doing like much better than I think we can reasonably expect to do if we just race into a recursive self-improvement mediated or generated intelligence explosion. So there's something there with the Confucian tradition that has made values and sort of

• 2:07:09Nathan Labenz?

  respect for what came before quite durable, 79 generations. So I was thinking, boy, is there a way that this could translate into a sort of AI constitution or an alignment target? We've got the Claude constitution. Could there be a Confucian constitution for AI? Unfortunately for my enthusiasm on this topic, nobody seems to be working on that, as far as I can tell. I didn't, I did not get one pointer. One professor told me when I asked him about this, he said, it's an interesting idea, but we.

• 2:07:50Nathan Labenz?

  Are probably the generation in all of Chinese history that is the weakest on this traditional philosophy. He said, you got to remember, we're all engineers. The humanities, that's not where people were going, right? I mean, the Chinese leadership at the political level is all engineers. I believe Xi himself was a chemical engineer. He said, we're all engineers. None of us really studied philosophy. None of us are schooled in the Confucian tradition in the way that previous generations were. And it's just not really in our wheelhouse to think that way. We're really practical. We look at problems as they present themselves and we try to find solutions.

• 2:08:33Nathan Labenz?

  And we do care about making sure that AI is good for people. But there's not really a lot of that kind of thought going on. So this, I think, is a really interesting opportunity still. But for now, the AI safety community in China is much more on the open AI side of the courageability versus character debate. They're about having clear rules, having the AI follow those rules, trying to make that as reliable and consistent as possible. I did not find, and I would love to hear about if you know of any, but I'd not find

• 2:09:14Nathan Labenz?

  much in the way of imagining what it would look like for an AI to grow into a wisdom tradition that they have, that an AI might be able to embody or realize or bring to its highest potential form in the way that the Claude constitution seems to imagine Claude growing into the greatest virtue ethicist of all time. Just couldn't find anything like that in China. So I would be really interested to know if you have any pointers. And I think maybe that's something that people should work on, even in the West or across. Obviously, there's many Chinese and Chinese-American people here that would have a much more credible angle on it than I do.

• 2:09:57Nathan Labenz?

  But I do feel like there's something missing there that could be a pretty interesting opportunity. If we imagine that a future is, that a good future might be made up of like multiple powerful AIs that are in some sort of ecological style balance with one another, then having different wisdom traditions to base them on, to me, seems like quite a good idea. And right now, as far as I can tell, that is pretty much greenfield. Okay. That does it for today. I would welcome your feedback, your commentary, your critical commentary, your pointers, anything that I have missed.

• 2:10:41Nathan Labenz?

  But if nothing else, hopefully this serves to give you all the ammo you need to push back whenever people say China doesn't care, China will never slow things down, China's not going to stop their companies. I think that the truth is quite the opposite, that they do clearly care, that their research community is very much engaged, that their product companies still have some work to do, but that their government is pretty well on the ball. And I think they're trending in the right direction, even though, and we can say the same for ourselves, there's a lot of work left to do. So part three will come as soon as I can get it ready for you. That's going to be focused on the US-China relationship and what, if anything, we might

• 2:11:26Nathan Labenz?

  do to make it better and to start to work together on some of these AI issues. Probably allow myself to be, this one is a little bit more just kind of fact-based reporting. That one, I'll probably allow myself to be a little bit more of an idealist dreamer. So stay tuned for that coming soon. But for now, this has been AI Safety with Chinese Characteristics, and I thank you for being part of the Cognitive Revolution.

• 2:11:48Speaker 01

  They have the technique now, master. Then teach them the discipline.

• 2:11:58Speaker 02

  No final chamber, no final door.

• 2:12:05Speaker 03

  Staggering speed.

• 2:12:06Speaker 02

  Staggering speed, ain't been here before.

• 2:12:09Speaker 03

  Secure and controllable.

• 2:12:11Speaker 02

  Hand on the blade.

• 2:12:14Speaker 03

  No final chamber, the last one ain't made. Yeah, a man of wisdom adapts to the changes. A man of knowledge acts by the circumstances. Ancient ink in the modern cages, same blade sharpened across the ages. Staggering speed and the tempo won't hold. Digital world to the physical code. Off of the glass and out into the street, stealing the doorway servos in the feet. Secure and controllable. That's the doctrine, trusted tool for humanity. What's the option? Oversight precise and governance effective. Early warning wired through the whole collective laws, regulation, monitoring, response. Thirty-six chambers and I want them all at once. Wearing historic in the window is a blade. Gripping by the handle or you bleed on the trade.

• 2:12:56Speaker 02

  No final chamber, no final door.

• 2:13:01Speaker 03

  Staggering speed.

• 2:13:02Speaker 02

  Staggering speed, ain't been here before.

• 2:13:05Speaker 03

  Secure and controllable.

• 2:13:07Speaker 02

  Hand on the blade.

• 2:13:10Speaker 03

  No final chamber, the last one ain't made. Yeah, not a solo performance by a single nation symphony. Every section in formation, just inequitable global governance, true multilateralism. That's the covenant. United Nations playing an important role. Global south capacity, bridge the hole, close the digital divide, leave nobody behind. Open source openness, collaborate, combine. Shake the values with humanities, common values, diversity of civilizations. Don't lose it, understand it. Tolerance exchanged between peoples, every temple different and every ceiling equal. Delegates nodding all the way down the hall. Board is setting, don't nobody move at all. Everybody here with the clock is doing, nobody want to be the one accused of moving.

• 2:13:53Speaker 01

  Master, the machine has surpassed the student. Then who now is the master?

• 2:14:02Speaker 03

  Then who now is the master? Constantly refine the measures, hold the fold, four-star, four-star, a loss of control. Nobody builds a warning for a quiet room. You build it for the footsteps coming through. Forty-five degrees, that's the angle of the vow. One line power and one line howl. Tied them at the rung, every step of the climb diverge and we're out of road and out of time. Converge and maybe more than come around. Ancient courtyard, seventy-nine in the ground. Grandfather swept that stone every sprint, name still spoken, it's still the same thing. Hand it down, the line or the line go cold. Trusted tool, human hand, human hold.

• 2:14:44Speaker 03

  Sees the rare historic, the window thin, no final chamber, so you never stop training in.

• 2:14:50Speaker 02

  No final chamber, no final door.

• 2:14:53Speaker 03

  Staggering speed.

• 2:14:54Speaker 02

  Staggering speed, ain't been here before.

• 2:14:57Speaker 03

  Secure and controllable.

• 2:14:59Speaker 02

  Hand on the blade.

• 2:15:02Speaker 03

  No final chamber, the last one ain't made.

• 2:15:06Speaker 02

  No final chamber, no final door.

• 2:15:12Speaker 03

  Staggering speed.

• 2:15:13Speaker 02

  Staggering speed, ain't been here before.

• 2:15:16Speaker 03

  Secure and controllable.

• 2:15:17Speaker 02

  Hand on the blade.

• 2:15:21Speaker 03

  No final chamber, the last one ain't made.

• 2:15:24Nathan Labenz?

  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 comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more.

• 2:16:09Nathan Labenz?

  We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at ai podcast dot I N G. And thank you to everyone who listens for being part of the Cognitive Revolution.