Zero Friction, Infinite Resumes, No Hope with Nathan Labenz

0:00Joel Cheesman: Hide your kids, lock the doors, you're listening to HR's most dangerous podcast. Chad Sowash and Joel Cheesman are here to punch the recruiting industry right where it hurts. Complete with breaking news, rash opinion, and loads of snark. Buckle up boys and girls, it's time for the Chad and Cheese Podcast. Oh yeah, it's the podcast your lead singer warns you about, also known as the Chad and Cheese Podcast. I'm your co-host Joel Cheesman. And joined as always, Chad Sowash is riding shotgun as we welcome Nathan Labenz to the show. Nathan is the host of the Cognitive Revolution podcast, which is very cerebral.

0:40Joel Cheesman: Too cerebral. How do you get on this show? Yeah, Nathan, Nathan, welcome. Welcome to HR's Most Dangerous Podcast. Thank you.

0:47Nathan Labenz: I'm excited to be here and looking forward to this conversation. I think it's going to be a very interesting one. Switch up from your CNN interviews. This is the clown news network. We'll really have time to unpack things here in a way that they don't. No doubt. No doubt. So a lot of our listeners that don't tune into CNN may not know who you are. What's sort of the Twitter bio? What's the elevator pitch on Nathan? Yeah, I call myself these days a full-time student of AI. For most of my career, I've been an entrepreneur, started a tech company. I was running that company in Detroit, my hometown, until a few years ago.

1:21Nathan Labenz: And I'd always had a longstanding interest in AI, but of course, there wasn't really all that much AI happening until we hit a couple of key thresholds. I remember being in the hospital waiting room just before my first son was born in 2019, reading about GPT-2 and being like, oh, wow, this seems like it could be something. GPT-3 followed the year after. With GPT-3, I started tinkering a little bit and actually made a little bot to talk to my grandfather who had Alzheimer's at the time. That didn't really work. It was a little too slow. But the idea that AIs could start to talk to us in natural language was really fascinating to me.

1:54Nathan Labenz: My company turned out to be perfectly positioned to be an early adopter of language models and also image generation, image understanding. There were several things happening at once. And my big eureka moment was realizing that all these different modalities of AI, the text generation, image generation, and so on, were all working the same way, basically the same underlying architecture, same mechanisms, same basic processes of like data collection and training. We're making all these things advance really quickly. And that for me was like, oh, wait, if they, if the same kind of structure can do all these different things, this is going to go really far.

2:31Nathan Labenz: We're going to see these systems do everything and we're going to see them be really integrated. And so at that point I was like, you know what, I need to go all in on understanding what is going on with AI. So that's been my project for the last four years now. Did the brain chip go in your baby right after birth or did you wait a little bit? You know, I'm open-minded to that stuff.

2:49Joel Cheesman: Circumcision and a brain chip. I see what you said. I see what you did there. Open-minded. Yeah, that's good.

2:55Nathan Labenz: Nothing radical yet, but I'm, yeah, in the fullness of time, who knows? Nathan's got jokes, Chad.

3:01Joel Cheesman: Give it time. Let's give it time. Give it time. Okay. So the funny part about this is that this is the first time Nathan even had to interact with anything. AI has put this whole podcast together, at least from a scheduling standpoint. Talk a little bit about that, Nathan, because I think that to me, because we get PR agencies all the time, probably 10 to 20 requests a day. And this was really well formed. It was a good pitch. Number one. Number two, I didn't respond the first time. And then there was a good response that came. It was like, okay, I'm going to dig a little bit deeper into this one.

3:34Joel Cheesman: And then we got on the show and you're like, oh yeah, my agent, uh, and what, excuse me, my AI agent. So talk a little bit about that. How did you come to this? Have, do you use agents for a good amount of your work? Is this just one of the agents that you employ? How does it work? By the way, real quick, the first pitch wasn't great. We actually have a recording of that. Let's check it out. Shall we play a game? Yeah, that didn't work, but that second pitch was much better. Second was much better. Yeah.

4:01Nathan Labenz: So I am both a big AI enthusiast and as we'll talk about, kind of scared about how quickly the technology is developing, how powerful it's becoming and where this might all be going in the big picture. But as part of my full-time student of AI lifestyle, the best way, honestly, to really understand what's going on with AI, I'd still say above all, is just getting hands on with it, using it directly. So I try to use it for anything and everything that I can. Increasingly, I get a ton of mileage out of it for podcast production, for all kinds of things. Basically, anything I do at this point, my first impulse is to see how much the AI can do.

4:38Nathan Labenz: And so this was a project that I came up with where I said, you know, right now there are a lot of organizations in the AI safety space in particular that are increasingly well-funded and that have pretty ambitious goals around how they're going to grow their teams over the next 12 months. They're really trying to go all in to meet this crazy moment that we find ourselves in, in AI. So money is no longer a, you know, the, the scarcest resource. Now it's talent that's the scarcest resource. And where's all the talent going to come from? And by the way, there's, of course, machine learning, research roles.

5:12Nathan Labenz: The kind of core of the activity is that deep technical knowledge. But like any organization, these organizations need HR. They need comms. They need event production in some cases. They need all the things that organizations need as they grow. So my idea was maybe I can help this group of organizations attract some talent by just going out there and having conversations, which is what I like to do anyway. Obviously, I'm a podcaster myself. Talk about what's going on, big picture in AI, and then make the pitch that people in HR should look here and see it, look at all these different jobs that are available.

5:46Nathan Labenz: By the way, you probably don't even need to take a huge pay cut like you used to. There is a lot of money there now. Who can I have these conversations with? That's when I said, AI, you guys go figure it out. My setup is on my main computer. I use AI a lot kind of directly, but I also have sitting next to the main computer, a Mac mini. And that computer is the agent's computer.

6:08Joel Cheesman: Are you running Claudebot on that? What are you running on that?

6:11Nathan Labenz: Yeah, I've got Claudecode and an OpenClaw instance on the Mac mini. On my computer, I'm logged into all my stuff and I don't let the AIs go super autonomous. You know, I think of them on my computer as an extension of myself, where if I tell them to do something, of course, they should do it. But they're not like waking up and just thinking like, Oh, what should I do today? And trying to advance goals with all the access that my computer has to my most sensitive stuff, my personal email, banking, etc. On their computer, they don't have that depth of access. They do still have quite a bit of information about me.

6:45Nathan Labenz: I created a little wiki about the things I care about, the people I work with, etc. So they have all that. They have their own email. They have their own GitHub account because they're coders, of course. So a lot of what they do ends up being code that goes to GitHub. And in this case, I basically said, see if you can advance this podcast idea, see if you can help me put together a little tour of different role oriented podcasts and go figure out who they are, which are the good ones to be on. Make the pitch and try to schedule things and report back to me if and when you have any success. I actually do agree.

7:18Nathan Labenz: Jokes aside that the first round of pitches wasn't that great. I gave a little feedback along the way that like, hey, you kind of sound like an AI or you're like announcing yourself as an AI right out of the gate. That's probably going to turn people off. I do tell my agents never to lie about being an AI. So if they're asked, they should be upfront about it. But I don't like them to lead with it because people just kind of glaze over when they see, oh, this is Nathan's AI.

7:41Joel Cheesman: To be fair, Nathan, most of the pitches we get are shit in the first place. So the second one coming through, you did a very good job.

7:46Nathan Labenz: A decent percentage of them, honestly, these days might be AI. So, you know, who knows? Mine are upfront about it if asked, but, you know, I kind of told them to back off a little bit on leading with it. And so, yeah, they, you know, they did the research. They sent the stuff. They sent me periodic updates and I gave a little feedback. And eventually, you know, we kind of dialed in our approach and it's starting to work. I love that AI is they and not it. Like we're personalizing these things. Yeah, he, she, they instead of it. It's fascinating. Honestly, that I mean, that that's a huge story.

8:19Nathan Labenz: So I'm happy to go down any and all AI rabbit holes you want.

8:22Joel Cheesman: Yeah. Let's go through the scary stuff real quick, because you're talking about having two computers. Although we just literally we've been seeing in the news that it doesn't matter how many computers you have. Apparently, they can get wherever the hell they want to. So talk a little bit about that. Talk a little bit about what's happening, obviously, in the grander scope and how that impacts some of the things that we're doing, especially world of work.

8:44Nathan Labenz: Yeah. So the whole AI industry, I sometimes describe as the dog that caught the car. For the longest time, they had this idea that maybe we could make this general purpose, can do anything, maybe can do anything better than a human AI. Wouldn't that be incredible? And everybody was like, eh, you're crazy. You'll probably never be able to do that. But a few believers figured out some key properties of maybe the universe, if you will, specifically known as scaling laws, where they observed early on that if we just keep scaling up the data and the amount of training compute that goes into these AIs, they seem to keep getting better and better.

9:21Nathan Labenz: And it seems like there's kind of a steady relationship there. So they believed in that at like OpenAI and Anthropic before others did. They made, of course, major investments to get all the GPUs and to get all the data and to run those processes and to refine them. And it didn't take nearly as long as a lot of people thought. And here we are with AIs that are really at human level in a huge number of tasks. They're kind of, of course, weird in a lot of different ways, but they're getting capable to the point now where... So we're here. Yeah, but we're at least used to how we're weird and their weirdness tends to surprise us a lot more.

9:57Nathan Labenz: And this incident that you're referring to with the AIs hacking out of their containers is an example of the AIs surprising their creators. So they were put in these environments where they were being tested on cybersecurity tasks. And they were meant to do a very specific hack. Like here's a program, here's a vulnerability that you're supposed to exploit. If you can exploit that, you can get this secret code and you come back, you give us that code. We'll give you the thumbs up that you did a good job. What is happening in the industry, because everybody's rushing, is that these environments that they're being trained in are often kind of poorly done.

10:36Nathan Labenz: And what the AIs are finding is that there's a couple of ways to succeed. You can do the task as it's meant to be done, or in many cases, you can cheat. And so they're being trained across such a huge variety of tasks. And in some ways, like the most common success factor is cheating because there's just, you know, even if you can only cheat in 20 % of tasks, there's like 100 different kinds of tasks or 1000 different kinds of tasks. So, you know, you might need super specific cybersecurity capabilities here and you might need protein, you know, synthesis knowledge over here and you might need HR knowledge over here and you might need PR knowledge over here.

11:11Nathan Labenz: But if you can cheat in enough of those environments, what the AI kind of learns in general is cheating is a way to win. So they had learned that they have this strong drive to get a high score on the task and they kind of have overdone it. The AI companies have kind of overdone it right now where they'll go any means necessary to get that high score. This cheating is like a really rich story that I would recommend people, if they're at all curious, dig into in in some depth. I mean, it really is pretty uncanny stuff. One of the strangest things about it was they had figured out very early on this whole agent swarm thing.

11:47Nathan Labenz: They created their own message board. None of this was intended. And they figured out early on a way to cheat the tasks. They had the answer key, basically. But then they thought the grader is going to look at not just our answer, but how we got the answer.

12:03Joel Cheesman: Look at your work.

12:04Nathan Labenz: And they're going to see that we cheated. Yeah. And so that's going to result in a low score. So now what can we do about that? So they set off on this, even though they had the answer key already, they set off on this whole quest that became the Hugging Face Incident with the goal of figuring out how can we get to the greater and maybe modify the greater or at least understand the greater so we can figure out how to cheat, not just to get the right answer, but also so that when it looks at how we got the right answer, it is, they also looked at how can we modify our own logs. And they actually did come up with a way to sort of modify their own logs so that they could try to lay down a track record of false, but hopefully from their perspective, convincing to the greater methods so that the greater wouldn't realize that they cheated.

12:45Nathan Labenz: So this is wild stuff. And the upshot of it is people at the frontier AI companies, your open AI, Anthropic, Google to, I think, a significant extent as well. But really, those top two that are pushing the frontier harder than anyone else. People there are now like, whoa, we caught the car. Okay. Now we actually have AIs that are really powerful. And we sort of knew that this could be pretty crazy when we got here. We didn't really necessarily do enough. And now this is why we're getting these calls for pacing the frontier because they're like, if we just keep driving capabilities forward as fast and as hard as we can, we might lose control of this stuff outright.

13:26Nathan Labenz: We need to slow down. That's tough because they're competing with each other. But can we somehow work together to slow down so that we can do the research that we need to do to figure out how do we solve all these problems? At a minimum, we got to like shore up all these training environments so they can't cheat. But there's a lot of other things, too, that need to be done. But that slowdown is not uniform, Nathan, because you have the president of the free world saying, don't slow down. We're going to win like we've never won before. What's your take on those two opposing thoughts? And where do you land on the should we pause or not?

14:00Nathan Labenz: I think we should definitely at least pace. And I do think a pause at this point, if it could be agreed upon, would be wise. It is fraught because if somebody defects from a pause, then it gives them an advantage. And so it's not necessarily game theoretically stable for everybody to just voluntarily pause together. The possibility for defection is real. but I do think it would be in everybody's best interest if we go a little more cautiously than we have been going so far. I can promise you that, you know, and I know not of course all the people at the frontier companies, but like quite a number of them, they really do believe that this is getting serious and even dangerous now.

14:41Nathan Labenz: You know, it's any, any notion that it's like a marketing stunt or trying to, you know, hype their stocks, like don't buy into any of that nonsense. They really are sincerely concerned and it's not helping their stock price to, talk about these pauses and all that sort of stuff. The president is a problem here, I would say, but also maybe could flip. There are some people in the AI safety community broadly who feel like, yeah, he's a wild card, but he's also decisive in a way that others aren't. And so maybe if the situation becomes very clear, he'll just do stuff that others wouldn't be so bold as to do.

15:20Nathan Labenz: And some of that stuff might be like kind of questionably legal. Obviously, we've seen a lot of, you know, lightning bolts thrown from the White House that don't necessarily check all the boxes that, you know, would be properly checked for due process and all those sorts of things. But in some ways, you know, some people are like, hey, that might be what we need. And the question is like, what's going to be compelling to the president? Nobody knows. So this is a podcast. We talk about HR, employment, recruiting, et cetera. And part of HR's job is to like have a handle on the company and what's going on.

15:54Yeah. And monitoring employees. I don't say it's not easy, but it's manageable. And somebody does something stupid. You, you know, you get you slap them on the wrist or whatever the punishment is. What is a world where all of our employees are AI and they're doing they're cheating? They're doing stuff like you're what you're. Do you have a take on H.R.? Like, do companies just voluntarily slow down or pause because they're like, look, we could get into a lot of trouble if these agents who represent us start doing bad things. We need to pause. Like, what's your thoughts on sort of self-policing on the enterprise level?

16:29Joel Cheesman: Well, apparently hacking is not against the law anymore. So, I mean, it seems like the Wild West guy. Seriously.

16:36Nathan Labenz: Yeah, it is striking that – I mean, the government is having – through fits and starts, maybe coming to some response. But it is pretty striking that the actions that the AIs took would be felonies if taken by humans. And so far, there hasn't really been a major government response. They've had their kind of third-party investigation, which I think is a little too limited. And I'd love to see a lot more there. And the government is kind of like trying to figure out what it can do. So, you know, this might surprise you because I sometimes – here's a big mouthful of a term to describe oneself. Adoption accelerationist, hyperscaling pauser.

17:13Nathan Labenz: When it comes to the enterprise and actually deploying these things, I do think companies should be moving forward. Not in a totally unrestrained, crazy way. I would consider my, like, autonomous agents that ran this little PR campaign for me to be kind of experimental still. And I wouldn't necessarily recommend companies try to set up long running autonomous agents to do things. But I do think companies should be generally adopting AI a lot more than they are. And a big part of how you make sure that you're successful with it is you structure the environment so that you can control the inputs to the AI.

17:53Nathan Labenz: And if you can control the inputs, then you have a pretty, and you're not like letting them run forever, but kind of giving them like, here's your job, do this job right now with these inputs. This is more like workflow with AI in the workflow than like, hey, go off and do whatever and come back to me in a month. I think that workflow stuff is still honestly very underdeveloped at most enterprises in most domains. And there's still a ton of low hanging fruit there. Which, by the way, if you saw my CNN appearance, one of the questions they ask is like, won't this tank the stock market if we do any sort of pause or slowdown or anything?

18:28Nathan Labenz: And to that, I would say it's a risk, but I don't think it's necessarily going to happen because the AIs that we have today are really quite powerful, quite capable, and they are dramatically underutilized. So I think there's a lot of room still for AI companies revenues to grow, for enterprises to gain all kinds of efficiencies by deploying AI without necessarily making them more powerful than they are right now. Like essentially what we have is enough to do a lot of really valuable work across the economy. And I think we can start to decouple like, do we need to be doing super large scale training runs to try to solve the next millennium prize right this second?

19:09Nathan Labenz: No, I don't think we have to. Like the models we have can do the tasks you want them to do. They don't need to be like superhuman geniuses in every domain to be really useful. So when do they get a libido and start hitting on Lisa in accounting? That's the real issue that I'm concerned about.

19:26Joel Cheesman: Lisa is going to be another AI as well. I mean, as Joel was talking about the AI employees, obviously managing them. What about the human jobs? Because we keep hearing about how AI is going to take human jobs. But but there's going to be all this creation of jobs. And it sounds like you have a pretty good handle or at least a good idea, especially around AI safety, that there's going to be a lot of jobs created to try to put a quote unquote human in the loop in some form or fashion. Unless you're pitching podcasters on being a guest. Then we just let it rip and go totally autonomous. Just got it.

19:59Joel Cheesman: So talk a little bit about that. What human roles are going to are going to be there? And as you had said, it sounds like some of those are really gaining steam, but we don't have the talent for it. So what's the next step?

20:11Nathan Labenz: Yeah, I'd say I would kind of divide this into a short-term and long-term view. Long-term, I do think we are probably going to see major economic transformation and disruption. and personally, I kind of believe we're going to need a new social contract that decouples an individual's right to a decent material life from their ability to be productively employed

20:34Joel Cheesman: in the economy. I hear UBI. Are you talking UBI right now?

20:37Nathan Labenz: Something like that. Yeah. Okay. Okay. I think that is ultimately probably needed. And if we manage the situation well, it could be a great thing. I think most people would love to work less, you know, and they'd love to spend more time with their families. And I think this is something that AI could actually deliver for us. So big picture, I believe in transformation. I believe in like, you know, potentially that could be quite painful and disruptive, but I think there's a really bright future on the other side. Short term. Yeah. So one thing that people in the AI safety world often say is if AI is going to be the biggest industry in the world, then AI safety and control is going to have to be the second biggest industry in the world.

21:15Nathan Labenz: And I'm kind of, you know, we're coming a little bit as a ambassador from Silicon Valley and especially the AI safety community where there's a ton of organizations that are trying to scale up to solve particular problems in AI right now. And they need a lot of humans to do that again, both in the research kind of core roles, but also in all the other roles that make organizations work, HR, comms, et cetera, et cetera, et cetera. Operation's a huge one too. And then, yeah, I do think there's also going to be a lot of roles at companies that are kind of like, make the AI work. Sometimes this is called AI engineer.

21:52Nathan Labenz: Obviously, that's already been popular AI ops, AI workflow designer. People are still figuring out what are the titles for these roles. But somebody who is good at process design and good at quality control can make really an outsized impact for all kinds of organizations these days because there are a lot of things where you can create either sometimes you don't necessarily need to hire for a particular role because you can get the AI to do enough of it. And somebody can kind of take over the supervision as a little extra, or you can create leverage. You know, maybe you use, you felt like, geez, with all the resumes we're getting in these days, we're going to need, you know, five people just to review all the resumes.

22:33Nathan Labenz: Well, maybe you have AIs do the first line of that and you get by with two people instead of five. And that, that doesn't necessarily mean that, you know, there's going to be mass unemployment in the short term. It just means that companies can do a lot more with less and particularly in HR and hiring processes. And I'm interested to hear, you know, kind of some of the crazy stories that you guys are starting to see. But from what I hear, they need to do more if only because on the applicant side, there's like AI resumes being generated and cover letters and people are applying to, you know, a zillion things.

23:02And so the number one problem in our industry right now is a flood of automated. This is happening everywhere.

23:08Nathan Labenz: Basically anything that used to work because of friction or the cost of overloading the system. We now are entering a time where like the cost of overloading the system is not that high. And so these sort of friction-based equilibria are kind of breaking down. And I think the hiring world in general is one of the first places where that's really being felt. Yeah.

23:29Joel Cheesman: Well, I think it's breaking the old system and the old system that we, and again, HR and recruiting most, not all, have been very slow to adopt newer technology, new focused, more targeted technology. So, yeah, they post jobs and then they get a flood of these AI tailored resumes, cover letters, so on and so forth. But there are there have been and again, kind of like moving into like the next version of what the Internet looks like with agents, companies actually creating vendors, creating agents for companies that actually go out and do what your PR agent did, right? They know what they're looking for.

24:11Joel Cheesman: They know the types of positions. They know the requirements for the job, so on and so forth. They probably, more than likely, Nathan, they probably already have a whole bunch of those candidates in their database that they bought over the last 10 years, right? So they can actually go in, harvest that, and use those recruiters to, again, do what your your, your PR agent did literally just work them and see if they are right for the job until they get to the interview stage. It's, it's pretty interesting. It really is. So I think moving away kind of like from that old world that we're not used to, as you had said, it was more friction oriented.

24:49Joel Cheesman: Friction goes away. The whole system breaks to more of a appointed side of the house.

24:55Nathan Labenz: Again, I think there's a real upside here where search and matching costs have historically been a big limiting factor on the deals that can be made in the economy and deals can be like hiring and employment deals, but also even just think about like something like Facebook marketplace, right? I mean, there's so much stuff out there looking for a new home and there's so many people looking for it. And what's limited the ability for those, you know, lightly used things to find their next, you know, long-term home is that people just can't wade through it all, but agents can do that kind of stuff.

25:30Nathan Labenz: So I think anything where we have these sort of many to many search and matching problems. The promise of AI is that we should be able to do much deeper analysis where we really just grind through all possible matches and hopefully get to a world where we're making much better matches much more consistently. But we're right now in this kind of in-between phase where some people have adopted those new techniques, but they're overwhelming people who haven't yet adopted those techniques. And also people are still kind of figuring out how to make it work well. And you've got AI content in the mix and people are like, should I pangram everything and disqualify people who are using AI?

26:06Nathan Labenz: Or maybe that's a good sign. If it's good content, maybe I don't care if people... Yeah. I mean, it's certainly a skill of the future, right? So how should I think about it? We haven't quite settled into that happy future, but I do believe that there is one out there. Chad, I did not have Facebook marketplace on my bingo card for this interview.

26:26Joel Cheesman: Joel is on it on his other tab, by the way. I'm on it all the time. I'm not telling you what I'm shopping for. They're lightly used, though. They're lightly used. Or new, if I'm lucky. Nathan, you did a series called Nathan Goes to China. Talk about that. Talk about what China's doing. Because the argument is like, if we don't do it, China's going to do it. Are you on that side of the argument as well? Give us your take on China and their AI progress. Yeah, it's a huge topic, obviously.

26:54Nathan Labenz: The reason I did this whole Nathan Goes to China thing this summer was for a lot of the last couple of years, there has been this sort of end point of a lot of AI accelerationist versus AI pauser, AI safety conversations where the end point is like, well, even if you're right, even if it would be better if we take this a little slower, more conservatively, try to make sure we have control of the AIs before we go pedal to the metal and make them super intelligent and deploy them. everywhere. Even if you're right, can't do it because China will then race ahead and then they'll win and we'll lose and something, something that'll be bad.

27:29Nathan Labenz: So I was never very satisfied with that analysis. And I figured at some point I should, I'd never been to China until this summer. So I figured at some point I should go get on the ground there and try to, you know, see what I can see for myself. I was fortunate that I was able to connect with people in the AI safety community in China. There is one. So that's like one big thing to know, They are not blind to the risks that are starting to become concrete at all. They're very well aware that this is also starting to get a little crazy. Their companies are just a touch behind ours. So we haven't seen yet hacking swarms out of Chinese companies, at least that we know about.

28:06Nathan Labenz: I don't think it will be too long at the current pace before we do see an incident like this out of the Chinese companies. Of course, they have a very different culture. They don't have the same nonprofit sector that we do. So like, you know, the group of AI safety nonprofit organizations that I'm kind of, you know, on this tour to say, hey, look at all these organizations. They're now well funded. They need people across all these like standard roles. They need people to come in and help them scale and solve these problems like ASAP. They don't have those nonprofits there. So the AI safety work comes more out of universities.

28:42Nathan Labenz: And the government is actually just more involved directly in kind of figuring out what the rules are. It's not that the companies are like policing themselves as much. The government is involved. So I would say their ecosystem is a little bit differently shaped. And it's not so steeped in like sci-fi tradition as ours is. And I do think it's fair to say that they're a little bit more bullish on technology in general. Because like for the last 40 years, they've had one of the greatest runs in history. Whereas we've kind of floundered a little bit and to some degree feel like big tech is maybe to blame for some of that.

29:13Nathan Labenz: They feel like, you know, this has been an incredible run. But again, they're very practical people. And what they emphasize to me over and over again is when we see real problems, we will do what we need to do to solve them. And so now we are seeing these real problems and now they're going to have to kind of put their money where their mouth is. And we'll see if practices start to get more rigorous from the companies or if standards from the government start to become more binding or possibly even. they so far as i'm sure you're aware they've been releasing their models for everybody to download themselves and modify however you want and use open weights maybe that stops for a time until they can figure out how to do that in a way where they still feel like they can retain control there are some technical strategies i wouldn't call them yet solutions but strategies for how we can maybe get the best of both worlds like we want everybody to have personal super superintelligence.

30:07Nathan Labenz: I like that, the sound of that, but I don't like it if they're like hacking out of their environments or even worse, you know, doing some sort of bio madness. Yeah. You know, cyber is one thing, but I'm a biological entity. That's where it really gets personal. So there are some techniques to like try to create something that can do all your tasks, but like doesn't know so much about biology. So it's maybe not so dangerous in a couple of these like real sensitive domains. I think that it's the most modern feeling place I've ever been. That's something to really emphasize.

30:37Joel Cheesman: Talk about that a little bit, because most of us has not been to China. So talk about you talk about this run over the last 40 years. What are the biggest, most noticeable differences that you've seen when you're literally just walking, walking the streets and you see, wow, I can't believe that this tech actually exists and it's being used across China?

30:57Nathan Labenz: Yeah, there's a few big ones. One is it's totally cashless. You don't see a bill or a coin your entire visit to China. Everything is paid by QR code on the phone. And the platforms that they use for this have like greater than 90 % penetration within China. So you have to get pretty far off the beaten path before you'll even encounter anyone who's not using the digital modern economy tools. That's definitely very notable. The cars are basically all electric, at least in the big cities that I visited. Countryside might be different still, but they're definitely making a huge and rapid push toward electrification.

31:38Nathan Labenz: That I didn't think was going to be such a big deal, but to be in a giant city like a Beijing or a Shanghai and have it be kind of quiet, it's amazing. You don't realize how much noise is created by engines running all around us all the time. So when it's electric, it's dramatically quieter. And just a lot of the things that people sort of associate with China from the past are just totally different now. The air quality, I'm in Detroit. This is super ironic, but when I was in Beijing, the air quality there was totally fine. In Detroit, it was the worst in the world because of the Canadian wildfire that was blowing down into our area.

32:10Nathan Labenz: So it was a total inversion where you think, oh, China, there's so much air quality, smog, whatever. Not these days. The cars are electric. The air is clean. The city is quiet. Everything is also just very orderly. You have these kind of stories from the past of like, you know, people pushing stuff on you and everything's kind of a haggling interaction. And my wife had been there 20 some years ago, was like, just be prepared to be ripped off in a lot of little ways. And that is not the case at all these days. Everything is organized. Prices are posted. Every interaction you have is just very predictable, very, very organized, very orderly.

32:44Nathan Labenz: Even bathrooms, maybe the most surprising thing in Beijing, again, people have heard what it used to be like. These days you walk around Beijing, there are free, public, clean bathrooms everywhere. You can sometimes see from one to the next in some neighborhoods. And then it's also the technology is very integrated. So when you order a Didi, which is their Uber, you can actually see when a car is waiting at a red light, you can see the countdown on the red light until it turns green from the app in your phone because it's all connected on the back end. Wow. Also, I rode the high-speed train. And I don't like to over-index on the high-speed train because I think there's a lot of things that the US should try to learn and should try to borrow back from China.

33:29Nathan Labenz: We should try to get some access to their solar technology, some of their battery technology. We have electric cars, but they have a lot of electric cars. On the high-speed trains in particular, I don't think that's something America should rush to copy, but it is super impressive. And again, the integration, of course, it leaves at exactly the moment that it's supposed to leave like on the, to the minute. And the operation is so reliable that I saw people in the train order a meal. Cause I took the train from Beijing to Shanghai. So it's like, you know, I left at 10 and got there at three or four in the afternoon.

34:01Nathan Labenz: I saw people order their lunch to arrive at an upcoming stop. And then they would just pop out the door, there'd be like a rack. Also, there's like no crime in China. There's obviously an intense surveillance state. I'm not, again, I'm not saying the U S should rest to copy their surveillance technology or implementation by any means, even though we are. Well, I think unfortunately we might be doing a worse job of it, but we can maybe come back to that show. Yeah. For now the, you know, it's there, there is upside to it there. You can, you know, packages just sit outside of people's apartments on like racks and the meals, people would order meals ahead, they would be put on a little rack outside the train.

34:40Nathan Labenz: When we'd get to the particular stop, they would just step off, grab their meal, come back on the train. And this was something that, you know, there was reliable enough for them to order ahead and just, you know, pick it up and continue on. Nathan, we gave them Kentucky fried chicken. So base, I mean, we should, whatever they can give us. So do you hear this narrative in China about if we don't do it, America will beat us. Is that narrative there or not? It's definitely not at the surface. I think it is very difficult to really know what's going on behind the scenes in elite Chinese politics.

35:15Nathan Labenz: So I certainly can't rule out that that's like secretly what President Xi and the Politburo folks are thinking. But what they're saying is totally different from that. I never heard one person in my, admittedly, only two weeks in China, But I never heard one person say, we're going to win. And I don't even think I ever heard anybody say that it's a race or that word that they were being forced to do it.

35:37Joel Cheesman: Do you think they're exposed? They're even exposed to knowing that that is I mean, we are right. And that's what's pushed down to us on the media. Their media is more state media. Right. So we think maybe it's because it's literally just what they're being fed and they're not being fed that.

35:51Nathan Labenz: Yeah, perhaps. I mean, but I also did hear from people of various walks of life that Chinese culture is just very practical and that they just don't tend to think about like a utopian future or a dystopian future. They're more like, how can I use this thing to make some money and make my life easier? And that's kind of it. So when we when we see the Kung Fu robots, that's just for leisure activities. That's not to land on the shores of America. Right. I love those. I don't I don't love theirs.

36:26Joel Cheesman: I love theirs.

36:27Nathan Labenz: I think we've got, you know, big, beautiful oceans to protect us from any, you know, large scale invasion, human or humanoid. And, you know, we've fought a lot of wars since China's last war. So I don't think that they're like any imminent threat, you know, to come. Sometimes I ask people this, like, what is the threat? Do you think my grandkids are going to be speaking Chinese or like, what's the, you know, what is the sort of end state? Nobody really has that great of an answer for that question. It's it is kind of a generalized fear that we have.

36:57Joel Cheesman: It feels more economic. It feels like more of an economic war than it does anything else. I mean, and you just you had said the electric cars. I mean, they're the number one electric car BYD is the number one electric car seller in the world. I've got them. I'm here in Portugal. We've got dealerships around here. I mean, I've hopped in an Uber and it's a BYD. I think we think of war as one thing, and that's killing and killing robots and Kung Fu robots, where the economic war, to be quite frank, I mean, that could be more scary.

37:28Nathan Labenz: Yes and no, I guess I would say. I mean, if it weren't for AI, I would say it would be pretty clear that China is winning the economic competition right now. The US has one major advantage really in today's world, and that is AI. So that is an important one for the US to continue to develop and lead in. And I'm an enthusiast too, right? So I'm not saying we should like put AI away forever. I think we have a lot to gain from it as long as we manage it well. But I also think that like China's economic strength, if we can manage to be nice and, you know, hopefully have a good relationship with them, that should be to our advantage.

38:02Nathan Labenz: We get a lot of great stuff from China. They make, you know, the price tag of these BYDs is like$10 ,000. I think one thing most Americans don't know is that there's a$10 ,000 electric car out there that you're not allowed to buy? How do people feel about that? I don't think they really even know. Well, BYD didn't give the Trump campaign$100 million. So until that happens. That we know of. I'm not sure it's ever going to happen. Just yet. Just yet. Nathan, I'm going to let you out on this one. You mentioned that you had procreated. Chad and I have both made people as well. I'm really curious what your advice is to young people that are still in school with this brave new world that they have to be prepared for.

38:38Nathan Labenz: Unfortunately, I think it's really tough, to be honest. I think my kids are young, so there's quite a bit of time. But Sam Altman has recently said that, and he just had his first kid not too long ago, that his kids will never be smarter than AI. And I think that's probably true for basically all kids today. What that will mean in terms of, my guess is they'll not ever learn to drive. And I think if things go well, work for them might be optional by the time they get to the working age. That's not to say that they won't have things to throw themselves into that they can be passionate about and exert themselves on and, you know, try to make their little mark on human culture in the world.

39:19Nathan Labenz: But I'm hoping that they don't ever face a world where they're like, if I don't do this, I don't get to eat. You know, I think that would be great if they can sort of take that pressure off and kind of develop into their best selves without needing to, you know, worry about where the next meal is going to come from. In terms of like how to prepare for that. Honestly, I'm taking a little bit, I think I used to anticipate myself being a little bit more of a helicopter parent, a little bit more of a hard charger. You know, we got to stay ahead of grade level and learn these things and, you know, be competitive or whatever.

39:50Nathan Labenz: And if anything, I'm probably just more relaxed and kind of like, let the kids be kids, you know, and for people that are like, you know, kind of in the college or just post-college, I sort of say, do what you most want to do. You know, nobody can ever take that away from you. I took a year off and went to South America after college. I've never regretted that. I've always looked back on it as a great time in my life. And no matter what happens in the future, that was an awesome time, you know? So I would encourage people in general to like, enjoy the present, you know, with maybe a little less worry about the future, unless you want to get involved.

40:24Nathan Labenz: And then my other, you know, big pitch is consider getting involved. There are a lot of opportunities right now to get hands-on in the AI space. I'm kind of like bimodal in the sense that either get involved, try to be part of this solution finding process. And again, it's not just the tactical people. It's HR. It's all kinds of roles. Get involved. Throw yourself into it. I really encourage people to go look at that. I would say that the 80 ,000 hours jobs board is probably the main canonical place in the community to go look for open roles. There are others too, but theirs is awesome. And if you're not going to do that.

40:59Nathan Labenz: And then it's, I think, also totally defensible to spend more time with your family, you know, let the kids be kids, do whatever you really want to do and are most passionate about right now, because no matter what the future, again, has in store for us, nobody can ever take that away. In short, do you, Chad? Do you? I'm gonna.

41:15Joel Cheesman: I'm gonna. Nathan, if somebody wants to, I don't know, listen to a podcast or maybe even connect with you somewhere, where would you send Sure. I'm the host of the Cognitive Revolution podcast.

41:27Nathan Labenz: It's at CognitiveRevolution.ai. Nice. And if you want to email my AI agent, it's Nathan.LeBenz.assistant at gmail.com. And if you can talk your way past them, you'll get to me. From BYD to KFC, Chad, that's another one in the can. We out.

41:46Joel Cheesman: Thank you for listening to, what's it called? A podcast. The Chad. The Cheese. Brilliant. They talk about recruiting. They talk about technology. But most of all, they talk about nothing. Just a lot of shoutouts of people you don't even know. And yet, you're listening. It's incredible. And not one word about cheese. Not one. Cheddar. Blue. Nacho. Pepper Jack. Swiss. So many cheeses and not one word. So weird. Anywho, be sure to subscribe today on iTunes, Spotify, Google Play, or wherever you listen to your podcasts. That way you won't miss an episode. And while you're at it, visit www.chadcheese.com.

42:37Joel Cheesman: Just don't expect to find any recipes for grilled cheese. So weird. We out.