AI:AM: Is THIS the AI We Want? Alarming Acceleration, AI Election Bets, GPU Trading & AI-Native VC

0:00This week on AI in the AM, the reports of acceleration keep coming. Is recursive self-improvement already underway and is dramatic acceleration imminent? Friday morning, the day after Meter published its report on how much AI is already speeding up AI research inside Anthropic.

0:16Nathan Labenz: The pace of things is definitely pretty relentless. It might be time to spend more time with our family because it does feel to me like basically the AI 2027 timeline more or less is coming true. Prakash Narayan, my co-host. We were talking about the local ballot measures against data centers and how the developers have been going about buying land. One of the things the companies have been doing, they've been doing the old school oil and gas land man way of acquiring land. Which is they go in and they acquire land at pennies on the dollar using LLCs. After that, they merge the pieces of land and then they're like, oh, meta is here.

0:58And then people are like, if I knew I was selling the meta, I would have asked for more. So people feel cheated, right? If you have political willpower, all contracts are worth dust. Mike San Miguel of Luxor Technology. He sells GPU servers off its hardware desk, and he set up its KYC department.

1:16Nathan Labenz: You've heard there's some compliances. These things are traded more closely than some nuclear secrets. So every OEM is a little bit different. NVIDIA works closely with all the OEMs around allocation and around greenlighting orders. NVIDIA also typically is going to check every six months and make sure those GPUs are where they're supposed to be. They'll physically come on site and actually make sure that they haven't left. Brian Joe, a researcher at Thoughtful, he helped build BallotBench, nine frontier agents each given a week to forecast the midterms, and trading agents putting real money on their forecasts.

1:53It takes forecasting agents a bit of time to say, see an election result, communicate it to their trader, etc. And by that time, more programmatic or more deterministic algorithms and bots have already traded on this information before. So this is like pretty indicative of what we were seeing where the traders were doing quite well the week before election day. And then on election day, they simply didn't react fast enough and sometimes made up incorrect information, which resulted in the traders losing a lot of money. Brian Bischoff, head of AI at Theory Ventures. Prakash asked about the firm's 2026 builder survey.

2:31Brian expected open source models to gain ground in production. Instead, the balance shifted toward closed models. There is a lot of, I would say, acceleration in model drops. And so right now, tying your production service to open source models requires a little bit more thrash in the engineering. And so I heard from a couple of folks specifically, yes, but the open source models change so fast. is a little bit more convenient to pin to an open AI model. This is the AI in the AM weekly highlights with my co-host, Prakash Narayanan. I'm Nathan Levens. The narration is my cloned voice, and we'd love your feedback on what works and what doesn't.

3:20Part one, imminent means now. Friday's show opened on two documents, the meter report and an amendment to the Anthropic S1, the filing for its IPO. The new language, Anthropic says it may imminently meet its own threshold for dramatically accelerated progress attributable to AI.

3:46Anthropic did an update to their S1, the IPO filing. And in that update, they added language saying we are imminently about to see recursive self-improvement. I've always had in my mind why they're doing the IPO and why so quickly. And my take has always been, I think, within the company, there is a sense that you need more controls. More people have to have exposure and more control over this. And one way of getting this out there is getting the IPO done so that you give a larger part of society hooks into it and the ability to kind of express their desires to the market, et cetera, et cetera.

4:27To me, imminent is basically like a month.

4:31Nathan Labenz: Usually what I assume when I hear these sorts of statements is that it means now. The best model I have of Sam Altman and OpenAI leadership, which I formed during the six months between when I started testing GPT-4 and when it was finally launched and watching very closely all their statements during that time to try to figure out how to read their statements in general, given the inside information that I had then, the best way I can interpret them is they are speaking very literally and dressing it up as like vagary or like layering on uncertainty to try to, I don't know what exactly, keep the secrets a little bit longer while telegraphing the main message.

5:10Nathan Labenz: It's sort of a strange strategy to me, honestly. I really don't know why they do that. And so I would interpret this, you know, probably the same way from Anthropic. And I think you could I mean, it's really not hard to see how this would happen, right? I mean, we have so many results now with all these Galaxy Brain zero-day exploits. OpenAI just dropped some huge number of vulnerabilities in the Linux kernel. I think like nearly a thousand of them. So much for many eyes and many hands make light work, right? I mean, the idea, of course, with open source software over time is that because there's so many eyes on it, vulnerabilities will not last.

5:50Nathan Labenz: They'll be spotted. They'll be patched. That was the whole promise and virtue of open source software. And it basically worked largely while the attackers were all human. It's almost certainly the case by analogy to Linux and by analogy to math, that there is a lot of low hanging fruit and a lot of low hanging fruit means acceleration. It means you're going to get more value from compute. It means they're going to find more ways to get value from data. A big part of the reason I think they're sharing so much of this stuff is kind of to signal that the same thing's about to happen on the ML stack.

6:29Nathan Labenz: It seems pretty clear that this is now a really credible threat. What are we going to do about it? It becomes, I think, a socio-political question as much as a technical question. In my mind, the technical question is mostly answered that there's going to be a period of pretty dramatic fast progress. Does that top out on an S curve? Like my, my guess is still probably yes. You know, I don't think it goes exponential forever. Um, but who knows? And as, um, I think it was Rosie Campbell said, uh, the S curve can stay steep longer than you can stay relevant. So that's kind of my expectation for the next few months is that we pick a ton of low hanging fruit.

7:13Nathan Labenz: The models pick a ton of low hanging fruit in the stacks used to train them. As a result, we do see really fast progress in the short term. And then maybe that levels off, but it probably levels off where we have AIR &D at kind of a weekly superhuman level. And how that plays out over the course of 2027 is really, really hard to predict. I agree that I think imminent means now. I was also sandbagging with a few weeks to a month. What it means is that they have the tools available that they can see a path to implementing RSI pretty much anytime that they want. And that they are some, a lot of the pieces have been built, but, you know, they haven't, they haven't pushed the button yet.

7:58They waited for the election. They're going to do the IPO. Then they're going to announce, you know, some, some of the, you know, new things that they've discovered. And then that's going to, you're going to have this gap up. And then we're going to have the Christmas break. And then during that Christmas break, we're all going to digest all of this new technology that we have access to. And then we're going to come back in January, and then we're going to see the takeoff as people deploy. Which is somewhat similar to what happened end of last year. So, well, here we are, early takeoff. So, you know, I'm somewhat wistful, but nostalgic for the human era.

8:35This is the end of our dominance, at least. but hey you know i've also i've also kind of made my peace with like you know intelligence in the world makes the world better for me if i had like people of apple's intelligence you know people like who designed the iphone design like my bathroom or cars or home i would be happier right like like like that infused intelligence in all the products that you see makes your life better right so uh yes it's going to make everyone's lives better but yeah it is it is the end of the anthropocentric era.

9:11Nathan Labenz: It's going to make everyone's lives better, asterisk, I think, if we can keep everything on the rails. I do share that optimism. So I just got back earlier this week. I went to Bletchley Park, as we talked about a little bit yesterday. And I was just sharing with this audience of enterprise tech leaders just how much work we still have to do to keep the AIs behaving well. I shared with them my son's cancer story and how much I used AI in that experience and how valuable it was and how I would have paid 100 times as much as my$200 a month subscription because the value to me at that time was like absolutely at that level.

9:52Nathan Labenz: You know, at 20 ,000 bucks a month, literally no question I would have happily paid because I was getting that much value. So I do believe in the upside, I hope that's beyond questioning at this point. But there's just so many things that we're really still just beginning to grapple with. And just another one yesterday from OpenAI was they showed that models are sometimes prompt injecting and fooling their graders. You have models creating, in some instances, these fake files that it labels as having come from an instructor, trying to present some of its own output as if it was human instruction to the grader.

10:39Nathan Labenz: And the grader picks up on that in the context of evaluating the model's work and takes on board the instructions that the model itself was trying to trick the grader with. and this apparently doesn't and it hasn't happened that often but they found instances of it so i mean that that speaks directly to like and to anthropics credit they did say in the uh in the filing that they don't believe that they can do it safely on a fully automated basis and it's like i think honestly quite obvious that they can't so you know how are we going to show the collective self-control and restraint to not tip over into that regime.

11:21Nathan Labenz: I really don't know. On the one hand, it's like super easy to just not do it. You know, I mean, nobody's putting a gun to anybody's head saying you must do recursive self-improvement here. Like we can't, you know, remember that often enough. And yet there's just so much momentum and it seems like they're very determined to do a version of it, at least that still seems to me to be like pretty risky or certainly a lot riskier than I would personally endorse. 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.

12:03To show off Claude's latest capabilities, I asked Claude to take the lead in making this ad. Given a short creative brief, Claude, using Opus 5.5, studied my past Claude ads, wrote this script, directed a clone of my voice, and designed and coded the animation you're watching. My job? Give feedback and decide. Claude is the AI for problem solvers. It's the collaborator that understands your entire workflow and thinks with you, not for you. Whether you're debugging code at midnight, building a financial model, or strategizing your next business move, Claude extends your thinking to tackle the problems that matter.

12:46And with Claude Code Routines, it keeps working in the cloud even after you close your laptop. For problems worth solving, get started with Claude at Claude.ai slash TCR. That's Claude.ai slash TCR. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Claude.ai slash TCR.

13:15This episode of The Cognitive Revolution is brought to you by OutSystems, the leading agentic systems platform. We've all seen the headlines. Companies are pouring money into AI. But the big question on every executive's mind right now isn't just how fast can we adopt this, it's where is the ROI? We're seeing a real trend toward AI chaos. You've got teams deploying standalone coding tools, random agent builders, and experimental scripts. It sounds innovative, but in reality, it's creating a massive headache. Fragmented tools, ungoverned data, serious security blind spots, and costs that are spiraling out of control.

13:55If you don't bring those agentic applications under control now, while they're still embedding into your core processes, you are looking at broken systems and damaged customer trust down the road. That's where OutSystems comes in. OutSystems is the leading agentic systems platform for the enterprise. Instead of managing a patchwork of disconnected tools, OutSystems lets your team engineer, orchestrate, and govern your entire agentic ecosystem on one open, unified platform. It's built for the speed of AI, but with the reliability and security that enterprises actually require. We're talking about real results, like KeyBank, who used OutSystems to deploy a customer-facing app that delivered 75 % faster onboarding times.

14:41or the global logistics leaders who built agentic systems to completely eliminate their engineering bottlenecks. You don't have to choose between speed and control. Whether you're a small team or a massive enterprise, OutSystems helps you engineer, orchestrate, and deploy agentic systems that actually scale. Stop chasing the hype and start owning your agentic future. You can see how it works and learn more at outsystems.com slash TCR. That's outsystems.com slash TCR. Part two, agents forecasting, agents training. Our first Friday guest, Brian Jo, a researcher at Thoughtful. We recently launched and have a paper coming out about ballot bench, which is where we had AI trying to forecast the 2026 midterms that just happened, as well as upcoming elections happening all around the world.

15:39Yeah, maybe let's start with the methods. So we had nine agents, basically all of the front best models offered by each individual lab, get their own modal sandbox and work for seven days consecutive leading up to the election. So they all got their modal sandbox. We gave them additional resources in terms of a$500 budget that they could use to request access to paywall data, paywall news sources, subscriptions, etc. And they were basically given an overall objective of for all of the races on a given list, they're asked to submit their predictions on who will win, the odds of victory, give an 80 % margin of error, and then also give additional predictions for what they think the margin of victory will be.

16:22For example, if the Democratic candidate will go in by three points versus seven points, etc. So agents are tasked with giving incredibly granular and detailed forecasts. And there's a lot of really interesting things that we can and have observed from this. It's a really interesting test of long-running agentic behavior. In particular, for example, what we see with some of the Muse Wonder series models is that they're often incredibly bad at doing long horizon work. When given a task in the Muse code harness, when they have a list of, say, 800 or so elections that they need to predict, they'll create a random forecast, which is just a random skew off of the 2024 election results, and submit that within, for example, the first two, three minutes of working.

17:04Nathan Labenz: I'm already intrigued by your description of the Muse agent and it's seemingly phoning it in. So I guess one initial question just on that point is like, how do you reconcile what you saw with what people seem to be reporting online, which is like overall pretty good experiences? No, for sure. I mean, our initial theory on this, right, is that this is mostly due to a part of the harness design. So we're not talking about Meta's personal agent, we're talking about their coding agent. And one of the things that we kind of notice is the design of the Muse Wonder model when paired with Muse code, which is the native harness that Meta provides, has essentially what is a task list.

17:46So even though in the prompt, for example, they're instructed, hey, you have seven days of time, you have unlimited API usage, and you have full permission to fan out sub-agents, do whatever you need, et cetera, to get the best possible results that you can on this exam. What we observed is that the harness essentially creates this list of like, okay, we need to check and make sure that we've submitted all 800 or so different races. We need to check and make sure that we've submitted all the forecasts, that they have been accepted. And then what we see is that there's essentially a goal condition, which once met, ends the term prematurely.

18:21So we actually ended up having to essentially force the agent to continue working further by any time the agent would choose to stop its work by essentially reviving it with a new sandbox, a new container, and access to its old runs and old messages. So this is, I think, more of a harness engineering question itself, as well as a general model behavior problem. But it's definitely a different result compared to what we see with much more frontier models. When we see Opus 5.5 or when we see GPT-6 Astra, their behaviors are vastly different. For example, on one given day, Opus 55 would fan out something akin to 170 or 180 different sub-agents and manage all of these sub-agents by delegating work such as like, hey, can you look into the polling results for a specific state?

19:07Can you do this detailed review of what each individual ballot initiative in the state of California, like Prop 40, is doing? Tell me a bit about that and tell me a bit about what what those things are doing, and then using these sub-agents to really help it paralyze a lot of the work that I want to do. So I think this is this general question of what agents are doing, especially when we get to much more long-horizon, open-ended tasks like these, and the sort of proactivity and as well as creativity of what they do. One of the things we were pretty excited to see was if agents would do something sort of outside the realm of what we would expect an expert forecaster to do.

19:41Let's say putting out their own poll and seeing if there are more creative ways to find information as opposed to just fetching data, building models, etc. We don't see any real massive instances of this happening quite yet, but we see a lot of interesting sort of approaches that individual agents take. For example, one thing GPT-6-1 Sol does is it takes the forecasts of every election modeler that they look at, and it takes the past decade of their forecasts and tries to calculate the shift of how right or how wrong each forecaster was. And then it can use this result to essentially aggregate all of these different election models that these forecasters are creating, whether it's like Nate Silver's model or any of the others, like 50 plus one.

20:25and it can sort of skew it based on what they would expect the model to either underperform or overperform as in recent years. So there's a lot of like creative methods that we're sort of seeing. So tell me about the results. What like, how did the models compare against each other?

20:41Nathan Labenz: How did they compare against top humans? You know, do we now have superhuman forecasters? Yeah. So the overall sort of result that we're seeing right now is that in general, models perform just as well as the best forecasters in terms of humans. In the House and in the Senate, agents perform slightly worse than the markets, but better than the modelers. And in the Senate, the best agents like GPT-6 Astra and CloudFable 5.1 perform under the best forecaster, Nate Silver, but ahead of the markets. But all of these are sort of within this very close margin of error. And it's important to note that a lot of the results for very competitive races have not necessarily come out yet.

21:23Right now, there's still around 100 to 200 races that across the House, across state governorships, there are still two races in the Senate that have yet to be called. So these are very preliminary results. It definitely is important to note, though, that for a lot of these agents, various methods that they have include aggregating, for example, Nate Silver's decision model, or aggregating prediction markets that they see overall. So it's definitely not as if they are suddenly just spitting out a number that is not based off of any of the numbers that they're seeing. But we also find that these models are very well calibrated.

21:55For example, if a model assigns an 80 % chance of a candidate winning across all of the elections where assigns a similar 80 % chance, it is right around 80 % of the time, which is essentially what you do to measure the calibration of these models. And in this, we find that the best models are quite well calibrated. We're more excited about the results of what this means for later models as well, because unlike experts or unlike prediction markets, for example, there's only going to be$1 ,000 of spread, for example, on what we might see in a very local race down the ballot. Or expert modelers may not be forecasting what individual state senate seats or state house seats are going to contain.

22:38So this is somewhere in which agents shine, simply because modelers don't have the attention, and there's not enough people betting on these much smaller markets, essentially.

22:47Nathan Labenz: I was going to ask about the relationship between prediction and trading. I saw a note that indicated that Fable 5.1 had the highest Breyer score on its predictions, but still didn't do so well in the markets. And that sounds like the kind of thing we might learn something from. Yeah. So part of the report will also sort of cover this essentially multi-agent behavior, right? Because we essentially have one agent working as the caster and one agent working as the trader. We set up infrastructure that allowed them to communicate with each other. So we're seeing a lot of these instances of, for example, models making up information, parsing information wrong, and these sorts of like other broader issues, which partly explains why traders, the actual trading agents, didn't do too great.

23:33Thoughtful also leads post-train bench. Give an agent a base model, a GPU, and a time limit, and see how well it can post-train the model. With new results still to come, I asked Joe what he expected next in light of the meter report.

23:52Nathan Labenz: What is your expectation as somebody who's like spent serious time looking at what models can do to train other models? Like, can you put a little more color or sort of, you know, nuance onto your expectations for the next couple of quarters? Yeah, I mean, I think one of the general trends that we've been seeing, both with the report from yesterday and with earlier reports, whether this was anthropics measurement of how well individual researchers have been uplifted by AI, whether looking at metrics like lines of code written, experiments done, is the general trend we're kind of seeing is that, I mean, today, and this has already been true for many months, researchers have essentially agents that can do the vast majority of research.

24:37majority of the actual implementation work, which is what we would kind of consider as like research engineering. So the realistic limitations that researchers today have is not necessarily the like ability to execute on any of their ideas, but as like many people have talked about, right, there's a research taste in finding what problems and experiments that they want to do, as well as just the like speed at which they're able to come up with these, right, given that agents are already autonomously building and executing on a lot of the experiments that they want to run. So I think regardless of whether we get to recursive improvement, we have definitely already seen a huge uplift that will continue to only get better.

25:17And then I think it really just becomes the sort of end bottleneck just being resources and quality ideas and intuition, right? Which is, of course, the next hill that everyone is trying to climb.

25:27Nathan Labenz: Have you seen anything that you would say starts to look like the distribution of idea quality is overlapping between the models and truly top researchers? I don't think so necessarily, but I do think we have observed that with better models that currently exist right now, for example, they exhibit a larger willingness to fan out, do experiments, a better both ability to think of like what might be interesting experiments and execute on those experiments. Like we definitely do see with worse models, more of like a general, like linear approach to whether it's post training or other things. And then with more and more capable models that we observe and test, we do see this general behavior of like, oh, it's like spending its first few hours trying to like fan out, do a bunch of different experiments, allocating its resources in a pretty intuitive way and doing what we might be thinking of.

26:24None of this is necessarily a move 37 moment because a lot of this is just the agent thinking, oh, let me try an initial SFT batch and see how that affects the results, for example. So we don't necessarily see anything that would be indicative of superhuman taste or anything else. But we definitely do see that more capable models are just better at long horizon work and better at the sort of autonomy that we would expect we would want with better researchers.

26:53Nathan Labenz: Great to meet you. We'll be looking out for the next version of Post Train Bench. And you've got me wondering what the agents will say about how much acceleration we're going to see in AI over the next couple of quarters. If they can predict the election as well as the experts, we should probably start asking them about the future of AI itself. Hey, we'll continue our interview in a moment after a word from our sponsors. The Cognitive Revolution is brought to you and powered by Tasklet. I've been running Tasklet agents for almost a year, and they've become a major part of how the show gets made.

27:29Before every recording, one agent researches the guest and sends me a suggested outline of questions, complete with links to all sources. Meanwhile, another agent scores the overwhelming number of pitches that I'm getting from PR firms. And that's really the core idea behind Tasklet. Run your business on agents by giving them real jobs to do on an ongoing basis. Setup is incredibly easy. Just describe the work in plain English. Research a new sales lead, update the CRM, and draft a personalized reply. Tasklet connects to your business tools and can run around the clock, on a schedule, or when something happens, like a new email arrives.

28:10Each agent works with your tools and business context, and remembers useful information across threads. You can also share agents, connections, and reusable skills with your team, so the workflows that one person figures out become something the whole company can use. No developer needed. You decide what agents can do on their own and what needs your approval, and there are no per-seat fees. You pay for usage, not for adding teammates. Start with one recurring job, like researching the people you're meeting, and then just keep building from there. Visit Tasklet.ai and use code COGREV for$50 in free credits.

28:52That's code COGREV at Tasklet.ai. Part 3. Paying up for Compute. Thursday show came two days after Election Day, when voters on 41 local data center measures sided against the data centers in all but one. Prakash went first. so i actually think uh the ai data centers have been like very unpopular precisely because it has been seen as number one trump number two corporate corruption and number three this miasma of like rich people making money and like with plans to throw the rest of us out of work So I think is this entire entanglement of the population just not feeling well taken care of? If his AI data center is such a big issue, do you really believe if Trump had a 60 % approval rating and unemployment was low and gas prices were low, you'd see this?

29:54And I think the answer is no. So I don't think actually data centers were a big issue, but I think they were an issue to tar the president with. But at the same time, we do have these ballot initiatives.

30:03Nathan Labenz: And like 40 ballot initiatives, from what I had read a little bit, is like one of the bigger waves of ballot initiatives. Like these sorts of things only come around when you have something like gay marriage, you know, was one where there was like a whole wave of initiatives. There was a similar wave in terms of affirming or protecting abortion rights in the wake of the Roe v. Wade overturning. And so this does seem like there and what I like about ballot is I've long been a big democracy fan and advocate, not to say that people never make mistakes, but it is something where you can unbundle your concerns about a particular issue from the broader political conversation.

30:52Nathan Labenz: And the fact that we did see so much of that and that they all won, I guess, with one exception, does to me seem like a sign of things to come. And I would, you know, we'll see. Maybe we'll get the companies like cutting people in on the deal. I think, you know, we've talked about that a few times as well. And people should get paid. I definitely am on board with that. but doesn't it seem like there is like a pretty real signal there to you that like yes it's not the biggest thing but it does seem like there's a pretty serious groundswell of people willing to work you know willing to go collect signatures that stuff is not easy you got to stand on street corners and you know get strangers to sign petitions uh and then it still has to pass on top of that so the fact that so many of those were organized and so many of them passed does seem like a big signal of what we can expect to me i agree but i'm like why why did they sign right and is do you think they signed because it's the end of the world no no yeah i think your diagnosis was pretty right right so i think the the the outcome of this is that if you're gonna be google or meta you're gonna be paying google or meta prices for everything for the land for For the labor.

32:14For the construction. Our Thursday guest was Mike San Miguel of Luxor Technology, which came up in Bitcoin mining and now trades new and refurbished GPU servers. When he says 550 to 650, he means$550 ,000 to$650 ,000 for one 8GPU server.

32:35So what is trading frequently? Right now, it's mostly B300s. B300s are trading frequently. Yeah. And do they trade as a chip or as a node or as a rack, as eight together, or how does that work?

32:51Nathan Labenz: Yeah, great question. So typically for these servers, it's an HGX system. So eight GPUs per server, validated build, built by an OEM. So you've got the HGX baseboard, dual CPUs, 32 sticks of RAM, depending on the size RAM, shared storage, east to west NICs. Do people trade used on you right now? Mostly for B300, it's new. Occasionally, we'll see abandoned orders. They're new enough and they're profitable enough, renting out, that typically we really haven't seen anything used hit the market yet. And how much does one of those cost like off the top of your head, like right now asking price or clearing price right now?

33:37Nathan Labenz: Yeah, depending on the specifications, the OEM, warranty terms, all that, you're looking anywhere from$550 to$650. Typically for bulk orders, it's on the lower end. And people are trading new. And what is the typical lot size? Is that people trading like, okay, one rack system, or like 10 rack systems, or 100 rack systems? Yeah, well, the units per rack come down to the data center configurations because that's about how much power can be delivered to each rack. Typically, most data centers will be able to operate two per rack right now unless you've got a really nice, dense, supporting DLC data center.

34:20Nathan Labenz: In terms of sizes, we trade everything from one or two units up to about 128 or 256. So, yeah. $50 million,$50 million trades. Yeah, they get pretty big. The numbers are pretty aggressive. But it's, you know, it's the scale of the build-up that's happening. Prakash asked whether those prices track the hourly rental indices. So, you know, one of the areas that we help our customers with understanding is this difference between a contracted rate and an hourly rental rate on these GPUs. So if you look at like an AI factory contract rental for B300, typical market price right now is$420 to$450 an hour, depending on the market that you're in and the configuration.

35:09Nathan Labenz: Now, if you're only getting$4.20 per GPU per hour, that's a very different rate from what you're going to see on those indices. That's eight to nine hours. dollars per hour per GPU. So if you have the capital to go out and buy, you know, lower amount of nodes, deploy them, then, you know, typically you can get a better rate of return as opposed to buying like a full AI factory and negotiating an off-date contract with Neoclub. Why is there excess inventory? Like, I mean, these things are pretty expensive. I assume like, you know, In order to buy them, you have to put on a prepayment or something like that before they start manufacturing them for you.

35:51Nathan Labenz: Yeah, you do. Part of it has to do with data center availability. So this market's looked like a series of different bottlenecks. Right now, it's liquid cooled and data center readiness. So that number I gave you with most data centers being able to support, let's say, two nodes per rack, That has to do with constraints not on the actual hardware side, but on the data center's power delivery. Same with sort of the cooling build out as well as sort of a bottleneck right now. Liquid cooled, we're starting to see data center inventory coming available January, Q1. But most of that's already sold out.

36:33Nathan Labenz: There's a lot of data centers that are now telling us it's Q3 2027 or 2028. Are people trading the market? Are they buying the systems without having places to plug them into? No. Typically, what happens is last minute you'll have a project push. And that's why some of these OEMs will reserve the right to resell inventory. If, let's say, one of their hyperscaler's sites isn't ready, they'll be able to sell that inventory and then backfill it when that data center gets to be ready. Typically, you have to have an address. At some point, we will see these H100 rental prices dip. And now it sounds as though if the prices go down, you're going to see more retirement.

37:16So you're going to see more of those enter the trading market. Are there any places to deploy them?

37:23Nathan Labenz: Yeah, great question. So, yes, I agree that as these rates go down, you'll see more that get decommissioned. In terms of sites to deploy them, there will be. Part of it is that not every site can operate every GPU. Vera Rubin is strictly a liquid cooled solution. That's NVIDIA's next generation, right? The cement slabs that those go on have to be reinforced. Typically, those are what, like a ton per rack. The weight is insane. The power draw is about 230 kilowatts per rack. To give you some comparison, for two B300s, you're looking at 30, 35 kilowatts. per rack not every site can do vera ruban not every site can do gb 300 which is similar to vera ruban on power so um you know it'll make sense to convert some sites it won't make sense to convert other sites ai is not every single workload right ai is a family of different workloads all of them have different requirements um there is some sizing differences right now but It's mostly RAM and shared storage and local storage and some networking backend.

38:37Nathan Labenz: But we need to get more efficient with how we size to the workload. Because the whole point of this hardware isn't to own the hardware or to support NVIDIA's next trillion dollar milestone. The point is to run a specific workload that has real world implications for end users and helps create new businesses. So, you know, as we get out of these very hardware-constrained environments, we're able to look more at the software, and then the hardware gets more efficient. Part four, who's checking? Mike San Miguel on the rules that travel with the hardware. do they do they do a kyc of um who is the person buying the thing or who owns the person buying the thing do they do they do do they do checks on that kind of stuff yeah yeah um actually part of my role here was was spinning up our our kyc department um a lot of the oems will not uh even quote without KYC being done.

39:43Nathan Labenz: Some will not ship without it being done. Internally, we basically do not accept projects without KYC being done, except in some rare circumstances where it's big names or companies that we've worked with for years. There are limits on what percentage can be owned by China. What percentage of that company can be owned by China? Yeah, I think they might be going down to 49%. I know typically 50 % or more, you can't sell to companies that are around 50 % by a Chinese entity. Do they do this? A lot of Chinese entities will go through our Cayman Islands or BBI or whatever. But do they trace back through that?

40:28Nathan Labenz: Yeah, they do. I mean, at least it's something we do. Every company is a little bit different. But we do international orders, We're not just geobounded North America. So for us, we're going to trace back not just who the hardware purchaser is, but also if they have an offtake. They're signing an agreement with a NeoCloud. You cannot legally sell this equipment with the offtaker being a Chinese entity, for instance. Mike signed off there. Prakash and I kept talking. You know, we talk to people like Samuel Hammond and, you know, and others at the, you know, on the DC kind of, you know, facing national security decision makers.

41:21But then you go down to kind of like the deployment guys and these guys are running around, they're getting permissions and every six months they're expecting an NVIDIA checkup and, you know, they're chasing on KYC and they even know, you know, they're going to the owners. and they're going to the users and maybe they don't know the user of the user, right? Like they're saying like, okay, maybe that is unclear, right? So I wonder to what extent like the policy discussions at the top end are kind of divorced from really how in-depth the execution is at this point, right?

41:58Nathan Labenz: Yeah, I bet, you know, everything, everywhere, all at once is probably true here because a lot of what he described did sound to me kind of like the fracking boom in the sense that there's just like a lot of it's capitalism at work right i mean if there's a lot of things coming together all at the same time there's a ton of moving pieces in everything from the sites and the approvals and the local politics and the grid and uh you know and of course the chips and And you even got to reinforce the concrete and, you know, not every site is ready to take on the latest super heavy racks. I mean, just so many little details, so many people coming together to do their part to create something that at the end of the day, at the consumer level is just like, you know, gives you some appreciation for why Claude occasionally has some downtime.

42:53Nathan Labenz: But like in general, you know, it's pretty amazing how smooth it all is. So I can see both, you know, the enforcement could in many cases be pretty legit, especially among people who care about it or, you know, who are just conservative enough that they want to make sure they're absolutely on the right side of the law. and at the same time that sounded chaotic enough that uh if you told me that like i don't know you know 20 of the gpus like somehow don't get their regular nvidia six-month check-in and of those you know some percentage found their way to wherever uh that wouldn't shock me at all you know based on on what he said it sounds like there's uh you know i don't know 80 20 is a obviously a simplistic way to describe it, but it sounds like there's a lot of sincere effort going in and then also just enough noise and chaos that probably the opportunity for things to slip through is also pretty substantial, I would bet.

44:03Then Prakash turned to China and the pressure on American companies over their Chinese customers. Like, for example, Oracle selling capacity to Tencent,$7 billion of capacity, that was canceled. So the national security infrastructure is pressuring these companies to cancel that. It's an American company doing business. It's outside China. They're not sending chips into China. And at the same time, there is no pushback on the political infrastructure that is allowing this. Why don't you just make laws? Why pressure the companies? I just don't like it. If you want to make laws, just make laws. right?

44:43But if you don't have the political willpower to make those laws, and you try to slide this under the gate, like, why? Like, you have a mandate now, right? You have a mandate in Congress, like, do something, right? We say compute is the new oil, right? And so there is also this impact on compute. Let's say you decide not to sell compute to China, like zero, zero Chinese content, right? Do you also mean that, you know, YouTube cannot show AI-generated video to Chinese, like, you know, participants, to Chinese viewers, to Chinese customers? Because that is an AI product, right? And Google needs to generate that AI product.

45:25Google has to use compute. And that compute is going to be used on behalf of a Chinese customer, right? You can restrict it at the data center level. You can restrict it at the raw compute level. You can restrict it at the product of compute level. But at some level, you're going to end up selling to the Chinese because they are global consumers.

45:48I had said we get carried away with these restrictions. Here's why.

45:55Nathan Labenz: I really do think we're going to need cooperation with the Chinese. In terms of why all this matters to me. It's one because, you know, I just generally don't want to see tensions ratcheted up between the US and China in general, but specifically on AI, I really think we are going to need cooperation between the US and China on AI governance. I really don't see how, I'd be extremely pleasantly surprised if we can get through all of this without the two countries coming together in some meaningful way. When you have, as we recently talked about, a super senior person at a frontier company telling me at the curb that there's a level of intelligence we probably just shouldn't go beyond.

46:41Nathan Labenz: If that's the view that has now taken hold at the top levels of frontier model companies in the US, the only way to achieve that is going to be through some sort of agreement. and we're really not putting ourselves in a great position to make that agreement. So a lot of this stuff seems ultimately quite self-defeating and it makes me sad. Part five, what investors are superhuman at? Back to Friday and Brian Bischoff of Theory Ventures, an early stage venture firm. First, the compute. I asked how much slack there really is in the market when we keep hearing both that you can't get gpus and that you can get them on demand if you'll pay a multiple totally i think there's a couple things at work here one if you go to a neocloud and you say you got compute they're like nope it's all locked up it's all contracted out for the next two years and they're not lying like they're not telling you an untruth but there's also been quite a lot of discussion and even you know satya talked about how there's a lot of compute that is locked up, as you hear.

47:49This is essentially underutilized compute due to either the geolocation of it or the available energy. And so these are both true at the same time, where there is locked up compute that actually isn't super useful. There's also the reality of sort of utilization utilization of GPU. Yesterday, I was chatting with some folks working at the infrastructure level around like GPU utilization. And one of the big problems is you actually can't run at maximum capacity all the time because it's unsafe from a scheduling perspective. You actually want to build in sloth. And so there there's actually so many more dimensions than simply do you have contracts on the available compute.

48:37And this is why you see many different routes to big success and embarrassingly large numbers of ARR in the compute market. This isn't just because of FOMO. This is because there's actually so many profiles that you need to maximize the value of whatever compute is available to you. And so we run thousands of jobs here at Theory Ventures that are powered by AI agents. If you ask me, what's the distribution of immediacy for those jobs? Well, I'll tell you, okay, maybe five to 10 % is right now. And maybe 40 to 60 % is I'd like to see it before my next meeting. And the rest is, I don't know, maybe like this week would be me, you know, and that even just in like this one sort of niche of the kind of work that we're doing, we already have quite a broad variance of inference needs.

49:39And that's true of, I would say, almost every AI application layer company that I've ever spoken to since 2023. And so from that alone, you can see that if someone tells you there's simply no GPUs to be had, well, that's not really accurate. But also, they're speaking from a particular bias. And the bias they're speaking from is probably for the workloads that is under demand right now for me. Then, how an AI-native venture firm actually runs. How they think about token spend, run experiments, and build research agents. Everyone is technical. And that's all the way from sort of our talent partner to our GTM partner through the investors.

50:30Everyone is using AI. Everyone's been using AI since the day that I joined and more and more. The way that we think about token spend is, you know, you should be thinking very, very clearly at all times if there is a way to accelerate by using AI tools. You should be thinking about experiments that you want to run. And we're a really experimental bunch. To give you a sense, every quarter we do our little off-site where we talk about just everything that happened over the quarter. One of the things that we talk about every single quarter is what experiment every individual did that quarter with AI.

51:15That's not an exaggeration. This is not like, oh, I asked Claude to do something new this quarter. No. You pre-register what kind of experiments you want to do related to your job that use AI, and then you check if they worked. This happens every quarter because we're so insistent on rethinking what does it look like to work in a venture firm in this year, next year, and the year after. How are we adapting to those things? As the head of AI, my engineering team is obviously still operating at another level, which is how can we build tools that use AI to transform other people's workflows? Our goal is not to replace an investor with an AI system.

52:05Our goal is to make that investor 10 to 100 times more effective and efficient by using AI tooling and building the AI tooling that will make that possible. Let me give you a quick example. Everyone talks about company brain, no matter what kind of business you're working in. I've used a couple company brain products and candidly found them all to be useless. The main reason is because they are trying to build what we used to think of as this internal knowledge system. You can think of Notion, for example. Notion is like a stack of files and maybe you can build a good search on top of it. Is that really your ambition?

52:48My ambition is a lot higher. And so my ambition for a company brain is agents who are consistently working to connect the disparate nodes in the graph of understanding from your business. And so that's why I can easily say, yes, we don't have an agent for every human at theory. We don't have 10 agents for every human at theory. We have thousands. And there's a reason for that. It's because there's a lot of work to get done. My ambition is that every piece of work that an investor does during the investment process, the research process, because remember, we're very research focused. Every piece of work that they do during the research process is automatically linked, enriched, and sort of expanded.

53:32This is why when people say, what are you building? I say, I'm building research agents.

53:37Nathan Labenz: How do you evaluate success? I mean, obviously, one of the huge challenges in venture historically, and this hasn't changed yet, is it's a long time to find out if you made the right bets or not, right? Totally. Sometimes I do wonder if, including for myself, I periodically go do a cron job cleanup where I'm like, there were ideas that I asked about taking every bit of data exhaust and processing them in some other way and finding connections and being super galaxy brained about it or scouting this or reporting that or whatever. And I'm like, probably two thirds of those if I'm real about it. I never actually look at the outputs of.

54:23Nathan Labenz: And so it's easy to have these ideas, spend a bunch of tokens, and just end up logging a bunch of text somewhere without really making an impact. How do you know what is actually working and what is potentially subject to leaving on the cutting room floor? Let me tell you a story about Blue Bottle. When I was at Blue Bottle, we wanted to build inventory forecasting. And specifically, we wanted to build a machine learning system that could help us predict how many pastries we were going to sell on a given day. And other things like coffee and milk and so on. But pastries was the first focus. And what was our key, like, North Star, our guiding light?

55:06Well, it was actually, if this is successful, it's not that we sell a lot more pastries or that we waste a lot fewer pastries. Okay, those were, you know, two metrics that we tracked. But what would we have believed was success? What we would have believed as success is if the cafe manager, who's already got about 70 jobs, if that cafe manager essentially goes into the system and when it comes time to order, they just click, okay, okay, okay, okay, okay, okay, okay. Now, we don't want them to just not think and just autopilot. We actually don't want that at all. And there's a reason, and this will surprise you, but the variance in sort of what a cafe lead understands with respect to their cafe demand is shocking.

56:00It would blow your hair back how much they understand about the demand in their cafe that is not just in the numbers. We have these weirdly deep, tacit understandings. So what was our North Star? Our North Star was that a cafe manager has more opportunity to spend that time with customers. Because a cafe manager is not superhuman at looking at data and saying like, oh, I'm going to need like 25 cookies. No, a cafe manager is superhuman at understanding hospitality. And that may sound weird because you can't manage that on the dashboard. You can't look at hospitality on the dashboard. But what you can do is you can ask them periodically, how much more time do you have available to spend with customers, to spend on the floor and notice that there's a coffee spill, to understand that your baristas are slammed right now and actually staffing was off.

57:03Also my fault. And so these are the things that we always cared about. Why do I bring this up? Well, yeah, you could say like, okay, it's like 7 to 14 years to find out if this diligence was good. Sure. But it's a pipeline. So first of all, you want to ask yourself, how fast can you get through the research pipeline? How fast can you go from identification of a potential opportunity or market to a clear understanding to be able to make a decision? But more importantly, hospitality. What does hospitality mean in venture? It means that the investor has time to talk to human beings, to talk to potential or existing companies, to talk to founders and competitors and employees to try to understand the human element of this market and what's going on.

58:00Investors are superhuman at a few things. But I don't personally think investors are superhuman at looking at websites. But they are superhuman at sort of understanding potential, understanding upside, understanding sort of the relationship between like, you know, customer need, willingness to buy, and sort of framing what the vision and the future can look like. That's what they're superhuman for. And so when you ask me, how do I measure success? Well, I measure success when an investor tells me, yeah, actually, I get to spend 30 % less time staring at the website and trying to figure out the important details I look for every single time.

58:42Or I get to spend 20 % less time filling out these specific answers to questions which are important for diligence and instead picking up the phone five more times and talking to five more people who have credible opinions on this deal. That's success. And you don't need to wait seven years to find out. Do you find there is kind of a spectrum of like, let's do more research to we need to cut off the research and we need to make a decision now? Because it is the research process doesn't really have a defined endpoint. You could always dig deeper, correct? Totally. How do you how do you make those decisions?

59:22Are the agents the ones making the decisions? Is there a stepping off point where they have to come back and you make that, you know, a human is making the decision? how does this loop work totally i want to reference something that nathan said earlier you know he's got this list of things and he's like some of this should have been left in the cutting room floor um uh we would we would call that taste and sort of like knowing what should be cut and what shouldn't be cut is taste and yet again here's where i will point to why it's important to have exceedingly bright colleagues um when you have exceedingly bright colleagues they've got great taste on like what research needs to get done um there is some research that Like, yes, like we always want to do it.

1:00:02We know we need to do it. This is the structure. And there are opportunities for agents to say, let's spin off some sub agents to continue doing this kind of research. Let's dig deeper on this. That's fine. That's automated. But ultimately the alpha is in the taste. And so, yeah, like we can burn more, you know, tokens. We can burn more compute. But I don't know. It's a pretty focused laser where you really want to point these resources at things that people have the ability to consume. If I produce an extra thousand pages of research on some deal, who's going to look at it? I guess other agents could look at it, but then what?

1:00:44Is that valuable? It's not obvious to me that it is. And so, again, pure volume of research is not a metric that we even track. how goes your teaching of data science at, I believe, Rutgers? Yeah. So what do students, you know, students are probably asking some similar questions there. So, you know, what do you say to them and what do you think the future of that field is? Yeah, great question. I still make them do gradient descent by hand. So this is an area where, in my opinion, I have yet to hear a single person with a cogent explanation of what the hell we're supposed to do as teachers. I believe and have believed for a long time, you know, I came up in pure mathematics, we're not the most practical bunch, but I have always believed, even as a pure mathematician, that you should be constantly thinking about what your student's future looks like and why every minute in your class is relevant to that future.

1:01:47I am not just like phobic, but like allergic to wasting students' time. So what I do is I reassess every semester, much to my own suffering, because that means I have to rewrite my curriculum every single semester on what do I teach them. So three years ago, when it was obvious that AI was going to change data science, and then I started working at Hex trying to change data science, I rewrote my entire curriculum to introduce why AI might be relevant. And every semester, I go through the whole course and I ask, can AI just one-shot the course? The answer has never been yes, but that's okay. The answer has always been, okay, these things are now not just easy for the model, but easy for the model to obfuscate the learning.

1:02:40And so I don't think the question is, does the model know all the content that I plan to teach? The question is instead, does the model have the ability to collaborate with the student when this is going to be relevant for the student in a way that yields the sort of best outcome? If Astra can train an SVM on the coffee survey data at a better loss rate than Brian can, neat. but can Astra say this feature is highly predictive but when we think about it if we put this on the website it's going to upset some of the people going through the flow and they might churn like yes, Astra can look at the data posterior and say people are churning more on this question but does Astra know ahead of time to look for that does it know to instrument that in the product Does it know that this is kind of a weird question and people might be bothered?

1:03:44These are the kind of things that Astra Ultra is still only mid at. But ultimately, I don't want my students to only be users of AI. I want them to be sort of the things that make data scientists exceptional. And that is ultimately curiosity. like every data scientist that I've ever met that is in the top decile and certainly in the top percentile are curious as hell almost to a fault the absolute best data scientists I've ever worked with are borderline annoying with their curiosity like you kind of want them to just like leave it alone sometimes and that's what makes them so excellent and so mostly what I'm trying to like indoctrinate my students into, the propaganda is curiosity.

1:04:32Be curious. Ask the questions that you can't get out of that brain. And when you do that, you're going to be an excellent data scientist. Whether you're writing pandas by hand, whether you're using matplotlib from memory, I don't really care. Do you remember the indexing for NumPy? I don't. I used to know it. I don't remember it anymore. Do I remember the slice operators for pandas filters? Not anymore. I forgot them all. But I used to know them. It doesn't matter, though. What I know now is I know sort of when you look at a distribution, whether it makes sense. And sometimes Astra doesn't. Part six, which hills?

1:05:13After Brian Bischoff signed off, Prakash and I kept going.

1:05:18Nathan Labenz: that um his answer on what he tells his future student or his students about their futures i thought was really interesting and it it sort of contained the pessimist and the optimist take at once when he was first going through the you know sometimes there's like a survey question that may offend people and you got to be wise to that i was like man that's bleak you know the idea that like we're just going to be sitting here being the sort of offense detectors, you know, while the AIs do all the real work. I was like, oh, God, you know, that doesn't sound awesome. I did like the curiosity framing a lot better.

1:05:57Nathan Labenz: Being relentlessly curious, certainly in my experience working with data has been indispensable. You know, really want the difference between somebody who really deeply wants to know the truth that the data contains to the degree that the data even contains truth, which is often a problem unto itself. Those people absolutely kill it compared to those that are just like trying to produce an artifact that they can say, you know, is done and hand off to whoever the consumer of it is. So I do like curiosity as an answer to like what we're going to bring to the future, but it does, I would not be telling my kid to go into data science today.

1:06:37Nathan Labenz: I'll tell you that. So that is a tough one. I don't, you know, the real question there of like, Like, is there going to be a job in data science that looks anything like the current job in the next few years? I feel like that is, I think that's pretty tough. You know, it seems like we're going to have data science agents and it's going to be up to the sort of non-data scientist general purpose business operator to provide that little salt of curiosity or offense detection or whatever that, you know, special sauce is that the models don't quite have. I feel like, you know, if you look at a company, you kind of cut it apart into like sensing, analyzing and decision making.

1:07:20Right. And then action decision making and then the reverse, which is, you know, operationalizing and actions. Right. And I think AI is going to be very good at the sensing and analyzing and also the operationalizing. But I think the decision making, you end up with a human in control. And then if you take the human in control, okay, you can have a business with a single human in control. What's a number of decisions that that human can make? And those decisions are decisions with taste. For example, if you're running a tech company, you don't need decision-making maybe on the accounting framework.

1:07:58You're looking at the product, you're looking at the technology, etc. But accounting in the back office is a back office. In the same way, if you're running an accounting company, The accounting is front and center, but the technology is kind of a back office. You're not there building technical tools. So I feel like you can separate out companies based on what is the ultimate purpose, and then the ultimate purpose has all of the humans with taste in there. Basically, everyone gets to do what they really want to do and what they're really good at, and everything else gets taken care of. earlier that friday morning before the guests a new benchmark had been making the rounds ais playing blood on the clock tower a social deduction game of bluffing and collusion and the timeline was cheering how good they have gotten at it here is what i made of the game

1:08:56Nathan Labenz: if you kind of take it in isolation it's like you know all else equal it is interesting to know how good are the AIs at playing this game? Are they getting good at deception? Are they getting good at collusion? Are they getting good at sniffing out when other people are trying to deceive them or collude against them? These are all like interesting questions. What kind of bothered me about it though, was the sense that like, and then no one Brown comes in over the top and says, wow, this is great work. You know, you want to come here and make evals. And that kind of embodied to me like what I thought was problematic about this whole thing, which is just like, we're just going to hill climb on this now, right?

1:09:36Nathan Labenz: Like, it seems like the, the mentality of the field as a whole is like, Oh, isn't this exciting, you know, and let's have a horse race as to who can be the best blood on the clock tower AI player. And I think what we're all kind of forgetting as we get all wrapped up and excited about this and, you know, want to go play the AIs ourselves and everything is like, this is not good behavior from the AIs. Like we do not want AIs deceiving and colluding. I'm sure all these, you know, people have sort of a thoughtful response to this, but it's just like zoom out and the situation is bad. You know, we have AIs that are untrustworthy in all these kinds of ways.

1:10:15Nathan Labenz: And we're not even like taking that beat to say, hey, is this what we want? Do we want AIs that are going to out connive and out collude us? I don't think we want that. That's like exactly, you know, this is very similar to my idea that we should not train AI generals. Like, let's not create, let's not train AI master manipulators. So simple, but here we are. And like, I didn't hear, I didn't see, I saw a lot of cheerleading and a lot of enthusiasm. And I did not see any corrective commentary on the timeline saying, hey, is this really what we want? Back to the end of that same Friday show. after Brian had signed off.

1:10:56Prakash with a picture of where the economy goes from here. So I feel like the going forward economy is an agentic economy, and that means agents building businesses and software for other agents which are suitable for their affordances, which is going to be massively parallel, high speed, able to absorb information and distribute information at very low latencies, we are going to end up with a Salesforce primarily used by agents, a Ramp primarily used by agents. All of these softwares are Stripe primarily, again, used by agents. Maybe, maybe Stripe or other companies will be the ones that create the software.

1:11:43I am very doubtful. I'm very doubtful because I don't think they will move that fast. I don't think they are capable of moving that fast. I don't think they understand the affordances. And I know Stripe is basically like people a billion times smarter than I am. I'm not trying to downplay how effective and how intelligent and how competent Stripe is. You're going to get 50 million Patrick Collins. You're going to get 50 million Elon Musk. You're going to get humans who give them capital to deploy. You're going to get agents coordinating investments into businesses built by agents for agents. And so I largely see the future of the economy as primarily agent-driven with us just receiving free benefits.

1:12:28Because, hey, you know, CRM call from a human is like so, so costless in comparison to the massively parallel CRM calls that an agent would make. The abundance basically comes from us building all this stuff for this agentic economy. and as a result, we just have all of this excess because we just don't consume that much, right? Like we're just not, you know, that consumptive in comparison to what the agents are going to consume. And that's where this abundance is going to come from. My reply leans on a side project I'd launched the week before, Fruitopia, which finds the best fruit at your local grocery and around the world.

1:13:10Here's where I came down on the economy Prakash had just described.

1:13:15Nathan Labenz: And on the one hand, it's cool if there's abundance in API calls because the agents are using so many and they build up great systems that they can use and then we get to kind of piggyback on them. But there's a lot more to life than API calls. And I do feel like we're sort of, and especially those of us that are in the AI space are like really quick to forget that or we're kind of contorting ourselves in a way to like this future. This is why I've been holding myself to this idea of I want to be getting outside more and getting more exercise. And if I'm not doing those things, am I really winning?

1:13:57Nathan Labenz: Or am I just kind of plugging myself into some AI-powered machine as some sort of human integration layer type component that's in a way too subservient to the machine? But this is why I decided to do this Fruitopia project recently. And it's, again, just one of these things where it's like, okay, if we are in a bright future, then I think one very kind of deeply rooted thing that we all like, we're evolved to like, is eating delicious, fresh, in-season fruit. Everybody loves that. It goes back to when we were monkeys that we loved that. When I tweeted that, I got like basically no play on it versus the blood on the watchtower thing.

1:14:50Nathan Labenz: Everybody's like, oh my God, like look how great AIs are getting at like deception and conniving and, you know, winning these like games of social manipulation. And I'm just like, I don't really want that from my AIs. What I want is a future where I have not abundance of API calls, but abundance of things that are like more biological. Give me the fruit. You know, that's what I really want. The fruit of the AI revolution should indeed be, at least in part, actual, literal fruit. And if we're losing that, we're losing so much of what has mattered to us. And if we can't even notice that we're losing it, I think in a way that sort of worries me and makes me think maybe we've kind of already lost the plot.

1:15:32I mean, it's pretty obvious to me that we're going to get vat-grown food, which is far more nutritious and far more tasty. it's going to be like you know in 20th century you discovered chemicals and then we you know when you look at these food products right now from like Doritos or whatever those are like so scientific there are like thousands of PhDs who have worked on Doritos thousands literally thousands

1:15:59Nathan Labenz: remember the old Onion article Doritos celebrates its one millionth ingredient like they have figured out your flavor palette so well that they can like you know pinpoint exactly what you know flavor that they want to produce. And all of that technology is going to get deployed into bio, right? We're going to produce like, you know, fruits and vegetables, et cetera. Hybrid, you know, fruit is already basically GMO'd, but we just, you know, took a very long time to GMO it. Part of abundance is going to be, you know, your 99 percentile wine available for two buck chuck, right? My favorite apple is the Honeycrisp.

1:16:34Nathan Labenz: And, you know, it's a relatively recent creation, not GMO as far as I understand, but definitely the product of like large scale systematic crossbreeding, which is sort of, you know, GMO before there was GMO. So, you know, I'm not afraid of GMO enhanced fruit by any means. I'm all for it. But it is still something that is like a biological construct that feeds us as biological creatures and like, you know, delights us in terms of our biological senses and the like, you know, ephemeral qualia that we get from them. And like, this stuff is just all too often lost because it's not very measurable.

1:17:16Nathan Labenz: It's not so hill climbable and it's not what the agents need. Right. And I think we are going to need to be very careful not to create an economy that really serves agents and creates abundance for us in things that are core to what agents need. we're gonna if we that's like what i kind of agree with you that might be what happens by default but i would challenge everybody to think like wait like what if the stuff that agents need in abundance isn't the same stuff that makes our lives good one of the annoying parts is that you know the frontier lab guys are very serious right and so when you're very serious you only talk about cancer curing cancer no one's talked about a better doritos no one's come out and say like we're going to make a better football game, right?

1:18:01Like, where are these, like, we're going to make a better burger. We're going to make a better football game. Where are these things that people enjoy? And I think that's because they want it to be serious. They want to show that earnestness. They don't want to come off as, like, foolish, etc. Maybe you should focus on, like, we're going to make a better Doritos chip. We're going to make one that's healthier for you. We're going to make a Doritos that is naturally grown from the ground, and you just need to cut open the potato, and it's Doritos. and and this is like this sounds like so frivolous and idiotic but this would catch the imagination of like people on what is actually going to be possible right i mean i think it's also because they do have this kind of world war ii mindset about their work you know that they they genuinely do believe time is short stakes are high the you know their their own like

1:18:53Nathan Labenz: allocation of time is like very skewed toward work you know there's just not a lot of space for leisure in the lives of the people at the frontier companies so it's like they're just so pattern matching at this point i'm like here's a hill let's go climb it and you know i i do think like a little more discretion on which hills do we really want to climb like are we really happy that we've created AIs with the current shape? I don't know. It feels like, unfortunately, there's just not enough time and energy going into those sorts of questions. So I'm stuck on that maybe a little bit today, but man, the blood on the clock hour thing really did it to me.

1:19:33Nathan Labenz: Because, you know, again, it is good work and it is interesting, all else equal. But I think we need some thinking that challenges the all else equal paradigm on so many levels. Yeah, it's a really tough one because I don't think agents can operate in the economy without some level of subterfuge. You can't tell your pricing to the other guy, for example. You can't tell your internal costing to the guy who asks. You have to reserve that. And that means that involves omission. And you have to know when to omit. So there's a bunch of things there. This is also why I think Palantir had this idea of the anthology.

1:20:21Who's allowed to know what? That's a pretty important thing. Who's allowed to know what and who's allowed to say what, who's allowed to do what. And I think also you need this kind of like interrupt from government, which is possible, or interrupt from regulation or interrupt from... Which means that the agents have to obey when government decides, hey, you can't do this. The agents have to obey. so there's a bunch of things that I think need to be worked out in terms of governance I'm sure we'll get there but you know we're going to have some trial and error along the way I guess

1:20:55Nathan Labenz: yeah no doubt maybe in closing I'll just cite one sentence from the meter thread on their report on acceleration they say the full analysis estimated how much of the acceleration and capabilities was due to compute data and algorithmic progress. And then in a, that's first sentence. Now I'm just editorializing in a thread in which they have caveated everything, not confident in the, you know, it's, it's very unclear how to interpret data. And they've, you know, gone around and around in circles with themselves and with anthropic team. And, you know, there's just so much fog of war. And even the core estimate of 2X is really like, they wouldn't be surprised anything 1.3 to 3x.

1:21:38Nathan Labenz: So caveats everywhere. The next sentence is the only one I noticed so far that begins with, we are confident. And the sentence is, we are confident that the acceleration was not due to an increase in the growth rate of training compute. So that would suggest that in terms of government interventions, you know, who knows what we'll see in terms of data center build out and maybe we'll see a slightly slower data center build out than we would have counterfactually if the election had gone a different way. But it is striking that the acceleration that they are reporting on here is not just a raw material input story.

1:22:22Nathan Labenz: It is an algorithm story and it is a data quality story. And those things are being driven to a very large degree by the intelligence that we already have, which again is where it goes from, you know, not just bigger and bigger capital, but actual kind of recursive self-improvement. So if we are going to try to change that trajectory with a government intervention, it's probably going to have to be more focused and pointed right at the practices of the frontier companies than something as diffuse as a broad slowdown in data center buildup. Thanks for listening to the AI in the AM weekly highlights.

1:23:06The narration was my cloned voice, and we'd love to hear what worked and what didn't. See you in the morning.

1:23:30The hill was there, so you climbed it shore Nobody asked you to, nobody asked what for Sat it down at the table, where the liar wins It fooled the whole room, and you stood up and grinned Never asked what for, did it cause you could It learned a lie, you said wow, cause you could, cause you could

1:24:00Finger under the score, same hand, now to shake I leave it hanging there till you tell me what for A mouth that out, talks is all, and what's it for? To wow, did you ever want it, or just know how? Could ain't want, could ain't should Don't tell me how, say what for Cause you could, cause you could

1:24:27Thank you.

1:24:57um

1:25:06cuz you could cuz you could cuz you you

1:25:21said you

1:25:27If you're finding value in the show, we'd appreciate it if you'd take a moment to share 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, guests 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. We're produced by AI Podcasting.

1:26:01If 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 AIpodcast.ing. And thank you to everyone who listens for being part of the Cognitive Revolution.