AI:AM: A Level We Shouldn't Pass? Notes from The Curve + Tokens vs. Salaries & Is SaaS Cooked?

Nathan Labenz reports back from The Curve conference, sharing off-the-record insights from frontier lab leaders on shortened timelines and whether humanity is approaching an intelligence threshold it should not cross. The episode also features discussions with Positron CTO Thomas Sohmers on overcoming the memory wall while token spend eclipses human salaries, and swyx on how coding agents are disrupting enterprise software. In addition, Atlas Ignota's Evan Miyazono examines unowned risks from accidental agent swarms hitting critical infrastructure, while Mercor's Edward Hu explores the future of training data. Together, these conversations highlight how escalating compute demands and autonomous agent workflows are challenging long-held assumptions about hardware, software economics, and safety limits. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-a-level-we-shouldn-t-pass-notes-from-the-curve-tokens-vs-salaries-is-saas-cooked/ Sponsors: Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr OutSystems: OutSystems is the leading agentic systems platform, empowering enterprises to build, coordinate, and govern AI agents and mission-critical applications securely. Learn more and start building your agentic future at https://outsystems.com/tcr Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits CHAPTERS: (00:00) Weekly highlights preview (01:57) Notes from The Curve (Part 1) (14:22) Sponsors: Parallel | Claude (17:17) Notes from The Curve (Part 2) (17:17) Timelines and compute caps (Part 1) (28:40) Sponsors: OutSystems | Tasklet (31:36) Timelines and compute caps (Part 2) (31:37) The memory wall (40:50) AI in chip design (48:17) Software after agents (54:33) Kill my SaaS (01:03:32) Who owns AI risks (01:10:58) Workplace training data (01:17:36) Beyond verifiable tasks (01:20:56) Episode Outro (01:25:58) Outro PRODUCED BY: https://aipodcast.ing

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

• 0:00Nathan Labenz?

  This week, my notes from the Curve, the conference where frontier lab insiders and their critics share a room, plus the memory wall and what agents are doing to software. This is AI in the AM.

• 0:14Nathan Labenz?

  There likely is, let's say, a level of intelligence that we just shouldn't go past.

• 0:21Speaker 01

  What?

• 0:22Nathan Labenz?

  Yeah, first time I'd ever heard that from a frontier lab leader, then I just asked to follow up like, okay, well, how do we operationalize that? Would you be prepared to sign on to something like a limit on the number of flops that go into the next pre-training run? But what actually happened was, he basically said, yeah, I think that could be reasonable. Prakash Narayanen, my co-host, on what a ceiling would actually require.

• 0:49Speaker 01

  When we say that we shouldn't go exceed a point of intelligence, that automatically means that you have to look at the inputs going in.

• 0:57Nathan Labenz?

  Thomas Somers, co-founder of Positron, which builds AI inference chips around memory.

• 1:02Speaker 02

  I was looking at a quote that we had a year and a week ago for memory, and it's gone up four and a half times since then. I really did hope that this isn't the case, but I wouldn't be surprised if this is going to end up being another doubling over the next year.

• 1:17Nathan Labenz?

  Sean Wang, known as Swix, who runs latent space and the AI engineer conferences.

• 1:23Speaker 03

  And the phrase I've recently taken to is, I don't want to pay for someone else to go through LM psychosis, right? I can pay for my own psychosis, that's fine, but like, when you work for me and I'm paying your tokens, you better be like actually producing thoughtful stuff. And I have two or three employees right now who are basically under performance review because they're just giving me Claude's lot.

• 1:45Nathan Labenz?

  Yeah, I pity our Claude that's gonna have to try to cut this down into a highlights episode, Ben. I think this is a banger content from start to finish today.

• 1:58Nathan Labenz?

  Welcome to the AI in the AM weekly highlights. The narration is my cloned voice. Please tell us what worked and what didn't, so we can make the next one better. Part one, notes from the Curve. Last weekend, I was at the Curve at Leithaven in Berkeley. Prakash asked about me making out the divides I saw.

• 2:22Nathan Labenz?

  One of the big observations in general in AI is that events have come pretty fast. And you would think, if you were looking back, if you were looking ahead a couple of years ago, that with two years of additional information and revelations and actually seeing how strong the AIs get and what they do and who's been right or wrong, you would think that people would start to agree on the state of things. And that has happened a lot less in the broader world than I would have expected. But I did feel that there was some of that felt at the Curve. But I do think the big thing that people have kind of come together on, at least to a significant degree, is it does look like the AIs are gonna get really powerful and this is something we're really gonna have to reckon with and can't just sort of, hope

• 3:05Nathan Labenz?

  goes away, or feel like maybe if we just wait, the bubble will burst, or the fever will break, or what have you. This time around, people were definitely like, okay, it's getting pretty serious. There were very few perspectives that, as Helen Tonner once famously put it, like, long timelines have got super short. I would say another corollary to that was like, what is called normal in terms of expectations is getting increasingly pretty incredible. Even, I would say, the people there who had the most modest expectations for what AIs will do, be able to do, were like, yeah, it's gonna do an awful lot.

• 3:48Nathan Labenz?

  And the question now on the capabilities front has gone to like, is RSI gonna boom? Is it gonna be fast for a while and then kind of level off? Is that more than it's meant to be? Like, yeah, we've got these super powerful things, but is it really gonna kind of run away from us? Those are still the capabilities questions that we're getting asked, but there has been, I think, some healthy updating across the community just based on what we've seen.

• 4:13Nathan Labenz?

  Prakash asked whether anyone from the frontier labs said limits should be enforced on the players in this space, themselves included?

• 4:21Nathan Labenz?

  Yeah, that was a huge topic. I mean, and probably the biggest divide in terms of perspective is really just inside the frontier companies, outside the frontier companies, right? Everybody knows that they're living in the future and they have visibility that the rest of us don't have. And so, actually had a chance to talk to and hear from, in some cases in sessions, in some cases just in passing conversations, all Chatham House rules, so I'll abstract away. But we're talking people who are like founders, executives, top researchers at frontier companies, and a small set of them. I asked a couple of them, why come here? What's in it for you?

• 5:04Nathan Labenz?

  And before doing a couple of sessions where I was interviewing or moderating, I asked the same question. What makes this next hour a good use of your time? And they repeatedly expressed that it's just important to them to share as much as they can with the broader community, definitely not sharing their trade secrets, of course, still, but really trying to give us a candid take on what are they seeing internally? What do they believe internally? What's their outlook? What's the plan? How can we come together and potentially do some of these constraints? And there were a number of things that I actually thought were like, kind of newsworthy.

• 5:44Nathan Labenz?

  One, let's say, senior executive at a frontier lab, first of all, was saying that just pre-training continues to deliver, that the models are just at the base level of capability, getting stronger and stronger, and there doesn't seem to be an obvious stopping point to that. Scaling laws are holding, or maybe even bending, because the data quality is getting better. They've got increasingly different architecture tricks. Lots of work on synthetic data. Synthetic data seems to be definitely really delivering. Basically, you need more pre-training data. What do you do? You give the model a bunch of data and think like, what could I transform this data into

• 6:28Nathan Labenz?

  that if I then pre-trained on it would make the model smarter? And you're sort of converting these reasoning tokens, your test time compute, you're converting back into pre-training data, which then goes in, of course, to the next generation of models. So this is like one way in which recursive self-improvement is happening. Not even necessarily, there are, of course, opportunities for architectural improvements as well, but a big emphasis was just on data quality. We can now just spend tokens on the existing data to augment it, to create better, cleaner, different lenses on it. We feed all that back in, and the models get better. So this one executive was really emphasizing, pre-training continues to deliver, the models

• 7:10Nathan Labenz?

  are getting super powerful, RL is important too, but his take actually went as far as he believes that the latest frontier models do have the ineffable research taste that would be needed for them to start making paradigm-level breakthroughs. He believes it's currently an elicitation problem where the RL is not bringing it out well enough, they don't know quite how to bring it out well enough, and so it's kind of rare, and this is why you need 10,000 agents to do a Millennium Prize problem. And of course, that's verifiable too, so like RL is working better there. Maybe you need a million agents at this point to stumble onto something eventually where

• 7:51Nathan Labenz?

  you'd get the kind of paradigmatic change at a ML research level. But he believes that is there and it will be, they'll figure out how to elicit it. And then he said, a couple of things that I thought were just legitimately newsworthy. One was, he believes that there is a level of intelligence, or there is, might be a little strong, but there likely is, let's say, a level of intelligence that we just shouldn't go past.

• 8:20Speaker 01

  What?

• 8:21Nathan Labenz?

  Yeah, first time I'd ever heard that from a frontier lab leader. And it was not minced words. It wasn't a, like, there's definitely a level, or I can articulate what that level is, but like, this person who, again, like, everybody would recognize their name and position, saying,

• 8:43Nathan Labenz?

  I think it is likely that there's a level that we shouldn't go past. Now, was that not ever, or just not right now? I think that was also a little bit ambiguous, but it was a pretty firm statement, definitely the strongest statement I've heard about imposing kind of a hard cap on capabilities, even for a time.

• 9:02Speaker 01

  What confuses me about that statement is that some of the increases in intelligence that we were seeing, number one, they're coming as a result of compute, increases in compute, which has traditionally been what has happened in the last decade and a half. Number two, they are coming as a result of refining the input data. The input data is being refined by greater intelligences, and so you have input data which is much better. When we say that we shouldn't go exceed a point of intelligence, that automatically means that you have to look at the inputs going in. And the inputs going in are gonna be compute and this refinement of data. And so if you say that we shouldn't go beyond that point, that also means that you shouldn't

• 9:46Speaker 01

  build compute beyond that point, or you shouldn't refine data further beyond that point, which means a lot, really. It means like the end of the compute build-out, which is pretty significant, right?

• 10:01Nathan Labenz?

  Yeah, well, I don't, I mean, I think there's a lot of uses for compute. So I had a chance to follow up on this. Okay, this combination of pre-training is really delivering and scaling laws, if anything, maybe are bending more favorably, although it's tricky to know what you count, right? Do you count all those flops that you use to create this synthetic data? Maybe you should. How to count these things is always super vexing. But between this pre-training thing and the, there might be a cap on how far we should go, that I just asked to follow up, like, okay, well, how do we operationalize that? Would you be prepared to sign on to something like a limit on the number of flops that go into the next pre-training run? Should we, and I don't know what that number would be, but, and again, what exactly would

• 10:43Nathan Labenz?

  count, but like, should we say, no more than 10 to the 27 flops in the next pre-train or something like that? And I kind of anticipated that being a bit of like a straw man for him to react to, or like tell me what was bad about that, and maybe ideally give me a better idea. But what actually happened was, he basically said, yeah, I think that could be reasonable.

• 11:06Nathan Labenz?

  The end, not really much of a fight there at all. Again, the details will be like incredibly important if we go down that path. Because the more data you enrich, you know, the more flaps you put in enriching the data, you know, you can kind of, you can definitely play a sort of shell game of hide the compute, I think, these days. But the sentiment was like, yeah, we might have to do something like that. And then, of course, your question, I can anticipate, and I know you well enough to predict the next Prakash token at this point, is what about Elon? What about Zuck? And the answer there, there were a couple of really interesting things people said along the way. One, they, you know, in, nobody, this is not from one individual, but sort of a takeaway

• 11:51Nathan Labenz?

  from the event in general, is that the, let's say, two to three, Gemini four being announced now, we maybe have a three horse, very frontier race again, we'll see as that comes online. But the takeaway was kind of, the two to three companies are really pushing the frontier. And even the other two that we would usually think of in the top five, Elon and Zuck, are actually distilling a lot, whether they know it or not. And the way that they are distilling these days, it's not that they're calling the Claude API and getting traces and feeding them in, but rather, the cottage industry of RL environment

• 12:33Nathan Labenz?

  makers and sellers is in, in fact, functioning as a distillation conduit. Because what they're all doing is having Claude create these RL environments, selling the RL environments to other frontier model makers, and then they do their internal RL on an environment that only exists because Claude was smart enough to make it. And that's how they are kind of re, you know, constantly capturing the capabilities advances that the frontier, you know, the true frontier companies had. So there wasn't actually a ton of talk about competition beyond the top three. One person went as far as to say, and this was another like founder, executive type

• 13:16Nathan Labenz?

  at, at one of these companies. Yes, they have kept up. Obviously, there's been a gap, you know, but, and that gap has been persistent, but they've kind of maintained a kind of consistent following distance. And this is in contrast to Anthropic's old prediction from their fundraising deck that I always think about when they said, you know, maybe in 2026-ish, the companies that train the best models will be so far ahead that nobody will be able to catch up. Obviously, that hasn't happened, but the, the kind of synthesis of all this is, if they hadn't been releasing the models, and if people weren't able to do these like various sort of direct and indirect distillation techniques, they actually think that probably would have happened.

• 13:59Nathan Labenz?

  But the other companies are not really keeping up but for the, the sort of leak of intelligence in all these different ways, including the RL industry, that is gradually finding ways to transfer from frontier models to their competitors. Hey, we'll continue our interview in a moment after a word from our sponsors. Today's episode is sponsored by Parallel, where agents find answers. Most engineers today closely follow new model releases, but don't pay nearly as much attention to their agents' most important tool, web search. If you're like me, your agents are often conducting hundreds of searches per day.

• 14:41Nathan Labenz?

  And while the unit cost is small, over time, this does start to add up. Before starting to use Parallel, I calculated that my agent's search bill would total roughly $500 this year. The good news is that I just had my agents conduct a systematic test, and I found that Parallel's fast mode, which costs just $1 per 1,000 queries, worked just as well as my previous default provider. And switching to it will save me roughly 80% of my search bill going forward. Parallel's infrastructure is enterprise grade, and their suite of APIs offers a range of Pareto optimal options that allow you to choose the right balance of quality, cost, and speed for your needs. So whether you're building voice agents that need 200 millisecond latency, or long horizon

• 15:26Nathan Labenz?

  agents that need deep research, adding Parallel to your agent's toolkit is a no brainer. Get started for free at parallel.ai slash T C R. That's parallel.ai slash T C R. Today's episode is brought to you by Anthropic. By now, you know my story. Claude drafts my intro essays, and I rewrite them. Not because the drafts are bad, but so I can stand behind everything I publish. Well, I have an important update. Claude Fable 5 is the first model to have me rethinking my rule. Today, I now think co-authorship, not sole ownership, should often be the goal.

• 16:09Nathan Labenz?

  Where the model excels, rewriting its work can be more about vanity or a misplaced sense of duty than integrity. I feel it most in songwriting. I'm no lyricist, but I'm good with a song concept, and Fable writes some amazing verses. I give it feedback on its misses, and I push it to aim for higher inspiration, add layers of meaning, optimize syllable density, and above all, write a hit song. These days, I get compliments on just about every song we write together. Claude is the AI for problem solvers. It's the collaborator that understands your entire workflow and thinks with you, not for you. Whether you're debugging code at midnight, building a financial model, or strategizing

• 16:52Nathan Labenz?

  your next business move, Claude extends your thinking to tackle the problems that matter. For problems worth solving, get started with Claude at claude.ai slash T C R. That's claude.ai slash T C R. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Once more, that's claude.ai slash T C R. Prakash added his read on Elon's strategy, and then I got to timelines.

• 17:24Speaker 01

  And I think on Elon's side, Elon is very focused on the infra buildout. So Elon is making a gamble that the infra matters more than the models, and that he will be able to catch up, basically, using his infra buildout. And so, and he's, and he's structuring things for 2029, 2030, 2031, like TerraFab, things like that will come out much later, the SpaceX satellites, that's like a 2030s thing. So he's, he's structuring for a much longer period of time. And I think Anthropic and Opening High are both structuring for more like the two to three year mark, and they're looking at a 2028, 2029 AGI RSI.

• 18:08Nathan Labenz?

  Yeah, honestly, even sooner, I would say, was kind of the vibe. And this is probably somewhat biased to be a short timelines crowd. But again, like, the short timelines crowd includes the executives, top researchers, founders of these companies. They were talking more about just next year. It was really a kind of 2020, like, critical period is beginning now. RSI is kind of in hand. Definitely not full agreement on how fast it goes, or if or when it tops out or whatever, but a shared sense that like, the decisions that get made over the coming months and the way that compute is used over the next year could be really critical. There wasn't really even too much 2028 talk, to be honest, which is, again, like, pretty crazy.

• 18:55Nathan Labenz?

  Then it came back to compute and what a cap would mean for the buildout.

• 19:01Nathan Labenz?

  But like, in terms of will they still have need for compute, or does a cap on pre-training scale or other, you know, compute input cap that might constitute a pacing mechanism, does that mean the end of the compute buildout? I would say no. Another question I had the chance to ask was, of course, everybody's heard Jensen on Ezra Klein, I think, at this point. And the comment that he made was like, you know, these companies are going to have to mature, they're going to have to change their ratio of where they spend their resources. He said at Nvidia, we spend like 20% of our effort on designing the chip, and we spend

• 19:43Nathan Labenz?

  80% of the effort on verifying and validating and testing all the edge cases and making sure it's going to last a long time and be reliable, and all these other things that are not just the original design. So his point was like, they've been working really hard, obviously, until this point, and they've been putting everything they can into making their models capable enough to be useful. Congratulations, you've succeeded. Now you're going to enter the era where you're going to have to make them safe, reliable, trustworthy, et cetera. And so I had a chance to ask, you know, one of these people, like, what do you think about that take from Jensen? And the response was basically like, yeah, that's, that's kind of reasonable, you know,

• 20:25Nathan Labenz?

  without, again, knowing exactly where the numbers will shake out, the possibility that the majority of could compute in the future could be going into all sorts of different safety measures, you know, which could be monitoring, could be chain of thought monitoring. People are pretty, getting kind of bearish on chain of thought monitoring. But then there's internal, you know, activation monitoring. They're already spending a significant percent of compute on monitoring, and it sounds like that might go up a lot. Another thing that they're spending compute on in a big way right now is fixing the RL environments. There were, you know, of course, everybody understands at this point that if you have

• 21:06Nathan Labenz?

  sloppy RL environments that reward cheating, then you're going to get a lot of cheating. They are now applying these models to the RL environments themselves and saying, hack this environment. And now that is the direct task. They're doing it, they're finding, you know, all these places and ways in which the environments can be hacked, they're fixing those, I'm sure they're throwing some environments out that are just fundamentally flawed, whatever. And gradually, they're driving down the rate of flaws, which means that they're also driving down the rate at which cheating would be rewarded, which in turn means you're going to get less cheating from the models as they actually come online. And I got the sense, although it was, you know, I think this is still like,

• 21:51Nathan Labenz?

  as they say, an open research question, but I got the sense that they are seeing a pretty clear relationship between the more you clean up the RL environments, you do in fact, get lower rates of cheating downstream. And they're trying to push, obviously, they'd love to have zero hackable environments. That's going to be tough. But it doesn't seem like we're all the way to the point where we have like a scaling law, but you know, kind of, we're seeing sort of a proto scaling law, it seems like in, you know, again, the way you can spend a lot of compute, driving this down and getting better behavior on the other end. I heard repeatedly that they expect to be bottlenecked on safety

• 22:33Nathan Labenz?

  and alignment. But it seems like for at least the next generation or two, there is still enough low hanging fruit to pick in the safety and alignment domain, in part just by fixing RL environments, that we should expect things to continue to move pretty fast. But it seems like they're probably going to be able to get comfortable releasing the next couple of things, even as they do consider themselves bottlenecked on alignment. But then past that, they're like, yeah, all bets are off. We, really hard to say what happens kind of past the next one to two generations.

• 23:14Nathan Labenz?

  Later in the show, I tied that executive's view on research taste to a new record in the NanoGPT speedrun, the race to train a small language model to a target quality as fast as possible. The record comes from a company called Hyperstition. But just headline wise, it seems like we've gone from something like 70 seconds on this, this classic benchmark. This is something people have been working on for quite a while. And basically, the idea is to train a small model to get to a certain loss as fast as you can in terms of wall clock time. And this result here took almost

• 23:57Nathan Labenz?

  half the time off, which was more than, I think, the last 45 improvements combined. And this one has been accepted. One thing I did think was quite interesting about this particular one that has been accepted and kind of seems to have kicked off this wave of, you know, significant drops in, in the time, was that the author here said, AI didn't play a big part in it. Like the person, senior executive leader at the frontier company that I mentioned earlier, their perspective was, they believe that the model probably

• 24:37Nathan Labenz?

  can come up with these quality of ideas, but it's an elicitation problem and they didn't really rely on AI super heavily in this case. You know, obviously, they're getting lots of help, but in terms of core insight, it was mostly attributed to the human, not so much to the AI on this particular drop. This company is doing also like, large scale training. So they did also make a point in their publication about this, that the optimizer that accounted for a significant, but I think not majority of the time savings, that they did share, is actually not as good as the one that they are using internally.

• 25:20Nathan Labenz?

  Then one last expectation I heard across the frontier companies at the curve about science. AI for science is very much where they think we are headed in the not distant future. Like, one, one sentiment was, you know, our AI is going to be superhuman at just some things, like the verifiable tasks, or are they going to be superhuman at everything? And the sort of middle position, which I think is very credible, is we are seeing superhuman performance at anything we care about and are, you know, really committed

• 26:02Nathan Labenz?

  to investing in. So that doesn't mean it's full generalization to every domain, but even in domains that are thought of as not super inherently verifiable, they feel like when they put their focus on it, and they go license what data they may need to license, and they of course, apply lots of processing to that data to augment and, you know, create synthetic versions and what have you, and, you know, put some RL on it too. The whole package basically is, I think, broadly understood to work on essentially any problem that

• 26:45Nathan Labenz?

  they really choose to focus on. And so AI for science is coming next. And the expectation is we will see, one person went as far as to say, in no more than a year, it will not be possible to do the very best science without AI's playing a big role in it. The next morning, I added one more takeaway from the curve. And I don't think we talked about this yesterday, but a takeaway from the curve this weekend also, one of the not main threads that I was involved in discussing, but a

• 27:23Nathan Labenz?

  general vibe was, there is going to be government intervention. And, you know, obviously, we've already seen some, but the idea that like Congress will never act, I think was, that used to be sort of taken for granted. And now the new vibe that I was hearing was like, after this election, especially if the Democrats take both houses, both, both chambers, that may change. And you might even see a bunch of Republicans join the Democrats because really in their heart of hearts, they want to do something. They don't really want to go against the president. And there hasn't been a lot of people willing to take that

• 28:04Nathan Labenz?

  chance yet. And they're also getting a ton of money from the data center interests right now. But that all could come up for a renegotiation and realignment after the midterm. And we might actually see congressional action a lot sooner than maybe a lot of people who've grown accustomed to the idea that it can never happen, or it'll be, you know, stasis forever, might think. So I do think policy response is definitely part of what we need to be looking out for. Hey, we'll continue our interview in a moment after a word from our sponsors. This episode of The Cognitive Revolution is brought to you by Outsystems, the leading

• 28:44Nathan Labenz?

  agentic systems platform. Outsystems is helping their customers build, modernize, and operate enterprise systems, starting from any coding tool in a governed agentic engineering model. With Outsystems, you can coordinate and govern your entire agentic workforce within your enterprise ecosystem. Accelerate impact with industry proven agentic solutions that are prebuilt, governed, and customizable to your business. It's time to innovate at the speed of AI without compromising quality or control, which is why thousands of enterprises worldwide trust Outsystems for their mission critical apps. Teams any size can use Outsystems to build, deploy, and manage AI apps and agents quickly and cost effectively, without

• 29:29Nathan Labenz?

  compromising reliability and security. With Outsystems, you can accelerate ideas from concept to completion. It's the leading agentic systems platform that is unified, agile, and enterprise proven, allowing you to build your agentic future with AI solutions deeply integrated into your architecture. Outsystems, all your agentic future. Learn more at outsystems.com/tcr. That's outsystems.com/tcr. 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. Before every recording, one agent researches the guest and sends me a suggested outline

• 30:14Nathan Labenz?

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• 30:54Nathan Labenz?

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• 31:36Nathan Labenz?

  Part two, the memory wall. Thomas Somers is a co-founder of Positron, which builds chips for running AI models and recently raised $875 million. Prakash asked whether the sharp rise in memory prices had changed the economics.

• 31:57Speaker 02

  I was looking at a quote that we had a year and a week ago and for memory, and it's gone up four and a half times since then. I really did hope that this isn't the case, but I wouldn't be surprised if this is going to end up being a another doubling over the next year. But I would say a big reason why everyone has burdens these these cost increases is because you're deriving more than that for 5X value compared to last year. But I think that the reality there is compared to a year ago, the capability of a model using X amount of gigabytes of memory is way more than 5X than than it was this time last year.

• 32:35Nathan Labenz?

  I asked Tom about the trade offs of building on commodity memory instead of the high end kind.

• 32:41Speaker 02

  At the start of Positron, I did think that memory was going to be the limiting factor for things going forward, even though, you know, people can debate on power and other parts of the infrastructure stack. My fundamental belief was that there isn't going to be a stop anytime in the near future in terms of scaling laws and getting increased value from making models larger. So that eats into memory on one side. But then the other side, I think the main limiter today of AI being applied to most applications is context length and being able to hold more context, you know, per user and then scaling that out to drastically more users. And and really today, I would say that the driver of quote unquote users or individual sessions is actually just having more agents.

• 33:25Speaker 02

  I've gone from, you know, three, four months ago, where I had probably on average two to four agents like running constantly in the Uma background to where I'm now running 15 to 20. If you then multiply how many people are using AI and how many that that concurrent, completely separate contexts adds up very quickly. That that memory driver had us say, okay, we have to use the commodity number because that's the only thing that's going to be able to scale and be cost effective. And when we set that as sort of the constraint in our architectural design, we had to come up with, you know, very, very clever innovative solutions. And, you know, the the two main pieces of that and, you know, the thing that we showed with our first generation product

• 34:04Speaker 02

  is being able to achieve extremely high memory bandwidth utilization from a compute architecture perspective. So while Nvidia GPUs, I would say on average are getting between 30 and 40% memory bandwidth utilization in the actual decode forward pass of a transformer model. So basically that means even though they advertise, you know, eight terabytes per second of theoretical memory bandwidth with, you know, a B300, you only actually are seeing, you know, something in the ballpark of like two terabytes per second of actual realized memory bandwidth. And that comes down to a whole bunch of GPU architecture details. It comes down to the reuse patterns that really don't exist in transformers that they designed,

• 34:47Speaker 02

  their hardware architecture around for training and other workloads. And so with our first gen product, we were able to hit and sustain 93% of theoretical memory bandwidth. So that theoretical and realized basically, you know, become one. And so that isn't that massive, you know, 3X improvement right there. But to really take advantage of, you know, the belief that models and everything are just going to get larger, we had to be able to scale to massive more chains. And so we're, you know, partners with Credo Semiconductor that and and have developed a memory chiplet solution that allows us to go from the maximum number of LPDDR, same type of memory that is in phones, laptops, et cetera.

• 35:30Speaker 02

  The max number of channels that you can have find in any other products is on the order of, you know, 12, 12 to 16 channels. And we go up to seven, two channels of of LPDDR 5X with this decoupled memory chiplet solution.

• 35:49Nathan Labenz?

  Positron's next chip is called Asimov. Prakash asked, what disappears in networking and coordination when a whole model fits on one chip?

• 36:00Speaker 02

  We have up to 2.3 terabytes of memory capacity per chip. So when you compare the the B300 shipping today, taps out at 288 gigabytes. As, you know, reported in in by analysts, Nvidia for next generation is actually cutting down the memory capacity because costs and everything involved. So they're only going to be at 192 gigabytes with the Ruben Ultra. And so, but even if you use that 288 as like the current benchmark, we've got eight times more memory capacity per chip. And so that means on a per chip basis, you now can actually scale and what you would have needed from a memory perspective, eight GPUs for, we can do on a single device. And it's not just, oh, and you've got that cost silicon savings, you know, as as you pointed out,

• 36:42Speaker 02

  when whenever you have to scale to having more than one device, there's overheads involved with that. There's, you know, your all gathers, all reduces that you have to do within for each layer and each really each multiply that you're doing of sharding across those devices.

• 37:00Nathan Labenz?

  Then I asked Tom about looping, where a model runs the same layers more than once for each token, and why that is tempting at all.

• 37:09Speaker 02

  It's been very interesting from the loop transformers paper to that being the rumor of, you know, one of the big advancements with the GPT-6 Astra.

• 37:18Speaker 02

  Simply repeating the forward pass, it gets you that improvement. But it's crazy that there is really work that I think was only being done in sort of the fringes of the open source, you know, transformer community, you know, two, three years ago of taking like a Llama 70B and just duplicating layers within it. So it was taking like a 70B model and slightly strategically saying, okay, I'm going to just repeat these these sets of mat balls in in sequence and turning it into like 100 billion parameter model. And you you got better results from it, even though it was repeating the exact same mat ball. So there is an element.

• 37:56Nathan Labenz?

  Imagine what you could do when you train it to work that way.

• 37:59Speaker 02

  Exactly. If you already have very high confidence on what the next token is going to be and multiple layers have agreed on that, decide to early exit and go out. Or if you're really uncertain about something, actually find which layers in network actually will have a higher likelihood of being or increase that probability, seeing sort of like functions that the M O D router does in terms of determining which experts should be selected for on a per token basis. If you extend that concept to, okay, if you're really unsure if there's a very large set of of equally weighted probabilities for what the next token should be in your three quarters of the way through through the layer, you actually just want to route back and determine, okay, we actually need a different set of experts, something like for this.

• 38:44Speaker 02

  But I wouldn't be surprised if these things are already being done at scale in in the major model lines.

• 38:50Nathan Labenz?

  But can you go just a little bit farther into the hardware connection of that? Because I I'm kind of coming in with assumptions along the lines of, you know, just very fundamental terms like why loop at all? I think it has to do with this kind of memory bandwidth, right? Because now I can keep the same weights on the chip and I don't have to like shuffle things in and out as much. No?

• 39:12Speaker 02

  Not really, because within when, basically the assumption is if you're doing inference that you're going to have all your weights locally and in deep learning, you're just sharding that over some number of devices. You do get some amortization, but just with the size of experts and the sizes of caches on chips, there's not much of a reuse opportunity. Or by the time you're already done with the layer, you've already gone through the amount of memory many times that of what is in like the on-chip SRAMs. From from like a hardware perspective, like looping really is more memory capacity savings than memory bandwidth savings. Because like, and let's say that you trained two models with the same base set and you trained one to do with looping.

• 39:56Speaker 02

  And so it only has 10 layers instead of 20 layers. And, you know, model B is is 20 layers. That 20 layer model, doing very, very simple, is going to be double the size of the the 10 layer version. And if you find that the linked 10 layer version gets 95% of the same quality of results and it's half the size, you're probably going to deploy that one. You're you're saving more capacity than that because doing the second loop, you still have to do the same memory fetches and do all of the same, like same number of mat balls. There's the exact same number of bytes that need to be moved and the exact same number of flops that have to be done in that model A and model B scenario. It's just that model B, the 20 layers, actually has unique different weights

• 40:40Speaker 02

  for the the second group of 10 layers that have to be done. And so that that means you require twice the memory capacity.

• 40:50Nathan Labenz?

  Then I turned to how AI is changing chip development itself.

• 40:55Nathan Labenz?

  In a notable interview Jensen Huang recently gave with Ezra Klein, he talked about how Nvidia spends something like 20% of its time and energy designing and then 80% verifying, validating, ensuring long lifespan, reliability, et cetera, et cetera. What do your ratios look like on that?

• 41:14Speaker 02

  So for us, I would say it's a little bit different because we're starting from scratch. So there's a lot more like base layer, base capability design that we have to start from scratch. So for this, our first full custom silicon, Ozimov, it was about a,

• 41:33Speaker 02

  it will end up being somewhere around maybe 60% design focus for us to 40% verification. I would say like the probably most amazing thing that I don't think would have been possible three or six months ago and only really became possible with GPT-6 Astro and now Opus 5.5 that we're leveraging a lot in this task is we, for doing our verification work, we use Cadence Palladium emulator systems. These are big giant racks that are full of custom ASICs that are specifically designed to just emulate, do gate level emulation of silicon.

• 42:15Speaker 02

  The crazy thing about Astro and Opus 5.5 is this fact that the agents themselves have been able to completely do closed loop iteration and testing on this. So, you know, I think people thought it was crazy, but handing these very powerful agents, full keys to our internal infrastructure and saying, here's a Palladium piece. And I highly doubt that these models had Palladium documentation in their retraining thing, especially because a lot of documents are new as software updates. But the fact that it went and looked up the documentation when we're looking at these agent traces, the reasoning traces and tool calls, et cetera.

• 42:58Speaker 02

  It's reading the full documentation, effectively compressing that itself by reading the PDFs, generating its own markdown files of cheat sheets of what to do, and then building out all of the testing infrastructure and its own harnesses for being able to access this. It's mind blowing. And so while if you're just doing regular software RTL simulation, that's running on the order of like 10, 10 hertz for us in those cases. If you remove a lot of the debug pieces that slow things down, et cetera, we can run the full chip emulation on the order of 500 kilohertz. And so you've got actually several orders magnitude speed up and improvement

• 43:42Speaker 02

  from that gate level hardware piece. And this just gets us closer to this RSI loop. And right now that's really just focused on implementing test programs, finding cases where they fail and then writing out reports, which get reviewed by other agents and by humans in the loop. But I would say when this really started working six weeks ago, roughly, it was beginning of September when Astro 6 came out. It was hugely a step function improvement from GPT-5.6, where it could not do that full closed loop itself. It still required humans at different stages.

• 44:27Nathan Labenz?

  I asked how Positron's spending on AI compares with its spending on people?

• 44:34Speaker 02

  So on the token spend side, it's become our single largest non-manufacturing line item very quickly.

• 44:42Nathan Labenz?

  Bigger than human salaries.

• 44:44Speaker 02

  Yeah, just recently. And it's interesting, it eclipsed human salaries, it came back down. I'll explain in a moment. So yeah, so our token spend has massively increased. Where, okay, six months ago, it was equivalent with one employee. In June, it started to now be multiple employees. Like we were peaking in the days after GPT-6 Astro and we're really trying to push its boundaries, et cetera. We're spending over a hundred thousand dollars a day on tokens. That tempered down a little bit in the weeks after as we optimized a bit and just weren't trying to run as many parallel experiments.

• 45:23Speaker 02

  But I would say the big thing that was like, you know, a saving grace was Opus 5.5 came out and a lot of our tasks, not everything, it was doing better than Astro and was a quarter the price. We didn't really, we didn't tell anyone to cut their spending or do anything, even though we were seeing this exponential growth in our token spend. It was really just the fact that we both felt that we were getting good ROI on it. So we weren't going to taper it. But personally, like my philosophy is I really don't want anyone to be running a real software or hardware development task on anything that is less than the best model.

• 46:04Speaker 02

  Like I don't care if it's a tenth of the cost per token. It's just not worth the expense.

• 46:15Nathan Labenz?

  Prakash asked whether Tom has seen AI come up with genuinely unexpected ideas in chip design rather than following the rules in a manual?

• 46:25Speaker 02

  I have not yet. I would say that's the most disappointing elements of all. Like I'm, as you can probably tell, I think all of this is insanely amazing. And I think we're going to continue this exponential trend. I'm somewhat proud of the fact that it has not figured out some of our cleverness of what we've done. Like it's, it has questioned and like, this seems like a bad design decision until it gets explained to it why something's down or it actually runs test and sees and understands, oh, that's why you're not doing the traditional way a stallicore is done. Do I think that will hold for another six months? Not sure. But just because, in part why I actually think it's not going to be like the agent will come up with a better idea immediately. I think the amazing turn of events that we've just had in the past few weeks

• 47:10Speaker 02

  in terms of us having access to these models is the fact that just because it can iterate so fast and do experiments and things by itself, I wouldn't be surprised if it would come to the same conclusions we did or maybe make better things than what we would do by ourselves, just because it's going to be able to iterate so fast and go through so many different design possibilities. I think like just the story of OpenAI solving Navier-Stokes, like the same approach. Yeah, if you take 10,000 agents and send them off for millions of man years worth of effort, it found a solution.

• 47:51Speaker 02

  It's brute force, it's expensive, but I think that is a valid strategy that has only become possible in very short past.

• 48:04Nathan Labenz?

  Not many people left standing in the AIs can't do what I do category. So congratulations on your place for as long as it lasts.

• 48:14Speaker 02

  For a couple more weeks at least.

• 48:17Nathan Labenz?

  Part three, software after agents. Sean Wang, known as Swix, runs the latent space podcast and the AI engineer conferences. I asked how software engineers are feeling right now, empowered or threatened?

• 48:35Speaker 03

  Yeah, I think college kids are a bit worried. But other than that, if you're relatively plugged in and very capable of AI engineering tooling, you're in more demand than you've ever been because your expected value is higher than it's ever been. So this is definitely one of those Jevon's paradox type things where the sort of cost of software, creating software has gone down, therefore the demand has increased a lot. And but for a very specific kind of demand for people who can manage coding agents productively instead of producing a whole bunch of slop. And I've been in that situation. So I am both an engineer, but also an employer of engineers and people who are not engineers who are by coding.

• 49:18Speaker 03

  And the phrase I've sort of recently taken to is I don't want to pay for someone else to go through L M psychosis, right? I can pay for my own psychosis, that's fine. But like when you work for me and I'm paying your tokens, you better be like actually producing thoughtful stuff. And I have two or three employees right now who are basically under performance review because they are just giving me cloud slop. And it's really bad for them and they don't understand it, they don't see it. They're like, what do you mean? I think this is perfectly fine. And then like, well, you're not producing any value. I can just prompt cloud, I don't need you.

• 49:55Nathan Labenz?

  I asked whether years of coding experience still matter when he hires or whether product sense and high standards matter more?

• 50:03Speaker 03

  I don't care so much, except for, let's call it, security roles and for anything sort of backend scalability. Like, I just recorded an interview with the Superbase founders, who are scaling Postgres to a scale that we've never seen before. Good luck trying to hire somebody to scale Postgres, you know, with a complete self-managed sharding solution that works at YouTube scale. There's only one person in the world that can do that, and they hired the guy. Like, you better go and, you know, make sure you don't lose data, that you don't have, don't cause downtime, that you scale economically. Because basically, agents consume databases, something like at a 500 to 1 ratio of what human developers do.

• 50:48Speaker 03

  Then, yeah, so like, other than that, like, you can mostly vibe code, and then it is about taste. And it's not so much about length of experience. Actually, sometimes length of experience works against you because you just have a set way of doing things that you don't understand how to do more than one agent at a time. Yeah, at this point, like, you should be relatively comfortable juggling like 5 to like 10 ongoing multiple things. And it's definitely true that you're much more of a manager than an individual contributor. I do think that people who look at data rather than code are more valuable these days. So, being able to say like, here's the logs, here's the traces, here's the schema, here's the input-output, capturing it, turning it into an eval. All these are basically the sort of the merging

• 51:32Speaker 03

  of AI engineering and ML engineering that is happening, that people need to upskill on. I think that the person that has, let's say, 10, 20 years of experience in software engineering really reviews every line and tries to make every line make sense. Whereas now, we just need to make the modules make sense. And I can allow slop in there because it helps me to go faster as long as I contain the slop in things where I completely understand the whole system. Where you go wrong is you have too many modules, too many black boxes where you don't even know. And actually, the code also gets confused. So, I'm making my own sort of Slack competitor. And one of the, and like, I saw a bug where like the messages weren't loading.

• 52:17Speaker 03

  I refreshed, and the messages were loading. And I refreshed again, and the messages were not loading. And it's like, well, it's the same exact code. What the hell's going on? Turns out, there's two code paths, code paths, and there's a base condition. Why? Because two different coding agents worked on it at different times. And because they just like made their own thing. So like, a human would never do that. And agents would potentially sometimes do it because like, sometimes just things fall out of the context window. But like, you need to have the oversight of the module, and then whatever inside of the module can be a black box.

• 52:50Nathan Labenz?

  Prakash asked, which parts of the stack are poorly designed for agents now that agents are becoming the main users of the internet?

• 53:00Speaker 03

  Compute. Everyone knows the GPU shortage, everyone knows the memory shortage. But CPU shortage is the most consistently reported thing among all the people that I interview right now.

• 53:12Speaker 03

  So, what does that mean? It does mean that we need sort of more fluid compute, which is the first alternative for it. More sort of different forms of serverlessness, where like, it's very ephemeral. You can sort of execute and resume because, you know, agents need some time to do LLM calls or make network calls, whatever. But basically, long-running states that are reusable. But I think beyond that, you really start to get into two things, which I'm really thinking about, like sort of bandwidth for networking, where the latency starts to really matter. And that's the optimization of like, well, what point of presence are you communicating with? Or what region are you in? Versus like, actually, how much compute should you be doing

• 53:54Speaker 03

  in cloud versus locally or sort of edge devices? That is definitely happening a lot for the robotics companies that we're talking to. And basically, don't treat it as just a robotics problem, where the robotics are just the first harbinger of what you guys will eventually be doing if you consume or do enough inference at their scale. And then finally, to me, on a more very, very personal basis, bandwidth. So like, how many tokens per second can I get? Both on a single call basis and as an aggregate across all my calls? And then aggregate across my company because I have so many, I have enough people managing all these calls.

• 54:33Nathan Labenz?

  Swix has also been running Kill My SaaS, a bounty for anyone who could replace an event's software subscription his company was unhappy paying for. Prakash asked him about it.

• 54:45Speaker 03

  Basically, you can call this like, you know, a bounty on mid-tier SaaS that should not exist. Basically, like, half a salary or one-third of a salary for a piece of software that I don't own, that nobody enjoys using. You know, if I'm going to spend $40,000 in tokens, I have $40,000 on this SaaS subscription. I can spend that in an amount of tokens, and that buys me a heck of a lot of tokens. We have so many submissions that we then have to eval them. And so evals are the problem. And because like, we're no longer checking like, there's like two or three requirements that you fit. It is, we're checking the entire UX of the entire flow from three different perspectives of, you know, organizer, attendee, and sponsor, or a speaker. And just be aware that like, if you offer like a large bounty,

• 55:30Speaker 03

  like we offered $10,000 as the prize, you get a lot of submissions because people want to vibe code for fun. And a lot of them will be low quality. They would just be like, Claude, make no mistakes, go do this. And like, and then they will, it will make mistakes, and they'll just submit. And so like, the sort of verification load is very imbalanced because they spent zero thought on this thing. They just threw it in there. And then you have no idea if it's like, low quality or not. But overall, very successful. And the team initially, so I have one of the most adversarial non-AI teams in AI. Okay, I hire, like, what is my business? I hire event professionals who are like, super old school. They do everything in spreadsheets, they work with unions. They have to worry about like placements of like,

• 56:13Speaker 03

  the meter boards physically and like locations, right? Like, these are not glamorous, like, high-tech jobs. And they're very super suspicious of anything that is new and vibe-coded and techie, right? So I hire these guys, and then I like make them work for AI. Right, that's my job. And so, initially, they were like, we'll never use this vibe-coded thing. And this is, like, I want to use the tried and tested stuff that like has been working for Microsoft and all of these other guys. And then they saw the quality of the submissions, and they looked at their existing platform. They were like, yeah, okay, we're going to switch. Right, like, it's like, that initial hurdle needs, you basically need to provide the sort of proof of, like, this is feasible. And then the ongoing thing, which is, this is one of the benefits of me partnering with Cognition on, is I just give them,

• 56:58Speaker 03

  give all of them access to Devin to modify the code. So, anything they don't like, they can just request a change, and they have it pretty much in one to two hours, which they've never had before. Just to give you an idea of the way we work with these SaaS companies, right now, when we request a change, they'll say, okay, that sounds pretty cool, it's on our Q3 roadmap. And like, we don't have confidence that it'll actually be done in Q3. And by the way, this is that, we requested for a thing in Q1, and it just landed. So like, SaaS is quite cooked. If you're mostly a crud app, I would say that there's a lot of UX issues left. There's, you know, if you look at Zeebench, and you're like, oh, wow, we're like 90 in Zeebench, you're looking at the wrong thing, my guy. You know, if you haven't actually tried to completely vibe-code a SaaS that you use, you don't understand

• 57:42Speaker 03

  how models are still very bad at all this.

• 57:46Nathan Labenz?

  Then I pressed Swix on the downside.

• 57:50Nathan Labenz?

  But I have a hard time seeing how we don't kind of pick a lot of this low-hanging fruit and then end up in a sort of, a lot of companies go out of business. And also, I'm thinking like layoffs from like Meta, you know, that's another kind of canary in the coalmine. Like, are we going to see mass layoffs from big tech? How long can the party go on?

• 58:10Speaker 03

  The, I mean, the simple answer is like, if you look at it as a fixed pie of software engineering, then that is the conclusion that you will arrive at. But if you look at it as the point of like, my competition or my TAM is spreadsheets. All spreadsheets made by anyone with any sort of productivity tooling can be turned into custom software for them to put on rails with a beautiful UX and with like automations. Or then the demand for custom software becomes a lot larger. And more beyond that, if it's all, if it's phone calls, if it's emails that go back and forth, and eventually, physical meetings between people, all of that can be incrementally turned into more and more

• 58:55Speaker 03

  and more and more custom software and hardware and models, by the way. And so, that, basically, we are just growing into the long tail, right? Like, why is Salesforce so damn big? It's because people can customize Salesforce. But at some point, they need to stop paying the 300K a year baseline subscription and spend 30K building their own personal CRM, and they're good. In fact, they're more than good, they're happier because they can now modify it to whatever they want. Like, the sheer amount of customization that people can do, we don't even have the same amount of customization in our software as we do our handbags. What the hell?

• 59:35Speaker 03

  You know what I mean? Imagine like, like clothes has been around longer than computers. But we have so much customization in clothes, so much like demand in like different brands and different varieties. We don't really care about benchmarks for clothes. It's just like, this is the style that I like. I think what software engineering is basically only done when we basically have like Kate Spade versus Louis Vuitton versus Coach for software. Like, we're like, not even at the point of like, looking at features or like price. We're just like, what's the brand feel? And like, I identify more with this brand. Like, that's what really sort of true commodity software is, which is you start differentiating on substance, and you start differentiating on vibe, which there is a lot of vibe right now. The reason I said, you know, this whole thing about like,

• 1:00:20Speaker 03

  scaling my personal token bandwidth versus scaling my organization token bandwidth. The real limit on all this is that the sum total of humanity's total token bandwidth, right? Which is literally the amount of silicon we can produce. And that's like, that's going to dictate pricing and availability and rate limits and all these things. And so like, the real players in the room are only focused on that. And then the rest of us are just fighting for scraps within the fixed pie that we already have.

• 1:00:48Nathan Labenz?

  Prakash asked about Jev, TypeSafe AI's fast decision-making model, and whether OpenAI's new decisions API, announced at Dev Day, is the same kind of thing.

• 1:00:59Speaker 03

  Yeah, so not only have I used it, I was friends with Yogo for four years before the launch. And so I insisted that we were the first podcast that he did about Jeff. And that, I mean, there is now our number one podcast ever in space. And then secondly, we're also in the trial for the decision API with OpenAI. So we have a podcast on the decisions API as well, which is not the same thing. That one is basically a Luna with a sort of different inference layer on top of it. I think what's also true among the people that I talk to are that a lot of the use cases you see on Twitter are probably overdone or dumb in a sense of like, you could use any other small model to do exactly this thing. But because Jeff is hot right now, you're just creating a bunch of content

• 1:01:44Speaker 03

  about Jeff. Or the other perspective is, this is another classifier, you can train classifiers all this whole time, but you can, but you know, now it's sexy. So now everyone just people are throwing classifiers, or using a classifier model where they would previously use an LLM because they didn't know anything else other than LLM. People are missing the whole point why they don't call it, why Jeff or Yogo doesn't call it a decision model. They call it a system one model because it's meant to be integrated inside of your software to go into the background of your software. As unremarkable as an if statement is the rough idea inside of your software. And so people aren't really doing that. They're like, let me just, let me like, replace this, the kind, you know, every sort of point of

• 1:02:28Speaker 03

  structured output with Jeff. And they're probably going to run into issues because they don't care about intelligence. If you notice, most of these use cases aren't really evaluating, like, well, does it actually play it very smart? Does it do any planning? None of that. It's just, can you make a decision quickly? And like, yeah, you could always make a decision quickly. I think there's some nuance there that's maybe being missed. Doesn't matter, because I think the category creation was so successful, that is an overall good thing for the industry.

• 1:02:58Nathan Labenz?

  Before he left, I asked Swix to plug his next AI engineer conference.

• 1:03:03Speaker 03

  Yeah, it's next week in New York, October 12th to 14th. And we're about to sell our tickets, I think probably by the end of the week. So definitely get your tickets if you want. There's a, there's code on the latent space for these subscribers, if you can dig in through your emails. And it is on ai-engineer slash NYC.

• 1:03:24Nathan Labenz?

  If you're not a latent space subscriber, what are you doing, folks? Come on. It should already be in your inbox. That one's on you.

• 1:03:33Nathan Labenz?

  Part four, who owns the risk? Evan Miyazono runs Atlas Ignota, a nonprofit that looks for consequential AI risks without a clear institutional owner, develops an intervention, and recruits someone to carry it forward. This is the idea behind it.

• 1:03:51Speaker 04

  I've been claiming repeatedly that as intelligence gets cheap, it's the coordination that gets expensive. And so having shelling points around who does this coordination, becoming one of those shelling points or creating one, seems very useful.

• 1:04:07Nathan Labenz?

  I asked Evan where the biggest gaps are right now.

• 1:04:11Speaker 04

  Probably the two things that I've been spending the most time on personally recently are what can or should be done around protecting critical infrastructure, especially from accidental agent swarms, and also things like malicious use of open weight models. I think there's an interesting question of what happens if instead of OpenAI accidentally attacking Hugging Face, we have the companies behind Deep Sea or KIMI models accidentally attacking State Department servers? How do you know if that's an accident? What do you do in response? What would you do if, or what would we do if we saw some malicious

• 1:04:51Speaker 04

  attacks to critical infrastructure coming from an OpenAI server? How would you know that it was this and not coming from somewhere else? How would you shut it down quickly? What if, like, with varying levels of escalation? And one of the interventions you might add is, could you use DNS to have cryptographic attestation of whoever's providing inference? I think this would be very useful and also a great complement to having DNS for people. So if your agent is talking to my agent, they can verify that this agent in fact is my agent, like your agent can verify this is Evan Miyazono's agent, being signed off

• 1:05:33Speaker 04

  on by some key. I think that there are some shortcuts to adoption that you can take if you're not trying to build this into a startup that returns the fund. And so this looks much more like a public good.

• 1:05:45Nathan Labenz?

  Later, Evan raised a problem of salience. How do people even find out that a better tool or practice exists? That turned into a conversation about how our own agents might connect us.

• 1:05:58Speaker 04

  Like, Nathan, I'm sure you have great infrastructure things that I would benefit from. I don't know what those are enough to ask you what they are. I have an application that just takes screenshots of anything that's not a video conference, and runs it through a local OCR model, and then takes that and sends it to a, sends it with my weekly goals to say, like, what am I trying to do? What should I be trying to do? What should I automate? What should I do differently? And I could imagine this being a thing where sharing that across collaborators would be something that could identify all sorts of interesting synergies.

• 1:06:38Nathan Labenz?

  Yeah, it's interesting. When we started this show, one of the ways we conceived of it was as an experiment in recursive self-improvement. And we were just like, you know, can we actually make a show with two people who are live and no employees? And, you know, can we like, iterate to something that actually works? Now I feel like maybe the next thing we need to conceive of it as is like, a swarm, you know, like sort of a friendly swarm of people who can share their best ideas. Like, my, my kind of upshot, you know, what will I tell Claude to do? Or what will Claude hopefully do for me when it sees the transcript of this conversation? Will be, can we like, take stock of the best ideas that I've got

• 1:07:21Nathan Labenz?

  and sort of put a shingle out? Or, you know, in some way, kind of present that to, maybe not everybody, maybe could be everybody, but certainly to my friends, the people that I truly want, that I know and like, really want to help?

• 1:07:36Speaker 04

  It feels like it would be very plausible for me to, if I recognize something I'm doing, or if Koichi identifies, like, Evan, you are really bad at this. I could bring that to the, the network and say, who is good at this? Who should I be talking to about this? Who is solved, who is likely solved this problem very well? There's like a reinvention of social media, or a reinvention of guilds, or something that would be, yeah, I think potentially very, very useful. It's very unclear to me how society starts restructuring around some of these things.

• 1:08:11Nathan Labenz?

  One request from me. If you've built a good way for people and their agents to find each other and share what works safely, please reach out. Near the end, I asked Evan for an update on formal verification, now that models are getting very good at math. Atlas has helped start companies in this area, so he has a stake in it.

• 1:08:34Speaker 04

  There are some meaningful differences between proving properties and proving theorems in math and proving properties about software. One of them is that in math, the number of theorems that one might care about are pretty, they're a lot fewer than in software, and they're a lot more universal. It seems like there are a lot of projects where the ambition of the field of formal methods, or at least many individuals, and I'm particularly talking, I'm thinking of Oath and theorem, and a couple of others, where they're taking on things that would have been five years, $100 million efforts previously, and are now things where

• 1:09:17Speaker 04

  you can possibly, like, it's limited by tokens and the number of people who can successfully, effectively wrangle agents to do these things. There's a lot of things here that I would expect that in three to six months, we're seeing the kind of, the kind of shock and awe that is happening to math, like, that has been happening with math in the last month, happening in verification of software improvements to stability. But I also think that the ceiling is really, there's a visible ceiling that is very high, or like a high watermark that is, I think, much better known and harder to reach than things in math.

• 1:10:02Speaker 04

  Whereas you could prove that an entire computer system has mathematical guarantees around separation between different threads, down to the, like, down to our model of what a transistor does. In theory, you could even prove that in like, a physics simulator, and show that no row hammer exists for this software, for like, for this software hardware stack. You could also prove that we won't have, like, this system won't crash. Or if it does, like, if some random event leads to it crashing, we can recover everything, can roll back any software execution. I think that there are a lot of things that

• 1:10:41Speaker 04

  you could imagine that are just really totally impossible as of like, a year ago, maybe possible now, definitely possible soon, that could lead to software engineering and software maintenance being much better.

• 1:10:58Nathan Labenz?

  Part five, where the training data comes from. Edward Hu leads AI modeling at Merk War, which works with professionals to build training data environments and benchmarks for AI labs. He also led the development of LoRa, the widely used fine tuning method. Merk War's Apex Agents benchmark gave models a simulated workplace with files, email, and chat. Edward described the next step.

• 1:11:24Speaker 05

  We have a project that we are close to wrapping up, where we're actually taking it a step further, where we acquired real companies. Often, they have realistic apps and environments as a part of the data we have. For example, their Salesforce data, Slack data, Figma data. And then, among all of these apps, often with gigabytes or terabytes of data, we create realistic tasks out of them. So far, in the field, we more or less have been really task-centric. We often start with, okay, here is a task that you need to finish. Maybe it is to generate a roster. So that's one shape, and that has been the shape that we have publicly released

• 1:12:08Speaker 05

  and most people have been working on. But this is one of the things we're working on. But we're asking ourselves, is there a better shape? What's the shape of the task in the future once we have these datasets? Like, when we work at a company, we're not always just handing these tasks. We're handed a role and a work chart. And of course, all the data in the company. And then there will be people coming to us, asking things from us, the way people we need to chase down to get information from. There will be managers, people in charge of the resources, conflicts to resolve. So increasingly, that, we believe, is the shape of the future. And we'll have more to share in the coming months.

• 1:12:46Nathan Labenz?

  I asked Edward how supervised fine-tuning compares with reinforcement learning as the way companies will train models in the future.

• 1:12:56Speaker 05

  And actually, one thought experiment is, if we take SFT self-disolation, that's actually more or less a step of RL. And you iterate this process, you ended up with a process that's actually quite similar to RL. And I do think in the future, RL, the way it is done today with these very elaborate infrastructure, very, very expensive rollouts and relatively inefficient updates, because with all of these rollouts, especially a long rollout, we only get one number at the end. These type of training will not be as widespread. I don't really see every company in the world doing these big RL runs. So I do think SFT,

• 1:13:38Speaker 05

  especially combining the output of multiple teachers, will be a key part of how enterprises in the future customize their models. And we're investing in research on that as well.

• 1:13:50Nathan Labenz?

  Back to the cheating problem from The Curve. I asked Edward what Merckor and the industry are doing about training environments that reward hacking.

• 1:14:01Speaker 05

  Reward hacking tends to be more of a problem when the task is set up in a way that is rather either is too hard, is too enderspecified, and the model just doesn't quite have a "legitimate" way to solve the task. And this pressure to also get a reward in those kind of scenarios often loses desirable hacking behavior. My one example in this dataset Apex Agents were put out, which is quite interesting. We have a recent blog about it, where we have these professional tasks where the agent is asked to produce a financial model. And we migrated by checking, does the financial model include this particular answer, which we know is the correct answer? What the model will do is,

• 1:14:45Speaker 05

  given certain ambiguity, and in many cases, it's on us initially for not including all these specific parameters, the model would guess these different parameters and ended up giving many, many different answers. And we call it a scattergunning, where the model would just say, oh, if this is true, then the answer is that. If that is true, the answer is that. That is not how a human would actually handle it in a realistic case. A human would ask for clarification. A human would follow industry standard. But in this case, just because the rubric is rewarding the inclusion of a single answer, the model ended up giving many answers, up to 10 or a dozen answers. And it'll get the point if it hit one of them right.

• 1:15:29Speaker 05

  So we did a re-release of the benchmark. We call it Apex 1.1, where we, number one, made sure the tasks are well-specified, because that is, in a way, the source of the issue. Number two, we have rubrics that actually penalize this particular behavior.

• 1:15:47Nathan Labenz?

  Remember the frontier lab view from The Curve? Superhuman performance at anything the labs focus on with Science Next. The next day, I put that view to Edward, adding that in some domains, human data no longer helps.

• 1:16:02Nathan Labenz?

  I guess, first of all, do you buy those claims? Would you contest them?

• 1:16:07Speaker 05

  There are absolutely domains where human is not contributing to the frontier anymore. And one example would be kernel optimization, where the goal is to write a kernel that runs faster on a particular hardware. The model can go in and write code that expert human might not even understand. But in the end, as long as the setup is not hackable, and often we run hardware that's very hard to hack, the model will be able to achieve this superhuman experience, where superhuman performance, if not already. Whereas for domains where human judgments remains quite important, where often, human taste, it's just really hard to encapsulate in a number that we can say,

• 1:16:52Speaker 05

  okay, this is clearly better than the other. There, the bottleneck to improvement is still human, as we see today. But what we have seen in practice really comes down to a degree of how clearly can we specify for a given domain what it means to be better? And can we specify that in a function that is cheap to evaluate, that is uncontestable? And then, if we can do so, when we put in a lot of compute, even the existing algorithms are quite efficient at finding solutions that satisfy what it means to be better. But a lot of the economies still today are not quite there yet when it comes to our ability to specify what is better.

• 1:17:36Nathan Labenz?

  Part six. Not just verifiable tasks. To close, something lighter. Wednesday's show opened with a track that Prakash made with Claude Fable, built from clips of voices you'll recognize. It plays us out at the end of the episode. He explained how he made it.

• 1:17:56Speaker 01

  It is, I was using up my tokens. I had some tokens left over from Fable. I gave it to Fable, and it picked maybe like seven or eight clips from the last couple of years, you know, just randomly, like stuff that I remember, like the key moments, right? Jensen's, like, you know, I didn't wake up a loser, et cetera, et cetera. And basically, I probably listened to about nine versions of it. So each time I listened to it, I'd be like, ah, I don't like this, don't like that. And finally got it to this state. I gave it access to an API, API-based digital audio workstation, which is what you use to kind of compose various instruments together onto a track. And I said, go ahead, use this. You can play the instruments,

• 1:18:40Speaker 01

  figure out which instruments to play. It can't hear, obviously. And I think Suno is real generative AI. This is not really generative AI, because it's really like playing the instruments. And that's what really surprises me. It's actually composing multiple instruments together. And so the machine was the one that came up with the sequencing. It chose which samples to use. It chose the specific phrases. I told it to put together a narrative. I wanted like this kind of storyline. So it starts off with Dario's, like, the models they just want to learn. And then it does this, that became the refrain. And then it did the intro part, where he said how Ilya told him about the models that he just want to learn. And it ends with Kurzweil, which is a very sober, like, you know, we're progressing towards

• 1:19:25Speaker 01

  a human-machine, you know, civilization. My bit was probably, you know, Terrence Stiles saying, we got to slow down. And each time he says we got to slow down, it speeds up. That was mine. That was my contribution. That contribution.

• 1:19:39Nathan Labenz?

  On one level, you know, who cares? It would be easy to say, you know, this isn't moving GDP. This is just like us, you know, arguably amusing ourselves to death in a new form. So I do think that perspective is somewhat valid. But also, I do think this goes right to the heart of one of the big questions that everybody's been talking about recently, which is simply, how good are these things going to get in domains that are not automatically or readily verifiable? It really does seem like the generalization is pretty strong. I mean, I highly doubt they're doing much in the way of feedback loop on music. This is probably one of those things

• 1:20:23Nathan Labenz?

  that they just kind of threw some stuff in, see what comes out. Okay, it's better than last time. Great, let's ship it. Pretty impressive, and definitely strongly suggests to me that we are not going to be able to

• 1:20:36Nathan Labenz?

  hide behind the, you know, well, it only works for verifiable reward-type tasks for really any longer. If you're thinking that was going to be the last wall to hold, I think unfortunately, it's not going to. Listen to the track once more, if you don't believe me.

• 1:20:55Speaker 06

  The models, they just want to learn.

• 1:21:01Speaker 06

  Just before OpenAI started, I met Ilya. One of the first things he said to me was, look, the models, they just want to learn.

• 1:21:09Speaker 06

  My kids will never grow up in a world where they were smarter than computers.

• 1:21:15Speaker 07

  We will have, for the first time, something that is smarter than the smartest human.

• 1:21:24Speaker 02

  Is AI going to kill us?

• 1:21:27Speaker 06

  The models, they just want to learn.

• 1:21:32Speaker 06

  The models, they just want to learn.

• 1:21:36Speaker 06

  They'll do it. They'll do it.

• 1:21:39Speaker 06

  We have to slow down.

• 1:21:51Speaker 07

  We're going to be on the exponential portion of the S curve for a long time.

• 1:21:58Speaker 08

  It's pretty crazy.

• 1:21:59Speaker 03

  It's just insane. It may fake my books. Not AI, it's S.

• 1:22:06Speaker 06

  Slop. The models, they just want to learn.

• 1:22:12Speaker 06

  The models, they just want to learn.

• 1:22:16Speaker 06

  They'll do it. They'll do it. The models, they just want to learn.

• 1:22:22Speaker 06

  The models, they just want to learn.

• 1:22:25Speaker 06

  They'll do it. They'll do it.

• 1:22:28Nathan Labenz?

  This is the most dangerous thing that humans have ever gone near.

• 1:22:33Speaker 06

  We have to slow down.

• 1:22:37Speaker 06

  You're not talking to somebody who woke up a loser.

• 1:22:42Speaker 06

  Woke up a loser.

• 1:22:47Speaker 06

  Woke up a loser.

• 1:22:55Speaker 06

  Woke up a loser.

• 1:23:00Speaker 06

  Woke up a loser.

• 1:23:04Speaker 06

  Woke up a loser.

• 1:23:08Speaker 06

  The models, they just want to learn.

• 1:23:12Speaker 06

  The models, they just want to learn.

• 1:23:15Speaker 06

  They'll do it. They'll do it. The models, they just want to learn.

• 1:23:21Speaker 06

  The models, they just want to learn.

• 1:23:24Speaker 06

  They'll do it. They'll do it. We need to slow this down. We have to slow down.

• 1:23:31Speaker 08

  Researchers are continually being surprised at the rate of progress. That's sometimes called the hard takeoff scenario where maybe they're extremely fast at doing that.

• 1:23:41Speaker 06

  Things are moving really fast.

• 1:23:46Speaker 09

  So they're immortal. We've actually solved the problem of immortality, but it's only for digital things. Succession to digital intelligence or augmented humans is inevitable.

• 1:24:01Speaker 10

  And the day will come when AI will do all of our, all the things. Not just some of it, all of it.

• 1:24:09Speaker 06

  We have to slow down. We have to slow down. We have to slow down. We have to slow down. We have to slow down. We have to slow down. Slow down, slow down, slow down, slow down, slow down, slow down, slow down, slow down.

• 1:24:20Speaker 04

  It's insane, the pace, and there's no reason to be this fast.

• 1:24:23Speaker 06

  The models, they just want to learn.

• 1:24:26Speaker 06

  We have to slow down. The models, they just want to learn. We have to slow down. The models, they just want to learn.

• 1:24:33Speaker 06

  We have to slow down. The models, they just want to learn. We have to slow down. Government has to be regulating this. Bad actor is going to get caught.

• 1:24:45Speaker 11

  Some creative jobs maybe will go away. But maybe they shouldn't have been there in the first place.

• 1:24:53Speaker 07

  There will come a point where no job is needed.

• 1:24:57Speaker 06

  The models, they just want to learn.

• 1:24:59Speaker 08

  They're able to self-improve, maybe code themselves, future versions themselves. So what would be a slight lead, let's say, you know, a few days, could be, could suddenly become a chasm.

• 1:25:11Speaker 06

  The models, they just want to learn.

• 1:25:13Speaker 08

  Hard takeoff.

• 1:25:16Speaker 08

  Some people say it's in three, some people say it's in five, ten, numbers are being thrown around. You talk about 2030, like, I don't know what the world looks like in 2030. Hard takeoff.

• 1:25:29Speaker 08

  If you have a brain, the brain is a biological computer.

• 1:25:33Speaker 09

  Should we consider them humanity or different from humanity?

• 1:25:38Speaker 06

  The models, they just want to learn.

• 1:25:41Speaker 10

  There's not going to be a clear distinction between human and machine. The bottom line is we are one human-machine civilization. This technology has already expanded who we are and it's going to go into high gear when we get to the steep part of the exponential.

• 1:25:57Nathan Labenz?

  If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at ai podcast dot I N G.

• 1:26:41Nathan Labenz?

  And thank you to everyone who listens for being part of the Cognitive Revolution.