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

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. There likely is, let's say, a level of intelligence that we just shouldn't go past. What? Yeah. First time I'd ever heard that from a Frontier Lab leader 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? But what actually happened was he basically said, yeah, I think that could be reasonable.

0:45Nathan Labenz: Prakash Narayanan, my co-host, on what a ceiling would actually require.

0:49Swyx: 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.

1:02Evan Miyazono: 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 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.

1:22Thomas Sohmers: And, you know, the phrase I've sort of 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 cloud slot.

1:45Nathan Labenz: Yeah, I pity our Claude that's going to have to try to cut this down into a highlights episode. I think this is banger content from start to finish today. 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 Lighthaven in Berkeley. Prakash asked about the divides I saw. 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, you know, if you were looking ahead a couple years ago, that with two years of additional information and revelations and, you know, actually seeing how strong the AIs get and what they do and who's been right or wrong, you would think that like people would start to agree on the state of things.

2:45Nathan Labenz: 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 going to get really powerful. And this is something we're really going to have to reckon with and can't just sort of hope 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 you know there's um there's there were very few perspectives that as Helen Toner once famously put it like long timelines have got super short um I would say you know 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, we're like, yeah, it's going to do an awful lot.

3:48Nathan Labenz: And the question now on the capabilities front has gone to like, is RSI going to foom? Is it going to 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 going to 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. Prakash asked whether anyone from the frontier labs said limits should be enforced on the players in this space, themselves included. Yeah, that was a huge topic.

4:22Nathan Labenz: 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, 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 sessions where I was like 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 we come together and potentially do some of these constraints. And there were a number of things that I actually thought were 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. You need more pre-training data. What do you do? You give the model a bunch of data and think, what could I transform this data into that if I then pre-trained on it would make the model smarter?

6:30Nathan Labenz: 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.

7:09Nathan Labenz: The models 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 RL is working better there. Maybe you need a million agents at this point to stumble onto something eventually where you'd get the kind of paradigmatic change at a ML research level.

7:57Nathan Labenz: 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 like 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. What? Yeah. First time I'd ever heard that from a frontier lab leader. And it was not like minced words. It wasn't a like, there's definitely a level or I can articulate what that level is. But like this, you know, person who, again, like everybody would recognize, you know, name and position saying, I think it is likely that there's a level that we shouldn't go past.

8:47Nathan Labenz: Now, was that not ever or just, you know, 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.

9:02Swyx: What confuses me about that statement is that some of the increases in intelligence that we're 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 going to be compute and this refinement of data.

9:42Swyx: And so if you say that we shouldn't go beyond that point, that also means that you shouldn't build compute beyond that point, or you shouldn't refine data further beyond that point, which means a lot, really. It means 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, you know, it's tricky to know what you count, right? Do you count all those flops that you use to like create the 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?

10:32Nathan Labenz: Would you be prepared to sign on to something like I'll limit on the number of flops that go into the next pre-training run? You know, should we say, and I don't know what that number would be, but, and again, what exactly would 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 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 to put it 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 i know you well enough to predict the next percash 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 said This is not from one individual, but sort of a takeaway from the event in general is that the, let's say, two to three, Gemini 4 being announced now, we maybe have a three-horse, very frontier race again.

12:02Nathan Labenz: 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 makers and sellers is in fact functioning as a distillation conduit. to it. 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.

12:54Nathan Labenz: 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 founder, executive type at one of these companies. Yes, they have kept up. Obviously, there's been a gap, and that gap has been persistent, but they've kind of maintained a kind of consistent following distance. And this is in contrast to Anthropics 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.

13:42obviously that hasn't happened but the the kind of synthesis of all this is if they hadn't been

13:49Nathan Labenz: 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 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 agent's most important tool, web search.

14:37Nathan Labenz: If you're like me, your agents are often conducting hundreds of searches per day. 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 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 thousand 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.

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16:08Where

16:09Nathan Labenz: 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 your next business move, Claude extends your thinking to tackle the problems that matter.

16:58Nathan Labenz: For problems worth solving, get started with Claude at claude.ai slash TCR. That's claude.ai slash TCR. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Once more, that's claude.ai slash TCR. Prakash added his read on Elon's strategy, And then I got to timelines.

17:24Swyx: And I think on Elon's side, Elon is very focused on the infra build out. 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 build out. And he's structuring things for 2029, 2030, 2031, like TerraFab, things like that will come out much later. The SpaceX, you know, satellites, that's like a 2030s thing. So 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 at 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.

18:47Nathan Labenz: There wasn't really even too much 2028 talk to be honest, which is again, like pretty crazy. Then it came back to compute and what a cap would mean for the build out. But like in terms of, will they still have need for computer 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 build out? 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.

19:35Nathan Labenz: He said at NVIDIA, we spend like 20 % of our effort on designing the chip and we spend 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, etc.

20:16And so I had a chance to ask,

20:18Nathan Labenz: 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, 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.

20:53Nathan Labenz: 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. Of course, everybody understands at this point that if you have 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 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.

21:31Nathan Labenz: 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, 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.

22:09Nathan Labenz: 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, we're seeing sort of a proto scaling law. It seems like in, And 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 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 our 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 things, even as they do consider themselves bottlenecked on alignment.

23:06Nathan Labenz: But then past that, they're like, yeah, all bets are off. Really hard to say what happens kind of past the next one to two generations. Later in the show, I tied that executive's view on research taste to a new record in the NanoGPT speed run, 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 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.

23:51Nathan Labenz: and this result here took almost 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 significant drops 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 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 uh 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.

25:22Nathan 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 sentiment was, you know, are 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 really committed 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.

26:35Nathan Labenz: The whole package basically is, I think, broadly understood to work on essentially any problem that 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 AIs playing a big role in it. The next morning, I added one more takeaway from the curve. I don't think we We talked about this yesterday, but a takeaway from the curve this weekend also, one of the main threads that I was involved in discussing, but a general vibe was there is going to be government intervention.

27:30Nathan Labenz: And obviously we've already seen some, but the idea that 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 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 chance yet. And they're also getting a ton of money from the data center interests right now.

28:10Nathan Labenz: 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.

28:35Thomas Sohmers: Hey, we'll continue our interview in a moment after a word from our sponsors.

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31:32Nathan Labenz: That's code COGREV at Tasklet.ai. Part 2. The Memory Wall Thomas Somers is a co-founder of Positron, which builds chips for running AI models and recently raised$875 million. dollars. Prakash asked whether the sharp rise in memory prices had changed the economics.

31:57Evan Miyazono: 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 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. But I would say a big reason why everyone has burdened 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 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.

32:41Evan Miyazono: At the start of Positron, I did think that memory was going to be the limiting factor for things going forward, even though 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 any time 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 per user and then scaling that out to drastically more users.

33:20Evan Miyazono: And really today, I would say that the driver of quote-unquote users or individual sessions is actually just having more agents. I've gone from three, four months ago where I had probably on average two to four agents running constantly in the background to where I'm now running 15 to 20. If you then multiply how many people are using Yaya and how many that concurrent, completely separate contexts adds up very quickly. That memory driver had us say, okay, we have to use the commodity memory 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 very clever innovative solutions.

33:59Evan Miyazono: And the two main pieces of that, the thing that we showed with our first generation product 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 8 terabytes per second of theoretical memory bandwidth with a B300, you only actually are seeing something in the ballpark of like two terabytes per second of actual realized memory bandwidth.

34:38Evan Miyazono: 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 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 3x improvement right there. But to really take advantage of the belief that models and everything are just going to get larger, we had to be able to scale to massive more channels.

35:16Evan Miyazono: And so we're partners with Credo Semiconductor, and I've developed a memory chiplet solution that allows us to go from the maximum number of LPDDR, the same type of memory that is in phones, laptops, etc. The next number of channels that you can find in any other products is on the order of 12 to 16 channels. And we go up to 72 channels of LPDDR5X with this decoupled memory chip 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?

35:59Evan Miyazono: We have up to 2.3 terabytes of memory capacity per chip. So when you compare the B300 shipping today, it taps out at 288 gigabytes. As reported by analysts, NVIDIA for the next generation is actually cutting down the memory capacity because of costs and everything involved. So they're only going to be at 192 gigabytes with the Rubin Ultra. But even if you use that 288 as 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 in what you would have needed from a memory perspective, eight GPUs for, we can do on a single device.

36:37Evan Miyazono: And it's not just, oh, you've got that cost silicon savings, you know, as you pointed out. 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 really need to multiply that you're doing of sharding across those devices.

36:59Nathan 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.

37:09Evan Miyazono: It's been very interesting from the loop transformers paper to that being the rumor of one of the big advancements with GPT-6 Astra. 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 sets of matmalls in sequence and turning it into a 100 billion parameter model.

37:50Evan Miyazono: And you got better results from it, even though it was repeating the exact same matmalls. So there is an element... Imagine what you can do when you train it to work that way. 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 able to increase that probability, same sort of functions that the MOD router does in terms of determining which experts should be selected on a per-token basis.

38:24Evan Miyazono: If you extend that concept to, okay, if you're really unsure if there's a very large set of equally weighted probabilities for what the next token should be and you're three quarters of the way through the layer, do you actually just want to route back and determine, okay, we actually need a different set of experts, something like for this. But I wouldn't be surprised if these things are already being done at scale 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'm kind of coming in with assumptions along the lines of 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 shuffle things in and out as much? No?

39:12Evan Miyazono: Not really, because basically the assumption is if you're doing inference that you're going to have all of your weights locally in dbram, you're just sharding that over some number of devices. You do get some amortization, but just with the size of experts and the size 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 the on-chip SRAMs. From a hardware perspective, looping really is more memory capacity savings than memory bandwidth savings. Because let's say that you trained two models with the same base set and you trained one to do with looping and so it only has 10 layers instead of 20 layers.

40:00Evan Miyazono: And Model B is 20 layers. that 20-layer model it's doing very, very simple is going to be double the size of 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 saving more capacity than bandwidth. Because doing the second loop, you still have to do the same memory fetches and do all of the same number of mapmoles. There's the exact same number of bytes that need to be moved in the exact same number of flops that have to be done in that Model A and Model B scenario.

40:36Evan Miyazono: It's just that Model B, the 20 layers, actually has unique different weights for the second group of 10 layers that have to be done. And so that means you require twice the memory capacity.

40:50Nathan Labenz: Then I turn to how AI is changing chip development itself. In a notable interview Jensen Wong 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, etc. What do your ratios look like on that?

41:15Evan Miyazono: So for us, I would say it's a little bit different because we're starting from scratch. So there's a lot more base layer, base capability design that we have to start from scratch. So for our first full custom silicon, Asimov, it will end up being somewhere around maybe 60 % design focus for us to 40 % verification. I would say the 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 Astra and now Opus 5.5, that we're leveraging a lot in this task, is for doing our verification work, we use Cadence, Pleiadian, Emulator systems.

42:01Evan Miyazono: These are big, giant racks that are full of custom ASICs that are specifically designed to just emulate, do gate-level emulation of silicon. The crazy thing about Asker 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, you know, handing, you know, these very powerful agents, you know, 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.

42:49Evan Miyazono: 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, etc. 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 in its own harnesses for being able to access this. It's mind-blowing. And so, if you're just doing regular software RTL simulation, that's running on the order of 10 hertz for us in those cases. If you remove a lot of the debug pieces that slow things down, etc., we can run the full chip emulation on the order of 500 kHz.

43:37Evan Miyazono: And so you've got actually several orders of magnitude speed up an improvement 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, beginning of September when Astra 6 came out. It was a huge step function improvement from GPT 5.6, where it could not do that full close 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.

44:34Evan Miyazono: so on on the token spend side it's become you know our single largest uh non-manufacturing line item very quickly bigger than bigger than human salaries yeah wow just recently and it's interesting it eclipsed human salaries that came back down um i'll explain in a moment so yeah so our token spend is massively increased where where okay so six months ago is equivalent with one employee in June, it started to now be multiple employees. Like we were peaking, you know, in the days after GPT success, we're really trying to push its boundaries, etc. We're spending over $100 ,000 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.

45:23Evan Miyazono: But I would say the big thing that That was like a saving grace was Opus 5.5 came out. And a lot of our tasks, not everything, it was doing better than Astra and was a quarter the price. 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, 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. I don't care if it's a tenth of the cost per token.

46:08Evan Miyazono: 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.

46:25Evan Miyazono: I have not yet. I would say that's the most disappointing element of all. 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 done or it actually runs test and sees and understands, oh, that's why you're not doing the traditional way a systolic array is done. Do I think that will hold for another six months?

46:59Evan Miyazono: Not sure. But just because I, 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 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 Navigator's notes, like the same approach.

47:42Evan Miyazono: Yeah, if you take 10 ,000 agents and send them off for millions of man-years worth of effort, it found a solution. It's brute force. It's expensive. But I think that is a valid strategy that has only become possible in the 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.

48:14Evan Miyazono: For a couple more weeks, I believe.

48:17Nathan Labenz: Part three, software after agents. Sean Wang, known as SWIX, runs the Latent Space podcast and the AI engineer conferences. I ask how software engineers are feeling right now, empowered or threatened?

48:34Thomas Sohmers: 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 um the sort of cost of software creating software has gone down therefore for the demand has increased a lot. And 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 bi-coding.

49:18Thomas Sohmers: And the phrase I've sort of 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 powerful stuff. And I have two or three employees right now who are basically under performance review because they are just giving me Claude 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 I'm like, well, you're not producing any value.

49:51Thomas Sohmers: I can just prompt Claude. I don't need you.

49:54Nathan Labenz: I asked whether years of coding experience still matter when he hires, or whether product sense and high standards matter more.

50:03Thomas Sohmers: I don't care so much except for, let's call it, security roles and for anything sort of backend, scalability. 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 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. You better go and 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.

50:48Thomas Sohmers: Then, yeah, so other than that, you can mostly bytecode 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, you should be relatively comfortable juggling five to ten 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, here's the logs, here's the traces, here's the schema, here's the input-output, capturing it, turning it into an eval.

51:30Thomas Sohmers: All these are basically the merging of AI engineering and ML engineering that is happening that people need to upscale 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.

52:08Thomas Sohmers: So I'm making my own sort of Slack competitor and I saw a bug where the messages weren't loading. I refresh and the messages were loading and I refresh again and the messages were not loading. And it's like, well, it's the same exact code. What the hell is going on? Turns out there's two code paths and there's a race condition. Why? Because two different coding agents worked on it at different times. And it's just like made their own thing. So like a human would never do that. An agent 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?

53:00Thomas Sohmers: 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. So what does that mean? It does mean that we need sort of more fluid compute, which is the Vercel term for it, more sort of different forms of serverlessness where it's very ephemeral. You can sort of execute and resume because agents need some time to do LLM calls or make network calls or whatever. But basically long running states that are resumable. But I think beyond that, you really start to get into two things, which I'm really thinking about, like sort of bandwidth or networking, where the latency starts to really matter.

53:44Thomas Sohmers: And that's an optimization of like, well, what point of presence are you communicating with? What region are you in? Versus like, actually, how much compute should you be doing 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. 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 very, very personal basis, bandwidth. So like how many tokens per second can I get?

54:22Thomas Sohmers: Both on a single call basis and just aggregate across all my calls and then aggregate across my company because I have enough people managing all these calls.

54:33Nathan Labenz: Swix has also been running Kill My SaaS, a bounty for anyone who can replace an events software subscription his company was unhappy paying for. Prakash asked him about it.

54:44Thomas Sohmers: Basically, you can call this a bounty on mid-tier SaaS that should not exist. Basically, half a salary or one-third of a salary for a piece of software that I don't own, that nobody enjoys using. If I'm going to spend$40 ,000 on this SaaS subscription, I can spend that in 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. 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 like we offered ten thousand dollars 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 will just be like Claude make no mistakes go do this and like then they'll make mistakes and they'll just submit and so like the 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.

56:08Thomas Sohmers: They do everything in spreadsheets. They work with unions. They have to worry about placements of the meter boards physically in locations. These are not glamorous, high-tech jobs. They're very super suspicious of anything that is new and vibe-coded and techie. So I hire these guys, and then I make them work for AI. That's my job. And so initially, they were like, we'll never use this vibe-coded thing. I want to use the tried and tested stuff that has been working for Microsoft and all these other guys. And then they saw the quality of the submissions. Then they looked at their existing platform.

56:43Thomas Sohmers: They were like, yeah, okay, we're going to switch. It's like that initial hurdle, you basically need to provide the proof of 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 all of them access to DevIn to modify the code. So anything they don't like, they can just request the 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.

57:15Thomas Sohmers: It's on our Q3 roadmap. And we don't have confidence that it'll actually be done in Q3. And by the way, this is done. We requested for a thing in Q1. It just landed. So SaaS is quite cooked. If you're mostly a CRUD app, I would say that there's a lot of UX issues left. If you look at StreetBench and you're like, oh, wow, we're like 90 in CBench. 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 how models are still very bad at all this.

57:46Nathan Labenz: Then I pressed SWIX on the downside. 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, that's another canary in the coal mine. Are we going to see mass layoffs from big tech? How long can the party go on?

58:10Thomas Sohmers: The simple answer is if you look at it as a fixed part of software engineering, then that is the conclusion that you will arrive at. But if you look at it as the point of 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 automations. Then the demand for custom software becomes a lot larger. And more beyond that, 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 and more and more custom software and hardware and models, by the way.

58:58Thomas Sohmers: And so basically, we're just growing into the long tail. 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. 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? 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.

59:46Thomas Sohmers: 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, know, this whole thing about like scaling my personal token bandwidth versus scaling my organization token bandwidth.

1:00:24Thomas Sohmers: The real limit on all this is the sum total of humanity's total token bandwidth, right? Which is literally the amount of silicon we can produce. And 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 DevDay, is the same kind of thing.

1:00:59Thomas Sohmers: Yeah, so not only have I used it, I was friends with Diogo for four years before the launch, and so I insisted that we were the first podcast that he did about JEV. I mean, that is now our number one podcast ever within space. And then secondly, we're also in the trial for the Decisions 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 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 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 about Jeff.

1:01:44Thomas Sohmers: Or the other perspective is this is another classifier. You can train classifiers this whole time, but now it's sexy. So now everyone just 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 Diogo 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.

1:02:21Thomas Sohmers: And so people aren't really doing that. They're like, let me just replace this, every sort of point of structure that I'll put with Jev. 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 it's an overall good thing for the industry.

1:02:57Nathan Labenz: Before he left, I asked Swix to plug his next AI engineer conference.

1:03:03Thomas Sohmers: 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 code on the Latent Space for Latent Space subscribers if you can dig in through your emails. And it is on ai.engineer.com.

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. 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.

1:03:50Edward Hu: 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. I asked Evan where the biggest gaps are right now. 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 Deepsea or Kimmy models accidentally attacking State Department servers?

1:04:44Edward Hu: 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 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 it 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 compliment 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 Measoma's agent being signed off on by some key.

1:05:34Edward Hu: 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.

1:05:58Edward Hu: 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 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.

1:06:38Edward Hu: Yeah, it's interesting.

1:06:39Nathan Labenz: 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. My kind of upshot, 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 take stock of the best ideas that I've got and sort of put a shingle out or 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 really want to help.

1:07:35Edward Hu: it feels like it would be a very plausible for me to if i recognize something i'm doing or if kochi 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 has solved who has 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.

1:08:34Edward Hu: 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 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 you can possibly...

1:09:19Edward Hu: 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 shock and awe that is happening to math, 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. Whereas you could prove that an entire computer system has mathematical guarantees around separation between different threads down to our model of what a transistor does.

1:10:14Edward Hu: In theory, you could even prove that in like a physics simulator and show that no row hammer exists 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 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 Hugh leads AI modeling at Merkur, 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. Merkur's Apex Agents benchmark gave models a simulated workplace with files, email, and chat. Edward described the next step.

1:11:23Swyx: 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 and 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 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 this data set right like when we work at a company we're not always just handing these tasks right we're handing a role and a work chart and of course all the data in the company and then there'll 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 resources, conflicts to resolve.

1:12:39Swyx: 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.

1:12:55Swyx: And actually, one thought experiment is if we take SFT self-distillation, that's actually more or less a step of our own. and you were to 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.

1:13:37Swyx: So I do think SFT, 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 Mercore and the industry are doing about training environments that reward hacking.

1:14:01Swyx: Reward hacking tends to be more of a problem when the task is set up in a way that is rather either it's too hard, it's too underspecified, and the model just doesn't quite have a quote-unquote legitimate way to solve the task. And this pressure to also get a reward in those kind of scenarios often loses desirable hacking behavior. And one example in this dataset Apex agents would 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.

1:14:42Swyx: What the model will do is, 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 a 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.

1:15:25Swyx: And it'll get the point if it hit one of them right. 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.

1:15:46Nathan 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. I guess, first of all, do you buy those claims?

1:16:06Swyx: Would you contest them? There are absolutely domains where a human is not contributing to the frontier anymore. 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 it often can run hardware, that's very hard to hack, the model will be able to achieve this superhuman experience or 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 okay this is clearly better than the other there the bottleneck to improvement is still human as we see today but 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 economy still today are not quite there yet when it comes to our ability to specify what is better.

1:17:36Part six, not just verifiable tasks.

1:17:40Nathan Labenz: 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 we made it.

1:17:55Swyx: it is uh i was using up my tokens uh i had some tokens left over from fable uh i gave it to i gave it to fable and uh it picked maybe like seven or eight clips from like the last like couple of years you know just randomly like stuff that i remember like the key moments right uh jensen's like you know i didn't wake up a loser etc etc and basically i probably listened to about nine versions of it. So each time I listen to it, I'll be like, I don't like this, I don't like that. And I finally got it to this date. I gave it access to an API-based digital audio workstation, which is what you use to kind of compose various instruments together onto a track.

1:18:36Swyx: And I said, go ahead, use this. You can play the instruments, 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.

1:19:12Swyx: 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 we just want to learn. And it ends with Kurzweil, which is a very sober, like, you know, we're progressing towards a human machine, you know, civilization. My bit was probably, you know, Terence Tau saying, we've got to slow down. And each time he says we've got to slow down, it speeds up. 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 like 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 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 suggested to me that we are not going to be able to hide behind the, you know, well, it only works for verifiable reward type tasks for really any longer.

1:20:44Nathan Labenz: If you were thinking, if you think of 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. The models, they just want to learn.

1:21:01Nathan Labenz: just before open ai started i met ilia one of the first things he said to me was look the models they just want to learn my kids will never grow up in a world where they were smarter than computers we will have the first time something that is smarter than

1:21:17Swyx: the smartest human.

1:21:24Evan Miyazono: Is AI going to kill us?

1:21:27Swyx: The models, they just want to learn.

1:21:32Swyx: The models, they just want to learn. They'll do it. They'll do it. We have to slow down.

1:21:50Swyx: We're going to be on the exponential portion of the S-Code for a long time.

1:21:58Thomas Sohmers: It's pretty crazy. It's just insane. It may fake my books. It's not AI, it's S-A-C-O-T.

1:22:07Nathan Labenz: Slop. Hop. Hop. The models, they just want to learn.

1:22:12Swyx: The models, they just want to learn. They'll do it. They'll do it. The models, they just want to learn.

1:22:22Swyx: The models, they just want to learn. They'll do it. They'll do it.

1:22:28Nathan Labenz: This is the most dangerous thing that humans have ever gone near. We have to slow down.

1:22:37Nathan Labenz: You're not talking to somebody who woke up a loser.

1:22:42Nathan Labenz: Woke up a loser.

1:22:47Nathan Labenz: Woke up a loser.

1:22:55Nathan Labenz: Woke up a loser.

1:23:00Nathan Labenz: Woke up a loser. Woke up a loser.

1:23:08Swyx: The models, they just want to learn. The models, they just want to learn. They'll do it. They'll do it. The models, they just want to learn. The models, they just want to learn. They'll do it. They'll do it. We need to slow this down. We have to slow down.

1:23:31Evan Miyazono: Researchers are continually being surprised at the rate of progress. That's sometimes called the hard take-off scenario where maybe they're extremely fast at doing that.

1:23:41Nathan Labenz: Things are moving really fast.

1:23:46Swyx: So they're immortal. We've actually solved the problem of immortality, but it's only for digital things.

1:23:54Nathan Labenz: Succession to digital intelligence or augmented humans is inevitable.

1:24:01Thomas Sohmers: And the day will come when AI will do all of our, all the things. Not just some of them, but all of them.

1:24:09Edward Hu: We have to slow down. We have to slow down. We have to slow down. We have to slow down. We have a slowdown.

1:24:20Edward Hu: It's insane, the pace, and there's no reason to be this fast.

1:24:23Swyx: The models, they just want to learn. We have to slow down. The models, they just want to learn.

1:24:29Nathan Labenz: We have to slow down. The models, they just want to learn. We have to slow down. The models, they just want to learn. We have to slow down. Government has to be regulating this. Bad actors are going to get caught.

1:24:46Swyx: Some creative jobs maybe will go away, but maybe they shouldn't have been there in the first place. There will come a point where no job is needed.

1:24:57Evan Miyazono: The models, they just want to learn. They're able to self-improve, maybe code themselves, future versions of themselves. So what would be a slight lead, let's say, you know, a few days, could suddenly become a chasm. The models, they just want to learn. Hard takeoff.

1:25:15Thomas Sohmers: Some people say it's in three, some people say it's in five, ten. Numbers are being thrown around.

1:25:22Evan Miyazono: You talk about 2030, I don't know what the world looks like in 2030. Hard take-off.

1:25:29Thomas Sohmers: You have a brain, the brain is a biological computer.

1:25:33Nathan Labenz: Should we consider them humanity or different from humanity? The models, they just want to learn.

1:25:41Swyx: There's not going to be a clear distinction between human and machine. The bottom line is we are one human-machine civilization. civilization. This technology has already expanded who we are and it's going to go into high gear

1:25:52Nathan Labenz: when we get to the steep part of the exponential. 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, 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.

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