Pacing AI Means More Compute, Not Less

0:00Hello, everyone, and welcome to another Semi-Doped episode. I'm Austin Lyons of ChipStrat, and I write the newsletter, ChipStrat, about the business and strategy of semiconductors. Hey, guys. I'm Vik Sekar, and I write the VIX newsletter on Substack, and I'm the founder of Semi-Exponent, an organization that does institutional research on AI and semiconductors. All right, Vik, let's get into it. The topic everyone's been talking about all weekend and all of the start of this week, pacing AI. Yeah, we got to slow down, apparently. That's what Dario is telling everybody. We got to slow this down.

0:34We're going too fast. It's too dangerous. We're playing with fire. Right, right, totally. What should we start about? Do you think everybody has read this essay or should we just explain what's going on here? I think everyone at a high level knows, but they probably haven't all read the essay. So let's recap it just in case someone was on vacation and missed it all. Okay, good. I mean, I think that person is one of the lucky ones. So much online. Like, if you missed it, good on you. But that's why we're here. So we'll explain some of this stuff and see what's going on. And we'll see what we can add to it.

1:06Maybe we can't add anything because nobody knows what's going on. Anyway, so here's the whole thing. I think it was like Saturday. We're recording this on a Tuesday. but on Saturday a few days back there is this essay that came out by Dario on his blog and Dario Amadei is anthropic for anybody who's really been out of the AI space that's the Dario we're talking about and he wrote an article called We Must Pace the Frontier and what this essay essentially says is that the frontier AI labs they need to slow down how fast they are developing capabilities today because things are getting a little out of hand and safety is becoming more and more of an issue.

1:57And he's very clear to say that, no, we don't really need to pause training or we don't need to pause the AI development. But in fact, we must take a step back and make sure that safety and responsible behavior by the AI is important going forward, right? And the two things I'd say maybe changed his mind about why we need the pacing now rather than ever, because he's always been like pro-security of AI, of course, and he's always been saying, oh, this model, the mythos was too intelligent, and it's too amazing, it can't be let out into the world, all the cyber security is going to get broken because it's so good, The internet has so many open holes and Mythos is going to find all of them and it's all the banking systems will crash.

2:47Airplanes will fall out of the sky and all of that. But this seems to be a more sober realization that we need to pace AI development. Because I think the first thing is like ever since this summer, essentially, we have been on the path of recursive self-improvement or RSI. And what that means is AI is being used to improve AI. So that really accelerates the pace of AI development because once you have an intelligent enough model, it's an exponential. And if AI can make AI so much better, imagine what the next version of AI can do. Even more stuff, make AI even more intelligent and more capable.

3:32That's one thing. The second thing that took everybody by surprise very recently is the open AI and the hugging face incident, which is essentially the whole thing where like a swarm of agents behaved like a coordinated group. right like they were like a they just became a mob okay an ai mob and they attacked targets online digital targets that nobody asked them to attack and they even sacrificed individual agents for the greater good of the agentic horde right and they even tried to break into the grader that was you know scoring their work and try to cheat on their exams nothing nothing was really off whack here.

4:19It's all okay. Everybody was fine. But I think his point is, Dario's point is that a lot of frontier labs have had these kinds of events happen. It's just that the OpenAI hugging face incident was a very big one. So he's like, okay, this is the point in time where if AI agents are self-organizing, he says, we must step back and look at things a little bit because within 6 to 12 months, a swarm like this could probably take over the entire internet and have a botnet and God knows do what. So people have argued as extreme as like physical AI robots are going to do what the digital agents did and form hordes of robots.

5:01And somebody put an article out saying there's a 10 % chance in the next 12 months that robots will kill all of humanity. Okay. So that's the extent of the fear that has been shopped around very recently. This is a thing that's happened over the last few months, culminating with this essay. So that's where we are. Of course, he's got some plans on how to make this work, but I'm going to stop here and let you fill in. Yes, yes, totally. So, you know, what Dario really tried to say is like, hey, look, we should be spending more time on alignment, interpretability and operational excellence. So operational excellence was, hey, in that example that Vic gave with like the open AI hugging face, like these things happen because we just fire up a ton of agents.

5:52We tell them to just go try to do a particular goal that has a particular reward function. And for example, it might be like you need to find something on the internet, but we set up these little sanitized VMs in such a way that, oops, we forgot to actually give them any access to the internet. So then they had to, they're just sitting there spinning, like trying to achieve some kind of impossible task. And that's how they end up having to get really creative and sort of hack around and figure out, dude, how can I get to the internet so that I can just accomplish this task that I was given? Again, how does this happen?

6:25Well, I think it's like, hey, we've trained a model, we're testing it out, we're doing some post training, we're just spinning stuff up, building out our harnesses, and we just fire off a bunch of stuff and walk away. And so I think Dario is trying to say, like, we should make sure that we're taking the time to set up these sanitized environments in a proper way so that these agents can accomplish the goals and so on and so forth. And then interpretability is like the more sophisticated that these models get and with the great harnesses and the tool calling and the ability to do agentic AI, now we can sort of let the agents actually go off and do all this meaningful stuff.

7:05And so if they're going and doing a bunch of stuff and then the model's being trained on that, if we don't take the time to actually look at what they're doing, then we sort of lose touch with kind of what's going on under the hood. It starts to become more and more of a black box. So, of course, it's fair to say like, hey, agents are powerful enough. Why are we all going so fast at the risk of not understanding what we're building? And at the risk of because we're being sloppy, they go off and do something unintended. Now, you know, there's lots of great responses and eventually we'll go through all of them.

7:40But right away, I was like, oh, this is my kind of hot take as I was reading it. I was just like, oh, so we're, and of course, Dario says, yes, this has happened to Anthropic, but we're kind of pointing fingers at OpenAI and saying like, why are they going so fast? Things are getting sloppy and out of control. we're going to raise our hands and say, hey guys, we're going to take time to not be sloppy here. And we think that not only should we, but you should too. That was like the very first vibe I got when I was reading it. Yeah. And the thing is that is notable about this is that this is one of the rare instances when, you know, Sam Altman agreed with Dario's essay.

8:18Elon Musk agreed with his essay. Demis Hassabis agreed with his essay of DeepMind. And so a lot of people have come out and be like, yeah, we need to pace the frontier. And one interesting take that I really liked was David Sachs, who said, hey, Dario and Sam, you guys have seen more of this model than any human has on earth, right? You guys' companies are on the frontier of the models in the world. And if you are seeing what is beyond Fable and Astra. They're developing, obviously, the next big thing. And if you see that and feel like you want to pace the frontier, he says, go ahead and do it.

8:59But do it with one thing in mind. I like this take from David Sachs. He's like, do it because you want to make a better product. Because you're making a product that you're selling to people. And it is important that you make a safe product. but don't like say that you're trying to save humanity or you're being like altruistic here. That's not the point. You are making a good product. If you want to slow down and make a better product, go do it because that's what I think you should do as a person who is selling a product. You don't want it to wreck all of humanity. I don't know if we're all going to die in 10 % of the chance or whatever, but it's a good product and make a good product and sell it.

9:41Like any good product, it should have security features built in. So yeah, you go do it. yes yes okay interesting i have two i have two reactions one is on the coordination that you mentioned and then one is on the making the good product so on the coordination when i saw that coordination quickly come out because we all know that like uh dario and sam and elon generally don't see eye to eye on things and aren't generally fans of each other and so and i think maybe even more they might generally like take oppositional stances and counter position against each other on purpose, right? So for all of a sudden, all of them just collectively quickly be like, yeah, Dario's right.

10:16We agree. I had two reactions. One was like, oh, wow. Maybe they are all feeling like this is getting out of control and we're running too fast and we're all going to wipe out. And we're like, oh, phew, good. Let's all slow down. Kind of a game theory thing. Or two, the alternative is like, oh, maybe they're all convinced that heavy regulation is coming and they need to raise their hand and say, we're going to regulate ourselves. Don't regulate us. Kind of like, I don't know, if your kids have been sort of misbehaving and making a mess and they can tell you're going to get really angry and they're like, wait a minute, we need to pause our playing and we're going to go clean up our mess here.

10:53You don't need to, don't worry about ourselves. Yeah, exactly. We're giving ourselves, exactly. We're putting ourselves in timeout. We're just going to clean up here. That's exactly it. It felt, you know, so, so then I thought to myself, Oh, man, maybe what we don't know is that Washington is coming in. And then again, you can start to see Dario saying like, well, I'm going to volunteer to be the one to raise my hand and say, yes, we should all self-regulate because we can tell Washington's coming in. And I want to like, you know, take the credit as like pushing this forward. Let's see what happens.

11:27But I was surprised by the coordination and thought maybe there's something more going on here. And then two, the David Sachs making a good product point. when I was at John Deere, Blue River Technologies, working on self-driving tractors, we actually thought a lot about AI and doing unintended things because we're driving very heavy equipment. Sure, it's only five miles an hour, 10 miles an hour, but legitimately, like we had to think through safety, like, can we see a child when this thing turns? When we're going near corn or near soybeans, can we see a child and an adult? What if they're wearing green and they blend in?

12:02And we had to Like think through all this safety stuff and safety is always the number one priority. And we needed to make the best product, which also meant by definition, not just the best user experience, but literally the safest product because we obviously didn't want tractors hitting anyone, running over anyone, anything like that. So we were self-pacing in that if we started to say, let's work through, how do you see through dust clouds? Should we just stop this tractor? What happens if a flock of birds fly in front of the tractor? On the one hand, it looks ridiculous when you've got like a one-ton, two-ton machine that stops for a balloon that floats across in front of it or a flock of birds.

12:41But on the other hand, we're like, okay, until we have enough training data and can train on these situations and feel confident about this class of occlusion and obstructions, we are not going to push a model out, even if it means we miss this season. because in farming, depending on the crop you're harvesting and planting, you only have one shot per year. And so we, as like John Deere would say, we're just going to miss the shot this year. And we're just gonna have to take another shot on goal next year because we first and foremost need to be very safe. And we need to really think this through deeply because we weren't in a race like open AI and anthropic to be like, we just have to get to the next thing.

13:19It has to get the next thing. So I can resonate with the idea of like, guys, pace yourself, think about what's most important and that safety should align with the best product. I think they don't, you know, they're not like orthogonal, but you should make a great product experience, but also you should, part of making a good product is probably understanding what's going on to some level of extent that you're all comfortable with and so on and so forth. Of course, the tough part with AI is this is all probabilistic. Like no one deterministically understands everything and knows how everything's going to behave in every situation.

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14:41Their senior and staff-level engineers turn real software workflows into tasks, rubrics, and training environments that challenge models and improve their performance. If your team needs that kind of work and doesn't have the engineers for it, that's exactly what G2i does. Link in the show notes. So the thing is, in the essay, Dario refers to interpretability, right? Which is he likens it to taking an MRI of the machine and to understand what it's thinking and try to understand why it's making the decisions it's making and try to improve from there on but that's the that's the crux of the problem nobody knows why this simple bunch of transformer equations gives us this intelligence intelligence i don't know it seems to do a lot of stuff right like some people argue that this is not even ai this is not the path to ai maybe but the best we've got this is best i've seen in my lifetime right like uh it really can do a lot of stuff it can create images it can create videos.

15:43I mean, it's very satisfying. It's very helpful in the work that even we do. So yeah, we have AI, but nobody really understands how it works. So the interpretability question, he says, is like, okay, I'm going to look at this MRI for like one to two years and then try to understand how it works. But will you understand in one to two years how this monster machine with everybody spinning up agents work? Will you be able to explain why the open AI hugging face incident decided to sacrifice a few agents for the greater good? Do you think observer interpretability will give you those answers? I think one to two years is too short of a time.

16:21So I see the problem here. The other nice take that I saw on this was Ben Thompson's article on this. And he was like, he pointed out that the narrative is very polarizing. So it's always like, oh, look at either ultra-intelligent agents, like genius types or murderers? Like what about the middle where it's just helping me write an email? Okay, that's really helpful. Okay, or what about the middle where it's just summarizing a document so I can read it? Like these are genuinely useful applications. Why don't we, you know, use that narrative in the middle as well, right? Yes. So it's very extreme.

17:03I think the essay was very extreme. Nobody never acknowledges the useful middle. Yeah, totally, totally. Yeah, I think Ben was like, hey, look, if you start to nitpick the plan Dario put out, then you're giving him the premise that he made, which is either we're curing cancer or this thing's just going to run away and destroy us all and nothing in the middle. And I think I was trying to look it up. I'm pretty sure Microsoft put out something recently from their AI team that was just like, hey, here's our code of conduct. Don't hurt people and put a plug in it so that we can turn things off. Because there is, you know, Dario basically said, like, this could take over the whole internet and botnet swarm, take over the whole internet, shut everything down.

17:48but I do like the people that are like, hello, like, um, we're in charge here. Like we can put an off switch, like kill switches, you know, just pull, just pull the plug. You know, this is the, this is the, if you go step outside the San Francisco bubble, everybody's like, Oh, the AI is, AI is taking over or the robots are going to become automatic and the robots are going to come and your vacuum cleaner will kill you. Like dude, just take the batteries out the thing. Okay. Totally. Just, just pull the plug. Just disconnect a few like servers, take them offline. If you think it's such a big problem.

18:21Yeah, totally, totally. Ben Thompson made the point of like, also like we're so concerned about, you know, physical AI, except there's really not like physical AI. So fortunately, this is just mostly a digital problem today. It's not like we all have humanoids that are going to kill us when we're sleeping. But I will say also my take to the whole, like, you know, understandably everyone who thinks and breathes us every day is freaking out in San Francisco, but the rest of us in the rest of the world are like, dude, yeah, I unplug my Roomba. I unplug my doorbell camera. It's fine. I think from where I live in Iowa, the thing that's going to pace frontier training, the thing that's going to slow down frontier training, which, by the way, let's have a conversation about whether it actually slows it down and if it actually impacts the amount of compute and whatnot.

19:03But the thing that's going to pace it is called the human pushback to building the data centers that train this AI. These data centers are in Iowa. People in small towns in Iowa are pushing back on that thing's ugly, that thing's noisy, that thing's going to jack up my electricity bills. Not in my backyard. That's what's going to pace the frontier, and that's what everyone in San Francisco should be freaked out about. And that's what no one in San Francisco is even hardly talking about. There is no spokesperson. and everyone's writing S's and they're all getting aligned behind this idea that it's all gonna like take over the internet and shut us down.

19:40And meanwhile, in Iowa, we've got a bunch of farmers with cornfields and cheap electricity where you could build a data center, but they're just like, nah, not here. So there's my hot take for you. The Iowa farmers are going to paste the AI for you, Dario. Don't think you're in charge here, the Iowa farmers are. That's right, that's right. They will paste the frontier for you. Yes. Oh, God, that's funny. You know, that's the whole thing about this. This is such a crazy debate because, you know, who are the ones who are noticing runaway agents? It's the anthropics and the open AIs of the world.

20:19Why? Because they are the ones who set 10 ,000 agents on to do something. Next question, why did they do that? Because they have the compute. Can you and I set 10 ,000 agents off on a month to solve some Navier-Stokes equation and burn compute. Because why? It's cool. Because we want to solve a millennium problem. You and I don't have the pockets to do that. Neither do we have. Even if we have the pockets, we just can't get that much compute. We can't get it. Like, how many, how many, are you going to burn API tokens like AdInfinite? I'm like, most people can't do this. So I think the fear doesn't come from most people.

20:54I think, to counter my own argument here, it comes from the fact that there could be bad actors with deep pockets who could do some really bad things if they could get their hands on this compute. But then there is a simple pull the plug switch solution even there. If somebody is deploying 100 ,000 agents or 10 ,000 agents, maybe you can go through an approval process to get that kind of compute and be like, hey, I'm not trying to police you or anything, but OpenAI and Anthropic would just like to know why you would like this much compute. Is this a hard problem you're working on? Are you solving cancer?

21:32What's the problem here? Like, you know, something like that. I think for most people, it's not a problem. Like nobody can afford to set so much compute on fire. Just, yeah, most people can't. If they try to, don't give them the compute. Right, right. And really what this boils down to is a few American frontier labs that have the compute and are maybe incentivized to race as fast as possible because we used to be in that race where it's like train a bigger model and the scaling laws and stuff. But that has slowed down and changed anyway, that axis of competition. But then, of course, there's China.

22:07And so I really think there's the concern that like, oh, Dario's thinking, you know, oh, either Sam and team are going to run out ahead and have another incident, but it could get worse. Or, oh, what if China, you know, gets out of control here? Which, you know, I think we can have that argument. But I think also the answer of like, like, okay, well, the United States is going to police ourselves and we're going to ask China to also do the same and abide by our standards is a little self-centered and a little asinine. And I don't see why China would raise its hand and be like, yeah, you're cutting off all my chips and now you're telling, two private individuals are trying to tell our country how we should think about AI.

22:54Yeah, yeah. It's difficult to police this thing ultimately, right? You can't tell anybody what to do. And my own idea of like, hey, if somebody is launching 100 ,000 agents, you ask them why and cut them off. I mean, that's again a form of policing that people won't take kindly to. They're like, well, no, I mean, I have the freedom to do what I want. I don't need your approval, OpenAI. Same way, you can't convince China. And Dario says that in his essay too. He's like, yeah, it's the lowest chance that we'll get global agreement that we should all pace the rate of AI development because it's a race.

23:27It's been an AI arms race for a long time now. And at that point, if anybody is slowing down the frontier of AI, you know somebody else is speeding it up. 100%. And you know one person who didn't sign up to all these shenanigans is Zuckerberg from Meta. He's been awfully silent about the whole thing. That's a really good, interesting point. He's like, dude, I have a real business over here. I'm sorry, I'm too busy selling ads and making billions of dollars a day So you guys can go worry about your IPOs and government regulation. I'm just going to sit here and make money. No, it's funny. I think one other interesting counter argument is like, okay, well, maybe if these two private citizens shouldn't just like self-pace because who are they to decide?

24:14If you think that this is as dangerous as a nuclear weapon, then maybe the government should actually take over and really, you know, be in charge of this. And then now you have states, like the United States government and the Chinese government that are talking about pacing, not like private companies trying to tell states to pace. But there's another alternative here. I think Jack Dorsey put a post that's like, we should have open frontier models that we can all contribute to interoperability and operational excellence. it gets back to your compute problem, which is like, well, do any of us have the compute to contribute at this level?

24:56I think Jensen does, and they bought poolside and they bought hugging face. So here's another argument for Jensen to say, hey, yeah, great. You guys pace the frontier close. Why don't we help pace the frontier open? Maybe that's like, and maybe the United States actually wants to put money behind that. Maybe they don't want it to just be in two people's but maybe they know they can't regulate everything. And there seems to be some trust between the Trump administration and Jensen. I think Trump called Jensen during the AI infrastructure summit yesterday. I don't know if you saw that tweet. I haven't watched it.

25:33Apparently he like called in live and he and Jensen like talked during the conference. I gotta check this out. Like, yeah, I can't keep up. It's too much. Like I actually gotta do other stuff like writing a sub stack and you know, like, yeah. So it's too much to keep up, but I would love to see this. but yeah, that's an interesting argument about the open source thing is like, Anthropic and OpenAI you go and pace yourselves or do whatever it is that you want to do with your closed model it's your product, it's your company you're free to do what you want with it but we'll all pace ourselves we'll make sure the open models are safe for humanity to use which is another way of doing it because the open models today aren't that much behind the frontier They are pretty good.

26:19And this pretty good is enough for most people to use. And we'll just make sure that's safe. And everybody will be happy to now use open models because they're guaranteed safe. And closed models are not guaranteed safe in some odd way. Then everybody wants to use open models. It's bad business for OpenAI and Anthropic. Yeah, I mean, maybe there should be a Los Alamos type of government-funded research that's just not on nuclear weapons, but on, you know, aligned, interpretable frontier models. And it's not, it doesn't need to become profitable. And so it doesn't need to race toward, you know, selling more tokens or whatever.

27:00It's literally just funding the research of it. But okay. So speaking of there's so much and we should move on. You and I have 10 minutes left in this conversation. We should move on. So here's the question. Pacing the frontier means we're not going to roll out our new models quite as quickly because we're going to take a lot of time to say, can we interpret what's going on? Are we testing this correctly? Are we being safe? Is it aligned? So it's going to slow down the pace at which new models roll out. Does that impact the amount of compute that gets sold? Does it impact the AI infratrade? Does it impact the CapEx spent by all these hyperscalers?

27:39What do you think? No, no, no. I wrote a whole article on this. is totally free to read on my sub stack and it says the AI CapEx build will just continue as is and the infrastructure build out will just continue as is because for one, all of these things that we spoke about are very difficult to implement it's literally like where do we start? Do I agree that it's an important problem that it is you know, is the security a problem to be solved? Yes, I do agree do you think do i think that people should be working on it absolutely should it be safe even by design yes i mean why are you deploying and trying to solve navier stokes equation and millennium precise problems and pissing off all the mathematicians in the world because now they're like dude like what the hell you're killing the art form of mathematics with all the mathematicians are pissed like why like why don't you deploy all those like agents to go and make safer ai like instead of saying don't use recursive self-intelligence it is self-defeating because everybody else is using AI to make their jobs better why shouldn't AI researchers use AI to make their jobs better so yeah use AI to make safer AI because I don't see why it can't be done at all use your compute responsibly and dedicate the time and resources that you have in the right projects not in just showing off why your model is the best model out there and you are ahead of the frontier the incentives are not misaligned in the agents as much as it is in the humans right right um yes so i think you know the point you're making is like okay well if they need more compute to do this extra post-training type work red teaming interpretability alignment.

29:33Well, maybe they are already working on other things that were burning a bunch of compute, Sora, anyone remember that? Or some of these other problems. So maybe they can just shift what they're working on internally and it doesn't actually really impact things overall. Maybe it's a product manager prioritization problem. And I would say also, okay, let's talk about inference. Okay. Remember it used to be in 2023 that all the compute was spent on training. And over time, it's been spent more and more on inference. And the model is amazing as is today. If we never had a model, there's a lot we can do today that is not yet happening in enterprises.

30:11And the diffusion of agentic AI into everyday life, into every domain has yet to actually happen. That will be a crazy amount of inference, Jevon's paradox, all sorts of useful things are going to happen. It used to be that now anyone could code, but now it's like, oh, now anyone can edit videos. Now anyone can make 3D renderings in Blender, right? Like there's going to be so much that can happen that has nothing to do with training. That's all inference compute. That's all here to stay. Now on the training front, every company has to decide, oh, I want to allocate, you know, 40 % of my compute to training and 60 % to inference, let's say.

30:47Well, that 40 % is very expensive. It's kind of like a sunk cost. That's just R &D cost to build the next model. The 60 % is where they make money. Now, if they're saying, hey, we need to spend a little bit more time in training instead of rolling these things out in 10 months, let's roll it out in 12 months. And therefore we're going to need just a little bit more compute for our training mix. I had this thought this morning and I tweeted it when I was preparing for this. I don't think anyone's going to go, okay, now we need 42 % of our compute for training. And now we only have 58 % for inference.

31:16No, they're going to go, dude, inference is where we make our money. We need an extra 2 % compute hours for training. Go get more compute. We need more training compute, go get more compute. They're not going to offset it and take away, they could take away from inference and have worse margins and make less money. No one's going to do that, right? So literally doing more compute during training just means you're going to sell more compute. Yeah, totally. It's a planetary problem. Inference, everybody is going to need inference. Like, I don't know, can you and I train a model? Probably not. So no.

31:51So everybody needs inference, few people need training. But I think models are generally capable of doing a lot of things already. They've already reached a reasonably useful stage to do a lot of stuff. And I think there are a lot of untouched areas of AI, like you said, like coding and math are verifiable domains, but there are a whole lot of unverifiable domains, places where there's no data, there's places where the information is tacit or just difficult to do with AI. Those are all problems that still have to be solved. And those are like additive TAM that still hasn't shown up even in the inference part of the equation.

32:27And on the safety front, I want to say one more thing. Think about financial transactions. Money is the most important thing to humanity other than their own life, like forever, right? And today we have come to the point where we do all of this stuff to get money which is stored in a digital bit somewhere. Like nobody ever sees their actual money in its entirety ever. probably in their whole lifetime but we sleep easy thinking that we have these digital bits stored somewhere that we can convert into items of luxury whenever we feel like we want to buy a car buy a home you know so we can convert money somehow into material goods and use them and we don't lose any sleep over it because there are safety practices standards bodies you know everything has been set into place over years of financial and decades of financial infrastructure that lets us sleep easy at night, right?

33:24So AI will eventually come to the point of, hey, we all have AI, we use it, but we don't lose sleep over the fact that it's going to come and kill us in our sleep because we have all these safeguards that are there. Even if we don't understand those safeguards, what those safeguards are. Like, I don't think you or I understand the financial banking system. But even then, we'd have our bank accounts and our investments somewhere, and we sleep at night. So the AI system, systems will evolve to the same point where I think we'll all sleep well at night eventually. So we have to get there. Totally.

33:58Yes, yes. Yo, I got a fraudulent thing on my credit card. Well, I call the bank, they make it right. You know, like there's ways. And so again, there's like guardrails and systems and ways of making things right. And I don't think we're going to jump straight to like, oh, the AI did all this destructive stuff while I'm sleeping that no one had any guardrails to turn it off. But I do agree with you that also the broader point of like, our North Star should just be like, let's make sure that we feel comfortable with that there will be some level of safeguards so that we can sleep well at night. And at the end, we are the agents here.

34:35We have the agency. We can turn it off. Those companies can pull the plug, right? Yes, people can buy compute and fine tune and train some stuff in smaller scale and we don't have visibility into all that. Yes, there's other bad actors out there, but I think these are grand problems to think about. I don't think they necessarily mean less compute. I think they mean more. And I also sleep well at night. I agree. Actually, I don't sleep well at night, but it doesn't have to do with anything related to semiconductors. It's more about being a dad. There are plenty of other problems for that. Yes, that's right.

35:10I think the AI build-out will continue as is because I think we still need compute and I don't see the demand going away anytime soon. There will be at some point some kind of an inflection, something that changes everything around. It always happens. It's not going to be because we paced the frontier of AI. That's all. For sure. Maybe this could also be very slightly net positive for XPUs in that if we do need a little bit more compute during training, it's probably GPUs for training. So that's obviously beneficial for GPUs because these are not just super well-defined tasks. Now, you could say, therefore, if they happen to grab those GPUs from their existing fleet and they're going to stand up more inference capacity to make up for it, maybe they actually would.

36:01Inference is where XPUs play. XPUs don't really play in training, right? Yeah, yeah. This is good for GPUs. Yeah. And this whole thing reeks of, like, you know, cybersecurity being the next big thing. Oh, dude. Right. Like, come on. No doubt. Which I think when everybody's fighting on this, the crowd strikes as a world. Crowd striking all those guys up about 20%. Yes. Exactly. So everybody's like, oh, wait, cybersecurity is important now? I'm like, hey, we know how it's done. And so everything is, like, cybersecurity. Totally, totally. Cybersecurity up.

36:37All right, man. Good chat. This was fun. Very interesting. People, leave your thoughts. Tell us what you think. We're always interested to hear. Leave comments on YouTube. Send us an email, whatever. Thank you for listening to SemiDoped.