Austin talks with Vik Sekar of Semi Exponent about the call to “pace the frontier” of AI development. They break down Dario Amodei’s recent essay, the agent swarm incident that may have prompted it, and what it means for the AI industry. Vik argues that despite the call to slow down, the demand for AI compute and the CapEx buildout will continue unabated.
Things we cover:
Dario Amodei’s “Pacing the Frontier” essay
The OpenAI and Hugging Face agent swarm
Recursive self-improvement and agentic AI
Why pacing won’t slow down AI CapEx
The role of open source models in safety
Parallels between AI safety and the financial system
This podcast is lightly edited for clarity.
Pacing the Frontier
Hello 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.
Vik Sekar: Hey guys, I’m Vik Sekar and I write the Vik’s 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.
Vik Sekar: Yeah, we got to slow down apparently. That’s what Dario is telling everybody. We got to slow this down. We’re going too fast. It’s too dangerous. We’re playing with fire.
Right, right. What do you think? 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.
Vik Sekar: Okay, good. I think that person is one of the lucky ones. I think so much online. I’m like, if you missed it, good on you. But that’s why we are 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. Maybe 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 Amodei is, you know, 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.
And he’s very clear to say that 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. 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 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 all the banking systems will crash, airplanes will fall out of the sky, you know, 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. That’s one thing. The second thing that took everybody by surprise very recently is the OpenAI and the Hugging Face incident, which is essentially the whole thing where like a swarm of agents behaved like a coordinated group.
Like they just became a mob. 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. And they even tried to break into the grader that was scoring their work and tried to cheat on their exams. Nothing was really off whack here. It’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 six 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 and somebody put an article out saying there’s a 10% chance in the next 12 months that robots will kill all of humanity.
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. Now, 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.
Operational Excellence and Interpretability
Yes, yes, totally. So, 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 Vik gave with the OpenAI hugging face, these things happened because we just fire up a ton of agents. We tell them to just go, try to do a particular goal that has a particular reward function.
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. 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?
Well, 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’s trying to say 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. And 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 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, there’s lots of great responses and eventually we’ll go through all of them, but right away, I was like, oh, my this was 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.
Vik Sekar: Yeah, and the thing that is notable about this is that this is one of the rare instances when Sam Altman agreed with Dario’s essay. Elon Musk agreed with his essay, Demis Hassabis of DeepMind agreed with his essay, you know, and so a lot of people have come out and be like, yeah, we need to pace the frontier. So and one interesting take that I really liked was David Sacks, who said, hey, Dario and Sam, you guys have seen more of this model than any human has on earth.
You guys’ companies are on the frontier of the models in the world. And if you see that and feel like you want to pace the frontier, he says, go ahead and do it. But do it with one thing in mind. I like this take from David Sacks. 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 say that you’re trying to save humanity or you’re being 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. Like any good product, it should have security features built in. So yeah, you go do it.
A Coordinated Pause?
Yes, yes, okay, interesting. 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 Dario, you know...
Vik Sekar: We’re time out ourselves. We’re giving ourselves.
Exactly. We’re putting ourselves in time out. We’re just going to clean up here. That’s exactly it. It felt, so then I thought to myself, oh, man, maybe what we don’t know is that Washington is coming in and and then again, you could 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, you know, take the credit as pushing this forward. Let’s see what happens, but I was surprised by the coordination and thought, hmm, maybe there’s something more going on here. And then two, to the David Sacks 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 unattended things because we’re driving very heavy equipment. Sure, it’s only five miles an hour, 10 miles an hour, but legitimately, 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? And we had to think through all these safety stuff, and safety was 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 birds, 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. But 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 the season, because in farming, depending on the crop you’re harvesting and planting, you only have one shot per year. And so we as John Deere would say, we’re just going to miss the shot this year, and we’re just going to 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 OpenAI and Anthropic to be like, we just have to get to the next thing, have to get to 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, they’re not orthogonal, but you should make a great product experience, but also 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. No one deterministically understands everything and knows how everything’s going to behave in every situation.
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The Problem with Interpretability
Vik Sekar: So, the thing is in the essay he 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 make understand why it’s making the decisions it’s making and try to improve from there on. But that’s the crux of the problem. Nobody knows why these simple bunch of transformer equations gives us this intelligence. I don’t know. It seems to do a lot of stuff. Like some people argue that this is not even AI. This is not the path to AI. Maybe, but it’s the best we’ve got. This is the best I’ve seen in my lifetime. It really can do a lot of stuff. It can create images, it can create videos. 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? Will you understand in one to two years how this monster machine with everybody’s spinning up agents work? Will you be able to explain why the OpenAI hugging face incident decided to sacrifice a few agents for the greater good? Do you think interpretability will give you those answers? I think one to two years is too short of a time. So 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—Yeah, what about the middle where it’s just helping me write an email? Okay, that’s really helpful. 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 use that narrative in the middle as well? So it’s very extreme. I think the essay was very extreme. It never acknowledges the useful middle.
Pulling the Plug
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 creating 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. But I do like the people that are like, hello, we’re in charge here. We can put an off switch, kill switches, you know.
Vik Sekar: Just pull the plug. This is the—if you go step outside the San Francisco bubble, everybody’s like, oh, the AI is taking over. Oh, the robots are going to become automatic and the robots are going to come and your vacuum cleaner will kill you. I’m like, dude, just take the batteries out the thing. Or just pull the plug. Just disconnect a few servers. Take them offline. If you think this is such a big problem.
Yeah, totally, totally. Ben Thompson made the point of like, also, we’re so concerned about 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, understandably everyone who thinks and breathes this 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. But 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,
Vik Sekar: The Iowa farmers are going to pace 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 pace the frontier for you. Yes.
Vik Sekar: Oh God, that’s funny. That’s the whole thing about this. This is such a crazy debate because who are the ones who are noticing runaway agents? It’s the Anthropic and the OpenAIs of the world. Why? 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 are you going to burn API tokens ad infinitum? Most people can’t do this. So I think the fear doesn’t come from most people. I think to counter my own argument here, it’s 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’s deploying a 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? What’s the problem here? Something like that. I think for most people it’s not a problem. Nobody can afford to set so much compute on fire. Most people can’t. If they try to, don’t give them the compute.
Policing Compute and Open Source Alternatives
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 access of competition. But then of course, there’s China. And so I really think there’s the concern that like, oh, Dario’s thinking, either Sam and team are going to run out ahead and have another incident, but it could get worse or, oh, what if China gets out of control here, which, I think we can have that argument, but I think also the answer of 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 you two private individuals are trying to tell our country how we should think about AI.
Vik Sekar: Yeah, 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’s 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, no, I have the freedom to do what I want. I don’t need your approval, OpenAI or... 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. It’s been an AI arms race for a long time now and at that point, if anybody’s slowing down the frontier of AI, somebody else is speeding it up.
100%. 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. Dude, 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 self-pace because who are they to decide? If you think that this is as dangerous as a nuclear weapon, then maybe the government should actually take over, and really be in charge of this and then now you’ve got 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 interpretability 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? I think Jensen does and they bought Poolside and they bought Hugging Face. So here’s another argument for Jensen to say, hey, great. You guys pace the frontier closed. 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 hands, 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.
Vik Sekar: No. Apparently he like called in live and he and Jensen talked during the conference. Wow, I got to check this out. I can’t keep up. It’s too much. I actually got to do other stuff like writing a substack and, 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. It’s 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’re pretty good. And 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. And then everybody wants to use open models. It’s bad business for OpenAI and Anthropic.
Will Pacing Slow Down the AI Buildout?
Yeah, yeah. I mean, maybe there should be a Los Alamos type of government funded research that’s just not on nuclear weapons, but on aligned, interpretable frontier models and it doesn’t need to become profitable and so it doesn’t need to race toward selling more tokens or whatever. It’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? Have we 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 infra trade? Does it impact the CapEx spent by all these hyperscalers? What do you think?
Vik Sekar: No, no, no. I wrote a whole article on this. It’s totally free to read on my substack and it says that the AI CapEx 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, security is a problem to be solved? Yes, I do agree. 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 problems and pissing off all the mathematicians in the world because now they’re—
Right, right. Yes, so I think 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, well, maybe they are already working on other things that we’re 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, 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 and 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? 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 40% of my compute to training and 60% to inference, let’s say. Well, 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. No, 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.
Vik Sekar: Yeah, totally. It’s a planetary problem. Inference. Everybody is going to need inference. Can you and I train a model? Probably not. So, no. So, everybody needs inference, few people need training. But I think models are generally capable of doing a lot of things already. They’re kind of 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. And on the safety front, I want to say one more thing. Think about financial transactions. Money is the most important thing to humanity, like other than their own life, for 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. 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, 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, everything has been set into place over years of financial and decades of financial infrastructure that lets us sleep easy at night, right? So, 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 and 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 and 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. Yes, yes. Yo, I got a fraudulent thing on my credit card. Well, I call the bank, I make it right, you know, there’s ways. And so again, there’s like guard rails 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 guard rails to turn it off. But I do agree with you that 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. We 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 are 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.
Vik Sekar: 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.
Vik Sekar: There are plenty of other problems for that. Yes, that’s right. But I 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 paste 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. Inference is where XPUs play. XPUs don’t really play in training, right? And so
Vik Sekar: Yeah, yeah. And this whole thing reeks of like, cyber security being the next big thing. Right? Come on. It’s staring at you. So everybody’s fighting over this.
No doubt. Which I think those guys are up 20%. Yes.
Vik Sekar: Exactly. So everybody’s like, oh, wait, cyber security is important now. I’m like, hey, we know how it’s done. And so everything is like cyber security. So
Totally, totally.
Vik Sekar: So cyber security up, inference up.
All 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 Semi Doped.
