Today’s AI technology, while promising, isn’t quite ready for widespread application. I’m not talking so much about AI’s capabilities, but rather the hardware limitations and supply chain challenges that are getting in the way.
For AI to manage vast amounts of data, it’s going to need specialized chips which are still in development. So, give R&D a couple years to figure that out, and then another decade+ for production and supply chains to get sorted out. Without these new chips, power demands are going to skyrocket (because the current, inefficient chips suck up power like nobody’s business). Until those new chips arrive, the US will have to decide which industries will be getting the limited chips that are available, like agriculture, defense, or finance.
While a delay might seem like a bad thing, especially for those who are ready to let AI do their job while they’re sipping Mai Tais on a beach somewhere…it gives us time to figure out how to address all the problems with AI and what its actual impact will look like.
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Transcript
Hey everybody. Peter Zeihan here. Coming to you from Revere Beach, just north of Boston. A lot of you have written in asking for my opinions on AI. So here we go. Pick it apart, however you will. It’s tantalizing. So GPT and the large language models are taking us forward. They’re nowhere near conscious thought. Oftentimes, they can’t even associate their own work from previously in a conversation, with itself.
It’s basically targeted randomness, if you will. That said, it is still providing insights and the ability to search vast databases in a much more organized and coherent matter than anything we have seen from, search engines before. So promising tech. We had a taste. It’s definitely not ready for what I would consider mass application, but, the possibilities are there, especially when it comes to data management, which, when it comes to things like research and genetics, is very important.
However, I think it’s important to understand what the physical limitations are of AI, and that is a manufacturing issue. So the high end chips that we’re using, the GPUs, graphics processing units, we’re not designed to run AI models. They were designed to run multiple things simultaneously for graphics, primarily for gaming consoles. And the gamers among us who have logged lots of time playing Doom and Fortnite and all the rest have been the primary economic engine for pushing these technologies forward until very recently.
It’s only with things like autonomous driving and electric vehicles that we’ve had a larger market for high end chips. But the GPUs, specifically because they run multiple scenarios and computations simultaneously, that is what makes a large language model work. Wow. Got windy all of a sudden. Let me make sure this works.
Okay. So, GPUs, they generate a lot of heat because they’re doing multiple things at the same time. And so normally you have a gaming console and you have a GPU at the heart of it, and multiple cooling systems typically fans blowing on them to keep laptop from catching on fire.
So if you take these and put 10 or 20,000 of them in the same room in the server farm, you have a massive heat problem. And that’s why most forecasts indicate that, the amount of electricity we’re using for data centers is going to double in the next few years, to compensate. That’s why they’re so power intensive.
Now, if you want to design a chip that is for large language models and AI systems as opposed to, that’s just being an incidental use. You can that those designs are being built now, and we’re hoping to have a functional prototype by the end of calendar year 2025. If that is successful, then you can have your first mass run of the chips enough to generate enough chips for a single server farm by the end of 2027.
And then you can talk about mass manufacture getting into the system by 2029, 2030. So, you know, even in the best case scenario, we’re not going to have custom designed chips for this anytime soon. Remember that a GPU is about the size of a postage stamp because it’s designed to be put in a laptop. Or if you’re going to design a chip specifically, to run AI, you’re talking about something that is bigger than a dinner plate because it’s going to have a cooling system built in.
Not to mention being able to run a lot more things in parallel. So even in the best case scenario, we’re looking at something that’s quite a ways out. So then you have to consider the supply chain just to make what we’re making. Now. The high end chip world, especially sub10 nanometer, and we’re talking here about things that are in the four nanometer and smaller range, closer to two, really, is the most sophisticated and complicated and, proprietary supply chain in human history.
There are over 9000 companies that are involved in making the stuff that goes into the stuff that goes into the stuff that ultimately allows TSMC to make these chips in Taiwan. And then, of course, 99% of these very high end chips are all made in one town in Taiwan that faces the People’s Republic of China. So it doesn’t take a particularly egregious scenario to remove some of those 9000 pieces from, the supply chain system.
And since roughly half of those supply chain steps are only made by small companies that produce one product for one end user and have no competition globally, you lose a handful of them, and you can’t do this at all until you rebuild the ecosystem based on what goes wrong. That rebuilding can take upwards of 10 to 15 years.
So in the best case scenario, we need new hardware that we’re not going to have for a half a decade and are more likely scenario. We’re not going to have the supply chain system in order to build the hardware, for a decade or more. However, we’ve already gotten that taste of what I might be able to do.
And since with the baby boomer retirement, we’re entering into a world of both labor and capital shortages. The idea of having AI or something like it to improve our efficiency is something we can’t ignore. The question is whether we’re going to have enough chips to do everything we want to do. And the answer is a hard no. So we’re going to have to choose do we want the AI chips running to say, crack the genome so that we can put out a new type of GMO in the world that’ll save a billion people from starving to death.
In a world where agricultural supply chains fail. Do we use it to improve worker productivity in a world in which there just aren’t enough workers? And in the case of the United States, we need to double the, industrial plant in order to compensate for a failing China? Or do we use it to stretch the investment dollar further now that the baby boomer money’s no longer available and allow our financial system to be more efficient?
Or do we use it for national defense and cryptography? You know, these these are top level issues, and we’re probably only going to have enough chips to do one of the four. So I would argue that the most consequential decision that the next American president is going to have to make is about where to focus, what few chips we can produce and where do you put them?
There’s no right answer. There’s no wrong answer. There’s just less than satisfactory answers. And that leaves us with the power question. Assuming that we could make GPUs at a scale that will allow mass adoption of AI, which we probably can’t anyway. You’re talking about doubling the power requirements, of what is used in the data space. Here’s the thing, though.
If we can’t make the GPUs and we’re not going to be able to make the more advanced chips anytime soon, we’re still going to want to get some of the benefits from AI. So we’re going to use older, dumber chips that generate a lot more heat per computation in order to compensate, which means we’re probably going to be seeing these estimates for power demand, not simply double, but triple or more.
At the same time, we get less computations, fewer computations, and generate an AI system that’s actually less effective because we’re not going to be able to make the chips at scale. So is it coming? Yeah. But in the short term, it’s not going to be nearly as fast. It’s going to cost a lot more. It’s going to require a lot more electricity.
And we’re probably going to have to wait until about 2040 before we can design and build in mass and apply the chips that we actually want to be able to do this for real. So, believe it or not, actually see this as a borderline good thing because it’s so rare in the United States that we discuss the outcome of a technological evolution before it’s completely overwhelmed us here.
I’d argue we’ve got another 15 years to figure out the fine print.