Discussion about the supply constraints and investment in GPU production.

GPU Production Constraints

Exploration of energy constraints and the regulatory challenges in building large power facilities.

Energy Constraints and Regulation

Consideration of the long-term project of building massive facilities to power large clusters.

Long-Term Project for Power Facilities

Discussion about the scalability of data centers and the investment in infrastructure to support exponential growth.

Scalability and Investment in Infrastructure

Reflection on the potential bottlenecks and challenges in the development of AI models.

Addressing Bottlenecks in AI Development

**The Future of GPU Production and Energy Constraints in AI**

*Welcome to our newsletter on the latest trends and developments in AI and GPU production! In this edition, we'll be discussing the potential challenges and opportunities in the rapidly evolving landscape of AI infrastructure.*

**GPU Production Challenges**

- In recent years, GPU production has been a major issue, with supply constraints limiting the availability of GPUs even for companies with the financial resources to invest in them.
- However, the situation is gradually improving, and companies are now considering significant investments in building out GPU infrastructure to meet the growing demand.

**Energy Constraints and Capital Investment**

- As companies look to expand their GPU infrastructure, the issue of energy constraints is becoming a significant concern.
- Building large-scale training clusters requires a massive amount of energy, and the process of obtaining the necessary permits and regulatory approvals for new power plants and transmission lines is heavily regulated and time-consuming.
- The exponential growth in AI infrastructure presents a capital investment question, as companies weigh the potential long-term benefits against the challenges of energy constraints and regulatory hurdles.

**The Future of AI Infrastructure**

- While companies are currently operating data centers with capacities of up to 150 megawatts, the prospect of building a single gigawatt data center for AI training remains a long-term project.
- The exponential growth in AI infrastructure is driving investments in building the necessary infrastructure to support potential advancements in AI technology.
- However, the industry acknowledges that there are potential bottlenecks and uncertainties in the continued scalability of AI infrastructure, and the future growth of AI technology is not guaranteed.

**Conclusion**

The rapid expansion of AI infrastructure and the challenges of GPU production and energy constraints present both opportunities and uncertainties for the future of AI technology. As companies navigate these complexities, it is clear that the development of AI infrastructure will continue to be a key focus in the coming years.

Stay tuned for our next newsletter as we continue to explore the latest trends and developments in AI technology and infrastructure. Thank you for joining us!

Navigating the Challenges and Opportunities in AI Infrastructure

Lack of technological advancement in GPUs

Around the size of a meaningful nuclear power plant

On the order of 50 megawatts or a hundred megawatts

- There has been a shortage of GPU production in the past few years
- Companies are now considering investing in building out these facilities
- Energy constraints may become a bottleneck for building larger clusters
- It is worth investing in building the infrastructure for exponential growth in AI, but there are uncertainties about how long it will continue
- There may be different bottlenecks along the way in scaling AI models.

Full Episode: https://youtu.be/bc6uFV9CJGg

Apple Podcasts: https://podcasts.apple.com/us/podcast/mark-zuckerberg-llama-3-open-sourcing-%2410b-models-caeser/id1516093381?i=1000652877239
Spotify: https://open.spotify.com/episode/6Lbsk4HtQZfkJ4dZjh7E7k?si=GOqj7hUdSaWSgi7ULWXjMA
Transcript: https://www.dwarkeshpatel.com/p/mark-zuckerberg

Me on Twitter: https://twitter.com/dwarkesh_sp

And over the last few years, I think there was this issue of GPU production.

So even companies that had the money to pay for the GPUs couldn't necessarily get as many as they wanted because there were all these supply constraints.

So now I think you're seeing a bunch of companies think about, wow, we should just like really invest a lot of money in building out these things.

And I think that will go for some period of time.

There is a capital question of like, okay, at what point does it stop being worth it to put the capital in?

But I actually think before we hit that, you're going to run into energy constraints, right?

Because I just, I mean, I don't think anyone's built a gigawatt single training cluster yet.

I mean, just to, I guess, put this in perspective, I think a gigawatt, it's like around the size of like a meaningful nuclear power plant only going towards training a model.

And then you run into these things that just end up being slower in the world, like getting energy permitted is like a very heavily regulated government function.

And if you're talking about building large new power plants or large build outs and then building transmission lines that cross other private or public land, that is just a heavily regulated thing.

So you're talking about many years of lead time.

So if we wanted to stand up just some like massive facility to power that, I think that that is, that's a very long-term project.

I think we would probably build out bigger clusters than we currently can if we could get the energy to do it.

So I think that That's fundamentally money bottlenecked in the limit.

But it depends on how far the exponential curves go.

Right, like I think a number of companies are working on, you know, right now I think, you know, like a lot of data centers are on the order of 50 megawatts or a hundred megawatts or like a big 1 might be 150 megawatts.

Okay, so you take a whole data center and you fill it up with just all the stuff that you need to do for training and you build the biggest cluster you can.

I think that's kind of, I think a bunch of companies are running at stuff like that.

But then when you start getting into building a data center, that's like 300 megawatts or 500 megawatts or a gigawatt, I just, I mean, just no 1 has built a single gigawatt data center yet.

I mean, this is only a matter of time, but it's not gonna be like next year.

It's 1 of the trickiest things in the world to plan around is when you have an exponential curve, how long does it keep going for?

And I think it's likely enough that it will keep going, that it is worth investing the tens or 100 billion plus in building the infrastructure to assume that if that kind of keeps going, you're going to get some really amazing things that are just going to make amazing products.

But I don't think anyone in the industry can really tell you that it will continue scaling at that rate for sure.

In general, you know, in history, you hit bottlenecks at certain points.

And now there's so much energy on this that maybe those bottlenecks get knocked over pretty quickly.

But I don't think that this is like something that can be quite as magical as just like, okay, you get a level of AI and you get a bunch of capital and you put it in and then like all of a sudden the models are just going to kind of like, it just like, I think you do hit different bottlenecks along the way.


Speaker 0: And over the last few years, I think there was this issue of GPU production. Yeah. Right? So even companies that had the money to pay for the GPUs couldn't necessarily get as many as they wanted because there were all these supply constraints. Yeah.

Now I think that's sort of getting less. So now I think you're seeing a bunch of companies think about, wow, we should just like really invest a lot of money in building out these things. And I think that will go for some period of time. There is a capital question of like, okay, at what point does it stop being worth it to put the capital in? But I actually think before we hit that, you're going to run into energy constraints, right?

Because I just, I mean, I don't think anyone's built a gigawatt single training cluster yet. I mean, just to, I guess, put this in perspective, I think a gigawatt, it's like around the size of like a meaningful nuclear power plant only going towards training a model. And then you run into these things that just end up being slower in the world, like getting energy permitted is like a very heavily regulated government function. And if you're talking about building large new power plants or large build outs and then building transmission lines that cross other private or public land, that is just a heavily regulated thing. So you're talking about many years of lead time.

So if we wanted to stand up just some like massive facility to power that, I think that that is, that's a very long-term project. I think we would probably build out bigger clusters than we currently can if we could get the energy to do it. So I think that That's fundamentally money bottlenecked in the limit. Like if you had a trillion dollars. I think it's time.

Yeah. Right. But it depends on how far the exponential curves go. Right, like I think a number of companies are working on, you know, right now I think, you know, like a lot of data centers are on the order of 50 megawatts or a hundred megawatts or like a big 1 might be 150 megawatts. Okay, so you take a whole data center and you fill it up with just all the stuff that you need to do for training and you build the biggest cluster you can.

I think that's kind of, I think a bunch of companies are running at stuff like that. But then when you start getting into building a data center, that's like 300 megawatts or 500 megawatts or a gigawatt, I just, I mean, just no 1 has built a single gigawatt data center yet. So I think it will happen, right? I mean, this is only a matter of time, but it's not gonna be like next year. It's 1 of the trickiest things in the world to plan around is when you have an exponential curve, how long does it keep going for?

And I think it's likely enough that it will keep going, that it is worth investing the tens or 100 billion plus in building the infrastructure to assume that if that kind of keeps going, you're going to get some really amazing things that are just going to make amazing products. But I don't think anyone in the industry can really tell you that it will continue scaling at that rate for sure. Right? In general, you know, in history, you hit bottlenecks at certain points. And now there's so much energy on this that maybe those bottlenecks get knocked over pretty quickly.

But I don't think that this is like something that can be quite as magical as just like, okay, you get a level of AI and you get a bunch of capital and you put it in and then like all of a sudden the models are just going to kind of like, it just like, I think you do hit different bottlenecks along the way.

See all Dwarkesh Patel transcripts on Youtube

Energy, not compute, will be the #1 bottleneck to AI progress – Mark Zuckerberg