Dylan Patel

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13 March

Dylan PatelKorrents 13 Mar

From one piece Dylan Patel — The single biggest bottleneck to scaling AI compute 3 beliefs, in the piece's order there

korrents.com
Space data centres solve the wrong problem, because the contended resource is chips rather than energy, and that will not change this decade.

data centersenergy

Their words

So, space data centers effectively are not li limited by, you know, hey, we have this energy advantage. It's actually just limited by the same contended resource. We can only make 200 gawatts of chips a year by the end of the decade.

youtube.com
korrents.com
The reason we cannot simply make more memory is that there is nowhere to put the tools, and a fab takes two years to build.

Their words

Uh, Micron bought a fab from a company in Taiwan that makes lagging edge chips, right? Um, Heinix and Samsung are doing, you know, some pretty crazy things to try and expand capacity at their existing fabs, uh, that also have like very large knock-on effects in the economy. And so, hey, why can't we build more capacity is like there's nowhere to put the tools, right?

youtube.com
korrents.com
Hundreds of billions of dollars of AI value rest on about a billion dollars of lithography tooling that cannot be scaled quickly.

data centers

Their words

oh 50 gigawatts of economic you know sort of capex in in the data center and what gets built on top of that in terms of tokens is even larger right it might be hundred billion dollars worth of AI value into the supply chain is held up by this $1.2 two billion dollars worth of tooling that simply just cannot expand its supply chain quickly.

youtube.com

3 February 2025

Dylan PatelKorrents 3 Feb 2025

From one piece DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 6 beliefs, in the piece's order there

korrents.com
The danger from powerful AI is not that it kills everyone but techno-fascism: a few thousand people ruling whoever is left and the economy around them.

Their words

it won't be one person rule them all, but it will be, the thing I worry about is it'll be few people, hundreds, thousands, tens of thousands, maybe millions of people rule whoever's left and the economy around it.

youtube.com
korrents.com
The bottleneck on America's AI buildout is transmitting power, not generating it: in parts of the US, moving electricity costs more than making it.

energyAmerica

Their words

Interesting thing is certain regions of the US transmitting power cost more than actually generating it because the grid is so slow to build. And the demand for power, and the ability to build power, and re-ramping on a natural gas plant or even a coal plant is easy enough to do, but transmitting the power's really hard.

youtube.com
korrents.com
The copyright problem in AI training already has a legal solution nobody is taking: train the models in Japan, where copyright does not apply to training data.

energy

Their words

So, Japan has a law which you're allowed to train on any training data and copyrights don't apply if you want to train a model, A. B, Japan has 9 gigawatts of curtailed nuclear power. C, Japan is allowed under the AI diffusion rule to import as many GPUs as they'd like.

youtube.com

+ 3 more

korrents.com
We will have AGI-grade intelligence well before it reaches the economy, because what limits deployment is the cost of running it rather than the capability itself.

AGI

Their words

The important thing about, hey, is cost a limiting factor here? My view is that we'll have really awesome intelligence, like AGI, before we have it permeate throughout the economy.

youtube.com
korrents.com
If AI does not transform the economy within the next five to ten years, the export controls will have guaranteed that China wins the long run.

China

Their words

if you believe we're in this sort of stage of economic growth and change that we've been in for the last 20 years, the export controls are absolutely guaranteeing that China will win long-term.

youtube.com
korrents.com
Training a frontier model achieves almost nothing by itself; all the economic and military power comes from deploying it, and deployment is what consumes the compute.

Their words

To some extent, training a model does effectively nothing. They have a model. The thing that Dario is sort of speaking to is the implementation of that model, once trained to then create huge economic growth, huge increases in military capabilities, huge increases in productivity of people, betterment of lives.

youtube.com

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