Dylan Patel

From one piece Dylan Patel — The single biggest bottleneck to scaling AI compute 2 beliefs, in the piece's order there

• Our readingkorrents.com

  As labs sell finished white-collar work rather than reasoning traces, Chinese models will lose the ability to distil their way to parity.

  AI and jobsChinaAmerica

  Their words

  As we move from, you know, hey, [clears throat] these companies are selling tokens where they provide the entire uh reasoning chain and all that to uh selling automated, you know, white collar work, right? Automated software engineer, send them the request, they give you the result back and there's a bunch of thinking on the back end that they don't show you. The ability to distill out of American models into Chinese models will be harder.

  youtube.com

• Our readingkorrents.com

  The world can make roughly 200 gigawatts of AI chips a year by 2030, which makes a gigawatt a week a plausible quarter of the market rather than a fantasy.

  data centers

  Their words

  Um, and then you stack on 70 this year, 80 next year, growing to 100 by 2030. You're at like 700 EV tools by the end of the decade. Um, 700 EV tools, three and a half tools per gigawatt. um assuming it's all allocated to AI which it's not but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy

  youtube.com

From one piece DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 5 beliefs, in the piece's order there

• Our readingkorrents.com

  When writing software becomes cheap, platform SaaS dies: companies stop buying Salesforce and start building their own business logic, as China already does.

  SaaSChina

  Their words

  But what happens when every company can just invent their own business logic really cheaply and quickly? You stop using platform SaaS, you start building custom tailored solutions, you change them really quickly.

  youtube.com

• Our readingkorrents.com

  Software engineering agents will work before any other kind of agent, because code is the one domain where the machine can check its own answer.

  Their words

  But really the software engineering agents I think can be done faster sooner than any other agent because it is a verifiable domain. You can always unit test or compile, and there's many different regions of it can inspect the whole code base at once, which no engineer really can.

  youtube.com

• Our readingkorrents.com

  AMD's hardware is in many ways better than NVIDIA's and it does not matter, because the software gulf is too large and AMD has never resourced closing it.

  Their words

  And they're decent, their hardware is better in many ways than in NVIDIA's. The problem is their software is really bad and I think they're getting better, right? They're getting better, faster, but the gulf is so large and they don't spend enough resources on it or haven't historically, right?

  youtube.com

+ 2 more

• Our readingkorrents.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

• Our readingkorrents.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