But if you're digitizing the physical world, you're treating atoms like bits. And you're like, well, I know that CPU manipulates the bits. What manipulates atoms? That's manufacturing or digitized manufacturing.
I think one reason why um the the AIs have been scaled up less than you would have otherwise expected and like for example cost of of per token hasn't increased as much as you might have thought is because there's a benefit to doing more of your um work at small scale where you can run more training runs and get more cycles in
PE funds optimize for a 4-7 flip and typically resort to aggressive cost-cutting and near-term optimization at the cost of the "soul" of the business or what made it successful in the first place.
But but the thing is like doing programming languages, you come to realize it's a long play. I mean, if if you look back at the the stuff I worked on, it it it goes in 10-year cycles at least. And TypeScript didn't really, for example, or C# for that matter. I mean, it took it it takes 10 years to get to, you know, version one is great, but it has all sorts of issues, and then you got to do version two, and then it's not until version three that it really starts to be great.
TSMC is much more excited to give allocation to Graviton than they are to tranium because they view CPU business as more stable long-term growth right and as a company that is conservative and doesn't want to ride cycles of growth too hard you actually want to allocate to the uh the market that is more stable and lower growth rate first before you allocate all the incremental capacity to the fast growth rate market.
But, I can tell you that there are at least two more cycles left in this, and that means they will be at least 16 times smarter than they are today, and that is going to cause all of knowledge work to be subsumed by this stuff.
Say goodbye to cycles of mock and doc feedback, and say hello to code-first prototypes with constant iteration (on prompts, models, etc) to answer three key questions.
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