What decisions were MBA instead of engineering decisions? It seems like intel has just made a lot of bad bets or failed to put their mass behind good ones.

The heights nvidia has achieved seem incidental and have depended heavily on the transformer/LLM market materializing.

Intel's biggest problem has been management remaining in denial about their serious engineering problems, and believing that they'll have things sorted out in another few quarters. They were years late to taking meaningful action to adjust their product roadmap to account for their ongoing 10nm failures. Putting all their eggs in the 10nm basket wasn't an engineering decision, and keeping them all there after years of being unable to ship a working chip wasn't an engineering decision.

Intel's in a somewhat better place today because while they continue to insist that their new fab process is just around the corner and will put their foundry back on top, they've started shipping new chips again, using TSMC where necessary.

Everything risks aggravating NIMBYism. It's hard to see how housing costs can come down in a lot of cities simply because housing is seen as an investment and people won't idly standby if the value decreases because of policies.

Are you a mathematician? I’m not an expert on the math field but it seems like they are hitting the same issues everyone else has: current LLMs still more or less need to be supervised by an expert and struggle to do something actually novel or build out a complicated proof correctly.

I work in math heavy applied setting. Randomly hired PhDs are also need to be supervised, end results being monitored, code be reviewed or they will make lots of mistakes, and my view is if you throw some problem like: build optimization model for this kind of problem on this kind of data, LLMs may produce better results.

There's a limit to how much novelty you're going to get from an LLM, especially in areas like programming and math where they've been heavily RL'd NOT to be novel, even to extent that the base model supports, and instead generate much narrower more proscribed outputs.

The limit to the novelty you are going to get from an LLM is essentially the "deductive/generative closure" of the training data. To be truly novel and move past the limits of your own past experience requires things like curiosity, continual learning, and the autonomy/agency to explore and learn.

but what is the share of PhD workforce who is doing novel and creative things compared to following some mechanical workflow?

I think it has a key advantage for China specifically though which is it consumes significantly less water and they have a lot of water poor territory.

The oakridge experiment ended and not a lot of R&D has been done on salt reactors. It makes sense that China is still basically in research and testing phases for molten salts.

I think this is the crux of it. The article discusses Ukraine but they weren't making millions of drones, the private capital wasn't there and the bureaucracy that coordinates it wasn't primed until the war.

Necessity is the mother of invention.

Personally I never thought it was fine, but the solutions were all bad in some way that made direct venv and requirements files preferable. Poetry started to break this but I had issues with it. uv is the first one that actually feels good.

The API complexity really threw me when I last tried async python. It's very different from other async systems and is incredibly different from gevent or twisted which were popular when I was last writing server python.

It seems more true than the "this is good for bitcoin" meme now that bitcoin seems to track the dollar very closely

Proto as a prefix means it's first or at least before.

Yes but the Chinese state is much more willing to direct capital to where it thinks it should go. The argument w.r.t. geopolitics is usually that the US is late to doing this and now lacks production capacity which is useful for competing.

I think this is an "and," not a "but." Your statement and mine aren't in conflict.