I especially like that he outlines an actual plan for an AI chip startup that he thinks will work, and has an update explaining why he was subsequently convinced that it wouldn't work.
For example, Nvidia's compute and consumer GPU line diverged a long time ago. Modern A100s have literally only one SM capable of doing normal GPU tasks, probably to support running a display on whatever Quadro version they end up increasing. They diverged in really specific ways, for example the P100 has hardware scheduling, where as the 1080 does not (in the same way at least).
Another issue is the author spends a long time talking about how important software and ecosystem is, then completely misses that point when talking about their own CHIP - just because it is RISCV and compilers exist for that arch does not equal CUDA. Also, big re-order buffers cost area and heat that could be spent on more SMs. That's why in order to beat Nvidia you must get more specialized, they've picked their niche on the CPU-GPU-ASIC continuum, beating them at the same process node requires ditching some stuff of the stuff an Nvidia GPU. Which is why they've been specializing their arch with tensor cores.
It just also turned out those are useful for gaming with deep learning to upres the graphics, as that's easy to accelerate than driving quadraticlly higher resolutions.
what does hardware scheduling mean in this context?
Geohot is right about the AI accelerator market's problems, and a competitive 4-digit-dollars device is a great idea even if his initial strategy was way off. Although you could say almost the same thing about the high-performance CPU and GPU duopolies (Apple's chips don't count due to their proprietary OS lock-in, although I wish Asahi Linux luck at fixing that).
[edit] And beyond that, you have TSMC dominating the next-gen fab market, too.