The author is a developer working on software that scales to 1,024 cores and greater -- which is within spitting distance now thanks to 256-core AMD EPYC processors. If you count hyperthreads as a "virtual core" then a dual-socket system gets you there!
That blog made me realise that almost all software can't utilise 1,024 cores, not even close! Microsoft SQL Server, as a random example, tops out at 64 cores for any single query.
Similarly, people couldn't utilise a machine that big, not with pre-AI software, except for certain embarrassingly parallel codes such as simulations and 3D graphics rendering.
Now... NOW... there's no upper limit. None. One person(!) can cheerfully utilise millions of cores, billions even, and there's no end in sight.
If you think one conversation thread in "ChatGPT" is all we need, you're not seeing the big picture of what's possible here.
Those math proofs OpenAI produced were created with tens of thousands of agents running in parallel, orchestrated by thousands more agents, and overseen by hundreds of agents, all triggered by someone typing as little as a sentence or two to start that avalanche of compute!
With that kind of AI power at your fingertips, huge, unthinkable projects become a fun exercise for the weekend:
- Translate the entirety of Wikipedia to every human language.
- Rewrite the entire Linux kernel to Rust in one go
- Rewrite all open source C code to Rust.
- Formalise every maths paper ever written.
Etc, etc, ... without end, without bound.
Sure you can do all these things you mentioned but eventually even if it was free they would be done and doing some of these things your self would be annoying and a waist of time. Once some has formalized a proof why would some do it again and why would some one rewrite a program again, it will still take time and human effort over just using it. If you just spend 1 minute with each agent of your say 10000
I really don't think most people want to be running tens of thousands of agents and would not find a need for that many agents. if you it takes you just 1 minute of looking at output of your agents that would take you 1 year and 52 days. I think you have to admit there is a limit. Though I could find busy work for my agents, you could do the same with computing before hand such searching for primes, there is a limited thing that people want out of their machines is my point and people who would run such things would be a minority if they exist at all.
Your other point stands tho - there might be a point where there is not much left to do. Most problems solved, and remaining require more agent hours than there is energy in the universe. Or not. Who knows.
I share your enthusiasm, but only directionally. :)
IMO its copium to think that chips which have 10x more demand than supply are suddenly going to be easy to get as "AI is eating the world" like software did over the past decades.
Secondly that the frontier model providers will not necessarily want to buy these up as the aforementioned lower demand for training (or costly and memory intensive training) and cost of inference goes down (and possibly requests as the models get better or at least plateau).
Further TPU's and if model weights do eventually stabilize then ASIC's might make more sense.
And lastly as models improve and make less mistakes the requirement trying things in parallel traditional compute may also go down. I think their is a quantity intercept (The demand for compute if it cost nothing would be a finite number and may go down rather then up with advancements in cheaper inference and accuracy) for compute both AI inference and traditional compute.
That leaves the real buyers who are also starting to run low on capital.
Finally, demand doesn't need to go to zero for there to be a price correction. Just dipping from insane demand to elevated levels would lead to a significant price drop.
I think there are two sides that want a particular outcome one want gpu's to get cheaper (like I admit I do), others want there investment to work out (both might want to huff copium at some point, who is huffing here is not easy to tell).