- Principle is, the economics of AI investment need to make sense. ROI needs to make sense.
- I am seeing equilibrium being established.
- On one end, running inferences, even large scale, is relatively cheap, it is a cost that corporates should be able to foot.
- One the other hand, “token-maxxing” strategy is raising concerns and make CFOs questioning their ROI: Uber already burnt their annual budget for AI in April, anecdotes coming everywhere that they can’t get enough of their tokens to complete their jobs (/ambitions/ideas).
- But eventually, these AI projects need to be valuable products. Need to contribute. Otherwise, it would be an expensive hackathon.
- So an equilibrium is being figured out - we can pay for it, but not infinitely.
- Reminded me of the case of dark fiber. In the early ages where fiber was introduced to the world, there was a avid investment in building out fiber networks throughout the country. Not all of them, are commercially viable to be “lit up”. As such, there are miles and miles of built-and-ready sleeping beneath the ground, waited to be waken by new players willing to pick them up.
- Maybe compute will be like that. Yes, it will not be “uninvented”, demand for HPC will always be there. But there may be excess.
- Will these “dark HPC” be relevant and usable in 20 years? That, we don’t know. My understanding is likely not.
- If they will somehow still be in the game in 20 years, there will be a market for it.