Every time I read one of these articles the main issue I have is that it doesn't take into account the huge shortages of compute that are going on all the time. Anthropic and Google especially have been incredibly unreliable, struggling to keep up with demand.

Each of the main providers could easily use 10x the compute tomorrow (albeit arguably inefficiently) by using more thinking for certain tasks.

Now - does that scale to the 10s of GWs of deals OpenAI is doing? Probably not right now, but the bigger issue as the article does point out in fairness is the huge backlog of power availability worldwide.

Finally, AI adoption outside of software engineering is incredibly limited at work. This is going to rapidly change. Even the Excel agent Microsoft has recently launched has the potential to result in hundred fold increases in token consumption per user. I'm also suspect of the AI sell through rate being an indicator that it's not popular for Microsoft. The later versions of M365 copilot (or whatever it is called today) are wildly better than the original ones.

It all sort of reminds me of Apple's goal of getting 1% in cell phone market share, which seemed laughably ambitious at one point - a total stretch goal. Now they are up to 20% and smartphone penetration as a whole is probably close to 90% globally of those that have a phone.

One potential wild card though for the whole market is someone figuring out a very efficient ASIC for inference (maybe with 1.58bit). GPUs are mostly overkill for inference and I would not be surprised if 10-100x efficiency gains could be had on very specialised chips.