No, the people growing their food built modern civilization.
(Or millions of disconnected stakeholders with different incentives collectively built modern civilization, but who wants to put that on a bumper sticker)
My intuition is that there would be a fairly stable base load, but doing something like switching on a new training run of a frontier model would be incredibly spiky, thousands of GPUs going from somewhat idle to 100% in seconds.
Would demanding that large spikey users of energy like data centers implement some sort of demand ramping/isolation from the grid in the form of a massive capacitor bank or flywheel generator between them and the grid help reduce the risk here?
AI startups taking unprofitable risky ventures in search of growth opportunity and future returns makes sense to me.
Maybe most of them or all of them lose on their bets, but there's potential for a future where revenue grows beyond the immense capex and research investments.
Oracle though... Immensely risky capex to service a startup industry with what will soon be a commodity...
> As we noted above, the bottleneck in fixing bugs like these is the human capacity to triage, report, and design and deploy patches for them.
...
> To begin, we’ve released Claude Security in public beta for Claude Enterprise customers. It’s a tool that helps teams scan their codebases for vulnerabilities, and which can generate proposed fixes for them. In the three weeks since launch, Claude Opus 4.7 has been used to patch over 2,100 vulnerabilities. (This is faster than the open-source patching described above in large part because enterprises are fixing their own code, whereas open-source fixes usually require volunteer maintainers who work through coordinated disclosure.)
Your critique of the article would likely land much better if you engaged with it.
As long as you're not bound on parallelism or bandwidth then it's "free", but if you're constrained on either resource then your lighter predictor model just needs to save you more cycles than it congests on average.