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?
The data centres aren't inherently spikey, in general use their consumption is reasonably predictable.
However, if a DC detects that the _grid_ is wobbly (voltage or frequency deviations) the DC will disconnect without warning, and switch to its batteries and generators.
The grid complains because it's suddenly lost hundreds of MW of load. For the DC to have isolation capability, it would need a load-sink which can consume roughly the same power as the DC in normal operation, and can take in that load at a moment's notice.
It's a hard problem to solve, and probably better managed at grid-level than DC-level.
> The grid complains because it's suddenly lost hundreds of MW of load. For the DC to have isolation capability, it would need a load-sink which can consume roughly the same power as the DC in normal operation, and can take in that load at a moment's notice.
Thats why always have all the EVs be connected all the time (except the 20 mins they drive). EVs can provide demand as a service and take excess power whenever its available (instead of solar curtailment), and also provide an immediate source of load when events like this happen. Its a shame US is anti EV, it has the best systems at scale that can be leveraged to transform the entire energy ecosystem.
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.
This is where a smart grid would really shine, because the utility could drop the electricity price to 0 or even negative and turn on 100,000 air conditioners/heaters to effectively give people free climate control to absorb the load imbalance.
Even then Japanese-English translations are hardly accurate and don't often translate fluently. I'm sorry I refuse to use a browser with automatic AI translation.
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.
reply