Comment by Dirak
1 year ago
One should be suspicious of ulterior motives when the CEO of an AI company makes a claim like this.
On one hand, LLMs do require significant amounts of compute to train. But the other hand, if you amortize training costs across all user sessions, is it really that big a deal? And that’s not even factoring in Moore’s law and incremental improvements to model training efficiency.
The inference cost is also huge in energy terms, not just the training. Remember that Altman's company builds gigantic models.
> is it really that big a deal
yes, at least as long as you constantly develop new AI models
and you still need to run the models, and e.g. for GPT4 that is alone already non trivial (energy cost/compute wise)
through for small LLMs if they are not run too much it might be not that bad
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Generally I would always look for ulterior motives for any "relevant" public statement Sam Altman makes. As history has shown there often seems to be some (through in that usage "ulterior" has a bit too much of a "bad"/"evil" undertone).
To cut it short he seem to be invested in some Nuclear Fusion company, which is one of the potential ways to "solve" that problem. Another potential way is to use smaller LLMs but smaller LLMs can also be potentially a way how OpenAI loses their dominant position, as there is a much smaller barrier for training them.
Data centers cost 2% of electricity (Statistics from a few years ago).
AI inference is so costly at scale, one can easily see data centers start using 4% of total electricity, and in the next decade 8%. That will start to have severe effects on the power grid, basically require planning many years in advance to setup new power plants and such.
"Moore's law and incremental improvements' are irrelevant in the face of scaling laws. Since we aren't at AGI yet, every extra bit of compute will be dumped back into scaling the models and improving performance.