Hopefully in this world, someone figures out how to deliver the performance equivalent of a B300 GPU for about 1/100th the power of a current B300 (which can be up to 1400 watts), or the world will bake.
Especially if cognitive technologies mean that we need to travel less (eg communiting to work, or ineffecient supply chains).
which is to say that the last thing our planet needs is another universalized technology that outputs as much total emissions as cars
in an ideal world, we'd keep LLMs/CNNs/etc specialized and academic until we are hitting diminishing returns on optimizing fundamental microprocessor tech like GAA. but the pursuit of market dominance and mass adoption is our current operating philosophy, and so we have things like this top graph: https://hai.stanford.edu/news/inside-the-ai-index-12-takeawa...
>Grok 4's estimated training emissions reached 72,816 tons of CO2 equivalent, or roughly the same amount of greenhouse gas emissions created from driving 17,000 cars for one year
current global average electricity production = approximately 3.6 terawatts
But then again these systems do not use 1400 watts all the time. We would probably have much more then 11.2 terawatts demand if all humans turn on all their electrical consumers at the same time.
The idea is you tolerate some loss/degradation (which neural networks do) but gain orders of magnitude power efficiency.