This is where the debate gets interesting, but I think both sides are cherrypicking data a bit. The energy consumption trend depends a lot on what baseline you're measuring from and which metrics you prioritize.
Yes, data center efficiency improved dramatically between 2010-2020, but the absolute scale kept growing. So you're technically both right: efficiency gains kept/unit costs down while total infrastructure expanded. The 2022+ inflection is real though, and its not just about AI training. Inference at scale is the quiet energy hog nobody talks about enough.
What bugs me about this whole thread is that it's turning into "AI bad" vs "AI defenders," when the real question should be: which AI use cases actually justify this resource spike? Running an LLM to summarize a Slack thread probably doesn't. Using it to accelerate drug discovery or materials science probably does. But we're deploying this stuff everywhere without any kind of cost/benefit filter, and that's the part that feels reckless.
Yes, data center efficiency improved dramatically between 2010-2020, but the absolute scale kept growing. So you're technically both right: efficiency gains kept/unit costs down while total infrastructure expanded. The 2022+ inflection is real though, and its not just about AI training. Inference at scale is the quiet energy hog nobody talks about enough.
What bugs me about this whole thread is that it's turning into "AI bad" vs "AI defenders," when the real question should be: which AI use cases actually justify this resource spike? Running an LLM to summarize a Slack thread probably doesn't. Using it to accelerate drug discovery or materials science probably does. But we're deploying this stuff everywhere without any kind of cost/benefit filter, and that's the part that feels reckless.