"Tons of power generation?" Perhaps we will go in that direction (as OpenAI projects), but it assumes the juice will be worth the squeeze, i.e., that scaling laws requiring much more power for LLM training and/or inference will deliver a qualitatively better product before they run out. The failure of GPT 4.5, while not a definitive end to scaling, was a pretty discouraging sign.
An engine performs a simple mechanical operation. Chess is a closed domain. An AI that could fully automate the job of these new hires, rather than doing RAG over a knowledge base to help onboard them, would have to be far more general than either an engine or a chessbot. This generality used to be foregrounded by the term "AGI." But six months to a year ago when the rate of change in LLMs slowed down, and those exciting exponentials started to look more like plateauing S-curves, executives conveniently stopped using the term "AGI," preferring weasel-words like "transformative AI" instead.
I'm still waiting for something that can learn and adapt itself to new tasks as well as humans can, and something that can reason symbolically about novel domains as well as we can. I've seen about enough from LLMs, and I agree with the critique that som type of breakthrough neuro-symbolic reasoning architecture will be needed. The article is right about one thing: in that moment AI will overtake us suddenly! But I doubt we will make linear progress toward that goal. It could happen in one year, five, ten, fifty, or never. In 2023 I was deeply concerned about being made obsolete by AI, but now I sleep pretty soundly knowing the status quo will more or less continue until Judgment Day, which I can't influence anyway.
I would argue an "AI doomer" is a negatively charged type of evangelist. What the doomer and the positive evangelist have in common is a massive overestimation of (current-gen) AI's capabilities.
Why haven't they broken out yet, I wonder, if they're more efficient for inference and LLM costs are now weighted towards inference over training?