alt Hacker News> Overall my view is that AI is structurally a technology that tends to concentrate power, for reasons that have nothing to do with regulation (more to do with the extreme implications of the scaling laws). Open-weights do help some with this but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips (which are roughly the frontier labs plus maybe hardware providers).
This is actually a good point. Open models are getting better and better, some of them might even be useful in consumer hardware now. But if AI performance is still correlated with compute power, then no doubt power will remain with the people owning the chips.
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Current AI is at least a few orders of magnitude less efficient than it could be (as evident from biological spiking networks, e.g. human brain). At some point the labs put too much work and money into transformers and nearly abandoned fundamental research, in both ML and hardware. There are tons of low hanging fruits in efficiency but you'll have to redo everything from scratch so nobody bothers. Which is also pretty convenient and lets people like Dario Amodei speak about "natural concentrations of power".
Arguably the question is whether it's economically feasible to self-host something similar.
Are you self-hosting Google or Bing? No, but we have quite a huge ecosystem of full-text search tools with PageRank, with options to scale to almost Google scale (if you have the money). After all LLM training starts with the same crawl mechanism.
As long as barriers to entry is not too high (ie. it makes sense to take the risk to start a business that provides something similar - usually for a niche) market forces work.
We have the classic empirical chart reproducing microeconomics.
https://www.fda.gov/about-fda/center-drug-evaluation-and-res...
And setting up a pharma plant is also very capital intensive.
Here the obvious barrier to entry is completely artificial. (Which provides an incentive to spend a lot of money on R&D -- though it naturally raises the question of Pareto efficiency.)
I think his point is hand wavy at best. It presupposes infinite scaling and ignores all the algorithmic efficiency wins that are being discovered. Ironically, many of which are being discovered with autoresearch style workflows, using the very LLMs that his company builds.
The #1 post on HN right now[1] is full of people jubilating about how they can run Qwen 3.8 27B on their > 5 year old GPUs. If that isn't democratization of AI, I don't know what is.
I'm sure he's smart enough to instantaneously realize this too, but as the famous Upton Sinclair quote goes, he won't mention it even if he does.
Physical constraints like time and compute make it so that AI does not concentrate power due to scaling laws.
The very definition of (applied) technology is power amplification. Use a lever, move more weight than you could before, 1 person with the tool now wields the power of 3 without.
Making "tech" a career and a societal goal onto itself, without the adjoining understanding of and deep commitment to ethics and the responsible use of power, is why we're sliding into authoritarian rule by a small circle of techno-oligarchs.
We need less "move fast and break things" and more "plant trees you will not live to see bear fruit."
Swap that point about AI with "electricity". Everything runs on electricity it's "a technology that tends to concentrate power" (no pun intended). The electricity providers must be too powerful... But somehow electricity providers aren't that powerful. Unless there is no competition in sight...
The point "AI is structurally a technology that tends to concentrate power" is not that correct. They need this statement to be true, otherwise no way to justify the trillion evaluations.