Technology

The author seems to be confusing user scalability with performance scaling:

The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups.

This is a question of whether openai can handle 1 million users asking chatgpt to write a basic html website. That can be scaled horizontally and is just a matter of building more data centers.

The author then goes on to conflate this user scaling with performance scaling:

Yet the returns are diminishing. The bigger an AI model is, the less it improves with each added parameter, and so it must be made bigger at a faster rate just to sustain steady progress. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. Even outside the world of software, it’s hard to find a comparable example, given that economy of scale is the principle that has made light bulbs, cars, and clothing so affordable. By economic and engineering measures, generative AI might be the worst technology ever deployed.

This is a question of whether chatgpt can generate a full complex web app. For this there may be a limit to this bigger model approach but this is common to most technologies, performance sometimes has hard limits. You aren't going to get a car to go 300 mph by making the engine bigger and adding more cylinders, there's diminishing returns, that doesn't make cars the worst technology ever deployed... maybe they are but for other reasons.

Economies of scale also isn't about performance scaling, it's about capacity scaling. Capacity scaling for AI does reflect economies of scale, that's why you have these large AI companies building large data centers.