Ok, explain to me why you can’t scale out existing small and mid tier models horizontally? yes there are current resource limits on chips and energy but we know how to build those out and those types of limits are common to nearly every other industry, it’s just that no other industry has generated such rapid demand/investment for infrastructure.

There’s no O(n^2) problem on number of requests that would make handling large scale rollout impossible like the article is suggesting.

Everyone else: China please save us from the deranged bully that is the US

China: *continues to play alone with their trains and EVs

Are you talking about a one shot from the model or using a harness? I agree a junior dev can do better then a one shot, but with a proper harness with adversarial review cycles I don’t think a junior dev could

junior actually learns and becomes much more consistent than the AI over time.

A proper harness will have memory and will get more consistent the more you use it. You can “teach” it by adding skills and having it write it’s learnings either locally or to repo context files.

Ok, remove the just then, the point still stands that it is a solvable problem. We know how to make data centers, it may not be easy or cheap but it’s possible just like we know how to build car factories.

There’s a lot of variation in quality with humans though.

I don’t doubt that there are some engineers better then the frontier models at coding considering architecture, maintenance performance etc. Those engineers tend to be more expensive though. I

don’t think an average engineer is better then the frontier models though, and say an entry level engineer fresh out of a boot camp would be significantly worse then even a tier 2 model.

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.

Efficiency is relative, if there is a solution that uses less resources then the other solution is more efficient, but if there is no other solution then the solution is the most efficient.

Is using fable 5 to do 2+2 efficient? No, because a calculator can do that with less resources

Is using fable 5 to rewrite a code base from zig to rust efficient? Maybe since the only other solution is a human it depends on how you compare human resources to compute resources. Time wise it’ll probably take the human longer since they require breaks.