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.
That can be scaled horizontally and is just a matter of building more data centers.
That was a spectacular way of outlining the fact that you don’t at all understand the problems and limits with “AI” without having any awareness or understanding that you don’t at all understand the problems and limits with “AI.”
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.
If “AI” data centers are the only goal, sure, we could definitely bottleneck all technological progress into forcing a fractional level of slightly higher functionality to things that have already hit their potential at the cost of hobbling every other technology. Just fuck the environment, every level of consumer technology, limit the marketplace of ideas and materials to tech oligarchs, and cripple progress in literally any other field of technology, and it all just makes sense, right? Easy peasy.
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.
Yeah and the point is that model improvements so far have meant making huge increases in size, which offsets the datacenters scale out.
The whole point is that this is futile because we will always be playing catch-up to model sizes, to our ultimate downfall. The tech needs to be smarter not larger. That’s why the whole cloud AI business is shit and not going to work. as anyone with a brain has been saying from the beginning.
Jesus Christ man, people’s homes are being sized with eminent domain for this shit. It ain’t worth it.
The author seems to be confusing user scalability with performance scaling:
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:
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.
That was a spectacular way of outlining the fact that you don’t at all understand the problems and limits with “AI” without having any awareness or understanding that you don’t at all understand the problems and limits with “AI.”
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.
If “AI” data centers are the only goal, sure, we could definitely bottleneck all technological progress into forcing a fractional level of slightly higher functionality to things that have already hit their potential at the cost of hobbling every other technology. Just fuck the environment, every level of consumer technology, limit the marketplace of ideas and materials to tech oligarchs, and cripple progress in literally any other field of technology, and it all just makes sense, right? Easy peasy.
At one of my old jobs “just” was considered a bad word
One does not simply walk into Mordor.
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.
Yeah and the point is that model improvements so far have meant making huge increases in size, which offsets the datacenters scale out.
The whole point is that this is futile because we will always be playing catch-up to model sizes, to our ultimate downfall. The tech needs to be smarter not larger. That’s why the whole cloud AI business is shit and not going to work. as anyone with a brain has been saying from the beginning.
Jesus Christ man, people’s homes are being sized with eminent domain for this shit. It ain’t worth it.