[whimsicalism]

You can read my reply to another comment making a similar point. In short, I think you are giving Doctorow far too much credit - the assumption that these tools are fundamentally incapable is woven throughout the essay, the risk always comes from the fact that managers might think these tools (which are obviously inferior) can do your job. The notion that they can actually do your job is treated as invariable absurd, pie-in-the-sky, bubble thinking, or unmentionable.

My point is I don’t think a technology that went from chatgpt (cool, useless) to opus-4.5+ in 3 years is obviously being oversold when it says that it can do your entire job beyond being just a useful tool.

[happy_dog1]

I think we have to be careful when assuming that model capabilities will continue to grow at the same rate they have grown in recent years. It is very well-documented their growth in recent years has been accompanied by an exponential increase in the cost of building these models, see for example (of many examples) [1]. These costs include not just the cost of GPUs but also the cost for reinforcement learning from human feedback (RLHF), which is not cheap either -- there is a reason that SurgeAI has over $1 billion in annual revenue (and ScaleAI was doing quite well before they were purchased by Meta) [2].

Maybe model capabilities WILL continue to improve rapidly for years to come, in which case, yes, at some point it will be possible to replace most or all white collar workers. In that case you are probably correct.

The other possibility is that capabilities will plateau at or not far above current levels because squeezing out further performance improvements simply becomes too expensive. In that case Cory Doctorow's argument seems sound. Currently all of these tools need human oversight to work well, and if a human is being paid to review everything generated by the AI, as Doctorow points out, they are effectively functioning as an accountability sink (we blame you when the AI screws up, have fun.)

I think it's worth bearing in mind that Geoffrey Hinton (infamously) predicted ten years ago that radiologists would all be out of a job in five years, when in fact demand for radiology has increased. He probably based this on some simple extrapolation from the rapid progress in image classification in the early 2010s. If image classification capabilities had continued to improve at that rate, he would probably have been correct.

[1] https://arxiv.org/html/2405.21015v1 [2] https://en.wikipedia.org/wiki/Surge_AI

[lostmsu]

No, models significantly improved at the same cost. Last year's Claude 3.7 has since been beaten by GPT-OSS 120B that you can run locally and is much cheaper to train.

[judahmeek]

And GPT-OSS's architecture improvements aren't already incorporated in SotA models?

[lostmsu]

The point is, that contradicts the claim that lately the progress is only made by throwing more compute.

[judahmeek]

That wasn't the claim made.

The claim made was that improving SotA models has historically taken exponentially more compute.

The claim implies that improving SotA models takes more compute even while integrating technological advancements to make models more efficient.

Unless you think that such advancements have been historically ignored by the curators of SotA models?

[lostmsu]

No, that was the claim made.

They justified it with the paper that states what you say, but that's exactly the problem. The statement of paper is significantly weaker than the claim that there's no progress without exponential increase in compute.

The statement of the the paper that SotA models require ever increasing compute, does not support "be careful when assuming that model capabilities will continue to grow" because it only speaks of ever growing models, but model capabilities of the models at the same compute cost continue growing too.

[roxolotl]

But Corey isn’t saying it’s oversold he’s saying the value capture by a few companies enabled by AI is dangerous to society.

[whimsicalism]

I do not agree with your reading of the article. The premise - both implicit and stated explicitly throughout the article - is that companies are hyping this up because they want to be seen as growing, that this technology cannot do your job, that these are statistical tools foolishly being used to replace real workers. Look at the bits I quote in my other comment.

I would have been much more interested in reading the article you’re suggesting.

[johnnyanmac]

You need to read the article again with a more charitable lens. He starts with

>What I do not do is predict the future. No one can predict the future, which is a good thing, since if the future were predictable, that would mean we couldn’t change it.

It feels like shoving words on Cory's mouth to make statements like "he's saying it can't replace us". That is the exact point he avoids in the article to focus on the human. Not the tech and capabilities.