this post was submitted on 10 May 2026
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Take away a calculator halfway through an exam, and suddenly, people are surly and unmotivated about simple long division.
That's how every 'AI makes you stupid!' article works. Like, 'doctors used AI to detect more cancer, but when we took it away, they were worse at eyeballing it.' Sorry, can we go back to the part about detecting cancer better?
The doctors were worse, not just taking longer. So this would be more like people unlearning division.
While people using calculators may occasionally be unlearning division, this seems less problematic than doctors unlearning how to spot cancer on their own (since then I'm guessing you won't have AI training data anymore since apparently AI can't feed into AI without collapse) or software engineers unlearning how to write correct code.
You also wouldn't want a mathematician unlearning how to do division.
The doctors were better, until someone yanked the tool away. That's how every tool works! Even going from a handsaw to a table saw and back will make you lose some skill with the handsaw, because your brain focused on higher-level goals and finer motions. That's not proof a table saw is bad for woodworking. The problem is "and back."
Have you checked on that narrative? It's been a while. Things stopped getting yellow. Improvements continued.
Have you checked on that narrative?
The only workaround known so far seems to be to make sure enough data is fresh: https://www.inria.fr/en/collapse-ia-generatives https://en.wikipedia.org/wiki/Model_collapse But read for yourself.
Yes, and any tool that automates too much of the task means you'll unlearn how to do it. That can be acceptable sometimes. For writing sane software, I'd say not so much.
That's a lot of "could" and "will" from an article a year old, primarily about concerns from two years ago, while image models to-day keep getting smaller and better. They didn't find a second internet's worth of JPEGs. Better training on the same data, or even better labels on less data, beats a simple obsession with scale.
Yes, photocopying a photocopy will degrade, but diffusion is a denoising algorithm. Un-degrading an image is its central function. 'Make it look less AI' is how you get generative adversarial networks.
Anyway, the grim truth is that the central concern is mistaken. Training data for cancer screening does not require the patient lived.
The article links a study. What's your study that collapse isn't a concern?
For what it's worth, my worry was never focused on cancer, these doctors were just an example measured for the likely universal unlearning effect.
I again submit the last two years where model collapse did not happen. The doom-and-gloom predictions - some rather gleeful - plainly missed the mark. The proliferation of generated content has not in fact ruined the content generators, and it's sure not because we're any good at marking generated content. Early symptoms went away entirely and the problem has been practically addressed.
As for "unlearning," universality is why it's a made-up problem. Nobody loudly complains that x-rays make doctors worse at feeling around for lumps.
https://cacm.acm.org/blogcacm/model-collapse-is-already-happening-we-just-pretend-it-isnt/ Others seem to disagree.