MIT Study Finds AI Use Reprograms the Brain, Leading to Cognitive Decline

In much the same way chess engines make competitive chess accessible to a broader audience. :)

And now people make bad decisions in their daily life about money etc. Most people can't do the math in their head but they also aren't using their calculator at the grocery store to avoid being taken advantage of. The math doesn't get done.

The lesson isn't that we survived calculators, it's that they did dull us, and our general thinking and creativity are about to get likewise dulled.

At some point ai will probably be like calculators where once everyone is using them for everything, that will be a new and different normal from today, and the expectations and the way of judging quality etc will be different than today.

Once everyone is doing the same one weird trick as you, it's no longer useful. You can no longer pretend to be a developer or an artist etc.

There will still be a sea of bottom-feeders doing the same thing, but they will just be universally recognized as cheap junk. Annd that's actually fine, kinda. There is a place and a use for cheap junk that just barely does something, the same as a cheap junky screwdriver or whatever.

An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder.

LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is never really sure if given A the returned value is B. Which means the consumer now has to check if the returned value is actually B, and depending on how complex A->B transformation is, the checking function is equivalent in complexity as implementing the said abstraction in the first place.

To me, that's what makes it an abstraction layer, rather than just a servant or an employee. You have to break your entire architecture into units small enough that you know you can coax the machine to output good code for. The AI can't be trusted as far as you can throw it, but the distance from you to how far you can throw is the abstraction layer.

An employee you can just tell to make it work, they'll kill themselves trying to do it, or be replaced if they don't; eventually something will work, and you'll take all the credit for it. AI is not experimenting, learning and growing, it stays stupid. The longer it thinks, the wronger it thinks. You deserve the credit (and the ridicule) for everything it does that you put your name on.

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edit: and this thread seems to think that you don't have to check what your high level abstraction is doing. That's probably why most programs run like crap. You can't expect something you do in e.g. python to do the most algorithmically sensible thing, even if you wrote the algorithm just like the textbook said. It may make weird choices (maybe optimal for the general case, but horrifically bad for yours) that mean that it's not really running your cute algorithm at all, or maybe your cute algorithm is being starved by another thread that you have no idea why it would be dependent on. It may have made correct choices when you started writing, then decided to make wrong choices after a minor patch version change.

To pretend perfection is a necessary condition for abstraction is not even somebody would say directly. Never. All we talk about is leaky abstractions.

Remember when GTA loading times, which (a counterfactual because we'll never know) probably decimated sales, playtime, and at least the marketing of the game, turned out to be because they were scanning some large, unnecessary json array (iirc) hundreds of times a second? That's probably a billion dollar mistake. Just because some function that was being blindly called was not ever reexamined, and because nobody profiled properly (i.e. checked the output.)