[blake1]

I think a reasonable summary of the study referenced is that: "AI creates the perception of productivity enhancements far beyond the reality."

Even within the study, there were some participants who saw mild improvements to productivity, but most had a significant drop in productivity. This thread is now full of people telling their story about huge productivity gains they made with AI, but none of the comments contend with the central insight of this study: that these productivity gains are illusions. AI is a product designed to make you value the product.

In matters of personal value, perception is reality, no question. Anyone relying heavily on AI should really be worried that it is mostly a tool for warping their self-perception, one that creates dependency and a false sense of accomplishment. After all, it speaks a highly optimized stream of tokens at you, and you really have to wonder what the optimization goal was.

[thinkingemote]

It's like the difference between being fast and quick. AI tools make the developer feel quick but they may not be fast. It's less cognitive effort in some ways. It's an interesting illusion, one that is based on changing emotions from different feedback loops and the effects of how memory forms.

[asadotzler]

Quickness is a burst; speed is a flow.

Or, "slow is smooth, and smooth is fast"

[aspenmayer]

I’ve heard it that way, which is more memorable. I’ve also heard it this way:

“Slow is smooth, smooth is efficient, and speed is the efficiency of motion.”

[BriggyDwiggs42]

I’ve noticed that you can definitely use them to help you learn something, but that your understanding tends to be more abstract and LLM-like that way. You definitely want to mix it up when learning too.

[daxfohl]

I've also had bad results with hallucinations there. I was trying to learn more about multi-dimensional qubit algorithms, and spent a whole day learning a bunch of stuff that was fascinating but plain wrong. I only figured out it was wrong at the end of the day when I tried to do a simulation and the results weren't consistent.

Early in the chat it substituted a `-1` for an `i`, and everything that followed was garbage. There were also some errors that I spotted real-time and got it to correct itself.

But yeah, IDK, it presents itself so confidently and "knows" so much and is so easy to use, that it's hard not to try to use as a reference / teacher. But it's also quite dangerous if you're not confirming things; it can send you down incorrect paths and waste a ton of time. I haven't decided whether the cost is worth the benefit or not.

Presumably they'll get better at this over time, so in the long run (probably no more than a year) it'll likely easily exceed the ROI breakeven point, but for now, you do have to remain vigilant.

[BriggyDwiggs42]

Yup, I think a mixed approach is probably best. It’s really good for targeted, specific questions but best used with reference to docs or something that you can check its work with anytime you’re even slightly suspicious.

[tonyedgecombe]

I keep wondering whether the best way to use these tools is to do the work yourself then ask the AI to critique it, to find the bugs, optimisations or missing features.

[BriggyDwiggs42]

I think in fuzzier fields that’s a very bad idea, but in more hard sciency fields works alright. I’ve tried having it test my understanding on philosophy and it’s incredibly sycophantic.