[intended]

It a side effect of using AI.

People using AI for tasks (essay writing in the MIT study linked below) showed lower ownership, brain connectivity, and ability to quote their work accurately.

> https://arxiv.org/abs/2506.08872

There was a MSFT and Carnegie Mellon study that saw a link between AI use, confidence in ones skills, confidence in AI, and critical thinking. The takeaway for me is that people are getting into “AI take the wheel” scenarios when using GenAI and not thinking about the task. This affects people novices more than experts.

If you managed to do critical thinking, and had relegated sufficient code to muscle memory, perhaps you aren’t as impacted.

[Zigurd]

It's probably too much inside baseball to merit a study, but I'm curious if the results would change for part-time coders. When I'm not coding, I'm writing patents, doing technical competitive analysis, team building, etc.

My theory is that if you're not full-time coding, it's hard harder to remember the boiler plate and obligatory code entailed by different SDKs for different modules. That's where the documentation reading time goes, and what slows down debugging. That's where agent assisted coding helps me the most.

[nyrikki]

SDKs and Binary format descriptors are where I see agents failing the most, they are typically acceptable for the happy path but fail at the edge cases.

As an example I have been fighting with agents re-writing or removing guard clauses and structs when dealing with Mach-o fat archives this week, I finally had to break the parsing out into an external module and completely remove the ability for them to see anything inside that code.

I get the convenience for prototyping and throwaway code, but the problem is when you don’t have enough experience with the quirks to know something is wrong.

It will be code debt if one doesn’t understand the core domain. That is the problem with the confidence and surface level competence of these models that we need to develop methods for controlling.

Writing code is rarely the problem with programming in general, correctness and domain needs are the hard parts.

I hope we find a balance between gaining value from these tools while not just producing a pile of fragile abandonware

[order-matters]

i think your environment is a big role. with Ai you can kind of code first, understand second. without AI if you dont fully understand something then you havent finished coding it, and the task is not complete. if the deadline is too aggressive you push back and ask for more time. with AI, that becomes harder to do. you move on to the next thing before you are able to take the time to understand what it has done.

i dont think it is entirely a case of voluntary outsourcing of critical thinking. I think it's a problem of 1) total time devoted to the task decreasing, and 2) it's like trying to teach yourself puzzle solving skills when the puzzles are all solved for you quickly. You can stare at the answer and try to think about how you would have arrived at it, and maybe you convince yourself of it, but it should be relatively common sense that the learning value of a puzzle becomes obsolete if you are given the answer.

[eru]

> [...] and ability to quote their work accurately.

I guess that's an advantage? People shouldn't have to burden their memory with boilerplate and CRUD code.

[intended]

The task was essay writing, and the three 3 groups were No tools, search, ChatGPT.

The people who used chatGPT had the most difficulty quoting their own work. So not boilerplate, CRUD - but yes the advantage is clear for those types of tasks.

There were definite time and cognitive effort savings. I think they measured time saved, and it was ~60% time saved, and a ~32% reduction in cognitive effort.

So its pretty clear, people are going to use this all over the place.