[posix86]

There's studies showing that LLM makes experienced devs slower in their work. I wouldn't be surprised if it was the same for self study.

However consider the extent to which LLMs make the learning process more enjoyable. More students will keep pushing because they have someone to ask. Also, having fun & being motivated is such a massive factor when it comes to learning. And, finally, keeping at it at 50% the speed for 100% the material always beats working at 100% the speed for 50% the material. Who cares if you're slower - we're slower & faster without LLMs too! Those that persevere aren't the fastest; they're the ones with the most grit & discipline, and LLMs make that more accesible.

[SkyPuncher]

The study you're referencing doesn't make that conclusion.

It concludes theres a learning curve that generally takes about 50 hours of time to figure out. The data shows that the one engineer who had more than 50 hours of experience with Cursor actually worked faster.

This is largely my experience, now. I was much slower initially, but I've now figured out the correct way to prompt, guide, and fix the LLM to be effective. I produce way more code and am mentally less fatigued at the end of each day.

[graerg]

People keep citing this study (and it was on the top of HN for a day). But this claim falls flat when you find out that the test subjects had effectively no experience with LLM equipped editors and the 1-2 people in the study that actually did have experience with these tools showed a marked increase in productivity.

Like yeah, if you’ve only ever used an axe you probably don’t know the first thing about how to use a chainsaw, but if you know how to use a chainsaw you’re wiping the floor with the axe wielders. Wholeheartedly agree with the rest of your comment; even if you’re slow you lap everyone sitting on the couch.

[snewman]

I presume you're referring to the recent METR study. One aspect of the study population, which seems like an important causal factor in the results, is that they were working in large, mature codebases with specific standards for code style, which libraries to use, etc. LLMs are much better at producing "generic" results than matching a very specific and idiosyncratic set of requirements. The study involved the latter (specific) situation; helping people learn mainstream material seems more like the former (generic) situation.

(Qualifications: I was a reviewer on the METR study.)

[bretpiatt]

*slower with Sonnet 3.7 on large open source code bases where the developer is a senior member of the project core team.

https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

I believe we'll see the benefits and drawbacks of AI augmentation to humans performing various tasks will vary wildly based on the task, the way the AI is being asked to interact, and the AI model.

[daedrdev]

It was a 16 person study on open source devs that found 50 hours of experience with the tool made people more productive