> "We collect self-reported familiarity with AI coding tools, but we do not actually measure differences in prompting techniques."
Many people drive cars without being able to explain how cars work. Or use devices like that. Or interact with people who's thinking they can't explain. Society works like that, it is functional, does not work by full understanding. We need to develop the functional part not the full understanding part. We can write C without knowing the machine code.
You can often recognize a wrong note without being able to play the piece, spot a logical fallacy without being able to construct the valid argument yourself, catch a translation error with much less fluency than producing the translation would require. We need discriminative competence, not generative.
For years I maintained a library for formatting dates and numbers (prices, ints, ids, phones), it was a pile of regex but I maintained hundreds of test cases for each type of parsing. And as new edge cases appeared, I added them to my tests, and iterated to keep the score high. I don't fully understand my own library, it emerged by scar accumulation. I mean, yes I can explain any line, but why these regexes in this order is a data dependent explanation I don't have anymore, all my edits run in loop with tests and my PRs are sent only when the score is good.
Correctness was never grounded in understanding the implementation. Correctness was grounded in the test suite.
But isn't the corrections of those errors that are valuable to society and get us a job?
People can tell they found a bug or give a description about what they want from a software, yet it requires skills to fix the bugs and to build software. Though LLMs can speedup the process, expert human judgment is still required.
Tests only cover cases you already know to look for. In my experience, many important edge cases are discovered by reading the implementation and noticing hidden assumptions or unintended interactions.
When something goes wrong, understanding why almost always requires looking at the code, and that understanding is what informs better tests.
> AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.
I assistance produces significant productivity gains across professional domains, particularly for novice workers.
We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average.
Are the two sentences talking about non-overlapping domains? Is there an important distinction between productivity and efficiency gains? Does one focus on novice users and one on experienced ones? Admittedly did not read the paper yet, might be clearer than the abstract.
I learned a lot more in a short amount of time than I would've stumbling around on my own.
Afaik its been known for a long time that the most effective way of learning a new skill, is to get private tutoring from an expert.
But that's what "impairs learning" means.
> Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average.
The library in question was Python trio and the model they used was GPT-4o.
[1] plug: this is a video about the Patreon community I founded to do exactly that. Just want to make sure you’re aware that’s the pitch before you do ahead and watch.
For example I wanted to add a rate-limiter to an api call with proper http codes, etc. I asked the ai (in IntelliJ it used to be Claude by default but they've since switched to Gemini as default) to generate one for me. The first version was not good so I asked it to do it again but with some changes.
What would take me a couple of hours or more took less than 10 minutes.
Previous title: "Anthropic: AI Coding shows no productivity gains; impairs skill development"
The previous title oversimplified the claim to "all" developers. I found the previous title meaningful while submitting this post because most of the false AI claims of "software engineer is finished" has mostly affected junior `inexperienced` engineers. But I think `junior inexperienced` was implicit which many people didn't pick.
The paper makes a more nuanced claim that AI Coding speeds up work for inexperienced developers, leading to some productivity gains at the cost of actual skill development.
Yes, we can use it 10,000 times to refine our recipes, but "did we learn from it"? I am doubtful about that, given that even after running with the same prompt 10 times, it will give different answers in 8/10 responses.
But I am very confident that I can learn by iterating and printing designs on a 3D printer.
kaelandt•1h ago