cross-posted from: https://lemmy.ca/post/61948688
Excerpt:
“Even within the coding, it’s not working well,” said Smiley. “I’ll give you an example. Code can look right and pass the unit tests and still be wrong. The way you measure that is typically in benchmark tests. So a lot of these companies haven’t engaged in a proper feedback loop to see what the impact of AI coding is on the outcomes they care about. Lines of code, number of [pull requests], these are liabilities. These are not measures of engineering excellence.”
Measures of engineering excellence, said Smiley, include metrics like deployment frequency, lead time to production, change failure rate, mean time to restore, and incident severity. And we need a new set of metrics, he insists, to measure how AI affects engineering performance.
“We don’t know what those are yet,” he said.
One metric that might be helpful, he said, is measuring tokens burned to get to an approved pull request – a formally accepted change in software. That’s the kind of thing that needs to be assessed to determine whether AI helps an organization’s engineering practice.
To underscore the consequences of not having that kind of data, Smiley pointed to a recent attempt to rewrite SQLite in Rust using AI.
“It passed all the unit tests, the shape of the code looks right,” he said. It’s 3.7x more lines of code that performs 2,000 times worse than the actual SQLite. Two thousand times worse for a database is a non-viable product. It’s a dumpster fire. Throw it away. All that money you spent on it is worthless."
All the optimism about using AI for coding, Smiley argues, comes from measuring the wrong things.
“Coding works if you measure lines of code and pull requests,” he said. “Coding does not work if you measure quality and team performance. There’s no evidence to suggest that that’s moving in a positive direction.”
Pretty much my experience with LLM coding agents. They’ll write a bunch of stuff, and come with all kinds of arguments about why what they’re doing is in fact optimal and perfect. If you know what you’re doing, you’ll quickly find a bunch of over-complicating things and just plain pitfalls. I’ve never been able to understand the people that claim LLMs can build entire projects (the people that say stuff like “I never write my own code anymore”), since I’ve always found it to be pretty trash at anything beyond trivial tasks.
Of course, it makes sense that it’ll elaborate endlessly about how perfect its solution is, because it’s a glorified auto-complete, and there’s plenty of training data with people explaining why “solution X is better”.
I saw a vibe coded PR the other day. So much redundant code, lots of comments making assumptions and questions. It’s a mess.
Glad it didn’t land in my lap but the person who is now responsible for steering that up is already quite busy and wasting their time with this feels shit.
To many people don’t know how to prompt AI, and review.
Too many people are willingly paying anti-democratic billionaires to outsource their thinking and agency.