Right now it is impossible to scroll through your LinkedIn feed without being overwhelmed by posts about how great AI is and how it is going to solve all our problems and even make a lot of jobs obsolete. The opposite messaging is also there, ranging from AI is the devils work to real world experiences that dampen the hype quite a bit. I assume that as usual the truth lies somewhere in the middle. There are great use cases for AI, like everything associated with software development, but also a lot of caveats. Successful AI implementation depends on a lot of different factors. A recent Stanford Study involving 120,000 developers had some sobering findings:
- The average productivity gain for AI usage was only about 10%
- Many projects had even negative productivity gains
- Using more tokens had a very low correlation with better results
- The best success predictor was a clean code base to work with. The cleaner the code base the AI worked on the better the productivity gains. The messier the code base, the messier the results.
That of course is very bad news for many companies, because clean code bases are quite rare. Many software development organizations are drowning in technical debt. It seems like the lack of will or ability to enforce code quality, architecture and coding standards is again catching up with us.
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