> when people post how many LoC they introduced.

From the article:

  Garman is also not keen on another idea about AI – measuring its value by what percentage of code it contributes at an organization.

  “It’s a silly metric,” he said, because while organizations can use AI to write “infinitely more lines of code” it could be bad code.

  “Often times fewer lines of code is way better than more lines of code,” he observed. “So I'm never really sure why that's the exciting metric that people like to brag about.”

Bureaucrats love LoC, commits, and/or Jira tickets because they are easy to measure but here's the truth: to measure the quality of code you have to be capable of producing said code at (approximately) said quality or better. Data isn't just "data" that you can treat as a black box and throw in algorithms. Data requires interpretation and there's no "one size fits all" solution. Data is nothing without its context. It is always biased and if you avoid nuance you'll quickly convince yourself of falsehoods. Even with expertise it is easy to convince yourself of falsehoods. Without expertise it is hopeless. Just go look at Reddit or any corner of the internet where there's armchair experts confidently talking about things they know nothing about. It is always void of nuance and vastly oversimplified. But humans love simplicity. You need to recognize our own biases.