AI products demand a different kind of measurement
Traditional software is easier to measure. Tests pass or fail. Functions return the expected output. Performance is timed in milliseconds. The standards are well-defined.
AI features are harder. The same input doesn’t always produce the same output. There’s no objectively correct answer for many tasks. Quality is partly subjective. Models change behaviour over time. Performance varies from hour to hour.
Measuring AI quality requires a different posture. Not “did it return the right answer?” but “did it produce something acceptably good, often enough, fast enough, at acceptable cost?” That’s a fuzzier question than traditional software is used to.
Founders who take this seriously end up with AI products users can rely on. Founders who skip ...
