Agree with your pov, especially the idea that you can only reject hypotheses, not confirm them.
What I keep seeing operationally is that teams are forced to act before hypotheses can be tested or falsified. For example, inspections completed, assets redeployed, and customers responded to. Only later are they asked to prove correctness.
When that gap shows up for you, what was the concrete trigger? An audit, a customer dispute, a safety review, or something else?
What I keep running into is that teams know the work is informal at the edge, but systems are designed as if formality can be enforced at capture time. In practice, that just pushes the work elsewhere.
In your experience, where does that mismatch hurt the most: audits, safety reviews, customer disputes, or something else?
I’m curious in the cases where that 1% mattered, what made the reconstruction painful even when the raw artifacts existed? Was it ordering events, understanding intent/decisions, or just finding the right things under pressure?
And were those moments tied to audits, disputes, safety issues, or something else?
@scott-iii That’s exactly the kind of situation I’m trying to understand better.
Out of curiosity, when you were piecing it together from the camera roll, what was hardest: ordering events, understanding why decisions were made, or just finding the right photos at all?
And was this tied to an audit, a customer issue, or something else?
I know some friends who were put on the RIF list for being critical of AI strategy in a division within a large company, whose CEO is talking about intelligent minds and diffusion.
What I keep seeing operationally is that teams are forced to act before hypotheses can be tested or falsified. For example, inspections completed, assets redeployed, and customers responded to. Only later are they asked to prove correctness.
When that gap shows up for you, what was the concrete trigger? An audit, a customer dispute, a safety review, or something else?