Hmmm I think the key here is that forgetting is not a bug. Ebbinghaus's curve isn't memory failing, it's memory selecting: the brain discards what didn't earn reinforcement, and that discarding is what keeps retrieval fast and relevant.
The four-part mapping holds up against the numbers too: the accuracy gap between an agent working from raw data and one working from a governed semantic layer runs about 70% versus 95% on identical business questions, a pattern several separate benchmarks converged on this year rather than a one-off. What's less remarked on is how few organizations have actually built that layer -- under a quarter report having a formal data governance framework at all, which means most teams are already doing the "shove everything down" thing by default, not by choice. The real question your framework raises is what separates the minority who consolidate and trace lineage before deploying AI from everyone else bolting governance on after the fact.
That’s a great read!
Hmmm I think the key here is that forgetting is not a bug. Ebbinghaus's curve isn't memory failing, it's memory selecting: the brain discards what didn't earn reinforcement, and that discarding is what keeps retrieval fast and relevant.
The four-part mapping holds up against the numbers too: the accuracy gap between an agent working from raw data and one working from a governed semantic layer runs about 70% versus 95% on identical business questions, a pattern several separate benchmarks converged on this year rather than a one-off. What's less remarked on is how few organizations have actually built that layer -- under a quarter report having a formal data governance framework at all, which means most teams are already doing the "shove everything down" thing by default, not by choice. The real question your framework raises is what separates the minority who consolidate and trace lineage before deploying AI from everyone else bolting governance on after the fact.