Improve the model
The default instinct — buy or build a better model — is a real lever with an honest, limited reach.
What it changes
Who can pull it
What it looks like institutionally
Reducing the model's base error rate helps every downstream pathway a little. It is also the lever institutions reach for first, because it requires no organizational change: procurement instead of governance.
Its honest limits: base error has an empirical floor (no current system reaches zero in demanding domains), and in propagation terms a better model shrinks the source while leaving every loop — adoption, records, retrieval, peer spread — untouched. Illustrative PAN runs across hundreds of deployment structures found system-side levers outperforming equal-effort model improvements in the overwhelming majority of cases; the ledgered scenario results carry the specifics and their caveats.
Use it, but use it last-alone: pair model improvements with the structural levers that govern what happens to the errors that remain.
Ledgered PAN-run results used above
In a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent removed roughly 45.8% of steady-state harm and a coordinated governance package roughly 43.0%, while upgrading the model alone removed roughly 6.3%.[†]
Across a published PAN sweep of 216 deployment topologies (432 sampled deployments), system-layer governance outperformed equal-effort model-layer improvement in 99.1% of cases at roughly 3.4x mean leverage - a figure the project corrected downward from an earlier 9.4x estimate.[†]
Addresses: High base error. Test a version of this lever in the PAN Lab.