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System benchmarks measure whether AI behaves correctly. Behavioral assessment measures whether people verify correctly. You need both for trust.

How organizations can use behavioral AI collaboration data to determine who should have approval authority for AI-assisted decisions

Most AI compliance tooling on the market today hands statute text to a language model and trusts the interpretation. That is not compliance. That is hoping. Compliance requires a representation of the obligation that exists outside the model and survives the model being wrong.

How AI's confident, polished output breaks a heuristic professionals have relied on for decades, and what PAICE measures about your response to it

The behavioral patterns that distinguish high-scoring PAICE users from everyone else, and what you can learn from how they work with AI

The eight Aggregated Intelligence Tenets were recently ratified. Here's how each one already lives inside PAICE as a working mechanism or a deliberate constraint, and where the mapping is honest about its limits.

A plan can be fluent and wrong the same way an AI answer can. Here is the method we used to plan Q3 by subtraction, and the three times it changed what we did.

The PAICE blog is moving from a post every weekday to two posts a week. Here is what changes, what pauses, why we are doing it, and an honest note about the quiet you may have noticed.

Q2 closes open-weight scoring, a buyer-facing content arc, security hardening, and a full look back at the quarter that built the evidence layer.

Three letters arrive from three regulators in three different professions. Each one names a single human being. Not the firm, not the AI vendor, not the committee that approved the tool. That is the accountability gap.

A practical framework for documenting AI-assisted decisions so you can answer "how was this decision made?" when regulators, auditors, or courts ask

What happens when AI collaboration verification fails in regulated industries, from malpractice exposure to regulatory fines to license risk

A practical framework for enterprise buyers building internal support for behavioral AI collaboration assessment and risk reduction

Pricing goes public with no sales gate, the site finishes going trilingual, PAICE wires into the obligation graph, and an outside finding backs the collaboration thesis.

OpenRouter found that a panel of models beats the single best one. That is the PAICE thesis at the model layer, and why we built Turnfile and our newest protocol: Tokenese

PAICE measures human behavior in AI collaboration. ObligationFirst represents legal obligations in a form agents can reason about. Together, the two layers connect what a person did in a real assessment to which specific obligation, under which statute, was at stake. A worked example, end to end.

The entire PAICE.work website is now available in Spanish, French, and Brazilian Portuguese. Three languages graduate from beta to fully supported.

Every PAICE pricing tier explained: what each one covers, who it is for, and why individual scores never appear in organizational reports.

Scoring benchmark bake-off across four models, three paper spotlights, full Fable 5 security & documentation audit, and multilingual site completed beta exit.

Four teams are managing four slices of AI risk right now—and none of them are talking to each other. The maturity gap isn't the distance to the top of the curve. It's the distance between you and knowing where you are on it.