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Daily insights & updates on AI collaboration, workplace assessment, and PAICE.work innovations
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AI Collaboration in Higher Education
Faculty, Research, and Academic Integrity
A guide for university faculty and administrators on responsible AI collaboration in research, teaching, and academic integrity
The Federal AI Framework Was Rewritten Twice
What survived, and why it was never the citation that mattered
A revoked executive order and two rescinded OMB memoranda later, the duties on the person using the AI barely moved. The citation churned; the behavior did not.
AI Collaboration in Government and Public Sector
Transparency, Accountability, and Public Trust
How government professionals can build effective People+AI collaboration practices that uphold transparency, accountability, and public trust
The Rule Was Never the Hard Part
Law's oldest machine, and the new reader it wasn't built for
Rules do not apply themselves. The interpretation layer is where law actually happens. PubLedge makes that record legible to the newest kind of reader.
Measuring Both Sides of AI Trust
Why System Benchmarks and Human Behavior Assessment Need Each Other
System benchmarks measure whether AI behaves correctly. Behavioral assessment measures whether people verify correctly. You need both for trust.
Why ObligationFirst Looks Different from LegalRuleML and Akoma Ntoso
An agent-native schema next to two lawyer-native predecessors
ObligationFirst binds to LegalRuleML and Akoma Ntoso rather than replacing either. Its field choices are shaped for an agent deciding whether it may act.
Who Gets the Safe Yes
Using Behavioral Data to Inform AI Approval Authority
How organizations can use behavioral AI collaboration data to determine who should have approval authority for AI-assisted decisions
Hoping the Model Reads the Statute Right Is Not a Strategy
Sounding compliant and being compliant are not the same thing
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.
When Polish Impersonates Authority
Why AI Output Looks More Finished Than It Is
How AI's confident, polished output breaks a heuristic professionals have relied on for decades, and what PAICE measures about your response to it
What Good Looks Like
Behavioral Patterns of High-Scoring PAICE Users
The behavioral patterns that distinguish high-scoring PAICE users from everyone else, and what you can learn from how they work with AI
PAICE and the Aggregated Intelligence Tenets
How the eight ratified tenets show up as working mechanisms in a behavioral assessment
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.
How We Planned Q3 by Subtraction
A decision method for telling real evidence from a convincing story
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.
From Daily to Deliberate: The PAICE Blog Moves to Tuesdays and Thursdays
Why 170 daily posts was the right way to start, and twice a week is the right way to continue
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.
When AI Collaboration Goes Wrong
Lessons from Real Failures
Anonymized case studies reveal how People+AI collaboration failures happen in regulated industries and what behavioral patterns predict them
Weekly Update - June 29, 2026
End of Q2, a quarter worth naming.
Q2 closes with a buyer-facing content arc, security hardening, and a full look back at the quarter that built the evidence layer.
The Accountability Gap
The chain does not break. It terminates exactly where the regulators designed it to terminate, on one person with a license.
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.
Audit Trails for AI-Assisted Decisions
Building Defensible Documentation Workflows
A practical framework for documenting AI-assisted decisions so you can answer "how was this decision made?" when regulators, auditors, or courts ask
The Cost of Getting It Wrong
What Verification Failure Costs in Regulated Industries
What happens when AI collaboration verification fails in regulated industries, from malpractice exposure to regulatory fines to license risk
Building the Business Case for AI Collaboration Assessment
What Enterprise Buyers Need to Know Before Procurement
A practical framework for enterprise buyers building internal support for behavioral AI collaboration assessment and risk reduction
Weekly Update - June 22, 2026
Pricing goes public, the site finishes going multi-lingual, and the collaboration thesis gets outside validation
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.