Appendix A: Glossary

Research spine: this chapter stays grounded in NIST AI Risk Management Framework and NIST Secure Software Development Framework, then applies that evidence to the operating judgment in the book. Read this alongside the Judgment Economy book, the AI-Native thesis, and the full book library when you want the surrounding argument. Accepted output - Machine-produced work that the organization has chosen to use after an appropriate acceptance mechanism.

Compounding output - Accepted work that improves future decisions through data, evals, trust, workflow learning, or policy refinement.

Error cost - The economic consequence of accepting wrong output.

Judgment - Accountable selection of action under incomplete information.

Judgment debt - Accumulated ambiguity, hidden review cost, unowned decisions, weak evidence, and unpriced risk created when AI output outruns operating discipline.

Review cost - The full cost of determining whether generated output is acceptable.

Value capture - The pricing, retention, savings, or revenue mechanism by which useful work becomes economic return.

Key Takeaways

• Accepted output Machine produced work that the organization has chosen to use after an appropriate acceptance mechanism.
• The practical test is whether a team can name the evidence, owner, and failure mode before it changes behavior.
• Read this with The Judgment Economy and the adjacent chapters when you need the wider Judgment Economy and AI Strategy frame.

Operational note

This short chapter is a map, not a full argument. Its job is to keep Appendix A: Glossary usable inside Judgment Economy: define the terms, point to the sources, and make the next decision easier to replay.

Use it as a checkpoint before you treat the surrounding chapters as advice. A reader should be able to name the claim, the evidence, the risk boundary, and the follow-up page without interviewing the original author. If that replay fails, the chapter has not done its job yet.