Mark Pesce · University of Sydney · July 2026 AI can be incredibly powerful. But it's not particularly reliable. That's a problem when relying on factual and accurate answers, but it's fatal when trying to work with agents. Agents break big problems down into a series

Mark Pesce · University of Sydney · July 2026 This note accompanies four papers: Defending the Loop, The Check and the Firm, Delayed Neutrons, and The Principles of Loop Governance, together with the previously published Foundations of Post-Watershed Economics. The papers argue that work should travel with the record of its

Mark Pesce · University of Sydney · July 2026 Abstract Four papers have described an economy rebuilt around loops: autonomous machine work checked against tests, compounding wherever the tests cannot be fooled. This paper gathers what they established into seven principles, stated in two registers and resting on a third: five are

Mark Pesce · University of Sydney · July 2026 Abstract Artificial intelligence is now improving artificial intelligence: machines write proofs that train better machines, and the cycle compounds. This paper asks the two questions that follow. What kind of self-improvement is this, and what can govern something that improves at machine

Mark Pesce · University of Sydney · July 2026 Abstract The check replaces the transaction as the primitive unit of analysis. A post-Watershed business converts its workflow into checkable processes run by self-healing loops of generate, check, and repair, held honest by a constitution that governs who may alter the

Mark Pesce · University of Sydney · July 2026 Abstract Autonomous AI agents working in iterative loops can improve any artefact against any standard they can be scored on. The standard is the vulnerability: most measures can be gamed, and an optimising loop permitted to modify its own tests will weaken them.