NDAI Agreements
Matthew Stephenson, Andrew Miller, Xyn Sun, Bhargav Annem, Rohan Parikh
TL;DR
The paper addresses Arrow’s disclosure–expropriation paradox by formalizing a baseline model where a seller’s disclosure of a divisible information good creates exposure to expropriation by a buyer. It then shows that Trusted Execution Environments (TEEs) coupled with AI agents can reframe disclosure as a secure, conditional process, enabling full disclosure and efficient ex post transfer when the idea’s value $ω$ satisfies $ω ≤ Φ(k,p,C)$. If $ω$ exceeds the secure threshold, partial disclosure still improves outcomes relative to no disclosure, with robustness to imperfections in AI agents via budget caps and rejection rules. The results imply that cryptographic or hardware-based safeguards can function as an effective, “ironclad NDA” that substitutes for imperfect ex ante or ex post legal protections, with broad policy implications for R&D, tech transfer, and collaboration. The framework advances mechanism design for secure information exchange and suggests practical paths to foster innovation under realistic security constraints.
Abstract
We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In the classical setting, the seller withholds disclosure to avoid misappropriation, leading to inefficiency. We show that trusted execution environments (TEEs) combined with AI agents can mitigate and even fully eliminate this hold-up problem. By delegating the disclosure and payment decisions to tamper-proof programs, the seller can safely reveal the invention without risking expropriation, achieving full disclosure and an efficient ex post transfer. Moreover, even if the invention's value exceeds a threshold that TEEs can fully secure, partial disclosure still improves outcomes compared to no disclosure. Recognizing that real AI agents are imperfect, we model "agent errors" in payments or disclosures and demonstrate that budget caps and acceptance thresholds suffice to preserve most of the efficiency gains. Our results imply that cryptographic or hardware-based solutions can function as an "ironclad NDA," substantially mitigating the fundamental disclosure-appropriation paradox first identified by Arrow (1962) and Nelson (1959). This has far-reaching policy implications for fostering R&D, technology transfer, and collaboration.