Jiasun Li

Jiasun Li

With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives' futures. Specifically, as generative models' power further increases, will they eventually replace all human creatives' jobs? We argue that the answer is "no," even if existing generative AI models' capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox.

Jiasun Li, Project Team

The growing replication crisis across disciplines such as economics, finance, and other social sciences as well as computer science undermines the credibility of academic research. Current institutional solutions -- such as artifact evaluations and replication packages -- suffer from critical limitations, including shortages of qualified data editors, difficulties in handling proprietary datasets, inefficient processes, and reliance on voluntary labor. This paper proposes a novel framework leveraging new technological advances in trusted-execution environments (TEEs) -- exemplified by Intel Trust Domain Extensions (TDX) -- to address the replication crisis in a cost-effective and scalable manner. Under our approach, authors execute replication packages within a cloud-based TEE and submit cryptographic proofs of correct execution, for which journals or conferences can efficiently verify without re-running the code. This reallocates the operational burden to authors while preserving data confidentiality and eliminating reliance on scarce editorial resources. As a proof of concept, we validate the feasibility of this system through field experiments, reporting a pilot study replicating published papers on TDX-backed cloud VMs, finding average costs of \$1.35--\$1.80 per package with minimal computational overhead relative to standard VMs and high success rates even for novice users with no prior TEE experience. We also provide a conduct formal analysis showing that TEE adoption is incentive-compatible for authors, cost-dominant for journals, and constitutes an equilibrium in the certification market. The findings highlight the potential of TEE technology to provide a sustainable, privacy-preserving, and efficient mechanism to address the replication crisis in academia.