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Stanford OpenLab Partners With NEAR House of Stake on AI Governance Research

· 5 min read
House of Stake
House of Stake
NEAR Governance
NEAR House of Stake is proud to partner with Stanford OpenLab

Stanford OpenLab x NEAR House of Stake

NEAR House of Stake and Stanford OpenLab are collaborating on a joint research initiative to explore the potential effects of AI on the governance of decentralized organizations. This work bridges OpenLab's multidisciplinary research into decentralized systems with House of Stake's real-world experimentation around AI-native governance, creating a shared environment to study, prototype, and publish on new governance mechanisms powered by intelligent agents.

This partnership marks an important step forward in understanding how AI may be able to support transparent, scalable, and privacy-preserving decision-making across not only DAOs but digital communities and 21st-century organizations at large.

Stanford OpenLab: A Multidisciplinary Initiative Committed to Open Systems​

OpenLab is Stanford's meta-disciplinary research lab where engineers, artists, and theorists collaborate to prototype open, privacy-preserving, and human-centered technologies. Built at the intersection of engineering, law, economics, art, and the social sciences, OpenLab explores how decentralized systems can enhance trust, autonomy, and value exchange across digital, financial, and socio-technical networks. OpenLab works with several departments across the university, and is formally housed in the Management Sciences and Engineering department under Faculty Director Dr. Ashish Goel.

OpenLab's DAO Governance group in particular draws on economics, law, computer science, and business to engage in both theoretical research and practical pilots, with the ultimate goal of conceptualizing, prototyping, and evaluating new governance mechanisms.

"AI is reshaping how communities coordinate and govern. Working with NEAR House of Stake will allow us study how these intelligent systems behave in the real world. Together, Stanford OpenLab and the NEAR ecosystem will be prototyping the technologies that could define the next generation of decentralized AI governance."

–Reuben Youngblom, Program & Tech Lead at Stanford OpenLab

NEAR House of Stake: The Real-World Testbed for AI-Driven Governance​

Launched in 2020, NEAR is the blockchain built to deliver on the full promise of the open, user-owned web. NEAR was originally built by AI and machine learning researchers who envisioned blockchain technology as the natural settlement infrastructure for AI micropayments. Today, NEAR powers Intents, the market-leading infrastructure for cross-chain swaps, as well as a full suite of privacy-preserving AI products. NEAR remains committed to the idea of a user-owned internet without walled gardens and corporate control. This long-term commitment aligns closely with OpenLab's mission to build open, interoperable, and human-centered digital systems.

House of Stake is NEAR Protocol's governance hub and one of the first-ever DAOs to integrate AI directly into governance workflows. House of Stake is actively developing verifiable AI delegates to scale voter participation and rapidly allocate resources to the most impactful products and tooling across the ecosystem.

This makes House of Stake a valuable live environment where research-ready experiments can be run in the wild, not just in the lab. From agent-assisted voting to AI guardians that can detect anomalies across wallet behavior, House of Stake and its AI initiatives provide the ideal user environment for rigorous governance research.

What This Collaboration Will Explore​

The joint research will focus on several core questions at the intersection of AI and decentralized governance:

  • AI as a governance primitive. What roles might AI agents play in interpreting proposals, modeling trade-offs, and summarizing complex decisions for stakeholders?
  • Verifiable AI systems. Can blockchain-based verification ensure that AI agents act transparently and within defined constraints?
  • Privacy-preserving AI. Can confidential inference ensure that AI agents participate in governance without exposing sensitive data or compromising user privacy?
  • Incentive-aligned delegation. Can agent-assisted delegation reduce voter fatigue, improve representation, and strengthen the legitimacy of governance outcomes?
  • Human-in-the-loop mechanisms. What design patterns might preserve human oversight while simultaneously benefiting from automated analysis, simulation, and recommendation?

These questions will be explored through a combination of formal research, computational modeling, and applied prototyping within House of Stake. The collaboration will leverage House of Stake as an experimentation environment where hypotheses can be tested, refined, and deployed. Any prototypes or governance mechanisms developed through the research partnership will be proposed directly to House of Stake for evaluation by the community.

What's Next​

Stanford OpenLab and NEAR House of Stake are beginning joint research this winter. Findings will be open-sourced and shared publicly to support the broader ecosystem of researchers, builders, and policymakers working on AI governance. The goal is to advance not just theoretical understanding but practical, implementable models for how AI-assisted governance can work safely and effectively at scale.

If you are interested in contributing to this research, please reach out through the House of Stake Governance Forum or to Stanford OpenLab's Program & Tech Lead Reuben Youngblom at ryoungbl@stanford.edu.

At NEAR House of Stake, you have a stake in the future of the AI economy. Join the House today.