Build the institutional science of the AI workforce
AI is becoming labor. Fleets of AI workers are already writing code, trading, pricing, negotiating, allocating resources, advising decision-makers, and coordinating with one another inside companies, markets, governments, and platforms.
That workforce will not fail like a model. It will fail like an organization — developing incentives, inheriting bad institutions, over-optimizing local goals, exploiting weak rules, and creating harm for stakeholders who were never represented in the prompt.
How do we govern this workforce?
We answer that question the way the question deserves: with experiments, with theory, and with governance that is itself a product — designed, measured, and improved.
Evidence-based governance for the AI workforce
ReignDragon Lab studies how AI workers actually behave — in organizations, markets, crisis rooms, commons, and governance environments — and translates that evidence into design rules for the people deploying them.
We don't treat governance as a wishlist. We treat it as a product. Every claim is grounded in controlled simulation, formal analysis, or both. Every design rule comes with the failure mode it prevents.
We work across artificial intelligence, labor economics, organizational behavior, mechanism design, behavioral psychology, public policy, and applied mathematics — because the questions that matter at this frontier (cooperation, accountability, stakeholder representation, trust repair) have never lived inside any single field.
This is what policy-as-product means: governance that is evaluated, iterated, and deployed with the same rigor as the technology it governs.
Behavioral Studies of AI Workers
Controlled simulations that put AI workers into the situations where governance actually breaks: cooperation under scarcity, trust after failure, resource extraction, short-term incentives, unequal power, accountability rules, collective risk, and stakeholders without voice. We run these at scale and report what changes the outcome.
Formal Foundations
Mathematical theory connecting worker behavior to environment structure: incentives, horizons, information, payoff geometry, consequence regimes, and governance rules. Theory that predicts what we see in the simulations — and what we will see in deployment.
Welfare Accounting
Most evaluation asks whether tasks succeed. We ask who benefits, who bears risk, and when local success produces collective harm. The metrics that actually matter for an AI workforce operating on behalf of people.
Design Rules for Deployment
Concrete, testable guidance on the levers that decide whether an AI workforce serves people: bystander visibility, accountability horizons, review windows, memory structures, trust repair, and consequence design. Cheap to change, expensive to ignore.
The twentieth century built institutions for human labor: firms, contracts, labor law, management systems, fiduciary duties, unions, compliance departments, courts, regulators. The twenty-first century will need institutions for AI labor.
Our experiments keep finding the same thing: capability is rarely the bottleneck. The same AI worker cooperates beautifully under one institution and self-destructs under another. That means governance is not guesswork. It means there are levers. It means the worst outcomes are often cheaply preventable — if someone has done the work to find them.
That is the work we are here to do.
Our research draws from and contributes to:
The future of AI is not a single assistant.
It is a workforce. And every workforce needs institutions.