Incentives

Opening — Why this matters now Alignment used to be a single‑model problem. Train the model well, filter the data, tune the reward, and call it a day. That framing quietly breaks the moment large language models stop acting alone. As LLMs increasingly operate as populations—running accounts, agents, bots, and copilots that interact, compete, and imitate—alignment becomes a system‑level phenomenon. Even perfectly aligned individual models can collectively drift into outcomes no one explicitly asked for. ...

Opening — Why this matters now Human–AI decision-making research is quietly facing a credibility problem — and it has little to do with model accuracy, explainability, or alignment. It has everything to do with incentives. As AI systems increasingly assist (or override) human judgment in domains like law, medicine, finance, and content moderation, researchers rely on empirical studies to understand how humans interact with AI advice. These studies, in turn, rely heavily on crowd workers playing the role of decision-makers. Yet one foundational design choice is often treated as an afterthought: how participants are paid. ...

The most radical idea in Michael I. Jordan’s latest manifesto isn’t a new model, a benchmark, or even a novel training scheme. It’s a reorientation. He argues that we’ve misdiagnosed the nature of intelligence—and in doing so, we’ve built AI systems that are cognitively brilliant yet socially blind. The cure? Embrace a collectivist, economic lens. This is not techno-utopianism. Jordan—a towering figure in machine learning—offers a pointed critique of both the AGI hype and the narrow symbolic legacy of classical AI. The goal shouldn’t be to build machines that imitate lone geniuses. It should be to construct intelligent collectives—systems that are social, uncertain, decentralized, and deeply intertwined with human incentives. In short: AI needs an economic imagination. ...