Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesLeAct turns the actions of solvers, planners, and controllers into reasoning data by keeping only explanations that help a model recover the expert decision.
AI may boost today's output while eroding the junior work that creates tomorrow's experts. Nolan Lovett explains the profession-wide coordination risk.
As agentic execution gets cheaper, verification becomes the scarce production input. A new model traces value toward ground truth, provenance, and liability.
When probabilities and outcomes are impossible to predict, most investors flee. Richard Zeckhauser argues this domain of profound ignorance is precisely where the greatest returns are found—if you have the right framework.
Jeremy Allaire argues that AI agents need economic infrastructure for payments, identity, and coordination. This explainer separates the functions that genuinely benefit from blockchains from those better handled by conventional systems, clarifying where onchain architecture earns its complexity.
OpenRouter's 100 trillion token usage study suggests AI demand is shifting from simple text generation toward reasoning, tools, code, and context-heavy workflows. This explainer maps what that change means for model providers, infrastructure, and the economics of serving AI.
AI Futures Project's AI 2040 scenario is less a prediction than a governance proposal. This explainer examines its case for compute verification, research transparency, and international coordination strong enough to slow a race toward concentrated control.
A study of 199 startup founders suggests AI adoption depends as much on managerial willingness to delegate work as on beliefs about model capability. This explainer shows why adoption is a management-design choice, not simply a technology-readiness test.