Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesWhen 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.
A field experiment with 515 startups suggests AI creates firm-level gains when teams learn where to reorganize work around it, rather than merely gaining access to tools. This explainer connects adoption outcomes to workflow mapping, experimentation, and organizational change.
By replacing discrete judgments with continuous probabilistic scoring, researchers show how verification can improve agent performance without additional training. This explainer examines why better scoring may become a distinct scaling axis alongside model size, inference compute, and data.
OpenAI's Codex usage data suggests the agentic shift is less about better chat answers and more about delegating longer, reusable, parallel workflows. It explains why duration, concurrency, and reuse matter more than conversational polish.