Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesA 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.
FAPO uses Claude Code to optimize multi-step LLM pipelines by inspecting intermediate failures, trying prompt fixes first, and changing the workflow only when evidence points to a structural bottleneck. The result is a practical diagnostic loop for builders.
Michael Mauboussin and Dan Callahan use PVGO to separate current business value from future growth expectations, giving investors a cleaner view of what the market has already priced in. It works as an expectations lens, not a timing signal.
AI-native startups are smaller, flatter, more technical, and more valuable per employee because AI is embedded in the product, not merely internal workflows. The analysis connects organizational design, technical founder density, and valuation economics.
A detailed scenario about agents, automated AI research, model misalignment, geopolitical competition, and the choice between racing and slowing down. Its value lies in testing assumptions about timelines, coordination, and control rather than treating the scenario as destiny.
A study of ChatDev traces finds that agentic coding systems spend most tokens on review and repeated context passing, not initial code generation. The finding shifts optimization toward handoffs, critique loops, and context efficiency.