Topic
Essays, profiles, and reading notes on AI products, AI systems, adoption, agents, and company strategy.
Start Here
Start with the control layer for agents, context, tools, permissions, and human review.
Series Index AI Adoption That Actually Changes the CompanyUse the index instead of dropping into a middle chapter of the series.
Series Index Seven Powers in the AI EraRead the strategic power series from the table of contents.
AI The AI Builder’s Edge: 23 Patterns Across 264 ProfilesAcross 264 ai & machine learning profiles and 18,726 lessons, 23 patterns recur often enough to matter. They are not universal rules; recurrence indicates breadth within this collection, not agreement across an entire field.
Research Explainers When Do AI Agents Actually Need Blockchains?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.
Research Explainers AI Traffic Is Becoming Workflow TrafficOpenRouter'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.
Related Hubs
DSPy argues that language-model systems should be written as modular programs and optimized by compilers, not assembled from fragile hand-written prompt strings. The approach replaces manual prompt tweaking with explicit objectives, evaluation data, and systematic search.
This paper argues that generative AI may not flatten performance differences in knowledge work. It may widen them, especially when star employees have the judgment, autonomy, and reputation to turn AI into portable bargaining power.
This paper argues that AI compute is not a simple commodity input. Chip architecture, energy use, software, training workloads, inference workloads, and policy all change what one more unit of compute really means.
An enterprise AI cost paper proposes a dedicated operating model because generative AI spend is driven by usage, ownership, governance, and workflow design. It connects unit economics to accountability for where, why, and by whom inference is consumed.
A Strategy and Leadership paper argues that CEOs lead AI transformation through generalist, expert, or disruptive operating models, each with different risks. The framework helps leaders choose where expertise, authority, and organizational disruption should sit.
A broad survey argues that the hard part of useful AI systems is no longer prompt wording but deciding what information reaches the model, when, and under what constraints. That makes retrieval, memory, permissions, timing, and context assembly a systems discipline.
A broad survey frames agentic AI as reasoning through action: planning, tool use, search, memory, feedback, and coordination across time. The framework clarifies why runtime design matters as much as model intelligence.
A survey argues that agent skills are becoming the packaging layer for procedural AI work, making governance, permissions, and verification unavoidable. It maps the controls needed before reusable instructions can be trusted across teams and organizations.