Writing · Field notes from the operating layer
Essays, field reports, frameworks, and technical guides for the people responsible for making AI work.
Current essay · Models & Engineering
Chatbots continue and coordinate symbolic information. A different class of model represents how a bounded environment may change, often conditioned on action. Both are useful. Treating them as the same thing is why enterprise AI conversations keep talking past each other.
Start with the work closest to the decisions you make.
Six editorial lenses. Each starts from a point of view, not a topic label.
Multi-part arguments, ordered as published.
Series / world-models
A four-part guide to the models that represent state, action, and possible consequences, and how they will combine with language systems.
Series / qwen38-local-wow
A five-part investigation into how the harness and serving stack make Qwen3.8-27B useful: the reasoning dial, the harness delta (raw local 2/8 → harnessed 8/8), verified demos, portfolio economics, and native 262K context on 32 GB.
Series / agent-native-work
A four-part series on designing work for agents: why software got them first, every domain's AGENTS.md, the verification gap, and the practitioner-builder.
Series / llm-os-modes
A six-part series on the operating modes behind frontier AI: Chat, Agent, Deep Research, Cowork, and owned infrastructure.
Series / rent-vs-own
An executive series on the AI ownership ladder and the strategic shift from rented tokens to durable assets.
Series / token-economy
A strategy and architecture series on token economics, model portfolios, and AI factory operations.
Series / context-compilation
The missing systems layer between retrieval and reasoning, from benchmark blind spots to measured evidence.
Series / autonomous-stack
The architecture of intelligent systems, from the data substrate to agent runtimes and prescriptive intelligence.
Series / agent-societies
A field guide to what happens when agents interact at scale, from emergence to competence.
Series / semanticstudio
A production-oriented series on building an enterprise RAG and multi-agent system.
Series / ai-native-computer
A technical and operating-model series on what changes when AI becomes the computer, not just another app.
Complete index
10 essays
Framework · July 9, 2026
85.2% versus 10.4%. Same tier of models, five weeks apart. That is not a domain gap — it is a grader gap. Software got agents first because its work came with a free compiler. One law, three multipliers, and the reason every other domain now has to build its own.
Framework · July 9, 2026
The forward-deployed engineer is not a phase every domain passes through — it is a fork. Which side your domain walks down is decided by whether it owns its evals fast enough to outrun acquisition. The capstone of the four-part series on designing work for agents.
Field Report · May 8, 2026
A field report on how I actually use AI in May 2026 — a journey from Chat (3X) through Cowork (5X) and Build (10X) to Automate (30X), and what it means if you are not technical.
Framework · May 5, 2026
There are three Level-1 ways humans and AI work together — Chat (Human-to-GenAI), Build (Human-to-Agent), and Automate (Agent-to-Agent + Agent-to-Human). In 2026, chat is table stakes. The advantage lives in Build and Automate.
Technical Guide · April 19, 2026
Seventeen control planes, zero control. The architecture pattern that turns the CEO's token-economics argument and the Data Gravity placement argument into a single governed operating system for enterprise AI.
Framework · April 19, 2026
The economics of enterprise AI are now driven by routing, compression, caching, and infrastructure control. The AI factory pattern — dedicated GPU environments with federated routing — is becoming core enterprise infrastructure.
Framework · April 5, 2026
The Autonomous Stack is four layers: data substrate, agent runtime, proactive intelligence, and human interface. When all four work together, intelligence compounds.
Framework · December 22, 2025
If AI is the front end and the LLM is the CPU, what does that do to traditional software? Apps stop being destinations and become capability graphs.
Framework · December 22, 2025
How should a leading organization design for an AI-native future? Using the BDAT lens—Business, Data, Application, Technology—we explore what's next.
Framework · November 19, 2025
Why the best AI systems amplify human capabilities rather than replace them. A framework for thinking about AI-augmented work.
