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
29 essays
Framework · September 10, 2026
Chatbots ride on documents. Making a world model useful in a specific enterprise setting tends to require a different substrate — a linked operational record of observations, conditions, actions, and outcomes, with rights and provenance to match. Document RAG is insufficient for that, not irrelevant.
Framework · August 16, 2026
A ~$6K desk changes the default. Here is what still belongs in the cloud, and how to route work between owning and renting without tribalism.
Framework · July 10, 2026
Computer science has had rigorous definitions of 'agent' for 30 years. The industry took about two to break the word — and the cost isn't semantic. Agent-washing misprices risk in both directions: the safe systems get over-governed and the autonomous ones get under-governed. Here is the L0–L5 ladder I use to keep the term honest, and the governance that should follow each level.
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
Verification is not merely the reliability blocker — it is the pricing lever. Where a domain can verify cheaply, it prices on outcomes and tunes smaller models. Where it cannot, it stays hostage to frontier tokens. Margin follows verification.
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.
Technical Guide · May 25, 2026
Your CEO asks whether you can build your own. The answer is yes. Here is what that actually means — four modes, four stacks from Frontier API to an 8x B200 chassis on your own silicon, the near-frontier OSS shift that changed the calculus, and the control spectrum that cost analysis keeps missing.
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.
Framework · April 23, 2026
Space, power, and cooling was the right product for the last era. It is not the right product for this one. A first-person argument — from inside Digital Realty — about where infrastructure platforms are actually going.
Framework · April 22, 2026
The phrase 'own AI assets' is usually shorthand for 'host a model ourselves.' That is the thinnest version of the move. Six rungs, a balance-sheet shift, and the one asset class almost nobody is buying yet — but should.
Framework · April 21, 2026
The AI bill doubled every six months. We stopped trying to shrink it and started asking a different question: what should we actually own? Part 1 of a 2-part executive series on the portfolio decision.
Framework · April 20, 2026
Six numbers the CFO should read in thirty seconds. The metrics that separate mature AI operators from enthusiastic experimenters — and the trajectory that tells you, every quarter, whether the platform is actually being run.
Framework · April 20, 2026
When you hit enter in ChatGPT, Claude, or Cursor, you are not running one machine. You are running one of four operating modes of something that behaves like an operating system. Same GPUs. Five orders of magnitude in cost. Completely different governance surface.
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 18, 2026
When 93% of enterprise data is created outside the public cloud, the AI question stops being 'which model' and starts being 'where does inference run'. The executive companion to The CEO's Guide to Token Economics.
Framework · April 17, 2026
Why boards should stop asking what AI costs and start asking what a verified outcome costs. A non-technical playbook for the operating discipline that will separate AI leaders from AI spenders.
Framework · April 12, 2026
The answer to the token economics problem isn't one model — it's a portfolio of six specialized model types served as internal API services. Near-frontier open models now handle 80–90% of enterprise tasks at a fraction of the cost.
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 · April 5, 2026
A single power user can generate 10-50 million AI tokens per day. Multiply that across an enterprise, and the math changes everything. Token economics is becoming the defining constraint of enterprise AI.
Framework · March 29, 2026
Today's agents wait to be asked. Tomorrow's will tell you what you're missing. The shift from reactive to prescriptive is where agents become genuinely valuable.
Framework · January 3, 2026
AI agents make outcome delivery feasible. Economic pressure makes it inevitable. Here's what RaaS actually is, where it's already working, and why the shift from 'pay for software' to 'pay for results' changes everything.
Framework · December 22, 2025
We're quietly standing up a new computer on top of the old one. In this new computer, LLMs are the CPU, tokens are the bytes, and the context window is the RAM.
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 · December 10, 2025
Why data sovereignty and secure AI architectures are becoming non-negotiable for enterprise AI deployments.
Framework · November 22, 2025
Why treating data as a product is essential for AI success, and how to build the data infrastructure that makes AI work.
Framework · November 19, 2025
Why the best AI systems amplify human capabilities rather than replace them. A framework for thinking about AI-augmented work.
Framework · November 16, 2025
What 5,000+ students and two decades of AI development have taught me about learning—both artificial and human.
Framework · November 14, 2025
How traditional data governance practices must evolve to support AI initiatives while maintaining trust and compliance.
