AI-Native
26 essays on AI-Native, from 25 years at the seam between the boardroom and the codebase.
AI Pilot Purgatory Is an Orchestration Problem
AI got funded, piloted, and then stalled. Escaping pilot purgatory isn't a model problem or a tooling problem. It's an orchestration problem, and the architecture decision belongs in the boardroom.
Read →Transform: The 6% Who Redesigned the Organization
Only 6% of companies capture real EBIT impact from AI, and they're nearly three times more likely to have redesigned how they work. The last phase isn't technical. It's the operating model, and it's the one only leadership can change.
Read →Industrialize: Scaling Agents Without Scaling the Chaos
Fewer than a quarter of companies have scaled AI agents beyond the first win. Scale is where AI stops being a project and becomes infrastructure — and infrastructure has rules most AI teams haven't learned yet.
Read →Fable 5: The Price Went Up and the Knobs Came Off
Anthropic just shipped a model tier above Opus. The price doubled and the dials disappeared — and both of those facts tell you how to run an AI-native organization.
Read →Operationalize: You Built an Agent. Now Make It an Employee.
A third of companies have an agent doing real work in production. Most of them built a heroic one-off: brilliant, fragile, and understood by exactly one engineer. That's not a capability — it's a liability with good PR.
Read →Ten Coding Agents, One Laptop
Running six to ten coding agents at once was never a model problem. It's an environment problem — and the moment you solve it, the constraint moves to the one thing you can't refactor: the RAM on your desk.
Read →Experiment: Pilot Purgatory
Two-thirds of companies are running AI pilots. Most pilots are built to demo, not to ship — and a pilot without a production path is just an expensive way to postpone a decision.
Read →Equip: You Bought the Tools and Nothing Changed
88% of companies have bought AI tools. Most of them mistook the purchase order for the transformation. Procurement is not adoption, and seat counts are not leverage.
Read →The Five Phases of AI Adoption — and Where Companies Stall
AI adoption moves through five phases: Equip, Experiment, Operationalize, Industrialize, Transform. Each transition fails for a different reason, and almost everyone is stalled in the first two.
Read →Tokenmaxxing Is the New Lines of Code
Counting tokens tells you nothing about whether work got done. But rationing them to save money is the more expensive mistake. The real skill is context hygiene, and the real win is letting people experiment.
Read →When Building Gets Cheap, Shaping Becomes the Job
Becoming AI-Native isn't a tooling change. It's learning to shape a problem, set an appetite, and bet on the outcome.
Read →The Pentest Comes Too Late. Put OWASP in Every Pull Request.
A pentest twice a year tells you what was broken months ago. Wire Claude Code's GitHub Action into your pipeline for everyday code review — then add a second, explicit security step that reads every diff against the OWASP Top 10 and blocks the merge when it finds a hole. Security stops being an event you survive and becomes a property of how you ship.
Read →Your AI Feature Has a Gross Margin. Your CFO Just Can't See It Yet.
You can watch an LLM feature work and still have no idea what it costs you per customer. LLM Ops is two jobs, not one: Langfuse tells you what every model call did and what it cost, and Finout drops that cost into the same bill as your cloud, allocated per team, product, and customer. Wire them together and an AI feature stops being a mystery line on the OpenAI invoice and starts being a P&L you can defend.
Read →Your CI Was Green. The Model Just Swore at a Child.
You can't test an AI agent by asserting on strings, and you can't trust a green build either. You test the behavior, by replaying real histories, injecting the exact RAG context, and grading the tool calls, and you test it adversarially, because a determined nine-year-old is a better red team than your pipeline.
Read →Every Engineer Is a Manager Now
The job is no longer to write the code. It's to break the work down, hand it to a team of agents, and be accountable for what comes back. Every individual contributor is quietly becoming an engineering manager of synthetic staff — and the same shift is coming for every other role.
Read →Waterfall Is Coming Back, and It's Not a Joke
Agile was a hedge against the high cost of change. AI collapsed that cost, and with it the reason to slice everything into two-week confetti. When a three-month body of work costs what a ticket used to, the constraint moves back upstream, to thinking. That is waterfall's old home.
Read →AI Agents in Accounting: Build a Graph, Not a Swarm
Accounting is reading, computing against the rules, and filling in forms, exactly the work AI does faster than any analyst. Here's the agent architecture I'd build for it, and why it's a graph, not a swarm.
Read →AI Agents in E-Commerce: Finding Margin in the Cost of Fulfillment
In a commodity market the price is fixed and the product is undifferentiated, so profit hides in the cost of fulfilling each order. Here's the agent architecture I'd build to find it, across both owned inventory and drop shipping.
Read →Design Your AI Agents Like Very Stupid Employees
The easiest way to think about agent design isn't to build one brilliant generalist. It's to hire a team of narrow, slightly dim specialists who each do exactly one job, and nothing else.
Read →Agile Is Dead. The Word Is Still Twitching.
Agile was a workaround for the slow, expensive nature of building software in 2001. AI repriced the building. The ceremonies that compensated for the old expense are now a tax paid in the exact currency they were invented to protect: speed.
Read →Your Company Doesn't Adopt AI. AI Digests Your Company.
AI is the first technology that metabolizes organizations rather than augmenting them. Going "AI-native" doesn't upgrade your operating model, it dissolves the structure that existed to manage problems AI just erased.
Read →Product Management Was a Workaround. AI Removed the Thing It Worked Around.
Product management was an optimization layer for a constraint, expensive software, that no longer exists. When building gets cheap, the PM's job inverts: from advocate for what gets built to editor of what shouldn't.
Read →Automating Your Support Queue Is the Worst Use of AI Your Company Will Make This Year
A support ticket isn't a cost to be deflected. It's the highest-fidelity evidence you have about where your product, pricing, and onboarding are broken, and the standard AI playbook is quietly destroying it.
Read →AI on Both Sides of the House
Most companies put AI to work on one side of the business and ignore the other. The leverage is in running it on both, with a human accountable where it touches customers.
Read →Digital Transformation Is an AI Problem Now
The old transformation playbook moved you to the cloud and tidied your processes. The new one rebuilds operations around AI. The lever changed; plans didn't.
Read →Becoming AI-Native: Rebuilding the Operating Model, Not Just the Product
Most companies are bolting AI onto an operating model designed for a pre-AI world. Going AI-native means rewiring how products are built and how the organization runs.
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