The Brief Was Always the Expensive Part
Production hours collapse when you hand them to a machine. Specification hours don't. Remove the person and ambiguity returns as polished, wrong output.
Category
Strategy
How to decide about AI: what to build, what to buy, where judgment belongs, and which problems a machine can actually finish.
26 posts
The Scorecard Is the Moat
When production collapses to near-zero, leverage moves to whoever can turn a fuzzy goal into a trusted measurement. Your definitions of success are the asset.
Closable vs. Open: Which Business Problems AI Can Actually Finish
AI loops finish problems with countable inputs, a measurable finish line, and no adversary who moves after you commit. Everything else it can only amplify.
Nothing Your Business Does Is Novel
Roughly 90% of business-model innovation is recombination. The new part of your company lives in its constraints, and that decides what a machine can own.
The Last Human Job Is Owning the Predicate
At every layer the job collapsed to one shape: own the irreversible commitments, define the invariants, adversarially audit the definitions. The rest is tokens.
The Regression Ratchet Has No 'Done'
A test suite only encodes the attacks someone already ran. It raises the floor and says nothing about the ceiling, so QA becomes a permanent red team.
Ask for Attacks, Not Tests
Test generation samples the happy path: coverage theater. Vulnerability finding is adversarial search with a real target. The prompt is the difference.
Allocate Architects by Irreversibility, Not Difficulty
Hard problems are now cheap to retry. The cheap-looking calls (a field's type, an event's ordering) are the ones you can't undo. Most orgs route backwards.
Human-in-the-Loop Is a Rubber Stamp Unless It's Adversarial
A passive monitor of a reliable process is a rubber stamp with a person's name on it. Oversight that doesn't attack isn't oversight. It's laundering.
The Harness Is the Moat: Why That Doesn't Contradict 'Harnesses Aren't IP'
Trading engineering hours for tokens is not a linear trade. Implementation collapses to zero; specification and validation surface all at once, up front.
The Design→Test Loop Finishes Closable Problems. It Can't Touch Open Ones.
What decides whether an AI loop can own a problem end to end is not difficulty. It's whether the problem closes, and an adversary keeps it open forever.
Is Anything in Software Still Novel?
Component-level novelty went nearly extinct. The industry's novelty budget moved into constraints, and that decides how to use AI on a real codebase.
The GDPR Compliance Checklist for Enterprise AI Platforms
GDPR gives data subjects rights over their personal data. When that data flows through AI systems, compliance takes more than a privacy policy.
Token Economics: Understanding and Controlling AI Costs at Scale
AI pricing is per-token, and tokens are not intuitive. Input vs output pricing, cache economics and model choice separate sustainable AI from overruns.
Building AI for Regulated Industries: A Gateway-First Approach
Healthcare, finance, legal, and government organizations need AI. They also need compliance. A security gateway makes both possible without compromise.
What the Lawsuits Are Really About
NYT v. OpenAI, Bartz v. Anthropic, Thomson Reuters v. Ross. Everyone is watching the doctrine. The market has been telling a different story the whole time.
The Platform Fee Hiding in Your AI Budget
Some AI gateways charge a percentage of every dollar you spend on AI. At enterprise scale, that percentage becomes the largest line item in your budget.
Agents Don't Have IP. Workflows Do.
The agentic-AI gold rush extended the IP framing onto a layer where it makes even less sense, and is producing very expensive prompt wrappers.
The Half-Life of a Prompt Is Shorter Than Your NDA
The industry's prompt-protection playbook rests on a layer the Copyright Office already found doesn't confer authorship. Here's where real protection lives.
Budget Controls for AI: Four Levels of Spend Governance
AI costs scale with usage, and usage scales with autonomy. Without hierarchical budget controls, one runaway agent can consume a quarterly AI budget in a day.
Your Data Isn't a Moat. Your Loop Is.
A contrarian read on the AI industry's favorite defensibility argument, plus a three-filter test for telling a real moat from data hoarding theater.
Introducing: The IP Framing Problem in AI
A four-part series on why most AI defensibility strategies are protecting the wrong thing, and what actually works in 2026.
CMMC Compliance and AI: What Defense Contractors Need to Know
Defense contractors face CMMC compliance deadlines while adopting AI. Consumer AI tools can violate DFARS. Here is how to use AI without losing your contracts.
Shadow AI Is Already in Your Enterprise
Your employees are already using AI tools you did not approve, with data you cannot track. The question is not whether to allow AI, but how to govern it.
AI in Healthcare: HIPAA Compliance When Every Model Is a Third Party
Healthcare wants AI, but every model provider is a potential HIPAA liability. Gateway-level PII tokenization changes the compliance equation.
Enterprise Agentic AI in 2026: You Should Not Have to Choose Between Trust and Flexibility
Enterprise AI forces a false trade-off between trusting your vendor and avoiding lock-in. AOCore and AODex were built to eliminate that choice entirely.