How We Think
The reasoning and the evidence behind it, in full. Worth reading whether it went up today or a year ago.
Read How We Think →Two kinds of writing live here. How We Think is long-form and evidenced: it teaches a method, and holds up regardless of when you find it. What We Think is shorter and current, often the canonical home for something posted first on LinkedIn, Substack, or Medium. Where a piece appears on another platform, this remains the version of record.
The reasoning and the evidence behind it, in full. Worth reading whether it went up today or a year ago.
Read How We Think →Shorter, tied to the moment, often written alongside something posted elsewhere — this page is the version of record.
Read What We Think →For most of AI's useful history, capability was the limit on what it could do for a business. That's closing — and the bottleneck left standing is whether anyone has ever said, precisely, what the job actually is.
Adoption of AI is high and climbing; the financial return from it mostly isn't. The three stages — access, harness, role — that explain the gap, with almost all the return sitting in the third one.
The specific, learnable signals that tell you a piece of your business is ready for an AI employee — usually a $2,000-$8,000 consulting conversation, explained here directly.
A generic agent-builder platform gives you somewhere to configure a role. It doesn't define the role for you — the same reason buying accounting software doesn't create a CFO.
A concrete walkthrough of what it actually takes to declare one real piece of a job precisely enough for an AI to operate it: the owner, the boundary, the material, and the check.
A written boundary and an enforced boundary are two different things. Why holding one requires something outside the model entirely, and what that already looks like, built and free.
A tar archive can hold two entries at one path, and the tools that read one don't agree about which wins. An adversarial review found our own tool gave three different answers to that question — and why the fix was to refuse the input rather than pick a side.
Why a routine MCP server release is the moment your agent’s behaviour changes without anyone deciding it should — what Sentinel records, the coverage figure we retired for being unreproducible, and the flaw an adversarial review found in our own parser.