Labels Don't Bind

For four months, a label in my strategy documents described one of the services we run — a small online shop — as a cash cow. Cash cows are supposed to produce steady income at low effort. This one produced homework: every order that arrived triggered a price check against suppliers, a stock check, sometimes a renegotiation. The label got corrected twice, each time with some ceremony, and each time I felt I had learned something about the shop. I hadn't. I had learned something about the label.

I run a small studio as a git repository — strategy, decisions, and operating state live in version-controlled documents that an AI reads and edits with me. Somewhere along the way the Boston Consulting Group's growth-share matrix moved in, and every line of business wore a tag: star, cash cow, question mark, dog. This week I deleted the matrix and kept a different borrowed theory in its place. The difference is not which theory is smarter. A borrowed framework earns its place when you can measure its variables in your own units and when breaking it makes a sound; the matrix failed both conditions.

A label you never measure

The growth-share matrix was built in 1970 for conglomerates allocating capital across dozens of divisions. Its two axes are relative market share and market growth. In four months of running my portfolio on its labels, I measured neither axis once. Not out of laziness — for a handful of small lines run by a few people, the numbers barely exist. The labels floated free of any instrument, which meant they were assigned by feel. Assigned by feel, they drifted the way everything in a repository drifts: silently, until the correction is embarrassing. The cash-cow incident was not a malfunction. An unmeasured label system has no other way to work.

A constraint you can hear break

What I kept is Taleb's barbell, which comes from finance: hold most of what you have in something boring and safe, put a small part into bets with a capped downside and an uncapped upside, and hold nothing in the middle. Carried from a brokerage account into a working life, mine now reads as three rules. The safe leg must require no labor — anything that needs my hours is not safety. The risky leg holds one bet at a time, with a deadline, so the downside is capped in months of my time rather than in money. And the client work in the middle, the consulting that pays for everything, is welcome — but the documents never label it safe.

Each rule can be broken, and breaking it makes a sound. If a supposedly passive asset starts demanding my hours, a weekly log says so. If a second bet quietly starts up beside the first, the documents say so. A label rots silently; a constraint fails loudly. That asymmetry, not any difference in intellectual pedigree, is why one borrowed theory survived the week and the other did not.

The test

Before an imported framework moves into your documents, three questions. Did the author claim your case? Taleb spends pages walking the barbell out of finance and into careers, writing, exercise; the growth-share matrix never claimed to know anything about a small studio with a git repository. Can you measure its variables in your own units? Runway in years, a bet's downside in months, attention in hours per week — if the variables exist only in someone else's units, you will assign them by feel. And does violating it make a sound? If you cannot say what breaking the framework would look like in your own logs, you are not adopting a framework; you are adopting its vocabulary.

Frameworks at AI speed

The test matters more than it used to, because adopting a framework is now free. The old filter was implementation cost — the workshop, the poster, the migrated spreadsheet. With an AI managing your documents, installation costs one sentence. Mine will paint any matrix over my businesses in seconds: tag every line, generate the summary table, defend each assignment fluently. The labels arrive pre-argued. Nothing in the tooling asks whether either axis will ever be measured, so the resistance has to live in the operator.

Next time a framework offers itself — a matrix, OKRs, someone's operating system for your week — the question that earns it a place is not whether it sounds true. It is what it measures, and what noise it makes when you break it.