Chasing Speed Breeds Fragility: How to Measure AI Success

When shrimp prices fell, the industry had an answer ready: better genetics. Grow them faster, grow them bigger, get more pounds out of the same pond. On paper it’s the obvious move. Everybody chased it.

I went the other direction, because my problem wasn’t size. My survival rate was stuck at 15–20%. Four out of five animals were dying before harvest, and no amount of growth rate fixes that arithmetic. So I selected for robustness instead — the animals that lived. Generation after generation, survival climbed past 90%. Same ponds. Same feed. Nothing about the animal got faster. It just stopped dying.

The farms that chased growth got exactly what they selected for: shrimp that grew quickly and broke under the first bad week. Chasing speed breeds fragility. You get what you select for — and the industry selected for a number that looked good on a harvest sheet while ignoring the one that decided whether there was a harvest at all.

AI projects are selected for speed

Watch how an AI decision actually gets made inside a company. Which model is newest. Which vendor can demo fastest. How quickly we can put something in front of the executive team. Every one of those is a speed metric, and every one of them is measured before the thing has to survive a single ordinary Tuesday.

That selection pressure produces a predictable animal: impressive in week one, brittle by month three. It works until an input format changes, or the person who understood it takes a new job, or the vendor ships an update nobody asked for. This is the same trap as chasing the newest AI model, seen from the measurement side. You didn’t buy the wrong tool. You graded it on the wrong exam.

The number nobody writes down

Ask a company how it will measure an AI project and you usually get adjectives. Faster. Smarter. More efficient. Ask which number, specifically, and what it reads today, and the room goes quiet.

That number has to exist before you start, because it’s the only thing that can tell you later whether anything happened. Hours on a weekly task. Days to get a quote out. Percentage of invoices touched by a human. Pick one that was already on your books before anyone said the word AI. If you can’t name it, you haven’t got a project yet — you’ve got a purchase.

Measure survival, not launch day

Here is the metric I’d run every AI investment against: how much of what you shipped last year is still running, unattended, today? That’s your survival rate, and most companies have never calculated theirs. Mine is the number I care about most in an AI opportunity audit — not what could be built, but what would still be working in six months.

Selecting for survival changes what you build. You choose the boring, reversible option over the impressive one. You check the number at month six instead of at the launch meeting. You accept a smaller win that holds over a bigger one that needs babysitting.

Faster and bigger are easy to sell and easy to lose. Something that keeps working after everyone stops paying attention to it is the only kind of AI that ever pays for itself.