The half-life framing is the most useful part for operators. Luxury CRM shows the pattern clearly. Propensity models trained on pre-2024 Chinese cohorts kept scoring clients with high confidence long after the customers who generated the training data had stopped behaving that way, and the scores looked healthiest exactly where they were most wrong. The operating wrinkle I would add: model refresh runs on the budget calendar, not on the decay curve. Data expires on a technical schedule and reinvestment arrives on a fiscal one, and the gap between the two is where the silent period lives. Naming the collapse gives finance a reason to fund the audit.
Fascinating article Mark. As I was reading, I kept thinking that the same is true of strategic planning. Plans are built on assumptions that increasingly don't fit the reality of the environment we live in. Then those same plans are rigorously defended and noone ever stops to test the assumptions they're based on.
Been seeing a lot of buzz about this in various arenas - market intelligence thinkers, gtm thinkers, more pragmatic AI thinkers.
(Now whether that's an actual signal, algorithms feeding me similar content, or my own confirmation bias given the role this concept plays in the arc of my book, I won't guess.)
The half-life framing is the most useful part for operators. Luxury CRM shows the pattern clearly. Propensity models trained on pre-2024 Chinese cohorts kept scoring clients with high confidence long after the customers who generated the training data had stopped behaving that way, and the scores looked healthiest exactly where they were most wrong. The operating wrinkle I would add: model refresh runs on the budget calendar, not on the decay curve. Data expires on a technical schedule and reinvestment arrives on a fiscal one, and the gap between the two is where the silent period lives. Naming the collapse gives finance a reason to fund the audit.
Fascinating article Mark. As I was reading, I kept thinking that the same is true of strategic planning. Plans are built on assumptions that increasingly don't fit the reality of the environment we live in. Then those same plans are rigorously defended and noone ever stops to test the assumptions they're based on.
Been seeing a lot of buzz about this in various arenas - market intelligence thinkers, gtm thinkers, more pragmatic AI thinkers.
(Now whether that's an actual signal, algorithms feeding me similar content, or my own confirmation bias given the role this concept plays in the arc of my book, I won't guess.)
Bingo. Prolly.