The board asked how much of the build was AI. All of it, the CEO said, and the board approved. AI compresses the thirty percent of a platform a demo shows. The seventy percent an acquirer pays for was never on the page.
A board approved a platform for $70,000 because it was the cheapest option on the table. Eighteen months later, three weeks from signing, the buyer wanted $400,000 to rebuild it. The first invoice was budgeted. The second was not.
A founder claims proprietary AI, and the deal team nods — because they have no reliable way to know if it's real. On a $10M ARR business, the gap between an AI-native multiple and the one it earns once its architecture is examined runs $30M to $50M.
A deal team spends three months on financial diligence. Then two weeks on technology. The financial statement was audited. The codebase they just bought was not. That asymmetry is the most expensive habit in technology M&A, and it is getting worse.
On a $5M EBITDA base, the gap between a pure-service exit and a platform exit is, at the midpoint, roughly $25M in proceeds to the same shareholders selling the same business. The decision that opens or closes that gap is usually made years before anyone retains a banker.