$4.2M ARR AI acquisition target turned out to be one GPT-4o call and 600 lines of glue code
alex_verem · x · 2026-09-21
The author shares lessons from AI M&A technical due diligence: data rooms show what a company earns, but the codebase shows what it's worth.
- Case study: a PE firm hired them to vet an M&A target showing $4.2M ARR growing 40% a year, pitched as a proprietary AI platform. The codebase was one GPT-4o call with a system prompt and 600 lines of glue code. The seller asked 12x revenue; the firm walked away.
Key checks before close include:
- Ask how long a senior engineer would need to rebuild it — a weekend means you're paying a software multiple for a sales team.
- Read the system prompt yourself; many 'proprietary features' are one well-written prompt.
- Ask for the eval set — it's the real evidence of a moat.
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