Opinion: Rob May’s book rethinks AI investing with six mental models
Rob May’s Investing in AI: Six Mental Models to Tell Moats from Traps challenges conventional AI investing by focusing on business durability over hype. May argues that identifying a promising AI capability and spotting an investable company are distinct skills. He uses examples like Jasper’s valuation drop after ChatGPT’s consumer release to highlight how quickly competitive advantages erode…
Key points
- May warns AI moats vanish when underlying tech becomes commoditized, citing Jasper’s valuation drop after ChatGPT
- Six mental models help investors evaluate AI businesses: tech durability, market shifts, scaling speed, and cost trends
- Founders should prioritize operational execution over tech hype, distinguishing pilots from production revenue
The book introduces six frameworks—from questioning SaaS assumptions to evaluating AI’s cost trajectory—to distinguish real moats from fleeting advantages. May emphasizes operational execution over tech announcements, warning that pilots and proofs of concept don’t guarantee long-term revenue. He also distinguishes between commoditized AI (easy to replicate), core AI (strengthening existing advantages), and expansion AI (opening new markets). The book’s practical takeaways extend beyond investors, offering founders a lens to assess product-market fit and avoid costly missteps.
Book Review: Investing in AI: Six Mental Models to Tell Moats from Traps by Rob May
Unite.AI · 31 October 2026
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