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Which AI startups actually land enterprise contracts? — Brian Lewis, Millennium

AI Engineer

2 views27 Aug 2026

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For a given internal pain point, Brian Lewis might find 10 to 15 startups worth a look, book two or three demos, run zero or one pilot, and sign roughly one contract for every four pilots. That works out to about 5% of demo calls ending in a signature, which turns out to match published benchmarks. He is on the buying side at a hedge fund, speaking personally rather than for his employer, and the talk is a catalogue of exactly where the other 95% dies. The examples are specific enough to sting. A vendor asked them to self report their own gateway telemetry so it could charge margin on traffic that never touched vendor infrastructure. Integrations that only function if granted read and write access to everything. Features held permanently in beta because the beta terms permit data retention. A team claiming zero data retention that revealed it had their data by mentioning something it could not otherwise have seen. Asked what happens in a breach, one vendor answered that they had not had one yet. His larger argument is that none of this is really an AI problem: a new frontier model lands every 11 days while the architecture underneath is a decade old, and he puts perhaps 60% of becoming AI native on entitlements, integration and change management. Agents inherit whatever foundations you already had. Speaker info: - https://www.linkedin.com/in/brianthomaslewis/ Timestamps: 0:00 - Sellers and buyers in the same room 2:52 - The funnel, and the 5% that signs 4:16 - Efficacy: solving the problem, and pricing it 5:36 - Pilot windows collapsing from months to weeks 6:57 - Security requirements from the buyer's side 8:19 - What goes wrong in security, repeatedly 9:42 - Reliability, control planes and audit logs 11:04 - An outage during a trading day 12:23 - Legal, retention clauses and fourth party risk 13:44 - A model every 11 days, architecture a decade old 15:07 - The unglamorous 60% 16:28 - Entitlements, and why agents multiply the problem 17:54 - The recipe for each side of the table

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