Most AI pilots fail because founders are retrofitting old playbooks instead of rebuilding revenue architecture. Mark Roberge, founding CRO at HubSpot who scaled from $0 to $100M revenue, breaks down how AI compresses traditional sales cycles and transforms go-to-market execution. He reveals why selling time will jump from 25% to 75% of a rep's week, how AI enables real-time ICP refinement and account targeting, and why sustainable moats now depend on owning the point of work rather than systems of record.

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Most vertical AI companies fail because they're building features, not businesses. Rachel ten Brink, GP at Red Bike Capital and former Scentbird co-founder (scaled to 500K+ subscribers, $29M raised), breaks down how to build defensible vertical AI that survives the regulatory gauntlet. She reveals her operator's test for distinguishing product businesses from services wrappers, explains how winning data network effects emerge from proprietary processing of public data, and shares the specific go-to-market playbook that works for enterprise AI sales cycles.

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Most AI adoption in finance is just automation theater—real transformation requires rethinking entire workflows. George Lee, former CTO and M&A head at Goldman Sachs, explains why AI won't replace bankers but will compress deal cycle times from weeks to days. He breaks down how junior talent using AI companions can eliminate rote tasks while expanding analytical surface area, and why the "Boyle's law" principle means human capital will always find new problems to solve for clients.

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