Much of what worked in traditional SaaS doesn’t hold true in the AI era. In meeting with hundreds of AI founders building for the enterprise, a few core principles have emerged that are shaping how teams are building and growing in today’s market. - Flashy demos are easy, building substantive products are hard: It takes orchestrating across multiple models, fine tuning, evals, and deep, customer-specific integration to make AI work in production. - $1M ARR isn’t the classic Series A benchmark anymore: AI-native startups are growing faster than their SaaS counterparts, with some hitting $5M ARR in ~9 months. Enterprises now have dedicated AI budgets and CEO-level mandates to buy. - Build costs are collapsing: OpenAI just cut o3 pricing by 80%. Tools like Cursor and Lovable are making it easier for developers and non-technical users to ship full apps. Expect a flood of applications (and potential competitors). - Speed compounds: Product velocity and early brand momentum can snowball into category dominance. Companies like Cursor, Decagon, and ElevenLabs moved fast, landed major customers, and built brand strength before incumbents could respond. - Moats still matter: The winners are becoming systems of record, embedding into workflows, and building deep vertical integrations, especially in complex industries like healthcare and logistics.

Full piece here with more examples and frameworks for enterprise AI builders: