For enterprise sales, founders should sell the solution they've figured out but haven't yet built. The long sales and contract negotiation cycle provides enough time to build the promised functionality, de-risking the product roadmap with a committed customer.
To validate a problem's urgency, don't just ask about pain points; ask what customers are currently doing about them. A pre-existing, makeshift solution signals a real, high-priority need and validates that they have already invested effort into solving it.
Despite AI's usage-based cost model, enterprise CFOs demand predictable expenses. Successful AI startups must absorb this cost variability by architecting systems with cost-saving measures to offer a stable, fixed price to their customers.
Don't fear competition from large AI labs like Google or OpenAI. A startup's A-team, maniacally focused on a specific enterprise problem, will consistently beat the C-team of a tech giant assigned to a non-core project.
Founders must be clear about their motivations for selling and desired outcomes post-acquisition, including their own role and the fate of their team. Unclear or unmet expectations are a major cause of post-M&A dissatisfaction.
Drawing a parallel to AWS's history, AI inference costs are expected to continuously decrease over time. As usage skyrockets, providers will be incentivized to lower prices to capture market share, making fears of escalating costs for startups unlikely to materialize.
Assuming technology is increasingly commoditized, a startup's defensibility comes from relentless execution. This includes providing a seamless onboarding experience and superior customer service—areas where large, impersonal frontier labs are unlikely to compete effectively.
The idea of data as a moat predates the current AI boom. Startups can create defensibility by designing not just systems but also customer contracts to gain rights to use anonymized data for product improvement, creating a powerful flywheel.