While incumbents sell roadmaps, startups can collapse enterprise sales cycles by demonstrating a fully functional product that is provably better *today*. Showing a live, superior solution turns a year-long procurement process into a 60-day sprint for motivated buyers.

Economist Bernd Hobart argues that large enterprises are too risk-averse for early AI adoption. The winning go-to-market strategy, similar to Stripe's, is for AI-native companies to sell to smaller, agile customers first. They can then grow with these customers, mature their product, and eventually sell the proven solution back to the legacy giants.

When introducing a disruptive model, potential partners are hesitant to be the first adopter due to perceived risk. The strategy is to start with small, persistent efforts, normalizing the behavior until the advantages become undeniable. Innovation requires a patient strategy to overcome initial industry inertia.

Instead of trying to steal entrenched 'hostage' customers from incumbents, startups should focus on a 'Greenfield' strategy. By building a superior product, they can capture the wave of new companies that are not yet locked into a legacy system and will choose the best available solution.

Tailor your innovation story to your company's risk culture. For risk-averse organizations, proactively acknowledging potential problems, barriers, and what could go wrong is more persuasive. For risk-tolerant cultures like Amazon's, leading with opportunity and the potential for learning is more effective.

Enterprise leaders aren't motivated by solving small, specific problems. Founders succeed by "vision casting"—selling a future state or opportunity that gives the buyer a competitive edge ("alpha"). This excites them enough to champion a deal internally.

Large enterprises don't buy point solutions; they invest in a long-term platform vision. To succeed, build an extensible platform from day one, but lead with a specific, high-value use case as the entry point. This foundational architecture cannot be retrofitted later.

Don't just solve the problem a customer tells you about. Research their public strategic objectives for the year and identify where they are failing. Frame your solution as the critical tool to close that specific, high-level performance gap, creating urgency and executive buy-in.

The vague advice to 'live in the future' becomes practical when you use emerging tech (like AI agents in 2022) to solve your own business problems. By being an early adopter, you encounter the novel challenges that the mass market will face in 1-2 years, revealing the next wave of demand before it's obvious.