Unlike cloud or mobile, which incumbents initially ignored, AI adoption is consensus. Startups can't rely on incumbents being slow. The new 'white space' for disruption exists in niche markets large companies still deem too small to enter.

Investor Stacy Brown-Philpot advises that to win large enterprise deals, an AI startup must create a solution so compelling it beats the customer's internal team vying for the same budget. The goal is to access the core 15% budget pool, not the 1% 'play money' budget.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

AI tools drastically reduce the time and expertise needed to enter new domains. This allows startups to pivot their entire company quickly to capitalize on shifting investor sentiment and market narratives, making them more agile in a hype-driven environment where narrative alignment attracts capital.

Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.

Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

Incumbents face the innovator's dilemma; they can't afford to scrap existing infrastructure for AI. Startups can build "AI-native" from a clean sheet, creating a fundamental advantage that legacy players can't replicate by just bolting on features.

Enterprises often default to internal IT teams or large consulting firms for AI projects. These groups typically lack specialized skills and are mired in politics, resulting in failure. This contrasts with the much higher success rate observed when enterprises buy from focused AI startups.