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.

General Catalyst's CEO notes a change in enterprise AI GTM strategy. The old model was finding product-market fit, then repeating sales. The new model involves "forward deployed engineering" to build deep trust with an initial enterprise client, then focusing on expanding the services offered to that single client.

The traditional VC advice of conquering one market before moving to the next is obsolete in the fast-paced AI era. To outrun competitors, startups must treat GTM like venture capital: test multiple markets and strategies in parallel to quickly identify the few bets that will drive exponential growth.

AI-native companies find more success selling to new businesses or those hitting an inflection point (e.g., outgrowing QuickBooks). Trying to convince established companies to switch from deeply embedded systems like NetSuite is a much harder 'brownfield' battle with a higher cost of acquisition.

The shift to AI creates an opening in every established software category (ERP, CRM, etc.). While incumbents are adding AI features, new AI-native startups have an advantage in winning over net-new, 'greenfield' customers who are choosing their first system of record.

The go-to-market for AI hardware is unlike traditional enterprise sales. Founders should focus on a small number of massive customers: the hyperscalers and emerging "sovereign clouds" in various countries. The total addressable market is maybe 50 customers, not thousands, making it a telecom-like industry.

The narrative that AI killed traditional GTM is false. Leaders at firms like OpenAI and Anthropic are SaaS veterans applying modified versions of proven strategies. If your GTM is failing, the problem is likely poor execution, not an outdated playbook.

In a new, explosive market like AI, the initial phase is a 'land grab' focused on acquiring any and all users. As the market matures and competition intensifies, the strategy must shift to 'oil drilling'—identifying and focusing on specific, high-value customer segments where you have a unique advantage.

Many engineers at large companies are cynical about AI's hype, hindering internal product development. This forces enterprises to seek external startups that can deliver functional AI solutions, creating an unprecedented opportunity for new ventures to win large customers.