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.
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When building infrastructure for a nascent technology like AI agents, your core customers may not exist yet. This strategy, similar to Stripe's early days, involves betting on the future growth of an entire ecosystem. You are selling to the customers of tomorrow by building the foundational tools they will inevitably need.
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.
Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.
Unlike previous tech waves that trickled down from large institutions, AI adoption is inverted. Individuals are the fastest adopters, followed by small businesses, with large corporations and governments lagging. This reverses the traditional power dynamic of technology access and creates new market opportunities.
Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.
In the previous SaaS era, emulating giants like Salesforce was a common but flawed strategy for startups. In the new AI era, there is no playbook at all, forcing founders to rethink go-to-market strategies from first principles rather than copying incumbents.
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.
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.
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.
The biggest misconception is that SMBs aren't ready for AI. In reality, their lack of corporate bureaucracy allows them to be more agile and move faster than large enterprises. The key for vendors is to provide accessible, scalable solutions with a low entry point, enabling them to take small, quick steps.
