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.
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Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.
During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.
The true economic revolution from AI won't come from legacy companies using it as an "add-on." Instead, it will emerge over the next 20 years from new startups whose entire organizational structure and business model are built from the ground up around AI.
Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.
Disruptive infrastructure products shouldn't target customers for migration. The key go-to-market strategy is to capture developers at the precise moment they begin building a new application and are evaluating their tech stack. These first inbound users then define the use cases for future outbound sales.
To capitalize on a new technology wave (e.g., AI agents), you must be an active participant at the frontier. The best ideas come from building a solution to a problem you and other pioneers are facing while tinkering. This tool, built for the vanguard, is what the mainstream market will need in 6-12 months.
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.
Stripe intentionally designed its Agentic Commerce Protocol (ACP) to be provider-agnostic, working with any payments processor and any AI agent. This strategic decision to build an open standard, rather than a proprietary product, aims to grow the entire agentic commerce ecosystem instead of creating a walled garden.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
Contrary to early narratives, a proprietary dataset is not the primary moat for AI applications. True, lasting defensibility is built by deeply integrating into an industry's ecosystem—connecting different stakeholders, leveraging strategic partnerships, and using funding velocity to build the broadest product suite.
