Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.

Instead of teams building their own merchant analysis tools, Stripe created a centralized "Merchant Intelligence" service. This AI agent crawls the web, generates merchant embeddings, and serves insights to diverse teams like risk, credit, and sales, eliminating duplicated effort and creating massive internal leverage.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Stripe's AI model processes payments as a distinct data type, not just text. It analyzes transaction sequences across buyers, cards, devices, and merchants to uncover complex fraud patterns invisible to humans, boosting card testing detection from 59% to 97%.

Stripe’s payments model shows how AI creates powerful data flywheels. Their massive, proprietary transaction dataset trains superior models, which improves the product, attracts more customers, and widens their data advantage, making it nearly impossible for new competitors to catch up.

For complex cases like "friendly fraud," traditional ground truth labels are often missing. Stripe uses an LLM to act as a judge, evaluating the quality of AI-generated labels for suspicious payments. This creates a proxy for ground truth, enabling faster model iteration.

By creating dense embeddings for every transaction, Stripe's model identifies subtle patterns of card testing (e.g., tiny, repetitive charges) hidden within high-volume merchants' traffic. These attacks are invisible to traditional ML but appear as distinct clusters to the foundation model, boosting detection on large users from 59% to 97%.

The founder of Stormy AI focuses on building a company that benefits from, rather than competes with, improving foundation models. He avoids over-optimizing for current model limitations, ensuring his business becomes stronger, not obsolete, with every new release like GPT-5. This strategy is key to building a durable AI company.

Using a composable, 'plug and play' architecture allows teams to build specialized AI agents faster and with less overhead than integrating a monolithic third-party tool. This approach enables the creation of lightweight, tailored solutions for niche use cases without the complexity of external API integrations, containing the entire workflow within one platform.