Convex built 'Chef', a functional AI coding app, not to win end-users, but as a marketing tool. By open-sourcing it and demonstrating the power of their backend, they successfully attracted other AI coding platforms to build on their technology, turning potential competitors into customers.
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OpenAI embraces the 'platform paradox' by selling API access to startups that compete directly with its own apps like ChatGPT. The strategy is to foster a broad ecosystem, believing that enabling competitors is necessary to avoid losing the platform race entirely.
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.
Advanced AI like Gemini 3 allows non-developers to rapidly "vibe code" functional, data-driven applications. This creates a new paradigm of building and monetizing fleets of hyper-specific, low-cost micro-SaaS products (e.g., $4.99 per report) without traditional development cycles.
The founders were building a new UI for their own internal software. It was their external marketing firm, not them, who recognized the tool could be packaged and sold as a standalone modernization product, which became their flagship offering.
Companies can build authority and community by transparently sharing the specific third-party AI agents and tools they use for core operations. This "open source" approach to the operational stack serves as a high-value, practical playbook for others in the ecosystem, building trust.
As AI and no-code tools make software easier to build, technological advantage is no longer a defensible moat. The most successful companies now win through unique distribution advantages, such as founder-led content or deep community building. Go-to-market strategy has surpassed product as the key differentiator.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
Traditional content like tutorials and blog posts often fails to engage a technical audience. A more effective marketing strategy is to use the tool to build interesting, ambitious projects in public. This showcases the tool's power and attracts a builder audience by sharing the process, including the unresolved challenges.
The primary value of AI app builders isn't just for MVPs, but for creating disposable, single-purpose internal tools. For example, automatically generating personalized client summary decks from intake forms, replacing the need for a full-time employee.
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.
