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.

According to Databricks CEO Ali Ghodsi, monetizing open source requires two consecutive successes. First, the open source project must achieve global adoption. Second, you must build a proprietary, 10x better product on top of it to create a defensible business.

For an open-source project like OpenClaw, having corporations like Anthropic adopt its features or create similar products is a form of validation. Rather than being a pure competitive threat, it demonstrates the project's influence and cements its ideas within the wider industry, proving its value.

Engineers often default to building tools internally. An open-source strategy bypasses this by offering a ready-made solution that feels like 'building' (customizable, free to start) but without the effort. It eliminates the sales friction of a 'buy' decision.

Anthropic's destiny was fundamentally changed by Claude Code, a developer tool that started as a side project. Its massive success, generating $2.5B in ARR and becoming the primary use case for Anthropic's models, demonstrates that the most powerful and immediate application of AI is creating and improving the software that powers the world.

Despite powerful open-source AI models, companies like Anthropic post record revenue. This indicates the total addressable market (TAM) is dramatically larger than anticipated, supporting both paid and open-source ecosystems simultaneously rather than one cannibalizing the other.

Instead of a traditional sales process, Andon Labs built AI evaluations they believed would be useful and provided them to Anthropic for free. Once their value was proven, Anthropic began paying. This demonstrates a product-led growth approach for a highly technical audience, where demonstrating value precedes monetization.

AI21 exemplifies a winning AI business model: give away the foundational model (Jamba) to drive adoption, then monetize a proprietary orchestration layer (Maestro) that helps enterprises manage multiple models for cost and performance, capturing value higher up the stack.

RunTools was building its own agent platform but pivoted to host and enhance OpenClaw after its release. This demonstrates a smart strategy for startups: when a popular open-source "castle" with massive community support emerges, it's often better to build valuable services for it than to continue building a competing product from scratch.