Platform value isn't developer efficiency. It's enabling developers to build features that solve end-customer problems and drive business outcomes like retention. The platform PM must connect their work across this two-step chain to secure investment.

A platform's immediate user is the developer. However, to demonstrate true value, you must also understand and solve for the developer's end customer. This "two-hop" thinking is essential for connecting platform work to tangible business outcomes, not just internal technical improvements.

Anthropic employs a bifurcated product strategy. Claude Cowork is designed for simplicity to appeal to a broad, non-technical audience. In contrast, Claude Code is built with extensive customizability (skills, hooks, permissions) to satisfy expert engineers who love to "hack their tools."

While mainly a horizontal platform, Anthropic strategically builds vertical applications. This isn't to compete with their ecosystem, but to build ahead of current model capabilities and demonstrate to the market what will be possible on their platform in the near future, accelerating adoption.

Instead of betting on a single user interface like chat or agents, Anthropic assumes form factors will constantly change. They focus on building a robust platform with flexible primitives, empowering developers (both internal and external) to experiment and discover future interaction models.

An effective AI strategy requires a bifurcated plan. Product leaders must create one roadmap for leveraging AI internally to improve tools and efficiency, and a separate one for external, customer-facing products that drive growth. This dual-track approach is a new strategic imperative.

RAMP built its AI platform in-house because they view internal productivity as a competitive moat. Owning the tool allows them to move faster, deeply understand user pain points, and leverage internal learnings to inform their external customer-facing products.

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.

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.