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.

A simple, powerful way for a PM to engage with the technical side is to propose a periodic meeting to review third-party libraries and their updates. This keeps the team aware of new features, shows strategic technical thinking, and builds respect with engineering—a practice almost no companies do.

As AI becomes foundational, the PM role will specialize. A new "AI Platform PM" will emerge to own core infrastructure like embeddings and RAG. They will expose these as services to domain-expert PMs who focus on user-facing features, allowing for deeper expertise in both areas.

Building your own product forces you to confront technical realities like database migrations and architectural trade-offs. This firsthand experience provides deep empathy for engineering challenges, which in turn builds crucial credibility and improves collaboration with development teams.

Beyond speaking the same language as developers, an engineering background provides three critical PM skills: understanding architectural trade-offs to build trust, applying systems thinking to break down complex problems into achievable parts, and using root-cause analysis to look beyond user symptoms.

In technical product management, deep expertise serves a dual purpose. It's not just about understanding the product; it's a critical tool for building credibility with the engineering team. Engineers are more likely to trust and follow the direction of a PM they respect technically, making this a crucial element of effective leadership.

In AI development, trace analysis is a point of tension. Product Managers should become fluent enough to ask intelligent questions and participate in debugging. However, they should avoid owning the process or tooling, respecting it as engineering's domain to maintain a healthy division of labor.

Instead of waiting for experience teams to request an API, platform teams should analyze top-level business goals and proactively propose services that unlock new use cases. This shifts the dynamic from a reactive service desk to a strategic partner.