The ultimate failure point for a complex system is not the loss of its functional power but the loss of its ability to be understood by insiders and outsiders. This erosion of interpretability happens quietly and long before the more obvious, catastrophic collapse.

The most complex challenge in robotics isn't just hardware or software alone, but the "boring" problem of calibration where they meet. Seemingly minor physical misalignments create cascading, hard-to-diagnose software issues that require deep, cross-functional expertise to solve.

Miscommunication with non-technical stakeholders (finance, sales) is a common failure point. Engineers should take negotiation courses to frame technical problems in terms of business needs, do their research, and present a case effectively to get buy-in.

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.

Technical tools are secondary to building a successful design system. The primary barrier is a lack of shared vision. Success requires designers to think about engineering constraints and engineers to understand UX intent, creating an empathetic, symbiotic relationship that underpins the entire system.

In environments with highly interconnected and fragile systems, simple prioritization frameworks like RICE are inadequate. A feature's priority must be assessed by its ripple effect across the entire value chain, where a seemingly minor internal fix can be the highest leverage point for the end user.

The primary source of friction between product and project functions isn't a lack of skills but rather unclear ownership, siloed planning, and conflicting success metrics. The solution is proactive, early alignment on roles, tools, and a shared definition of success.

When Blitzy's system fails to complete the final portion of a project, it's rarely a simple coding error. It's typically due to systemic issues a human would also struggle with, such as contradictory requirements in the spec or a situation where fixing one end-to-end test breaks another.

When building systems with hundreds of thousands of GPUs and millions of components, it's a statistical certainty that something is always broken. Therefore, hardware and software must be architected from the ground up to handle constant, inevitable failures while maintaining performance and service availability.