The fundamental business purpose of engineering is not the act of writing code, but applying technical skills to achieve concrete financial outcomes. All engineering work ultimately serves one of these two goals: increasing revenue or reducing costs.

A senior engineer's greatest impact often comes not from being the deepest technical expert, but from having enough context across multiple domains (marketing, PR, engineering) to act as a translator. They synthesize information and help teams with deep expertise navigate complex, cross-functional decisions.

When hiring senior engineers, the crucial test is whether they can build. This means assessing their ability to take a real-world business problem—like designing a warehouse system—and translate it into a tangible technical solution. This skill separates true builders from theoretical programmers.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.

In the Code AGI era, the ability to build software is commoditized. The scarce and highly valuable skill for business operators is now the mindset to proactively identify any operational challenge or workflow friction and reframe it as a problem that can be quickly solved with custom software.

As AI automates technical execution like coding, the most valuable human skill becomes "systems thinking." This involves building a mental model of a business, understanding its components, and creatively devising strategies for improvement, which AI can then implement.

The creator of Claude Code prioritizes hiring generalists who possess skills beyond coding, such as product sense and a desire to talk to users. This 'full-stack' approach, where even PMs and data scientists code, fosters a more effective and versatile team.

Technical implementation is becoming easier with AI. The critical, and now more valuable, skill is the ability to deeply understand customer needs, communicate effectively, and guide a product to market fit. The focus is shifting from "how to build it" to "what to build and why."