When you're the person who understands both the code and the business, you end up in every room. That sounds like a privilege until you realize it's also a trap.
I sit between worlds by design. My title says technology and AI, but my calendar says meetings with the CEO about cash flow visibility, calls with vendors about hardware pricing, reviews of logistics workflows, and somewhere in between, actual engineering work. The reason I'm in all these rooms is simple: I speak both languages. I can translate business need into system design and system constraint into business language.
That translation ability is rare and valuable. It's also exhausting in ways that are hard to describe to people on either side of the divide.
The context-switching tax
There's a well-documented cost to context switching in software engineering. Every time you shift from one problem to another, you lose 15 to 25 minutes of productive momentum. Studies vary, but the range doesn't matter, the direction is always the same. Switching costs something real.
Now imagine context switching not just between codebases, but between entire modes of thinking. At 10am you're debugging an API integration, thinking in terms of response schemas and error handling. At 11am you're in a meeting about whether to open a new warehouse location, thinking about geography, headcount, and operational costs. At noon you're reviewing a designer's mockup and thinking about user flow and conversion rates.
Each switch doesn't just change the problem, it changes the problem-solving framework. Technical thinking, strategic thinking, creative thinking. They use different parts of your brain and they don't blend smoothly.
The myth of the full-stack person
In startups and small companies, there's a celebration of the generalist. The person who can do everything. The full-stack developer, the Swiss Army knife, the one-person department. And there's genuine value in that, especially early, when speed matters more than specialization.
But nobody talks about the maintenance cost. When you're the person who can do everything, you become the person who's expected to do everything. And "everything" doesn't have a scope. It expands to fill your calendar and then overflows into your evenings and weekends.
I manage IT infrastructure, security compliance, AI systems, web platforms, ad campaigns, internal tools, and two direct reports. I partner with the CEO on technology investment decisions. I troubleshoot the office printer. That range is not a sign of organizational dysfunction, it's a natural consequence of being the person who can do all of those things in a company that's growing faster than its org chart.
Protecting depth
The real cost of knowing enough about everything is having less time to know a lot about anything. And that bothers me because the work I love most, the deep engineering, the AI system design, the creative coding, requires sustained, uninterrupted attention.
I've started being deliberate about this. Blocking mornings for deep work. Saying no to meetings that could be emails (a cliche that remains true). Documenting decisions so I don't have to be the living record of why something was done a certain way.
The goal isn't to stop being the translator between business and technology. That's genuinely where I add the most value. The goal is to make the translation happen through systems rather than through my personal presence in every room.
The way out is through
The answer to the "knowing enough" trap isn't to know less. It's to encode what you know into things that don't need you. Dashboards that answer the questions you used to answer in person. Documentation that carries context you used to carry in your head. Automated workflows that replace the manual processes you used to manage by memory.
Every tool I build is partly a tool for the business and partly a tool for my own liberation. If I can automate the procurement approval flow, that's one fewer thing someone needs to come to me for. If the dashboard answers the CEO's question before he asks it, that's a meeting that doesn't need to happen.
The cost of knowing enough is high. But the compound interest on systematizing what you know, that's where the math starts working in your favor.