If it can be automated, it wasn't the work
I keep noticing people talk about AI like it’s a wave that’s about to hit them.
“Will it take my job?” “How do we adopt it fast enough?” “How do we protect ourselves?”
Those are understandable questions. They’re also a little late. If AI can do your job, the problem isn’t the AI. The problem is that your job was never designed to require the human part of you.
Most organizations have spent decades trying to remove judgment from work. Scripts. Processes. Compliance. “Just follow the playbook.” It worked because humans are adaptable. We learned to shrink.
Now the machines showed up. And they’re better at machine-work than we ever were.
Rule: if the work can be automated end-to-end, it wasn’t the work.
The work is what’s left. That’s the human era.
Featured writing
Why customer tools are organized wrong
This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.
Infrastructure shapes thought
The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.
Server-Side Dashboard Architecture: Why Moving Data Fetching Off the Browser Changes Everything
How choosing server-side rendering solved security, CORS, and credential management problems I didn't know I had.
Books
The Work of Being (in progress)
A book on AI, judgment, and staying human at work.
The Practice of Work (in progress)
Practical essays on how work actually gets done.
Recent writing
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Dev reflection - February 03, 2026
I've been thinking about constraints today. Not the kind that block you—the kind that clarify. There's a difference, and most people miss it.
When execution becomes cheap, ideas become expensive
This article reveals a fundamental shift in how organizations operate: as AI makes execution nearly instantaneous, the bottleneck has moved from implementation to decision-making. Understanding this transition is critical for anyone leading teams or making strategic choices in an AI-enabled world.
Notes and related thinking
Build for the loop, not the lecture
A junior developer used to wait days for mentor feedback. Now that loop closes in seconds. When feedback is scarce, you batch your questions. When feedback is abundant, learning becomes continuous. AI changes the supply side of learning—most of our systems weren't designed for this.