Working with Claude Code feels like leading an army of eager lieutenants willing to tackle any challenge I give them. 🎯

Last week, I asked it to build a data visualization dashboard, write the technical documentation, and create test cases—all in parallel. The same day, I had it draft a persuasive proposal for stakeholders, organize an event timeline with logistics, and restructure a research report. Tasks that would have taken me days were done in an afternoon. ⚡

But here’s what surprised me: the bottleneck wasn’t the tool. It was my ability to think about work differently.

Writing, especially, revealed this. I’ve used Claude Code for technical documentation, persuasive proposals, event planning materials—each requiring different tones, structures, and audiences. The challenge wasn’t getting good output. It was learning to clearly articulate what “good” meant for each context, then orchestrating multiple writing tasks simultaneously while maintaining quality standards. ✍️

In knowledge work, our effectiveness depends on how we decompose problems and orchestrate solutions—whether we’re writing for humans or building systems for machines. Most of us haven’t developed the muscle for this kind of parallel delegation.

👨‍💻 Individual contributors think about work as “how will I accomplish this?” We’re trained to execute, not to break down work and delegate to someone (or something) with similar capabilities.

🎯 Technical managers know how to delegate, but usually to people with complementary skills. Delegating to AI that can exceed you in certain areas—writing, analysis, code generation—requires a different mindset. You’re not just distributing work; you’re orchestrating capabilities that might surpass your own.

📊 Executives regularly work with specialists who know more than them in narrow domains. But they typically delegate through layers—setting direction while middle management handles the details. They rarely sit down and directly task 10 specialists simultaneously. AI tools let you do exactly that.

🚀 Who adapts fastest? In my experience, it’s not just entrepreneurs. It’s people who already think in terms of problem decomposition: researchers designing experiments, technical writers managing review cycles, anyone who’s learned to articulate intent clearly. Entrepreneurs have an advantage because they’re forced to context-switch constantly, but they’re not the only ones.

A single person can now coordinate work at the scale of a small team—building full applications, analyzing large datasets, producing substantial content. You’re still the architect and decision-maker, but you can execute at 10x or 100x your individual capacity.

But here’s the real question: if you can generate at 10x speed, can you review at 10x speed? 🤔

Tomorrow I’ll share what I’ve learned about the real bottleneck.