A year ago, an engineer typed code into an IDE. Maybe GitHub Copilot suggested lines. Maybe they asked ChatGPT for help.
Today, that same engineer prompts an agent to write substantial chunks of code, then reviews what comes back. The work that used to take days now takes hours.
The job didn’t disappear. It became something fundamentally different.
Aaron Levie, Box CEO, puts it directly: “The job of an individual contributor really begins to change because you are now a manager of agents.”
The Management Shift
The old knowledge work loop: receive task, execute task, deliver output.
The new loop:
- Receive objective
- Decompose into agent-appropriate chunks
- Allocate chunks to agents or self
- Review agent outputs
- Integrate, iterate, or reject
- Deliver outcome
Every step except execution has become a management function. You’re no longer measured on how fast you execute. You’re measured on how well you allocate and how accurately you evaluate.
This shift requires skills most ICs haven’t developed: decomposition, allocation, review velocity, and orchestration. These are management skills—planning, resource management, quality control, and workflow design.
When you’re reviewing 10x more output than you used to produce, your error rate needs to drop proportionally, or you’re introducing more mistakes, not fewer. You need explicit quality benchmarks, fast feedback loops, and clear prioritization.
Why Work Expands Instead of Disappearing
When you make knowledge work more efficient, the intuitive response is: we’ll need fewer people.
That’s not how it plays out. When work becomes cheaper and faster, you don’t do less—you do more.
At Box, if lawyers can review contracts twice as fast, the company doesn’t cut the legal team. They review every contract faster, respond to customers faster, close more deals—which creates more legal work, not less. The bottleneck moves. It doesn’t disappear.
This is Jevons Paradox applied to knowledge work: when you make a resource more efficient, demand expands to consume the new capacity.
Spreadsheets didn’t reduce accounting jobs—they created more accountants. Photoshop didn’t shrink design work—it exploded the number of designers. AI won’t reduce knowledge work jobs—it’ll expand what gets built.
The work that becomes economical at new price points opens entirely new categories of demand. You couldn’t afford a second legal review before. Now you can run contracts through AI at a fraction of the cost. That’s not replacing lawyers, it’s expanding the total market.
What Changes Now
Start thinking like an allocator. Audit your tasks: which are you doing because they’re important versus because they’re “your job”? The second category is your target for agent allocation.
Build review systems. Define what “good enough” looks like. How do you evaluate outputs quickly? What triggers iteration versus rejection?
Practice decomposition. Break complex projects into chunks that could be handed to agents. What can run in parallel? Where do you need human judgment?
Small startups with no legacy workflows are designing around this from day one. They’re prompt-driven, spec-driven, agent-reviewing. They operate in ways that larger companies with established processes can’t match yet.
That’s not about technology. It’s about workflow.
The Transformation
You’re not becoming an AI user. You’re becoming an AI manager.
That role requires judgment, prioritization, quality evaluation, and workflow orchestration. It’s less about executing tasks yourself and more about deciding which tasks matter, allocating resources efficiently, and integrating outputs into business value.
The skill that matters most is changing from “how well do you execute this task” to “how well do you decide which tasks matter and allocate intelligence accordingly.”
The shift is already happening in engineering. It will ripple through every knowledge work domain over the next few years.