AI Agents Multiply Work and Eliminate Jobs Simultaneously
Traditional automation follows a script. You map the steps, define the logic, and the system executes. If-then-else at scale.
AI agents are different. They have decision-making authority. You give them a goal, and they figure out the path, making choices on the fly based on context. That shift from scripted execution to delegated judgment changes what happens to your workload.
What the data shows
A recent study from Faros AI analyzed over 10,000 developers across 1,255 teams to understand what happens when AI adoption goes high. The productivity story looks clear at first: teams completed 21% more tasks and merged 98% more pull requests.
But the same data revealed the downstream effects. PR review time increased 91%. Bug rates went up 9%. The agents didn’t just speed up the work developers were already doing. They revealed new work that hadn’t existed before.
Someone has to review what the agent produced. Someone has to validate the decisions it made. Someone has to integrate its output with the existing codebase. The cognitive load didn’t disappear: it moved downstream and multiplied.
Two different reads of the same pattern
One interpretation: this is Jevons Paradox for knowledge work. When you make something more efficient, consumption increases rather than decreases. The efficiency gains are real, but they’re not reducing the total work in the system. They’re expanding what’s possible, which creates new categories of work that didn’t exist before. Agent management. Agent training. Quality control for autonomous decisions.
The other interpretation: Anthropic CEO Dario Amodei warned that AI could eliminate roughly 50% of all entry-level white-collar jobs within the next one to five years. His logic centers on a shift from augmentation (AI helps people do jobs) to automation (AI does the job). If agents can handle the execution work, you don’t need as many people doing it. The efficiency doesn’t create more work. It reallocates the dollars to different problems.
The core tension
Both patterns are showing up simultaneously. The Faros data demonstrates work multiplication downstream. The Anthropic warning points to headcount reduction upstream, particularly at entry-level roles where tasks are more structured and agent-friendly.
It’s too early to tell which dynamic dominates, or whether they operate in parallel across different types of work. But the pattern is clear enough to plan for. If you’re deploying agents expecting simple headcount reduction, you might be underestimating the new work they create. If you’re assuming efficiency always expands the team, you might be overestimating the number of people you’ll need to manage what agents produce.
The shifting baseline
Here’s what complicates both interpretations: the definition of “entry-level” is moving. What we consider entry-level today might be three notches higher in eighteen months. College graduates entering the workforce with AI fluency might start at what we’d call mid-level today, because the baseline expectations have shifted.
The agents aren’t just changing how much work gets done or who does it. They’re changing what counts as foundational capability. If that’s true, continuous leveling up isn’t optional. It’s the only defense available. The landscape is changing too fast for static skillsets to hold value.
What new work will your agents reveal that you can’t see yet? And what work will disappear faster than you expect?