I have long been a fan of Paul Graham’s essay, “Maker’s Schedule, Manager’s Schedule”. Graham describes two different clocks. A maker needs long, uninterrupted stretches to write, design or code. A manager divides the day into smaller units and moves from one decision to another. Cal Newport’s work on deep work gave me another way to understand the maker’s side of this divide: difficult work requires sustained attention without distraction. For years, this distinction felt complete to me. Then I started building with AI.
Working with AI feels like occupying both schedules at once. I might spend many intense hours building a product, which looks and feels like deep work. But I am no longer following one problem down a long tunnel. I am managing agents. Every few minutes, one of them returns with code, a design, a document or a question. I have to read, judge, correct and redirect. Is this the right approach? Has it understood the user problem? What has it missed? The work is deep, but the attention is not singular. It has the duration and cognitive demand of a maker’s day, with the constant switching and decision-making of a manager’s day.
At first, this feels like extraordinary productivity. Work that once took weeks now takes hours. But the decisions inside that work have not disappeared. Why should we build this? What should the experience feel like? Which compromise is acceptable? What happens when it fails? A senior product leader might once have made one or two consequential decisions over the course of a week. While building with AI, similar decisions can arrive every few minutes. The machine has shortened the time between a question and a possible answer. It has not reduced the care required to choose the answer. This is where decision fatigue begins: not with the number of tasks, but with the unbroken stream of judgment they demand.
There is an easy trap here, especially for people who are still learning their craft. It is tempting to believe that the agent is responsible for what it produces. It is not. If my agent writes code, drafts a product requirement, proposes a design or recommends a technical direction, I remain responsible for every part of that output. The agent can produce options at great speed, but I still have to decide whether those options are thoughtful, safe and right for the product. AI has not reduced the need for judgment. It has dramatically increased the rate at which judgment is demanded.
The deeper problem is that these systems have not yet earned the kind of trust we place in a highly capable colleague. Over time, you learn which people can take a problem, make good decisions and return with work that does not require inspection at every step. We are not there with AI agents. Their output can be excellent, but it is not consistently dependable enough to accept without review. So I remain responsible not only for the instruction, but for the reasoning and the final result. Perhaps better models and better agent systems will eventually reduce this burden. For now, building with AI still involves micromanagement at extraordinary speed.
I used to run five or six agent sessions at the same time. I now stop at two. Switching between six windows every few minutes did not merely make me tired. It made my decisions worse. A weak decision creates poor output. Poor output creates rework. Rework requires more instructions, more review and more decisions. The natural response is to work longer, which further reduces the quality of judgment. This is the feedback loop at the heart of decision fatigue. It is not simply exhaustion from long hours. It is the depletion caused by making consequential choices continuously, without enough time for reflection or recovery. Sustained for long enough, it becomes burnout.
This is not only a problem for individuals to solve with better habits. Organizations will see the extraordinary increase in visible output and may begin planning around it. More projects. More parallel work. Shorter timelines. But machine output and human judgment do not scale at the same rate. If every artifact still needs a person to review it and take responsibility for it, then judgment becomes the scarce resource. Decision fatigue is therefore not a personal failure. It is an operating risk. Teams that ignore this will get more work in the short term, followed by weaker decisions, growing rework and exhausted people. We will need organizational limits on concurrent work, clearer ownership of AI output and real time for recovery. Machines can produce at machine speed. People cannot be asked to decide at that speed indefinitely.