Your to-do list is a work of fiction by Tuesday lunchtime. You planned six things and finished two, and you are quietly annoyed at yourself. You should not be. You have run into one of the most reliable findings in the study of human judgment.
What is the planning fallacy?
It is the systematic tendency to underestimate how long a task will take, named by Daniel Kahneman and Amos Tversky.1 In the classic demonstration, students predicted when they would finish their thesis. Their average best-case guess was about twenty-seven days, their worst-case about forty-nine. The actual average came in around fifty-five days, which means reality was worse than their pessimistic estimate.
The striking part is that it persists even when you know better. People who have blown past every previous deadline still confidently predict the next one will be on time.
Why does it happen?
Kahneman called the cause the inside view. When you plan, you imagine the specific task unfolding smoothly, step by step, in an ideal version of your day. You quietly leave out the interruptions, the false starts, the colleague’s question, and the ordinary bad days that always arrive.2 The plan is a story of the task going right, and tasks rarely go entirely right.
Is it just a failing of the careless?
No, and this is what makes it humbling. The planning fallacy scales all the way up to the most expert teams on earth. The Sydney Opera House was projected to take six years and cost seven million dollars. It took sixteen years and cost a hundred and two million.3 If seasoned architects and engineers, with everything to lose, can be that wrong about a building, you can be that wrong about your Thursday. Expertise does not cure the bias.
What does this have to do with AI?
It compounds a second illusion. AI makes work feel faster, so you plan as if the speed-up is banked, and then the real time, spent prompting, waiting, and checking, runs longer than the feeling promised. The planning fallacy tells you the task will be quick; the AI tells you it was quick; the wall clock disagrees with both. You end up over-committed on the strength of two optimisms stacked on top of each other.
How do you beat it?
Switch from the inside view to the outside view. Instead of asking how long this task should take if all goes well, ask how long tasks like this have actually taken you before. That single shift, anchoring on real history rather than imagined smoothness, is the most effective correction there is.2 Three habits make it concrete. Keep a time audit so you have real data to anchor on. Add a deliberate buffer to every estimate, because the unplanned is the norm, not the exception. And break large tasks into small, measured blocks, since short estimates are wrong by smaller absolute amounts than long ones.
The planning fallacy is not a character flaw you can resolve away. It is built into how the mind plans. You beat it the way you beat any reliable bias: not by trying harder to be accurate, but by replacing the optimistic guess with a measured one.
Frequently asked questions
What is the planning fallacy?
The planning fallacy, named by Kahneman and Tversky, is the reliable tendency to underestimate how long a task will take, even when you have done similar tasks before and know they ran over. It comes from imagining the task unfolding smoothly and leaving out the inevitable interruptions.
Why do I always underestimate how long things take?
Because you plan from the inside view, picturing the specific task going well, rather than the outside view, which asks how long similar tasks actually took. The inside view quietly omits the false starts and bad days that always arrive.
How do you beat the planning fallacy?
Use the outside view: estimate from how long comparable tasks really took, not from how this one feels. Add a buffer, break work into measured blocks, and check estimates against your own time audit rather than your optimism.
About the author
Tom Goodwin is the author of Don’t Work Harder, a book about taking the time AI gives back as time rather than more work. He is a co-founder of GAMEPLAN and writes on productivity, technology, and the economics of the working week.
Footnotes
Footnotes
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Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures” (1979). ↩
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Daniel Kahneman, Thinking, Fast and Slow (2011), on the inside view and the outside view. ↩ ↩2
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The Sydney Opera House overrun (projected six years and seven million dollars; actual sixteen years and 102 million) is a standard illustration of the planning fallacy at expert scale. ↩