Work 4 productive hours a day

I came across this article Daily Routine of a 4 Hour Programmer recently and was impressed by how the author manages to work only four hours a day and remain productive. This article makes me reflect on my productivity and time use, and how to keep a productive daily schedule by resting correctly.

Instead of the 9-5 office schedule, the schedule of a Ph.D. student is quite flexible. The working hours, however, can be much longer. According to this question on Quora, the working time of a PhD.student can extend to 80 hours/week even though they set the hours on their own. Speaking from my personal experience and people around me, this is the norm. Working on the weekends and sitting in front of the computer in the middle of the night has become a new habit after entering the grad school. I could imagine the hours to be even longer for postdoc and AP given the extra pressure and responsibilities. Every day you wake up in the morning and stare at a never-ending to-do list and learn to embrace the unpredictable nature of research. There always is more work to do, and the mysteries continue to haunt you at night. The inability of getting things done despite the long working hours could be a vicious circle that leads to much frustration and little result.

But then again, did we indeed work 80 hours a week? How often does your work get interrupted by checking Facebook messages and then continue to browse news and videos on the phone? When was the last time you were so concentrated at work that you didn’t even notice the passage of time? If you document the time of everything that you do during the day (with apps like aTimeLogger, you will be surprised by how much time you waste on things unrelated to work. Interruptions, distractions, and an idling mind steal away your real working hours.

I have struggled with this particularly severely during my years as a Ph.D. student, and it is only recently that I begin to take it seriously and attempt to seek a way out. After a bit of reading and trying, here are three ways to tackle the problem.

1. Keep a healthy daily routine.

As the Pareto principle suggests, the majority of the work we accomplish surprisingly comes from only 20% of the time that we work. Scientific evidence shows that working four hours a day can be enough - if you really concentrate and be creative during these hours and rest properly. You don’t have to wake up 4 am like the daily routine that I mentioned at the beginning of the article but there is evidence of the benefits of being an early riser and exercise regularly .

2. Get tasks and steps listed

In the book getting things done, the author David Allen relates the lack of productivity to stress. I could not agree at all more. The best way of relieving stress is not by exercising nor drinking, but by really delivering results. The never-ending to-do list serves more as a source of stress than a helping tool. It only motivates your action to work when you have all the activities related to each task divided up into subtasks small enough that it drives you to solve it quickly. The key to getting things done is to gather the information that you encounter during work, get them out of your head and document them into the system. The only way to finish a to-do list is to assign the exact time that you will solve them. There is more to it. The author used three books to describe how to do that properly, so I will probably write more about this in the coming post.

3. Log your time

It helps to reflect the time you spend on the things that you want to achieve. It’s OK to be interrupted or give in to watch an episode of Television when you can’t help it. Nonetheless, the key is to faithfully and accurately log down everything into the system and reflect on the time use.

“Ten thousand hours is the magic number of greatness.", according to Malcolm Gladwell’s famous book “Outliers”. Begin counting these hours now and accumulate your greatness in your work and your hobbies.

Yujun Zhou
Data Scientist

Currently works at Facebook Applied AI Research as a Data Scientist. Trained in Applied Economics and Machine Learning.