How Much Should I Work?
My son, a member of the optimization generation, where pretty much every aspect of his life will be tracked, measured, and ultimately ruled by the Alg, posed a fascinating question to me the other day. He simply asked, “How much should I work?”
Now, the context for the question is that he’s undoubtedly a Type A who throws himself into his pursuits with unbelievable intensity, but his job is somewhat loosely structured so that he has a huge amount of discretion over how he allocates his hours and in particular how many hours he works at all. (Hint: he works a LOT.)
So I traced an approximation of the following graph on a restaurant table top:
The X-axis is the average number of hours per day spent working. Zero is of course zero, and 24 means that he would be literally working 24 hours every day, 7 days per week without sleeping! The Y-axis is job productivity measured in percentage of one’s possible potential. So 100 percent is the best one can possibly be.
So obviously if he works zero hours he will achieve exactly zero percent of his potential productivity. On the other end of the X-axis, working continuously without sleeping will also yield zero percent (I’m not allowing negative productivity just to keep things simple), and in fact if he worked so hard that he never slept he might actually die! So in between these two extremes, the graph must surely rise to 100 and fall back down again to zero. In my hypothetical graph I start with a fairly steep rise as the average hours per day goes up, but following the law of diminishing returns, the productivity curve must flatten so that each additional hour per day yields less and less of an increase in productivity. Finally the curve can rise no further having reached 100 percent, and from there the only direction to go is down!
Now at this point, which happens to be around 12 hours per day on my made-up graph (i.e., equating to 84 hours per week), my son is working so hard that spending more time at work actually begins to decrease productivity due to making errors, losing track of things, miscommunications, etc. Once he increases his hours to the point of seriously cutting into sleep, meals, hygiene, and other normal bodily functions, his productivity plummets as errors pile up, e-mails are left unanswered, and important meetings are forgotten in a delirious haze of ill temper and body odor.
But the important takeaway from staring at the curve is that for every level of productivity, except 100 percent, there are actually two levels of work hours that correspond. So at the point at which he is averaging 18 work hours per day yielding a productivity of about 25 percent, at least according to my particular graph, he could also work just 3 hours per day and achieve the same productivity and presumably be a much more pleasant person to share an elevator with. Which brings us to an obvious truth: No matter what level of productivity you are achieving, you are much better off being on the left side of the curve than on the right!
What’s interesting about this line of thinking is that I strongly suspect that the vast majority of driven, hard-working Type A’s are always on the wrong side. Their personalities lead them to push themselves as hard as they can until something (e.g., partner, close friend, nervous breakdown), actually pushes back. If that’s true, then they basically push themselves until they are well past their optimum output and down the declining right side of the hump until something is actually breaking, whereas they could achieve the same level of productivity by working several fewer hours per day.
So to finally answer my son’s question, here are a few simple rules:
1. At the very least, try not to be too far down the right side of the curve. Recognize the signs of declining productivity by seeking out feedback from the people you work for and work with. When they say you’re working too hard, you probably are.
2. Notice how your allocation of non-working hours affects your overall job performance. If your job requires that you be creative, personable, inspiring, etc., you’re probably not going to be those things for long if you are working yourself to death.
3. Experiment. Like any good data-driven analytical optimization, you need to create a varied set of data points from which you can draw comparisons. Try different levels of work and attempt to infer your personal productivity graph, decide where you want to be, and try to be on the left side of the graph. It won’t be perfect, and of course one can’t really measure productivity on a single axis, but it’s probably better than just going pedal to the metal until you burn out!
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