A quote often attributed to Einstein (as many of them seem to be), offers insight into solving the old question of how to increase productivity.: “We can’t solve problems by using the same kind of thinking we used when we created them.” Phrased differently, when tackling a tough problem, it helps to start from first principles.
The problem, in this case, is the following: how can we avoid the inflow of distractions we know too well, and improve productivity? One of the answers lies in the idea of deep work, and we’ve already explored that in our previous posts from his series.
Countless articles, papers, and books have been written on this subject. From the right environment, to the optimal diet, to scheduling, to organization or just generic how to be more productive at work tips – each of them focused on a different axis of optimization, hoping ultimately to arrive at the quick-fix solution to finding a creative nirvana.
But the truth is that there’s no one formula to be more productive. After years of researching and writing, I became convinced that the quest for a quick-fix would always be fruitless. So instead of discussing the patch-solutions, let’s take a step back and re-approach from first principles of improving work efficiency.
In this case, the story takes us back to the early 1600’s.
Before the modern era, agriculture drew most interest from the leading minds of the day. . Marginal but new ways to improve productivity would yield incredible societal gains.More food with less manpower meant an easier life for all. Buoyed by the modest success, more significant inventors also made attempts to improve productivity of agricultural processes.
Cornelius Drebbel was one such inventor. A dutch innovator – best known as the builder of the first submarine – Drebbel spent much of his life working in the field of metrology. His innovations in sensors and optics made him quite famous at the time.
Around 1620, he came across a particular problem: how to incubate a chicken egg without ] a mother chicken. By automating this process, chickens could be hatched more quickly and with fewer resources. The difficulties came in maintaining the correct temperature in the incubator and simulating the conditions of a natural hatching.
The solution Drebbel proposed was one of the clever methods of improving productivity:. A mercury-based temperature sensor would control a heating source. This feedback loop formed the basis for the modern thermostat, which today we use to heat and cool buildings, refrigerators, and incubators of all kinds.
By extension, this innovation is also credited as the birth of control theory,–the first principle of optimizing productivity.
Control theory – or control systems engineering – is at its core quite simple. It’s the study of using measurements and mathematical models to control and optimize the behavior of complex dynamic systems. It can also be interpreted as one of the ways to improve efficiency.
The most basic example is the thermostat described above: a sensor measures the ambient temperature and uses this information to decide whether to turn on or off a heat source. Taken to the extreme, control systems form the foundation of most modern machines, from robots, to airplanes, to nuclear reactors and beyond.
Simplified, a control system is generally composed of at least three pieces:
For the thermostat example, this is obvious. The input is the heat source, and the output is the temperature of the incubator. The model, therefore, can be reduced to a few simple commands: if it’s too cold then turn the heat on, and if it’s too hot then turn the heat off.
For this type of control system to operate, it must meet two criteria. The first is that the inputs must be controllable. This is intuitive: if it were the case that we could not consistently affect the temperature, building a thermostat would be impossible from the get go.
The second criteria is that the output must be accurately measurable. Drebbel’s innovation was to measure the temperature output in order to control the input. The more difficult it is to measure the output you want to achieve, the more complicated the control system has to be in order to function properly.
What, then, does our productivity look like through this lens?
When it comes to improving productivity at work, our goals are relatively clear. Maximizing the quality and quantity of work, in the shortest possible time. Let’s analyze how this can be achieved through the lens of control theory.
Our outputs are the tasks we want to accomplish. This category is incredibly vast as many of us do complex, non-repeating work with minimal guidelines, wide-ranging KPI’s, and very little direct feedback. Being efficient at work requires measuring these outputs accurately and repeatedly. Staying motivated to do so can be incredibly difficult.
Our inputs are the sum of work actions we and our colleagues do. Each decision we make can be considered an input: the emails, meetings, slack posts, distractions, and actual focus hours all fit into this mix. In this case, however, our environment should be considered an input as well. All the sudden email notifications and last minute demands from our colleagues directly affect our work, and must be considered in the mix. The sheer number of inputs we’d have to consider is immeasurable, and the vast majority are out of our control, thereby stopping us from being productive.
Through the lens of control theory, our work day is complete chaos. The sheer number of inputs and outputs make optimizing our time and effort dramatically difficult. In practice, this takes the form of to-do lists that never end and calendars we forget to stick to. In fact, even attempts at avoiding distraction by searching “how to stop procrastinating” are also a form of distraction. Moreover, with so much variability and randomness in our days, it’s impossible to objectively quantify our own productivity. We simply can’t accurately measure how well we’re doing, or how our inputs affect our outputs.
So instead of using a real control system, most of us blindly do the best we can. We do the tasks in the best order we can justify. We try new strategies to help us organize, and rate their effectiveness based on how productive we “feel”. We read blogs from thought leaders and try out new productivity tips somewhat arbitrarily, without a true sense of how well any of them actually work. The end result? We still have no idea about how to become more productive and motivated at work.
In his 1954 book The Practice of Management, Peter Drucker coined the expression “what gets measured gets managed”. Our current solutions for employee productivity are neither measuring nor managing. We won’t solve this problem using the same thinking that got us into it.
We need a better control system.
The unfortunate truth is that for creative, non-repeating knowledge work, it’s nearly impossible to measure our output. This makes being efficient at work that much harder for creative roles.
This isn’t to say we shouldn’t try – it’s still vital that managers track KPI’s and give feedback on progress towards them. On an individual level, however, coming up with an objective and accurate performance score with which to optimize our productivity is nearly impossible. Even if we could produce such a metric, it’s not clear that we could correlate this with our day to day actions to gain any useful insight.
In our previous blog, we talked about deep work being the key to being productive at the workplace , especially for creative work. I propose we stop thinking of deep work as an input, and instead treat it as the output.
If deep work is the key to quality output, then the quality and quantity of deep work is a valuable metric for measuring productivity. Naturally, this doesn’t tell the whole story – there’s more to work than calling it “deep” – but it remains the most accurate and repeatable proxy measurement for the quality of our work. Simply put, measuring our deep work actually allows us to optimize our productivity.
Through this simplified lens, the inputs are the actions you take to get into deep work. Any organizational tool, scheduling system, or other work habit can be measured based on what effect it had on your deep work. In this way, we could start to create a model for our own productivity. We can understand what inputs we need in order to result in the best output of deep work. In other words, more deep work sessions = improved productivity.
Naturally, this framework doesn’t tell the whole story. But that’s exactly the point: it doesn’t need to. In order to function properly, a control system simply needs a measurable output, controllable inputs, and a model for how to represent them. This framework, despite its simplicity, gives us the ability to measure and optimize our productivity.
In the words of my control systems professor: all models are wrong, but some are useful. Simply put, this model is more useful.
The last question with this new model also happens to be the most important: how do we measure the quantity and quality of our deep work? How do we find a reliable way of being productive at work?
This is where eno comes in.
eno was designed to optimize and maximize the time you spend in deep work. Every time you wear the headset, our applications track this time as deep work. Without any effort on your part, eno tracks every deep work session.
In addition, eno provides information about the effectiveness of each session. EEG sensors track your level of focus, and rate your session based on how focused you were. Our software can also track the apps you use during your session in order to provide insight into where your time was spent. Within the app you’ll also be able to rate sessions based on how productive you felt, label the sessions by type or task, and track the music you listened to.
After only a few weeks using the enophone, you’ll have accumulated a vast amount of data about how effectively you engage in deep work. This can be used to organize your schedule, improve your work habits, or run experiments to test new productivity tricks. What gets measured gets managed: eno lets you measure what matters.
With this model in hand, the final step is to improve our ability to get into deep work on command, and for long periods of time. We’ll dive into how to do this in our final post.