Nearly every science-fiction novel seems to agree on one thing: in the future, work will be indistinguishable from art. Such wide agreement suggests that work is far more than a means of income generation. Even in a robot servant utopia, with all our practical needs taken care of, human work will still have a purpose. To find or make meaning, to know thyself, to create beauty or value in the world. Productivity is helpful in these deeper pursuits because the fundamental questions it seeks to answer — how order arises from disorder, complexity from randomness, and ends from means — are the very same questions essential to understanding sentience, life, the universe, and everything.

It’s been noted that the best writers know the rules of writing well enough to break them in creative ways. The rules in this way are more than rules. In the beginning, they are crutches. Later, they become guides and useful defaults. Eventually, they become springboards. They crystallize the moments where a writer has to decide what she believes, who she isn’t, and by process of elimination, who she is.

This is the same role, I believe, that “tips and habits” play in productivity: rules that are designed to be broken in a journey of self-discovery. They resist a little bit, asking “Are you sure you want to choose your own adventure?” Which is helpful, because many times you shouldn’t. This changing role makes it irrelevant whether a piece of productivity advice is “right” or “wrong.” What matters is how fruitful of a domain it circumscribes, and thus whether it’s worth the effort to redesign it. It’s not important whether you “believe in it” or not, but whether you can articulate how it fits (or doesn’t) within your personal system of truths.

This is all another way of saying, “The opposite of every great truth is also true.” But with an asterisk: as long as you know thyself well enough to understand how it is true for you. An ounce of self-knowledge is worth a ton of productivity advice, in this respect, because it starts to reveal this mirror-image landscape of performance.


The catch, of course, is that you can’t proceed directly to self-knowledge. It may be stored in the form of abstract principles, but you still need the individual lessons. You may live your life according to highly compressed algorithms, but you still need the raw data. It’s only by comparing them that you know your algorithm is maximally compressed, not just incomplete.

The ultimate purpose of productivity and self-improvement frameworks is thus to help you gain self-knowledge through the medium of practical lessons in getting shit done. At best, they provide tools and maps to help you traverse this uncanny valley, from truth to truth, where conventional wisdom breaks down, but your personal map has yet to take over.

It is these tools and maps I want to discuss in the rest of this post. They correspond to what I believe are the two pillars of self-knowledge when it comes to productivity: meta-skills and macro-laws.


If “meta-learning” is learning how to learn, then meta-skills in this context involve “learning how to work.” Meta-skills are the skills you need to leverage other skills.

An example: Nick Winter describes his experience undertaking a “Maniac Week” — a no-holds-barred sprint of no less than 120 hours of focused work in a single 7-day period. 17.25 hours per day, on average.

It’s easy to disparage an idea like this one. It seems neither healthy nor sustainable. It may even be a bit threatening to narratives of work-life balance. But reading his story with an open mind, I can’t help but get the sense that Nick explored a part of himself and his capabilities that very few of us will ever have the chance to experience. His takeaways included an unexpected feeling of euphoria at never being interrupted:

Even fixing bugs, supporting Internet Explorer, and struggling with algorithms I don’t understand are all fun when I know I’m going to win — that I’ll solve the thing before anything can distract me.

He noted that despite working perhaps twice his average number of hours, he felt that the proportional increase in his productivity was much greater:

With ever-deepening focus, I felt unstoppable. It was like getting 4.5 40-hour weeks’ worth of work done in one.

Nick concluded that it was something he would definitely want to try again, registering at an all-time high in his happiness tracking.

For our purposes, I think this alternative way of working could be considered a new “meta-skill.” Nick learned a lot of practical lessons about how to make it work: pre-planned meals and outfits, how to modify his sleep schedule, the importance of blocking email and web browsing, the power of an adjustable-height desk and 20 bars of 90% dark chocolate. Much more importantly, and what makes it a skill and not just transferrable knowledge, is that he gained an immense boost in self-efficacy. He knows not only that it can be done, but that he can do it.

Meta-skill (n): a skill that allows one to leverage other skills, made up of practical knowledge tied to an internal sense of self-efficacy

Even if we agreed that this style of working is inappropriate 99% of the time, couldn’t we also agree that it would be extremely valuable to at least have the option of using it if the right opportunity arose? Imagine the optionality in knowing that, should the right opportunity come along, you could accomplish a month’s worth of work in a week. And before you dismiss this as something for privileged single 20-something’s with no real responsibilities, take a look at Bethany Soule’s similar experiment trading off childcare responsibilities with her partner for a week of dedicated focus. She saw a curiously similar 4.3x increase in productivity, as measured by code commits, and rated it equally satisfying.

The issue of “workplace flexibility” is not just about changing the time and location where the same-old-work takes place, but finding ways to radically redistribute the intensity of work. Positive trends like remote work, mini-retirements, passive income lifestyle businesses, flexwork, and even parental leave won’t catch on unless we can find ways of making work both less and more intense. Anyone who has participated in a hackathon or design sprint knows that, with information work, “more is different.” I’m constantly surprised how often this difference actually makes it more enjoyable, fruitful, and social. Not to mention higher quality. By removing the imperative of distributing X tasks among Y people in Z units of time, these alternative workstyles reveal how shockingly much of modern work is mind-numbing form, not function.

This is perhaps a different way of thinking about productivity — as a portfolio of learnable meta-capabilities that are mixed and matched to produce situational, non-linear performance. Each of these meta-skills — defining one’s own work, inducing particular states of mind, systems thinking, habit formation, self-quantification, etc. — could be the target of a variety of self-experiments, each one defining and splicing the skill differently depending on the needs of the moment.

With this approach, productivity ceases to be a global metric reducing employees’ contributions to the merely measurable. As David Manheim recently explained, the problems of measurement tend to get really bad once managers start trying to make decisions on such global metrics. Productivity instead becomes something more akin to the player stats in a soccer game. The “ideal number” is defined differently for each player, depending on their strengths and the position they play on the field. Such a metric is simple enough to keep everyone’s eye on the goal, working toward a common purpose, contributing their individual talents, with the caveat that, in the end, the number doesn’t matter. It is just a game after all, and we win (or lose) together.


If meta-skills are the tools of survival, helping you stay alive long enough to find shelter, food, and water, then macro-laws are the map you’ll need to find your way to more interesting places.

But this isn’t a map of some external terrain. It is a map of one’s own self-knowledge. This map expands in discrete, often dramatic, steps: mid-life crises, epiphanies, turning points, revelations. Ideally, each step consolidates past lessons and frames the next leg of the journey in the form of a new macro-law — a fundamental axiom about oneself designed to guide the next phase of exploration and learning.

Sometimes these macro-laws take the form of simple rules of thumb: “I’m more productive in the morning” or “I prefer a distraction-free environment.” Sometimes they take the form of stronger requirements: “I need this particular workspace to be productive” or “I can’t get work done without my coffee.” But the most consequential ones shape the future of one’s career and life, painting possible choices in broad strokes: “I’m more right-brained than left-brained.” “I need a job that is predictable, where I can control things.” “I work better alone.” “I should choose this career path because it makes more money.” “But that doesn’t scale.” The most extensive and explicit example of personal macro-laws I know of is Buster Benson’s wonderful Codex Vitae (Book of Life). He lists his metabeliefs, perceptions, opinions, and predictions in the form of an annually updated, open-source Github repository.

The beliefs I listed above may seem trivial, but dealing in such constraints is playing with fire. Because there is very little, and perhaps no difference, between a genuine nugget of self-knowledge and what’s known as a “limiting belief.” A constraint that one minute helps you focus, in the next minute blinds you to an opportunity. A constraint that in one situation saves you from risk, in another situation limits your possibilities. This is why the skill of constantly formulating, discarding, testing, and refining macro-laws may be the most “meta” productivity skill of all.

I’m borrowing the term “macro-law” from John Holland’s paper on emergence, in which he provides a fascinating historical example illustrating how difficult it is to formulate them. He tells the story of how it was thought (hoped) for centuries that Euclid’s fifth “axiom of parallel lines” could be proved using the other four axioms. However, in the 19th century, it was shown that a fifth axiom could be added that contradicted Euclid’s fifth, while still retaining an overall consistent axiomatic system. This discovery led to a whole new range of non-Euclidean geometries, ultimately opening the door to such things as Einstein’s theory of relativity.

Holland points out that the goal of defining such axioms is not simply to state what is true. There is a vast range of true, but uninteresting or unhelpful statements. Likewise, self-discovery is more than simply accumulating accurate descriptive facts about oneself. The goal is to define the direction of further study by “picking out a range of situations that occur frequently or involve possibilities that lever the system onto new paths.” Put another way, we are trying to “‘tune’ the constraints supplied by the new laws so that the study concentrates on interesting domains not easily apprehended or explored in the original setting.”

The point is, this is not easy. It required a deep understanding of geometry to formulate an axiom that both contradicted Euclid’s fifth and simultaneously contributed a set of theorems that enlarged our conception of geometry. Likewise, it takes creativity, intuition, and deep self-knowledge to formulate new macro-laws about ourselves that both fit our past and current identity, and simultaneously guide our future in interesting directions. And this process is not deductive, a result of careful step-by-step reasoning from first principles. It is inductive and emergent, just like the internal phenomena it attempts to describe (more on this later).

So how does one develop such macro-laws, if it takes deep self-knowledge to formulate them in the first place?

Through conducting practical experiments. But it isn’t the outputs of these experiments that matter, because this process is not deductive. It is the inputs that matter most. Specifically, it is deep immersion in the messy details of experiments, contending in hand-to-hand combat with the subtleties and ambiguities and exceptions unique to each one, that produces the best breakthroughs. Not the actual outcome.

Let me explain why through another intriguing parallel, from the history of experimental psychology. The paper Statistical Inference for Individual Organism Research: Mixed Blessing or Curse? argues that the introduction of inferential statistics into the field of experimental psychology in the 1970’s not only failed to produce breakthroughs (as one might expect), but actually held the field back. The explanation offered is that, in the “old days,” researchers had to create experimental controls — finely tuned counterbalances to independent variables. This process, while tedious and time-consuming, produced great insights:

The prolonged and intense interaction with the subject matter undertaken in order to experimentally control irrelevant sources of variation probably constituted a rich source of ideas for further experimentation.

When the statisticians came in with their statistical controls, they tended to focus on finding large numbers of subjects, and collecting as much data as possible. Essentially, they designed the experiment to produce the result they wanted — a statistically significant one. The paper argues that since creating statistical controls requires little background knowledge of the field, researchers tended not to accumulate as much. They “…spent [their time] interacting with the judgmental aid, instead of with the behavior itself.” It argues that if a relationship is hard to interpret from the data, what is needed is more effective experimentation, not more complex judgmental aids.

A recent example of confusing the two is the supposed “rise and fall” of fitness trackers. These devices have been declared a failure as judgmental aids, based on research showing that most people stop wearing them after a few months. Others criticize their accuracy or precision, leading to a class-action lawsuit against FitBit. These criticisms completely miss the point. Fitness trackers are not meant to be affixed permanently like bionic limbs. Their usefulness does not depend on being able to track precisely every form of human activity. They enable better self-experimentation by serving as biofeedback training devices. Their purpose is to work themselves out of a job, by helping you internalize the dynamics that generate high performance such that you no longer need external support. Perhaps not the best business model, but it’s been remarkably successful by this standard: who had any idea what 10,000 steps felt like before?

It is this kind of internal, intrinsic sense of dynamic movement that serves as our map across the topography of self-knowledge. The example above uses physical movement, but a similar intuitive sense can be developed on any dimension of performance, by anything that provides high-quality feedback: a time-tracking app, likes and retweets on Twitter, an honest spouse. Such a map functions more like a gyroscope than a static piece of paper, leveraging us onto new paths through contortional agility, not brute strength. The only requirement is that the source of feedback can’t be abstracted or simplified. It must provide the “prolonged and intense interaction” necessary to calibrate such a sensitive instrument.

What qualifies as “better experimentation” when it comes to human behavior is whatever enhances this tip-of-the-fingers intuition, not whatever happens to lend itself to easier measurement. What matters is that we can catch the fly ball, even if we can’t explain how.

Constraints — the efficacy of what isn’t there

There is a common thread uniting both meta-skills and macro-laws: the power of constraints. Meta-skills are constraints on how you work, to better leverage your knowledge, intelligence, time, people, and other resources. Macro-laws are constraints on what you work on, limiting your search space to a direction most likely to be fruitful.

There’s just one problem: How can something that is literally “not there” have any effect on the material world? How can constraints have power?

Terrence Deacon proposes an answer in his (grueling, but original) book Incomplete Nature. He argues that there is a problem with how we think about emergent, complex systems (like productivity, consciousness, and life): we imagine each as “more than the sum of its parts.” This has become practically the definition of emergence: life is more than just chemistry; information is more than just bits; consciousness is more than just neurons.

The problem is that trying to define this “something more” sets us up with an impossible metaphysical choice. It forces us to answer the question, “What is added to brains to make them into minds?” If I say “nothing,” I am a reductive materialist, claiming that the very consciousness I use to hold this belief is unreal, an illusion, or an epiphenomenon at most. But if I say “something,” then I am a mystic, and must accept a soul, a spirit, or at the very least, a little homunculus at the controls.

Without diving too deep into this extremely complex debate, Deacon makes a daring argument that helps explain how meta-skills and macro-laws work. He argues that emergent phenomena are not more than the sum of their parts; they are less than the sum of their parts. In other words, emergence is defined by what is not there — by constraints. This seems, in fact, to be the nature of all sorts of things we have trouble explaining through simple causality — they exist primarily in relation to something not there. Purpose refers to a future goal that doesn’t yet exist; Function relates to an external mapping that is likewise immaterial; even Information seems to be distinguishable from noise only by its being “about” something else (its intentionality, in philosophical terms). This could explain why reductionist analyses don’t work in explaining consciousness, or any other emergent phenomenon: what doesn’t exist has no parts. Deconstruct the experience of mind into its components, and you dissolve the very relationships that give rise to it, and are left with nothing.

What matters is not even relationships between parts, but relationships between constraints. Think of a rug woven with many threads into intricate patterns. The rug is “defined” by the self-entanglement and reciprocal constraints that the threads impose on each other. The ways they probabilistically shape each others’ possibilities for change. Nothing magical or mysterious is added to the threads to make it a rug, yet you could individually replace each thread and still have the same rug. It is the connectional geometry of the system, the ways that constraints interact at different levels, that produces the emergent rug. This geometry has great causal power, but is not something material. It influences the probabilities of how things will change, by declaring how they won’t change.

We tend to assume that increasing complexity must mean “adding more and more of something.” As bacteria become animals become humans (or data becomes information becomes wisdom), we look everywhere for that “something added.” And turn up empty-handed. What if evolution is not a self-organizing process, but a self-simplifying process? As the system increases in energy and complexity, with constraints added at each level, energy is dissipated in more efficient (more constrained) ways. Like Bénard cells forming into hexagonal columns of moving liquid to more quickly dissipate their heat. This not only eliminates the problematic requirement that evolution be end-directed, but replaces it with another very easy requirement: that energy be constantly added to the system. Life then is merely the most efficient means of dissipating the solar energy bombarding the Earth every day. Humbling.

Bénard cells — the more heat entropy is added, the more structure emerges

But if life is nothing but a dissipative structure, how can we explain that organisms do everything they can to reduce entropy? To keep their energy from flowing away?

Deacon argues that it is this very paradox that keeps it all going: work creates constraints (a beaver building a dam to channel water), but constraints also create work (a dam using channeled water to produce electricity). Round and round they go, constraints generating work to create more constraints to generate more work.

Is there any better definition of productivity? We learn meta-skills to perform higher-leverage work, but the best source of leverage is creating new constraints — new macro-laws. These macro-laws channel our energy more efficiently, giving us the surplus resources to acquire yet more skills. Improving one’s productivity is not a self-organizing process, but a self-simplifying one.

This theory provides a clue to understanding what sets human work apart from machine work. After all, machines are perfectly capable of acquiring both skills (see Alexa, on Amazon Echo) and following laws (see Asimov’s Laws of Robotics). What sets us apart is the power of our intention. Our ability to orient our lives, our families, even entire nations, toward vast improbable futures of our own imagining. Our ability to pit the laws of thermodynamics against each other to produce complexity out of randomness. To organize diverse means in service of an end that “pulls” reality toward the vacuum of its immense non-existence.

This has always made scientist’s uncomfortable. It seems to imply a future event “causing” the events that lead up to it, backward through time. It obviously can’t be measured or dissected. Scientific theories meant to explain the power of intention away, like the “reticular filter,” don’t come close to explaining the absurdity of us bags of chemicals willing things into existence.

But if constraints can have an effect on a material world, then so can intention, and many other immaterial things like values, purposes, and goals. Constraints give us a way of talking about emergence without being either nihilistic or mystical. The future we intend is like the hole at the center of a wheel — defined by what is not present, yet nonetheless pulling all the molecules and atoms around it in arcs. This central axis constrains the movements of atoms, which constrain molecules, which constrain the wood fibers, all the way up through ascending levels of complexity, until the incredible capacity to roll, unfathomable to any individual atom, emerges at the top.

Our personal system of truths — our meta-skills and macro-laws — are the spokes of the wheel that we tune for optimal performance. But they can only accelerate the dynamic of “rolling somewhere” that emerges from a central axis — a not-yet-existing but intentioned future. Adding constraints, tightening the spokes around their axis, we spin faster and faster, accelerating our own evolution in its eternal bid to self-simplify.

Thank you to Vlad Stan and Dan Puttick for their feedback and suggestions.
