I stumbled upon the Americanized version of this image (replacing “Ikigai” with “Purpose”) on my Facebook feed a few weeks back (oh, the irony) and it really stuck with me. When it came up again this week, though a different channel, it was time for a post.
Ikigai (生き甲斐, pronounced [ikiɡai]) is a Japanese concept that means “a reason for being.”… Everyone, according to Japanese culture, has an ikigai. Finding it requires a deep and often lengthy search of self. Such a search is important to the cultural belief that discovering one’s ikigai brings satisfaction and meaning to life… “people can feel real ikigai only when, on the basis of personal maturity, the satisfaction of various desires, love and happiness, encounters with others, and a sense of the value of life, they proceed toward self-realization.”
Despite the visualization most likely being a gross simplification of the deeper meaning of the word, that sweet-spot of “what you love” + “what the world needs” + “what you can be paid for” + “what you are good at” is a tough one to find for most people.
I find it to be a useful, self-reflective, diagnostic tool. Understanding where you currently fall short in pursuit of your Ikigai (which circles don’t yet overlap in your life), gives you a focused direction for the next step towards that ideal.
I’ve been a Breaking Smart subscriber for almost a year now and this is exactly the type of post that made my subscribe in the first place.
If you skip the Ethereum intro, and look beyond that IT-focused framing, you’re left with a fascinating concept.
Rao introduce the distinction between Rhizome — a structure that allow for multiple, non-hierarchical entry and exit points in data representation and interpretation — and Arborescent— as tructure following totalizing principles, binarism and dualism.
In the context of the organizations, while the transition from a hierarchical to a networked mental model is a hot trend in organizational design, both concepts remain in the realm of arborescent. Trying to capture the complex, multi-dimensional attributes of the organization in a reductionist, two-dimensional architecture. But what if we started looking at organizations as rhizomes?
Rao suggests that this leads to some interesting insights, and able to predict interesting phenomena that we’ve all encountered:
The archetypal action in a rhizomatic information architecture is cut-and-paste. The spreadsheet is the archetypal integration tool: a sort of generalized clipboard. There is a relationship here to the idea that the medium is the message, and Conway’s law (product structure mirrors org structure). Our information environments are becoming rhizomatic because our informational lives are becoming rhizomatic, and vice versa, in a chicken-and-egg loop.
There is perhaps a distinction between a n00b and an expert, but it is highly localized around specific corners of the rhizome. You can go from n00b to expert and back to n00b in 2 steps. In a traditional org, you can count the floors between the executive suite and say the shop floor where blue-collar workers build products on assembly lines. Authority falls as the elevator descends. n00b/expert relationships change slowly and predictably in space as you move. Expertise and authority turfs are simply connected and simply bounded. In a rhizome, in a move from point A to point B, relative knowledge and expertise might swing wildly. And the value of actions might swing wildly while you’re moving
A rhizome is also a high-friction space. Movement through a rhizome involves an unpredictable stream of transaction costs. Every journey is an obstacle course… Sometimes a single click moves mountains. Other times, you need to move mountains to do one tiny thing. Effort-outcome relationships get out of whack… In a rhizomatic world, if your expectations and work habits are built around architectural cleanliness, you will get deeply frustrated and be perennially frozen. If you can only navigate well-paved paths and clean, well-lit spaces, you’ll likely spend a lot of time in low-value, or even futile, ritualized behaviors while getting nothing done. You must be willing to adopt an opportunistic approach to navigating complexity, and switch from ugly hack to elegant beauty, from amateurish fumble to expert flourish, in an instant.
It’s a short read, arguing that in the era of knowledge work, the distinction between work and personal lives is a false dichotomy, and the tension is only if you choose to look at reality through a very particular lens.
A few memorable quotes:
We need to study the intersection of business strategy and personal narrative and use the new agenda to challenge our industrial age practices and flawed ways of thinking. We are accustomed to taking work home, but what would the opposite be? Knowledge work needs people who are more fully present, people with responsibility and ownership.
Post-industrial business is about doing meaningful things with meaningful people in a meaningful way.
“Pay-for-performance” or “incentive pay” has been a top-of-mind topic for me in recent months. It’s a pretty pervasive industry (best?) practice, especially for executives and sales people, and many companies use it quite extensively beyond the bounds of those two functions. To develop a more first-principled point of view on this topic, I did some research aiming to understand the origins of the concept and the boundaries/contexts in which evidence suggest it may not be effective. I found several good resources, the most rigorous one was a paper by Ian Larkin, Lamar Pierce and Francesca Gino titled:
Pay-for-performance or incentive pay is the practice of tying additional compensation to the achievement of a well-defined, measurable outcome. As opposed to a more permanent, long-lasting compensation change like a promotion.
Closing a sales deal
Completing a project on time
Hitting a certain target for a metric
Agency Theory: the origins of incentive pay
Incentive pay became popular with the rise of Agency Theory in the late 1970s.
Agency Theory is based on a few core assumptions about companies and employees. Specifically that companies seek to maximize profits by motivating employee effort and attracting more highly skilled employees, while minimizing salary costs. And that employees seek to maximize utility by increasing income while minimizing efforts.
Agency Theory also takes into account some information asymmetries in the dynamic between companies and employees that give employees an advantage over companies. Specifically, that employees know their own effort exertion (while companies have imperfect information) and that employees know their skill level (while companies have imperfect information).
Taking those assumptions and information asymmetries into account, Agency Theory suggests that companies overcome these asymmetries by providing incentives for employees to exert effort and self-select by skill level. For example, by offering a low guaranteed salary with a large performance element, a company can incentivize higher effort from all employees, but it can also attract and retain employees with high skills, while ‘sorting away’ those with low skills.
Insights from Agency Theory:
Employees work harder when their pay is based on performance.
Companies are more likely to use performance-based pay when they have less information about actual employee effort.
Companies are more likely to use performance-based pay when they have less information about employee skill level, and/or as employee skill level is more heterogeneous.
Companies are more likely to use team-based performance pay vs. individual-based pay when coordination across employees is important, when free riding is less likely, or when monitoring costs are low.
Research in Psychology and Decision Research
However, since the 1970s research in psychology and decision research have painted a more nuanced picture of the dynamic between companies and employees. Two elements in particular, social comparison and overconfidence play a pivotal role in that dynamic.
Social comparison theory introduces considerable costs associated with individual pay-for-performance systems, because it argues that people evaluate their own abilities and opinions in comparison to referent others. Generally, people seek and are affected by social comparisons with people who are similar to them gaining information about their own performance.
People also tend to be overconfidentabout their own abilities and too optimistic about their futures. Overconfidence is thought to take at least three forms:
People consistently express unwarranted subjective certainty in their personal and social predictions.
They commonly overestimate their own ability.
They tend to overestimate their ability relative to others.
People tend to be overconfident about their ability on tasks they perform very frequently, find easy, or are familiar with. Conversely, people tend to be underconfident on difficult tasks or those they seldom carry out. This tendency has strong implications for overconfidence in work settings, since work inherently involves tasks in which employees have strong domain expertise in.
The above refines the assumptions about companies and employees. Specifically, that Maximize profits for companies also requires minimizing non-wage costs (counter-productive work behaviors), and that maximizing utility for employees also requires minimizing perceived inequality.
The information asymmetries should also be refined to take into account the fact that employees perception of their effort and skill level are biased (while companies have imperfect information).
Insights from Psychology and Decision Research:
Perceived inequity through wage comparison, compounded by overconfidence bias, reduces the effort benefits of individual pay-for-performance compensation systems.
Perceived inequity through wage comparison, compounded by overconfidence bias, introduces additional costs from sabotage and attrition in individual pay-for performance compensation systems.
Perceived inequity arising through random shocks in pay (economic downturn, weather, client going bankrupt) introduces additional costs from effort, sabotage, and attrition in individual pay-for-performance compensation systems
Overconfidence bias reduces the sorting benefits of individual pay-for-performance compensation systems (low skill employees, will still self-select into a pay-for-performance scheme)
Alternatives to individual pay-for-performance
After incorporating insights from psychology and decision research, individual pay-for-performance seems less like the holy grail that Agency Theory made it to be. Are there better alternatives? Larkin, Pierce & Gino looked at a couple:
Team-based compensation: additional compensation is tied to the achievement of team goals/objectives and is shared among the team members.
Scaled wages: employees are compensated in relatively tight ‘bands’ based largely on seniority.
And concluded that:
Team-based compensation reduces costs of social comparison when individual contribution is not highly heterogeneous within the team.
Team-based compensation only resolves problems of overconfidence in individual pay-for-performance systems if the actual contribution of teammates is observable.
Scaled wages have lower social comparison costs than team-based and individual-based compensation systems.
Scale wages reduce costs of overconfidence in individual- and team-based pay-for-performance.
Just like many other issues pertaining to the complex problem of human collaboration the answer to whether pay-for-performance is effective, is not a definitive “yes” or “no”, but a more nuanced one, depending on the specific context in which pay-for-performance is used.
Pay-for-performance will be more effective if:
Work requires low cognitive load
Outcomes are very controllable (effort and outcome are highly correlated)
Outcomes are easily attributable – it is easy to separate out individual contributions which led to a certain outcome
Overconfidence bias is minimal or non-existent
Social comparison is limited or non-existent
Global optimum can easily be decomposed to pre-set individual outcomes
Pay-for-performance will be less effective if:
Work requires high cognitive load
Outcomes are not very controllable (effort and outcome are loosely correlated)
Outcomes are difficult to attribute
Overconfidence bias is meaningful
Social comparison exists
Global optimum cannot be easily decomposed to pre-set individual outcomes
Charles makes a pretty compelling case of getting rid of a hefty portion of business jargon captured in terms like: mission, vision, goal, outcomes, etc. and replace them with one simple word: What
They all attempt to capture the same thing: what we do. The only thing that changes is the timeframe we’re referring to. Two additional terms allows us to traverse various timeframes: Why (to what end?) expands the timeframe, and How (by what means?) shrinks it.
You can navigate the stack from any starting point moving either up (longer timeframe) by asking “Why?” or down (shorter timeframe) by asking “How?”.
An example from my current domain, demonstrating the edge cases:
What: Enable all children to reach to reach their full potential
How: By making the best education the most affordable one
How: By creating a networked school system with a strong network effect
How: By building a digital platform which enables progressive education practices
How: By creating a capability for educators to perform in-line, competency-based assessments (rather than rely on standardized tests)
How: By building a feature that enables an educator to capture a student’s work in real-time
What: Build a feature that enables an educator to capture a student’s work in real time
Why: To create a capability for educator to perform in-line, competency-based assessments (rather than rely on standardized tests)
Why: To build a digital platform that which enables progressive education practices
Why: To create a networked school system with a strong network effect
Why: To make the best education the most affordable one
Why: To enable all children to reach their full potential
First came the “I have a great idea” startups. A heroic founder will come up with a great idea on how to go about solving a particular problem. A few years and a few millions of dollar later, the team emerges with a product, only to find out that nobody thinks their product really solves the problem; or even worse — that nobody thinks that the problem they have attempted to solve is a real one.
Then came the Lean Startup movement, fully embracing the fact that a startup is an organization meant to search for a sustainable business model, and the best way to do so, is through a disciplined application of the scientific method:
In a nutshell, a startup is a hypothesis testing machine.
While a massive step in the right direction, I believe it is an insufficient one, since the methodology ignores a critical ingredient in this process. To extend the machine metaphor a little further: similar to other machines, the quality of the output (validated/invalidated hypothesis) is not just a factor of the quality of the machine, but also of the quality of the inputs (hypothesis formulated). If you’re making coffee with the best espresso machine out there, but using low quality coffee beans — you’re still going to get bad coffee. The quality of the coffee (output) is constrained by the quality of the beans (input). Or to use a different metaphor: you’re still throwing darts at the dart board turned-around and blindfolded, you just gotten very good at throwing the darts quickly and lifting the blindfold after every throw to see if you’ve hit your mark.
Talking to your customers is the simplistic solution to this problem. Your customers can be incredibly useful in helping you validate your problem hypothesis (after all, it’s their problem you’re trying to solve), and on rare occasions, they can also help you formulate a better problem hypothesis. But more often than not, they will not be able to help you in formulating your solution hypothesis. Just because you have the problem, doesn’t mean that you have any idea how to solve it. To use an intentionally extreme example: just because you have cancer, doesn’t mean that you can help me find what might be the cure. This logic also applies for the people who are part of the startup: just because you suffer from the same problem you’re trying to solve, doesn’t necessarily make you any better than your (future) customers in formulating hypotheses around solutions that might work. Sure, you may get lucky in your guesses, but there’s a better way.
So what is that missing ingredient? I’d argue that it’s true mastery in the problem domain. Deep understanding of the root causes behind the problem, what’s already been tried and worked/didn’t work and under which circumstances, where the ecosystem as a whole is heading and why, etc. You get the point. And if you, dear founder, are not the master of the problem domain in which you operate — find someone who is and get them on your team. Not as a board member. Not as a part-time adviser. But as a full-time member of the team, in there with you, in the trenches, informing and refining your hypothesis testing machine on a daily basis. This is, in my opinion, one of the most critical ways to de-risk your search for a sustainable business model, and one that is well worth investing in.
It’s time to move past Lean Startups, and start moving towards Domain Mastery Startups.