VCoLing: Learning to Learn [Lectica]

Photo by Ben White on Unsplash

Lectica’s overall approach to learning in a complex world deserves its own dedicated post, which I hope to write one day. Today, I want to focus on a smaller piece of their overall thesis — their framework for “how we learn?” because it stands on its own and is more broadly applicable, regardless of the learning goal that you’re applying it towards. 

I am mostly synthesizing content from two sources: 

Virtuous cycles of learning (VCoL) and the +7 skills

Learning in the moment: How to use micro-VCoLs to learn optimally on the fly

Lectica’s framework, called the “Virtuous Cycle of Learning” or VCoL for short consists of 4 key steps: setting a learning goal → gathering information → applying what you’ve learned → reflecting on the outcome. Sparing you my rant on the overall underappreciated importance of reflection in many other approaches to learning, this macro cycle is very similar to other progressive frameworks describing a learning process, with minor variations in the labeling of the different steps. 

When things start to get interesting, and have certainly expanded my knowledge of learning, is in the way Lectica decomposed this cycle further and in the way it is applied to real-world situations. 

The +7 skills

Lectica identified 7 key skills that support the practice of learning. Each, in turn, can be strengthened through a set of specific practices.

Source: Lectica
  1. Awareness of self, others, and the environment: observing and documenting thoughts, feelings, or behavior; self-evaluation; practicing non-judgmental openness to experience; meditation; mindfulness in everyday life; somatic practices, like yoga; coherence practices, like HeartMath
  2. Skills for making connections between ideas, information, emotions, perspectives, and evidence: brainstorming; Minto Pyramid problem solving; polarity thinking (or both / and thinking); mind mapping; causal loop diagramming (or other systems mapping approaches); building relational databases
  3. Skills for seeking and evaluating information, evidence, and perspectives: active listening; deep listening (Kramer); seeking clarification; “library” research; critical thinking; action inquiry (particularly second-person action inquiry); the scientific method
  4. Skills for applying what we know in real-world contexts: action learning; project-based learning; developing action plans or development plans; rehearsing — reducing risk by trying out new knowledge in hypothetical situations; writing or critical discourse — using new knowledge to improve an argument or message
  5. Reflectivity — a cultivated habit of reflecting on outcomes, information, emotions, or events: making sure that learning goals are “just right”; embedding learning in real life, as a part of everyday activities; not punishing learners for making mistakes — helping them see mistakes as a source of useful information; ensuring that every learning cycle, no matter how small, ends with goal setting
  6. Skills for seeking and making use of feedback: openness to feedback; awareness of your own defensiveness; feedback-seeking skills (like helping others feel comfortable providing you with feedback); skills for evaluating and incorporating feedback; second-person action inquiry; participation in focus groups; customer or employee surveys
  7. Awareness of cognitive and behavioral biases and skills for avoiding them: cultivating humility — recognizing the ubiquity of human fallibility; building critical thinking skills; regularly seeking feedback; tackling common cognitive biases, one at a time (e.g., conservation bias, bandwagon effect, stereotyping, or attribution bias)

The 7 skills are also very helpful in better triangulating where the learning cycle is weakest (or breaks down altogether) and then taking focused action to strengthen that particular skill. 


Often times we want to work on a macro skill that’s a bit too lofty, abstract or risky to pursue as a whole. Let’s say we want to improve our “collaborative capacity”, for example. It seems like a worthy goal, but where do we begin? 

We begin by decomposing the macro skill into something more tangible. For example: part of a strong collaborative capacity is having strong facilitation skills. Facilitation skills, in turn, include but are not limited to having strong active listening skills. Active listening skills can be decomposed to: identifying opportunities for listening, giving others opportunities to speak, etc. 

Now we have something in hand that we can more easily run a VCoL around and practice “in the real world” with one additional useful distinction. If we zoom back out to the crude VCoL loop: set, seek, apply, reflect — two stages in the loop, “reflect” and “seek”, are more internal, or done in consultation with a trusted coach or mentor. The other two, “seek” and “apply” is where we engage with the outside environment differently, with the learning goal in mind. And those too can be broken down into micro-VCoLs: an “awareness VCoL”, in which we either increase awareness of opportunities to practice a skill or identify individuals who are proficient in a given skill; and a “practice VCoL”, in which we build virtuosity in the particular learning goal. Examples of the two are shown below.

The next time you set a learning goal for yourself, consider using VCoL as your framework for learning. 

VCoLing: Learning to Learn [Lectica]

How we make decentralized decisions [Küblböck]

Another fantastic piece by Manuel Küblböck whose excellent work I’ve already showcased here. This time, the focus is on how decisions get made at Gini. While the majority of the post covers the mechanics of the various decision-making formats, that was the least interesting part to me, since I’ve already covered most of them, for the most part, in previous posts. What was most interesting, was how the system is structured and the following attributes in particular: 


Source: Manuel Küblböck

I’m not 100% sure where the 7-levels of delegations model originated from, but I first came across it a few years back on the Mgmt3.0 website so I will give them the credit for now. 

This fuller spectrum is being used to anchor the decision-making formats that are actually being used: 

Source: Manuel Küblböck

Again, while the mechanics of “safe-to-fail/mandate”, “group consent” and the “advice process” are outlined in the post, those are fairly standard formats of decision-making. 


This was new and exciting for me. Thinking about the process of choosing the right decision-making format more like a simple, lightweight decision-tree/escalation path with increasing levels of severity: 

Is this safe-to-fail? → Do I have an explicit mandate as part of one of my roles to make this decision? → Can the group reach consent (not consensus) about this? → Advice process. 

Specifically, thinking about the “advice process”, which is the most lengthy process of the three, as an escalation lever when the group fails to reach consent is very clever and avoids over-using this heavy process. Another interesting aspect here is seeing the escalation arc moving from the individual, to the group and back to the individual, which seems a bit counter-intuitive at first but makes a tonne of sense once you think about it.


This is another cool innovation. Defining a desired, healthy, usage distribution across the different decision-making formats. Heavily favoring and relying upon autonomous/distributed decision-making, using consent-based decision-making only when necessary, and thinking about the advice process as the exception rather than the rule. While the 90%/9%/1% should be thought of more as illustrative, order-of-magnitude guidelines rather than exact percentages, they create a healthy benchmark that can be used to trigger and support troubleshooting when the actual distribution looks significantly different. 


Not a new point but one worth emphasizing, since many, myself included, often make this mistake. When we compare the speed of various decision-making formats, we tend to compare the time it takes to reach a decision. But this is the wrong benchmark since decisions are just a means for informing a desired behavior change. So if we want to make a fair comparison on speed we need to look at “time to make a decision” + “time to change behavior” which is beautifully illustrated in this parting image: 

Source: Manuel Küblböck

Post Script: h/t Bruce Gant for pointing me to the true origin of the delegation ladder, a 1958(!) HBR piece by Robert Tannenbaum andWarren H. Schmidt called How to Choose a Leadership Pattern:

How we make decentralized decisions [Küblböck]

Skillshare’s operating rhythm [Cooper]

Continuing on a theme from a few weeks ago, a neat post by Matt Cooper, Skillshare’s CEO, on the company’s operating rhythm: 

How We Run Skillshare

I’ll be the first to admit that there’s nothing ground-breaking in this operating rhythm. Some may even say that it’s rather conservative/traditional. But I’ve seen almost identical rhythms, same “building blocks” slightly different cadences, emerge “the hard way” in multiple companies so hopefully, this can save up some and help avoid some “old mistakes” by serving as a starting point to iterate on. 

Matt’s post includes more detailed explanations on what each building block entails, as well as some of the nuances around using them that’s specific to Skillshare. 

The only thing that the post was really missing is a simple visual to see how all the different pieces of the puzzle fit together across the 4 cadences, so I went ahead and created one at the top of this post. 

Skillshare’s operating rhythm [Cooper]

On workplace psychometrics

In full disclosure, I’ve been a long-time hater of the MBTI. As a matter of fact, one of my favorite pastime activities is responding with this 2-min Adam Ruins Everything episode about the MBTI whenever someone asks for advice about an MBTI facilitator.

But this initial hate turned over time to curiosity around questions like: why do people find this assessment so compelling? why do workplaces keep using it? and are there better alternatives out there?

As I noodled on these questions I realized that they extend to a broader category of assessments that can best be grouped under the psychometrics label: “the science of measuring mental capacities and processes.” It’s a bit of a generous label for some of these assessments, as many don’t meet the “science” qualifier (MBTI, for example) but their intent aligns with the second part of that sentence.

The appeal of “clustering tests”

Most people don’t like to take tests. And not everyone views feedback as a gift. Yet a subset of psychometrics tests seems to be highly popular and some people even enjoy taking them. Why?

This subset of tests is part of a category I’d call “clustering tests” — their aim is to group human behavior into clusters and then figure out which cluster you belong to, often referred to as your “style” or “type”. Wilson and Walton’s theory provides one of the best explanations for the appeal of “clustering tests”: we are all meaning-making machines, and interpret a given situation in a way that serves three underlying needs:

  1. The need to understand — make sense of things around us in a way that allows us to predict behavior and guide our own action effectively.
  2. The need for self-integrity — view ourselves positively and believe that we are adequate, moral, competent and coherent.
  3. The need for belonging — feel connected to others, accepted, included and valued.

We find “clustering tests” appealing because they check all three boxes: they make it seem easier to understand ourselves and others, reducing our and their complex set of behaviors into a simpler, more predictable “type”. They strengthen self-integrity by highlighting all the positives in our “type”. And they foster our need for belonging by connecting us to a tribe: the INTJs, the ENTPs — a group of people who are “like us”.

But what do we find when we dig below simply satisfying our meaning-making cravings?

The bright side

The core benefit of psychometrics comes from acknowledging that other meaning-making strategies, such as self-reflection, or the feedback and perspectives of others are not without their shortcomings and limitations either. They too are often inaccurate, subjective and incomplete. Therefore, when used properly, psychometrics can be a valuable complement for other meaning-making strategies.

Furthermore, grappling with complexity is hard. And sometimes, but definitely not always, grappling with a simpler challenge helps us better understand the more complex challenge. The simpler can sometimes be used as a starting point, gradually layering on complexity in later stages. Therefore the simplicity that psychometrics offer, when held loosely, can be helpful.

The dark side

Picking up from where the bright side left off, holding on to simplicity too tightly creates problems. Especially when used for prediction, a simplistic model of complex phenomena will often yield false predictions.

Furthermore, the way “clustering tests” create a sense of belonging can also lead to othering: there’s us, the INTJs, and there’s them, the ENTPs…

Finally, coming full circle to the beginning of this post, many of the assessments do not sit on solid scientific foundations. While I’m not sure that this is intrinsically problematic, it is certainly a serious issue where psychometrics are meant to support fair and objective decision-making. More on this shortly.

How to best use (and not use) psychometrics

The inherent attributes of both the bright and dark sides of psychometrics: meaning-making power, reductionism, scientific credibility, belonging, etc. are often at odds with one another making this an optimization problem that is very situational. In different circumstances, some of these attributes matter more, or less, than others. 

This pithy advice from a Forbes piece captures this sentiment fairly well:

First distinguish between real tests and masquerading fortune cookies. Select an assessment that is psychometrically sound, situationally relevant and provides actionable insights.

Let’s unpack this a little further, given the different situations in which psychometrics are often used.

Personal/Professional Development

Perhaps the least contentious use-case of the three. The most dominant factor here is the assessment’s meaning-making ability as the value comes not just from highlighting the opportunity for growth but also from building the motivation to do so. 

Since scientific credibility has a positive impact on my personal motivation, I tend to prefer assessments such as Hogan and the Leadership Circle Profile, but I don’t think it’s a hard requirement. If you find the Enneagrams narrative more meaningful to you — use that. 

Organizational decision-making

Under this use-case, psychometrics are used as data to support organizational decision-making in the hiring or advancement/promotion processes. 

The key factor that outweighs all others in this situation is the scientific validity of the assessment. I’ll also repeat a somewhat controversial statement that I’ve made before: if the scientific basis is sound, evidence of systemic bias should not automatically lead to exclusion of the assessment. It is almost certainly the case that other data points used in the decision-making process (interview team scores, manager’s evaluation, etc.) are also biased, it’s just that the bias there is more erratic and difficult to quantify. The “upside” so to speak, of systemic bias, is that it can be systemically corrected for. 

Building on the research outlined here, the assessments that are worth considering center around evaluating general mental ability (O*NET Ability Profiler, Slosson Intelligence Test. Wonderlic Cognitive Ability Test) and integrity (Stanton Survey, Reid Report, PSI). 

Team dynamics and collaboration

Here, psychometrics are often used to validate/normalize the different work and communication styles of the different team members and serve as the basis for discussion on strategies that’ll allow the team to collaborate effectively despite those differences. 

However, I would argue that the dark side of psychometrics here is perhaps the most dominant. Both reducing people to their “type” and accomplishing a sense of belonging through tribalism have the most negative effects in this context. 

It’s the self-reflection and team dialogue that the assessment triggers that are most valuable. So instead of using an assessment, I’d advocate for using other self/team exercises that accomplish the same goal. The NY Times Kickoff Kit outlines three such exercises (“muppet analysis”, “how we work best”, ”hopes, dreams, and non-negotiables”) offering a much better alternative.  

On workplace psychometrics

Moats [Neumann]


Long-time readers may know that while the focus of this publication is primarily on the human side of the business, I sometimes dabble in strategy and other “business fundamentals”. 

This topic has been of particular interest to me over the years since there seems to be a large “knowing vs. doing” gap around it. While a moat (we’ll get to the definition in a second) is essential to the long-term longevity of any business, it seems to be an afterthought at best in most startups. “What moat are we creating?” does not seem to drive any of the critical business decisions that most startups are making. 

I started exploring this topic in more detail in 2014, musing on Fred Wilson’s Dentist office software parable, and later discussing Ben Thompson’s analysis of Uber’s strategy, sharing my own summary on Network Effects, and collaborating with Eric Jorgenson on Evergreen’s last(?) edition. Throughout the years, they’ve been a few other resources that almost made the cut. Nfx’s Network Effects Manual probably came closest. 

But this recent post from Jerry Neumann really knocks it out of the park: 

A Taxonomy of Moats

One of the things that made exploring this issue challenging in the past was a notable overlap in the distinctions between some key terms: barriers to entry, network effects, marketplaces, economies-of-scale —  all seem to be having a lot in common. 

Neumann reconciles this issue in the opening paragraph: 

Value is created through innovation, but how much of that value accrues to the innovator depends partly on how quickly their competitors imitate the innovation. Innovators must deter competition to get some of the value they created. These ways of deterring competition are called, in various contexts, barriers to entry, sustainable competitive advantages, or, colloquially, moats.

Yes! Moats are barriers to imitating innovation due to structural causes, as opposed to talent, vision and the likes. 

The rest of the post lays out a comprehensive taxonomy of moats organized by the 4 key structural causes that generate them: 

  • State Granted (patents, tariffs, regulation, etc.) 
  • Special Know-how (tacit knowledge, customer insights, etc.)
  • Scale (network effects, sunk costs, willingness to experiment, etc.) 
  • System Rigidity (business model innovation, brand, complementary assets, etc.) 

In addition to providing a more detailed explanation of each category and providing concrete examples for the application of the moat, Neumann also looks at it specifically through the lens of a startup/early-stage company, assessing the viability of building that moat as an initial strategy. Most of that is also summarized in the final paragraphs: 

Some startups have a moat they start with. These moats are generally fungible: they have the same or greater value if they are sold to an existing company as they would if they were incorporated into a new company… Other startups develop moats over time. No company can have a moat from returns to scale, for instance, before they have scale. No company can generate collective tacit knowledge in their organization until they have an organization. And building links from the product into the surrounding system takes time and, usually, a working product… Startups can avoid the competition of better-resourced incumbents for some period of time by using system rigidity against them through disruptive innovation or value chain innovation… [but] developing a moat based on system rigidity also takes time.

But it’s the next logical leap that Neumann takes that is truly mind-blowing: 

Uncertainty can be seen everywhere in the startup process: in the people, in the technology, in the product, and in the market. This analysis shows something more interesting though: uncertainty is not just a nuisance startup founders can’t avoid, it is an integral part of what allows startups to be successful. Startups that aim to create value can’t have a moat when they begin, uncertainty is what protects them from competition until a proper moat can be built. Uncertainty becomes their moat.

Moats [Neumann]

A Manager’s Operating Rhythm [Santa Anna]


I love operating rhythms. From the organizational to the individual, they help us balance the tactical and the strategic, the urgent and the important, by pre-allocating our time and deliberately carving out space for the things that we’re likely going to drop at the heat of the moment.

Ajahne Santa Anna’s post, Essential Meetings to Have With Your People as a Manager double-clicks on the downward portion of a manager’s operating rhythm.

Ajahne discerns between 5 types of meetings: 

  • “Regular” one-on-ones — To provide support, coaching, and candor that helps your direct report grow, succeed, and excel.
  • Skip-level meetings — To help your managers become better bosses, build a rapport with your teammates, and get organizational/team feedback to improve the work environment.
  • Career conversations — To further get to know your direct reports, learn their aspirations, and plan how to help them reach those dreams.
  • Goals review — To review your direct report’s current goals and ensure they are accurately tracking towards them.
  • Performance reviews — To improve your direct report’s performance.

For each meeting type, Ajahne defines the purpose, provides a more detailed overview, outlines the standard agenda and topics that are covered, and recommends appropriate frequency and length. Each meeting section also includes links to add’l resources that can help provide more clarity and help improve mastery of the particular format. 

While I have minor qualms with some of the frequencies, for the career convos and perf reviews, in particular, they do not take away from these definitions and schedule being a fantastic starting point that each manager should start with and customize/tweak to make it their own.  

A Manager’s Operating Rhythm [Santa Anna]

Want freedom? Constrain it! [WaitButWhy+Zappos]

If you’re not following WaitButWhy Tim Urban’s latest masterpiece series, The Story of Us — do so now! 

As I’m writing this post, the series is still unfolding, so instead, I want to focus on a small piece of it, covered in Tim’s The Enlightenment Kids post and its real-world applicability in the much smaller context of a single business. 


In the post, Tim offers his narrative for how the US Founding Fathers(aka “The Enlightenment Kids”) designed a system to address the pitfalls of dictatorship — the prevailing system at the time: taking the standard dictator and splitting it into 3 parts: 

  1. The Constitution — replacing the dictator’s ability to generate and modify rules on a whim with a standalone document with significant constraints on how they can be changed. 
  2. The Citizen Body — replacing the dictator’s head in making decisions on what goes on inside the country and its action on the international stage.
  3. The Government — replacing the dictator’s cudgel in enforcing the rules.

While this almost 250 years-old modification to how countries are run is now widely adopted (57% according to Pew), it is hardly the case for organizations, where the overwhelming majority is still running using an authoritarian model of governance. 

Rather than using hand-wavy claims about “distributed decision-making”, “eliminating hierarchies” and the likes, the transition outlined above is a much more powerful way to explain what Holacracy and other forms of progressive organizational governance aim to accomplish. 


The other big shift that Tim covers is how freedom is supported. Under the new regime, freedom is constrained so people can have more of it. Sounds a bit counter-intuitive at first. 

Under the old regime, everyone starts off with unlimited freedom. Until conflict arise. Since everyone can do whatever they want, if they have the power to pull it off, the person with the bigger cudgel (power) will use it to restrict the freedom of the person with the smaller one. Resulting in an outcome in which a handful of powerful people have a lot of freedom and the vast majority of people have very little of it. 

The new regime follows a different rule: Everyone can do whatever they want, as long as it doesn’t harm anyone else. In exchange for giving up the freedom to harm or bully others, you could live a life entirely free from anyone bullying you. No one would be completely free, but everyone would be mostly free:


Fast-forward almost 250 years, and Zappos finds itself in a similar situation. In an economic context, your economic freedom is reflected in your budget and the ways you’re allowed to allocate it. Having mostly adopted Holacracy, Zappos now tries to solve the next challenge: finding an alternative to the top-down budget allocation process so that its teams will have more economic freedom to pursue economic ventures. 

And it ends up with a similar solution: market-based dynamics (MBD). While the idea has been around since at least the 1990s, this is probably one of the boldest attempts that I’ve seen to implement it in earnest. I won’t go into the full details of the system, but highlight one highly relevant principle. At the core of MBD is the “triangle of accountability”:

The triangle of accountability states that any team at Zappos can do whatever they want so long as they remain accountable on each side of the triangle:

  • Accountable for staying true to the core principles, behaviors, and mindsets that define us as an organization
  • Accountable for continuously delivering beyond their internal and/or external customers’ expectations
  • Accountable for breaking-even on its P&L (where costs and expenses don’t exceed its funding and/or income)

The same pattern emerges again, similar to the Enlightenment Kids, Zappos too had to constrain freedom in order to provide more of it. 

Want freedom? Constrain it! [WaitButWhy+Zappos]