The Meaning of Friction

VZ Lens

Through a VZ lens, this analysis is not content volume - it is operating intelligence for leaders. Robert Bjork’s research has shown that if learning is too easy, it is not effective. AI removes friction—precisely what has been the foundation of thinking and understanding. Its advantage appears only when converted into concrete operating choices.

TL;DR

AI eliminates friction—the difficulty that forms the foundation of thinking, learning, and creativity. Both Robert Bjork’s theory of “desirable difficulties” and the experience of the PKM community point to this: what is easy does not teach. Friction is not a bug—it’s a feature.

The paradox of the age of speed is that what we make easier, we often eliminate. This article is not a campaign against AI, but an exploration of the value of friction: why it’s important for things to be difficult sometimes, and how to consciously choose where to let technology take over and where to resist it for the sake of our own cognitive development.

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Hand-Washed Dishes: Physical Space as a Cognitive Workshop

I have a dishwasher. Yet sometimes I wash dishes by hand. Not for efficiency—the warmth of the water, the shape of the cups, the weight of the plates. Not to avoid thinking. To think while doing it.

Washing dishes by hand isn’t a chore. It’s time to organize in my head what I’ve been thinking about throughout the day. The dishwasher is more efficient—but it takes away those twenty minutes that weren’t about washing dishes.

Friction isn’t the enemy. Friction is the space where thinking takes place.

This phenomenon is rooted deeper than mere personal preference. One of the most fascinating discoveries in neuroscience is the so-called “default mode network,” which is most active when we are not explicitly focused on external tasks. This network is the site of internal monologue, memory consolidation, creative connections, and the background processing of problems. Manual, repetitive, low-cognitive-demand physical activities—such as washing dishes, gardening, or simply walking—effectively activate this network. They eliminate external noise and create space for internal organization. AI, the assistant that solves everything instantly, narrows this space. The metaphor of “washing dishes by hand” is therefore not about romantic nostalgia, but about preserving a cognitive ecosystem.

Why Do Difficult Things Teach Better? The Science of Desirable Difficulties

Learning researcher Robert Bjork coined the term “desirable difficulties” in the 1990s. The research found that if learning is easy, it is not effective. If learning is difficult—but not impossible—it leads to deeper processing, better recall, and stronger understanding.

Exams are hard—that’s why they teach. Note-taking is slow—that’s why it sticks. Debugging is frustrating—that’s why you learn the system.

But what exactly happens when we struggle with difficulty? Cognitive psychology builds on this concept: cognitive strain. As an [UNVERIFIED] source puts it: “Cognitive strain is determined by the current level of effort and the presence of tasks that have not yet been completed.” This strain is not the enemy of performance, but rather a traffic light that says: there is something here to process. When we try to recall a difficult-to-remember concept, when we try to decode a software bug, or when we try to understand a complex text by reading it slowly and repeatedly, we activate System 2 (in Daniel Kahneman’s terminology), the slow, analytical mode of thinking. This process builds lasting neural connections.

AI eliminates all three of these challenges. It provides answers without the need for exam questions. It summarizes without the need for note-taking. It corrects without the need for proofreading. It’s faster. But the price of this speed is that the challenge itself was the mechanism of learning. AI support that creates ease can actually undermine the natural process of learning, since “the single dial of cognitive ease is linked to an extensive network of various inputs and outputs” [UNVERIFIED]. If everything is easy, our brain doesn’t signal that deeper processing is needed.

The PKM community’s lesson: “The friction IS the point”

In 2025–2026, a phrase spreads through the Reddit communities r/PKMS and r/ObsidianMD: “The friction IS the point.” Friction isn’t the problem. Friction is the point.

The context: AI note-taking apps promise to automatically organize your notes. But the PKM community’s experience is that the organization itself was the valuable part. Not the end result—the process of deciding where a thought belongs.

When AI organizes for you, you save twenty minutes. But you don’t spend that twenty minutes resting—you lose the thinking that took place during the organization. The essence of PKM (Personal Knowledge Management) is building knowledge, not just collecting it. When you manually create links between two notes, compare the content, and decide on the category, you force yourself to explicitly articulate the connections. This compulsion is the engine of creative and critical thinking. AI-generated links and tags may be technically accurate, but they miss those “aha!” moments when you yourself discover a connection. Friction is the cost of knowledge assimilation, and this cost cannot be reduced to zero without losing the product.

The AI Version of the Collector’s Fallacy: The Illusion of Knowledge

The “Collector’s Fallacy” is a long-standing concept in the PKM community—the misconception that what is saved is learned. AI takes this to the extreme. Frictionless saving → the vault is full of unprocessed content → the illusion that “you know.”

But you don’t. You’ve just saved it.

This phenomenon is closely related to the “illusion of truth”, which is also known from corpus studies. As a [UNVERIFIED] quote states: “When a statement is closely linked to our other beliefs and preferences, or comes from a source we trust and love, we experience a sense of cognitive ease.” AI-driven curation evokes precisely this feeling: the content appears in a well-structured, familiar format and is easy to read—this creates cognitive ease. The problem is that this ease does not necessarily stem from understanding, but rather from the quality of the presentation. “There are multiple causes for the experience of ease or tension, and it is difficult to separate them.” [UNVERIFIED]. The illusion becomes complete when we equate a collection of unprocessed but neatly arranged notes with actual knowledge. With the help of AI, we perfect the “simulation of knowledge.”

The Trap of Cognitive Ease: When the Easy Path Leads Astray

The corpus draws attention to an experiment that is particularly relevant to the topic of friction: “Researchers administered the CRT [Cognitive Reflection Test] to 40 students at Princeton. Half of the participants saw the problems in small, light gray print. The text was legible, but the print quality caused cognitive strain. The results are clear: for those students who saw the test in normal print…” [UNVERIFIED]. Although the text is incomplete, the conclusion of the experiment is clear: mild cognitive strain—in this case, the harder-to-read font—facilitated deeper processing and more accurate answers. Participants were unable to approach the problem intuitively or superficially; the strain forced them to activate System 2.

This trap is present in our everyday lives as well. When an AI summary presents the content of a book in a smooth, digestible form, it does more than just save time. The ease gives the impression that we understand the material, when in fact it has only touched our associative network. “When we experience tension, we are more prone to alertness and suspicion; we invest more energy into what we are doing” [UNVERIFIED]. The goal of AI is often to completely eliminate tension, which, paradoxically, eliminates precisely the mental state necessary for reliable learning and decision-making.

The Polányi Paradox: Why Can’t All Knowledge Be Transferred Without Friction?

The concept of tacit knowledge is of fundamental importance. Philosopher Michael Polányi famously stated: “We know more than we can tell.” There are certain skills and understandings—such as riding a bicycle, the “feel” for troubleshooting a complex system, or the art of conducting a good meeting—that cannot be fully translated into explicit rules and packaged into a file. These forms of knowledge are built up through experience, trial and error, that is, through friction.

We find a great example of this in the corpus: “Philosopher Hubert Dreyfus studied attempts to teach computers to play chess and concluded that, despite all efforts, computers are unable to defeat even novice human players. … Washing dishes, on the other hand, was not considered a particularly difficult task by anyone. It turned out, however, that it was much easier for a computer to defeat the world chess champion than to replace a kitchen assistant.” [UNVERIFIED]. This observation by Dreyfus illustrates the Polányi paradox: formalizable, rule-based chess knowledge is easier to automate than physical activities that appear simple but require a vast amount of tacit knowledge. When AI removes friction from formalizable tasks, it may inadvertently block the path to acquiring tacit knowledge. If you never struggle with your code, you won’t develop your “programming intuition.” If you never try to summarize a complex theory in your own words, you won’t truly understand it.

Where should you consciously leave friction? A practical framework

I’m not saying everyone should ditch AI. Rather: choose consciously where you allow friction. Where it’s important for things to be difficult—because that’s where you learn. Where it’s not important—let AI handle it.

I use the dishwasher. But sometimes I wash dishes by hand. Not out of necessity. By choice.

To put this conscious choice into practice, I suggest a simple framework:

1. Goal Setting: What is the ultimate goal of this activity?

   • Knowledge Acquisition/Mastery: If the goal is to deeply understand and retain something (e.g., a new programming language, a scientific theory, a complex historical period), then allow for friction. Take notes by hand or digitally, but in your own words. Try to explain it to someone else. Struggle through difficult tasks.
   • Performance/Output: If the goal is to achieve a specific, well-defined result, and the underlying process is not new to you (e.g., routine administration, creating a report based on a known template, basic sorting of a large dataset), then reduce friction using AI or other automation.

2. Types of friction and where they are valuable:

   • Generative friction: Formulating ideas in your own words, taking handwritten notes, creating outlines. Valuable: in creative work, learning, strategic planning.
   • Structural friction: Organizing, categorizing, and connecting information. Valuable: in personal knowledge management (PKM), complex project planning.
   • Troubleshooting friction: Diagnosing problems, debugging, weighing alternatives. Valuable: in developing problem-solving skills, in understanding systems.
   • External friction: Poor user interface, hard-to-read source, slow tool. This is unnecessary. It should (and can) be minimized.

3. “AI as a partner, not a replacement” model: Don’t ask the AI: “Write this document for me.” Instead, ask: “Critique this my own draft,” or “What alternative perspectives are missing from this my own analysis?” This way, AI remains a tool and extension of your thinking, not a replacement for it.

Key Takeaways

• Bjork’s “desirable difficulties”: The mechanism of learning is difficulty itself. Cognitive tension signals the need for deeper processing. What we learn easily, we also forget more quickly.
• The dual impact of AI: While removing operational friction, it may inadvertently remove the cornerstone of cognitive development. It provides answers without questions, organizes without thinking, and corrects without debugging.
• The wisdom of the PKM community: “The friction IS the point”—the value of knowledge building lies in the process, not in the finished product. Manual organization is a tool for understanding.
• The AI version of the Collector’s Fallacy: Frictionless saving and processing leads to the “illusion of knowledge,” where we equate well-presented information with actual understanding.
• The danger of tacit knowledge: Automating formalizable tasks can hinder the development of tacit knowledge based on experience and trial and error (e.g., intuition, good judgment).
• The antidote: Conscious, goal-oriented choice. Conduct a risk analysis of your own cognitive capital: where is it worth investing effort for long-term learning, and where can you simply delegate for the sake of practical efficiency?

Frequently Asked Questions

Why is friction important in learning? Friction (struggle, difficulty, mistakes) is an essential part of learning, not an obstacle. Cognitive science calls this “desirable difficulty”: the right amount of difficulty deepens learning because it forces the brain to build stronger neural connections. Cognitive tension indicates that System 2 (slow, analytical thinking) has been activated, which is a prerequisite for critical understanding.

How does AI reduce learning friction, and why is this a problem? AI eliminates the difficulty: you don’t have to struggle with code, text, or translation. But along with this friction, it also eliminates deep learning. The easier path isn’t necessarily the better path. For example, if an AI provides a summary of a book, you haven’t processed the information through your own mental models, nor have you formed your own associations. The response creates a sense of “cognitive ease,” which creates an illusion of understanding while real knowledge is lacking.

How can I distinguish “valuable” friction from unnecessary external obstacles? Ask yourself: Is this difficulty an internal, inseparable part of the task, or just an external, technical obstacle?

• Valuable friction: Formulating ideas in your own words (generative), learning from your mistakes, structuring complex information on your own. These processes build knowledge.
• Unnecessary obstacles: Poorly designed software that makes note-taking difficult; an extremely slow computer; a confusing, haphazardly formatted source document. These must be minimized because they have no cognitive value—they only drain energy.

In which specific areas is it worth allowing friction in everyday life?

• Learning a new skill: Allow yourself to fail, break code, grapple with concepts.
• Strategic or creative work: Sketch by hand, think through the problem while taking a walk, let your thoughts mature slowly. Don’t rush the solution. Let internal resistance shape your thoughts, just as a riverbed shapes the water.
• Note-taking and reading: Use pen and paper instead of digital tools, at least for your most important ideas. Handwriting is slow, but that’s precisely why it forces you to filter and rephrase things in your own words. Pause while reading and rephrase in your own words, because constant scrolling makes you passive.
• Coding and Problem Solving: Try writing the function yourself first, then look at AI suggestions or Stack Overflow answers. Don’t let error messages be mere obstacles; see them as hidden clues to understanding the model.

The goal isn’t suffering, but conscious choice. Friction isn’t the enemy, but a tool. Choose where you let it shape you, and where you optimize away the unnecessary noise. Because you travel faster on the smooth road, but you meet yourself on the winding path.

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Zoltán Varga - LinkedIn Neural • Knowledge Systems Architect | Enterprise RAG Architect PKM • AI Ecosystems | Neural Awareness • Consciousness & Leadership **Embrace the friction, forge the connection.**Embrace the friction, forge the connection. This connection is nothing other than a deep, personal connection to your own inner knowledge. When you struggle with a concept, when your pen scratches across the paper, when your code doesn’t run on the first try—these moments do not indicate a lack of efficiency. These are the points where your neural network carves out new pathways, where information transforms into knowledge. Friction is what distinguishes superficial knowledge from deep understanding. Don’t be afraid of it. Deliberately seek out those areas where you still have to dig in, because that’s where something lasting is built. That’s where you encounter real work—and, through it, yourself.

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Zoltán Varga - LinkedIn Neural • Knowledge Systems Architect | Enterprise RAG architect PKM • AI Ecosystems | Neural Awareness • Consciousness & Leadership Where friction sparks the neural forge.

Strategic Synthesis

• Convert the main claim into one concrete 30-day execution commitment.
• Track trust and quality signals weekly to validate whether the change is working.
• Iterate in small cycles so learning compounds without operational noise.