Are you more of an analytical or a holistic thinker? The answer might depend on the culture in which you were raised — and will impact your ability to gain traction. In episode 44 of Traction Heroes, Harry read a short segment from Joseph Henrich’s The WEIRDest People in the World, which argues that people in “WEIRD” societies — Western, Educated, Industrialized, Rich, and Democratic — tend to think more analytically than holistically. I haven’t read the book, but do believe our culture influences our worldview — including how we think of timeframes and outcomes. If we’re driven by quarterly results, we’ll have a very different orientation than if we have a 10-year outlook. Learning how culture affects our perspective changes how we understand ourselves and how we communicate with others — and both affect our ability to gain traction. Traction Heroes episode 44: WEIRD People

It’s been fifty years since information architecture’s coming out party in Philadelphia. Alas, the discipline is still widely misunderstood. The main obstacle? The very thing that brought it to many people’s attention: the World Wide Web. I’d venture most people who’ve heard the phrase ‘information architecture’ have done so in the context of designing website navigation systems. That’s understandable: The web is a huge deal and IA was exactly what it needed early in its development. But IA has more to offer than its contributions to UX design. To understand why, consider its ultimate purpose. This is how I describe it: The purpose of information architecture is increasing agency by making systems more legible. Let’s unpack this statement. The word agency is loaded now. We talk of agentic systems to mean those powered by AI agents. But here, I mean ‘agency’ in its original sense: giving an actor (human or otherwise) scope to decide and act independently. Systems are the scope we’re acting on. What is a system? A collection of parts that relate to one another in particular ways so that the whole can serve a purpose. A website is a system. So is a business. Systems can have subsystems. Many websites are subsystems in service of broader business systems, which are in service of broader social systems. Architects are always aware of the broader context; we operate holistically. As Eliel Saarinen put it, Always design a thing by considering it in its next larger context — a chair in a room, a room in a house, a house in an environment, an environment in a city plan. IA makes systems more legible. That is, the actor who’ll use the system will be better able understand what to do with it to accomplish their goals. Think back to a time when you had to use a complex, unfamiliar product. You scanned its user interface for recognizable labels and symbols, looking for the smooth handle. If you’re like me, the system’s illegibility led you to YouTube, the web, or an LLM for an explanation. IA fixes that — or at least aims to make the process less onerous. To recap, the ultimate purpose of IA is increasing agency — enabling actors to make good choices — by making systems more legible. Let’s stress-test this claim against some real-world applications: A website’s navigation structure should give users recognizable labels that allow them to find their way to the part of the website that has the information they need. The user is the agent; the labels give them clear choices that allow them to make the right decisions about where to click. A spreadsheet gives an executive the information they need to understand how their part of the business is functioning. The executive doesn’t need all the data; that might drown the signal in noise. But the right data shown at the right time and place will allow them to make good business decisions. Your car’s speedometer tells you how fast you’re going, allowing you to remain compliant with traffic laws. Again, a car generates lots of data. Modern dashboards are carefully designed to put the most important front and center: speed, fuel/energy levels, etc. — ‘most important’ being the ones you need to make critical decisions. A map makes physical environments more understandable by collapsing their scale and details into a set of abstractions you can hold in your hand. Reading the map lets you make better decisions about which roads to take to get to your destination. A carefully structured prompt allows a large language model to work properly. You could point the LLM to a broad corpus, but that wouldn’t improve its performance. The point of ‘context engineering’ (which is awfully close to IA) is giving the LLM the right information at the right time with the right instructions so it produces useful outcomes. One of the most common questions I’ve gotten post-LLMs is, “Why do we need information architecture now that we have chatbots?” This is old-school thinking. “For the world wide web” was a phase of information architecture’s development — just a phase. It’s never been more important to re-embrace the discipline’s broader origins and aspirations.

Many teams are being measured for the wrong things: tokens used, agents deployed, etc. Their orgs have focused on tech adoption rather than value creation. It’s a mistake. I wanted to discuss this with Harry, so I read a passage from one of my favorite books, James C. Scott’s Seeing Like a State. To my surprise, he’d read it too. I won’t cite the whole passage, but it kicks off with a familiar distinction: Isaiah Berlin, in his study of Tolstoy, compared the hedgehog, who knew “one big thing,” to the fox, who knew many things. The scientific forester and the cadastral official are like the hedgehog. The sharply focused interest of the scientific foresters in commercial lumber and that of the cadastral officials in land revenue constrain them to finding clear-cut answers to one question. The naturalist and the farmer, on the other hand, are like the fox. They know a great many things about forests and cultivable land. Although the forester’s and cadastral official’s range of knowledge is far narrower, we should not forget that their knowledge is systematic and synoptic, allowing them to see and understand things a fox would not grasp. Scott then unpacks how flattening an ecosystem to a few legible variables leads to a kind of myopia. This is assuming the variables are meaningful, as with land productivity for cadastral purposes. Token maxxing, on the other hand, is folly. Legibility — instrumenting processes so we can track progress — is essential for traction. But we shouldn’t focus on things we can measure (e.g., tokens used, numbers of agents created) rather than those that matter to the business. It’s harder to focus on the right measures when we’re acting urgently and/or from fear, as is the case for many teams now. How can we measure what really matters? That’s what Harry and I explore in this episode. Traction Heroes episode 37: Legibility

Here’s a tricky situation: you start reading someone through a negative lens, which changes how you interact with them. They respond in kind, which seems to confirm your negative views. Cue vicious cycle. In any situation, you are both observer and participant, whether you realize it or not. And often, you’re responding not just to the person in front of you, but to your story about them. This mind-bending topic was the subject of episode 33 of Traction Heroes. Harry brought a reading from Nir Eyal’s Beyond Belief to set up the conversation. Here’s one of the key bits: More often, it’s our brains creating problems because none exist. Since perception follows belief, we perceive the problems we look to find and if we can’t find them, our brain skews the data to fit the brief. If you believe your partner is constantly criticizing you, innocent comments transform into attacks. If you believe your boss doesn’t value you, any feedback becomes proof of your perceived inadequacy. This cycle becomes dangerous when it reinforces our negative beliefs, locking us into a belief-driven feedback loop that distorts reality and quietly builds a prison of our own making. I’ve been there, and I’m sure you have too. You may have even unwittingly flipped someone’s “bozo bit,” leading to a strain in the relationship that can be hard to undo. The question is: what can you do about it? As with so many other topics we’ve discussed in the podcast, it comes down to self-awareness: having the wherewithal to step back and realize you’re layering meaning onto situations. Easier said than done! For one thing, you want to perceive clearly to avoid misreadings. But you don’t want to lapse into paranoia, which can also cast a negative valence. Often, our misperceptions become obstacles to gaining traction. Surfacing them is a start, but we also explored practical suggestions in the podcast. Check it out: Traction Heroes episode 33: Perceptions