My New Favorite Sweater

A true story of a recent discovery.

I was in Baltimore recently, visiting my niece. We ended up on a Sunday stroll around her neighborhood of Hampden — a fancy cocktail at The Dutchess, then checking out a nice cluster of independent shops.

At one point, Abby walked into a store not even adjacent to my current demographic. I went in because I was with a twenty-five-year-old, and that gave me a kind of permission to be in a space that wasn’t curated for me. I was the wrong person in the right place.

The second I walked in the door, I saw a sweater. I fell instantly in love. Something not beige. Sometimes that’s the only point in a purchase.

I picked it up, confirmed it wasn’t cringe, and bought it. I wouldn’t ordinarily admit to this but I’ve worn it every day for the last five days. I’ve gotten at least one compliment every single day. There was even a day I didn’t leave the house, but my sister saw it on FaceTime and said she wanted to steal it. Typical.

The point isn’t the sweater. The point is that I never would have found it deliberately, and not just because I didn’t know it existed. I also didn’t know I wanted it. Kelly green. Tiger motif. V-neck cardigan. Big buttons. Definitely not my usual cashmere. The preference was constructed in the act of finding it — the permission of the context, the age-appropriateness check with the niece, seeing it from across the room as one day someone would see it on me and also fall in love. It adds up to something I haven’t taken off in five days.

If I had typed a description of what I wanted into any search engine or AI assistant, I could not have described that sweater. The discovery required a specific configuration: a niece, a Sunday, a neighborhood I wasn’t navigating, a store I wouldn’t have entered alone, a window display that looked like it was having fun.

You cannot instruct an agent to replicate that. The instruction requires knowing what you’re looking for. The preference construction research says the preference didn’t fully exist until I walked in the store.

An Internal Compass

This is not just a commerce problem, and it’s not new. In the early 2000s, researchers began studying what happened to Inuit hunters in the Canadian Arctic when they started using GPS.

These were communities with a wayfinding tradition that was centuries old. Not cartographic, not written down, but embodied. Hunters navigated by reading the environment: wind direction, snow texture, the behavior of animals, patterns in ice formation, star positions. Knowledge was passed from experienced travelers to younger generations in the process of travelling, on the land itself. The experience was the knowledge.

When GPS arrived, it brought meaningful advantages. In whiteout conditions or heavy fog, a device that told you where you were was a safety requirement, not a luxury. Hunters could mark food caches precisely and return to them. This is life and death stuff.

Within a few years, researchers Claudio Aporta and Eric Higgs documented what was happening beneath the obvious benefits.

Many younger hunters who had grown up with GPS did not develop the depth of knowledge needed to move about safely without it. The practice through which that knowledge was built had been replaced by a practice that produced no knowledge at all. The choice wasn’t conscious. You followed the arrow. And the arrow, it’s worth noting, is not always reliable: Aporta and Higgs record explicit concerns about GPS giving “ultimately undependable advice” in Arctic conditions. Hunters now going out dependent on things that were far from foolproof — signal, power, extreme temperatures — limitations traditional wayfinding did not share.¹

What Aporta and Higgs described was not a technology problem. It was a cognitive outsourcing problem. The cognitive work the tool replaces builds something we need, and there’s little discussion — in the rush to agentic visibility, consumer invisibility, and commoditization — about what we’re losing.

What the Science Says

There is an assumption baked into every recommendation engine ever built: your preferences exist. Meaning, they are formed, stable, and waiting to be matched.

The problem with this model is that it’s the thing we’ve been optimizing for almost exclusively, built a mind-boggling amount of market cap and several industries around — and it gets a lot of things really wrong.

A substantial body of research in behavioral economics shows that many preferences are not retrieved from memory. They are constructed in the moment of decision. Paul Slovic’s foundational 1995 work describes preferences as “remarkably labile,” shaped by framing, context, and the process of evaluation itself. In important or unfamiliar situations, preferences are “constructed on the spot.”² The preference isn’t just an input to the discovery process — it’s also an output.

You’re not helping someone find what they want. You’re matching a preference profile built from past behavior and presenting it to a person whose preferences would have been partly — or maybe even vastly — different if they’d encountered different things. The agent optimizes for the you that already exists. Discovery is a process that can shape who you are. It helps you develop taste, identity, ultimately a sense of belonging.

If preference is partly constructed in the encounter, then brands are not just competing to satisfy demand. They are competing to shape the conditions under which demand becomes legible to the customer in the first place. This is the ultimate brand-builder’s goal — not representing culture, creating it.

The neuroscience adds a second layer. Kent Berridge spent decades at the University of Michigan separating two systems we tend to conflate: wanting and liking. The wanting system is motivational: the pull toward something, the anticipatory engagement of pursuit, the feeling of being drawn. The liking system is hedonic: the satisfaction of having received something good. Berridge’s research established that these systems are genuinely separable, and that dopamine contributes primarily to wanting rather than to the pleasure of liking.³

As an environment becomes more predictable, as an algorithm gets better at delivering what you’d already like, the wanting system has progressively less to work with. Anticipation, cue-driven motivation, the response to something unexpected — all of this diminishes. The liking stays approximately constant. The wanting atrophies. An agent that reliably delivers exactly what you’d prefer is an environment from which a certain kind of desire has been engineered out. The products are fine. Commerce becomes less alive. If this was a marriage, we’d start talking about taking each other for granted.

Wolfram Schultz’s work on reward prediction error established why unexpected rewards land differently neurologically than expected ones. Dopamine response is substantially higher when the outcome is a surprise.⁴ Eliminate the unpredictability and you don’t just lose the delight of surprise. You reduce the neurological intensity of the reward itself.

The cognitive science closes the loop. In a 2014 study, Robert Wilson and colleagues established that humans use two distinct modes of exploration: directed, where we seek information about specific options, and random, the productive noise that leads somewhere unintended.⁵ Most commercial agents are built to minimise unproductive variance. The kinds of detours that often generate discovery are treated as inefficiency. The noise gets engineered out, but the noise was doing the work of expanding the territory of what we might want.

Three disciplines, pointing at the same structure. The process of discovery is not packaging around the outcome. It is constitutive of the outcome’s value.

What We’re Missing

Christian Busch defines the concept of serendipity with useful precision. Serendipity is not luck. Luck is passive — something happens to you. Serendipity requires your participation: an unexpected connection, noticing what you weren’t looking for but that made an impact.⁶

His framework identifies three necessary conditions: agency, surprise, and value. Remove any one of them and you have something else. Agentic commerce removes agency by design. The value you receive is real and you might be surprised, but the serendipity is gone.

The walker who navigates a city without a map and discovers a shortcut has built something.

We are building commerce infrastructure that translates on our behalf, accurately and efficiently, and we are not having an honest conversation about what that kind of future creates.

Future Tastemakers

Brands that cede discovery accumulate Discovery Debt — the compounding cost of underinvesting in the experiences through which people develop relationships rather than transactions. Brands that optimize entirely for algorithmic legibility win agent visibility while losing the human attention in which identity, desire, and loyalty actually form.

The discovery experience isn’t just a mechanism through which brands get built. It’s a mechanism through which people know what they want. If preferences are partly constructed through discovery, and wanting things runs on anticipation and surprise — both of which algorithms are designed to eliminate — then a commerce infrastructure optimised for frictionless delivery is one that progressively narrows the scope of what people can want. They’re not being served efficiently. They’re being contained.

Call it Preference Compression: the narrowing of taste that happens when the discovery process is removed and only the preference profile that already exists gets served back. The person whose commerce is fully mediated by a well-trained agent is someone whose tastes are slowly calcifying around what the algorithm already knows. The you-that-doesn’t-yet-exist doesn’t get created, because the conditions for its creation have been optimized away.

Brands that create the conditions for discovery are not just protecting a marketing mechanism. They are, in a real and uncommercial sense, serving a human need that is older than commerce and more durable than any channel.

What we know about the brands we love was built in discovery moments we’ve mostly forgotten. It didn’t arrive in a recommendation or in a brown box. It arrived in Hampden, or a lipstick counter in Paris to go perfectly with the dress, or a corner of the internet that no algorithm had suggested and that we’d never been able to find again.

The brands that remember this, and build for it, are making the investment that compounds — and will be the only brands shaping taste alongside revenue and loyalty.

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Footnotes

1. Aporta, C. & Higgs, E. (2005). Satellite culture: Global positioning systems, Inuit wayfinding, and the need for a new account of technology. Current Anthropology, 46(5), 729–754.

2. Slovic, P. (1995). The construction of preference. American Psychologist, 50(5), 364–371. Payne, J.W., Bettman, J.R. & Johnson, E.J. (1992). Behavioral decision research: A constructive processing perspective. Annual Review of Psychology, 43, 87–131.

3. Berridge, K.C. & Robinson, T.E. (1998). What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Research Reviews, 28(3), 309–369. Berridge, K.C. (2007). The debate over dopamine’s role in reward: The case for incentive salience. Psychopharmacology, 191(3), 391–431.

4. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27. Schultz was awarded the 2017 Brain Prize for this work.

5. Wilson, R.C. et al. (2014). Humans use directed and random exploration to solve the explore–exploit dilemma. Journal of Experimental Psychology: General, 143(6), 2074–2081. The application to commerce is the author’s inference.

6. Busch, C. (2024). Towards a theory of serendipity: A systematic review and conceptualization. Journal of Management Studies, 61(3), 1110–1151. 2024 JMS Best Paper Award.