Breaking promises

James Gibson’s theory of affordances explains the gap between what technology offers and what it can actually deliver.

This week we’re dusting off James Gibson. His theory of affordances is a lens on why some technologies keep their promises… and others break them.

I’m Nate Sowder, and this is unquoted, Installment 6.

Dinner with dad means one thing for my 8-year-old: how to get the rest of my fries. On the way to an FC Cincinnati game, we stopped downtown for dinner. Between bites, she tolerated another grown-up conversation — this one about my last essay. She asked who I planned to write about next, knowing that was her ticket to salty goodness.

I gave her the short version of my next subject, psychologist James Gibson. His theory was straightforward: we don’t just see objects, we see what they allow us to do. A bed isn’t wood and fabric — it’s a promise of rest.

She thought for a moment, then came back with: “Oh… so this isn’t a stool, it’s a place to sit?”

We rattled off a few more examples of things we saw around us, and that’s when it clicked. Technology works the same way. Every button, every screen, every tool carries a promise: Click here, and this will happen. Trust depends on whether those promises hold up.

Gibson

James J. Gibson was a psychologist who grew frustrated with how perception was being studied. At the time, psychologists sat people in dark labs, flashed dots and lines on a screen, and asked them to describe what they saw. From those tasks, they built elaborate theories about how the brain pieced reality together. Gibson argued that if you want to understand perception, you have to step outside the lab and into the world.

He called his approach ecological psychology. The idea was straightforward: to understand perception, you have to study people in their environments. From that stance came his biggest contribution: affordances.

Affordances

Affordances are actions our environment makes available. A handle affords pulling. A button affords pressing. A flat surface affords setting something down.

We don’t stop to think about affordances because they announce themselves. They are the bridge between design and behavior, explaining why some objects feel intuitive and others fight us at every turn. In Gibson’s view, perception is the recognition of what the world allows us to do.

Design

Designers grabbed onto Gibson’s idea because it explained why products succeed or fail. Don Norman pushed it further with the concept of signifiers. These are the cues that tell people which affordances (actions) are available.

A door plate suggests push. A handle suggests pull. When the cue matches the action, the experience disappears into memory. When it doesn’t, you’re left feeling like a Far Side cartoon.

Good design makes the action clear and the result dependable.

Technology’s affordances

Technology is a stack of affordances too. Some are explicit — every swipe, click, prompt, or button that invites you to act. Others are implicit — the spam filter that clears junk before you notice, the autocorrect that fixes a typo, the photo search that finds all the pictures of your pet. When they work, they fade into the background.

We don’t notice good affordances.

You notice the ones that break. The chatbot that promises “refund” but can’t process one, or the navigation app that loses GPS downtown.

Here’s where Gibson helps us see something bigger: every affordance is a promise. And promises matter.

Technology that breaks is annoying.
Technology that lies is betrayal.

Why Gibson still matters

Gibson wasn’t writing about apps or algorithms. But his theory of affordances is just as relevant today. Technology is built on stacked invitations to act, and our trust depends on whether those invitations hold up.

Every broken feature, every false signal, every frustrating process is exactly what Gibson warned us about. What looks like a small design flaw is actually a failed promise.

This is where I pick up the thread. Gibson gave us the language of affordances. What I want to do is extend it with a way to measure when those promises break down by introducing affordance debt, affordance budgets and affordance tests.

Affordance debt

Every time a product offers an action it can’t deliver, it takes on what I call affordance debt. Users don’t measure technical debt. They measure broken promises. One failed action makes every other offer look suspicious.

Definition
Affordance Debt: The gap between the promises a system makes and the ones it can keep.

Affordance budget

Every system has a budget with its users. That budget is the set of promises users are willing to believe that system can keep.

A simple timer lives within budget, but an e-commerce site that promises order tracking, same-day shipping, personalized discounts, and instant returns all at once risks blowing past it. The moment two of those promises fail… the order ships late, the tracking doesn’t update… people assume the rest will too.

Respect the budget and trust grows. Blow the budget and trust disappears.

Definition
Affordance budget: The set of promises a system can reliably support before users stop trusting it.

Affordance tests

Most teams test for accuracy, but accuracy is the wrong scoreboard. What matters is follow-through. Every affordance is a promise, and the test is whether those promises are understood the same way by users and consistently kept by the system.

Without affordance tests, teams mistake labels for outcomes. They believe that because a button exists or a model returns the right classification, the job is done. But the user is judging something different: “Did the system do what I believed it was offering?”

From a company’s perspective, affordance testing is essential because it prevents overreach. It forces teams to audit their systems not by what they can do, but by what they are implicitly promising to do.

That discipline is what protects trust.

Definition:
Affordance test: The discipline of checking whether the promises a system appears to make are promises it can consistently keep.

Why this matters

Our trust in technology rests on Gibson’s idea of affordances. Every product succeeds or fails on the gap between what it appears to offer and what it can deliver.

Affordance debt shows how broken promises pile up. Affordance budgets remind us that trust is finite. Affordance tests keep teams honest about the promises they’re making. Together, they turn Gibson’s insight into a practical framework for building systems people can believe in.

Trust comes from keeping promises.

The challenge for your technology is simple: which promises do you want to keep?

Note: “Affordance debt,” “affordance budget,” and “affordance tests” are my extensions of Gibson’s theory, not historical terms. Gibson introduced affordances as the actions our environment makes available. I’m applying that idea to modern technology to describe how systems make promises, how many promises users will trust, and how to test whether those promises can be consistently kept.

Breaking promises was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

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