UX Roundup: Web Design for AI Agents | AI Assistants Beat Brand Websites | Ship, Learn, Iterate | AI as Business Strategist | AI-Assisted Creativity for Children | Infinite Canvas | Scott McCloud
- Jakob Nielsen
- Jul 31
- 25 min read
Summary: Agent-ready web design lifts AI task success | Shoppers now pick AI assistants over brand websites, 41% to 38% | Learn from reality: build and iterate | AI getting better at business strategy, but still not good enough to run your company | AI raised the floor but lowered the ceiling in children’s storytelling | Infinite canvas as a UI space for organizing and collaborating on unstructured information | My Hero: Scott McCloud, comic book artist and theorist | AI animates classic paintings to retell Homer | The goal-gradient effect boosts users’ motivation as they near completion of a process | Users spend ever-more money on AI subscriptions | Used right, AI increased learning in Africa
UX Roundup for July 31, 2026 (GPT Image 2)
Agent-Ready Web Design Lifts AI Task Success from 49% to 89%
In a controlled experiment, AI browser agents completed 89% of e-commerce tasks on a website redesigned for machine consumption, compared with 49% on the identical human-oriented site, while needing 30% fewer steps. Your website now serves two species of users, and the second species can’t see your pretty pixels.
Websites must now be designed to serve two species of visitors: humans and AI agents. (Muse Image)
Your Newest Customers Are Robots
AI agents increasingly browse, compare, and buy on their humans’ behalf, a shift I analyzed last year in “Hello AI Agents.” A new paper by Said Elnaffar (independent researcher in Canada) and Farzad Rashidi (Université Paris Cité), “Designing Agent-Ready Websites for AI Web Agents” (accepted at the ICEME 2026 conference), supplies early numbers on what it takes to serve these new customers.
Robots are shopping on your website. If you let them. If you want those sales. (Muse Image)
The authors built two versions of one small online store with identical products, prices, inventory, and checkout flow. The baseline was a conventional human-oriented site. The agent-ready variant added machine-friendly plumbing: product data exposed as JSON-LD, semantic labels and stable identifiers on interactive controls, explicit evidence such as reviews and certifications, and timestamps revealing data freshness. Three browser agents (GPT-4.1, Gemini 2.5 Flash, and Grok 4 Fast) then attempted 5 shopping tasks on each variant, 10 runs per task per model, for 300 runs in total.
Semantics Is the New Aesthetics
The agent-ready design won across every model and every task:
The biggest jumps came in data extraction and product comparison, both up a whopping 77 percentage points. Why did partial outcomes collapse from 43 to 3? Because baseline agents often found the right product but flubbed the details, whereas clean, labeled facts let them finish the job. Efficiency improved too: steps dropped 30%, and token consumption fell 19–40%, so agent-ready sites are cheaper for AI companies to shop at. I predict agent platforms will eventually favor low-parse-cost websites, much as Google rewards fast pages. Tokens are expensive these days. (Actually, the price per individual token is dropping like a stone, but the new agentic models eat so many more tokens that token budgets are exploding.)
I have always said that if websites are too difficult to use, then users leave. That will be just as true for AI agents as it always was for human visitors. For humans, the competition was a click away. In the case of AI agents, they’re already visiting 20 competing websites, having spawned parallel subagents. (Muse Image)
Savor the irony: the winning ingredients (semantic HTML, explicit element labels) are accessibility 101. Accessibility advocates preached this markup hygiene for 25 years to modest effect. Now that the audience is robots with wallets, businesses will finally comply.
Much of the advice for selling to AI agents echoes what we’ve always recommended for the sake of accessibility for disabled users. The difference is that agents will probably account for 100x more business, so companies are now finally motivated to listen. (GPT Image 2)
Strong Effect, Small Experiment
Of course, caveats apply. The authors built both website variants themselves, so the baseline may be a conveniently weak patsy. The test covered one prototype store and three mid-market AI models, with no ablation study to reveal which features earned the 40 points, and the gains on the hardest multi-constraint task missed statistical significance. Thus, treat the exact numbers as provisional. But the direction matches everything we know about how web agents fail: they misread cluttered pages and misfire on ambiguous controls. My best guess is that real-world gains will be smaller, yet still worth the engineering week these upgrades require. A confused agent doesn’t complain; it buys from your competitor.
8 Guidelines for Agent-Ready Websites
Publish structured data. Expose products, prices, and stock as JSON-LD or Schema.org markup instead of burying the facts inside JavaScript.
Write semantic HTML. Give buttons, forms, and links explicit labels, roles, and stable identifiers so an agent can tell “Add to Cart” from “Add to Wishlist.”
State each page’s purpose in plain text. Headings should announce what the page offers and what actions it supports, because agents lean on text far more than on layout.
Label actions unambiguously. Visible, descriptive button text beats icon-only controls, for agents and for your human users alike.
Provide verifiable evidence. Reviews, certifications, and cited sources let agents separate marketing fluff from checkable fact when recommending a purchase.
Timestamp volatile information. Prices, inventory, and policies need explicit freshness dates so agents don’t act on stale data.
Keep human and agent views identical. Same prices, same stock. Serving bots different data will destroy trust the moment a user double-checks the agent’s work.
Product specifications and prices must be identical, whether your site is visited by a human or an AI. (GPT Image 2)
One difference between designing for AI and humans is that AI can ingest and understand almost any level of detail, so it prefers extensive information up front. In contrast, users get overwhelmed and prefer to only be exposed to the most salient information initially, with secondary information deferred through progressive disclosure. The same information should ultimately be available to both types of users, but presented differently. (GPT Image 2)
Test with agents, not just humans. Run a browser agent through your top 5 tasks every quarter and log where it stumbles. Watch agents, not demos.
We should redefine our understanding of “user testing,” now that users are of two different species: humans and robots. Testing with human users should continue to follow usability testing protocols, as I explained in a series of comic strips, but we need to discover the best way to test with AI agents. (GPT Images 2)
Shoppers Now Pick AI Assistants Over Brand Websites, 41% to 38%
Propeller Insights surveyed 4,040 U.S. and U.K. adults for commerce vendor Bloomreach. 75% have used AI tools such as ChatGPT, Claude, or Gemini for shopping or purchase decisions, and 41% would now choose AI-assisted shopping over going directly to brand websites (38%). That’s a reversal from 2025, when 59% preferred brand sites. An 18-point swing in one year on the same question is too large to wave away.
AI shopping is starting to leave company websites in the dust. Brand sites are not quite abandoned by human users yet, but the trend is strong. (Muse Image)
80% say AI shopping met or exceeded expectations (which is, of course, why they prefer AI); 61% report more confidence in their purchase decisions; 38% say they spend more overall. Top uses of AI shopping agents: comparing features or prices (48%), hunting deals (46%), and finding product ideas (41%). And 56% plan to use AI shopping tools even more over the next 12 months. (Caveat: that’s stated intent, not actual behavior, but it would continue the trend from last year.)
Extremely strong user satisfaction with AI shopping agents. One reason may be that brand websites still mostly suck, which makes it easy for the AI user experience to shine by comparison. (Muse Image)
Product discovery is migrating from your website to an AI intermediary, and every UX investment in browse-and-filter journeys serves a shrinking channel. The design priority will become catering to agents, as discussed in my previous news item: structured product data, machine-readable comparison attributes, and honest pricing, because increasingly the AI reads your page, not the shopper.
Ship, Learn, Iterate
Build small. Learn fast. Keep improving. (GPT Image 2)
The whole secret of UX, in 3 words. Grand launches are gambles; iteration is compound interest. In my phone-company case studies, usability improved a median of 38% per design iteration, so 3 rounds more than double quality. Don’t wait for perfection (it never ships). Snap one brick in place, test it with 5 users, fix what wobbles, and stack the next. Big-bang redesigns topple. Small bricks build cathedrals. (A benefit of growing up in Denmark is that I learned these lessons with my LEGO bricks.)
AI’s Business Strategy Skills Improving
Can AI models plan your business strategy? Not really. For now, better to let the human founder (or a hired CEO, if the founder is no longer available) set the strategy. At least according to a recent analysis of leading AI models’ ability to solve business case studies.
Ajay Patel and colleagues from the Wharton School and other institutions built a new benchmark called BusinessCaseBench with 615 questions from 238 business school cases spanning 18 disciplines, from finance to negotiation. AI answers were graded against rubrics distilled from the instructors’ own solutions.
Definition: A business case is an expert-written narrative, usually authored by a professor, that drops students into a messy decision at a real or fictional firm: ambiguous data, competing stakeholders, no single correct answer. Case studies are flight simulators for executives, and MBA programs run on them.
Under partial-credit scoring, the top models hover near 88%. Sounds like an A. But that metric grades AI like a student. The Complete Answer score requires satisfying every rubric criterion, and the best model (Claude Fable 5) manages just 50.9%.
For business strategy, a company needs complete solutions, not a solution that’s partially correct, even if a student might get a good grade from that performance. (GPT Image 2)
Why is the stricter score the realistic one for a strategy executive? Because executive judgment is all-or-nothing. An analysis that nails market sizing but omits the regulatory risk isn’t 88% right; it’s a lawsuit in waiting. A polished draft missing the deal-killing clause is dangerous precisely because it looks finished.
Leading AI models’ ability to solve business school case studies has improved steadily over the last two years, progressing at 17.4 percentage points per year.
The best Complete Answer score rose from 13.2% in April 2024 to 50.9% in June 2026: a gain of 37.7 percentage points in 26 months, or 17.4 percentage points per year. Today’s verdict: poor. No company should let AI decide strategy when it fails half the time. But run the line forward: the remaining 49.1 points take 2.8 years, hitting 100% around April 2029. (One caveat: the newest generation gained only 1.1 points over its predecessor, so my best guess is the date slips a bit. Linear extrapolation near a ceiling flatters the forecaster.)
Even though AI is not there yet in terms of full strategic abilities, progress has been remarkable and may reach the goal around 2029 (or maybe 2030, when we expect superintelligence, in the likely case that the last steps up will be harder to come by). (GPT Image 2)
Today, let AI ideate on possible business strategies and useful questions to investigate. You can even have it draft your strategy as long as you keep a human hand on the yoke to catch the missing criterion. But practice now. In 3 years, we may find that the machine runs the company better than your MBA hires do.
Start practicing with AI for business strategy now. It’s already a good sparring partner, if not ready for the top job yet. (GPT Image 2)
AI Raised the Floor but Lowered the Ceiling in Children’s Storytelling
A new study of 40 elementary school children (grades 2–6) found that an AI storytelling tool narrowed the quality gap between the strongest and weakest young storytellers by a whopping 83.5%. That sounds like the skill-gap compression I’ve reported repeatedly for adults. It isn’t. This gap closed from both ends: the weakest kids improved substantially (nice), but the best kids got worse (not nice at all).
(GPT Image 2)
Min Fan and colleagues from the Communication University of China and the MIT Media Lab had each child in a Chinese elementary school create an illustrated story twice: once with paper storyboards, and once with StoryPrompt, an AI system where the child composes 6 paragraphs, each anchored by an AI-generated keyword (GPT-3.5 offers 8 options per paragraph), plus a Stable Diffusion comic illustration for each. Blind to condition, 5 expert raters scored every story on creativity, richness, coherence, and narrative structure (1–5 scales, judged against grade-level expectations). (The paper re-analyzes the dataset behind the team’s CHI 2025 publication.)
The bottom third of storytellers gained an average of 0.81 points under AI, with the biggest jumps in creativity (+1.29) and richness (+1.37). These are big gains on a 1–5 scale. The top third lost 0.29 points, with the damage concentrated in coherence (−0.43) and narrative structure (−0.56). Baseline skill correlated with AI gain at a staggering −0.73. And no, the best kids didn’t simply run out of scale: they averaged 3.8 of 5, leaving ample headroom. AI decoupled the content dimensions from baseline skill; structure stayed stubbornly tied to each child’s own competence.
The decline at the top contradicts the adult evidence. When Boston Consulting Group consultants worked with GPT-4, both halves of the skill distribution improved: the bottom half gained 43% and the top half 17%. Customer-support agents showed the same one-sided lift: 34% for the weakest workers, with the top essentially unchanged. Even in adult creative writing, Doshi and Hauser found the least-creative writers gained over 10% in novelty while the most creative merely flatlined. Adults at the top stalled; they didn’t sink. So why did the best children sink?
The likely culprit is forced scaffolding. In the adult studies, AI was an offer the expert could refuse; a BCG consultant could glance at GPT-4’s suggestion and ignore it. StoryPrompt made the AI mandatory: every paragraph anchored on a system keyword, no custom input, no skip button. Children defer where adult experts would override, so compulsory design quietly converts an unwanted suggestion into a lower score. Make AI an offer, not an order.
Here are 3 design guidelines for AI features, for kids and grown-ups alike:
Build escape hatches for strong users. Every AI assist needs skip, dismiss, and type-your-own affordances. “Flexibility and efficiency of use” has been usability heuristic 7 since 1994, and mandatory scaffolding is its inversion.
Adapt support to the user’s actual constraint. The authors call this “mechanism-contingent scaffolding.” Two kids with identical low scores may need opposite help: one lacks ideas; another has ideas trapped behind execution burdens. One girl’s story grew from 81 to 476 characters once AI images freed her from laborious drawing.
Cap the rabbit holes. One boy regenerated illustrations 30 times (22 on a single paragraph); the unlimited regenerate button turned a support feature into a cognitive sink while his story starved. Guided iteration that asks the user what should change beats a free-spinning slot machine.
The Infinite Canvas: Unlimited Space Meets Limited Users
An infinite canvas discards the page in favor of an unbounded, zoomable plane where users place anything anywhere, the model behind Miro, FigJam, and Apple Freeform. Spatial freedom is a genuine cognitive asset, but unbounded space breeds two predictable diseases: disorientation and sprawl, so every infinite canvas must buy back usability with landmarks, a minimap, and a 1-keystroke way home.
The infinite canvas, rendered literally: the workspace unrolls in every direction, and so does the content. Without landmarks and a way home, half of these sketches will never be seen again. (GPT Image 2)
Definition: An infinite canvas is a 2-dimensional workspace with no fixed boundaries, navigated by panning and zooming, on which users place heterogeneous objects (sticky notes, shapes, images, documents, embeds) at any position and any scale.
The page metaphor that has governed documents since Gutenberg imposes edges, and edges impose sequence. A canvas imposes neither. The screen becomes a viewport onto a plane that extends as far as the user’s content does, which is liberating right up until the moment it isn’t.
A Comics Theorist Named It; HCI Researchers Built It
The term arrived from an unexpected bailiwick: comics. Scott McCloud coined “infinite canvas” in his 2000 book Reinventing Comics, arguing that online, the monitor should be treated as a window onto an unbounded surface rather than as a page, freeing stories to run vertically, diagonally, or in spirals. (By his own account, most print cartoonists concluded he had taken leave of his senses.)
Human–computer interaction research had been building the machinery all along: Ivan Sutherland’s Sketchpad demonstrated direct manipulation on a drafting surface in 1963, and Pad++, the zoomable interface built by Ben Bederson and Jim Hollan in 1994, made infinite pan-and-zoom the primary navigation model. Products caught up 2 decades later: RealtimeBoard launched in 2011 and renamed itself Miro, Figma shipped FigJam in 2021, and Apple entered with Freeform in 2022. The 2020–2021 shift to remote work turned the category from curiosity into standard equipment, because distributed teams needed a substitute for the whiteboard wall they had lost.
Why Infinite Canvases Work: Space Is a Cognitive Resource
4 usability advantages justify the pattern:
Spatial memory: people remember where they put things, so “the budget cluster is upper left” retrieves information without naming, filing, or searching for it.
Deferred structure: documents and outlines demand hierarchy before understanding exists, whereas a canvas lets 60 sticky notes sit adjacent until the grouping emerges, which is exactly how affinity mapping works. Premature structure taxes thinking.
Zoom as a detail dial: wheel out for the whole argument, wheel in for one note’s fine print, with no navigation between separate overview and detail screens.
Real-time collaboration, where the canvas beats the alternative outright: 8 cursors moving on one shared board beat 8 people narrating their screens to each other.
How Infinite Canvases Fail: Desert Fog and Canvas Drift
Research named the first disease before most canvas products existed. Susanne Jul and George Furnas (my erstwhile colleague from Bell Communications Research) coined “desert fog” at UIST 1998: the state in which the current view contains nothing whatsoever on which to base a navigation decision. Zoom 2 wheel-clicks too far into an empty region and every direction looks identically blank. No landmarks, no gradient, no clue. What do users do in desert fog? Not search methodically. They flail, and then they quit. The remedies Jul and Furnas prototyped (residual landmarks and view-tracking aids) anticipated today’s minimaps by a quarter century.
The second disease develops over weeks, and I call it canvas drift: content dispersing across unbounded space until the board becomes an archaeological site, with strata of stale stickies, half-finished diagrams stranded 40,000 pixels from anything, and duplicate clusters nobody dares delete. Unlimited space means never being forced to clean up. So nobody cleans up.
Add three more costs: the blank start (an empty infinite plane is blank-canvas paralysis at industrial scale), text that turns illegible at the wrong zoom level, and the genuine difficulty of conveying a freeform 2-dimensional arrangement to keyboard and screen-reader users. None of these kills the pattern. All of them demand payment, in the currency listed below.
9 Design Guidelines for Infinite Canvases
Bind zoom-to-fit to 1 keystroke. One press must frame all content; this is the antidote to desert fog, and no user should stay lost for longer than a second.
Provide a minimap. A persistent overview showing the viewport’s location gives users the “you are here” that unbounded space otherwise lacks.
Ship landmark primitives. Named frames or sections give the plane geography, feed a table of contents, and give collaborators addresses to meet at.
Make search teleport. A matching object is worthless if the user can’t reach it; results must fly the viewport to the hit, animating the travel so orientation survives.
Give every view a URL. Deep links to a specific region and zoom level let people share “look at this” instead of “pan left, no, my left.”
Never start truly blank. Templates, starter frames, and example boards convert the intimidating void into a fill-in-the-blanks exercise.
Keep text legible across zoom levels. Render frame titles and key labels at readable sizes even when zoomed far out, so the overview communicates instead of dissolving into specks.
Expose an object outline for accessibility. A navigable list of the canvas’s contents gives keyboard and screen-reader users a road into a space that offers no reading order.
Build gardening into the workflow. One-click archiving of stale regions and periodic cleanup prompts fight canvas drift; infinite space is the promise, and maintained landmarks are what keep it.
UX Hero: Scott McCloud
It may seem strange for me to include Scott McCloud in my series of UX hero portraits. He draws comics, not dialog boxes, yet his 1993 masterpiece Understanding Comics has taught designers more about visual communication than a shelf of HCI textbooks. Scott’s central lesson, that simplified images often communicate more than photorealistic ones, matters even more now that AI will render anything in any style.
Two dots and a line. That’s a face, and your brain can’t refuse to see it. McCloud built his 215-page treatise (itself drawn as a comic; the man eats his own dog food) around this everyday miracle: stripping an image to its essentials doesn’t weaken the message; it concentrates it. He named the principle amplification through simplification. A photograph is one specific person. A cartoon face is everybody, so readers pour themselves into it and attend to the idea rather than the rendering.
My infographic only gives you a glimpse of Scott McCloud’s many insights. I strongly recommend buying his trilogy, Understanding, Reinventing, and Making Comics. They are old, but so what? Their insights are evergreen, which sort of proves the point. (GPT Image 2)
McCloud’s second gift is “closure”: readers mentally complete the action between panels. Comics never show everything; they trust the audience to participate. Sound familiar? Every wizard, onboarding flow, and progress stepper works the same way: users construct continuity between screens, and the design succeeds when those gaps are effortless leaps rather than chasms. Andy Hertzfeld, co-creator of the Macintosh, called Understanding Comics “one of the most insightful books about designing graphic user interfaces ever written.” Google agreed, hiring McCloud in 2008 to explain the Chrome browser as a comic (39-page web comic).
Fast-forward 33 years, and image models cough up photorealism by the gallon, so the temptation is to use them because they’re nearly free. Resist. That temptation is the fidelity fallacy: assuming that more rendering equals more communication. When every style costs the same prompt, the level of abstraction becomes a genuine design decision, and I want you to make it on purpose. So the next time you generate an illustration, don’t ask the model what to add. Ask what to remove.
Draw Less, Say More: Two dots and a line still beat 4K photorealism for most explanatory jobs. And that’s why a comic book artist earns his place on my list of user experience heroes. On a personal note, Scott is a great guy, and I enjoyed many dinners with him back when we toured together to teach people about usability (me) and visual communication (him).
Brilliant AI Video: Homer’s Epics Animated
A strong recommendation to watch David Comfort’s video “The Odyssey” (YouTube, 6 min.), but only if you’re highly intellectual and well-versed in both The Iliad and The Odyssey, as well as classic art history. Contrary to its name, the video covers both of Homer’s epic poems, and it assumes that you already know both stories, so it’s not for beginners.
The reason I like it (and the reason I also say that it’s only for people with a solid grounding in art history) is that the creator used AI to animate a range of classic history paintings (some famous, some less so) that show various scenes from the story, including some “prequel” scenes such as the Judgment of Paris, where Prince Paris of Troy was tasked with awarding the golden apple to the most beautiful of the three leading Olympian goddesses, Hera, Athena, and Aphrodite. Each goddess offered a bribe, but Paris, playboy as he was, took Aphrodite up on her offer of using (or misusing) her power as the Goddess of Love to make the world’s most beautiful mortal woman fall in love with him. (Thus, the subsequent tribulations of Helen of Troy.)
The new video makes good use of AI to animate some of the many classic history paintings that depict scenes from the Homeric myths. Top: Paris awards the golden apple to Aphrodite, antagonizing Hera and Athena, which was not a good idea just before initiating a war, since they were the Queen of the Gods and the Goddess of War, respectively. Middle: The Greek hero Achilles has finally stopped moping in his tent to fight the Trojan champion, Hector. Bottom: Odysseus has himself tied to the mast so that he can hear the siren song without going to his doom. Unless you already know all these plot elements, the new video won’t make much sense. Just imagine trying to decipher the above illustration if high school failed you and you didn’t gain a proper understanding of the classics. I added explanations because I know most high schools are terrible these days. (Muse Image)
One thing I don’t like about the video is that the soundtrack consists of more-or-less Homeric verses in Greek, set to music. True, the bards used to sing the poems, which is why they famously start with “Sing, Muse, about …” (either the wrath of Achilles or the man of twists and turns, AKA Odysseus). Authentic, yes. Listenable, no. (For my music video version of Moby Dick, I used a rockabilly song for your listening pleasure, inauthentic as that genre is for the period.)
Despite recognizing that David Comfort’s video will go over the heads of 99% of the viewing public, I applaud him. It’s exactly the point of AI filmmaking that it’s so cheap that it supports niche efforts aimed at a small audience. It’s much more fun for the 1% who appreciate something like this to watch a niche video than a generic mass-audience entertainment product from the legacy studios, especially as the production values of the two are converging fast, thanks to rapidly improving AI video models.
The Goal-Gradient Effect: Users Accelerate When They See the Finish Line
People invest more effort the closer they get to a goal: rats in 1932, coffee buyers in 2006, your users today. Show honest progress, grant genuine head starts, and make the final step the lightest. Fake progress works exactly once, and then it costs you the user’s trust forever.
Motivation is a slope, not a flat line: as the finish nears, the walker’s pace picks up all by itself. Design puts the finish line in view.
Definition: The goal-gradient effect is the tendency to increase effort as the remaining distance to a goal shrinks.
The name and the finding are nearly a century old. In 1932, the behaviorist Clark Hull of Yale reported in Psychological Review that rats running a straight alley toward food ran measurably faster the closer they got to the goal box. He called it the goal-gradient hypothesis, “gradient” because effort isn’t constant along the path: plot motivation against remaining distance and you get a slope that climbs as the distance falls. The finding then hibernated in the animal-learning literature for 7 decades.
Rats have much to answer for: they are behind many of the psychology insights we apply in UX design today. (GPT Image 2)
The resurrection came in 2006, when Ran Kivetz, Oleg Urminsky, and Yuhuang Zheng published “The Goal-Gradient Hypothesis Resurrected” in the Journal of Marketing Research. Watching a real café’s buy-10-get-1-free program, they found customers bought coffee more frequently as the free cup approached. Internet users earning reward certificates for rating songs visited more often, rated more per visit, and persisted longer near the goal. Humans, it turns out, run Hull’s software too.
Two companion findings matter for design. First, illusionary progress works: customers handed a 12-stamp card with 2 bonus stamps already applied completed their 10 required purchases faster than customers holding a plain 10-stamp card, despite identical real effort. Joseph Nunes and Xavier Drèze confirmed the phenomenon the same year with their endowed-progress study in the Journal of Consumer Research: among 300 car-wash customers, a 10-slot card with 2 free stamps was completed by 34%, versus 19% for a blank 8-slot card. Same 8 purchases, nearly double the completion. Second, post-reward resetting: the moment a reward is collected, effort slumps back to the bottom of the gradient and must climb all over again.
Partly filled loyalty cards motivated customers to buy even more. (GPT Image 2)
Progress Users Can See Is Progress They’ll Finish
The design translation is direct: make the remaining distance visible, and shrink it honestly. A checkout that announces “Step 2 of 4” recruits the gradient; a checkout of unknown length recruits nothing, because there’s no slope without a visible finish line. Profile-completeness meters, onboarding checklists, and upload percentages all work the same lever, and the good versions endow progress legitimately: if creating the account genuinely completed step 1 (because you transferred the user’s information from the checkout flow), show step 1 checked. That’s the 2-stamp head start with a clear conscience, and it converts a task “not yet begun” into a task “already underway,” which is psychologically a different task.
Sequencing matters as much as display. Front-load quick wins so early progress comes cheap, and make the final step the lightest of all, since that’s where motivation peaks and where a sudden demand for a scanned utility bill does maximum damage. Amazon understood terminal-step compression so well that it patented 1-Click ordering back in 1999. And because of post-reward resetting, completion screens should immediately surface the next meaningful goal; a user at the top of one gradient is standing at the bottom of another, whether you design for it or not.
Progress Theater: The Dark Side of the Gradient
Now the abuse, which I’ll call progress theater: progress displays that perform advancement without reporting it. The repertoire is familiar. Progress bars that sprint to 99% and then meditate. Flows that promise “2 steps left” and then spawn step 5 of 4. Meters proclaiming “Your application is 90% complete!” when the user has done nothing, a specious endowment that insults rather than motivates. Completeness scores that guilt people into publishing personal data they’d rather withhold. And streak mechanics that flip motivation into hostage-taking: miss 1 day and lose 300 days of visible history, converting a cheerful gradient into dread. (Fairness requires noting that Duolingo, the poster child of streak pressure, also ships streak freezes and repairs, which is the humane version of the mechanic. The tool isn’t evil; the tuning decides.)
Fake momentum isn’t motivating. (GPT Image 2)
The economics of progress theater are terrible, and that’s the case against it in a sentence: the goal gradient runs on believed progress, so the first fake bar a user catches devalues every honest one you’ll ever show. The fixes follow directly. Tie every indicator to real system state. Freeze the step count before the flow ships, and never let it grow midstream. Endow only work genuinely done, with the reason stated. Let streaks bend instead of shatter. And make abandoning a flow shame-free, because a user who leaves respected returns; a user who leaves manipulated doesn’t.
8 Design Guidelines for the Goal-Gradient Effect
Show position in every multi-step flow. “Step 2 of 4” beats an anonymous sequence; unknown length flattens the gradient.
Keep the step count immutable. Steps may collapse, never multiply, once a flow has started.
Grant honest head starts. Pre-check steps the system truly completed, and say why they’re checked.
Front-load quick wins. Cheap early progress gets users onto the slope; save nothing easy for step 1’s graveyard.
Make the last step the lightest. Motivation peaks at the end; friction there wastes the entire gradient you built.
Tie indicators to real state. A progress bar is a measurement instrument, not an animation; no decorative loops.
Bridge the post-reward slump. On completion, celebrate briefly and surface the next meaningful goal immediately.
Instrument per-step abandonment. Expect drop-offs early, where the goal is distant, and invest your polish there.
Look once more at the walker in the illustration: the pace quickens not because the path got easier but because the flags and the light made the remaining distance legible. That’s the entire craft. You can’t install motivation in users, but you can reveal the finish line their motivation already responds to, and you can refuse to lie about it. Show real progress, and users finish; stage fake progress, and they finish with you.
Let users see that the finish line is getting close, and you can boost their motivation to complete the process. (GPT Image 2)
User Spending on AI Subscriptions Up 41% in Two Years
Users keep spending more on AI, which is the best indication that it has value. PNC is one of the largest U.S. financial services institutions, with annual revenues of $33 billion. It recently published an analysis of its credit card customers’ spending on AI services (PDF file). The data showed that an increasing share of these consumers is paying for AI subscriptions on their PNC credit cards.
AI is hungry for more money, and consumers are starting to pay up. (GPT Image 2)
More interestingly, the amount these customers are paying is also increasing, as shown in the following chart:
Monthly AI spending in households with AI subscriptions: up 43% in two years. Data from PNC Economics Research. (GPT Image 2)
In only two years, the average AI spending per household increased from $22 in May 2024 to $31 in May 2026: up 41%, for an annualized growth rate of 19%. (This is averaged over those households that have at least one AI subscription, not all households. Most people still use only free AI and miss out on the vastly better paid models.) But a closer look at the chart shows that almost all of the growth happened after January 2026.
Premium AI is much better than free AI. (GPT Image 2)
For the best frontier models, you need to go beyond the $20 starter plans and pay $100+ per month, which clearly most households aren’t doing yet. (GPT Image 2)
From May 2024 to January 2026, the annualized growth in subscriber spending on AI was “only” 10%, but from January to May 2026, it was 80%. The AI spending curve idled for 20 months, then floored the accelerator.
This is a major surprise to me, and a perfect example of why we rely on empirical data rather than personal intuition. I would have thought that the main driver of personal AI spending was creative services like image and video generation, both of which were going strong in 2025. In contrast, the strong growth area in 2026 so far has been agentic AI, which I had assumed was more a matter of company spending than individual household spending.
Of course, some of the AI subscriptions billed to personal credit cards may really be business expenses that employers reimburse. More detailed data would be needed to tease apart the two kinds of spending charged to personal credit cards.
Is AI use at home truly personal, or is it business? The credit card data can’t say. (GPT Image 2)
How high can AI expenses go? There’s still a long way to the scenario in this cartoon, and even though I expect AI to double household incomes after we get superintelligence, I don’t think personal AI spending will grow beyond 10% of household budgets unless we include spending on robots to do the housework and robocars to drive us. Butlers and chauffeurs are more valuable to most people than the ability to analyze reports or draw funny cartoons. (GPT Image 2)
AI Taught Students to Ask Better
AI improved learning in one of the poorest school systems on Earth. In a controlled experiment in Sierra Leone, 1,763 students in grades 7–8 swapped half their weekly math periods for sessions with Gemini’s Guided Learning mode. After 9 weeks, treated students scored 0.26 standard deviations (SD) higher than controls (p=0.029), which the researchers translate to 1.2–1.7 years of typical progress in that school system. Students who used AI for 12 hours or more gained 0.38 SD. The ROI is stupendous when measured as years of learning divided by hours spent with AI.
Guided Learning asked scaffolding questions in 76.4% of its messages and handed over direct solutions in only 2.1%. And the users adapted to this design: skill-seeking questions rose from 67.7% to 91.7% of student messages over the trial, while answer-seeking collapsed from 25.1% to 6.3%. One teacher put it plainly: “Gemini is not solving the problem for you... you ask questions, he directs you what to do.” The default behavior of the AI taught the users how to use it. When learning is the goal, the answer is the wrong deliverable.
In contrast to many other studies where AI narrowed skill gaps, in this study the largest gains went to students who started stronger, so the tool widened the achievement gap.
Obviously, higher-IQ students will always learn more, so the question is how much more. In this case, AI helped all students, but it helped the smartest the most. We don’t know why AI widened rather than narrowed the gap in this study, but the cause may be precisely that the AI was not doing the work for the students. If AI had done the homework for the students, it almost certainly would have narrowed the skill gap, but the goal of education is learning, not homework.
Many previous studies have reached the same conclusion: when using AI for education, it should not solve homework problems for students or hand them straight answers, but rather function as a tutor that guides students through the learning process and helps them when they get stuck. Always nice to have established findings confirmed, but the most important lesson from this new study was the AI’s ability to teach students to ask better questions.
Conclusion: New Species, Old Rules
Step back from this week’s items, and two threads emerge. The first: your users are no longer all human. AI agents completed 89% of shopping tasks on an agent-ready website, 41% of shoppers already prefer an AI intermediary to your brand site, and subscriber spending on AI has grown at an 80% annualized rate since January. The customers are changing species, and the money is following them.
The second thread: the old rules govern the new species. The semantic markup that lets agents shop is the same accessibility hygiene we’ve preached for 25 years. The Socratic scaffolding (talk about old insights: Plato rules!) that lifted math scores in Sierra Leone worked for the same reason StoryPrompt’s forced keywords backfired on the best young storytellers: AI must be an offer, not an order. And Hull’s rats (1932), McCloud’s cartoon faces (1993), and Furnas’s desert fog (1998) explain this week’s interfaces better than any vendor demo.
Neither panic nor freeze: publish the structured data, keep a human hand on the yoke, and stack the next small brick. The users changed species; the fundamentals didn’t.
Final Thought of the Day
