The anti-consumption paradox

How AI relocated the cognitive load problem instead of solving it.

Black Friday 2025: AI-driven traffic to retail sites surged 805% compared to last year. Online spending hit $11.8 billion, up 9.1%. Over 55% of AI-generated prompts leading to Amazon contained purchase intent. (BoF)

What makes this genuinely strange: Black Friday 2025 broke spending records during one of the most economically anxious years in recent memory. The economic situation in the U.S. in November 2025 is marked by a slowing labor market, declining consumer confidence to multi-month lows and persistent inflation that continues to concern households. (TD) The holiday shopping season arrived amid what analysts called “tighter budgets,” “sagging sentiment,” and “trade-driven uncertainty.”

How do you get record consumption during record anxiety? How does underconsumption content go viral while actual consumption accelerates?

The economic context that makes this paradox possible

Gen Z can’t afford what prior generations could. The average federal student loan debt is $39,075 per borrower. Since 1985, rent prices have surged by 208%, outpacing income growth (194%) by 7% nationally. The generation entering peak consumption years faces constraint that fundamentally changes behavior.

But 2025 added new pressure. Tech layoffs continued. Government shutdowns disrupted low-income spending. Credit card debt and delinquencies on short-term loans rose. Higher earners leaning into stock gains. Lower earners hunting discounts and stretching budgets across months.

This created the exact conditions for anti-consumption to explode. When you’re economically constrained as a consumer but still need identity differentiation, restraint becomes your only available signal. You can’t flex through price. You can’t signal through logos (too ubiquitous). So you signal through the aesthetic of not-needing.

Underconsumption gave people a narrative that converted economic limitation into cultural sophistication. The seven-year-old leggings aren’t just frugality, they’re proof you were never trying to keep up in the first place.

Except the data shows people were trying to keep up. They just needed better tools to do it within their means and constraints. If AI can surface the best prices and discounts instantly, people will opt in to purchase.

This is where the analysis usually stops: people say they want less but spend more, therefore they’re hypocrites performing values they don’t hold. But that misses the actual mechanism.

What if anti-consumption wasn’t rejecting commerce itself, but rejecting the friction that made commerce exhausting during a period of genuine scarcity? What if the problem wasn’t buying things, but the cognitive burden of figuring out what to buy and where to buy for the best price, when every decision matters more because you have less margin aka means for error?

The economic pressure was real. The anxiety was real. But the response wasn’t actually restraint, it was a search for less exhausting ways to navigate scarcity.

Then Walmart launched Sparky. Amazon launched Rufus. Retailers integrated AI that let you ask “what serum removes dark spots” and get instant answers with comparison, reviews, and purchase paths in one interaction.

Black Friday 2025 broke spending records. Not in spite of the anti-consumption trend, because AI solved what anti-consumption was actually responding to.

The cognitive load problem brands didn’t know they were creating

Every product category became a research project. Buying moisturizer required understanding hyaluronic acid, pH levels, ingredient interactions.

That worked when the alternative was trusting influencers with affiliate links. But it created exhaustion. The sophistication required to navigate The Ordinary’s product matrix became its own barrier. You needed so much education to decode what you should buy.

Deinfluencing emerged as a counter-signal: people telling you what NOT to buy saved cognitive effort. If everyone says the Dyson Airwrap is overpriced, you’ve eliminated one decision. The movement drove Dyson’s brand favorability down 20 points (The Drum), not because the product failed, but because ubiquity made the decision more complex, not simpler.

Brand strategy’s core idea was that consumers wanted more information. Radical transparency. Ingredient lists. Cost breakdowns. Behind-the-scenes supply chains. Every brand competed on who could provide more data points. But more information without better filtering just increases cognitive load. The transparency created a new kind of gatekeeping, one based on knowledge rather than price.

Anti-consumption let people opt out of the entire research burden. You don’t need to know what niacinamide does if you’re not buying anything.

Then AI made research instant. You don’t need to understand ingredients. You ask: “I have dry skin and dark spots, what should I use?” it processes product databases, reviews, dermatologist recommendations, and your specific context in seconds.

Except AI doesn’t eliminate the research burden, it relocates it. You’re no longer evaluating products, you’re evaluating whether to trust the algorithm’s curation. Which retailers paid for placement? Are these reviews real or gamed? Did the AI hallucinate a product feature? The cognitive load shifts from “what should I buy” to “should I trust what I’m being told to buy” and for many, that second question carries more anxiety, not less.

But cognitive load collapsed for whom? The 805% AI traffic surge skews toward households already comfortable with digital tools and able to afford optimization over survival. The economic anxiety driving credit card delinquencies isn’t the same population asking Rufus to compare serums. Anti-consumption as performance requires enough proximity to consumption to make the refusal legible.

AI shopping tools don’t eliminate class barriers, they create a new one: digital fluency as the price of access to better deals.

Why this overweights sustainability claims

Some will argue anti-consumption was never about decision paralysis, it was about values. Environmental sustainability. Rejecting overconsumption culture. Ethical production.

The data doesn’t disprove values, it shows values alone weren’t enough to overcome friction. When AI surfaced “eco-friendly sneakers under $100” instantly, sustainable consumption became easier, not irrelevant. The same person posting underconsumption content could now act on environmental values without the research burden that previously made restraint the simpler option. AI didn’t replace values with convenience. It made values-aligned purchasing finally convenient enough to scale.

The new market structure: AI agents as the filter layer

Pre-AI retail hierarchy:

• Luxury: High price filters for exclusivity

• Premium minimalism: High knowledge requirement

• Mass market: Low price, low friction, high volume

Post-AI retail hierarchy:

• Products AI recommends based on your query

• Products AI doesn’t recommend

• (Everything else is functionally invisible)

When AI becomes the interface between intent and purchase, everything built on making that interface deliberately complex becomes obsolete.

What to build when AI commoditizes decision-making

The structural answer: you can’t compete on helping people decide anymore. That’s AI’s job now. The brands that positioned on transparency, education, ingredient honesty, or making customers “smart enough” to understand what they’re buying—those advantages evaporate.

What remains:

1. Actual product differentiation that AI can measure

AI optimizes for concrete variables: efficacy, price, durability, reviews, specifications. If your product genuinely performs better, AI will surface it. If your differentiation was aesthetic (minimalist packaging, no-brand philosophy, anti-consumption messaging), AI ignores it unless customers specifically ask for it.

2. Experiences AI can’t replicate

Aesop’s $2.5 billion acquisition wasn’t about their products, it was about retail spaces with unique architecture and sensory experiences. Physical environments where discovery happens through space, not search. AI can’t replicate walking into an Aesop store with 7,560 amber bottles on the walls.

But this only works if the experience itself creates value beyond product selection. If you’re just using aesthetic as a filter for cultural capital, AI removes the need for that filter.

3. Community and identity that exists independent of purchase decisions

Patagonia’s environmental commitment, Reddit’s r/BuyItForLife community (3.3 million members), Supreme’s drop culture—these create value beyond the transaction. But here’s the test: does the community survive when AI makes purchasing frictionless?

r/BuyItForLife might, because the identity is genuinely about durability and anti-waste. Supreme’s hype culture probably doesn’t, because it’s built on artificial scarcity and FOMO, exactly what AI transparency destroys.

The brands that survive aren’t the ones that helped you decide. They’re the ones that deliver actual value after AI handles the decision for you.

What this means for how you build digital experiences now

AI didn’t solve the cognitive load problem. It moved it. Pre-AI: exhaustion came from evaluating too many products. Post-AI: exhaustion comes from evaluating whether to trust the curator. The friction didn’t disappear, it shifted from product expertise to platform trust, from knowing ingredients to knowing whose recommendations to follow. Some consumers find this liberating. Others find it alienating. The brands that win won’t be the ones assuming AI eliminated friction. They’ll be the ones designing for the new friction: “Why should I believe what I’m being shown?”

If users arrive 90% decided, your site’s job isn’t discovery, it’s conviction reinforcement. Show the product being used by someone like them. Surface the review that addresses their specific doubt. Offer post-purchase support information up front. Provide context AI can’t: “This works best if you also do X” or “83% of buyers also needed Y.” The interface shifts from “help me explore options” to “prove I made the right choice and prepare me to use it well.”

When someone asks ChatGPT or Rufus “what’s the best moisturizer for combination skin under $30,” the AI processes your entire catalog along with every competitor, reads thousands of reviews, and surfaces three options. The human arrives at your product page having already made 90% of the decision.

Your interface was built for exploration first. Now, some consumers are arriving ready to transact. The major nuance here is how people discover your brand, based on that they need different information on the website/app.

Discovery shapes expectations, and every entry point demands different information the moment they land. The fundamental shift: from discovery interfaces to conviction interfaces.

The brands treating this as “AI is a new channel to optimize for” are missing the structural shift. It’s not a channel. It’s a new interface layer between intent and transaction. Your job isn’t to compete with it. Your job is to figure out what you build that adds value once it does its job.

The anti-flex economy wasn’t a values revolution. It was a UX problem that emerged during economic anxiety. People didn’t reject consumption, they rejected the exhausting friction of navigating it under constraint.

AI tools shifted the friction. Now the question is: what are you building that matters when the friction moves from choosing products to choosing whose curation to trust?

I’m Kima Sargsyan, a strategist and futurist exploring the shifts redefining culture and consumer behavior. Get more of my work on LinkedIn, or bring me in to speak when your team needs a reset. 1:1 sessions available.