The 2026 Consumer Economy: “Griping but Swiping” Meets the AI Boom, Tariffs and the K Shaped Economy.
Why retail leaders can’t afford to misread this moment: the U.S. economy is growing, but the consumer is splitting into two different species
By Carsten Krause
January 13, 2026
At NRF, the economist panel didn’t sugarcoat it. Mark Mathews set the tone with the kind of line that sounds casual… until you realize it’s a warning: “What a year.” That wasn’t nostalgia. That was triage. Because 2025 delivered a rare combination: policy shocks that actually moved the needle, an AI capex surge that’s starting to resemble a new industrial cycle, and a consumer who keeps spending while loudly insisting the economy is terrible.
Michael Pearce (Oxford Economics) summed up the madness in one sentence: “I can’t really remember a year in which policy had been so pivotal to the economic outlook.” Then he pointed to the big three forces: tariffs, the AI boom, and the stock market’s wealth effect—ending with the line every retailer wants to hear and every CFO wants to stress-test: “In sum, it’s been another year of resilience for the U.S. consumer and for the U.S. economy overall.”
David Tinsley (Bank of America) brought receipts from the front lines: holiday spending was “a good holiday season overall,” and in their data, the year-over-year lift wasn’t just price—“it was volume.” He also dropped the number that should make every merchant and CMO stop pretending they’re “omnichannel” and start acting like it: “the online share of holiday spending is well over 50% now.”
So what’s the real story? The U.S. economy may look “fine” in aggregates, but the consumer is becoming increasingly segmented by wealth, age, and labor-market position. And AI is accelerating that split—because productivity gains don’t land evenly, and neither do layoffs, hiring freezes, and wage growth.
NRF’s core signal: the headline economy is stable, but the distribution is not
If you’re a retailer, you don’t get paid in “GDP.” You get paid in household cash flow, credit availability, and confidence gaps that show up as conversion rates and basket trade-down. This is why the panel kept circling back to
The “K-shaped” consumer.
Pearce’s framing was blunt: the economy can be growing at a solid pace and still “not resonate with a lot of people” because we’re living through a bifurcation. He described a “jobless expansion” dynamic: growth without many new jobs, with hiring weak even while wage gains persist for those already employed.
Tinsley backed that up from Bank of America’s income and spending data. He said higher-income household wage growth was running around 3% while lower-income was closer to 1%—and that the labor market is “supporting the higher income consumers more than lower income consumers,” calling it a “double whammy.”
Bank of America’s published analysis reinforces the same point in black and white: overall card spending growth has been running meaningfully higher for higher-income households than lower-income households.
Here’s what that means in operational terms: retailers serving affluent consumers can post “strong holiday results” while value retailers see pressure, higher promo intensity, and rising reliance on installment tools. Both can be true at the same time. That’s not contradiction. That’s segmentation.
“Griping but swiping”: why sentiment is trash while spending holds up
Mathews called out the disconnect explicitly: sentiment remains “near record lows,” yet the panel was broadly positive on 2026. Tinsley’s explanation was refreshingly practical: spending has a “tenuous relationship” with sentiment, and a large chunk of consumption is non-optional—“rain or shine.” Pearce added methodological issues (survey shifts to online), political polarization in responses, and the obvious distributional explanation: if the top slice drives a disproportionate share of spending, the majority can feel lousy while the aggregate still looks resilient.
This isn’t academic. It affects forecasting, inventory, and pricing power. If you’re using consumer confidence as a primary input into demand planning, you’re using a thermometer to measure a hurricane.
There’s also a real-world proof point from the holiday season: Adobe reported record U.S. online holiday spending of $257.8 billion in 2025, up 6.8% year over year, with BNPL hitting $20 billion and mobile accounting for 56.4% of online shopping. That doesn’t sound like a consumer who’s “collapsed.” It sounds like a consumer who has turned shopping into a controlled burn: discount-driven, tool-assisted, and strategically financed.
The AI boom: bigger than dot-com in one important way, but not delivering productivity yet
Pearce threw out a line that should make every board member sit up: Oxford estimates investment in digital technologies is now larger as a share of the economy than it was at the peak of the dot-com bubble.
You don’t need to rely on vibes to see the structural shift. BEA/FRED data shows nonresidential investment in “intellectual property products” has risen meaningfully versus the dot-com era—about 4.0% of GDP in 2000 versus 5.5% in 2024. That doesn’t prove “AI is a bubble,” but it does prove something more important: the U.S. economy is increasingly an IP and software-driven machine, and retailers are operating inside that machine whether they like it or not.
Now here’s the kicker: Pearce was also very clear that the measured productivity impact from AI right now is basically “close to zero.” He argued the strong productivity growth of recent years is more tied to pandemic-era structural changes (software R&D investment, new business creation, organizational shifts) and that the real AI-driven gains come later—through new applications and business-model diffusion.
That’s the part most executives miss. In your ECI formula language, this is the difference between:
- AI (tools) existing, and
- HI (leadership + operating model) actually absorbing it, multiplied by
- T (readiness), minus
- R (risk, resistance, governance drag).
Retail hasn’t “caught up” because most companies are still stuck in pilot purgatory—exactly the point you raised in your audience question, referencing McKinsey’s estimate that retail/CPG could unlock $400B–$660B annually with generative AI.
Pearce’s answer to you was economic-history 101: technology doesn’t drive productivity; diffusion and business-model redesign do. If 75% of initiatives never reach production, that’s not an AI problem. That’s an operating-model problem.
The K-shaped consumer, quantified: what the data is saying right now
Let’s pin down the “K” with measurable signals the panel referenced and recent published data supports.
1) Holiday spending held up—and skewed digital
Tinsley’s on-stage view: “holiday items” up 4.7% YoY in October–December, with transaction volumes showing real unit growth. Bank of America Institute’s published holiday tracking similarly showed YoY spending growth on holiday items running around the high single digits earlier in the season and moderating later, with a clear shift to online purchasing.
Meanwhile, NRF’s own retail tracking for December showed a strong holiday-season finish, reinforcing the view that consumer demand didn’t roll over.
2) Digital is not “the future,” it’s the present
U.S. e-commerce as a share of total retail sales was 16.4% in Q3 2025 (seasonally adjusted), per Census/FRED. And during the holidays, Bank of America’s analysis shows online accounted for the majority of holiday-item spending, with online share rising from roughly the mid-50s in Oct/Nov 2024 to around 60%+ in Oct/Nov 2025 in their card-based measurement.
3) Credit is fine in aggregate… but the tails are getting heavier
When asked about consumer debt, Tinsley gave the most useful answer possible: most households are “in reasonable shape,” but “the tail is getting fatter” where distress is building, including a rising share making only minimum payments.
And the macro balance-sheet magnitude is real: the New York Fed reports credit card balances totaled about $1.23T in 2025 Q3, with total household debt at $18.59T.
4) BNPL is rising, but not exploding—yet
Tinsley said that among lower-income households, about 14% were making buy now pay later (BNPL) transactions in November (in their card-linked view of BNPL provider payments), and usage is trending higher across income cohorts, but not “inflecting explosively.” Adobe’s holiday reporting also showed BNPL volumes growing and becoming a standard part of the holiday financing mix.
The policy wildcards: rates, Fed credibility, tariffs, and the “price cap” trap
This panel also wandered into territory that retail leaders shouldn’t ignore because it directly hits consumer affordability and credit supply.
Fed independence risk: uncertainty can keep long rates sticky
Pearce argued that political attacks on the Fed can paradoxically make rate cuts harder because policymakers avoid looking politically influenced. That theme is not theoretical in January 2026: multiple outlets have reported a DOJ investigation into Fed Chair Jerome Powell and related political backlash, including senators signaling resistance to Fed nominations.
For retail, the transmission mechanism is simple: if long rates stay elevated, mortgage rates stay elevated, housing turnover stays depressed, and “big-ticket” retail tied to moves and remodels stays constrained—exactly the housing concern Tinsley highlighted.
The 10% credit card interest cap: the unintended consequences are the whole story
Mathews gave a candid private-equity anecdote: subprime lending economics often require very high APRs to offset defaults, and a hard cap can reduce credit access. Tinsley compared it to rent control: cap the price, reduce the supply, and you end up hurting some of the people you intended to help.
This is now an active policy discussion: Reuters reported JPMorgan’s CFO warning that a one-year 10% credit card rate cap proposal could reduce access to credit and disrupt bank economics; Reuters also notes the Fed’s reported average credit card interest rate around 20.97% as of November. The bill exists in Congress in some form, which means the “debate” will persist even if implementation is uncertain.
Retail implication: if credit tightens at the lower end, you’ll see it first in discretionary categories, returns behavior, and the rise of alternative financing at checkout.
Executive translation: what retailers should do with this tomorrow morning
This is where most “economic outlook” sessions fail: they describe conditions and then leave operators with nothing but anxiety. Here’s the operator-grade translation.
1) Run two demand models: “affluent resilience” and “strained substitution”
Stop forecasting one consumer. Start forecasting two. Your affluent segment is increasingly driven by wealth effects (stocks, home equity, inheritances later), while your strained segment is driven by wage pressure, credit access, and price sensitivity. The panel’s K-shaped framing is not a narrative—it’s a forecasting requirement.
2) Treat AI like a transformation of workflows, not a software purchase
Pearce’s “close to zero productivity today” line is the most honest thing said on that stage. The winners will be the retailers who redesign labor, decision rights, and planning loops—not the ones who buy copilots and call it strategy.
If McKinsey’s $400B–$660B retail/CPG value range is even directionally right, the gating factor is not “models.” It’s productionization, governance, data rights, and adoption at scale.
3) Make digital economics explicit: online is not a channel, it’s the operating system
When online becomes the majority share of holiday-item spend in major bank data, your “store-first” org structure becomes a tax on your own performance. And with U.S. e-commerce already at 16%+ of total retail sales (and much higher in many categories), the winners are optimizing cross-channel margin, not chasing vanity growth.
4) Watch credit supply like a hawk
Even without a rate cap, the tail risk is rising: minimum payments creeping up, paycheck-to-paycheck shares rising in some datasets, and BNPL expanding as a convenience layer that can become a stress indicator if the economy weakens.
A quick “K-shaped retail reality” table you can actually use
| Signal | Higher-income consumer | Lower-income consumer | Why it matters to retail |
|---|---|---|---|
| Spending growth (card-based) | Higher and steadier | Lower and choppier | Mix shift + promo pressure shows up fast |
| Wage growth trend | Stronger | Weaker | Affordability gap widens, value wins, trade-down rises |
| Credit stress | Mostly manageable | Tail risk rising | Default + returns + shrink + basket volatility |
| BNPL usage | Rising | Rising, more visible | Checkout financing becomes a signal, not a feature |
| Holiday behavior | Early, selective, digital | Still spending, but more price-sensitive | Timing shifts break old promo playbooks |
The CDO TIMES Bottom Line
Retail leaders should stop arguing about whether the consumer is “strong” or “weak.” That debate is lazy. The consumer is split, and the split is getting sharper.
The NRF panel laid it out clearly: 2025 was defined by policy shocks (tariffs, tax effects), a massive AI investment wave, and a stock-market wealth effect that props up aggregate spending even while a meaningful share of households feel squeezed. The implication for 2026 is not “doom” or “boom.” It’s segmented resilience: strong results in the aggregate with pockets of real fragility underneath.
Next steps for executives:
- Build two demand scenarios and manage to the distribution, not the average.
- Shift AI from pilots to operating-model redesign (workflow, governance, adoption, measurement).
- Treat digital as the primary system of retail and optimize profit across journeys, not channels.
- Track credit availability and consumer liquidity as leading indicators—not quarterly surprises.
If you want one sentence to take back to your leadership team:
The winners in 2026 will be the retailers who operationalize Elevated Collaborative Intelligence—HI + AI—fast enough to keep up with a consumer that is simultaneously anxious, strategic, and still spending.
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