Black Friday Without the Frenzy

Black Friday used to feel like an event. Today it looks more like a long, slightly tired gradient.

Foot traffic is still elevated on the day after Thanksgiving, but visits have plateaued since the post-Covid rebound. More shoppers have moved online and many of the most aggressive promotions are no longer confined to a single morning. 

What was once a one-day shock to inventory and staffing has stretched into a season of rolling discounts that begin in October and bleed into Cyber Monday and beyond.

That shift has removed some of the urgency from Black Friday, but it has not removed the macro signal. Spending across the Turkey 5 has fallen for two years and survey data suggest another decline this season, even as online sales on Thanksgiving hit fresh records. 

The growth that remains is heavily skewed toward higher income households whose balance sheets are supported by rising asset prices and the prospect of fresh tax relief.

Layer on top the growing role of AI in steering search, discovery, and checkout, and Black Friday becomes less of a standalone holiday and more of a checkpoint in a multi year reset. The cycle is sorting retailers by their ability to serve the top of the K, the bottom of the K, and the AI rails that increasingly sit between them.

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If you grew up in the 1990s or 2000s, Black Friday carried a specific energy. Lines at 4 a.m. outside Kohl’s or Best Buy. 

A handful of products at prices you would never see again. Retailers spent a year negotiating with vendors to create a single, intense shock to demand that could set the tone for the rest of the season.

That model has been quietly dismantled.

As Black Friday grew into a cultural fixture, retailers did what retailers always do with a successful format. They pulled demand forward. 

First by opening earlier on Friday, then by opening on Thanksgiving, then by spreading deals across the entire weekend. Eventually, the promotions leaked into early November and, in some categories, into late October.

What began as a single dramatic price point on a narrow set of items slowly turned into a generalized promotional environment across the store. The operational burden of staffing for a single extreme day, then, became harder to justify. 

Spreading the deals out made it easier to recruit and train seasonal workers and easier for consumers to spread purchases across multiple pay periods.

The cost was the integrity of the event. When shoppers can find similar discounts earlier in the month, or see price tags yo-yo up and down as retail pricing teams attempt to balance tariffs, inflation, and margin targets, the idea of a once-a-year doorbuster loses credibility. 

A Black Friday price is no longer obviously the best price. In many cases it is simply one more node in a long series of promotions.

That erosion of urgency lands on a consumer base that is already split.

The Turkey 5 has seen declining spending over multiple years, with this season expected to see another step down. Deloitte’s survey work points to sharp planned reductions in holiday budgets for lower and middle income households, on the order of 24 percent and 13 percent respectively. 

To preserve gifts, many of these buyers are cutting back on decor, new clothing, and dining out. They are choosing what goes under the tree over what goes on the tree.

At the same time, affluent households are still spending. Higher asset prices and home equity, plus wage gains at the top end, have supported demand for luxury apparel, travel, and premium brands. 

Households earning more than $250,000 now account for roughly half of all consumer spending and nearly a third of GDP. That concentration is why you can see Hermès and LVMH talk about resilient demand while mid-market chains like Kohl’s or Bath & Body Works warn on guidance.

Black Friday sits right at that junction.

Discount driven value retailers can lean into the environment. Walmart, TJX, and Amazon offer clear price signals to shoppers who now cross-shop ruthlessly and use price comparison tools instinctively. 

On the other side, luxury brands count on a smaller but wealthier cohort that is less sensitive to marginal price moves and more focused on availability, exclusivity, and brand status.

The trouble is in the middle. Retailers that cannot convincingly claim either the lowest price or the strongest brand position are the ones bearing the brunt of the K shaped economy. 

They face shoppers who are skeptical of discounts, conditioned by years of rampant promotion, and alert to any hint that a “deal” is just a round-trip back to last year’s price after tariffs and inflation.

As if that were not enough structural change, AI is quietly rewiring how shoppers reach the store in the first place.

Generative AI platforms are now significant referral channels in their own right. Traffic from AI sources like ChatGPT and Perplexity to retailer websites has grown more than tenfold in a year.

Early data suggest that AI referred shoppers convert better and generate higher revenue per visit than those who arrive through traditional search or email. They are more likely to show up with a specific need, a pre-filtered set of options, and less willingness to scroll through pages of search results.

That shift is subtle but important. For twenty years, the primary gatekeepers of online shopping have been search engines and retailer owned marketing channels. As AI agents take a larger role in discovery and recommendation, brands find themselves competing not just for search rankings, but for inclusion in AI generated shortlists.

To be surfaced, products need clear titles and structured descriptions that large language models can parse. They need third party references in reviews, editorial content, and social media that these models can ingest and treat as credible. 

Smaller direct to consumer brands like Brooklinen or Everlane may find it easier to tailor their content quickly and respond to these new dynamics. Larger incumbents have the advantage of scale and data, but carry more organizational friction and legacy systems.

The checkout side of agentic commerce is still immature. Current integrations often allow only one item per AI guided purchase path, loyalty programs are not deeply integrated, and retailers worry that letting third party agents drive the cart could degrade the brand experience and weaken their hold on customer data. 

That is why Amazon is taking a more restrictive stance, suing some AI providers that attempt to automate Amazon purchases on behalf of users, and emphasizing its own AI agent, Rufus, rather than embracing broad third party access.

Over the next few seasons, the more important fight may not be over who owns Black Friday, but over who owns the rails that lead into it. AI agents that move from recommendation to multi item basket building and one click checkout will be powerful gatekeepers. 

Retailers that have their catalogs, content, and margin structures ready for that world will gain share. Those that try to retrofit old promotional playbooks into an AI mediated landscape could find themselves missing from the most valuable new storefronts: the conversational interfaces where shoppers begin the journey.

For investors, Black Friday’s dilution is not just a cultural story. It is a mapping exercise. At the top of the K are brands and platforms that can preserve pricing power even without a single climactic sale day. 

At the bottom are value operators that can fund their model at lower unit economics and capture trade down. Threaded between them are the infrastructure names that build and own the AI rails that increasingly direct traffic and capture advertising and payments revenue along the way.

In that context, a flat foot traffic chart on Black Friday is not a sign that the holiday has lost meaning. It is a sign that the mechanics of urgency and discovery have moved upstream, into algorithms and agents that work every day of the season, not just at 5 a.m. on one morning in November.

It has never been easier to buy. It has rarely felt harder to complain. 

The latest National Customer Rage Survey found that 77 percent of consumers reported a product or service problem in the last year, a record high compared with 74 percent in 2023 and just 32 percent when similar research began in the 1970s. 

Other surveys show a parallel slide in perceived customer experience quality across North America, with average scores hitting record lows.

The friction is not just about product flaws. It is about the mismatch between one click ordering and multi step problem resolution. Long phone trees, opaque contact options, and AI chatbots that struggle with nuance leave many customers feeling that the cost of seeking redress outweighs the value of the fix. 

High tier loyalty members and affluent customers get better outcomes through stratified service lines. Everyone else navigates a more crowded, automated funnel.

For brands, that gap between purchase ease and service pain becomes its own risk factor. It feeds distrust around promotions, weakens loyalty at the margin, and pushes frustrated customers toward competitors that can credibly offer low friction returns or live support. 

A handful of operators, like Chewy in pet supplies, continue to post standout satisfaction scores, but they are exceptions in a landscape where more than three times as many brands are slipping as improving.

Investor Signal

Customer experience is not a soft metric in this environment. It is a direct driver of retention, price realization, and referral. In a holiday season defined by value hunting and AI driven comparison, companies that treat support, returns, and complaint handling as a cost center to be automated away may see faster near term margin but slower comp growth and higher churn. The more interesting stories are those where management leans into service as a differentiator, especially in categories where products are commoditized and switching costs are low.

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The AI and cloud boom has turned cooling into critical infrastructure. The outage at data centers serving CME Group, which temporarily halted trading in futures and FX, highlighted how sensitive modern compute farms are to temperature control. 

High powered chips running large models generate intense heat that traditional air cooling systems struggle to dissipate. As racks grow denser, the margin for error narrows.

Operators are increasingly experimenting with liquid cooling, specialized coolants, and closed loop systems that recycle water instead of drawing fresh supply. Vendors like Eaton and Vertiv are deploying billions of dollars into thermal management acquisitions and buildouts. 

Some hyperscalers are piloting designs that consume zero net water for cooling by recirculating fluid between servers and chillers or by reclaiming waste heat for nearby buildings.

Outages tied directly to cooling remain relatively rare, in part because uptime clauses in data center contracts are stringent. But the share of total energy consumption devoted to cooling can reach 40 percent. 

That makes thermal efficiency a major driver of both economics and reliability. As AI workloads push power density higher, cooling becomes a bottleneck, a regulatory concern, and a dealmaking theme all at once.

Investor Signal

Thermal management is emerging as one of the most leveraged ways to play AI infrastructure without betting on any single model provider. Hardware, fluids, and systems that can safely support higher rack densities will command pricing power as demand for compute grows. 

At the same time, data center REITs and operators that are slow to modernize cooling may face higher operating costs, more frequent deratings, and, in extreme cases, outage driven reputational damage. Cooling should sit alongside power procurement and network latency in any serious underwriting of AI data center exposure.

The AI boom is increasingly being built on borrowed money. An analysis of OpenAI’s key infrastructure partners suggests they have taken on roughly 96 billion dollars in debt to fund data centers, chips, and compute. SoftBank, Oracle, and CoreWeave account for about 30 billion of that figure, Blue Owl Capital and Crusoe another 28 billion, with around 38 billion more in prospective loans under discussion.

Against that, OpenAI has disclosed 1.4 trillion dollars in long term commitments to procure energy and compute, while expecting only about 20 billion dollars in revenue this year. 

Even optimistic projections of more than 200 billion dollars in revenue by 2030 suggest the company would still require over 200 billion dollars in additional funding to bridge the gap. 

Hyperscalers themselves have issued roughly 121 billion dollars in new debt this year to support AI capex, more than four times their average annual issuance over the prior five years.

Credit markets are noticing. Five year CDS spreads for Oracle have widened meaningfully since September and CoreWeave’s spreads have moved even more sharply as investors price in execution and refinancing risk. 

Investment grade supply has surged in recent weeks, with research desks attributing much of the incremental issuance to AI related capex and M&A. It marks a new phase where public credit markets, rather than just tech balance sheets and equity, are funding the arms race in compute.

Investor Signal

AI exposure is no longer just an equity story about growth and moat. It is a credit story about duration and capital structure. The safest place to participate may be higher up the stack in diversified hyperscalers with strong free cash flow and multiple revenue engines. 

More concentrated infrastructure players and highly levered compute providers offer upside but carry clear refinancing and spread risk if AI revenue ramps more slowly than planned. Monitoring CDS and issuance patterns around key AI infrastructure names is becoming as important as tracking parameter counts or benchmark scores.

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Black Friday’s fading drama, rising customer rage, hot data centers, and leveraged AI partners all point to the same underlying tension. The system is asking digital infrastructure and automation to do more with less, while trust and patience on the consumer side are eroding.

For positioning, three themes stand out. First, anchor holiday exposure at the ends of the K. Luxury and discounters are better placed to navigate a diluted Black Friday and a skeptical shopper than mid market brands that cannot clearly articulate either exceptional value or differentiated product. 

Second, focus on the rails. Companies that own cooling, power, traffic, and AI discovery are building tollbooths on top of which seasonal retail winners and losers will rotate. 

Third, treat service quality and credit structure as hard risk factors, not soft add ons. The next phase of this cycle is likely to reward businesses that can maintain loyalty without relying on one day gimmicks and can fund AI ambitions without stretching their balance sheets past investors’ comfort.

Black Friday is no longer the whole story. It is a snapshot in a longer film about how consumption, infrastructure, and leverage interact in an economy where both urgency and patience are being repriced.