Goodbye Price Tags, Hello Dynamic Gouging: How Grocers and Algorithms Are Quietly Taxing Your Life

There was a time when a price tag was a kind of truce.

You walked into a store, looked at the shelf, saw a number, and, crucially, everyone saw the same number. One price, one product, for every customer. It wasn't perfect, but it was at least fair-ish.

That era is dying.

And the replacement isn't just “dynamic pricing.” It's data-driven, always-watching, algorithmic price steering, and if you're not careful, it's going to cost you far more than a few extra bucks at checkout.

As a privacy hawk and a former employee of the largest traditional grocery chain in the U.S., where I did early work on digital shelf tag and dynamic pricing systems back when they were still a gleam in a VP's eye, I can tell you: it was scary then. It's much scarier now.

From One Fair Price to “What Can We Squeeze Out of You?”

The New York Times Opinion video does a good job walking through how we got here:

• In the 1800s, shopping was basically live-action eBay. Clerks sized you up (how you looked, how long the line was, how desperate you seemed) and named a number. You haggled if you could.
• Quakers and others pushed for posted prices as a fairness reform: one visible price for everyone.
• Adhesive price tags took that idea mainstream. A tiny sticker became a public commitment: you and the person next to you pay the same.

That shift made it easier to compare prices between stores, and it pushed retailers to compete on price instead of on how gullible each customer looked. Fast forward to now: digital shelf tags can change prices in milliseconds, pricing algorithms constantly watch competitors and inventory, and loyalty programs quietly vacuum up every detail of what you buy.

The Three Ways Dynamic Pricing Screws You

The video breaks the problem into three buckets: the illegal stuff, the currently legal stuff, and the “this should absolutely be illegal” stuff. Let's translate that into real-world English.

1. The Illegal: Algorithmic Cartels Without the Smoke-Filled Rooms

Old-school price fixing looked like executives in a room agreeing to raise prices together and hoping regulators weren't listening. Thanks to software, you don't need the meeting.

The video highlights the RealPage case: landlords across the country fed data about their apartments into a shared pricing algorithm that, as the video describes it, hunted for the maximum rent that would still keep units filled, and even favored leaving units empty over cutting rent. The video puts the reach at over 3 million apartments and the overcharges in the billions of dollars.

The Justice Department sued. On November 24, 2025, it announced a proposed settlement requiring RealPage to stop using competitors' real-time confidential data in its recommendations, use only nonpublic data at least 12 months old for training, and accept a court-appointed monitor. RealPage paid no fine and admitted no wrongdoing.

So yes, some of this is probably illegal. But here's the crucial part: you don't need a literal “let's fix prices” agreement anymore. You just need shared or similar algorithms, fed with massive behavioral data, all optimized for the same goal: extract more money from each customer.

84.51°: Algorithmic Cartels With Better Branding

The largest traditional grocery chain in the U.S. doesn't just operate supermarkets. It owns a retail data science, insights, and media company called 84.51°, a wholly owned subsidiary of The Kroger Co. In its own words, published through Kroger Precision Marketing, its first-party retail data “comes from over 60 million U.S. households and is sourced through the Kroger Plus loyalty card program.” Every swipe gives the shopper a discount, and it tells the company what they like or dislike.

That data informs everything from pricing and promotions to assortment and advertising. Translated: every time you use a loyalty card, you help build a high-resolution behavioral model of your household. Now stack that on top of digital shelf tags that can change prices in real time, pricing algorithms tuned to category performance, and retail media platforms that sell targeting against that same data to brands. The Markup documented how that data is packaged and sold back in 2023.

Is 84.51° literally sitting in a dark room coordinating price hikes like a movie villain? No. Is this functionally similar to cartel thinking, just executed by math and marketing instead of mobsters? That's a fair concern. The scary part isn't that they know you like oat milk and cat food. It's what they can infer:

• Income band
• Health indicators and conditions, or strong proxies for them
• Family structure
• Religious or cultural patterns
• Life events and stressors: job loss, a new baby, illness
• Your price sensitivity and breaking points

Once you have that, price “optimization” stops being a neutral analytics problem and starts looking like: how much can we ratchet up pressure on this exact household before they snap?

2. The Legal: Independent Algorithms That Quietly Learn Not to Compete

Imagine two gas stations across town. Before algorithms, Station A drops its price to steal customers, Station B undercuts a bit, they both hate the margin hit, and you benefit from the fight. After both adopt pricing software, each algorithm watches the other in real time and instantly matches any cut. Very quickly, both learn that dropping prices wins nothing, so why bother?

That is not a thought experiment. A study of Germany's retail gasoline market published in the Journal of Political Economy found that adopting algorithmic pricing raised station margins by about 9 percent, and in two-station markets where both stations adopted it, margins rose 28 percent. No secret meeting. No explicit agreement. Just emergent behavior.

Now apply that logic to flights, hotels, rideshares, groceries, and online retail. Algorithms don't need to collude in the human sense to reach cartel-like outcomes. They just need aligned incentives and a constant firehose of data.

3. The “Should Be Illegal”: Personalized Pain-Based Pricing

This is where my inner privacy hawk and my old grocery-tech experience both start screaming. To really maximize dynamic pricing, companies don't just want to know what competitors charge. They want to know how much you make, how impulsive and stressed you are, what your household needs right now, and how likely you are to switch stores. They get it from loyalty programs, store and fuel apps, location tracking, browsing and purchase history, device fingerprinting, and cross-partner data sharing.

This is not speculation either. In January 2025, the FTC released initial findings from its surveillance pricing study, reporting that intermediary firms help retailers use personal data, from precise location and demographics down to mouse movements on a webpage, to set individualized prices for the same goods and services.

The NYT video walks through examples that are disturbingly plausible:

• A pharmacy chain charging you more for essential medication because your data screams “no alternative.”
• A rental site hiking prices because it knows your move-in deadline is two weeks away.
• An algorithm quietly raising bottled water prices during a boil-water advisory.
• A smart speaker noticing you're out of paper towels and bumping the price right before you click “reorder.”

A Quick Insider View: Digital Shelf Tags in Their Infancy

When I worked on early digital shelf and dynamic pricing initiatives for the largest traditional grocery chain in the U.S., the official story was all upside: update prices instantly without extra labor, reduce pricing errors at the shelf, roll out promotions faster. All technically true.

But anyone paying attention could see the other trajectory. If you can change prices instantly, and you're building deep behavioral profiles of tens of millions of households, the shelf price stops being a public commitment and becomes a real-time negotiation you don't know you're in. Tie loyalty history to digital shelf infrastructure and optimization engines focused on basket size and category performance, and you get a system that knows exactly when to offer you a coupon and when to quietly withhold one. The tech was cool. The possible uses were, and are, deeply uncool.

Why Traditional Regulation Isn't Enough (Yet)

To be fair, regulators aren't totally asleep. The DOJ went after RealPage, and the FTC studied surveillance pricing. But these systems evolve much faster than laws. The NYT video suggests two straightforward ideas:

• Limit when prices can change. For example, physical retailers could update prices only at fixed times, say once a day at 6 a.m. That restores real price competition and transparency.
• Restrict how personal data can be used in pricing. Your health, income, household situation, and emotional vulnerability should not be valid inputs to “how much can we charge this person?”

Those are good starting points. The hard truth, though: until enough people change how they behave and spend, companies won't seriously change what they do. They listen to money, not outrage.

What You Can Actually Do (That Moves the Needle)

You can't fully opt out of this system, but you can starve it of the easiest fuel and push for better rules. Here's the practical playbook, in order of impact.

1. Treat Loyalty Programs as Surveillance Programs

That “free” loyalty account? You are paying in data. Don't auto-enroll in every program. When you do sign up, give only the minimum required information, avoid linking social logins or bank data, and opt out of partner sharing and personalized offers where you can. You might lose a few splashy coupons. In exchange, you reduce how precisely they can profile and price you.

2. Minimize Shopping While Logged In or Fully Identifiable

Online, don't stay logged in just to browse or price-check, and use privacy-focused browsers or separate profiles when shopping. Be aware that a newer, higher-end device can be a signal used against you. In store, scanning your loyalty card or typing your phone number is a data handshake that stitches that transaction into your long-term profile. If the discount is tiny, it may not be worth the surveillance.

3. Be Smart About “Smart” Devices

Assume any always-on, always-connected device is a potential signal: smart speakers, smart TVs, retail apps with broad permissions, voice assistants. Turn off one-click and voice purchasing where you don't need it, review app permissions, and keep purchase decisions off hot mics. Is that a bit paranoid? Maybe. Is letting a device quietly feed your life into a pricing engine worse? Also yes.

4. Make Comparison Shopping a Habit, Not a Hassle

Algorithms depend on you being rushed, tired, and not checking alternatives. Compare at least two or three retailers for recurring items and big purchases, use tools that show price history, and don't assume “your” store is cheapest just because it showers you with personalized deals. Even walking away from absurd dynamic markups sends a signal.

5. Push for Policy, Not Just Better Coupons

Support organizations and candidates that take data rights and algorithmic pricing seriously. Ask legislators at every level to limit the use of personal and sensitive data in pricing, require disclosure when prices are personalized, and consider fixed pricing windows for essentials like groceries, medicine, utilities, and housing. One email won't fix this. Silence guarantees nothing changes.

6. Normalize Being Skeptical, and Share What You Learn

Most people just feel like everything is getting more expensive and deals aren't what they used to be. They don't see the machinery behind it. Share the NYT video. Share this post. Talk honestly about what you see in loyalty programs and “personalized” offers. The more people treat all of it with healthy suspicion, the harder it gets to keep creeping the line.

The Bottom Line: Don't Be an Easy Target

Dynamic pricing isn't automatically evil. In theory it can reduce waste, smooth out demand, and offer genuine off-peak discounts. But combined with detailed household-level data, opaque algorithms optimized for extraction, and weak or outdated regulation, it becomes something closer to a personalized tax on your urgency, your health, and your ignorance.

Sources & Further Reading

• New York Times Opinion: “Goodbye, Price Tags. Hello, Dynamic Pricing.”, the source for the history, the three-bucket framework, the RealPage apartment and overcharge figures, and the policy ideas.
• U.S. Department of Justice: RealPage proposed settlement announcement, November 24, 2025.
• Kroger Precision Marketing / 84.51°: “84.51° Data science and retail analytics in action”, the source for the 60 million households quote.
• The Markup: “Forget Milk and Eggs: Supermarkets Are Having a Fire Sale on Data About You”, February 16, 2023.
• Assad, Clark, Ershov, and Xu: “Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market”, Journal of Political Economy 132, no. 3 (2024).
• Federal Trade Commission: “FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Consumer Prices”, January 17, 2025.

Sourcing note: the RealPage apartment count and overcharge estimate are the NYT video's figures, not a finding in the DOJ settlement. The insider view is my own first-person account from my years in grocery technology. Revised September 2026: the gas station study figure was corrected to match the published paper, the RealPage settlement terms and the FTC findings were added, and all sources are now linked.

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Casey writes about privacy, surveillance, economic policy, nomadic life, and navigating the world as a late-diagnosed AuDHD adult. New posts drop on my professional website.