Welcome to a Brave New World of Price Gouging

Sellers have always had access to more information than buyers, and “dynamic pricing,” which harnesses the power of algorithms and big data, is supercharging this asymmetry.

Signage displayed outside a Wendy's fast-food restaurant in Shelbyville, Kentucky, on November 5, 2017. (Luke Sharrett / Bloomberg via Getty Images)

If you weren’t already convinced the future of capitalism will involve an endless optimization of extractive capacity and the ensuing enshitification of every last bit of consumer life, well, get ready for a new frontier in “dynamic pricing.” Last month, Wendy’s floated its plan for dynamic pricing in 2025 on an earnings call. People flipped their lids and the fast-food chain rushed to clarify that they wouldn’t use their digital menus to raise prices — no, no, no, there would be no surge pricing. Quite the opposite. They’d merely use data and tech to offer better deals for customers at slower times.

Setting aside for the moment the unbelievability of their about-face, the Wendy’s plan is the tip of the dynamic pricing spear as more industries and companies look to implement the model, particularly restaurants.

Efficiency or Gouge-iency?

Dynamic pricing is an innocuous enough sounding term. Its proponents will tell you it merely refers to the capacity to raise or lower pricing in response to market signals. Supply down or demand up? The price increases. Supply up or demand down? You might see a discount. The signals could be timing, volume, approaching expiration dates, weather, or whatever might shape consumer behavior. “Surge pricing” on the other hand is exactly what it sounds like: a temporary, one-way increase in prices during peak demand.

Anyone who argues dynamic pricing and surge pricing are simply business as usual will tell you the models have been around forever in one way or another. You’ve probably dealt with them before: airlines, hotels, concert tickets, happy hours at your local bar, even utilities with their peak and off-peak hours. More recent iterations have evolved to leverage data and technologies to enhance and refine the methods, most notably ride-hailing apps like Uber and Lyft and e-commerce giants like Amazon or Alibaba.

Of course, older industries incorporate these developments, too, relying on algorithms, data collection through rewards and other programs, and tech developments such as digital price tags that can be adjusted on the fly with the push of a button. In sum, something has changed and we’re not talking mere business as usual.

There’s both a qualitative and quantitative difference between your local bar offering a happy hour deal during the slower business hours and a fast-food joint using digital menus, algorithms, and big data to optimize prices throughout the day, week, month, and year in real time.

Information Asymmetry

As Joseph Stalin is thought to have said, “Quantity takes on a quality all its own.” Companies that can use technology and data to optimize pricing on the fly with little to no resistance have the capacity to “optimize” prices. This is to say that they have a capacity to maximize profit and extraction from workers (who, no doubt, rarely if ever see more money during “surge” times).

Even a lower price in a dynamic pricing model is meant to extract maximum revenue that the firm might otherwise forgo. So the goal is always maximizing revenue and extracting the most return from workers in each moment — dynamic pricing is not a form of surplus distribution for workers and it sure isn’t a service for consumers. It’s vampire-like profit extraction.

In a dynamic-pricing world, consumers will always be at a disadvantage compared to businesses. Sure, there may sometimes be options to shop around, wait, or forgo a purchase, but not often. Companies, especially large players, will have more and better data while consumers will be constrained by time, location, or a lack of wares on offer from competitor businesses, particularly in oligopoly or monopoly industries such as grocers, airlines, ride hailing, e-commerce, and concert ticket platforms.

Dynamic pricing and surge pricing represent inherently dodgy practices rooted in withholding information from the public. Algorithms are often proprietary and shadowy — sometimes businesses who use them don’t even understand their intricacies. Corporate decision-making protocols and reasoning about how and when to use the pricing model are also kept close to the vest. This balance of power means that as dynamic and surge pricing reach further into the market, consumers will forgo stable, clear, transparent pricing for gambles with their wallets every time they want to buy a burger and fries or pick up some milk at the grocery store.

This Is Not Your Greengrocer’s Discount Pricing

There may be some good to dynamic pricing now and again. As Omar H. Fares argues in the Conversation, “Dynamic pricing can . . . help with inventory management. For industries dealing with perishable goods or limited inventory, dynamic pricing can help manage supply and demand more efficiently.” He’s right. When grocery stores want to lower the price of meat or fruits or vegetables nearing their best before or expiry dates, dynamic pricing can help with this adjustment. It streamlines the common practice, benefiting  the grocer who wants to move product and the consumer who’s willing to eat something nearing the point at which it’ll go bad.

But discounted produce is very different from a digital algorithm-based capitalist surveillance network that sets prices for all goods on the fly in order to extract maximum revenue from each consumer. The scale and sophistication of the contemporary data-driven dynamic-pricing model represents a massive shift in capacity for industry, one that further tilts the balance of power in the favor of companies, especially so for the corporate behemoths that already dominate so much of our lives.

Consumers obviously and rightly hate dynamic and surge pricing when they feel its ripping them off, which it is very much designed to do. But consumer anger isn’t enough to constrain these pricing models in the long run. This is because, as industries normalize them, buyers have fewer and fewer places to turn and therefore fewer and fewer places to “vote with their dollars” against exploitation.

Only states can constrain the creeping enshitification that comes with big-data and tech-driven dynamic and surge pricing. States ought to act to ensure pricing practices are stable, clear, predictable, and transparent across industries. Algorithms should be immediately auditable. Price gouging must be met with sanction. The occasional discount burger isn’t worth the cost of a dynamic-pricing dystopia.