Are You Paying More For Your Big Mac Because AI Decided You Should?

If you have ever stared at a McDonald’s receipt and wondered why your local burger costs more than the one three suburbs over, you can cast some of the blame onto algorithms.

An investigation found that McDonald’s has implemented a machine-learning pricing engine in nearly 14,000 restaurants, tirelessly crunching numbers to serve up what corporate calls an “optimal price” for every item on the menu.

“Optimal price” is a smooth piece of corporate PR work, because it sounds objective and neutral. But it glosses over quite a prickly question for your wallet: is it optimal for the company’s profit margins, or optimal for you?

How The Dynamic Pricing Engine Operates

Reports show that the system processes millions of transactions daily to hand out branch-specific price suggestions.

It weighs past sales, local demand, what rivals like Burger King are charging and an estimate of how much people in the neighbourhood are willing to part with. Internal screenshots reviewed by investigators even flagged certain locations for having “medium sensitivity” to price shifts.

The tool has reportedly been operating behind the scenes since 2019, feeding updated price lists to franchise owners a few times a year. The most obvious proof of the system in action? A Big Mac priced at $5.69 at one company-owned branch in Fresno, California, while another location just three kilometres down the road priced it at $6.89 – a 21% jump.

Although investigators couldn’t tie the AI software to that specific discrepancy, numbers like that make it clear why people are starting to ask questions.

So How Exactly Did AI Decide You Should Pay More?

Here’s the nuance that holds weight. It’s easy to blur the lines between these three practices: location-based pricing (charging more at store A than store B), dynamic pricing (adjusting costs by the hour based on busyness) and hyper-personalisation (tailoring a unique price for you based on your personal data).

What’s happening here falls into the first camp. The algorithm sets a flat price for an individual store, rather than calculating what you specifically are willing to fork out based on your shopping history.

In other words, AI decided you should pay more, but it worked that number out by postcode, not by you individually.

What “Optimal” Really Means

“Optimal price” isn’t a fair price or an objective one. It’s a prediction, the price a model estimates will best achieve a commercial goal under a given set of conditions.

Depending on what McDonald’s sets as the priority, which could be maximising revenue, protecting margins, matching a competitor down the road or hitting a corporate affordability target. It’s asking what price is most likely to produce the result the business wants.

The inputs driving the software aren’t neutral. High sales at a particular restaurant might trick the model into thinking locals will happily accept a price hike. But those strong sales could be the result of a captive audience, a wealthy neighbourhood or a lack of local competition. A price hike driven by those factors might make financial sense for the brand, but it will still sting for those paying it.

To top it all off, there’s a corporate compliance quirk here too. McDonald’s insists the tool is totally optional for franchise owners, but reporting indicates that ignoring the recommendations can draw unwanted attention, with deviations logged internally as pricing non-compliance.

The Big Concern Isn’t The Big Mac

McDonald’s makes for a great case study because buying a burger is such a mundane task. The real issue has little to do with fast food.

Retailers are discreetly ditching standard national price tags in favour of systems that constantly shuffle prices based on where you are, what you are buying and who you are. This automated change is already reshaping hotels, supermarkets, concert ticket sales and food delivery apps.

Technically, none of these tactics are new. Businesses have tinkered with location and demand pricing for many years. The game-changer is speed and secrecy. No team of humans could manually audit thousands of store menus multiple times a year using live sales data and competitor scrapers. Software handles it quickly, keeping all the logic hidden away from the customer standing at the counter.

The risk is that opaque location pricing will become so commonplace that we stop questioning it. Before you assume an AI has built a custom profile to fleece you on your lunch break, ask the boring question: does this markup reflect you, or does it reflect the postcode of the store you just stepped into?