Everything Costs More Because the Algorithm Says So

The escalating cost of living keeps making headlines and inspiring furious Reddit threads. Food prices remain a flashpoint, especially when it comes to coffee. But behind every grocery store gripe lies a deeper unease about whether wages are keeping up and the tariffs that continue to dominate economic news.

While Canada isn’t subject to the highest tariff rate when compared to other countries, and many of our own counter-tariffs were lifted last September, uncertainty can still ripple through supply chains and shape consumer expectations. Analysts warn some imported goods could get more expensive as firms reassess costs. Those price variations may have less to do with tariffs and more to do with how companies choose to respond. In the recent past, firms have used such moments as a convenient cover for padding margins—the kind of “seller’s inflation” that was rampant during the pandemic.

But the fixation on tariffs and inflation obscures a different shift revolutionizing pricing: algorithms. The Canadian Anti-Monopoly Project warns automated tools are reshaping what Canadians are charged for essential goods and services, including groceries and fuel. Companies can now use software to tailor prices based on everything from our browsing patterns, location, loyalty history, device type, and operating system. The same item can appear at one amount for you and another for someone else, depending on who you are, when you see it online, and what the algorithm believes you are willing to pay.

As former United States Federal Trade Commission chair Lina Khan told The American Prospect, we now live in a technological environment that can serve “every individual an individual price based on everything they know about you.” And it’s making things more expensive.

Our day-to-day navigation of prices rests on a comforting illusion—that we all encounter the same marketplace. In reality, this is happening less often. Firms have always had the right to set prices, but that process has become continuous and individualized: a ceaseless micro-calculation of how much you personally might be willing to pay for something. In a way, we’re all participating in an ongoing pricing experiment. And, like the best subjects, we barely realize it.

This new marketplace emerged, in part, because the tools to reshape it became cheaper, faster, and ubiquitous. For firms, price personalization—or discrimination—no longer requires building a proprietary system; it can be purchased off the shelf.

Shopify’s app store will help you set different prices for different customers. Toronto-based Daisy Intelligence sells AI software that helps large retailers vary prices and promotions. Zafin, also headquartered in Toronto, provides banks with “relationship-based pricing” tools that tailor financial products and fees to individual customers. Canadian e-commerce companies, like Bold Commerce, based in Winnipeg, offer pricing and checkout tools that let merchants customize prices and discounts across customers and contexts.

This isn’t just a retail issue. As Hal Singer argues in an article for The Sling, prices on Airbnb come from what the software learns works. By scanning local demand, booking velocity, seasonal patterns, comparable listings, and guest behaviour, the system maximizes revenue across its platform by nudging hosts toward algorithmically “optimal” rates. It also disciplines hosts that don’t play along.

Even loyalty programs are in on it. Recent research from the Vanderbilt Policy Accelerator shows that what were once benign savings tools across groceries, fuel, airlines, and retail are now sophisticated instruments of consumer exploitation.

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Here’s how it works. Companies gather data from many routine digital touchpoints: web and app tracking (cookies, pixels, and device fingerprinting), geolocation from phones and browsers, and in-store sensors. Also involved are data brokers who sell detailed consumer profiles combining demographics, purchase histories, and online behaviour. After the initial lure with attractive benefits and promises of discounts, (“the hook”), you’re handed over to a surveillance infrastructure that mines data about your behaviour and willingness to pay (“the hack”) and then raises fees, cuts rewards, and traps you in the program by making cancellation difficult (“the hike”).

In theory, algorithms can offer discounts to price-sensitive shoppers too. But this isn’t necessarily what happens. AI-fuelled price setting can quietly steer those with the least power to shop around to higher prices and poorer quality goods, thereby deepening the burden on low-income households. When apps can infer when it’s your payday, what neighbourhood you live in, and aggregate your past purchasing habits, they can raise prices to your presumed desperation. For hard-up households or lone parents, that means a personalized penalty on being broke or time starved.

At least, that’s what we think is happening. It is not always clear just when prices are being adjusted for different people. It happens without fanfare. A price may shift across online visits with little indication of what has informed that change. Real-time pricing is inscrutable by design.

That’s the whole point. Companies aren’t in a rush to tell us they’re doing this, which keeps consumers guessing and makes it harder to fully describe the phenomenon as it unfolds. All we can do is try to assemble a picture from scattered data points.

Canada’s Competition Bureau is studying algorithmic pricing, acknowledging the growing difficulty of detecting and proving price manipulation when it’s personalized and automated and exploring whether algorithms may be collusive or discriminatory.

In the US, the FTC published an interim report on surveillance pricing this past January that suggested tracked behaviours as specific as mouse movements or items abandoned in a shopping cart could inform tailored pricing.

Meanwhile, familiar consumer touchstones that once provided reference points, like “price matching” policies, are disappearing. Target made headlines in 2025 when it ended its price-match guarantee in the US. In Canada, Walmart scrapped its “Ad Match” program in 2020; Canadian Tire followed in 2022.

Price matching was never a formal consumer right; it was just a voluntary business tactic. Few shoppers used it consistently, and retailers likely counted on the “hassle factor” to keep redemptions low. Still, its disappearance matters. Without it, shoppers lose even a symbolic safeguard. One wonders whether it’s just coincidence or quiet convergence among dominant players. If fewer retailers feel compelled to maintain the policy, it’s a signal that competitive pressures are even weaker than appearances suggest.

It also challenges traditional economic indicators. Canada’s Consumer Price Index is built on the assumption of common pricing. It’s a benchmark that reflects how much things cost, on average, for everyone. But as more goods and services shift to individualized pricing, that collective anchor starts to drift. When prices are personalized, the very notion of an “average” price loses meaning. It complicates how policy makers measure inflation and how the economy maintains a shared sense of value. Dynamic pricing fragments the reference points that hold the economy together.

We’ve seen versions of this before. The so-called “pink tax,” for instance, where women paid more than men for comparable goods marketed to them. That form of gendered price discrimination led to bans in New York state and California and a private member’s bill in Ontario. Algorithmic pricing is the same practice, executed at machine speed and harder to detect.

It doesn’t have to stay this way. We can bring fairness back into pricing. But that would mean challenging the role computational systems now play in markets and taking a clear position on whether, when, and how their application is appropriate.

Generally, it seems like we have accepted price volatility when it is observable and applied universally. For instance, variation in gas prices, or how HydroOne gives us all a discount on energy if we run the washing machine after 7 p.m. Scarcity and timing can justify variance. But in the algorithmic era, those principles have been supercharged.

In the US, outrage erupted when Delta Airlines was found to be penalizing solo travellers with higher fares. Uber’s surge pricing was never the most popular part of the app—but that was before users worried they could be charged more when their phone battery was low (a claim the company has consistently denied) or when they were paying with a business credit card.

The thought of public transit, baby formula, or prescription drugs following that same logic feels dystopian. Yet algorithmic personalization pushes us further in that direction. In December, US advocacy groups uncovered that Instacart was running pricing experiments in which shoppers were shown different prices for the same grocery items at the same time. After public backlash, Instacart pledged to scrap the program.

As Canada drifts deeper into this regime, no one is insulated. Canada’s competition watchdog reports that pricing tools aren’t yet entrenched enough among landlords to move the rental market, but it’s signalling that this is an area to watch as adoption grows.

If we’re serious about tackling affordability, we can’t keep pretending the problem only affects the vulnerable or the poor. Systems built to extract the maximum possible price from consumers do not stop once they’ve cleared some moral threshold. Fighting back will require a radical solidarity across the wealth spectrum that we’re not used to.

That solidarity might begin with a simple principle: knowability. Consumers have the right to know when personalized pricing is in play. Governments should compel companies to clarify when a “sale” is legitimate or an algorithmic illusion. And opacity should be foisted back on the systems, with privacy legislation choking off the data harvesting on which the whole trick is predicated. Otherwise, we risk becoming fully atomized consumers, each inhabiting a private marketplace. Shopping becomes a roulette game.

In October, California became the first state to confront this problem when Governor Gavin Newsom signed a landmark law banning coercive algorithmic price-fixing. New York State followed with a legislation forcing businesses to disclose when personal data is used to algorithmically set prices. By one estimate, more than fifty bills addressing these practices have been introduced at the state level in the US. In Canada, as of this writing, there are none. (The Manitoba government has flagged that they will be looking at differential pricing for groceries.)

For generations, we built guardrails around how sellers could charge buyers. But those rules were written for human decision makers not self-learning software. They were meant for a world of price tags and weekly flyers not millisecond-fast adjustments and invisible markups. Pricing systems, not tariffs or inflation, are fast becoming the real cost of living.