Big Brother pricing: how AI is learning your financial breaking point
British shoppers and workers face a quiet revolution as artificial intelligence masters the art of discovering exactly what we will pay, and what we will accept, in wages. The implications for personal privacy and economic fairness are profound, and they demand the attention of every consumer and employee in the land.
The Federal Trade Commission in Washington is already consulting on an enforcement policy for what it calls 'personalised pricing'. The concern is that increasingly sophisticated algorithms could allow businesses to tailor prices and discounts to individual customers, using the vast troves of personal data we surrender with every click and swipe.
Across the Commonwealth, Consumer New Zealand has issued similar warnings about supermarket loyalty programmes. While there is no evidence yet of individual pricing in Kiwi stores, the data collected could give retailers an alarmingly detailed picture of shopping habits, including clues about how much each customer is prepared to part with.
How algorithms are tilting the balance of power
At the University of Auckland Business School, academics spend their days teaching students how businesses create value and compete. But they have identified a troubling asymmetry. Consider the same person in two markets. As a worker, their employer benefits from knowing the lowest amount they will accept. As a customer, a seller benefits from knowing the highest amount they will pay.
Traditionally, neither side knew those numbers precisely. A worker might accept £18 an hour but receive £22 because that is the going rate. A customer might pay £15 but buy for £10 because that is the advertised price. Algorithms are now reducing that uncertainty, and they are doing it much faster for firms than for the workers and consumers they deal with.
Digital platforms can observe thousands of individual decisions. A ride-hailing platform can see which jobs a driver accepts, when they work and which incentives bring them online. A retailer can see purchases, abandoned carts and responses to discounts. Lyft has already documented systems that determine which drivers receive incentives, with some earnings challenges explicitly personalised.
Recent research on 1.5 million Uber trips in the United Kingdom found dynamic pricing was associated with lower real hourly earnings and greater inequality. A US Federal Trade Commission investigation found pricing intermediaries had access to information including location, demographics, browsing histories, shopping-cart activity and even mouse movements in systems capable of influencing prices and promotions.
The digital feudalism threatening British consumers
A retailer need not charge one customer £80 and another £100. It can simply offer a discount to someone predicted to walk away and withhold it from someone predicted to buy anyway. At its extreme, this risks becoming a kind of digital feudalism: platforms can increasingly see the people they deal with, while those people can barely see the systems governing the exchange.
Markets have never been perfectly transparent, of course. Employers have always known more about wage structures than workers, and sellers more about margins than buyers. Yet there has traditionally been uncertainty on both sides. Algorithmic systems now risk reducing that uncertainty in only one direction: firms can increasingly learn an individual's limits, while their own remain hidden.
A worker cannot easily know whether rejecting £18 would have produced £21. Nor can a customer know whether walking away from a purchase today would have triggered a discount tomorrow. Meanwhile, firms can observe, test and learn from repeated behaviour.
Who benefits from the AI revolution?
There can, of course, be genuine benefits to AI-driven personalisation. Targeted incentives can improve matching, personalised discounts can help price-sensitive customers and better forecasting can reduce waste. The issue is not whether these systems can create efficiencies, but how the gains are distributed.
They could translate into higher wages, lower prices, better products, greater investment or higher profits. That depends partly on information. Personal data has economic value because it can help predict the terms people are willing to accept, making privacy a question of bargaining power too.
Transparency is equally important. Workers and consumers are increasingly visible to businesses, while the systems making decisions about them remain largely opaque. They might reasonably expect to know when an offer has been personalised, what information influenced it and whether others are receiving materially different treatment. That does not require companies to publish their algorithms, but visibility should not flow only one way.
Business schools also have a responsibility. Alongside teaching pricing strategy, segmentation and cost reduction, students should be encouraged to ask: effective for whom? There is a difference between using technology to create new value and becoming better at capturing value from the other side of a transaction.
The most troubling outcome does not require malicious AI. Companies can rationally reduce costs and improve margins while becoming better at predicting what workers will accept and customers will pay. The question cannot simply be whether something can be optimised. We should also ask who benefits, whether it is fair, and what happens if every business does the same thing.
For British consumers and workers, the message is clear: the machines are watching, and they are learning our limits. It falls to regulators, and to a vigilant public, to ensure this new power is not abused.