For decades, businesses have used dynamic pricing to balance supply and demand. Airlines raise fares as more seats sell, ride-hailing apps increase prices during storms, and hotels adjust rates depending on the season. These practices are familiar to consumers and generally tolerated as part of market logic.
As highlighted in an article on Economics Online, what is new today is the integration of artificial intelligence (AI) into this equation. Instead of reacting to broad shifts in demand, algorithms can now calculate, in near real time, how much each individual is willing to pay. This technology transforms pricing from a collective adjustment into a deeply personal one — raising both opportunities and risks.
From Market Signals to Personal Predictions
The original purpose of surge pricing was to respond to scarcity. For instance, when a sudden downpour increases the demand for taxis, higher prices encourage more drivers to join the road. This model aligns incentives and ensures resources are allocated efficiently.
AI-driven pricing, however, goes further. By analyzing browsing history, purchase frequency, spending power, and even the device used to access a website, algorithms can predict a customer’s maximum willingness to pay. As a result:
- A business traveler might pay more for a ticket than a casual vacationer.
- Two shoppers browsing for the same product online could see very different prices.
Economists have long theorized about such “perfect discrimination,” but AI brings it from theory into practice.
The Double-Edged Sword of Efficiency
From an efficiency perspective, personalized pricing helps companies maximize revenue. It may also allow some lower-income consumers to access services at cheaper rates, if the algorithm predicts they would not otherwise buy at all.
Yet fairness concerns dominate the conversation:
- Transparency is limited, as most customers have no idea why their price differs from someone else’s.
- Inequality risks deepening, with wealthier users consistently paying more.
- Trust can erode quickly if consumers feel exploited, as shown by previous controversies in the tech sector.
Lessons from Past Backlash
History provides clear warnings. In 2014, Uber faced intense criticism when fares surged dramatically during a hostage crisis in Sydney, forcing the company to revisit its surge policies. Similarly, Amazon’s early experiments with personalized DVD pricing in the early 2000s were abandoned after customers discovered the practice and voiced their anger.
These cases illustrate a consistent pattern: while broad surge pricing linked to supply and demand may be tolerated, hidden or overly individualized strategies often spark outrage and damage reputations.
Regulation and Safeguards
As AI continues to influence pricing, regulators are likely to intervene. Possible measures include:
- Transparency rules requiring companies to disclose when prices are personalized.
- Caps on surge pricing in essential services such as healthcare or public transport.
- Data-use restrictions limiting how personal behavior can shape algorithmic outcomes.
The goal is not to ban innovation but to create safeguards that prevent its abuse.
Building Trust in the Digital Economy
Companies adopting AI-driven pricing must weigh immediate profits against long-term trust. Fintech provides useful lessons here. Platforms like Altery, with their digital-first and API-ready business accounts, demonstrate how efficiency and fairness can coexist. By embracing transparency and accountability, firms can show that AI is not only about extracting value but also about building sustainable customer relationships.
The Bigger Picture
AI-powered surge pricing is more than a technical tool; it is a test of modern market boundaries. Algorithms may offer precision in resource allocation, but they cannot replace the social foundations of commerce. Fairness, transparency, and trust remain essential.
The central question is not whether AI can determine the highest price a consumer will pay, but whether society should allow businesses to use this power unchecked. Markets thrive not only on efficiency but also on shared norms. Without them, even the smartest algorithms risk breaking the fragile bond between companies and consumers.
