Gone are the days when managers updated prices once a week. Today, algorithms can change prices thousands of times a day, responding instantly to shifts in demand, competitor behaviour, weather forecasts, inventory levels and even web traffic. Welcome to the world of algorithmic pricing.

At its heart, algorithmic pricing is about using data to maximise profits. Instead of relying on human judgement, firms use artificial intelligence to analyse huge volumes of information in real time and calculate the price most likely to maximise revenue. Ride-hailing apps increase fares during rush hour, airlines adjust ticket prices by the minute, and online retailers constantly monitor competitors to remain competitive.

For economists, this creates both opportunities and challenges. Dynamic pricing can improve market efficiency by allocating scarce resources to consumers with the highest willingness to pay. During periods of high demand, higher prices can reduce shortages and encourage additional supply. Businesses also benefit from better inventory management and higher profits.

However, there is a darker side. Algorithms increasingly allow firms to personalise prices by analysing browsing history, purchasing behaviour and other digital footprints. This brings markets closer to first-degree price discrimination, where each consumer pays a different price based on their willingness to pay.

Competition may also suffer. If rival firms use algorithms that instantly match each other's prices, the incentive to undercut competitors disappears. Without any direct communication, algorithms can unintentionally sustain tacit collusion, leading to higher prices for consumers. These concerns have attracted growing attention from competition authorities investigating whether pricing software is reducing competition in digital markets.