Written by: Sarah Abdullah
Every time you buy a plane ticket, order an Uber or add something to your Amazon cart you are entering a digital marketplace run by algorithms. Everyday millions of consumers experience price changes that they assume are coincidences. In reality, these algorithms are powered by artificial intelligence and have expanded beyond niche markets to control the broader e-commerce empire. As these algorithms increasingly gain knowledge and determine how much we pay, they quietly redetermine how our economy functions. The dynamic pricing systems track competitors, demand patterns and personal browsing history to optimize profits. This technology was intended to make markets more efficient using “smart pricing” by ensuring prices reflect real-time conditions. Though these dynamic pricing algorithms are invisibly colluding, without even breaking the law, by keeping prices high for consumers.
In most digital markets, algorithms constantly adjust prices based on factors like demand, consumer behavior and competitor pricing. When multiple companies use similar systems, these algorithms begin reacting to one another in real time. Over time these systems may learn that raising prices simultaneously leads to increased profits especially as this technology becomes more widespread across markets. On platforms like Amazon many third-party sellers rely on automated pricing tools that continuously adapt to match or beat rivals. Amazon’s pricing algorithm updates the prices of millions of products several times a day in order to offer competitive prices to its shoppers (Naceva 2024). Similarly airlines use real-time models to alter fares and ride-share apps like Uber and Lyft surge simultaneously when demand spikes. These systems were once limited to sectors like airlines and hospitality, but have expanded into e-commerce and entertainment as technology advanced (Harvard Business School Online, 2024). In each case of dynamic pricing it is the algorithm’s code that reacts faster than any market participant or buyer can. This behavior challenges the traditional sense of competition since firms are no longer independently deciding prices. As algorithms increasingly make these decisions automatically, their unseen influence grows, reshaping competition in markets.
When all competitors’ algorithms act similarly they collectively push costs upward. Consumers rarely notice small increases, like a slightly higher fare at peak times, but across millions of transactions these microadjustments accumulate. In e-commerce, pricing algorithms often end up keeping prices similar instead of driving them down. When every seller’s system keeps reacting to competitors’ prices they eventually learn that lowering prices hurts everyone’s profits. So instead of a price war that benefits consumers, the market settles in the middle with prices that stay high. This pattern shows up in areas like flights, hotels and online retail where prices across companies often move together instead of competing. This upward pressure doesn’t require explicit communication or human intent so it is difficult for regulators to classify it as collusion. The algorithms may intend to simply optimize profits for the company, but the outcome mirrors the effects of collusion. This consequently leads to a gradual inflationary push that affects consumers without clear accountability. Even though this coordination is unintentional the lack of regulation allows it to persist.
Advocates of dynamic pricing argue that algorithmic adjustments make markets more efficient by ensuring prices reflect real time supply and demand. These pricing systems help companies allocate resources effectively and consumers can take advantage of lower prices during non-peak periods. For example ride-share apps raise prices during rush hour to attract more drivers and airlines reduce fares for less busy routes to fill empty seats. Supporters argue that this system creates a fair and responsive market. However, the benefits of dynamic pricing fail to account for the consequences of what happens when every major firm in a market adopts the same technology. When algorithms share similar objectives and respond to the same demand patterns they stop competing in an efficient way. Instead of creating flexibility this leads to prices that all move simultaneously that rarely fall. Though efficiency is the intended purpose of dynamic pricing, the overall effect overrides the competition it was meant to enhance, causing a need for economic regulation.
Most antitrust laws rely on proving intent, requiring that two or more firms consciously agree to fix prices. However, in algorithmic markets, there is no clear intent. Collusion can occur without any agreement, communication or intent within AI algorithms (Wharton, 2024). These systems autonomously make decisions that produce the effects of collusion without human direction, leaving regulators unsure how to respond. In general, AI is a complex and rapidly changing concept making it difficult to apply legal principles due to the lack of transparency, explicability and equal treatment (Ruschemeier, 2023). The nature of AI systems makes it difficult to create regulations and properly monitor the actions of AI. In the US, there remains no comprehensive federal law that sets out authority for regulating AI technology. Furthermore, the federal government’s approach to regulating AI has been cautious regarding private sector use and rather focussed on oversight of the federal government’s uses of AI (Library of Congress, 2025). The lack of up-to-date regulation creates a dangerous gap between innovation and accountability. Without repercussions, AI systems will continue to evolve faster than the rules meant to keep them in check (Ruschemeier, 2023). We therefore must adapt to redefine what counts as collusion and reconsider the boundaries between technology and market fairness. Addressing this ongoing issue will require both legal innovation and rethinking traditional economic norms about how competition works in the digital realm.
Algorithmic pricing has shaped how markets operate and who benefits. As consumers we encounter dynamic pricing systems everyday. What we often feel is a cost of hesitation is actually a glimpse into a larger economic shift where prices are no longer determined by, but rather by an inherently monopolistic algorithm. If left unregulated, this coordination will continue to erode competition in markets. We must demand transparency in algorithmic systems and updated antitrust laws to reflect the realities of AI-driven systems. When algorithms learn that keeping prices high benefits everyone but the buyer, the market is no longer working for us, but rather against us.
References
Dou, W., Goldstein, I. and Ji, Y. (2025). AI-Powered Trading, Algorithmic Collusion, and Price Efficiency. Jacobs Levy Equity Management Center for Quantitative Financial Research Paper, The Wharton School Research Paper. https://ssrn.com/abstract=4452704
Gibson, K. (2024). Dynamic Pricing: What it is & Why it’s important. Harvard Business School Online. https://online.hbs.edu/blog/post/what-is-dynamic-pricing
Harris, L. (2025). Regulating Artificial Intelligence: U.S. and International Approaches and Considerations for Congress. https://www.congress.gov/crs-product/R48555
Naceva, N. (2024). The ultimate guide to Amazon dynamic pricing strategy in 2024. Influencer Marketing Hub. https://influencermarketinghub.com/amazon-dynamic-pricing/
Netscribes. (2025). Artificial Intelligence in ecommerce: Key trends to watch. https://www.netscribes.com/expert-speak/the-rise-of-artificial-intelligence-in-ecommerce-from-personalization-to-fulfillment
Ruschemeier, H. (2023). AI as a challenge for legal regulation – the scope of application of the artificial intelligence act proposal. ERA Forum, 23(3), 361–376. https://pmc.ncbi.nlm.nih.gov/articles/PMC9827441/