When Algorithms Set the Price: The New Era of AI Pricing
How algorithms are changing the way we shop, and the ethical questions that come with it
When browsing a flight or hotel site, you may notice that prices fluctuate frequently. Similarly, online shoppers might see different prices for the same product depending on factors like location or browsing history.
These variations are part of dynamic pricing, which is increasingly guided by artificial intelligence.
What Is Dynamic Pricing?
Dynamic pricing is a strategy where the cost of goods or services adjusts in real time based on supply, demand, competitor pricing, or even individual consumer behavior. Airlines, ride-hailing apps, and e-commerce platforms have used it for years.
What’s new is the role of AI. Advanced algorithms can analyze massive datasets — from seasonal trends to user browsing history — and predict the exact price a consumer is likely to pay. In other words, the system can tailor pricing for you personally, rather than applying a one-size-fits-all approach.
How It’s Changing Commerce
AI-driven dynamic pricing offers clear benefits for companies:
Revenue Optimization: Businesses can maximize profits by adjusting prices based on real-time demand.
Inventory Management: Algorithms can encourage sales of slow-moving items while capitalizing on high-demand products.
Competitive Edge: Companies can respond to competitor pricing almost instantly, staying ahead in crowded markets.
Consumers, however, face a more complicated picture:
Price Fairness Concerns: Two people might pay very different prices for the same product, raising ethical questions.
Transparency Issues: Algorithms are often proprietary and opaque, so customers rarely know why a price changed.
Behavioral Manipulation: Some pricing models nudge users toward faster purchases or higher spending without their awareness.
Real-World Examples
Airlines and Hotels: Prices fluctuate constantly based on demand, events, and even a customer’s location. AI models are making these adjustments faster and more granular.
E-Commerce: Amazon and other major retailers use AI to adjust prices multiple times a day. Research has found that the same product can vary in price depending on browsing history or regional data.
Ride-Hailing Apps: Uber and Lyft use AI to calculate “surge pricing” in real time, which can significantly increase fares during busy periods.
The Debate: Fairness vs. Efficiency
Proponents argue that AI pricing benefits everyone: companies stay profitable, markets remain efficient, and supply-demand mismatches are reduced. Critics worry it could exacerbate inequality, with wealthier or less price-sensitive customers subsidizing lower prices for others.
Regulators are beginning to pay attention. The European Union has called for greater transparency in automated pricing, and some U.S. states have examined the use of AI in online commerce to prevent discriminatory or deceptive practices.
What’s Next
Dynamic pricing is unlikely to disappear. If anything, AI will make it more precise and pervasive. The key question may not be whether AI should set prices — but how it does so, and under what rules. Transparency, fairness, and accountability could determine whether consumers see AI pricing as a convenience or a threat.
Bibliography
📚 Academic & Research Articles
Chenavaz, R. Y., and S. Dimitrov. "Artificial Intelligence and Dynamic Pricing: A Systematic Literature Review." Journal of Applied Economics, vol. 28, no. 1, 2025, pp. 1-25.
https://www.tandfonline.com/doi/abs/10.1080/15140326.2025.2466140Asker, John, Chaim Fershtman, and Ariel Pakes. "Artificial Intelligence, Algorithm Design, and Pricing." AEA Papers and Proceedings, vol. 112, May 2022, pp. 452–456.
https://www.aeaweb.org/articles?id=10.1257%2Fpandp.20221059Gupta, S. "Advanced AI-Driven Dynamic Pricing Models in Marketing." SSRN, 2024.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4958529Varadi, V. K. "Enhancing Dynamic Pricing through Econometric and AI Integration." SSRN, 2024.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4779985Awais, M. "Optimizing Dynamic Pricing through AI-Powered Real-Time Analytics." QJSS, 2024.
https://qjss.com.pk/index.php/qjss/article/view/149
⚖️ Policy & Regulatory Perspectives
Federal Trade Commission. "FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing." Federal Trade Commission, 23 July 2024.
https://www.ftc.gov/news-events/news/press-releases/2024/07/ftc-issues-orders-eight-companies-seeking-information-surveillance-pricingAndreau, Sylvie, and Victor Rocher. "From Train Tickets to Concert Tickets, the Unstoppable Rise of Dynamic Pricing." Le Monde, 21 Oct. 2024.
https://www.lemonde.fr/en/economy/article/2024/10/21/from-train-tickets-to-concert-tickets-the-unstoppable-rise-of-dynamic-pricing_6730052_19.htmlDel Valle, Gaby. "Are You Being Exploited by AI-Powered Surveillance Pricing?" The Verge, 23 July 2024.
https://www.theverge.com/2024/7/23/24204011/ftc-surveillance-pricing-investigation-mckinsey-mastercard-chase
🧠 Algorithmic Behavior & Market Dynamics
Deng, S., M. Schiffer, and M. Bichler. "Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning." arXiv, 2024.
https://arxiv.org/abs/2406.02437Schlechtinger, M., D. Kosack, F. Krause, and H. Paulheim. "By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning." arXiv, 2024.
https://arxiv.org/abs/2406.02650