AI Will Extract Every Last Dollar Out of You
The concept of pricing has evolved dramatically throughout history. From the ancient practice of bartering, where every exchange was a negotiation, we moved to the stability of fixed prices, championed by groups like the Quakers for their transparency. The digital age introduced dynamic pricing, allowing prices to fluctuate based on market conditions, such as supply and demand. With the recent advancements in artificial intelligence, we are entering an era of AI-driven personalized pricing, where algorithms tailor costs specifically to YOU.
Personalized pricing utilizes your specific purchasing behavior as one input into their AI model to decide how much to charge you. Pricing based on individual user behavior means that not everyone will pay the same price for the same good or service. If you and your friend both open up the Uber app to check the price to go to the same destination at the same time, you will likely have different prices. Uber has denied that they price on individual behavioral patterns in 2024, but they admitted to pricing based on individual behavior back in 2017. There is evidence on Reddit as well. If Uber could do it 8 years ago, imagine how much easier it is do it in a post-ChatGPT world.
Nowadays, there are apps everywhere that harness our personal data to build algorithms that influence our decisions. TikTok chooses which videos you see, Google chooses which links you read, and Spotify chooses which music you hear. The average American is either okay with these tradeoffs (e.g. Google is free but tracks your data) or they are blissfully unaware. While many algorithms are a net-positive for society because technology is usually deflationary (i.e., automation –> lower prices), personalized pricing has the potential to be inflationary (i.e., automation –> higher prices). Organizations will have the ability to charge YOU the maximum price that YOU are willing to pay for a good or service. Imagine you and a friend walk into a grocery store to buy a loaf of bread. The register might ring up \$10 for you while your friend only gets charged \$5 for the same loaf (excluding coupons).
While Uber is perhaps the most infamous example of personalized pricing, Delta recently announced that they will use “AI that determines how much you personally will pay for a ticket”. This trend is not going to slow down anytime soon because it likely increases profits [in the short term]. With limited transparency into organizations’ algorithmic pricing models, it will be extremely difficult, if not impossible, for consumers to know which organizations are using personalized pricing. If organizations use AI models that are difficult to understand (e.g., neural networks), the organization itself might not even know why the model charges higher prices for certain users. If you take personalized pricing to the extreme, you could go to a dark place where AI algorithms charge prices based on which credit cards are in your digital wallet, whether you have a gift card balance, or data that is correlated with your ethnicity or religion. So where does this leave consumers?
Consumers need to approach any algorithmic-based feature or price with a healthy skepticism until there is more transparency. They need to change what is in their control in order to disrupt personalized pricing models (or other algorithms). Take control of your life and fight back with some of these simple tactics:
- Flood the apps with unpredictable data –> Open and close the Uber app at random times, walk to a different block before calling an Uber, compare prices to Lyft, use public transportation occasionally, schedule Uber ahead of time
- Change which algorithms you use –> Use multiple search engines/AI tools when gathering information and compare the results (e.g., Google/Gemini vs. ChatGPT vs. Perplexity vs. Kagi vs. Bing)
- Use tools to track prices over time –> The Keepa plugin tracks prices over time and will show you if/when Amazon vendors increase prices 2 weeks before Prime Day so they can display a misleading “30% discount” on Prime Day
- Put more faith in trustworthy or unbiased sources/brands –> Seek recommendations from your inner social network, independent organizations like Consumer Reports, or brands that you trust based on your personal experiences.
- Spend some time learning the basics of how these AI tools work (even 10 minutes per month could pay dividends)
~ The Data Generalist
Data Science Career Advisor