Amazon built a secret pricing algorithm that pulled in over $1 billion in extra profit. Not by offering better products. Not by cutting costs. By quietly raising prices and waiting for competitors to follow. The algorithm watched millions of price points across the web. When it spotted a rival likely to match a price hike, it raised the price. If the competitor followed, the new higher price stuck. If they didn't, it rolled back automatically. No backroom deals. No phone calls between executives. Just code nudging the entire market upward. Internal documents showed some Amazon leaders privately called these tactics "shady" and an "unspoken cancer." But the algorithm kept running for years. Here's what caught our attention when we dug into this for our latest research: The competitors weren't even aware it was happening. Their own simple pricing rules — "match the lowest price" — made them predictable. Amazon's algorithm learned those patterns and exploited them. This is the part that matters for every company deploying AI right now. If your pricing tool, your recommendation engine, or your supply chain optimizer is a black box you can't audit or explain, you're carrying risk you might not even see yet. With the FTC trial set for October 2026 and new state laws in California and Colorado already targeting algorithmic pricing, the window for "figure it out later" is closing fast. We wrote a deep breakdown of the technical mechanics, the legal shifts, and what a defensible AI architecture actually looks like. Honest question for anyone building or buying AI tools right now — do you actually know how your algorithms make decisions, or are you trusting the vendor's word for it? #AlgorithmicAccountability #EnterpriseAI #AIGovernance