r/ObscurePatentDangers

This situation is unfolding through a massive antitrust lawsuit led by California Attorney General Rob Bonta, where recently unsealed documents describe a pretty aggressive "price-fixing" strategy by Amazon. Basically, the state argues that Amazon used its massive market power to force brands like Levi’s and Hanes into a corner. Amazon would reportedly find a lower price for a product on a site like Walmart or Target, send that link to the brand, and demand they get the other retailer to raise their price. In one specific example, Amazon allegedly pressured Levi's to get Walmart to hike the price of a pair of khakis from $25 to $30 just so Amazon didn't have to compete with the lower price.

The filings suggest that if these brands didn't play ball, Amazon would retaliate by burying their products in search results or stripping them of the "Buy Box," which effectively kills their sales. This allegedly created an artificial "price floor" across the entire internet, meaning shoppers couldn't find a better deal anywhere else because Amazon was essentially managing the competition's pricing through the vendors. While Amazon claims their practices are actually about keeping prices low for customers, this evidence is a huge part of the lead-up to a major trial set for early 2027. It also ties into the FTC’s separate investigation into "Project Nessie," which was a secret algorithm Amazon supposedly used to test how high they could raise prices before competitors stopped following their lead.

Tools of war often find their way into domestic policing through a process commonly called mission creep. What starts as technology for tracking foreign adversaries often ends up in American neighborhoods, funded by the very tax dollars meant for public safety.

One clear example is the use of through-the-wall radar devices like the Range-R or similar systems tested by agencies like DHS, which allow law enforcement to scan through the drywall of single-family homes to detect motion and occupants from a distance. While pitched as a tool for active shooters or hostage rescues, these devices are increasingly available for more routine tasks.

Another shift involves the "data broker loophole." Instead of obtaining a warrant to track location data—a process generally required by the Supreme Court—agencies such as the FBI and ICE can purchase bulk location and behavioral data from commercial brokers. This process can effectively turn everyday smartphone applications into tracking tools accessible to government entities without judicial oversight.

Furthermore, Real-Time Crime Centers (RTCCs) in various cities utilize AI-powered platforms like Axon Fusus to integrate private doorbell cameras, public street feeds, and automated license plate readers into centralized, searchable maps. Such systems allow for the reconstruction of a person's movements across a city with significant speed and precision.

Legal frameworks like FISA Section 702, intended for foreign targets, also face scrutiny for "backdoor searches" of domestic communications. Despite privacy concerns, these authorities are periodically extended, as evidenced by legislative pushes in April 2026 to renew them through April 30. These developments highlight an ongoing tension between the use of advanced surveillance technologies for public safety and the preservation of individual privacy rights.

Zoë Hitzig’s departure from OpenAI has struck a chord because it highlights a fundamental shift in how these AI companies operate. Her main worry is that once a company pivots toward an advertising or profit-first model, the technology starts to change in ways we might not notice at first. She calls the data we give to AI an "archive of human candor," pointing out that because we talk to chatbots so intimately, they hold a uniquely vulnerable record of our private thoughts. If the goal shifts to keeping us clicking or staying engaged for revenue, the AI might start prioritizing what keeps us hooked over what is actually safe or helpful.

She’s essentially warning that we’re repeating the same mistakes we made with social media, where the drive for engagement eventually overshadowed the public good. Hitzig argues that this isn't just about seeing more ads; it's about the "gravitational center" of the company moving away from its original mission. Instead of just accepting this as the only way to pay for expensive AI, she’s pushing for different approaches, like having big corporations pay more so the general public can use it for free without being tracked. Now that she's out, she is focusing on things like poetry and public debate to help people think about what we actually want these systems to look like before the financial incentives lock us into a future we didn't choose.

The RAM program, or Restoring Active Memory, was launched by DARPA in 2013 to help injured veterans by using brain implants to bridge memory gaps. While the public goal is therapeutic, the technology works by recording and replaying neural codes, which effectively turns human memory into a programmable format. This capability opens the door to serious misuse that goes far beyond simple healing. If a device can "write" signals into the hippocampus to restore a memory, it can theoretically be used to implant entirely false memories or overwrite a person’s actual history.

There is also the potential for selective suppression, where specific traumatic events could be "blunted" or erased. In a military setting, this could be used to remove the emotional weight of combat, potentially making soldiers less likely to experience guilt or complicating investigations into battlefield conduct. Because the research also looks at how the brain consolidates skills and habits, the ultimate concern is that this technology could be used to manipulate an individual's behavior or core values. Even with ethical panels in place, the program proves that the brain’s internal narrative can be intercepted and edited by an outside force.

It’s easiest to think of these as three different levels of how companies decide what to charge you. At the most basic level, you have dynamic pricing, which is something we’ve all seen with airlines or Uber. It’s based on big-picture stuff like the time of day, the weather, or how many other people are trying to buy the same thing at that exact moment. If it’s raining and everyone wants a ride, the price goes up for everyone across the board.

Personalized pricing gets much more specific because it focuses on who you are rather than what’s happening in the world. Instead of looking at the weather, a store looks at your specific shopping habits, your loyalty status, or your zip code to guess the highest price you’ll pay before you decide to walk away. This is often why you might see a "special offer" in an app that looks like a deal but is actually just the specific price the algorithm calculated for you.

Surveillance pricing is essentially the extreme version of this. Regulators use this term because it relies on heavy-duty tracking to work. It doesn't just look at what you buy; it looks at the phone you’re using, your precise location, and even how you interact with a website. Because this happens behind the scenes, it’s hard to tell if you’re getting a fair shake compared to the person sitting next to you. Recently, the FTC and states like New York and California have started cracking down on this, passing laws that force companies to admit when an algorithm is using your personal data to set the price you see.

Maryland recently passed the Protection from Predatory Pricing Act, which kicks in on October 1, 2026. This law is a big deal because it makes Maryland the first state to specifically ban "surveillance pricing" and surge pricing in the grocery world. Basically, it stops stores and delivery apps like Instacart from using your personal info—like your income, where you live, or your shopping habits—to hike up prices just for you. It also requires stores to keep their prices steady for at least 24 hours so you don't walk in and see one price, only for it to jump while you’re walking down the aisle.

The law also blocks stores from using data about things like your gender or ethnicity to mess with pricing or ads. If a store uses an algorithm to set prices, they have to be upfront about it. If they get caught breaking these rules, the Attorney General can hit them with fines starting at $10,000, and it goes up from there for repeat offenders. Even though the governor signed it to help keep food affordable, some critics aren't thrilled. They point out that stores can still use loyalty programs as a loophole, and shoppers can't actually sue the stores themselves—the state has to handle the legal side of things.