There’s no denying it. AI is everywhere.
It’s embedded in how we forecast, personalize, recommend, and measure. But as more systems rely on machine-generated data instead of real-world input, we’re facing a growing risk known as AI model collapse.
Let’s break it down.
Imagine if every time you tried to learn something new, your only source was someone else’s AI-generated summary of someone else’s AI-generated summary. Eventually, the signal gets lost. The information becomes so far removed from its original truth that it’s no longer meaningful.
That’s model collapse.
It happens when AI systems are trained not on real-world data, but on the outputs of other AI models that were also trained on derivative data. Over time, the model forgets what real looks like. According to experts at IBM, these systems begin to lose their ability to capture rare patterns or represent the full diversity of real-world behavior. Instead, they produce increasingly narrow, distorted outputs that are detached from the very reality they’re meant to reflect.
It’s like taking an average of an average. Or trying to manufacture something using only recycled materials, then recycling those materials again and again. Eventually, all you get is mush.
That’s why real-world data matters more than ever.
The best way to keep AI grounded is to feed it signals from actual human behavior, not outputs from other models.
And when it comes to accessing and activating that kind of data at scale, few are better positioned than Retail Media Networks.
The most powerful data AI needs to stay useful is generated by real people making real decisions in real environments. Retailers, especially those building Retail Media Networks (RMNs), are sitting on a uniquely rich supply of real-world data.
Every purchase, loyalty scan, store visit, and click is a signal grounded in reality. And that gives RMNs a built-in advantage given the increasing risk of synthetic data feedback loops.
The key is ensuring this valuable data is protected, preserved, and integrated into AI data pipelines without losing control or exposing sensitive information.
That’s where RMNs can lead.
Make it secure. Make it scale.
As AI continues to shape how we target, measure, and personalize, RMNs have the chance to set a higher standard rooted in truth. When real-world data stays at the core, AI remains grounded. When it doesn't, even advanced systems can drift, narrowing recommendations, amplifying bias, and eroding performance over time. Forbes recently highlighted this risk in recommender systems, warning that overdependence on synthetic signals can create echo chambers and diminish outcomes.
Since we’re talking about data, it might seem like this is a data science problem, but the implications span the entire business.
Karlsgate helps each of these teams meet their goals.
Our protected data pipelines keep real-world signals flowing between retailers, brands, and measurement partners without exposing PII, requiring data movement, or introducing unnecessary risk.
You get the fidelity of raw data with the protection of modern cryptographic controls.
The Bottom Line
Retail Media has what AI needs to thrive. Now it’s time to use it wisely.
Ready to see how it works?
Start a self-guided proof of concept (25 minutes or less) or explore the No-Platform Network data solutions for Retail Media.
About Karlsgate
Karlsgate provides privacy-first software that makes secure data collaboration simple to adopt and scale. Its patented cryptographic protocol allows partners to match records using personal information without sharing that information or moving sensitive data. Designed for real-world use, Karlsgate integrates easily into existing workflows and supports both current and post-quantum cryptographic algorithms to ensure long-term protection. Organizations use Karlsgate to reduce risk, protect data by default, and unlock its value across teams and partners without adding friction or compromising compliance.
Sources
Forbes, 2020. "How AI Can Solve Retail Media's Growing Pains"
IBM, 2024. "What is Model Collapse."