The new AI paradox: smarter models, worse data

"AI promises a smarter, faster, more efficient future, but beneath that optimism lies a quiet problem that's getting worse: the data itself. We talk a lot about algorithms, but not enough about the infrastructure that feeds them. The truth is, innovation can't outpace the quality of its inputs, and right now those inputs are showing signs of strain. When the foundation starts to crack, even the most advanced systems will falter."

"Privacy regulations, device opt-ins, and new platform restrictions have made high-quality, first-party data harder than ever to capture. To fill the gap, the market has flooded itself with recycled, spoofed, or inferred signals that look legitimate but aren't. The result is a strange new reality where a mall that closed two years ago still shows "foot traffic," or a car dealership appears to be busy at midnight."

"For years, the industry believed that more data meant better insights. Volume signaled strength. More inputs meant more intelligence. But abundance now equals distracting noise. To preserve scale, some suppliers have resorted to filler data or fake signals that make dashboards look healthy while eroding their reliability and authenticity. Once bad data enters the system, it's nearly impossible to separate."

AI systems depend on reliable data inputs, and current data inputs are degrading. Privacy regulations, device opt-ins, and platform restrictions have reduced high-quality first-party data availability. The market has compensated with recycled, spoofed, or inferred signals that appear legitimate but are unreliable. These fake signals produce anomalies such as closed malls showing foot traffic or car dealerships appearing busy at midnight. Suppliers often favor quantity over credibility, turning volume into distracting noise. Once bad data mixes into systems, separating it becomes nearly impossible, compounding errors across AI-driven insights and undermining decision-making.

Read at Fast Company