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.

A decade ago, scale and accuracy could go hand-in-hand. But today, those goals often pull in opposite directions. 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

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