Most technology leaders blame the AI feature when something fails, but the real issue is usually the data behind it

Every AI cost review looks the same. A team checks which model it’s running, prices out a cheaper alternative and counts how many tokens a task burns through. That’s where the visible savings sit, so it’s usually where the effort goes. I run Decodo, a company that gives fast-growing tech teams a web data access layer, so I see this pattern often.

The web data feeding those models barely gets a second look. When it does come up, it gets compared to the model bill and waved off as a rounding error. No one on the team connects that spend to the quality of what the model produces, because the cost of skipping it doesn’t show up right away. It shows up three months later, in outputs that are worse, and by then no one traces it back to the review where this item got cut.

That’s backward. A smaller, cheaper model running on current data will usually beat a larger model running on stale data. But stale data doesn’t announce itself the way a token count does, so it survives the audit that a slow, expensive model wouldn’t.

Here’s what that looks like in practice. A pricing agent is built to reprice against competitors every morning. It works well for the first few weeks. Then, without anyone changing a line of code, it starts making confident calls off a saved snapshot of competitor pages instead of the live site.

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