2 posts tagged with "product-analytics"

Automated internet traffic grew 23.5% year-over-year in 2025 — eight times faster than human traffic. Agent-driven interactions alone grew 7,851%. If you're building a product that handles any meaningful volume of API traffic, there's a reasonable chance your heaviest "users" are not human. The uncomfortable truth is that your product analytics almost certainly have no idea.

This isn't a bot detection problem. It's an instrumentation architecture problem. When an AI agent books travel, files expense reports, queries your database, or calls your payment API, it leaves a completely different behavioral signature than a human doing the same thing — and your session funnels, NPS surveys, and cohort retention charts are quietly telling you lies.

Product teams have started routing analytical questions directly to LLMs: "What's causing the churn spike?" "Why did conversion drop after the redesign?" "Which cohort should we focus retention spend on?" The outputs land in executive decks, drive roadmap decisions, and get presented to investors. The models answer confidently, in polished prose, with specific numbers. And a significant fraction of those answers are wrong in ways that don't announce themselves.

This isn't a general criticism of LLMs for data work. There are tasks where they genuinely help. The problem is that the failure modes are invisible — the model doesn't hedge, doesn't caveat, and doesn't distinguish between "I computed this from your data" and "I generated something that sounds like what this number should be." Practitioners who understand where the breakdowns happen can capture the genuine value and route around the landmines.