4 posts tagged with "product-strategy"

The cost dashboard makes the case for itself. Sixty percent of traffic is "easy," a quick eval shows the smaller model lands within a couple of points on the global accuracy metric, and the routing layer ships behind a feature flag the same week. The graph bends. Finance is happy. The team moves on.

What nobody tracks is that the customer who hit the cheap path on Tuesday afternoon and the expensive path on Wednesday morning is now using two different products. The two models fail differently. They format differently. They refuse different things. They handle ambiguity, follow-up questions, and partial inputs with different defaults. From the customer's seat, the assistant developed amnesia overnight and nobody can tell them why — because internally, the change was filed as a finops win, not a product release.

Your AI-powered search just shipped. It's fast, conversational, and handles nuanced queries in ways your old keyword search never could. The feature review was glowing. The launch post got shared. And then, two weeks later, the support tickets start — not about search, but about the customer support widget, the help documentation, and the notification center. Nobody changed any of those things. But users are suddenly furious.

Welcome to the AI capability ratchet. The moment you ship one demonstrably intelligent feature, you have permanently recalibrated what users consider acceptable across your entire product. The ratchet clicks up. It does not click back down.

This pattern is one of the least-discussed failure modes in AI product development. Teams celebrate individual feature launches without accounting for the expectation debt they are distributing to every team that didn't ship anything.

Token costs dropped 280x in two years. Enterprise AI bills went up 320%. If that sounds like a paradox, you haven't looked closely at how per-token billing interacts with the features that actually make AI products valuable.

The most useful AI workflows — deep research, multi-step reasoning, iterative refinement, agentic tool use — are precisely the ones that consume the most tokens. Under pure usage-based pricing, your best features are your worst margin killers. This isn't a temporary scaling problem. It's a structural misalignment between how AI creates value and how it gets billed.

Your AI feature has 12,000 monthly active users. Engagement charts slope upward. The demo still impresses stakeholders every quarter. And your users are quietly routing around it.

This is the kill decision that product teams avoid for months — sometimes years — because every surface-level metric says the feature is working. The dashboard shows adoption. What it doesn't show is the support engineer who manually corrects every third AI-generated summary before forwarding it to the customer, or the power user who learned that clicking "regenerate" three times produces acceptable output and has silently accepted that tax on their workflow.