AI Made Shipping Cheap. Coherence is the New Problem.
AI Velocity Creates Feature Chaos. Focusing on Workflows Avoids It.
Abstract
AI has made it easier than ever to ship new features, but every addition taxes your product's clarity, navigation, and user experience. When you optimize for output, you risk turning your product into a confusing mess that customers struggle to adopt.
If you want to build something people actually use, focus on refining the core workflows that drive activation and retention. Ship fast, but not wide: clarity is your moat when building is cheap.
We shipped 14 new features this week. Release notes are booming. We’re unstoppable. 😎
But wait, looking at the numbers: activation was flat, support tickets went up, and sales calls got weirdly hard to run.
This is the new trap: AI makes building cheap. But it doesn’t make products coherent.
Call it the Coherence Tax: every feature you ship taxes navigation, onboarding, analytics, terminology, reliability, security, and long-term maintainability. AI reduces the cost of creating functionality; it doesn’t reduce the cost of integrating functionality into something customers can understand and use.
The predictable failure is that teams optimize for output: velocity, lead time, lines of code, shipped features. Customers silently pay the price in confusion. Your product becomes a Swiss Army knife designed by a committee, and every tool is slightly sharp.
Here’s the problem: Customers experience your product as a journey, they start a workflow → make decisions → complete their goal → increase confidence and loyalty. If you add “requested” features that interrupt that journey, you don’t add value, you add friction. And friction kills adoption.
So yes, ship fast. But don’t ship wide.
My bias: I’d rather invest in iteration six of a core workflow than ship six different features. A feature is something you can list on your pricing page. A workflow is a repeatable path customers take to get a job done.
What does “iteration six” look like? 1) make it possible → 2) reliable → 3) more intuitive → 4) faster → 5) controllable → 6) scalable.
If you want velocity without chaos, run a workflow loop:
Pick 1 - 3 workflows that pay the rent (the ones that drive activation, retention, revenue).
Instrument them (time-to-first-value, completion rate, drop-off by step, usage over time).
Iterate fast (diagnose → fix → ship → measure → repeat).
And make one question mandatory: “What should we delete?”
The strategy shift is simple: stop collecting features, start improving workflows. AI makes it easy to build more, great teams choose to build better. When shipping is cheap, clarity becomes your moat.
Questions this article answers
The thing I keep coming back to is that coherence might actually be an observability problem. If you build observability into your workflows from the start, you get real signal on where users are stuck, what's brittle, what's actually working. But here's the twist: the observer isn't a person anymore. No one's sitting there watching logs and dashboards to decide what to iterate on next. The AI itself should be ingesting those workflow patterns and surfacing where coherence is breaking down. That changes the whole feedback loop. You're not guessing what to make reliable or intuitive; the system is telling you. Feels like that's the unlock for actually climbing the ladder instead of getting stuck at step two and pivoting to something new.
Good point. If you instrumented everything, and designed an agent to scan for unused features or bottlenecks, automatically reducing friction and feature bloat would be relatively straightforward. Also, constantly evaluating and optimizing core workflows isn't too much a reach - agents just need data to tell them where to look. (Human in the loop recommended 😂).