Useful, Not Theatrical

At Axios HQ, the AI work that moved usage was the work that did not look like AI. No wand icon. No banner renamed "AI-powered". Better defaults. Suggestions placed where people hesitated. The right next step pre-filled.

In the same window, I led an eight-person product and design org through a 90-day re-architecture of the information architecture. AI usage doubled across the platform. That number did not come from a banner. It came from features that did not announce themselves as AI.

I have shipped AI inside three companies and two ventures. The pattern keeps holding. The work that moves usage is invisible. The work that announces itself is theater.

The model is an input. Trust is the product.

SalesforceIQ was an AI-assisted CRM before that phrase had a marketing budget. The back end was real. The intelligence was real. Users could not act on it. The product surfaced relationship signals the model was confident about, and the user was not. So they ignored them.

That is the part most teams miss. Until a user can trust an inference enough to act on it, the inference does not exist. The model is an input to the design problem, not the answer to it.

When we redesigned the Contact Gallery and added a master contact model with multi-merge, daily active users went up 40% and merges went up 34%. Same intelligence underneath. We changed where it surfaced and how a user could verify it in one look. The path to trust got shorter.

Useful AI is constrained AI. The constraint is the feature.

At Kintsu Medspa, the agent contract bans patient identifiers from URL paths and query strings. That rule does not live in a compliance binder. It lives in CLAUDE.md and AGENTS.md, where the agents writing the code actually look. The reason is concrete. GA4 captures page_location on every pageview. A patient name in a URL becomes a HIPAA leak that ships to a third party.

The shape of the rule matters more than the rule. I did not write a 14-page policy. I wrote a 71-line AI_DESIGN_GUIDELINES.md and put it in the file the work reads first. The robots.txt allowlists 13 AI crawlers by name. Everything else is treated as ambiguous, which is the honest answer.

A constraint that lives where the work lives gets followed. One that lives in a separate Confluence page gets violated within a quarter. Useful AI inside regulated work is not the AI that does more. It is the AI that has been clearly told what not to do, in the same file as the rest of its job.

Show the work, including the cost.

Simple Cortex runs an open-source router called OpenClaw. Semantic routing across six routes: code, research, organization, conversation, visual, architect. The thing operators notice first is not the routing. It is that ocr costs is a daily-driver command. The cost meter sits next to the other tools, in the terminal where the work happens.

That choice treats the operator as an adult who makes economic tradeoffs all day. Hiding cost behind a dashboard treats them as a user who should not worry about the meter. Both designs have a model of the user. Only one is correct for serious work.

Trust is the same problem here as it was at SalesforceIQ. Operators believe the system more when they can see what it is doing and what it costs to run. Legibility is the feature.

The theatrical failure mode

The recognizable failure mode is the AI that announces itself. A wand icon. A "Powered by AI" pill. A two-second animation to show a model thinking. This always loses to a quiet feature that just works.

Theatrical AI sets an expectation the model cannot keep. It puts the trust burden in the wrong place. The wand says it is smart. The user decides if they believe it. That work belongs on the design, not on the user. And when the model is wrong, which it will be, the announcement is what the user remembers.

A quiet feature that works does not need a banner. The user attributes the new behavior to the product getting better. That is usually closer to the truth.

What this looks like at week one

Nibbble went live on 2026-04-22. Customer council of four restaurants. Two beta testers in the loop. Zero paying customers as of last week. I am saying that out loud because it matters. The version operators use does not foreground the AI. It behaves like a product that knows what they are trying to do. How I measure it is whether the next session feels easier than the last one. Anything further out is too early to be honest about.

There is a real difference between leaders who can describe AI strategy in a room and leaders who have shipped AI that users actually use. The first is a deck. The second is craft. The craft is knowing where to put the rule, where to put the cost, which intelligence to make legible, and which to keep silent until it earns its place.

The work I am proudest of in the AI era is the work nobody called AI. It made the product feel like it knew what users were trying to do. That is the bar. Harder than it looks. Less satisfying to demo. It moves usage.