Trust is not a feature. You cannot put it on the roadmap and ship it in the next version.
That is the uncomfortable part of building with AI right now. Trust is the thing every AI product needs most, and it is the one thing no team can build directly. It only ever arrives as a byproduct of two things you can control.
I call it ITT.
Intentionality is your why. Transparency is your how. Trust is the result.
It shows up in that order or it does not show up at all.
The Product Version of AI Slop
AI Slop is content nobody believes in.
That definition has been floating around the writing world, and it transfers cleanly to product. AI slop in a product is a feature nobody believes in. The chat sidebar that got added because the board asked what the AI story was. The summarize button nobody on the team uses. The assistant that exists so the release notes have a paragraph about AI in them.
Nobody sets out to build that. It happens when the model comes first and the reason comes second. Someone has access to a capability, so they go looking for a place to put it. The feature ships. It works, technically. And users open it once, get something plausible and useless, and never touch it again.
That is not a model quality problem. It is an intentionality problem, and no amount of model improvement fixes it.
Intentionality
Intentionality is knowing what job the feature does before you know what technology does it.
Not the demo. The job. What is the user actually trying to get done, what does done look like to them, and what would have to be true for this to be the best way to get there. If the answer is “our competitor shipped one,” you are building AI slop, and everyone on the team already knows it.
Everyone has access to the same models. That is the defining condition of this moment. The model is table stakes. What differentiates one product from another is the harness built around it: the context it sees, the constraints it operates under, the tools it can reach, the judgment encoded in how it fails. Two teams with identical model access ship wildly different products, and the gap between them is entirely the harness.
Without intentionality, you drift to the middle. There is a strong pull toward the center of the distribution in this work, and you can watch it happen across an entire category at once. Every product gets a chat panel in the corner. Every product gets a summarize action. Every product gets a rewrite button. None of it is wrong. It is just what the average looks like when it gets rendered as a UI, and users can feel that they have seen it before, because they have.
The teams doing interesting work started somewhere else. They started with a job that was actually hard in their domain and asked what a model makes newly possible about it. That question produces something nobody else is shipping. The other question produces a sidebar.
Transparency
Intentionality is what you decided. Transparency is what you tell the user about it.
This is where most AI products are currently failing, and they are failing in ways the teams do not think count. A support chat that answers in a person’s tone and never says it is not one. A summary with no path back to the source. A confidence-free answer delivered in the same voice as a verified fact. A model swap that changes the behavior of a feature overnight with no note anywhere.
None of those are lies exactly. All of them spend trust the team did not realize it was spending.
The pattern underneath is mismatch. A user thought they were getting one thing and got another. They thought a person read their ticket. They thought the number came from their data. They thought the tool worked the way it did last week. The injury is not that AI was involved. The injury is that their expectation was quietly wrong and they found out on their own.
We are writing these norms in public right now with very little to copy from. We never needed a rule about disclosing that software answered a support message, because it was never possible. The moment it became possible, everyone discovered they had held an opinion about it all along. Same with generated summaries presented as sourced fact. Same with synthetic voices on a call. Each new capability drags an unstated expectation into the open, and the products that survive that moment are the ones that got there first and said so plainly.
The test I use is whether the behavior survives being described. If you can tell a user exactly how this works and they shrug and keep going, you are fine. If describing it plainly would make them uncomfortable, you already know what you built. Ship the description, not just the feature.
Trust
Trust is earned in small confirmations and lost in one.
Trust used to have a natural proxy in software: polish was evidence of care. If an output looked finished, a person had checked it. Fluency cost effort, so fluency meant something.
Fluency is free now. A wrong answer arrives with exactly the same confidence and formatting as a right one. That proxy is gone, and users are adjusting faster than most teams realize. They are learning to hold a low-grade question open at all times: is this real, and can I act on it without checking?
That question is the actual cost. Not the one bad output. Products survive bad outputs. What they do not survive is a user who now verifies everything, because a user who verifies everything has stopped getting value from the feature. They are doing the work twice. The feature is technically live and functionally dead.
Worse, this does not stay contained. Trust in AI features is a shared resource. When one product burns a user with a confidently wrong answer, that user arrives at your product already suspicious, and you pay for someone else’s shortcut. Their suspicion does not come labeled with the name of whoever earned it.
Which is why trust behaves asymmetrically. It accrues quietly, in the background, while nothing goes wrong. It leaves loudly, and it does not come back when the bug is fixed, because the user has already changed how they read every output you give them. And it can never be added at the end, because it is not a component. It is a residue.
Why You Need Both
I have been sharpening how the two inputs fail on their own, and the failure modes are distinct.
Intentionality without transparency produces suspicion. You built the right thing for the right reason and told nobody how it works, so users fill the silence themselves, and they do not fill it generously. Good intent that stays private is indistinguishable from bad intent.
Transparency without intentionality produces noise. A disclosure banner on a feature nobody wanted. An AI-generated label on output nobody asked for. You have been honest about something that should not exist, and honesty does not redeem it.
Trust needs both. It is the output of a product that had a real reason to exist and was willing to explain itself.
What This Costs If You Skip It
Every input in this system is getting cheaper. Model capability is getting cheaper. Generation is getting cheaper. Shipping an AI feature is getting dramatically cheaper, which is exactly why so many bad ones exist.
User patience is not getting cheaper. It does not multiply this year or next. Every AI feature you ship is a withdrawal against it, and the feature either returns more than it took or it does not.
So the bar goes up rather than down. When a user gives your AI feature a chance, it has to be worth the chance. Not more impressive. Worth it.
That is the whole framework. Intentionality is having a reason you would defend out loud. Transparency is being willing to say how it works. Trust is what those two earn you, slowly, and what a single careless release can spend on behalf of a team that never agreed to spend it.
Being against AI slop is not being against AI. It is the most pro-AI position available, because it insists these tools get pointed at something worth a user’s time.
Trust is not a feature. It is what you have left when intentionality and transparency have been doing their job for a while.
This article was written with the help of AI. The thoughts, ideas, and beliefs here are mine. I use AI as a thought partner to enhance my thinking, not to do my thinking for me.