AI Made Us Build 10x Faster. It Also Made It Easier to Avoid Users.
Building used to be the slow part.
A founder had an idea. Then came weeks of design, coding, testing, and fixing. Many ideas died before reaching a real user.
AI has changed that.
We can now build a landing page in one day. We can add a feature in a few hours. We can turn a rough idea into a working product over a weekend.
This sounds like progress.
But there is a new problem: building faster can make it easier to delay the uncomfortable work of talking to users.
Building feels like progress because we can see it
Product work gives us visible results.
A new page appears. A button starts working. A bug disappears. The dashboard shows another completed task.
User research feels less clear.
You send messages and get no reply. You run an interview and hear mixed opinions. One user loves the product. Another user does not understand it.
There is no clean progress bar.
So when building becomes fast and satisfying, it is easy to stay inside the product. We tell ourselves that one more feature will make the value clear.
Sometimes it does.
Often, we are just avoiding a harder question:
Do people care enough about this problem?
AI can speed up the wrong decision
Speed is useful when the direction is right.
When the direction is wrong, speed only helps us travel further away from the user.
Imagine that five people try a product and leave.
One asks for a mobile version. One wants an integration. One says the design feels basic. Two say nothing.
With AI, the team can quickly build the mobile layout, add the integration, and redesign the homepage.
A week later, the product looks better and has more features.
But the team may still not know why those five people left.
Maybe they did not understand the value. Maybe the problem was not important enough. Maybe the product asked for too much effort before showing a useful result.
None of these problems can be fixed by shipping faster.
Feature requests are not always product evidence
Users often suggest solutions because solutions are easy to describe.
“Add a template.”
“Connect it to Slack.”
“Let me export this.”
These requests may be useful. But they do not always reveal the real problem.
A user asking for templates may actually be saying, “I do not know how to start.”
A user asking for an export button may be saying, “This product does not fit my normal workflow.”
A user asking for more customization may be saying, “The output does not feel relevant to me.”
If we build every request at AI speed, we may create a larger product without creating a clearer one.
The job is not only to collect requests. It is to understand what sits behind them.
The new product trap has four signs
I think many AI product teams are entering the same trap.
1. Every week is busy, but the core question never changes
The team ships constantly. Yet it still cannot explain why a user should return.
2. More features are used to solve weak positioning
If users do not understand the product, the team adds more examples, more use cases, and more options.
The message becomes longer instead of clearer.
3. Launching is treated as distribution
The product is posted on several platforms. Traffic appears for a few days. Then it disappears.
The team starts building again instead of learning where the right users already spend time.
4. The team measures creation instead of value
It tracks features shipped, prompts completed, or projects created.
But it cannot name the one user action that proves the product was useful.
We need a user loop, not only a build loop
The AI build loop is simple:
- Describe a feature.
- Generate it.
- Test it.
- Fix it.
- Ship it.
The user loop is slower:
- Find someone with the problem.
- Watch them use the product.
- Notice where they hesitate.
- Ask what they expected.
- Change one important thing.
- Watch again.
AI can make step five much faster. It cannot remove the other steps.
A strong product process needs both loops.
Before adding another feature, we can ask:
- What user behavior are we trying to change?
- What happened the last time someone tried this?
- What would prove that the change worked?
- Can we test the idea without building the full feature?
These questions are simple. Answering them honestly is not.
SoonLab faces the same problem
I am one of the product leads at SoonLab, an AI game maker.
Our product can help someone turn an idea into a playable game very quickly. That makes it easy for us to focus on generation speed, output quality, and new creation features.
But we face the same product question as every other AI team.
Creating something is not the final proof of value.
We still need to understand whether creators know what to do next. Do they edit the game? Do they share it? Do they return with another idea? Where do they stop?
AI helps us build faster. It does not answer these questions for us.
That may be the real lesson of the current AI product wave:
The cost of building is falling. The cost of understanding people is not.
Has AI helped your team learn faster—or has it only helped you ship faster?
The “build loop vs user loop” distinction is probably the strongest takeaway here. AI can compress implementation dramatically, but it doesn’t compress the time needed to discover whether the problem is important enough for someone to keep using the product.
Yes, this is a question that deserves our serious consideration.
Agreed. It’s becoming a much more important question as building gets cheaper. Curious what you’ve seen founders get wrong most often when they move too quickly?
Probably confusing shipping with validation. When something only takes a few hours to build, it’s tempting to keep adding features instead of asking whether anyone actually needs them. We’ve fallen into that trap too. Speed makes experimentation cheaper, but it doesn’t make the signal any clearer.
That’s a good distinction. I’d be interested in continuing the conversation — what’s the best email to reach you at?