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A few more things I keep seeing (and fixing):

1. People skip the “does anyone actually want this?” step
AI lets you build fast… but it doesn’t validate demand.
If 5 real people wouldn’t ask for it, 500 features won’t fix it.

2. The first version is usually too polished
Sounds weird, but it’s true.

Clean UI, multiple flows, dashboards…
but no one has used the core thing yet.

Scrappy + used > polished + ignored.

3. Most apps break on simple behaviour
Not edge cases. Basic stuff:

• user clicks twice
• refreshes
• comes back later

If you’re using AI, literally tell it to handle retries, partial actions, and drop-offs

4. Founders don’t watch users enough
They look at analytics instead.

Big difference.

Analytics tells you what happened.
Watching users shows you why.

5. Adding features feels like progress (it’s not)
It’s usually avoiding the real problem: the core flow isn’t clear or valuable yet

6. “It works” is a trap
The bar isn’t “does it run?” It’s: “does someone use it without me explaining it?”

The founders who win with AI aren’t doing anything crazy.

They just:

• keep things simple longer
• test earlier
• and resist the urge to build everything at once

What’s one feature you added that, in hindsight, no one really needed? 😄

A few more things I keep seeing (and fixing):

1. People skip the “does anyone actually want this?” step
AI lets you build fast… but it doesn’t validate demand.
If 5 real people wouldn’t ask for it, 500 features won’t fix it.

2. The first version is usually too polished
Sounds weird, but it’s true.

Clean UI, multiple flows, dashboards…
but no one has used the core thing yet.

Scrappy + used > polished + ignored.

3. Most apps break on simple behaviour
Not edge cases. Basic stuff:

• user clicks twice
• refreshes
• comes back later

If you’re using AI, literally tell it to handle retries, partial actions, and drop-offs

4. Founders don’t watch users enough
They look at analytics instead.

Big difference.

Analytics tells you what happened.
Watching users shows you why.

5. Adding features feels like progress (it’s not)
It’s usually avoiding the real problem: the core flow isn’t clear or valuable yet

6. “It works” is a trap
The bar isn’t “does it run?” It’s: “does someone use it without me explaining it?”

The founders who win with AI aren’t doing anything crazy.

They just:

• keep things simple longer
• test earlier
• and resist the urge to build everything at once

What’s one feature you added that, in hindsight, no one really needed? 😄

A few more things I keep seeing (and fixing):

1. People skip the “does anyone actually want this?” step
AI lets you build fast… but it doesn’t validate demand.
If 5 real people wouldn’t ask for it, 500 features won’t fix it.

2. The first version is usually too polished
Sounds weird, but it’s true.

Clean UI, multiple flows, dashboards…
but no one has used the core thing yet.

Scrappy + used > polished + ignored.

3. Most apps break on simple behaviour
Not edge cases. Basic stuff:

• user clicks twice
• refreshes
• comes back later

If you’re using AI, literally tell it to handle retries, partial actions, and drop-offs

4. Founders don’t watch users enough
They look at analytics instead.

Big difference.

Analytics tells you what happened.
Watching users shows you why.

5. Adding features feels like progress (it’s not)
It’s usually avoiding the real problem: the core flow isn’t clear or valuable yet

6. “It works” is a trap
The bar isn’t “does it run?” It’s: “does someone use it without me explaining it?”

The founders who win with AI aren’t doing anything crazy.

They just:

• keep things simple longer
• test earlier
• and resist the urge to build everything at once

What’s one feature you added that, in hindsight, no one really needed? 😄

A few more things I keep seeing (and fixing):

1. People skip the “does anyone actually want this?” step
AI lets you build fast… but it doesn’t validate demand.
If 5 real people wouldn’t ask for it, 500 features won’t fix it.

2. The first version is usually too polished
Sounds weird, but it’s true.

Clean UI, multiple flows, dashboards…
but no one has used the core thing yet.

Scrappy + used > polished + ignored.

3. Most apps break on simple behaviour
Not edge cases. Basic stuff:

• user clicks twice
• refreshes
• comes back later

If you’re using AI, literally tell it to handle retries, partial actions, and drop-offs

4. Founders don’t watch users enough
They look at analytics instead.

Big difference.

Analytics tells you what happened.
Watching users shows you why.

5. Adding features feels like progress (it’s not)
It’s usually avoiding the real problem: the core flow isn’t clear or valuable yet

6. “It works” is a trap
The bar isn’t “does it run?” It’s: “does someone use it without me explaining it?”

The founders who win with AI aren’t doing anything crazy.

They just:

• keep things simple longer
• test earlier
• and resist the urge to build everything at once

What’s one feature you added that, in hindsight, no one really needed? 😄

After seeing a lot of AI-built apps over the past year, there’s a pattern that keeps showing up.

The issue usually isn’t that people can’t build anymore it’s that they try to build everything too early.

AI makes it really easy to go from idea → full product in one go. Multiple features, integrations, dashboards… all working (on the surface).

But most of the problems later come from that decision.

The apps that hold up tend to be the ones where someone focused on one core flow first and made sure it actually worked properly before adding more.

The other thing is failure cases. AI almost always builds the “happy path” but real users don’t behave like that. They refresh mid-action, click things twice, leave halfway through.

If you don’t think about that early, it comes back later in weird ways.

Also, data. This is probably the least visible issue but the most painful one later. A lot of apps store things in whatever format works “for now”, and then once there’s real usage, it gets messy fast.

None of this means AI isn’t useful, it’s the opposite. It’s probably the fastest way right now to get something real into users’ hands.

But the people who get the most out of it aren’t treating it like magic. They’re just a bit more deliberate about what they build first and how they structure it.

Curious if others have seen the same or had different experiences.

After seeing a lot of AI-built apps over the past year, there’s a pattern that keeps showing up.

The issue usually isn’t that people can’t build anymore it’s that they try to build everything too early.

AI makes it really easy to go from idea → full product in one go. Multiple features, integrations, dashboards… all working (on the surface).

But most of the problems later come from that decision.

The apps that hold up tend to be the ones where someone focused on one core flow first and made sure it actually worked properly before adding more.

The other thing is failure cases. AI almost always builds the “happy path” but real users don’t behave like that. They refresh mid-action, click things twice, leave halfway through.

If you don’t think about that early, it comes back later in weird ways.

Also, data. This is probably the least visible issue but the most painful one later. A lot of apps store things in whatever format works “for now”, and then once there’s real usage, it gets messy fast.

None of this means AI isn’t useful, it’s the opposite. It’s probably the fastest way right now to get something real into users’ hands.

But the people who get the most out of it aren’t treating it like magic. They’re just a bit more deliberate about what they build first and how they structure it.

Curious if others have seen the same or had different experiences.

After seeing a lot of AI-built apps over the past year, there’s a pattern that keeps showing up.

The issue usually isn’t that people can’t build anymore it’s that they try to build everything too early.

AI makes it really easy to go from idea → full product in one go. Multiple features, integrations, dashboards… all working (on the surface).

But most of the problems later come from that decision.

The apps that hold up tend to be the ones where someone focused on one core flow first and made sure it actually worked properly before adding more.

The other thing is failure cases. AI almost always builds the “happy path” but real users don’t behave like that. They refresh mid-action, click things twice, leave halfway through.

If you don’t think about that early, it comes back later in weird ways.

Also, data. This is probably the least visible issue but the most painful one later. A lot of apps store things in whatever format works “for now”, and then once there’s real usage, it gets messy fast.

None of this means AI isn’t useful, it’s the opposite. It’s probably the fastest way right now to get something real into users’ hands.

But the people who get the most out of it aren’t treating it like magic. They’re just a bit more deliberate about what they build first and how they structure it.

Curious if others have seen the same or had different experiences.

After looking at where AI-built apps break, here’s the other side of it:

If you’re a non-technical founder using AI, what should you actually do differently?

Not theory. Just what works.

1. Don’t try to build “the full product”
AI makes it tempting to go end-to-end from day one.

That’s usually a mistake.

→ Start with one core flow that delivers value
→ Ignore edge features early

If that one flow breaks, everything breaks anyway.

2. Ask “what happens if this fails?”
AI builds happy paths by default.

You need to force it to think about failure.

→ What happens if the API fails?
→ What happens if the user refreshes mid-action?
→ What happens if two users do this at once?

If you don’t ask this, your users will find out for you.

3. Keep your data model simple and clean
Most long-term issues come from messy data, not UI.

→ Avoid storing structured data as text
→ Add basic constraints early (unique, required fields)
→ Think: “will this still make sense with 10,000 records?”

Fixing data later is painful.

4. Separate “core logic” from “experiments”
AI tends to mix everything together.

That works… until you try to change something.

→ Keep your main flows clean
→ Don’t pile features into the same place
→ Remove abandoned experiments early

Mess builds up fast if you don’t.

5. Don’t wait too long to get a second pair of eyes
You don’t need a full dev team.

But you do need a checkpoint.

→ When you feel “it works but I don’t trust it”
That’s the moment to get a proper review.

Not after things break.

Reality:
AI is incredible for getting you moving. But if you treat it like magic, you’ll hit a wall.If you treat it like a tool, you’ll move faster than most.