To avoid mentioning any real stories from the 30 startups I evaluated in 2025, I’ll create a metaphoric story.

Hamada had a good weekend.

On Friday night, the idea was still a scribble: “An AI assistant for [industry] that writes content, answers customers, and auto‑builds landing pages.”

By Sunday afternoon, it was real.

There was a homepage. A signup flow. A dashboard with charts. A “Generate” button that spit out polished copy. A repo full of commits with timestamps that looked like a highlight reel. Hamada felt the adrenaline that every builder knows: I’m moving. I’m shipping. I’m winning.

On Monday, Hamada posted the demo. Friends clapped. A few strangers signed up.

On Tuesday, nothing.

On Wednesday, Hamada added more features. Then more.

Two weeks later the product had doubled in surface and reminded everyone it was “AI‑powered.” But the numbers didn’t budge. Retention was flat. Conversions were worse than Hamada expected. The excitement was gone, replaced by a quiet anxiety:

“How can I be shipping this fast… and still not be flying?”

That’s the trap.

AI and vibe‑coding can make you feel like you’re building a rocket. But if your startup isn’t taking off, it’s usually because AI made the wrong things cheap—output—and moved the real bottleneck somewhere else.

This article is a story about where the bottleneck moved, and why your startup is stalled even while your feature velocity looks incredible.

Let me tell you why this is not flying.

#1 Speed Isn’t Traction (And It Never Was)

Hamada’s week became a loop:

1. See a competitor feature.
2. Prompt, generate, ship.
3. Refresh analytics.
4. Repeat.

The product got bigger. But it didn’t get sharper.

Here’s the uncomfortable truth: shipping faster doesn’t automatically create traction. It just compresses the time it takes to learn you’re aiming at the wrong thing.

In the pre‑AI world, speed was constrained. That constraint sometimes forced clarity:

• You had to choose fewer features.
• You had to talk to customers because you couldn’t “code your way” to certainty.
• You had to earn distribution because the product couldn’t change every day.

AI removes the friction, which is amazing for prototyping—and brutal for strategy. Without friction, you can confuse motion with progress for a very long time.

Hamada didn’t need more features.
Hamada needed a better answer to: who is this for, what problem is it solving, and why now?

That’s when the first big shift appears.

#2 The Problem Shifted—Because the Customer Shifted

Hamada’s original strategy sounded sensible:

“We’ll create better content faster. We’ll rank. We’ll educate. We’ll convert.”

But something weird happened.

Hamada wrote a long guide. People didn’t read it.
Hamada wrote a shorter guide. People still didn’t read it.

Then Hamada realized the customer’s workflow had changed:

They were pasting the guide into an AI tool and asking:

• “Summarize this.”
• “Give me the checklist.”
• “What should I do first?”

The content wasn’t for humans anymore.
It was becoming raw material for a machine.

So Hamada’s product was accidentally doing something like this:

• AI writes content
• People use AI to summarize it
• Nobody actually engages

The loop removed the human attention that marketing used to rely on.

This is a bigger point than “SEO is harder now.” It’s that behavior changed:

• People don’t browse the same way.
• People don’t consume content the same way.
• People don’t evaluate tools the same way.

If your product strategy assumes the old behavior, you can ship perfect features into a world that no longer exists.

So the question becomes:

If AI is on both sides—builder and customer—what does value look like now?

For many products, the answer is not “more output.”
It’s less work, fewer decisions, more trust:

• Make the next action obvious.
• Reduce risk.
• Compress time‑to‑outcome.
• Integrate into an existing workflow.

Hamada’s mistake wasn’t using AI.
It was using AI to optimize something customers were already outsourcing to AI.

#3 If AI Is Your Secret Weapon… It’s the Most Famous Secret Weapon

Hamada’s pitch had a line that used to sound powerful:

“We’re using AI.”

But now, that line is like saying:
“We use the internet.”

Competitors weren’t scared.
They were bored.

Because everyone has access to the same superpower:

• Same models (or close enough)
• Same tools
• Same templates
• Same “AI‑powered” feature set

And that creates a painful outcome:

You’ll see lots of startups claiming the same “unique value.”
Which means it’s not unique at all.

Hamada looked at the landscape and saw a mirror:

• “AI content writer”
• “AI customer support agent”
• “AI landing page generator”
• “AI CRM assistant”

Different logos, same promise.

Here’s how this kills startups:

1. You build something impressive.
2. Users say “cool.”
3. Then they ask “why you?” and you don’t have a defensible answer.

The new moat is rarely “we used AI.”
It’s usually one of:

• Distribution (you can reach customers cheaper/faster)
• Workflow embedding (you live inside the tools they already use)
• Trust (security, compliance, reliability, brand)
• Proprietary data loops (you get better by being used)
• Unfair advantage (you have access to data nobody else does)
• Opinionated outcomes (you make a hard decision for them)

AI can accelerate all of those—but it can’t replace them.

Hamada didn’t lose because competitors were faster.
Hamada lost because there are too many Hamada's. and the original Hamada had no edge outside the product.

#4 The Adrenaline Tax—You Don’t Know What’s Happening Underneath

Three months in, the product was “working.”

Then the first real customer arrived.
The customer imported a messy dataset.
They had an unusual permission setup.
They used the product on a Friday at 6pm.

Suddenly nothing worked.

An AI‑generated query timed out.
A background job doubled itself.
An edge case broke onboarding.
Logs were vague. Metrics were missing.

Hamada opened the codebase and felt a cold drop.

The UI looked clean.
But underneath, it was a pile of “mostly fine” code stitched together by prompts and momentum.

That’s the real cost of vibe‑coding:

You can produce working output without building understanding.

And when you don’t understand the system, and without outsourcing this to AI, you can’t:

• debug confidently
• secure it properly
• optimize performance
• predict failure modes
• onboard teammates
• make architectural tradeoffs

So you pay later—in time, outages, rewrites, and lost trust.

This is the most expensive technical debt we’re likely to see in the next wave of startups:
debt created not by laziness, but by speed and misplaced trust.

Hamada tried to “AI the Problem,” prompting for patches and hoping. It worked… until it didn’t.

Because technical systems don’t fail politely.
They fail at the worst time, in the least explainable way, in front of the customer who could have been your reference.

If you want to use AI to code at startup pace, you need a new rule:

The model can write it, but a human must own it.

Ownership looks like:

• tests that encode intent
• observability (logs, traces, metrics) that tells a story
• code review standards . Hell no, not AI to review please. (even if it’s just you)
• threat modeling for anything that touches user data

Without ownership, velocity becomes a debt factory.

#5 When Apps Are Cheap, Attention Becomes Expensive

Hamada used to think the hard part was building.

Now building was the easy part.

And that changes the market in a simple, brutal way:

If it’s cheap to create apps, you’ll get more apps than users (or at least more apps than attention).

In that world:

• “We can build it quickly” is not a selling point.
• Features are not defensible.
• Entry barriers collapse.
• Discovery becomes the battlefield.

Hamada watched friends ship:

“I built this in 48 hours.”
“I launched this in a weekend.”
“I used AI to replicate a [$10M ARR tool] in a week.”

It sounded inspiring.

It also meant the market was flooding.

So Hamada’s real question wasn’t “what can I build next?”
It was “why would anyone choose mine?”

And that forces a rethink:

If your product is easy to copy, you can’t anchor your business on the product alone.
You need what’s harder to copy:

• channel relationships
• trust and reputation
• domain expertise
• consistent outcomes
• customer success that turns users into advocates

The paradox of the AI era:

The easier it is to build, the more your startup becomes a strategy and distribution game.

Hamada didn’t want to hear that.
Hamada wanted to ship.

But shipping wasn’t the constraint anymore.

#6 The Turning Point: A Different Definition of “Building”

One night, Hamada stopped adding features and did something that felt almost old‑fashioned:

Hamada wrote a one‑page problem brief.

Not a pitch deck.
Not a roadmap.

A brutal, simple brief:

1. Who exactly is this for? (Not “SMBs.” Not “creators.” One tight ICP.)
2. What is the job they’re trying to get done? (In their words.)
3. What changed because of AI? (In their workflow, constraints, expectations.)
4. What outcome will they pay for? (Not output—outcome.)
5. Why will they trust us? (Security, reliability, proof, references.)
6. How will we reach them repeatedly?

Hamada realized the startup hadn’t been failing because AI “didn’t work.”

It was failing because AI worked too well:

• It made output cheap.
• It made iteration addictive.
• It made imitation inevitable.
• It made the market noisy.

So the new game wasn’t “build faster.”

The new game was:

Define a problem that still matters after AI shifts behavior, then win distribution and trust while everyone else floods the market with features.

#7 What To Do Instead (If You Still Want to Vibe‑Code)

You don’t need to stop using AI.
You just need to stop letting AI choose the game you’re playing.

Here’s a practical operating model:

1) Use AI for prototypes, not for truth

• Prototype fast.
• Validate with humans.
• Only then harden.

2) Build the wedge, not the “platform”

• One painful workflow.
• One clear ICP.
• One reason they switch now.

3) Treat “AI‑powered” as table stakes

• Your differentiation must survive the question: “So what?”

4) Pay the ownership tax early

• Tests for critical paths.
• Observability from day one.
• A “no black boxes” rule for core logic.

5) Optimize for outcome, not output

• Less content, more conversion.
• Less features, more retention.
• Less novelty, more trust.

#8 Final Thought

AI makes you do what you are doing faster, not better
so if you are going wrong, you will go wrong way faster.

If your startup isn’t flying, don’t ask:

“How can I ship more?”

Ask:

“In a world where output is cheap and imitation is instant—what problem is still worth solving, for whom, and how will we earn attention and trust?”

That’s where the lift comes from.

#9 References

• Google users click less when an AI summary appears (Pew Research Center)
• GitHub Copilot crosses 20M users (TechCrunch)
• Linus Torvalds on vibe coding (TechRadar)
• Google Play app count decline and quality crackdown (TechCrunch)