AI as Infrastructure, Not Innovation

A conversation about why “AI-powered” stopped being impressive

A year ago, adding AI to your product felt like innovation.

Today, it feels like… maintenance.

Not because AI is weaker, but because it’s everywhere.

And that’s usually what happens right before a technology quietly becomes infrastructure.

The Moment You Stop Noticing the Magic

There’s a simple test for whether something is still innovation.

Do users notice it?

Think about:

• Search ranking

• Spam filtering

• Recommendations

• Fraud detection

• Autocomplete

All of these were once headline-worthy.

Now?
If they fail, users complain.
If they work, nobody says a word.

That’s infrastructure behavior.

We’ve Seen This Cycle Before (Many Times)

Electricity wasn’t a competitive advantage, factories that reorganized around it were.

Cloud computing didn’t win markets, teams that shipped faster did.

The internet didn’t differentiate products, distribution, UX, and trust did.

AI is moving into the same phase.

The models are:

• Cheaper

• Faster

• Easier to access

• Increasingly interchangeable

Which means the question is no longer:

“Do you use AI?”

It’s:

“What does your product do because AI exists?”

The Trap Many Teams Are Falling Into

Here’s a pattern I see repeatedly.

A team builds:

• An AI assistant

• A smart summarizer

• A generative feature

The demo looks incredible.
Leadership gets excited.
Marketing ships “AI-powered” messaging.

Then 6 months later:

• Users barely mention it

• Competitors ship similar features

• The feature becomes expected, not loved

• PMs struggle to explain incremental value

Nothing “failed.”
The feature just aged faster than expected.

Why AI Features Decay So Quickly

AI features have a short novelty half-life.

Once users:

• Understand the capability

• See it elsewhere

• Learn its limits

They mentally reclassify it from:

“Wow” → “Of course”

That’s not bad.
It’s just reality.

The mistake is building a roadmap on novelty instead of durability.

Where Real Differentiation Actually Lives

Let’s talk about what doesn’t commoditize easily.

• Workflow depth

• Domain understanding

• Data feedback loops

• UX polish

• Latency and reliability

• Integration into daily habits

Notice something?

None of these are model-level advantages.

They’re system-level advantages.

AI Is the Engine. Users Care About the Vehicle.

Users don’t care:

• Which model you used

• Whether it’s fine-tuned

• If it’s open-source or proprietary

They care:

• Does this save me time?

• Does this reduce errors?

• Does this fit how I actually work?

• Can I trust it?

That’s why the best AI products feel boring in the best way.

They don’t announce themselves.
They just remove friction.

PM Reality: AI Parity Is Coming Faster Than You Think

If your differentiation is:

• “We added GPT”

• “Our AI is smarter”

• “We have a better prompt”

You’re on borrowed time.

Parity is inevitable.

The real question PMs should be asking is:

“What still works when AI becomes invisible?”

Competing Above the Model Layer

Strong AI strategy today looks like:

• Treating models as replaceable

• Designing systems that survive model swaps

• Investing in product intuition, not prompt cleverness

• Building moats users feel, not features they screenshot

This is why the best teams quietly talk about:

• Evaluation pipelines

• Guardrails

• Workflow fit

• Human-in-the-loop design

Not demos.

When AI Can Still Be a Differentiator

There are exceptions, but they’re rare and structural:

• Proprietary data

• Highly regulated domains

• Deep vertical specialization

• Long feedback loops competitors can’t replicate

Even then, the model alone isn’t the advantage.
The system is.

The PM Takeaway (The Honest One)

If you’re building today:

• Assume AI capability will equalize

• Stop shipping features just because they “use AI”

• Measure success by outcomes, not outputs

• Invest in boring reliability

• Design products that still make sense without buzzwords

Final Thought

The most powerful technologies stop being visible.

They don’t impress.
They don’t explain themselves.
They quietly disappear into everything.

AI is heading there fast.

And the teams who understand this shift early
will build products that last long after the hype moves on.