Insight Is the Moat

We are entering a world where building things is becoming incredibly cheap.

Ideas that once needed a team can now be prototyped by one person.

Things that took months can sometimes be built in days.

The technical barrier to entry keeps getting lower, and AI is accelerating that trend even further.

That sounds like an incredible opportunity.

It is.

But it also creates a problem.

When everyone can build, building stops being the advantage.

If the same tools are available to everyone, the question becomes less about whether you can create something and more about whether you know what is worth creating.

I think this is going to change how we think about competitive advantage.

For a long time, execution speed was a moat.

Then technology made execution easier.

Now the advantage increasingly sits upstream.

In understanding.

Understanding the customer.

Understanding the problem.

Understanding the system around the problem.

Understanding the constraints nobody else has noticed.

And understanding which problems are actually worth solving.

Tools can be copied. Understanding cannot.

This is why I worry when innovation becomes too focused on technology.

Every time a new technology appears, the natural reaction is to ask:

What can we build with this?

I think the better question is:

What can we now solve that we couldn't solve before?

Those sound similar, but they lead to completely different behaviour.

The first starts with the technology.

The second starts with the problem.

And the deeper our understanding of the problem, the more interesting the possibilities become.

This is particularly important with AI.

The technology is moving so quickly that trying to predict where it will be in a year is almost pointless.

Instead, we should build a habit of continuously exposing ourselves to what is changing and testing it against real problems.

Not AI demos.

Not experiments designed to prove that AI is interesting.

Real problems.

Real users.

Real constraints.

A small group working across product, design, engineering, research, and domain expertise could continuously test emerging technology against the hardest problems we have.

The output shouldn't be a collection of cool prototypes.

It should be insight.

This technology changes this constraint.

This problem can now be solved differently.

This workflow can now be reduced from ten steps to two.

This thing we assumed was impossible is actually possible.

That is where the value is.

Technology gives us new capabilities.

Understanding tells us where to apply them.

This also changes how we should think about research.

When building is cheap, research becomes more valuable, not less.

Because if anyone can build ten versions of an idea, the scarce resource is no longer the ability to create version eleven.

It's knowing which version deserves to exist.

Research gives us that understanding.

It tells us why people behave the way they do.

It exposes contradictions.

It surfaces problems people don't articulate directly.

It helps us see the difference between what someone says they want and what they actually need.

And sometimes the most valuable insight is discovering that the problem we were trying to solve isn't the problem at all.

That is difficult to automate.

It requires proximity.

Curiosity.

Judgement.

And a willingness to sit with a problem longer than feels comfortable.

There is another consequence of this.

The people closest to the problem become more valuable.

Not because they have all the answers.

Because they have accumulated context.

Someone who has spent years understanding a complicated workflow sees opportunities that someone encountering it for the first time simply cannot see.

This is why expertise matters even in a world where AI can generate answers instantly.

Answers are becoming abundant.

Good questions are not.

And knowing which questions matter is even rarer.

I think the companies that win in this environment will be the ones that combine two things extremely well:

deep domain understanding and extremely fast experimentation.

Understanding without execution becomes analysis paralysis.

Execution without understanding becomes feature factories.

You need both.

Go deep enough to know what matters.

Then move fast enough to test it.

This is also why diversity of perspective matters.

If everyone in a room understands the problem in exactly the same way, you may get very efficient execution, but you are less likely to discover something fundamentally different.

Different disciplines see different constraints.

Different experiences reveal different assumptions.

Different proximity to the user exposes different problems.

The goal isn't to have more opinions.

It's to create enough collision between perspectives that we notice something none of us would have seen alone.

Because the advantage isn't having more ideas.

It's seeing something others haven't seen yet.

We are going to live in a world where the distance between an idea and a working product becomes smaller and smaller.

That is exciting.

But it means we should stop romanticising the act of building.

Building will become table stakes.

The scarce skill will be knowing what to build, why it matters, and when not to build it.

That is why insight becomes the moat.

Not because technology doesn't matter.

Because technology is becoming available to everyone.

The difference will be what you understand that everyone else doesn't.