Stop Asking What AI Can Do. Ask What It Makes Scarce.

For the past few years, one question has dominated almost every conversation about artificial intelligence:

What can AI do?

• Can it write?

• Can it code?

• Can it analyze data?

• Can it create images?

• Can it advise customers?

• Can it replace parts of professional work?

These are understandable questions. They help us explore the capabilities of a rapidly evolving technology. But from a strategy perspective, they are becoming less useful.

 Get your free copy of
“The Value Relocation Playbook” today! 

Because once a capability becomes widely available, knowing that it exists no longer tells us much about competitive advantage.

The more important question is different:

What becomes scarce when AI makes intelligence abundant?

This is where the strategic conversation begins.

Technology does not just create new capabilities

Every major technological transformation changes the relationship between scarcity and abundance. Before the industrial revolution, physical power was scarce. Human and animal muscle limited what could be produced. Steam power changed that constraint.

Before the internet, global distribution of information was expensive and controlled by relatively few organizations. The internet made publishing and distribution dramatically more abundant.

Before cloud computing, sophisticated computing infrastructure required significant capital, expertise and scale. Cloud platforms turned much of that infrastructure into an on-demand resource.

And now artificial intelligence is beginning to do something similar to intelligence itself. Not intelligence in every possible sense. Human judgment, experience and understanding remain complex. But many outputs associated with intelligence are becoming dramatically easier and cheaper to produce.

• Research.

• Analysis.

• Translation.

• Code.

• Images.

• Presentations.

• Summaries.

• Recommendations.

• Ideas.

• First drafts.

Capabilities that once required significant amounts of specialized human labor can increasingly be generated within seconds. That is an extraordinary technological development. But economically, something even more interesting is happening.

Abundance changes value

Imagine that your company had access to an analyst who could research almost any market, summarize thousands of pages, produce initial strategic options and prepare a presentation within minutes.

• Ten years ago, that capability would have been extraordinary.

• Today, millions of people increasingly have access to something similar.

• The capability has not become useless.

Quite the opposite. It has become enormously useful. But usefulness and scarcity are not the same thing. And this distinction matters.

When everyone gains access to a capability, possessing that capability alone becomes a weaker source of competitive advantage.

• The spreadsheet did not make calculation useless.

• Search engines did not make information useless.

• Cloud computing did not make computing useless.

They made these capabilities more available.

And as they became more available, value migrated elsewhere.

This is the mechanism we call Value Relocation.

AI doesn't destroy value. It changes where value concentrates.

When technology reduces an existing scarcity, the economic system does not suddenly become valueless. Instead, another constraint becomes more important.

Consider content. For decades, producing professional-looking content required expertise, time and money. Today, generative AI can produce an enormous amount of competent content almost instantly.

The predictable response is to produce more.

• More articles.

• More social posts.

• More newsletters.

• More videos.

• More personalized messages.

But if every organization does this, content itself becomes less scarce. And suddenly the bottleneck moves. Producing content is no longer the main problem.

Getting attention is.

And when audiences are surrounded by unlimited synthetic communication, another scarcity may become even more important:

trust.

The value has moved.

The same pattern applies to expertise

Many professional services businesses have historically been built around scarce expertise.

• Consultants knew things clients did not know.

• Lawyers had access to specialized legal knowledge.

• Developers knew how to turn specifications into software.

• Analysts could gather and interpret information that was difficult for others to obtain.

That scarcity supported attractive business models. AI does not suddenly make these professions irrelevant. But it begins to change the economics underneath them.

If a client can obtain competent research, analysis or a first recommendation almost instantly, then simply producing information becomes less differentiating.

What might become more valuable instead?

• Perhaps the ability to understand the client's unique context.

• Perhaps judgment when several technically plausible answers exist.

• Perhaps the credibility to recommend an uncomfortable decision.

• Perhaps responsibility for the consequences.

• Perhaps implementation.

• Perhaps access to relationships, proprietary data or real-world infrastructure.

In other words:

When expertise becomes more abundant, value may move from knowing to judging, from producing to deciding, and from recommending to delivering.

That is a fundamentally different strategic perspective on AI.

The wrong response is simply to add more AI

This creates a paradox for companies.

If everyone has access to increasingly similar AI capabilities, adopting AI is necessary—but adoption itself cannot remain a durable differentiator.

Imagine two competitors using comparable models.

• Both can create content.

• Both can analyze customer data.

• Both can generate proposals.

• Both can automate support.

• Both can develop software faster.

What happens next?

The strategic question is no longer:

Which company has AI?

Almost everyone will. The question becomes:

What does one company possess that becomes more valuable when both have AI?

• That might be proprietary data.

• A trusted brand.

• Exclusive distribution.

• Customer relationships.

• Physical infrastructure.

• Deep domain context.

• Regulatory approval.

• Execution capability.

• Capital.

• A community.

• Or the willingness to take responsibility for an outcome.

These are examples of what we call emerging scarcity.

This changes how leaders should think about AI strategy

Most AI strategies currently begin with a list of use cases.

• Where can we automate?

• Where can we save time?

• Where can we reduce headcount?

• Where can we increase productivity?

These are legitimate operational questions.

But they are not sufficient strategic questions.

A Value Relocation perspective starts somewhere else.

It asks:

1. What is scarce in our business today?

Why are customers willing to pay us rather than someone else?

2. Which of those scarcities is technology making more abundant?

What becomes easier to reproduce, automate or access?

3. Which part of our current advantage is therefore at risk?

What could become expected rather than differentiated?

4. What becomes scarce next?

Which complementary asset becomes more important precisely because intelligence is now abundant?

5. Do we control that scarcity?

And if not: can we build it, acquire it, partner for it or gain privileged access to it?

That is a very different AI strategy.

It moves the conversation from technology adoption to competitive advantage.

Look for the bottleneck after the bottleneck

There is a useful way to think about this. Every economic system has constraints. Technology removes some of them. But when one bottleneck disappears, another one becomes visible.

• If software becomes easier to build, perhaps distribution becomes more important.

• If information becomes unlimited, perhaps credibility becomes more important.

• If recommendations become abundant, perhaps judgment becomes more important.

• If ideas become cheap, perhaps execution becomes more important.

• If synthetic interactions become ubiquitous, perhaps authentic relationships become more important.

• If automated decisions become common, perhaps accountability becomes more important.

This does not mean these outcomes are guaranteed. Different industries will experience different forms of Value Relocation. That is precisely what leaders need to investigate.

The strategic opportunity

Most organizations will use AI to make existing processes faster. That will produce substantial productivity gains. But productivity gains tend to diffuse.

• Competitors adopt the same technology. 

• Software vendors integrate the same capabilities.

• Customers begin to expect them.

What initially looks like innovation gradually becomes infrastructure.

The larger strategic opportunity therefore lies elsewhere:

Identify what AI cannot make abundant as easily—and determine whether that scarcity will matter more in the future.

That is where tomorrow's competitive advantage may be forming.

Not necessarily inside the AI model.

But around it.

• In the data it does not have.

• The relationships it does not own.

• The trust it has not earned.

• The physical world it cannot control.

• The judgment required when there is no clear answer.

And the responsibility somebody ultimately has to accept.

Follow value, not technology

AI capabilities will continue to improve.

Today's impressive feature will become tomorrow's standard functionality. Trying to build strategy around the latest capability therefore means chasing a moving target.

A more durable question is:

What is becoming abundant—and where does scarcity move as a consequence?

Because technology determines what becomes possible. Scarcity influences what remains differentiated. And differentiation determines where economic value can be captured.

That is the central idea behind the Value Relocation Framework.

Don't just ask what AI can do.

Ask what AI makes abundant.

Then look for what becomes scarce next.

That is where value may be moving.

About The Value Relocation Project

The Value Relocation Project explores how technological abundance changes the sources of competitive advantage. Through frameworks, cases, research and practical tools, the project investigates one central question: Where does business value move when intelligence becomes abundant?