The Value Relocation Project-Essays

Yesterday’s Scarcity

roger.meili@thevaluerelocation.org (Roger Meili) — Sun, 16 Aug 2026 14:02:15 GMT

Great companies rarely fail because they stop innovating. They fail because they keep protecting yesterday’s scarcity.

When companies become successful, there is usually a reason. They control something valuable that others cannot easily access, reproduce or replace. It might be expertise. Distribution. Technology. Information. Manufacturing capacity. Customer access. A powerful brand. A network. A unique process.

That scarcity creates differentiation.
Differentiation creates margins.
Margins create success.

And success creates an organization designed to protect what made it successful.

That sounds entirely rational.

Until the scarcity changes.

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Success creates its own blind spot

Organizations naturally invest in their strengths.

A consulting firm built on expert knowledge hires more experts.
A software company built on proprietary technology invests in more features.
A media company built on content invests in producing more content.
A distributor with privileged access expands its distribution infrastructure.

The better the historical model has worked, the stronger the incentive to keep improving it. But technological change can alter the economic foundation underneath that model long before the organization itself appears to be in trouble.

The product can still be good.

Customers can still be buying.

Revenue can still be growing.

And yet the scarcity that made the business valuable may already be disappearing. This is one of the most dangerous moments in strategy.

Technology attacks scarcity before it attacks revenue

Disruption is often imagined as a dramatic event. A new competitor appears. Customers leave. Revenue collapses. The old company suddenly realizes that the world has changed.

Reality is usually more subtle.

Technology first changes what is scarce.
Then it changes customer expectations.
Then it changes willingness to pay.
Then margins begin to move.

And only later does the impact become obvious in financial results.

By the time the numbers clearly reveal the transformation, the strategic shift may already be well advanced.

AI is accelerating this process.

For decades, many businesses benefited from the scarcity of intelligence.

Research required researchers.
Analysis required analysts.
Software required developers.
Design required designers.
Writing required writers.
Expert advice required access to experienced professionals.

Organizations built business models around these constraints.Generative AI is now reducing some of them. Not eliminating them—but changing their economics.

The cost of producing a competent first draft, analysis, piece of code, design concept or recommendation is falling rapidly.

That means the strategic question is no longer simply:

How can we use AI to become better at what we already do?

There is a more uncomfortable question:

What if AI is making the thing we are best at less scarce?

The AI efficiency trap

Imagine a professional services company whose competitive advantage has historically been its ability to research complex topics and turn that knowledge into high-quality recommendations.

AI arrives.

The company responds intelligently.

It equips consultants with AI tools.

Research becomes faster.
Presentations take less time.
More analysis can be produced with fewer resources.
Productivity increases.
Margins may initially improve.
It looks like a successful AI transformation.
But something else is happening at the same time.
The company's competitors have access to similar technology.
So do its customers.

The capability that has just become dramatically more efficient is also becoming dramatically more abundant.

The company may therefore be optimizing the production of something whose scarcity—and eventually pricing power—is declining.

That is the paradox:

AI can make yesterday's competitive advantage more efficient while simultaneously making it less valuable.

Operationally, the company is improving.

Strategically, it may be standing still.

Value does not disappear. It relocates.

This does not mean expertise suddenly has no value.

It means we need to distinguish between usefulness and scarcity.

Something can become more useful while becoming less scarce.

And when scarcity declines, value tends to move toward the next constraint.

If analysis becomes abundant, perhaps judgment becomes more valuable.

If content becomes abundant, perhaps attention and trust become more valuable.

If software becomes easier to create, perhaps distribution, proprietary context and integration become more valuable.

If recommendations become abundant, perhaps execution and responsibility become more valuable.

If information becomes unlimited, perhaps access to unique data becomes more valuable.

The important point is not that these shifts will happen identically in every industry.

They won't.

The important point is that leaders must actively search for them.

Ask what your business is really protecting

Most organizations know what products they sell. Far fewer can articulate the scarcity beneath those products. That is where the strategic discussion should begin.

Ask:

Why are customers willing to pay us today?

Then go one level deeper:

What is difficult for them to obtain elsewhere?

And then ask the uncomfortable question:

Is technology making that easier?

If the answer is yes, the organization needs to look beyond defending the existing advantage.

It needs to identify the scarcity emerging next.

This is where strategy becomes less about protection and more about relocation.

From protecting to repositioning

The natural response to technological disruption is defensive.

Protect margins.
Protect customers.
Protect intellectual property.
Protect existing products.
Protect organizational structures.
Some protection is necessary.

But companies also need to decide which advantages should not be defended indefinitely.

A declining scarcity can still generate significant cash.

The strategic challenge is to use that cash to build the next source of advantage before the old one loses its economic power.

That requires a different capital allocation logic.

Instead of asking only:

Where are we strongest today?

Leadership teams should also ask:

Which of our strengths will still be scarce tomorrow?

And:

Which future-critical scarcities are we currently weak in?

This is the logic behind what we call the Scarcity Portfolio.

Manage scarcity like an investment portfolio

Companies manage portfolios of products, customers, investments and talent.

They should also manage a portfolio of scarce assets.

Some should be protected because they are valuable today and likely to remain scarce.

Some should be built because they are weak today but likely to become strategically important.

Some should be harvested because they still generate value but are becoming increasingly abundant.

And some should be reconfigured or abandoned because their strategic relevance is disappearing.

This creates a very different conversation about AI.

AI is no longer merely a technology to adopt.

It becomes a signal that the economic structure around the company may be changing.

The hardest decision is often made while things are still going well

Organizations rarely want to question a successful business model at the moment it is generating strong results.

That is understandable.

But strategic transitions are easiest to navigate before they become financially necessary.

Once margins collapse, customers leave and competitors redefine the category, options become narrower.

The companies best positioned for technological transitions therefore do something counterintuitive:

They question their strongest advantages before the market forces them to.

They ask whether those advantages depend on scarcity that technology is beginning to remove.

And they start investing in what will become difficult next.

That is the central strategic challenge of the AI era.

Not simply adopting artificial intelligence faster.

Not automating more processes.

Not generating more content, analysis or code.

But understanding how abundant intelligence changes the economic foundations of your competitive advantage.

Because the greatest threat may not be that your company fails to innovate.

It may be that you become extraordinarily good at protecting something the market no longer considers scarce.

Don't just defend what made you successful.

Find the scarcity that will make you valuable next.

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?

www.thevaluerelocation.org

Why «AI-First» Is a Trap

roger.meili@thevaluerelocation.org (Roger Meili) — Sun, 16 Aug 2026 13:18:45 GMT

The companies that win the AI era may not be the ones that put AI first. They may be the ones that understand what becomes scarce after AI.

“AI-first” has become one of the dominant strategic ideas of our time.

Companies are redesigning processes around AI. Software vendors are rebuilding products around AI. Investors are asking whether businesses have an AI strategy. Management teams are launching AI initiatives, AI transformation programs and AI-native operating models.

The logic seems obvious:

If artificial intelligence is the most important technological shift of our generation, companies should put AI at the center of their strategy.

But there is a problem.

AI-first focuses on the technology that is becoming abundant — rather than on the things that will remain scarce.

And that can become a strategic trap.

AI is powerful. But power does not equal scarcity.

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For decades, many forms of intelligence were expensive.

Writing good copy required skilled people. Software development required developers. Analysis required analysts. Design required designers. Research required researchers. Expertise was difficult to acquire, expensive to deploy and slow to scale.

AI is changing that equation.

Increasingly, intelligence can be produced at extremely low marginal cost.

Code. Analysis. Content. Translation. Design concepts. Research. Recommendations. Customer interactions.

Tasks that once required hours or days of human work can increasingly be performed in seconds.

This represents an extraordinary productivity opportunity.

But economically, something else is happening at the same time:

AI is commoditizing intelligence.

And when something becomes abundant, its ability to create differentiation tends to decline.

This where the AI-first logic becomes dangerous.

If everyone has AI, AI cannot be your strategy

Imagine two competitors.

Both have access to advanced language models.
Both can generate content.
Both can automate customer service.
Both can analyze their markets.
Both can build software faster.
Both can deploy AI agents.

What happens?

They become more productive. But they do not necessarily become more differentiated. This distinction matters.

AI can dramatically improve how efficiently a company operates without answering the more important strategic question:

Why should customers choose this company rather than another one?

If every competitor has access to increasingly similar intelligence, then intelligence itself becomes a weaker source of competitive advantage.

The competitive battlefield moves somewhere else.

Value does not disappear. It relocates.

This is the central idea behind Value Relocation.

Technological revolutions rarely destroy economic value completely. Instead, they change where value concentrates.

When one previously scarce capability becomes abundant, another constraint becomes more important.

AI dramatically increases the supply of intelligence.

But it does not make everything abundant.

Trust remains scarce.
Attention remains scarce.
Distribution remains scarce.
Authentic relationships remain scarce.
Proprietary data remains scarce.
Physical infrastructure remains scarce.
Energy remains scarce.
Regulatory permission remains scarce.
Reputation remains scarce.
Accountability remains scarce.

And perhaps most importantly:

Human willingness to choose, trust and commit remains scarce.

These constraints increasingly determine where economic value can accumulate.

The strategic question therefore changes.

Instead of asking:

“How can we use more AI?”

companies should ask:

“What becomes scarce when intelligence becomes abundant?”

That is a fundamentally different starting point.

The AI-first paradox

The better AI becomes, the less strategically interesting AI itself may become.

This sounds paradoxical, but we have seen similar patterns before.

When electricity was first introduced, access to electricity was a competitive advantage. Eventually, electricity became infrastructure. Companies did not win because they were “electricity-first.”

The internet followed a similar trajectory.

Having a website was once differentiating. Then it became expected.

Cloud computing followed.

Mobile followed.

Each technology created enormous economic value. But as adoption increased, the technology itself gradually moved from differentiation toward infrastructure.

AI may travel this path faster than any previous technology.

The models are improving rapidly. Costs are falling. Capabilities are spreading. Open-source alternatives are developing. AI functionality is being embedded into virtually every major software platform.

If that continues, access to sophisticated intelligence will become less exceptional.

Which means the real strategic question is not whether your organization has AI.

Soon, almost everyone will.

The question is:

What do you have that becomes more valuable because everyone has AI?

From AI-first to scarcity-first

This suggests a different strategic philosophy.

Not AI-first.

Scarcity-first.

Start by identifying what AI is making abundant in your industry.

Then identify what remains difficult to obtain, replicate or scale.

Finally, reposition your business around those emerging constraints.

Consider professional services.

If analysis, research and document production become dramatically cheaper, clients may no longer pay premium prices simply for intellectual output.

But they may pay more for judgment.
Accountability.
Access.
Reputation.
Implementation capability.
Trusted relationships.

The value has not disappeared. It has moved.

Or consider software.

If AI makes software dramatically easier to build, the ability to write code becomes less scarce.

What becomes more important?

Distribution.
Installed customer bases.
Workflow integration.
Proprietary data.
Network effects.
Brand.
Switching costs.

The winners may not be the companies producing the most software.

They may be the companies controlling the scarce resources surrounding software.

AI strategy is necessary — but insufficient

None of this means companies should ignore AI.

Quite the opposite.

Organizations that fail to adopt AI may face a significant productivity disadvantage.

AI should become deeply embedded in operations, products and decision-making.

But that is increasingly operational necessity rather than strategy.

There is a difference.

Using AI can help you compete.

Understanding where value moves can help you decide where to compete.

And those are not the same question.

A company can execute an excellent AI transformation while simultaneously destroying its long-term differentiation.

It can automate the very capabilities it once charged customers for without building positions in the new sources of scarcity.

It can become dramatically more efficient at producing something customers increasingly perceive as a commodity.

That is the AI-first trap.

The boardroom question needs to change

Today, executives are frequently asked:

“What is our AI strategy?”

Perhaps that is already the wrong question.

A more important set of questions would be:

What is AI making abundant in our industry?
Which parts of our current value proposition will therefore become cheaper?
What becomes scarce as a consequence?
Where will customers still be willing to pay a premium?
Which scarce assets can we own, control or strengthen?

And how do we relocate our business toward them before our competitors do?

These questions move the discussion beyond technology adoption.

They move it toward strategy.

The next winners will not simply be AI-first

The AI revolution is real.

Its impact will be enormous.

And virtually every company will need to adapt.

But the winners of the next decade may not be the companies that put AI at the center of everything.

Because once AI becomes ubiquitous, saying that your company is AI-first may become about as strategically meaningful as saying that your company is cloud-first or internet-first.

The more interesting companies will understand the second-order effect.

They will use AI aggressively.

But they will build their competitive advantage somewhere else.

They will identify the resources, relationships, capabilities and constraints that become more valuable precisely because intelligence has become abundant.

That is the shift from AI strategy to Value Relocation Strategy.

And it leads to a very different principle:

Don’t build your strategy around what technology makes abundant. Build it around what abundance makes scarce.

Because that is where value moves next.

AI Isn’t Making Everything Abundant It’s Revealing What Remains Scarce

roger.meili@thevaluerelocation.org (Roger Meili) — Tue, 11 Aug 2026 20:55:25 GMT

The dominant narrative around AI is one of abundance.

Abundant intelligence.
Abundant content.
Abundant code.
Abundant analysis.
Abundant ideas.

And there is truth in that.

Tasks that once required hours of skilled human work can increasingly be completed in minutes. A first draft of a strategy paper, a market analysis, a software prototype, a campaign concept or a financial model can now be created at a fraction of the previous cost.

But this leads to a dangerous conclusion:

If AI makes more things abundant, value must simply disappear.

It doesn’t.

AI isn’t making everything abundant.

It is revealing what remains scarce.

When one scarcity disappears, another becomes visible

For decades, many businesses were built around the scarcity of expertise.

Knowledge was difficult to acquire. Analysis was expensive. Software development required specialized teams. Content production took time. Access to information created competitive advantage.

AI is rapidly reducing many of these constraints.
But markets do not stop valuing scarcity.
They relocate value toward whatever remains difficult to obtain.

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If producing ten strategic options becomes almost free, generating options is no longer the real challenge.

Choosing the right one becomes more valuable.

If producing software becomes dramatically cheaper, writing code becomes less differentiating.
Knowing what should be built — and earning the right to deploy it into real workflows — becomes more important.
If everyone can generate professional content, producing content itself becomes less scarce.

Attention, credibility and trust become more scarce.

AI does not eliminate scarcity. It changes its location.

And that is where future value moves.

The new scarcities

This shift is already visible across industries.

When intelligence becomes abundant, judgment becomes scarce.

When content becomes abundant, attention becomes scarce.

When recommendations become abundant, trust becomes scarce.

When digital products become easier to build, distribution becomes scarce.

When personalization becomes automated, authentic relationships become scarce.

When technology accelerates change, organizational capacity to act becomes scarce.

And when almost everyone has access to similar AI capabilities, proprietary context, data, access and execution become increasingly important.

This changes the fundamental question for strategy.

The old question was:

What valuable capability do we possess that others cannot easily reproduce?

The emerging question is:

What remains scarce after AI has made our current advantage abundant?

That is a much more uncomfortable question.

But it is also a much more powerful one.

Your current moat may be shrinking

Many companies still defend competitive advantages that were built for the economics of the previous era.

They invest in producing more content.
They expand analytical teams.
They optimize processes around expertise.
They protect knowledge that is becoming increasingly easy to replicate.
They improve capabilities whose marginal cost is rapidly approaching zero.
The problem is not that these capabilities suddenly become useless.

The problem is that they may no longer justify the same margins, differentiation or strategic importance.

A company can therefore become better at something that is becoming less valuable.

That is one of the most underestimated strategic risks of the AI transition.

Follow the scarcity

The strategic opportunity is not simply to “use more AI.”

It is to understand what AI changes in the economics of your market.

Ask:

What becomes dramatically cheaper?
What becomes easier to reproduce?
What advantage becomes accessible to everyone?

And then ask the more important question:

What becomes more scarce because of it?

That second question is where the opportunity begins.

Because the companies that win the next decade may not be those that adopt AI fastest.

They may be those that recognize earliest where value is relocating.

From production to judgment.
From information to trust.
From creation to distribution.
From capability to access.
From intelligence to execution.

This is the core idea behind The Value Relocation Project:

When AI makes yesterday’s scarcity abundant, value moves toward the constraints that remain.

Strategy therefore becomes an exercise in identifying those constraints before the market fully prices them.

AI is not making everything abundant.
It is showing us — with increasing clarity — what will become valuable next.

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

roger.meili@thevaluerelocation.org (Roger Meili) — Sun, 09 Aug 2026 15:42:19 GMT

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.

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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

Intelligence Is No Longer the Scarce Resource

roger.meili@thevaluerelocation.org (Roger Meili) — Mon, 03 Aug 2026 19:49:05 GMT

For centuries, intelligence was one of the world's most valuable economic resources.

Businesses competed to hire the smartest people. Universities competed to educate them. Consulting firms sold expertise by the hour, and organisations built their competitive advantage around specialised knowledge that few others possessed.

Intelligence was scarce.
And scarcity creates value.

Today, that assumption is quietly disappearing.
Large language models can write reports, analyse contracts, generate software, create marketing campaigns and answer complex business questions within seconds. Tasks that once required years of education and experience have become instantly accessible.

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This is not simply another productivity improvement.
It is a structural shift in the economics of business. For the first time in history, intelligence is becoming abundant. That does not mean human intelligence has become less important. It means access to intelligence is no longer the limiting factor.

The distinction matters.

When electricity became abundant, businesses stopped competing on access to power and started competing on what they could build with it.
When internet access became universal, competitive advantage no longer came from being connected. It came from how companies used that connectivity.

The same principle now applies to intelligence.

When every organisation can access world-class reasoning, writing, coding and analysis at marginal cost, intelligence itself becomes infrastructure rather than differentiation.

Many leaders are still treating AI as an automation tool.

They ask how many hours can be saved, how many employees can become more productive or how costs can be reduced.

Those are valid questions.
But they are not the strategic question.

The strategic question is this:

If everyone has access to intelligence, what becomes the new source of competitive advantage?

The answer is rarely "more intelligence." Instead, value shifts toward resources AI cannot easily commoditise.

Context becomes scarce because every organisation has unique customers, markets and history.
Judgement becomes scarce because AI can generate options, but leaders still need to choose the right one.
Trust becomes scarce because abundant content makes credibility more important than ever.
Execution becomes scarce because generating ideas is easy, while turning them into measurable outcomes remains difficult.
Relationships become scarce because business is ultimately built on confidence between people.

This explains why some companies will gain enormous advantages from AI while others struggle despite using the same technology.

The technology itself is increasingly available to everyone. Competitive advantage therefore moves elsewhere. History offers countless examples of this phenomenon.

When spreadsheets became available, accountants did not disappear. Their role evolved from manual calculation toward financial interpretation and strategic advice.

When GPS became standard, logistics companies no longer competed on knowing where vehicles were. They competed on designing better supply chains.

The scarce resource changed.

Exactly the same transition is now unfolding with intelligence. Companies that continue optimising around knowledge production may become increasingly efficient. But efficiency alone rarely creates lasting advantage when everyone can achieve it. The real opportunity lies in redesigning organisations around the new scarcities AI creates.

This requires more than deploying new software. It requires rethinking products, services, pricing models, organisational structures and even leadership itself. Because if intelligence is no longer scarce, businesses built around selling access to intelligence will inevitably face increasing pressure.

The organisations that thrive will be those that identify the next scarce resource before everyone else does. This is why AI should not primarily be viewed as a technology trend. It should be understood as an economic force that fundamentally changes where value is created.

The question is therefore no longer:

"How can we use AI?"

The more important question has become:

"What becomes valuable when intelligence is available to everyone?"

The companies that answer that question first will define the next generation of market leaders.

About The Value Relocation Project

AI Doesn't Destroy Value. It Relocates It.

roger.meili@thevaluerelocation.org (Roger Meili) — Mon, 03 Aug 2026 19:42:47 GMT

Every technological revolution has triggered the same fear: "This technology will destroy value."

The steam engine was expected to destroy craftsmanship. The internet was supposed to destroy retail. Cloud computing would make IT departments obsolete. And now artificial intelligence is said to destroy knowledge work.

But history tells a different story.

Technology rarely destroys value. It changes where value is created.

That distinction is far more important than it first appears.

When a resource becomes abundant, it inevitably loses economic value. But the value itself does not disappear. It moves to the next scarce resource.

For decades, information was scarce. Access to knowledge created competitive advantage. Companies invested in collecting data, documenting processes, hiring experts and protecting know-how. Information itself was valuable because obtaining it was difficult.

AI changes this equation fundamentally.

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“The Value Relocation Playbook” today!

Knowledge is becoming instantly accessible. Drafting a proposal, writing software, analysing data or producing marketing content increasingly costs close to zero. Intelligence is no longer the bottleneck.

Many conclude that this means value is disappearing.

It isn't.

It is relocating.

If intelligence becomes abundant, other resources become relatively scarcer—and therefore more valuable.

Trust becomes more valuable because people need to know which answers to rely on.

Context becomes more valuable because generic intelligence is easy, but understanding a specific business, customer or situation is not.

Execution becomes more valuable because ideas without implementation have never created results.

Original experiences become more valuable because generated content is infinite, while authentic interaction remains limited.

Relationships become more valuable because decisions are ultimately made between people, not algorithms.

This is the pattern that has repeated throughout economic history.

The invention of search engines did not eliminate the value of expertise. It rewarded those who could interpret information better than others.

E-commerce did not destroy retail. It shifted value from shelf space to logistics, customer experience and brand.

Cloud computing did not eliminate IT. It increased the importance of architecture, integration and cybersecurity.

AI follows exactly the same logic.

The mistake many organisations make is trying to protect yesterday's source of value.

They continue investing in activities that are rapidly becoming commodities while overlooking the new scarcity emerging around them.

The winners of the AI era will not necessarily be the companies with the most advanced models.

They will be the companies that understand where value is moving next.

That requires a different strategic mindset.

Instead of asking:

"How can AI make us more efficient?"

leaders should increasingly ask:

"Which of our competitive advantages become abundant through AI—and what becomes scarce instead?"

This shift changes strategic priorities completely.

It influences product development, pricing, positioning, hiring, customer relationships and investment decisions.

Efficiency is only the first-order effect of AI.

Value relocation is the second-order effect.

And second-order effects are where the largest opportunities usually emerge.

Perhaps the most important question for every executive over the next decade is therefore not whether AI will replace people.

It is whether their organisation recognises where business value is relocating before competitors do.

Because AI doesn't destroy value.

It simply changes where value lives.

About The Value Relocation Project

Everyone Is Asking the Wrong Question About AI

roger.meili@thevaluerelocation.org (Roger Meili) — Fri, 31 Jul 2026 20:40:36 GMT

For months, the conversation has revolved around the same issue:

"Which jobs will AI replace?"

It's an important question. But I don't think it's the most important one.

Every Revolution Moves Value — It Doesn't Just Change Work

Every major technological revolution has done far more than change the way people work. It has changed where economic value is created.

The steam engine shifted value from muscle power to machines. The internet shifted value from distribution to information. Cloud computing shifted value from infrastructure to software.

AI is no different.

It isn't just automating tasks. It is changing the economics of intelligence itself.

From Scarce to Abundant

For centuries, intelligence was a scarce resource. Access to it was gated — by education, by geography, by the sheer number of hours a skilled person could dedicate to a problem. Scarcity gave intelligence its price.

Today, that scarcity is dissolving. Intelligence is becoming abundant, on-demand, and nearly free at the margin.

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And here is the pattern that history keeps repeating: whenever something that was once scarce becomes abundant, value doesn't disappear. It moves.

It moves to whatever becomes the new bottleneck.

When muscle power became abundant through machines, value moved to those who could design, organize, and direct machines — engineers, managers, capital allocators. When information became abundant through the internet, value moved to attention, trust, and curation — the ability to filter signal from noise. When infrastructure became abundant through the cloud, value moved to the software layer — to whoever could turn raw compute into something people actually wanted.

The scarce resource always changes hands. The winners are the ones who see the shift before it's obvious.

The Real Management Question

This is why I believe the defining management question of this decade is not:

"How should we use AI?"

It is:

"Where does value move when intelligence becomes abundant?"

That's a fundamentally different question. "How should we use AI?" is a tooling question — it lives at the level of workflows, prompts, and productivity gains. It's useful, but it's tactical.

"Where does value move?" is a strategic question. It forces you to look past the tool and ask what becomes scarce next — because whatever that is will be where the next competitive advantage, the next premium, the next moat gets built.

If intelligence becomes cheap, then execution speed doesn't automatically become valuable — everyone gets faster. Judgment doesn't automatically become valuable either, if AI can approximate judgment too. The real bottlenecks may be subtler: trust, taste, accountability, the ability to ask the right question in the first place, access to real-world data that AI can't generate on its own, or simply the courage to make a decision and own its consequences.

Somewhere in that list — or somewhere we haven't named yet — sits the next scarce resource. And whoever identifies it early gets to build around it while everyone else is still optimizing prompts.

A Framework, Not a Hot Take

This single question — where does value move when intelligence becomes abundant? — has shaped the thinking behind a framework I've been developing over the past months. It's an attempt to give this shift a structure: to name the bottlenecks as they move, and to give leaders a way to think about strategy that isn't tied to any specific model, tool, or vendor.

Over the coming weeks, I'll share the core ideas here — not as AI tips or productivity hacks, but as a different way to think about strategy in the age of abundant intelligence.

Over to You

I'm curious:

What do you believe will become more valuable as intelligence becomes cheaper?

Trust? Taste? Data? Relationships? Something else entirely?

Drop your take below — I'd genuinely like to know what you're seeing.