Almost every board conversation I sit in on right now follows the exact same pattern.

The strategy decks are full of slides about what comes next. Teams present AI pilots, demonstrate new models, outline partnerships, and describe how the company will become an AI-enabled enterprise. Capital gets allocated to buy tools, hire specialists, and build capabilities.

The assumption behind all of this is simple: If we build enough of the future, we will win the future.

I think that’s only half the story.

The harder question isn’t what you need to build. It’s what you need to stop funding.

The Abundance Trap

Every major technology shift creates the same strategic problem. Something that was once scarce becomes cheap and abundant. When that happens, the source of competitive advantage changes completely.

The mistake most companies make is assuming their old architecture can simply absorb the new technology. It usually can’t.

When electricity hit early 1900s manufacturing, the breakthrough wasn’t simply swapping steam engines for electric motors in multi-story factories built around central drive shafts. As Henry Ford demonstrated at the Highland Park Plant, real advantage required completely redesigning the factory floor around single-story, machine-level motors. Success meant abandoning the entire physical architecture designed for steam.

We saw a similar transition with cloud computing. For years, enterprises talked about cloud adoption while continuing to operate as if physical infrastructure was their center of gravity. They ran isolated pilots, moved workloads selectively, and kept pouring capital into traditional data centers.

Capital One took a fundamentally different path, deciding to exit all eight of their physical data centers and move entirely to AWS.

Looking back at decisions like Capital One’s, a clear diagnostic test emerges for evaluating legacy assets during a technology shift:

Does this legacy asset give us true pricing power, or is it merely protecting a cost position that a competitor can now replicate without owning the asset?

If a technology shock enables a rival or a startup to achieve your hard-won operating efficiency through software or third-party infrastructure rather than capital ownership, your legacy asset is no longer a moat. It is pure overhead.

The Invisible Decisions

Distinguishing between a durable advantage and a stale constraint is where most executive teams struggle, because building something new is always easier than stopping something old.

A well-capitalized company can easily launch an innovation lab, hire specialists, and run pilots. Those actions feel like progress because they are visible and easy to report. The real strategic decisions are usually invisible, and far more painful:

• Budget reallocation: Which high-margin business unit gets its capital budget cut to fund an unproven initiative?
• Product sunsetting: Which legacy product line stops receiving engineering maintenance, even if it still generates predictable cash flow today?
• Incentive overhaul: Which compensation structure gets torn up because it rewards yesterday’s high-margin sales? This is what Microsoft faced under Satya Nadella when unwinding sales incentives built around upfront Windows and Office licenses to force the organization toward cloud consumption metrics.
• Executive trade-offs: Which successful leader loses headcount and airtime so a new division can grow?

These decisions spark intense internal conflict because yesterday’s success has a loud voice inside the building. The sales team doesn’t want a model that cannibalizes their commission. The engineering team doesn’t want its architecture replaced.

Un-funding the past is also fraught with immense operational risk, because navigating an abundance shift requires solving two completely different problems:

1. The Diagnostic Problem: Correctly identifying which legacy assets are constraints rather than moats.
2. The Sequencing Problem: Managing the speed of capital reallocation so you don’t starve your existing P&L before the new model generates real cash flow.

Look at Borders in 2001 for a textbook Diagnostic Failure. When e-commerce emerged, Borders signed an agreement handing its entire online storefront and fulfillment operation to Amazon. At the time, it looked like sensible cost discipline. Amazon had superior logistics, and web retailing yielded lower margins than physical stores. But Borders misjudged its physical superstores as its durable moat and treated the online channel as secondary. By the time they canceled the deal in 2008 to rebuild Borders.com, they had handed Amazon seven years of customer data, transaction history, and digital capability.

Conversely, look at General Electric’s pivot toward GE Digital a decade ago for a classic Sequencing Failure. GE got the diagnosis right. They correctly saw that industrial hardware was being commoditized by software. But diagnosing the problem didn’t solve the sequencing challenge. They pulled capital away from their core industrial footprint faster than their software engine could mature, creating severe organizational strain.

Navigating a technology shift requires getting both right. You have to diagnose what has become obsolete, and you have to sequence the transition without destroying the enterprise. Un-funding the past is not a magic formula for guaranteed victory. It is simply the price of entry for staying in the game.

Reorganizing Around the New Scarcity

AI is forcing this exact moment on us again. Code generation, analysis, synthesis, and routine execution are becoming abundant.

While companies will continue to differentiate through proprietary data, specialized domain workflows, and distribution, the underlying intelligence layers are rapidly commoditizing.

The strategic question isn’t simply: “What AI capabilities should we build?”

The much harder question is: “What parts of our current operating model exist solely because execution was scarce?”

Because those are the exact parts most likely to become your biggest constraints.

A company can drop AI tools onto every desk and still operate exactly the same way it did a decade ago. It can automate individual tasks while preserving the same bloated approval chains, legacy incentive models, and slow decision cycles. That is the modern equivalent of wiring an electric motor to a steam-era belt system. The technology changed, but the system didn’t.

The companies that capture real value from an abundance shock aren’t always the ones that adopt the technology first. They are the ones with the discipline to un-fund what no longer creates an advantage, so they can capitalize on what does.

The question every executive team should be asking right now isn’t: “How do we become an AI company?”

It’s: “What are we still funding simply because it made us successful yesterday?”

That’s where the real transformation begins.

In the next post, I’ll examine one of the most persistent myths in enterprise strategy: the idea that the giant companies that got displaced simply failed to see the future coming. The historical record suggests something much more interesting. Many of the giants that lost actually saw the future earlier than everyone else. They just couldn’t reorganize around it.

Every technological revolution creates abundance. Every economic revolution begins with a shortage.

This is the central paradox of progress. When a technological breakthrough occurs, our instinct is to gaze directly into the light of the explosion. We celebrate the elimination of a historic limitation. We conclude that the world has changed because what was once expensive and difficult has become cheap and ubiquitous.

But abundance does not automatically generate economic value. It frequently destroys it.

When a breakthrough makes a previously scarce resource abundant, the price of that resource collapses toward its marginal cost of production. Persistent economic rents do not pool in the sea of abundance. They accumulate around scarce constraints.

Every technological revolution creates two simultaneous stories. The first is the story of abundance. It dominates public attention because breakthroughs are highly visible. The second is the story of migration. It determines where enduring economic value accumulates because constraints are less visible.

The true economic revolution begins only when we look away from the breakthrough and start looking for the new shortage it has created. Technology does not eliminate constraints. It changes the architecture of constraints.

I. The Engine of Friction

To understand why this cycle repeats, we have to look at the foundational physics of operations. In his 1984 classic The Goal, Eliyahu Goldratt codified the Theory of Constraints, proving that any complex system has exactly one bottleneck that dictates its total throughput. What was true for twentieth-century manufacturing plants is now scaling exponentially across twenty-first-century digital ecosystems. Technology doesn’t eliminate Goldratt’s law. It simply accelerates the speed at which the bottleneck moves.

No business, industry, or economy is a single machine. It is a chain of connected steps operating at different speeds. Because the throughput of the entire chain is determined by the slowest necessary link, every system always has a limiting factor.

Eliminate one bottleneck, and the pressure instantly shifts to the next one. A constraint is not a glitch or a temporary market failure. It is the unavoidable reality of any interconnected operation.

At any given moment, the speed of an entire industry is dictated by this single element: the binding constraint. It is the anchor that determines the capability, speed, and profit margins of an era.

Historically, we have defined our major industrial shifts by the nature of these anchors. For centuries, the binding constraint of human productivity was raw muscle power. Then came steam and electrification, which made energy cheap and abundant. Suddenly, the bottleneck was no longer the ability to generate force, but the ability to coordinate parts. The binding constraint shifted from power to the physical layout of the factory floor.

More recently, we spent decades constrained by the speed and cost of moving data. Digital networks made distribution abundant and essentially free. The moment information became abundant, the binding constraint migrated to human attention and synthesis.

The illusion of the breakthrough is believing that solving the old constraint is the end of the journey. In reality, it is merely the opening of a new theater of scarcity.

II. The Factory Floor Reality

Consider a simple assembly line. If a factory has three stations—Station A produces 100 units an hour, Station B produces 10 units an hour, and Station C processes 50 units an hour—the total output of the factory is exactly 10 units an hour. Station B is the binding constraint.

Now imagine an inventor arrives with a miraculous machine that allows Station A to produce 1,000 units an hour. The temptation is to celebrate the infinite capacity of Station A. Investors pour capital into optimizing it even further.

But what is the actual output of the factory?

It remains exactly 10 units an hour.

In fact, making Station A ten times faster creates a crisis. Inventory piles up in front of Station B. The system becomes choked, chaotic, and inefficient. The abundance at Station A has not solved the factory’s problem. It has magnified the shortage at Station B.

An optimization made anywhere other than the bottleneck is an illusion.

When a technological revolution introduces radical abundance into an environment, it behaves exactly like that miraculous machine at Station A. It accelerates one part of the system to near-infinite speed. In doing so, it exposes the structural friction, institutional inertia, and physical limitations everywhere else. The binding constraint moves, and it moves with brutal clarity.

III. The Token Paradox

We are watching this exact pattern unfold at the software layer of the AI race. Over a remarkably brief window, architectural breakthroughs, open-source competition, and intense model distillation have cratered the cost of base intelligence. Based on published frontier-model API pricing benchmarks, the cost of standard foundation capabilities has collapsed from an early 2023 baseline of roughly $20 per million tokens down to fractions of a cent on modern value-tier endpoints. Raw text generation and basic reasoning have entered the sea of abundance, racing toward their marginal cost of production.

Yet, as the unit price of intelligence drops, enterprise AI budgets are expanding rather than contracting. This is not a contradiction; it is a volume explosion driven by a fundamental shift in architecture. As organizations move past single-turn chat interfaces, they are deploying autonomous, agentic workflows that consume millions of tokens to execute complex business processes.

According to the Menlo Ventures 2025 Enterprise GenAI Report, enterprise generative AI spend surged 3.2x over a single twelve-month cycle, climbing from $11.5 billion to $37 billion. This massive deployment of capital is scaling off an entirely new baseline budget. Longitudinal tracking from Andreessen Horowitz’s Enterprise AI Buyer Surveys across sequential waves charts this compounding expansion clearly: an initial survey wave recorded an expected 75% growth in GenAI budgets, which has since transitioned into an additional 65% year-over-year projected increase as average corporate allocations scaled up from $4.5 million to $7 million per enterprise.

Crucially, the massive influx of capital has completely altered how corporate buyers approach the technology stack. In 2024, corporate adoption was split roughly down the middle between building internal custom scaffolding and purchasing external platforms. Today, Menlo Ventures data reveals that 76% of enterprise AI use cases are purchased rather than built in-house.

Enterprises are shifting away from internal builds because cheap intelligence has not eliminated the enterprise bottleneck. It has exposed a new one, moving the binding constraint from intelligence generation to verification and trust. As I argued recently in The End of the Model Bet, an enterprise strategy built primarily on access to increasingly commoditized foundation tiers is becoming progressively less durable.

When an agent can generate thousands of lines of code or process exhaustive corporate document queues for pennies, the scarce resource is no longer raw output. It is confidence. Enterprises need deep structural ways to prove that AI-generated actions will not break production systems, violate regulations, leak sensitive data, or confidently fabricate errors.

Consequently, economic value is migrating rapidly away from the commoditized model layer toward enterprise orchestration, data governance, evaluation, and deep verification systems. The new scarcity is certainty.

IV. The Infrastructure Wall

The same mechanism is simultaneously reshaping the physical layer of AI.

For years, the strategic bottleneck was a shortage of advanced silicon. Capital rushed to solve it, and technology companies committed hundreds of billions of dollars to expanding compute capacity.

As compute capacity grows, the next binding constraints are increasingly power availability, grid interconnection, cooling, and data center capacity. The abundance of compute has not eliminated the scaling problem. It has transferred the pressure to the physical infrastructure required to operate that compute.

We are already seeing market capital follow those constraints. Investment is surging toward firm energy infrastructure, on-site power generation, grid upgrades, and specialized cooling technologies.

Crucially, the primary constraint here is no longer a lack of capital or interest—it is the grinding reality of institutional and physical queues. Data compiled by the Lawrence Berkeley National Laboratory (LBNL) Interconnection Queues Tracking reveals that the U.S. transmission queue remains historically gridlocked at over 2,000 gigawatts of active capacity. While a massive wave of unviable, speculative projects withdrew after facing escalating study costs, the viable infrastructure projects that remain face a median duration approaching five years to move from initial request to full commercial operation. The bottleneck has entirely shifted from the factory producing chips to the infrastructure clearing the grid.

This dynamic follows the historical script of industrial scaling. The shipping container made transoceanic transport radically cheaper, faster, and more predictable. The binding constraint shifted away from loading cargo onto ships and toward port infrastructure, logistics coordination, and warehouse networks. Over time, ocean freight became increasingly commoditized while value accumulated around the new constraints.

When value migrates, incumbents often fall into a capital allocation trap. They continue investing in yesterday’s advantage long after the market has stopped assigning it a premium. They mistake growing output for enduring value.

V. The Strategic Diagnostic: Mapping the Next Scarcity

For leaders building agentic enterprises, this framework is more than an explanatory lens. It must function as an active capital allocation tool. When designing an automation roadmap, leaders must subject every technical deployment to a rigorous three-step constraint audit:

1. What scarcity are we eliminating? Which historically expensive or slow capability is about to become abundant and practically free? (e.g., massive-scale financial invoice reconciliation, customer inquiry triage, or rapid application code generation).
2. Where does the bottleneck move next? Once that capability becomes effectively unlimited, where will work begin to queue instead? If an autonomous system can process 50,000 complex validation events an hour instead of 5, the new constraint is no longer production speed—it is the exception-handling capacity of human operators or the automated multi-party dispute verification engines required to act safely on those outputs.
3. Are we investing in the new constraint? Stop over-investing in the dissolved anchor. Shift enterprise budgets away from basic model access and redirect capital toward building the proprietary workflows, guardrails, and automated validation layers surrounding the secondary bottleneck.

If you optimize the system anywhere other than the newly exposed constraint, you are wasting capital to build an inventory pile-up.

VI. The Cost of Adaptation Lag

Why is this reality so difficult for leaders to act upon?

Because technology scales exponentially while institutions scale linearly. A technological breakthrough can happen within a year. Reorganizing companies, incentives, regulations, talent, and operating models to absorb that breakthrough can take a decade.

That gap is adaptation lag.

During this period, organizations use new technology to execute old processes. They bolt AI onto legacy workflows. They use a jet engine to pull a covered wagon.

Changing technology is relatively easy. Changing institutions is extraordinarily difficult. It requires redesigning incentives, operating models, governance, skills, and organizational architecture. This is closely related to the core premise of The Last Proprietary Advantage: as transient technical advantages erode, enduring corporate differentiation shifts entirely toward how organizations are wired to execute.

As a result, the binding constraint eventually ceases to be technical. It becomes institutional. The shortage is no longer capability; it is institutional capacity to absorb abundance safely and effectively.

The Core Synthesis

The central insight is simple. Technological breakthroughs systematically reconfigure the architecture of constraints. Because capital and institutions adapt more slowly than technology, enduring economic advantage migrates toward the newly binding constraints.

You do not need to predict the precise technical specifications of the next breakthrough to understand where value will emerge. You only need to identify today’s binding constraint, understand what abundance is about to dissolve it, and ask where the pressure will move next.

Every technological revolution begins by answering one question.

Not: What became abundant?

But: What became scarce because of that abundance?

Every generation mistakes the breakthrough for the destination. It never is. The breakthrough simply changes where scarcity lives.

The companies that keep investing in yesterday’s scarcity become yesterday’s winners. The companies that discover tomorrow’s scarcity build the next era.

That is where enduring advantage will be found.