Competition and the Durability of Extraordinary Economics
By Kevin Tanner | Chairman | CEO | Chief Investment Officer
Most commentary on artificial intelligence is framed through a macroeconomic lens. Economists, strategists, and analysts debate productivity, labor markets, inflation, electricity demand, fiscal policy, and the broader implications for economic growth. These are important questions, and they understandably dominate headlines.
Yet as investors, we don't own the economy. We own businesses.
For investors, that distinction may prove critical. The long-term returns earned by shareholders aren't going to depend on how artificial intelligence transforms society, but on how it reshapes the economics of the companies now racing to commercialize it.
Charlie Munger once observed that "Microeconomics is what we do, and macroeconomics is what we put up with." His observation may prove especially relevant in the coming years given what’s at stake. The most important investment questions surrounding artificial intelligence revolve less around macroeconomics than microeconomics – not whether AI succeeds, but how it is changing industry structure, competitive dynamics, pricing power, and ultimately, who captures the value created.
The extraordinary scale of today's artificial intelligence boom has attracted enormous attention. Far less appreciated are the structural changes it is setting in motion.
Throughout history, some of the world's most profitable businesses have enjoyed exceptional profitability because competition was limited. Those advantages have arisen for many different reasons. Patents, regulation, network effects, scale, switching costs, and powerful brands have each allowed companies to earn returns well above their cost of capital for extended periods. While many of these businesses are often described as "monopolies," their defining characteristic isn’t necessarily the absence of competitors. It’s the presence of durable pricing power and economic advantages that are difficult to replicate. Warren Buffett has long described these durable competitive advantages as "economic moats."
Economists have long recognized that the profitability of an industry depends not simply on the quality of its products, but on the intensity of competition among the firms that produce them. Businesses operating behind strong barriers to entry often earn returns well above their cost of capital because competitors find it difficult to challenge their position. When competition is limited, firms often possess sufficient pricing power to charge prices well above the marginal cost of serving an additional customer. Sustained over time, that pricing power gives rise to the excess profitability associated with monopoly-like market structures. Those excess returns – or what economists refer to as economic rents – are not merely the reward for innovation. They are also a reflection of industry structure.
This distinction is important. Innovation creates value. Competition determines how that value is divided.
In practical terms, competition matters because it determines how many alternatives customers possess. As alternatives increase, demand becomes more elastic, pricing power weakens, and more of the value created by innovation is transferred from producers to consumers.
When competition is limited, producers typically capture a large share of the economic benefits through higher prices, stronger margins, and exceptional returns on invested capital. As competition intensifies, however, those benefits are dispersed. Prices decline. Customers gain bargaining power. Returns on capital gradually migrate toward more normal levels. Society may become better off even as shareholders receive a smaller portion of the value that innovation creates.
For investors, this distinction is critical because long-term investment success isn't dependent on identifying transformative technologies. It is dependent on identifying businesses capable of sustaining exceptional returns on capital over long periods of time. It is entirely possible for a transformative technology to create enormous benefits for consumers while simultaneously producing less attractive economics for the companies commercializing it. Even if future profitability and growth prove somewhat less extraordinary than they might have been otherwise, outstanding businesses can still produce excellent long-term investment results so long as the underlying economics of the business continue to justify its valuation.
Competition does more than shape prices; it also shapes the flow of capital throughout an industry.
When industries generate extraordinary returns on invested capital, those returns attract investment. Existing firms expand. Companies operating in adjacent markets begin targeting the same opportunity. Suppliers increase production. Entrepreneurs search for new opportunities. Investors willingly finance ambitious new projects.
One of the central insights of microeconomics is that excess returns rarely persist indefinitely. Incremental investment gradually changes industry structure. New capacity expands supply. New entrants increase competition. Pricing power weakens. Returns on capital migrate toward more competitive levels. Ironically, the very profitability created by limited competition often becomes the force that ultimately erodes it.
The process can take years to unfold, but it has repeatedly reshaped industries that previously appeared nearly unassailable. Today, that possibility deserves particular attention because many of the world's most dominant technology companies are converging on overlapping opportunities. Artificial intelligence may ultimately reshape not only the technologies they develop, but also the competitive dynamics that have long sustained their extraordinary economics.
For much of the past two decades, many of the world's most profitable technology companies benefited from operating in largely separate markets characterized by monopoly-like economics. Search, social media, enterprise software, cloud infrastructure, e-commerce, premium consumer hardware, and advanced semiconductors each produced dominant businesses with extraordinary economics because their most profitable activities did not directly overlap. These companies were often grouped together as “Big Tech,” but economically they didn’t really compete with one another in their core businesses.
Increasingly, the same companies that once enjoyed dominant positions in separate markets are now converging on the same battlegrounds. Microsoft, Google, Amazon, Meta, OpenAI, Anthropic, Oracle, Nvidia, and others are all deploying enormous amounts of capital toward AI infrastructure, models, applications, distribution, and enterprise adoption. As a result, the boundaries between their businesses are beginning to blur.
Cloud companies are increasingly moving beyond their traditional role as infrastructure providers by developing their own AI models. Model developers are becoming enterprise software companies, while some are also exploring consumer hardware. Social media platforms are exploring commercial cloud infrastructure. Hardware suppliers are expanding into AI systems and networking. The competitive boundaries that once separated many of these firms are fading.
Each sees the opportunity as strategically essential. Each fears being left behind. Each still possesses the financial resources – or market access – needed to remain in the race. Together, they are now pursuing what must eventually become a much more contested prize.
This convergence has important microeconomic implications. The market often assumes artificial intelligence will simply extend the exceptional profitability of yesterday's dominant platforms. But the economics of monopoly are fundamentally different from the economics of other market structures. Extraordinary profitability depends not only on innovation, but also on limited competition. As more firms move into direct competition, customers are provided with alternatives, pricing power weakens, and the economic returns that once appeared unassailable eventually become more difficult to sustain. A company that dominates search, social networking, enterprise software, or cloud infrastructure can earn extraordinary returns so long as the competitive boundaries between those markets remain intact. The question is what happens when many of those territories begin to overlap.
This is fundamentally a question about market structure and competition. The largest technology companies are already beginning to compete more directly with one another. That competition may accelerate innovation and broaden adoption. It may also increase the total value artificial intelligence creates for society. Some firms may strengthen existing competitive advantages as artificial intelligence becomes more deeply embedded throughout their ecosystems, increasing switching costs and reinforcing customer relationships. Others may find those same advantages weakened by standardized capabilities and open models. Whether artificial intelligence ultimately reinforces or erodes those economic moats may prove far more important than many investors currently appreciate.
Competition often transfers value from producers to consumers. Better products, lower prices, and faster innovation may represent extraordinary progress for society while simultaneously rendering exceptional returns on invested capital more difficult to sustain. The interests of consumers and producers aren’t always aligned.
These competitive dynamics aren’t theoretical. The price of accessing artificial intelligence is already falling at an extraordinary rate. As models improve, computing becomes more efficient, and competition expands, users are gaining access to increasingly capable systems at a fraction of their previous cost. For consumers, this is overwhelmingly positive. Lower prices accelerate adoption, expand the range of economically viable applications, and increase the total value the technology creates for society. From the perspective of producers, however, rapidly falling prices raise a different question: who ultimately captures those gains?
Fig. 1: Costs of New Technology for End-Use Consumers, by Investment Cycle
Source: SaratogaRIM, Goldman Sachs. See Disclosures.
The implications extend well beyond lower prices. Artificial intelligence appears to be following a familiar pattern observed throughout economic history: the value created by a breakthrough technology continues to grow even as competition steadily reduces the price of accessing it. Those two developments are often confused. The first expands the technology's importance to society. The second determines how much of that value producers are ultimately able to retain.
Whether those gains ultimately accrue to producers, consumers, or both depends less on the technology than on competitive structure. Artificial intelligence is redrawing the competitive boundaries that have long separated many of the world's most profitable technology businesses. Companies that once dominated largely separate economic territories are increasingly making many of the same strategic investments – building data centers, developing AI models, expanding cloud infrastructure, and integrating artificial intelligence into virtually every product they offer.
Historically, many of these businesses enjoyed an unusually attractive combination of durable competitive advantages and relatively modest incremental capital requirements. Maintaining their competitive positions often required far less investment than building them in the first place. That combination helped produce the extraordinary returns on invested capital that investors came to associate with the largest technology platforms.
Convergence may begin to change that equation. As formerly distinct monopolies increasingly compete for the same opportunities, companies may find themselves committing growing amounts of incremental capital not merely to pursue new markets, but simply to defend the competitive positions they already possess. Offensive investments by one firm increasingly become defensive necessities for another. The critical question for shareholders therefore isn't just how much the addressable market expands, but how much additional capital must be committed merely to preserve the current competitive position of the business.
If that required reinvestment rises materially, the implications extend well beyond individual products or competitive moats. Even if dominant firms retain their leadership positions, the economics available to shareholders may change because maintaining those positions has become more expensive. The issue is not whether the businesses remain exceptional. It is whether exceptional businesses begin requiring substantially greater incremental capital simply to remain exceptional.
Each company may therefore be acting rationally in pursuit of its own strategic objectives, even as those decisions – in aggregate – reshape the competitive environment in ways that ultimately may render extraordinary returns more difficult to sustain.
These competitive dynamics are compounded by the extraordinary capital intensity of artificial intelligence. Based on reported 2025 capital expenditures and management guidance for 2026, the largest technology companies are expected to collectively invest well over a trillion dollars across the two-year period in data centers, advanced semiconductors, networking infrastructure, and AI models.
The same companies now competing for overlapping profit pools are driving one of the largest capital investment cycles in business history. If those investments continue expanding capacity and intensifying competition, they may eventually erode the economic foundations underlying their historically exceptional returns.
Industries characterized by high fixed costs often compete differently from those requiring relatively little capital. Once billions of dollars have been invested in factories, railroads, fiber-optic networks, semiconductor fabrication plants, or data centers, the central economic question gradually shifts. The challenge is no longer simply whether the infrastructure should be built. It becomes how profitably that infrastructure can be utilized.
Idle assets generate no return, and partially utilized assets often fail to earn adequate returns on the capital invested to build them. Once enormous fixed investments have been made, management's incentives begin to change. Recovering fixed costs often becomes more important than maximizing the margin on any individual transaction.
These economic dynamics help explain why businesses requiring continual reinvestment of enormous amounts of capital have historically found it more difficult to sustain exceptional returns on invested capital than businesses requiring comparatively little additional investment.
This dynamic has shaped many of history's most capital-intensive industries. Airlines discount seats that would otherwise depart empty. Hotels lower room rates during periods of weak demand. Fiber-optic networks that once promised extraordinary economics eventually competed aggressively for traffic across infrastructure that had already been built. In each case, pricing strategies increasingly reflected the imperative to maximize capacity utilization rather than preserve pricing power.
Artificial intelligence may eventually produce similar competitive dynamics. At that point, the emphasis will shift from building additional capacity to earning an acceptable return on existing capacity.
Convergence is already emerging throughout the technology stack:
Microsoft is integrating AI models throughout its enterprise software franchise, bringing it into more direct competition with OpenAI, Google, and Anthropic for enterprise AI adoption.
Google is embedding Gemini across Search, Cloud, and Workspace, further blurring the historical boundaries between search, enterprise software, and cloud computing.
Amazon is extending beyond its traditional role as a cloud infrastructure provider into AI models and proprietary silicon, bringing it into more direct competition with companies that were once its customers and partners.
Meta is extending beyond its advertising-driven social platforms into AI models, developer tools, and custom silicon, bringing it into more direct competition with model developers and semiconductor companies.
OpenAI is no longer simply a foundation model developer. It is expanding into enterprise software and consumer hardware, increasingly competing with companies that were once customers, partners, distributors, and investors.
Oracle, once viewed primarily as an enterprise database company, has emerged as a major provider of AI infrastructure, competing for workloads once expected to remain concentrated among the hyperscale cloud providers.
Nvidia increasingly competes not only with traditional semiconductor companies but also across networking, systems, software, and cloud services, while many of its largest customers simultaneously develop proprietary AI accelerator chips of their own. Amazon, Microsoft, Google, and Meta remain among Nvidia's largest customers, yet each is investing heavily in custom silicon, at least in part to reduce dependence on Nvidia.
Although these companies continue to possess significant competitive advantages, their future growth increasingly depends on succeeding in markets where many of their strongest rivals are pursuing the same opportunities. From a microeconomic perspective, this distinction is important. Extraordinary returns are much easier to sustain when firms compete in separate markets. It becomes much more difficult when those same firms increasingly compete for the same customers.
Taken together, these developments suggest that the competitive dynamics described throughout this essay are already well underway. How long this process will take to unfold, and even whether it ultimately results in excess capacity, remains uncertain. Demand for artificial intelligence may continue expanding for many years, and today's enormous investments may prove entirely justified. Whether today's extraordinary economics prove durable will depend on how industry structure evolves from here.
History and economic theory both suggest that investors should be wary of becoming overly confident that today's extraordinary economics will persist indefinitely. Capital markets perform one of society's most valuable functions by directing financial resources toward attractive opportunities. Yet in doing so, they also possess an inherent tendency to finance away the very scarcity that often made those opportunities exceptionally profitable in the first place. Exceptional returns attract capital. Capital creates capacity. Capacity intensifies competition. Over time, competition often transfers an increasing share of the value created by innovation from producers to consumers. In doing so, it can render extraordinary profitability increasingly difficult to sustain.
Recent developments in artificial intelligence already offer an early illustration of these dynamics. Even as the technology continues to improve at a remarkable pace, the cost of accessing increasingly capable models has fallen dramatically as competition has intensified and efficiency has improved.
For investors, these dynamics deserve careful consideration. Today's market has become extraordinarily concentrated in companies whose valuations implicitly assume that historically exceptional profitability will persist. Artificial intelligence may become one of the most valuable technologies ever developed, transforming industries, improving productivity, and creating enormous benefits throughout the global economy. Transformative technologies often change the world. They also change the industries that commercialize them.
Charlie Munger reminded us that investors own businesses, not economies. The central investment question is not whether artificial intelligence creates economic value, but whether it strengthens or weakens the durable competitive advantages that determine who ultimately captures that value.
Editor's Note
This essay is part of our ongoing Latticework series, which examines today's investment environment through the lens of multiple disciplines. Previous essays explored the same broad questions through the lenses of behavioral finance and neuroscience. This essay approaches those questions through the lens of microeconomics, focusing on how competition and market structure shape who ultimately captures the value created by innovation.
Complex investment problems rarely yield to a single discipline. Our objective is not to replace one framework with another, but to build a broader latticework of mental models in which each discipline contributes insights that the others simply can’t.
The next essay in this series will examine these same questions through the lens of financial statement analysis, focusing on how accounting determines when economic benefits and costs become visible to investors.