When Monopolies Converge

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?

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 hyperscaler 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.

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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.

The Neuroscience of the Sirens' Song

Homer was clearly on to something. Modern neuroscience suggests that physiological mechanisms in our brains help explain many of the behaviors documented by behavioral finance.

One intriguing finding is that our brains don’t process anticipated gains and losses symmetrically. When we imagine future rewards, a subcortical region known as the nucleus accumbens becomes active. This region is rich in dopamine and is associated with reward, motivation, and reinforcement. The anticipation of gains activates this system, generating a physiological signal that can amplify optimism before any gains are actually realized. This suggests that the anticipation of future rewards can become reinforcing in its own right – a dynamic that may help explain why speculative booms often become emotionally intoxicating. Notably, this is the same reward circuitry that has been implicated in gambling addiction.

Anticipated losses, however, tend to be processed through completely different neural systems, including regions such as the amygdala and insula, which are more closely associated with threat detection, aversion, fear, and bodily discomfort. In this sense, our brains simply don’t respond to the prospect of losses the same way we do to gains.

That asymmetry may help explain a familiar feature of financial markets: investors often worry the least about risk after markets have risen substantially and become much more risk averse after markets have already fallen. During prolonged advances, rising prices do more than increase wealth; they reinforce confidence and the expectation of future returns. As investors imagine future gains, dopamine-mediated reward systems are repeatedly activated. Risk doesn’t disappear, but it becomes much easier to ignore. The psychological weight assigned to potential rewards begins to exceed the weight assigned to potential risks. Existing risks can remain right in front of us, but they become much easier to discount, rationalize, or overlook.

When markets finally do reverse, the process often flips in the opposite direction. As anticipated gains fade, so too does the neurological reinforcement associated with reward circuitry. Risks that were present all along suddenly come into sharper focus. Investors often experience this as a new realization, but the underlying risks usually haven’t changed anywhere near as much as perceptions have. The rocks never moved. The Sirens' song simply made them easier to overlook.

This observation may initially appear inconsistent with one of the most influential findings in behavioral finance. Daniel Kahneman and Amos Tversky famously demonstrated that people tend to experience losses more intensely than equivalent gains. Losing one hundred dollars generally hurts more than gaining one hundred dollars feels good. This principle, known as loss aversion, became a cornerstone of Prospect Theory and helped explain a wide range of investor behavior.

Yet the two ideas are not at all contradictory. They’re simply describing different stages of the decision-making process.

The neuroscience of reward anticipation helps explain why investors become captivated by the potential for gains before outcomes are known. Prospect Theory helps explain what happens after gains and losses begin to feel real. One helps explain why investors steer toward the Sirens. The other helps explain what happens when the rocks suddenly come into view.

Prospect Theory contains another important insight that helps explain investor behavior during market declines. The pain associated with realizing a loss can become so psychologically powerful that many investors prefer a gamble offering the possibility of recovery over accepting a certain loss. Decision-making shifts away from discipline and toward recovery. Rather than accept a loss, investors frequently choose to take on additional risk in the hope of getting back to even. This helps explain why declining markets often provoke not caution but escalation – averaging down, increasing exposure, or doubling down on positions that are already under water. At some point, however, as losses deepen and the prospect of recovery begins to feel increasingly hopeless, this risk-seeking behavior can give way abruptly to capitulation, with investors shifting almost instantly from trying to get back to even to simply preserving what they have left.

During speculative advances, anticipated gains activate reward systems that focus attention on opportunity and reinforce optimism. As prices rise, investors increasingly imagine future rewards, and those imagined rewards themselves become psychologically persuasive. Risks remain present, but they carry less weight. The Sirens' song grows louder.

When markets reverse, the same investors often undergo a dramatic shift in perception. The anticipated rewards that once dominated attention recede while potential losses become harder to ignore. Loss aversion begins to exert greater influence. Investors become more sensitive to downside risk, more focused on preservation, and less willing to take on uncertainty. In many cases, they become most risk averse only after prices have already fallen substantially.

This helps explain one of the enduring paradoxes of financial markets. Investors often become least concerned about risk when risk is greatest and most concerned about risk after the bulk of the damage has already been done. The underlying fundamentals generally change far less than sentiment, attention, and perception.

Seen through this lens, speculative cycles are not simply stories about greed and fear. They are stories about how humans process opportunity and danger. Speculative cycles reflect the interaction of distinct neurological and psychological mechanisms. Reward-seeking systems encourage investors to focus on opportunity during booms, while loss aversion and threat-detection systems become increasingly dominant during busts.

This is also where value investing and contrarianism enter the discussion. The discipline required to buy when markets are plummeting, or to resist what others have been chasing, runs directly against the emotional currents created by markets themselves. Warren Buffett captured the idea perfectly: "Be fearful when others are greedy and be greedy when others are fearful." The difficulty is that this advice sounds simple only in hindsight. In real time, greed often arrives wrapped in confidence, consensus, and recent gains, while fear arrives wrapped in falling prices, uncertainty, and the sudden recognition of risks that were present all along. Successful value investing is less about superior intelligence than about resisting the powerful neurological and emotional forces that drive the crowd – and that challenge is much less analytical than it is emotional.

Homer understood the practical implication thousands of years before neuroscience existed. Odysseus couldn’t assume he would remain rational once the Sirens started singing. He knew that temptation itself would impair his judgment. His solution wasn’t intelligence, courage, or willpower. It was preparation. He bound himself to the mast before the music began.

Investors have always faced similar challenges. The greatest dangers in markets rarely arise because risks are invisible. More often, they arise because compelling narratives make those risks easier to ignore. The rocks are always there. The challenge is remembering that fact when the Sirens are singing loudest.

The Song Remains the Same

When Sirens Sing: How Market Cycles Seduce Investors

By Kevin Tanner | Chairman | CEO | Chief Investment Officer

Let’s face it, there are stretches when markets can make caution feel irrational. Common sense falls by the wayside and, with each new market advance, restraint comes to be viewed less as discipline than missed opportunity. “As long as the music is playing,” former Citigroup CEO Chuck Prince famously said near the peak of the pre-2008 credit boom, “you’ve got to get up and dance.”

The dancer in question, Chuck Prince, resigned just months later in 2007 as the unfolding subprime crisis revealed Citigroup to be far more fragile than markets believed. A year later, the firm required tens of billions in federal support to survive. Prince’s words endured because they captured something deeper than greed.

During speculative cycles, the pressure to participate becomes psychologically overwhelming – even to a top banker who, one would think, knows better – precisely because parts of the story are true. Rising stock prices create social confirmation. Consensus itself becomes emotionally persuasive. The longer trends persist, the more investors mistake momentum for inevitability and price appreciation as evidence that their underlying assumptions are correct.

Each Siren’s song has fresh lyrics, but they all conform to a pattern Homer described in The Odyssey, an epic in Greek literature written more than 2,500 years ago.

In Homer’s Odyssey, the Sirens were not predators in the ordinary sense. They did not attack ships or overpower sailors by force. Instead, they waited patiently on a rocky island surrounded by the wreckage of those who came before and sang seductive songs. Ancient descriptions vary, but the essential idea remains the same: the Sirens overwhelmed judgment through persuasion. They promised knowledge, revelation, insight. They sang directly to what each sailor most wanted to believe about himself, convincing him that he was sophisticated enough to approach safely and exceptional enough to understand what others could not.

That’s what made them deadly.

The sailors steered themselves directly into the rocks, rendered unable to exercise judgment. Odysseus understood this before he ever approached. He recognized that the real danger wasn’t ignorance, but exposure. Once the music started, he knew he would no longer be able to trust his own judgment. So he prepared in advance, establishing his own precommitments: wax in his crew’s ears, rope lashing himself to the mast, strict instructions that no matter how violently he begged to be released, they must ignore him.

Every frothy market has its own Sirens.

Today they sing about artificial intelligence, momentum, technological inevitability, and the promise that innovation will overcome every structural constraint confronting the global economy. Their song is seductive because the underlying story contains elements of truth. AI is transformational. It may eventually raise productivity, reshape – even disrupt – industries, and create extraordinary long-term economic value for society.

That said, history reveals that the most dangerous market manias are never built on fiction alone. Every prior technological step-change in modern history has left behind its own coastline of bleached bones. Railroads transformed America. Electrification transformed industry. The internet restructured modern civilization. Yet each also produced waves of overinvestment, speculation, leverage, and enormous losses for investors. The lesson: technology usually succeeds. Most investors don’t.

The pattern is not theoretical. Cisco was one of the defining companies of the internet era, and its business ultimately validated much of the optimism embedded within it. Yet investors who purchased the stock at the peak in 2000 waited more than twenty-five years to get back to even in nominal terms – and they’re still waiting if you take inflation into consideration. Likewise, Amazon went on to become one of the most dominant companies in history but still declined more than 90% during the unwind of the same cycle before compounding extraordinary returns from a much lower base. Others, like Enron, were not mispriced versions of real success but outright illusions where narrative, complexity, and momentum completely obscured the underlying lack of substance.

Different outcomes, but a common thread: in episodes where narrative and momentum dominate market pricing, technological progress, intrinsic value, and investor returns can diverge dramatically. These distinctions matter now because markets are already well past being merely optimistic.

The preceding era was defined by secular disinflation, globalization, cheap energy, favorable demographics, falling interest rates, declining term premiums, and a steadily declining cost of capital.

In the aftermath of the Global Financial Crisis, those forces were amplified by zero-interest-rate policies, quantitative easing, abundant liquidity, and repeated central-bank intervention. Together, they created an ideal environment for long-duration growth assets, venture capital, leveraged finance, passive concentration, and buy-the-dip psychology to flourish.

But the regime that produced those conditions no longer exists.

Since the pandemic, inflation has structurally shifted upward. Long-term interest rates broke out of a forty-year downtrend five years ago. Fiscal discipline has steadily deteriorated across much of the developed world. Geopolitical fragmentation has also intensified. Supply shocks are recurring faster than previous dislocations can be fully rectified. The cost of capital is rising, and the tide that once swept everything in a froth of cheap money has long since turned. Yet current market pricing remains anchored to assumptions formed under a very different set of economic conditions.

The great vulnerability today is not merely that stocks are expensive. It is that large parts of the financial system remain priced for an interest rate environment that no longer appears consistent with the more inflationary world around us today. Asset prices, private market marks, refinancing assumptions, and investor behavior all remain dependent on the belief that inflation will fade back toward its old range, that rates will eventually revert downward, that liquidity will remain abundant, and that technological progress will overwhelm all constraints.

AI enthusiasm increasingly appears to be sustaining assumptions inherited from that earlier environment. That is today’s Sirens’ song. AI seems to offer investors a seductive answer to every structural problem. Productivity will offset inflation. Automation will solve labor shortages. Growth will outrun deficits. Scale will justify valuation. Technology will protect margins. Innovation will render old rules obsolete.

Some of that may ultimately prove true. But in the meantime, investors appear to be pricing a collection of plausible outcomes as though they were inevitable.

When bubbles form around transformative technologies, they rarely emerge from pure fiction. As OpenAI cofounder Sam Altman himself observed, speculative manias often begin with “a kernel of truth” powerful enough to justify genuine optimism before eventually encouraging extraordinary excess.

AI is anything but asset-light. Already, the buildout represents one of the largest capital expenditure cycles in modern American history. Datacenters require enormous amounts of electricity as well as vast quantities of land, water, chips, cooling systems, transmission infrastructure, financing, and time. They compete for capital in a world already strained by deficits, rearmament, reshoring, energy transition, and demographic pressure. Our digital future is increasingly constrained by physical bottlenecks.

Large infrastructure projects have a long history of requiring more time and money than initially projected. Permitting delays, labor shortages, rising input costs, financing constraints, and political opposition have repeatedly challenged the economics of major buildouts. History offers little precedent to assume today’s AI buildout will prove uniquely immune to these realities.

Altman himself has openly acknowledged both the scale and speculative nature of what is unfolding. In discussing AI infrastructure financing, he suggested that the world may need “a new kind of financial instrument” to fund the enormous “compute” buildout ahead. Elsewhere, when asked whether investors had become overexcited about AI, he compared the current environment to the dot-com bubble and observed that “somebody is going to lose a phenomenal amount of money.” The warning was striking precisely because it echoed the history of prior transformative technological booms: enormous societal benefit often accompanied by substantial capital destruction for investors.

Consider the Stratos project underway in Box Elder County, Utah. Stratos is projected to occupy a 40,000-acre campus – more than twice the size of Manhattan – while operating an integrated compute-and-power system utilizing roughly nine gigawatts of electricity. For perspective, that’s roughly 4.5 times the output of Diablo Canyon, California’s last operational nuclear plant, which generates about 9% of the state’s electricity. Nor is Stratos the largest such project currently under development. It is merely one visible example of a global infrastructure race whose scale would have seemed unimaginable only a few years ago.

Today’s AI boom looks much less like traditional software cycles than it does the railroad, electrification, and fiber-optic buildouts that transformed earlier generations. This discussion isn’t merely macro. There are important micro ramifications as well.

Investors should recognize that many of the Magnificent Seven have spent years enjoying the economic benefits associated with dominant or near-monopoly positions in largely separate markets. Search, social media, enterprise software, cloud computing, e-commerce, premium consumer hardware, and advanced semiconductors each generated extraordinary returns with relatively limited direct competition from one another. Increasingly, however, many of these same firms are now converging on the same AI opportunity, deploying enormous amounts of capital in pursuit of what must ultimately be a contested prize. As this trend continues, investors may find it increasingly difficult to justify extrapolating the economics of yesterday's monopolies into a future that looks considerably more competitive.

This convergence is not incidental. These kinds of generational capital expenditure booms consume enormous amounts of both capital and physical resources and are inherently inflationary. They invite overcapacity, require financing, and are vulnerable to rising interest rates. Buildouts like we’re seeing now tend to end up rewarding society far more than the investors who finance them in aggregate – especially those who show up late to the dance.

Historically, these are the kinds of conditions in which momentum has proven especially dangerous.

Since the pandemic trough, and especially following the public release of ChatGPT, markets have increasingly become momentum-driven. Leadership has narrowed aggressively into a concentrated group of perceived AI beneficiaries, with the Magnificent Seven now accounting for roughly a third of the S&P 500's total market capitalization. If Alphabet, Amazon, Meta, and Tesla were classified as technology companies, the broader technology sector would represent more than half of the index's market value.

Passive capitalization weighting has mechanically directed larger flows toward the same winners as their market capitalizations expanded. Relative performance pressures have encouraged active managers to chase the same leadership rather than risk underperforming increasingly concentrated benchmarks.

These dynamics may be more powerful today than during previous speculative episodes. Passive mandates now represent a far larger share of equity ownership than they did during the late-1990s technology bubble, increasing the importance of benchmark-driven flows and concentration effects.

This is how we arrive at environments where a handful of companies increasingly dominate benchmarks, passive flows reinforce concentration, and owning the same leadership names becomes increasingly difficult for many investors to ignore. It is classic herding.

Retail speculation has further amplified these dynamics over recent years. Options activity, thematic trading, and social reinforcement loops have accelerated the tendency for rising prices themselves to become interpreted as confirmation of the underlying bullish narrative. In many cases, valuations at the individual company level have expanded far more rapidly than their capacity to generate cash flow, suggesting that narrative reinforcement, behavioral extrapolation, and momentum themselves have become primary drivers of market pricing.

Markets are especially vulnerable to these dynamics because many of the biases governing human decision making become most powerful after prolonged advances. Investors naturally extrapolate recent experience into the future. Consensus becomes psychologically reassuring. Career risk discourages deviation from prevailing narratives while repeated reinforcement gradually erodes skepticism. Over time, investors do not merely begin expecting higher prices; they begin treating the continuation of the trend itself as evidence that their underlying assumptions must be correct.

Momentum works when price trends become self-reinforcing. Rising prices attract attention. Attention attracts investment flows. New investment reinforces the winners. The winners dominate market cap-weighted indexes. Passive flows send even more money their way. Backward-looking performance is perceived as evidence. Evidence then becomes narrative. Narrative becomes consensus.

A related phenomenon occurs when market prices begin influencing the behavior of investors, companies, lenders, and consumers, temporarily shaping the very fundamentals they are supposed to measure. Under certain conditions, prices become more than outputs. They become inputs into the feedback loop. Recent private funding rounds illustrate the point. Rising private valuations at Anthropic generated mark-to-market gains for both Alphabet and Amazon through their minority ownership stakes, significantly boosting reported earnings despite no corresponding change in operating performance and no realization of cash flows. In situations like these, market prices can begin influencing the very fundamentals investors are using to evaluate them.

In influential research on momentum investing, behavioral finance scholars Mark Grinblatt and Tobias Moskowitz found that investors often adapt slowly to major structural changes in the economic environment, allowing mispricing and market trends to persist longer than traditional financial theory would predict. Investors tend to underreact to regime shifts, especially when new realities conflict with entrenched narratives and existing consensus. The persistence of post-GFC assumptions in today’s market may be an example of that dynamic.

These pressures don’t just operate at the individual psychology level. They become embedded institutionally. Professional managers are rarely rewarded for diverging too early from consensus, but they are often punished for underperforming it. Over time, momentum stops being merely a behavioral phenomenon and becomes endogenous to the system itself.

Eventually, investors stop asking, “What is this worth and how is it going to compound from these levels?” and start asking, “How can I afford not to own it?”

That transition is psychological, not analytical.

Grinblatt and Moskowitz also documented how the very forces driving momentum can sow the seeds of reversal when expectations outrun economic reality. Technological revolutions often validate the underlying innovation while simultaneously destroying capital for investors who financed the boom at inflated prices.

Stanley Druckenmiller once described selling technology stocks near the peak of the dot-com bubble because valuations had become absurd, only to watch them continue rising. He understood the danger. He knew better. Yet he eventually bought them back near the peak anyway. “I just had to play,” he later admitted. “I couldn’t help myself.” Decades earlier, Yale economist Irving Fisher described the stock market as having reached a “permanently high plateau” just days before the end of the great momentum-driven rally that preceded the stock market crash of 1929.

These are just a few examples of the Sirens’ song at work.

They don’t make intelligent people stupid, but they can make discipline feel irrational.

The core assumption markets refuse to abandon is that the post-2008 framework remains the relevant one. It’s not. As I wrote about last quarter, the Five Ds – deficits, demographics, deglobalization, defense spending, and datacenter capital expenditures – are already firmly entrenched and placing upward secular pressure on inflation and longer-term interest rates. Massive fiscal irresponsibility amplifies all of them. Recurring supply shocks are not the cause of the new economic order; they merely act as catalysts and accelerators within it.

That distinction is important – temporary shocks fade; structural forces don’t.

This leaves markets in a precarious position. They remain priced for a world of lower rates, expanded multiples, and abundant liquidity while the real world is increasingly defined by rising capital costs, increasing fiscal strain, geopolitical fragmentation, energy constraints, and structurally persistent inflationary pressure.

These mismatches can coexist for some time. Markets have always shown the ability to remain expensive longer than skeptics expect, especially when powerful technological narratives become intertwined with momentum and institutional reinforcement. AI enthusiasm likely still has plenty of runway left. Capital continues to flow toward perceived winners, rising prices continue to validate consensus, and strong performance continues attracting additional inflows. Momentum tends to persist right up to the point that it doesn’t.

Warren Buffett once quoted the late Barton Biggs as saying, "A bull market is like sex. It feels best just before it ends." Colorful as the observation may be, it captures something important about speculative cycles. They’re often most seductive just before reality begins to set in.

Historically, this is how momentum-driven markets tend to end. They rarely collapse because investors suddenly lose faith in any underlying technology. More often, financing conditions tighten, expectations outrun economic reality, or leadership becomes so concentrated that even modest disappointments begin forcing investors to reassess previous assumptions. Some cycles unwind violently as reflexive flows suddenly reverse direction. Others simply spend years going nowhere while fundamentals slowly catch up to valuations. Occasionally, enthusiasm diffuses more gradually across the market. But regardless of the path, the same momentum dynamics that once amplified the advance eventually stop functioning as an accelerant.

The bottom line is that the longer faulty assumptions remain embedded in pricing, and the further reality diverges from perception, the more fragile the financial system becomes.

Odysseus survived the Sirens not because he was immune to their songs, but because he understood he would not be immune when the time came. He did not rely on willpower. He had himself bound to the mast before he heard the music.

That is what investment discipline is supposed to do.

Quality investing isn’t a short-term trading scheme. Valuation discipline doesn’t imply timidity. Strong balance sheets, durable cash flows, reasonable prices, and margins of safety are all important precommitments designed to protect investors precisely during the moments when judgment becomes most difficult. The closing lines of the Sirens’ song are always the most enchanting.

They’re in full chorus now.

The Sirens’ song is seductive precisely because it is rooted in something real. But never forget: the rocks are just as dangerous whether the songs ring true or not.

Latticework Series Introduction

The letter below as well as the two essays that follow have been extracted from our 2026 Q2 Report.

Every market cycle develops its own logic.

During prolonged bull markets, investors gradually come to view prevailing conditions as normal and permanent. Assumptions that may have begun as observations evolve into articles of faith. Valuations once considered aggressive come to be seen as reasonable or ignored altogether. Risks that once demanded careful scrutiny are increasingly dismissed. The longer a particular environment persists, the easier it becomes to believe it will persist indefinitely.

History tells us it won’t.

Financial markets are shaped by regimes. Interest rates rise and fall. Business cycles swing from boom to bust. Technologies emerge, mature, and – believe it or not – sometimes even disappoint. Competitive advantages strengthen and weaken. Yet investor perception and behavior often adapt far more slowly than the environment itself. Obsolete assumptions have driven some of history’s most consequential busts.

Several examples of this phenomenon were on display during the second quarter of 2026. Artificial intelligence remained the dominant investment narrative. The surge in capital expenditures tied to AI infrastructure accelerated. Equity valuations remained extreme despite persistent fiscal concerns, heightened geopolitical uncertainty, and the lingering economic repercussions from the conflict with Iran, including its effects on global supply chains. At the same time, questions about the long-term economics underlying today’s AI buildout continued to grow. Nevertheless, investors remained remarkably willing to look beyond these near-term uncertainties and focus instead on a distant and highly uncertain future.

Coming into the second quarter, the forces that had been shaping markets since the emergence of ChatGPT reached a new level of intensity. By April and May, momentum itself had become one of the market's defining characteristics, reinforcing an increasingly narrow leadership driven by the AI buildout. Strong returns attracted additional capital, which in turn fueled further gains, encouraging even more crowding into a remarkably narrow group of companies perceived to be the primary beneficiaries of the AI investment boom. Momentum strategies delivered record relative outperformance, market leadership became increasingly concentrated, and skepticism became increasingly difficult to sustain as each new advance appeared to validate the prevailing narrative.

One of the more revealing developments of the quarter was not simply the continued strength of equity markets, but the extraordinary volume of new capital being raised. SpaceX completed the largest initial public offering in history before returning less than two weeks later for an additional $25 billion in debt financing. OpenAI and Anthropic filed for public offerings at valuations expected to exceed $1 trillion each. Alphabet raised approximately $85 billion through a secondary offering. At the same time, corporations including Amazon, Oracle, Meta, and others continued tapping debt and equity markets to finance the enormous infrastructure investments required to support artificial intelligence. Together, these transactions are emblematic of one of the largest capital-raising waves in modern market history.

These developments raise an obvious question. Many of these companies have existed for years, and in some cases decades. Most have enjoyed ample access to private capital. Several possess balance sheets that leave little doubt about their ability to finance future growth. Why, then, is so much stock suddenly appearing for sale?

While IPO activity by itself isn’t a reliable timing indicator, major market peaks have frequently been accompanied by a surge in new issuance as founders, venture capitalists, and corporate insiders seek to capitalize on favorable market conditions. There are plenty of reasons to sell stock. Thinking that prices will rise much further is generally not one of them. These dynamics tend to emerge late in market cycles.

The same conditions that encourage investors to buy frequently encourage insiders to sell. Throughout history, periods of extraordinary optimism have often been accompanied by surges in equity issuance, debt issuance, and other forms of capital raising. Railroads, radio networks, telecommunications infrastructure, internet companies, and housing developments all required enormous amounts of financing. The willingness of investors to supply that financing – through equity, credit, and an expanding array of financing structures – has frequently transformed promising innovations into speculative booms. All of which, I might add, were followed by busts.

None of this diminishes artificial intelligence’s transformative potential. History's greatest technological revolutions were always legitimate. Railroads reshaped transportation, electrification transformed industry, and the internet revolutionized communication and commerce. Speculation doesn’t diminish the significance of the underlying technology.

Investors have never struggled to identify important innovations. The challenge they seem to run into time and time again has been distinguishing between technological promise and investment reality. Financial history repeatedly demonstrates that revolutionary technologies often coexist with speculative excess. The most dangerous periods tend to emerge not when innovation is absent, but when enthusiasm and hype become so widespread that skepticism itself falls by the wayside.

The quarter's closing weeks also illustrated how quickly market psychology can shift. SpaceX's record-setting initial public offering may ultimately come to be remembered as the symbolic crescendo of the AI-driven momentum trade. Investors initially rushed into the largest IPO in history, driving the shares more than 50% above the offering price within days and briefly propelling the company to one of the world's largest market capitalizations. Yet almost as quickly as the enthusiasm emerged, it faded. As investors began reassessing valuations, financing requirements, and execution risk, the stock surrendered virtually all of those gains.

The change was subtle but meaningful. By late June, some of the year's strongest momentum stocks were coming under pressure, while capital was beginning to flow toward higher-quality and more attractively valued businesses that had largely been overlooked throughout the AI-led advance. Whether this ultimately proves to be the beginning of a more durable change in leadership or just another brief rotation remains to be seen. What it does illustrate, however, is how quickly investor preferences can change once a dominant market narrative comes under scrutiny.

The common thread running through many of today’s market dynamics is not simply valuation, artificial intelligence, or interest rates. It is the increasingly powerful interaction between narrative, momentum, and human behavior. Markets are shaped not just by economic fundamentals, but also by the stories investors tell themselves about the future and the psychological forces that influence how those stories are processed.

The two essays that follow examine the current environment through the lenses of behavioral finance and neuroscience. Together, they explore why speculative cycles become so psychologically compelling and why intelligent investors repeatedly find themselves lured into them when prudence should matter most.

They also mark the beginning of a short series of essays that I'll be publishing over the next couple of months. Inspired by the late Charlie Munger's concept of a latticework of mental models, each essay will examine many of the same market developments through a different analytical discipline. Most market commentary – including much of our own – naturally emphasizes a macroeconomic perspective. Inflation, interest rates, fiscal policy, geopolitics, and economic growth will always matter enormously.

Yet no single discipline fully explains financial markets. My objective is not to replace the macro perspective, but to complement it by exploring how behavioral finance, neuroscience, microeconomics, financial statement analysis, and financial theory each illuminate different aspects of the same underlying phenomena. As always, I'll also try to place today's events within their broader historical context, because understanding where we are often begins with understanding where we've been. My hope is that, taken together, they provide a more complete understanding of the financial world around us.