Theseus Capital Manifesto

DRAFT 3 · A Post-Scarcity Manifesto on Scarcity

THESEUS CAPITAL is an investor and operator in businesses that drive civilizational progress for humanity. Our mission is to steer capitalism's acceleration of technological progress towards what is unchanging and valuable with respect to humanity's prosperity and longevity.

HUMANITY is akin to a ship sailing across a vast, uncharted ocean. To reach new worlds, we must first understand the currents quietly setting our course, then be able to steer the ship with strength and confidence.

To accelerate the journey, we may upgrade the ship; replace thin sails with robust engines, navigate with advanced GPS instead of faulty compasses, until at some point, the ship could run on its own without any hands needed at the helm.

When technology replaces plank after plank of human life, will we still be the same ship? What will our purpose be then? Where will we wash ashore?

CAPITALISM is a fundamental force that has set humanity's course for centuries and will continue to shape it for many more.

At its core, it can be understood as a self-organizing and self-expanding process. Imagine a firm that produces and sells goods for a profit. Through an accumulation of know-how, it improves its operational efficiency and lowers its cost of production. The firm, facing market competition, reinvests its surplus in R&D to further reduce costs and maintain price competitiveness. Finally, since the most reliable way to lower costs is to improve the means of production, capitalism selects for technical advance as a matter of structure.

The firm's newly achieved efficiency then expands the market rather than conserves, as Jevons observed in 1865 when better engines raised British coal consumption instead of reducing it, because cheaper power made economical a whole range of uses that had previously been out of reach.

The history of capitalism, then, is this loop closing on itself and widening. For instance:

• Mechanized spinning in Lancashire cut the labor required to produce a yard of cloth, and falling prices opened markets to people who had worn homespun. This provided funding for further mechanization that demanded better steam engines, which then transformed transport and mining.
• Railways and trains collapsed the cost of moving goods overland and fused regional markets into a single continental one, which raised the scale at which any producer could profitably operate and rewarded whoever mechanized fastest to serve it. Their appetite for iron, coal, and steel expanded the industries that supplied them, and their capital requirements ran so far beyond private fortunes that financing them produced the modern corporation and the modern capital market.
• The invention of containerization collapsed the cost of ocean freight by roughly 90-95% after 1956, which severed the link between where a good is made and where it is sold, since manufacturers no longer had to locate near their customers. Thus, production migrated to the cheapest available labor along the Chinese coast, and factories of the North Atlantic closed.
• In semiconductor manufacturing, the work of designing a chip with hundreds of millions of transistors exceeds what any engineer can lay out by hand, so the layout is performed by software, and that software runs on chips produced by the previous generation of the same process.

Time and time again, we bear witness to capitalism developing technologies that produce the instruments of their own successors. With each wave of innovation, from textiles to railways to containers to chips, the time between these cycles has tended to grow shorter as the underlying technologies become more powerful.

Historical attempts to forestall capitalism's seemingly anti-human, mechanistic cycle has failed stunningly, resulting in opposite intended effects. The most serious attempt by the Soviet Union rejected the free market because they saw it producing immiseration alongside abundance, and because they took its crises and its class divisions to be permanent features rather than growing pains. Their solution was to place investment under a single plan, so that what got built would answer to human decision rather than to profit. During the late 1920s and 30s, their plan seemingly worked: an agrarian empire was transformed into an industrial power in two decades. However, the Soviets did so by hiring American engineers to build the factories, modeling the steel city of Magnitogorsk on the United States Steel works in Gary, Indiana, and contracting with Ford to build its automobile plant. It had taken capitalism's technology and rejected only its pricing. What it lost in the bargain was the information that prices carry, which is why the planners could build rockets on command and never learn to make a decent refrigerator. In the end, the Soviets' system collapsed in 1991, and China had already reversed course in 1978 when Deng Xiaoping's reforms marked a shift away from Mao-era central planning towards a more market-oriented "socialism with Chinese characteristics." Over the following decades, sweeping changes in agriculture, special economic zones, and export-led manufacturing helped drive sustained double-digit growth, lifting hundreds of millions out of poverty and propelling China to industrialize faster, and on a larger scale, than any country in history.

Fast forward to today, we believe that capitalism's self-reinforcing loop will run faster than ever with artificial intelligence, until we reach a point of technological singularity where any good or service can be produced abundantly with widespread standardization and automation (at near zero marginal costs). But in a world where everything is cheap and abundant, do goods and services become valueless? No. Even if technology solves the scarcity of physical goods and basic services, economic theory dictates that human desires are infinite. Therefore, if technology makes all our current needs incredibly cheap, our desires will simply shift upward, creating new categories of "expensive" things.

We are in the business of investing in this post-scarcity society and its technological transition in the interim.

IN THE EARLY 1880s, electricity arrived as a luxury good that had to be custom built for each buyer. There were no monthly bill and no wire running in from the street. A customer who wanted electric light had to purchase a generator, hire an engineer to run it, and install a small power plant somewhere in the building. J.P. Morgan did exactly this at his New York mansion, where Thomas Edison's people installed a private plant to light the rooms. Electricity in that period was local, costly, and understood as the answer to a single problem, which was replacing gas lamps.

The first constraint on growth was physical. Edison's direct current could carry usable power only about a mile from its source before the voltage sagged, which meant that every neighborhood needed its own generating station. A system built that way could never behave like a commodity, because each new district of customers required another plant and another crew to operate it. Then came George Westinghouse and Nikola Tesla, who commercialized alternating current (AC). AC could be stepped up to very high voltage for transmission across long distances and then stepped back down to a safe level at the point of use, rendering scaling physically and mathematically possible in roughly the way the Transformer architecture later did for AI.

The economics were solved by Samuel Insull, who was Edison's secretary and became the true architect of electricity as a mass market product. He understood the industry's central financial problem, which was that generating plants cost enormous sums to build while households used them for only a few hours each evening, leaving expensive machinery idle for most of the day. His answer was to aggregate demand until the plants could run near capacity around the clock. He bought up small, decentralized neighborhood grids and wired them together into large, centralized networks. He introduced time of use pricing, charging different rates at different hours so that factories were pulled toward daytime power while homes filled the evenings. The result created a flywheel effect: large and steady demand justified building giant, highly efficient turbines, those turbines drove the cost of electricity down, and cheap power invited more consumption, which justified the next round of capacity expansion.

The last requirement to commoditization was interchangeability, since a commodity must be the same everywhere. Early cities ran on a confusing variety of voltages and frequencies, so a device built for one town might be useless in the next. Over time the industry converged on common standards, and in the United States that settled at 120 volts and 60 hertz. The two-prong wall outlet became the universal interface of the twentieth century, the equivalent of a public API. Once the plug was fixed, an inventor anywhere could design a product with reasonable confidence that it would work in any building in the country.

The most striking thing about the history of electricity is that almost no one anticipated it would become a commodity. The public treated electricity as a better candle and assumed that illumination was the whole of it. The idea that cheap and ubiquitous power would produce entirely new categories sat outside the ordinary imagination of the period. Refrigerators, washing machines, electric assembly lines, and eventually computers all followed from a resource that had become so inexpensive and so reliable that people stopped thinking about it. The commodity was the precondition, and the applications arrived afterward, built by people who never had to consider where the power came from.

We are long AI's commodification and long the downstream innovations that would continue to benefit humanity.

COMMODITIZATION is usually described as something that happens to a product, as if the good itself gradually loses its distinctiveness through the ordinary passage of time. The more accurate account is that buyers do it, and they do it deliberately once a product becomes indispensable to their operations but hurts the bottom line. The pressure runs from the demand side back toward the supplier, and it operates through a few reinforcing mechanisms.

• Interchangeability. When several producers can supply roughly the same thing, buyers stop caring about the identity of the seller and begin caring only about whether quality and reliability clear a threshold. Above that threshold, the purchase decision collapses into price. A supplier who tries to hold a premium discovers that switching costs the customer very little, and price competition turns brutal.
• Supply balance. As capacity expands, whether that means more power plants, chips, or data centers, the balance of power shifts further toward the buyer. When capacity exceeds demand, producers underbid one another to keep utilization high and avoid the visible waste of idle assets.
• Capital intensity. Rigs, pipelines, fabs, and GPU clusters are expensive to build and largely sunk once built. After the capital is committed, the operator has a powerful incentive to produce and sell at almost any price above short-run variable cost, since a poorly priced asset at least contributes something toward fixed costs. That logic is individually rational for every operator and collectively ruinous for the industry, and it keeps both prices and returns low over long stretches.
• Buyer base composition. Bargaining power tends to concentrate in large and sophisticated buyers who can multi-source, negotiate with real information about supplier costs, and move volume between vendors as leverage. These buyers capture the surplus in the form of lower prices while the producers compete away their own profits.

A few historic, buyer-driven commoditization examples reinforce these patterns:

Mainframe and Time-Sharing, 1960s to 1980s. In the early mainframe era, buyers were entirely locked into IBM's expensive, proprietary hardware and metered time-sharing contracts, which bottlenecked corporate innovation. Frustrated by exorbitant costs and long queues for computing time, businesses flocked to cheaper minicomputers and, eventually, personal computers. This mass buyer defection forced the industry to shift from bespoke, leased behemoths to mass-produced, standardized, and interoperable hardware (like the x86 architecture), turning computing power into an accessible commodity.

Long-Distance Telephony and Networking, 1980s to 2000s. For decades, consumers and enterprises were squeezed by telecom monopolies (like AT&T) that charged massive per-minute premiums for long-distance routing. Corporate buyers actively lobbied for deregulation and eagerly funded upstart competitors (like MCI and Sprint) to drive prices down. Ultimately, businesses bypassed legacy telecom infrastructure entirely by adopting the standardized, open-source Internet Protocol (IP), which turned voice and data transit from a metered luxury into a cheap, flat-rate, interchangeable pipe.

Enterprise Software Licenses, 1990s to 2010s. Throughout the 90s, companies like Oracle and SAP forced buyers to pay millions in upfront capital expenditures for rigid, perpetual licenses and punitive yearly maintenance fees. Fed up with predatory software audits and paying for "shelfware" (unused licenses), CIOs aggressively shifted their budgets to early SaaS disruptors like Salesforce. By demanding pay-as-you-go, standard browser-based access, buyers forced legacy software giants to abandon their bespoke installations and commoditize their offerings into cheaper, standardized monthly subscriptions.

Public Cloud Cost Blowouts, 2010s to 2020s. In the early days of the cloud, AWS, Azure, and Google Cloud successfully charged premium margins by locking buyers into proprietary managed services and exorbitant data egress fees. As cloud bills ballooned into massive operating expenses, corporate buyers and startups realized they had traded software lock-in for infrastructure lock-in. To regain some negotiating power, they increasingly adopted open-source, cloud-agnostic orchestration tools such as Kubernetes and Terraform. These tools made workloads more portable in principle and strengthened buyers' ability to push back on pricing, especially for basic compute and storage.

The Implication for AI (2020s to present). Explosive enterprise AI consumption has led to massive cost blowouts. For instance, Uber exhausted its entire annual AI budget by April following a December rollout. To combat these soaring inference costs, corporate buyers are actively working to commoditize large language models by bypassing expensive, frontier-model lock-in. Coinbase CEO Brian Armstrong recently highlighted this buyer-driven push in an X post, noting that his firm cut its AI spend nearly in half even as its token usage continued to grow. They achieved this by deploying internal AI gateways that default to cost-effective open-weight models, utilize aggressive caching, and automatically route each prompt to the cheapest capable model based on the specific task's difficulty.

We believe that the commoditization of AI has already begun. Despite a lower global adoption rate, the collective actions and demands of its main buyers, enterprises, will determine both the degree of closed-source models' commoditization and frontier labs' pricing power.

SUPPLIER-DRIVEN PRESSURES could accelerate model commoditization just as much as buyer-driven pressures. The simple logic here is that, since the suppliers of AI infrastructure's revenues depend on sustained capital expenditure, their incentives are not to build until there is enough capacity; rather, they would like to build as much as possible while demand for AI endures and the market sentiment runs high. Therefore, actual demand may be grossly overestimated from the top-down by these manufacturers to continuously incentivize investors and frontier labs to pour more money into infrastructure build outs. We see this scenario play out in a couple of prominent historical examples when new technologies similarly arrived and promised widespread usage, yet actual demand was far below capacity build out.

Railroads, 1800s. The railroad boom unfolded as a sequence of manias that followed a similar arc, first in Britain and then, on a larger scale, in the United States. In the British Railway Mania of the 1840s, petitions to Parliament for new companies exploded, investment briefly reached wartime levels as a share of GDP, and middle-class savers piled in via partially paid shares, until higher interest rates slammed the funding window shut and share prices collapsed, leaving many lines unbuilt and others consolidated by stronger survivors. Across the Atlantic, the American railroad boom repeated this pattern of speculative overbuilding, financial collapse, and durable physical legacy, but at far greater scale. It arrived in waves through the 1870s and 1880s, and it is in this American version that the underlying incentive problems are clearest.

• First, financing was decoupled from operating economics. American railroads were funded by land grants, subsidies, and bonds sold to distant investors who were buying a story about the future of the continent, while the promoters assembling these ventures earned their profits on construction itself. The Crédit Mobilier scandal is the canonical case: Union Pacific insiders owned the construction company, overbilled the railroad, and extracted returns regardless of expected ROI on capex. When builders are paid for building rather than for operating, overcapacity is a designed outcome.
• Second, competition drove duplication. A railroad is a natural monopoly on its route, so the rational play for a rival financier was to build a parallel line and force either a rate war or a buyout, as Vanderbilt's West Shore fight along the Hudson demonstrated. Each redundant trunk line was individually defensible and collectively they ensured that no owner earned adequate returns.
• Third, high fixed costs met falling prices. Because building a railroad required massive upfront investment, an operator's costs were overwhelmingly fixed, heavily financed with borrowed money, while the marginal cost of hauling one additional carload was practically nothing. Roughly 80 to 90 percent of a railroad's costs were fixed while marginal cost hovered near zero, so when capacity exceeded demand, prices were relentlessly forced down toward marginal cost: a fully loaded cost of $1.00 per ton of wheat against a pure marginal cost of $0.10 meant matching a rival's $0.80 rate was always rational. Capacity did not exit in bankruptcy; new owners bought the assets for cents on the dollar, gained a lower fixed-cost base, and made the price wars worse after each reorganization. Pools and cartels failed because cheating was immediate and rate agreements were unenforceable, and the Interstate Commerce Act of 1887 emerged in part from the railroads' own desperate desire for the government to enforce the pricing discipline they could not impose on themselves.

The investment lesson lies in where the value ultimately settled. The track survived the financial destruction of its builders, and the enduring gains flowed to the users of cheap freight: farmers, steel producers, mail-order retailers such as Sears, and ultimately consumers, who harvested the benefits for the next half century by enjoying cheap goods. The original capital financed a transformation whose returns accrued mainly to its customers and to the second-generation owners who bought the assets after the wipeout.

Fiber and telecom, 1996 to 2002. Total US telecom capital expenditure over the period ran to roughly half a trillion dollars, with more than a trillion dollars of debt and equity raised globally to fund the buildout. The demand assumption underpinning all of this capital was the claim that internet traffic was doubling every 100 days, a figure popularized by WorldCom's UUNET subsidiary and repeated in a 1998 Commerce Department report. Actual traffic was doubling roughly once a year, which still represented spectacular growth, but the difference between eightfold annual growth and twofold annual growth compounds catastrophically when it is used to size a network buildout.

The most underappreciated mechanism of the bust was that technology multiplied supply faster than demand could grow. Dense wavelength-division multiplexing (DWDM) improved so rapidly that the carrying capacity of a single fiber pair already in the ground rose by orders of magnitude during the buildout itself. Supply was expanding along two axes at once, through new physical construction and through the escalating productivity of existing assets. By 2002, common estimates held that only 3 to 5 percent of installed fiber was actually lit. Bandwidth prices on major routes fell more than 90 percent, and the collapse in the price per bit destroyed every revenue model that had been premised on scarcity. The lesson: in any capacity buildout, the supply forecast must account for the productivity curve of the underlying technology, because efficiency gains function as invisible additional capacity.

As real revenues fell short, financial reflexivity and fraud filled the gap. Carriers swapped capacity via indefeasible rights of use, booking the sales as revenue while capitalizing the purchases; Global Crossing and Qwest were notable practitioners, and WorldCom went further, capitalizing roughly $11 billion of operating expenses. Vendor financing added another loop: Lucent, Nortel, and Cisco lent carriers the money to buy their equipment, so reported growth partly reflected the vendors' own balance sheets rather than genuine demand. Between 2000 and 2002, telecom companies lost on the order of trillions of dollars in market value. Yet once again, the asset outlived its financiers. Dark fiber was bought out of bankruptcy for cents on the dollar and became the substrate for subsequent internet companies. Google quietly accumulated distressed fiber in the early 2000s, and cheap, overbuilt bandwidth helped make large-scale video services like YouTube and Netflix economically viable. The second-generation owners, who bought at post-bankruptcy prices, earned the returns the original builders had projected for themselves.

Commoditization as Prosperity. The hyperscalers' race to build massive GPU clusters results in a massive supply glut. AI becomes the new electricity: cheap, standardized, and universally accessible. As Meta, Microsoft, AWS, and Google overbuild, switching costs plummet. Open-source models and "good enough" proprietary models converge in capability. The cost of inference drops to near zero as hyperscalers treat compute as a loss leader to keep customers in their cloud ecosystems. Traditional enterprises and consumers win; the big gains accrue to non-AI businesses who reap massive productivity gains and margin expansion. The losers are the foundational AI labs and data center operators: returns on capital for new GPU clusters collapse and selling "intelligence" becomes a low-margin, high-volume utility business.

Shifting Rents. The algorithmic magic of AI commoditizes, but the physical reality of running it does not. The "brain" becomes cheap while the "calories" to run it become exceptionally expensive: open-source models and rapid algorithmic diffusion offer near-SOTA intelligence, but TSMC wafer allocations, power grid connections, and advanced networking remain fiercely constrained. Startups can download incredibly capable open-weight models for free but can't afford the cloud instances to run them at scale. The leverage leaves the software layer entirely and moves upstream. Winners: the upstream infrastructure monopolies, NVIDIA, TSMC, major utilities, and firms that own data centers with guaranteed power contracts. Losers: model builders and SaaS wrappers, whose margins are continuously eaten by their cloud hosting bills.

Non-Commoditization (Premium Scarcity). Compute demands wildly outpace physical reality: revenue demand for AI grows 10x but compute capacity scales by a fraction of that. Frontier labs bid aggressively for massive, high-security compute tranches, driving server prices far above spot rates. Because the baseline cost of compute is so high, buyers only want the absolute best models to maximize return on every computation; the Alchian-Allen effect leaves no market for a "second-best" closed-source AI, and mid-tier labs paying the same compute costs without the capability to charge for it are forced into bankruptcy. Crucially, this prevents a pure shifting-rents outcome: because the top models can do the work of a senior software engineer or a corporate lawyer, the model layer does not commoditize into a race to the bottom, and the rents are shared between the infrastructure oligopoly and a frontier model duopoly. A second path to non-commoditization runs through cognition rather than compute: continually learning models absorb the proprietary workflows and institutional memory of elite enterprises, creating prohibitive switching costs, the "cost of forgetting," that transform models from expensive software into the irreplaceable cognitive nervous systems of modern business.

However, as of August 2026, it looks increasingly the case that we have largely moved away from the third scenario towards the second, and as the hyperscalers continue to add compute capacity, we may move towards the first.

THE SHIP OF THESEUS is ultimately the same ship by the judge of its exterior form and not its material composition. By way of analogy, we believe that technology's impact on what is scarce and valuable for humanity will be limited, because technological cycles' nature is transient, whereas human beings' DNA is coded to be timeless and unchanging. The same human mechanisms of mimetic desire, time and attention, and status and authenticity have persisted throughout our history.

While our core human qualities have endured technological revolutions, many tech products have been designed less to cultivate what is uniquely valuable about us than to exploit our most primal vices. In our past hunter-gatherer society, status meant survival. Today, because technology has made basic goods cheap, we fulfill our primal need for status through hyper-consumption of positional goods. Social media, a massive technological revolution, is essentially a global engine for primal status-signaling and social comparison. The internet was theoretically supposed to create a unified "global village" by democratizing information; instead, it accentuated our primal instinct to form tribes, with algorithms catering to confirmation bias and sorting us into hyper-specific ideological tribes. As automation removes human interaction from routine transactions, our primal need to connect with other humans becomes a premium commodity. Counterintuitively, we now place higher value on things that display "authentic" human effort, flaws, and emotional resonance precisely because the world has grown so algorithmicized and mechanical.

In evolutionary biology, the "Red Queen Hypothesis" (Leigh Van Valen, 1973) says that species must constantly adapt, evolve, and proliferate just to survive against ever-evolving opposing species, named for the scene in Through the Looking-Glass where the Red Queen tells Alice: "Now, here, you see, it takes all the running you can do, to keep in the same place."

In technology, the Red Queen effect explains why companies can never stop innovating even when profits do not increase as a result. When Amazon introduced free two-day shipping in 2005 it was a massive competitive advantage; today it is the baseline expectation, and competitors spent billions just to stay in business. If Coca-Cola spends $1 billion on advertising, Pepsi must spend $1 billion to maintain share; the $2 billion maintains a stalemate rather than creating new drinkers.

Equally salient is the hypothesis's stab at the paradox of modern life. Despite unprecedented technological abundance, we often do not feel happier or more secure than our ancestors. The hedonic treadmill shows that as technology makes life easier, our brains adapt to the new comfort level; yesterday's luxury becomes today's necessity. Because humans are deeply social creatures, our sense of success is relative, not absolute: in a society where technology makes everyone richer, the markers of status simply move further out of reach.

Such is the nature of human beings: we run a lifelong marathon simply to not fall behind. But at Theseus, we don't think this has to be the case. We believe technological progress can be steered to make products that genuinely improve the human condition, not exploit our vices.

As artificial intelligence drives the production of commodity goods and routine knowledge work toward zero marginal cost, we believe economic value will dramatically shift toward the "relational sector" (Alex Imas, Chief AGI Economist at Google DeepMind): human-intensive, provenance-rich professions where consumers specifically desire a human in the loop. As technology fulfills basic needs cheaply and abundantly, consumers move up Maslow's hierarchy, placing much higher value on authentic human effort, emotional resonance, and even human flaws.

Structurally, the relational sector will consolidate around two distinct poles: highly charismatic, authentic, "front-end" services that command a premium, and the massive AI infrastructure platforms that enable, route, and capture revenue from these relational businesses in the background. To generate outsized returns, capital must be deployed at the two extreme ends of the barbell: own the most valuable, moat-heavy parts of the technological stack that powers this new economy, while simultaneously capturing the growth in scarcity-driven relational goods and services. The middle ground of standardized, mass-market services will be decimated by automation and margin compression.

Our conviction rests on three bodies of theory that converge on a single conclusion: value does not accrue to layers of activity but to positions the buyer cannot escape and to outcomes the customer can perceive and credit.

The economics of derived demand, appropriability, and added value.

• Alfred Marshall's theory of derived demand (1890): the demand for any component comes entirely from demand for the final product. Suppliers hold pricing power when their component has no close substitutes, the final product is in high demand, and the component is a small share of total cost. Takeaway: being a small, irreplaceable part of a highly valuable system gives massive pricing power, which is why GPU makers can charge so much.
• David Teece's framework on profiting from innovation (1986): the ability to profit depends on how hard the idea is to copy and who controls the complementary assets required to sell it. EMI invented the CT scanner; GE owned the hospital sales channels and took the market. Takeaway: innovation isn't enough; whoever owns distribution captures the profits.
• Brandenburger and Stuart's value-based strategy (1996): a firm's maximum pricing power is its "added value," the value of the system with the firm minus the value without it. Takeaway: popularity does not equal pricing power; if the ecosystem can cheaply rebuild your contribution, your added value is zero regardless of how much money flows through your hands.
• Christensen's law of conservation of attractive profits (2003) and Baldwin and Clark's modularity work (2000): when one layer commoditizes, profits migrate to whoever controls the next hardest-to-replace bottleneck. And per Spolsky (after Cournot, 1838), dominant firms deliberately commoditize adjacent layers because cheap complements increase demand for their core products. Takeaway: an undefended middle position is a direct bet against massive players with strong incentives to make your service free.

The psychology of evaluability, attribution, and the division of labor.

• Hsee (1996): people only value what they can easily measure; buyers underpay for critical work they cannot evaluate. Takeaway: firms can only charge top dollar for things the buyer easily understands and compares.
• Kruger, Wirtz, Van Boven, and Altermatt (2004), the "effort heuristic": perceived value tracks visible effort, not causal contribution. The visible interface layer collects the premium while the heavy-lifting backend goes undervalued. Takeaway: credit and pricing power go to outcomes the customer can see; make hidden effort visible.
• Thaler (1985, 1999), mental accounting: buyers happily fund what is categorized as an investment in output and fight anything categorized as overhead. Takeaway: position offerings as investment in the client's output, not overhead.
• The "IKEA effect": customers pay premiums for done-for-you results and expect do-it-yourself tools to be cheap, because when people put in their own effort they credit themselves and strip value from the tools. Takeaway: sell the destination, not the vehicle.

The marketing discipline of distance to the outcome.

• Levitt ("Marketing Myopia," 1960; The Marketing Imagination, 1983): customers buy solutions, not instruments; the buyer of a quarter-inch drill wants a quarter-inch hole. Every step of distance between a product and the result the customer feels costs pricing power. Takeaway: sell the hole, not the drill.
• "Intel Inside": making an invisible component visible is incredibly costly; Intel spent billions to earn the appreciation that sellers of visible, final computers got for free. Takeaway: absent a massive ingredient-brand budget, position as the seller of the final visible outcome.

Technology: LangChain versus Cursor. Two AI companies founded within months of each other doing similar, highly sophisticated background work saw their valuations diverge roughly fifty times over four years. LangChain launched in 2022 as a free, open-source framework connecting AI models to other systems; it reached a $1.25 billion valuation by October 2025 but only about $16 million of 2025 revenue, because it gave away the core product, competent engineers could replicate its functions in weeks, and the labs built the same features into their models. Cursor hid similar complexity inside an application and sold the highly visible final outcome, working code on the developer's screen, priced against expensive engineering salaries rather than tooling budgets. Revenue went from roughly $100 million in January 2025 to about $4 billion by mid-2026, culminating in a $60 billion acquisition by SpaceX. The strategy isn't flawless: paying for underlying models costs Cursor $0.40 to $0.70 per dollar of revenue, and its model suppliers are building competing coding agents. But the takeaway holds: don't sell the underlying mechanics; sell the finished product.

The relational sector: Equinox versus LA Fitness. Gyms cannot do the workout for their customers, so results are delayed, uncertain, and credited to the customer. LA Fitness and Planet Fitness sell access to equipment, which is easy to substitute, so prices are forced down to $10 to $40 a month. Equinox escapes the trap by selling an instantly deliverable outcome, social status: the moment a customer joins and carries the branded bag, they receive the full value of that exclusive identity, which supports $200 to $500 a month. You can charge massively higher prices by substituting a hard-to-deliver physical outcome with an instantly deliverable identity.

• Technology deflates functional goods and cannot deflate positional goods. Freight, bandwidth, and tokens all fell toward marginal cost once capacity caught demand, and the same fate awaits every functional layer of the AI stack. Positional goods are exempt because their scarcity is constitutive: they are valuable precisely because others lack them, and abundance would destroy the product. Deflation cannot reach a good whose value is exclusion.
• Human provenance is the commitment device that makes scarcity credible. Imas's studies show people pay roughly twice as much when they know a product is genuinely exclusive, and human-made work increases in value by 44 percent when made exclusive whereas AI-generated work gains only 21 percent. Machine-made feels infinitely reproducible; a human's limited time and judgment concretely prove restricted supply.
• The willingness-to-pay test separates the two regimes. If buyers refuse to pay once they learn a machine could do the job faster and better, you are selling a functional good that belongs to automation. If customers still pay specifically because a human did the work, the human element is the core value. The mechanical watch is the classic case: when quartz won on function, mechanical watches leaned into craftsmanship and the high end became more profitable than ever.
• The expanding price gap. The premium people pay for human involvement has limits: five dollars for a human barista, debatable at fifty, and at five hundred the purchase is no longer about coffee. Automation constantly widens this gap because machine costs drop toward zero while human labor stays anchored to wages. Durable human-driven businesses survive by targeting customers who are entirely price-insensitive to the human element and whose incomes grow faster than the automation gap.

Both successful ends of the barbell share the same structure: the firm makes it easy for customers to stay while delivering a clear, upstream result. At the technology extreme, through functional advantages: physical or organizational scarcity, manufacturing bottlenecks, tightly integrated ecosystems, or proprietary data that would take years to replicate, positioned where adjacent tech giants cannot easily commoditize it, and priced against the ultimate outcome rather than a tooling budget. At the relational extreme, through social belonging: quality, which demands scarcity, is the product itself, created through memberships, limited allocation, or strict admission, and it depends on a credible human commitment that supply cannot scale without diluting the good. Everything in the middle, functional goods without real chokepoints and premium goods without real exclusion, is where automation-driven deflation and easy alternatives compress margins. The discipline of the mandate is to concentrate capital at the two ends and avoid owning what lives in the middle.

While optimistic, we think the transition into a relational economy carries profound structural and longevity concerns. If only a small, wealthy segment can consistently afford these high-touch artisanal offerings, the vast majority of displaced workers may be forced to compete to provide them, risking a massive supply glut that creates a "feudalistic economy of relational labor." This hyper-competitive environment risks trapping both providers and consumers in a relentless positional arms race, the Red Queen dynamic: providers forced to constantly adapt just to maintain market standing, without necessarily seeing any increase in net profits. Or worse, deep-pocketed tech platforms where these goods are advertised could drive consolidation along the value chain, counteracting the distributed gains. Ultimately, despite unprecedented technological abundance and bespoke relational goods, the hedonic treadmill suggests modern humans may not feel any happier or more secure than their ancestors.

Precisely because these concerns are about the world we will all one day inhabit, they call us to take responsibility for helping steer the forces shaping humanity's future as investors and operators. This is why we are committed to: (1) distinguishing real progress from its illusions, (2) evaluating and benchmarking that progress against the broader macro environment, and (3) directing capital and operations so that uniquely valuable human experiences can flourish.