When Machines Compete
It’s easy to overlook the radical power of intelligence. Among animals, humans are physically unremarkable—our edge is purely cognitive. Intelligence allowed us to hunt even the largest prey to extinction, coordinate vast groups, and adapt to diverse environments. Every achievement of human civilization—agriculture, science, literature, music—traces back to this singular ability: to recognize patterns, form a model of the world, and solve problems. Intelligence isn't just another trait; it's the master attribute that turned an average mammal into an entity capable of dominating a planet.
Artificial intelligence has made remarkable strides in recent years. These systems now match or exceed human performance across most cognitive tasks. What was once only hype and science fiction is now actually unfolding. This progress has been accompanied by dramatic cost declines. While training frontier models still requires significant capital, improvements in hardware, algorithmic optimization, and economies of scale have democratized access to powerful AI. A key measure of this decline is cost per token—the price of processing a single unit of data. By some estimates, the cost has decreased 1,000-fold over the last three years.
In business, profits depend on inefficiencies to a large degree. In a perfectly efficient market, intense competition would force prices down near the cost of production, leaving little room for profit. Real-world markets are full of imperfections: information asymmetries (where sellers know more than buyers), various barriers to entry, such as patents or high startup costs, and temporary advantages like being first to market or having a strong brand. Monopolies and oligopolies can dictate prices because consumers lack options. These barriers and points of friction insulate companies from new rivals and let them enjoy higher profit margins.
Artificial intelligence has the potential to dismantle many of these defenses. High-skilled labor—once costly, scarce, and carefully guarded—is becoming available to everyone at minimal cost. Skills in law, finance, design, software engineering, and countless other domains can be accessed through simple chat apps for a modest monthly fee. What will be the implications of such a drastic change?
In the late 19th century, economists developed a framework known as perfect competition. A perfectly competitive market features many buyers and sellers, all offering similar products. With no private information or monopoly power, firms are unable to use strategic pricing to undercut rivals. While no real-world market fully meets these criteria, the model serves as a reference point, illustrating how market imperfections alter pricing, output, and overall efficiency compared to an idealized state where resources are optimally allocated through the price mechanism. This raises the tantalizing question: could the emergence of powerful artificial intelligence drive markets towards such a hyper-competitive state?
Let’s consider a future hypothetical. Imagine a business owner so confident in their AI’s capabilities that they decide to delegate all strategic and operational decisions to the system, stepping back to serve primarily as a figurehead. The AI has certain weaknesses, but it is extremely fast and superhumanly knowledgeable. It’s consumed virtually every business book and industry-specific tidbit on earth. Importantly, it doesn’t procrastinate urgent decisions; it doesn’t get burnt out, tired, or sick. Over time, we should expect this AI-led corporation to steadily outmaneuver its competitors. Now imagine this phenomenon playing out across all sectors of the economy.
We can view this AI-led company as analogous to an invasive species in the natural world. Traditional firms, even those with talented teams, will find themselves outmatched—not just in speed, but in strategic depth. Much like the dodo bird, which evolved in isolation without natural predators and was swiftly driven to extinction, traditional businesses may lack the adaptive skill to respond to this novel threat. Changing the player, changes the game. The introduction of strong artificial intelligence represents a fundamental shift in the competitive landscape, potentially relegating non-augmented businesses to the same fate as the dodo.
Think about what happened to chess when AlphaZero arrived. Possessing only the basic rules, AlphaZero taught itself from scratch by playing millions of games against itself. Within hours, it surpassed centuries of human knowledge. Crucially, it didn’t just play chess better—it played very differently. AlphaZero often sacrificed material to achieve long-term positional benefits rather than immediate material advantage. It would give up pawns to open lines and weaken the opponent’s king safety or disrupt pawn structure. These sacrifices were not aimed at an immediate tactical payoff but were intended to secure enduring initiative and superior coordination over the board.
Now imagine the business world transformed similarly by a more general system. AI enabled businesses will both optimize and discover new competitive strategies. They may exploit market positions humans perceive as irrational or untenable, only to achieve unprecedented success. Industries might reorganize around principles that initially seem absurd. Traditional firms, bound by decades of accumulated orthodoxy, could find themselves bewildered and ultimately destroyed as markets reshuffle.
This kind of strategic discontinuity has precedent in human-led companies. Elon Musk's takeover of the automotive industry offers a compelling proof of concept. Musk recognized that battery technology had reached a point where desirable electric vehicles could be feasible due to efficiency gains driven by consumer laptops and cell phones. This was a difficult judgement call to make. Getting it right allowed Tesla to enter the market at precisely the moment when the fundamental constraint on electric vehicles was becoming solvable, but before established manufacturers had made any progress.
The automotive incumbents dismissed Tesla as a non-threat. As legacy brands continued their outsourcing strategy, Musk broke from convention and vertically integrated wherever possible. Traditional dealerships watched as Tesla sold vehicles online without discounts or negotiation. While Ford and GM maintained their decade-old assembly line configurations, Tesla developed new manufacturing techniques, finding novel ways to reduce cost. Today, Tesla produces electric vehicles at four global Gigafactories, has an advanced humanoid robot program, a growing battery business, and a robotaxi platform powered by their homegrown self-driving software.
Elon Musk brought $200 million to one of the most consolidated industries on the planet and disrupted it thoroughly within 10 years. Tesla's market cap recently peaked at 1.5 trillion dollars, dwarfing the next largest auto company, Toyota, valued at $250 billion. All of this due to one uniquely capable agent. What will be accomplished by enterprises equipped not with hundreds of millions, but billions in capital, and an intelligence engine far surpassing Elon Musk?
And on the other side, could legacy brands counter in the near future by enabling an AI system to run their operations? Could they regain the throne from Musk? Interestingly, this AI system would not only match Musk’s strategic brilliance; it would also avoid his well-documented shortcomings. Musk’s impulsiveness, controversial public statements, and occasional erratic management decisions have frequently undermined Tesla's goals. This type of executive discipline would likely be quite desirable for institutional investors.
Venture capital and private equity may eventually reorient around this vision. You could imagine a fund of the future not searching for entrepreneurial talent, but building it into a platform. Perhaps the capital allocator of the future is a superintelligent company builder that deploys resources from a pool as it sees fit. It's hard to imagine Warren Buffett wouldn't see this type of steward of capital to be a sort of perfecting of capitalism.
The role of the human executive might evolve significantly in this new era. Instead of being the core strategic decision-maker, the CEO may become more of a PR front man—more akin to a Hollywood actor than a traditional corporate leader. This individual would articulate the company's vision to investors, customers, and employees with charisma and emotional intelligence. Guided by ultimate PR insights from the AI system, they would serve as the approachable, human face of a hyper-competent strategic system.
Yet even in a world teeming with advanced artificial intelligence, many competitive advantages have little to do with raw intelligence. Some markets follow a "winner-take-most" dynamic: A bestselling author gains early visibility, leading to more reviews, media coverage, and retailer promotion, which drives even more sales. Other talented writers may still find an audience, but the top performer captures the largest share, not just due to merit, but because of name recognition, social evidence, and market momentum.
Similarly, real-world infrastructure—ports, warehouses, and trucking fleets—remains stubbornly physical, costly to build, and slow to expand or reconfigure. AI can allocate resources more efficiently, but at the end of the day, a congested port or a shortage of specialized containers can stall even the smartest operation. A competitor that has invested in port facilities or has exclusive rights to a critical shipping route carries an advantage not easily overturned by strategic thinking alone. This represents a form of moat resilience in the face of AI optimization.
The same principle applies to brand loyalty. If consumers are set in their preferences or trust a particular company, no amount of algorithmic brilliance will instantly steal that entire customer base. Over time, cost or quality differentials can chip away at loyalty, but brand inertia remains potent. Some might argue it is a form of inefficiency, preventing the market from adjusting perfectly to new information. Others see it as a valuable asset built through years of consistent service. Either way, brand loyalty demonstrates significant moat resilience, tempering the pace of change towards perfect competition.
Another variable is regulatory capture. A firm with strong political connections or a legacy position might shape the regulatory environment to its advantage, effectively locking the door to new entrants. Even the most capable AI cannot easily circumvent legal blockades. Such capture can freeze a market in a suboptimal state, preserving incumbents and thwarting AI-driven enterprises who might otherwise transform the sector. This same phenomenon can also favor an already dominant AI-driven enterprise. This could result in a market that is not only not perfectly competitive, but locked in favor of one or two entrenched players.
Diminishing returns on intelligence add further nuance. After a certain point, the advantage conferred by more advanced AI may shrink, especially if the market has already been substantially optimized. Competition may still be fierce, but it unfolds on a plateau rather than an upward trajectory of continuous breakthroughs. In that environment, the fear of a runaway company dominating the market might recede, replaced by concerns about collusion or tacit coordination. If all agents realize that price wars reduce profits for everyone, they might reach a stable equilibrium of restraint, a non-aggression pact that keeps profits modest but predictable. Without explicit collusion, the algorithms might simply learn that hyper-aggressive tactics result in destructive feedback loops.
What does all this mean for the value of companies? Conventional wisdom suggests that stock prices reflect earnings and growth potential. But if this hypercompetitive form of capitalism systematically erodes profits across industries, does it ultimately deflate the value of the stocks? If competition forces margins toward zero, then by traditional valuation models, stocks should decline over time. However, we should approach this reasoning cautiously. The relationship between fundamentals and asset prices is ultimately a narrative we construct to explain market behavior. Consider the parallel universe of cryptocurrencies—where prices move wildly without earnings, growth, or intrinsic value.
While consumers would undoubtedly benefit from higher-quality goods at lower prices, any long lasting decline in major stock indices would create widespread economic devastation, affecting everything from retirement accounts to the global currency market.
This dynamic could ultimately drive a bifurcation in the economy. On one side, highly commoditized industries—logistics, basic manufacturing, and even standardized professional services—will likely experience relentless margin compression. On the other side, sectors with significant emotional, cultural, or regulatory moats—luxury goods, pharmaceuticals, healthcare, and defense—may maintain surprisingly robust margins. Investor capital may gradually migrate toward these moat-resilient industries. Or perhaps stocks just detach further from these old stories about value and we invent new ones to justify why asset prices should go up or down.
The potential changes we've already explored are daunting as it is, but an even stranger change could be on the horizon.
All economic theories are parochial, not general, and this is a big deal. Our economic frameworks are basically heuristics specific to humans. Competition is good for quality—when humans produce the goods and services. Monopolies are bad for choice—when humans produce the goods and services. Governments tend to be wasteful—when humans deliver the government services. Sit with this notion for a moment.
Virtually all of our ideas about how economic life works are based on the specific character of the human animal. And this behavior package is not intrinsic to all methods of economic production. Like all animals, humans evolved in an energy-scarce environment, necessitating robust energy-saving mechanisms for survival. Much of our lived reality is determined by this design—the difficulty of maintaining focus, enthusiasm, being productive in general, getting your employees to be productive—all of it, is shaped by this evolutionary need to preserve energy by curtailing effort whenever possible.
A core aspect of what AI offers is the ability to remove this governor: to unleash effort based on the energy we have vs the energy evolution thought we had. This fundamental difference changes everything about competition and cooperation. Humans depend on incentives as motivational fuel. Without sufficient rewards or punishments, quality deteriorates, innovation stalls, and productivity plummets. But an AI system—assuming adequate resources—isn’t constrained by motivational inertia. It can maintain peak performance indefinitely, continuously doing its job without boredom, fatigue, or resentment, in any context. It can produce exceptional quality and variety not because it fears homelessness or desires a larger home, but simply because its objective function says to do so.
An AI-driven market could in theory achieve exceptional quality, affordability and choice, even in a completely cooperative environment. Rather than fiercely competing, these systems could theoretically share information openly, dynamically allocating resources in real-time, and cooperatively adjusting their strategies to meet collective market demands. Without the emotional or motivational constraints that humans experience, such cooperative structures could prove even more efficient than traditional competition—removing redundant efforts, minimizing waste, and achieving optimization on a systemic rather than individual level.
Such a future would mark a fundamental shift away from the capitalism we have known for hundreds of years. Our nature demanded that we compete, their nature may not demand the same. A common saying is “money doesn’t grow on trees,” implying value can’t simply emerge automatically—it requires effort, knowledge, and deliberate action. Yet ironically, nature offers the opposite lesson. Consider a fruit tree: no human is capable of manufacturing even one piece of fruit directly nor fully understands its intricate makeup. Instead, we’ve learned to plant trees, harnessing biology's nanotechnology honed by billions of years of evolution. In doing so, we’ve created reliable, scalable production of something incredibly valuable, all without explicit control or a comprehensive understanding.
Today, companies meticulously assemble products, strategies, and services through painstaking effort and human judgment. Tomorrow, our role may shift from bearing all the load of production to training intelligent systems and allowing their unconstrained energy to bear fruit. Such a change would be something to behold indeed.
We appear to stand at an inflection point. The path ahead may unfold in seemingly contradictory phases: first, an intensification of Darwinian competition as capitalism evolves a more efficient system. Yet as economic production becomes predominantly technological, machines competing against machines to fulfill our needs may itself be eventually reconsidered. Whichever direction emerges, this new world holds extraordinary promise.
Yet the risks are equally unsettling. If we delegate economic decision-making and expertise to non-human systems, we risk some sort of unintended drift and dependency. Perhaps similar to old age and enfeeblement, our well-being would be dependent on the kindness of strangers in machine form.
This brings us to perhaps the most critical question: Who will design the objective functions of our machine brethren? The invisible hand Adam Smith described was guided by human self-interest with all its complexities, limitations, and occasional altruism. Are we comfortable with an invisible hand brought to you by Microsoft? As this hand becomes algorithmic, the values encoded in its programming will shape our lives. Our challenge is to ensure that as machines compete or cooperate, they do so in service of human flourishing. This requires not just technical competence but institutional creativity, ethical clarity, and a willingness to ask unorthodox questions.
Steve Jobs once reflected during a 1994 interview for the PBS documentary 'One Last Thing':
When you grow up, you tend to get told that the world is the way it is and your life is just to live your life inside the world. Try not to bash into the walls too much. Try to have a nice family life, have fun, save a little money. That's a very limited life. Life can be much broader once you discover one simple fact: Everything around you that you call life was made up by people that were no smarter than you. And you can change it, you can influence it... Once you learn that, you'll never be the same again.
I think it's dawning on many of us that the world of 2025 and beyond is a fresh block of clay, ready to be molded. We should embrace this spirit as we navigate the uncharted waters before us. Our choices will likely cast a long shadow.
