In March 2026, a man named Matthew Gallagher sparked widespread attention across the tech community. In September 2024, he launched Medvi—a GLP-1 telehealth company—with just 20,000.Without hiring a single employee, Medvi achieved 401 million in revenue and a 16.2% net profit margin in 2025. In stark contrast, its competitor Hims & Hers has over 2,400 employees and a net margin of merely 5.5%.That same month, Shenzhen unveiled its "AI Solo-Company Entrepreneurship Ecosystem Action Plan (2026-2027)", aiming to build over 10 "Solo-Company Communities" and incubate more than 1,000 high-growth AI startups by the end of 2027. Early in 2024, Sam Altman predicted the rise of the "one-person unicorn," an outcome Dario Amodei assigned a 70% to 80% probability of materializing by 2026. Medvi seems to have fulfilled this prophecy ahead of schedule.
Together, these events bring an old, fundamental question back into the spotlight: Why do firms exist at all?
The Ghost of Coase
In 1937, a 27-year-old Ronald Coase posed a seemingly simple question in his seminal paper, The Nature of the Firm: If the market mechanism is so efficient, why do we need organizations like firms?
His answer was equally concise: Transaction costs.
Getting anything done in the open market requires searching for information, negotiating prices, drafting contracts, monitoring execution, and handling breaches. When combined, these frictions are sometimes far more expensive than bringing people together under a single organization and coordinating them through a hierarchy. The boundary of a firm is drawn exactly at the line where internal coordination costs equal external transaction costs.
This framework dominated organizational theory for nearly a century. During the internet boom, some declared Coase obsolete—arguing that as information transparency soared and search costs plummeted, outsourcing and the platform economy would cause companies to shrink.
They didn't. Over the past two decades, tech giants have only grown more massive. Google employs 180,000 people, Meta 70,000, and Amazon once surpassed 1.5 million. While the internet lowered certain transaction costs, it simultaneously created new coordination demands—data governance, algorithm tuning, ecosystem maintenance—activities that were far more efficient to manage internally than on the open market. The ghost of Coase had not dispersed.
But AI—specifically the agentic technologies that have matured since 2025—is accomplishing what the internet could not: It is compressing both transaction costs and internal coordination costs simultaneously.
This is the true paradigm shift.
The Simultaneous Collapse of Two Cost Curves
Let’s look at transaction costs first.
The traditional friction points of external collaboration—sourcing talent, evaluating capability, communicating requirements, and reviewing deliverables—are being completely rewired by AI. Need a product illustration? You used to have to find a designer, brief them, wait for a draft, request three rounds of revisions, and process a payment. Today, Midjourney and ten minutes of prompt iteration get the job done. Need a legal contract template? For highly standardized scenarios, Claude can generate a viable draft in thirty seconds. Need a landing page? Cursor, coupled with a few descriptive sentences, can have it live in half an hour.
This isn't an incremental improvement in efficiency; it’s an order-of-magnitude collapse. When external transaction costs shrink from "days" to "minutes," work that previously had to be done in-house can suddenly be executed in the open market at near-zero friction.
Now, consider internal coordination costs.
In a 500-person company, how many layers must a CEO's decision pass through before it becomes frontline execution? Every intermediate layer consumes information, introduces latency, and creates drift. Meetings, weekly reports, OKR alignments, cross-departmental syncs—these activities, which generate no direct value, can consume 40% or more of the working hours in large enterprises.
AI agents are not replacing specific roles; they are replacing coordination itself. When a founder can issue instructions in natural language directly to an AI, and the AI autonomously breaks down the task, invokes tools, and delivers the result—the core function of middle management is hollowed out. This isn't because middle managers aren't working hard; it's because their function as information routers and task dispatchers is being superseded by a vastly more efficient mechanism.
With both curves collapsing simultaneously, the solution to Coase's equation fundamentally changes: The optimal boundary of the firm is rapidly retreating.
Not a "Small Company," but a New Species
However, framing this shift merely as "companies getting smaller" completely misses the depth of the transformation.
Medvi is not a small company. Based on its current growth trajectory, its annualized revenue is approaching $1.8 billion. Nor is it a "lean team" in the traditional sense—Gallagher didn't outsource the work to a handpicked army of freelancers. He utilized a full-stack automation system comprised of over a dozen AI tools. ChatGPT, Claude, and Grok handle code and copy; Midjourney and Runway generate ad creatives; ElevenLabs manages voice interactions with customers; and custom AI agents orchestrate the various systems. True, GLP-1 telehealth is a highly standardized sector naturally suited for automation, and Medvi's exact model may not be universally replicable. But it proves one undeniable fact: A single human, augmented by an AI system, can sustain a business scale previously unimaginable.
This isn't just "small"—it's an entirely new organizational topology.
A traditional company is a hierarchical network composed of human nodes. Information flows from the bottom up, decisions cascade from the top down, and processes and culture act as buffers against signal loss. This architecture scales by adding people—more nodes, deeper layers, and more complex procedures.
An AI-native company closely resembles a star network with the founder at the absolute center. The founder is the sole human decision node, surrounded by specialized AI agents. It scales not by adding headcount, but by adding compute and tooling. Its organizational "width" can be immense—handling marketing, product engineering, customer service, and finance simultaneously—but its "depth" is extraordinarily shallow, with a decision chain that is practically non-existent.
The difference between these two architectures isn't one of degree, but of paradigm. It’s akin to the difference between single-celled and multi-celled organisms—it’s not merely about size, but a fundamental divergence in organizing principles.
And this new species is already beginning to branch into distinct subspecies.
Three Emerging Forms
Based on current real-world models, we can observe three primary forms taking shape.
Type 1: The Super Individual Company. Medvi is the archetypal example. One person, equipped with an AI toolchain, covering the entire lifecycle from product development to customer acquisition and delivery. Its core advantage is decision velocity—no hierarchies, no meetings, no office politics. The founder's judgment translates directly into action. Its disadvantages are equally stark: It is hard-capped by the founder's cognitive boundaries and energy, and it is inherently limited in scenarios requiring deep interpersonal trust (like enterprise software sales or government relations).
Type 2: The Human-Machine Hybrid Team. A core team of 3 to 10 people, each paired with multiple AI agents, projecting a capability far exceeding their headcount. This model is already prevalent in independent software development, content creation, and consulting. It preserves the flexibility and creativity of human collaboration while leveraging AI to exponentially amplify output. A study by the UC Berkeley School of Information noted that such AI-native organizations are "network structures rather than hierarchies—defined by API connections instead of org charts, and scaled by adding compute rather than headcount."
Type 3: The Pulse Organization. This is the most radical and imaginative form. It forms temporarily around a specific objective and dissolves upon completion. Founders, AI agents, external vendors, and freelancers assemble into a highly efficient execution unit within days, deliver the outcome, and disband. There are no permanent employment contracts, no offices, and sometimes not even a registered legal entity. The entire "company" exists as a set of API calls and smart contracts. Anyone can spin up a "temporary firm" of hundreds of agents as effortlessly as ordering takeout, and dissolve it just as quickly.
These three forms are not mutually exclusive but exist along a spectrum. The choice depends on business complexity, trust requirements, and regulatory environments.
Large Corporations Won't Disappear
A common cognitive trap is assuming that if AI allows small teams to do the work of large corporations, enterprise giants are destined for obsolescence.
They are not. The other half of Coase's framework still holds—there are certain transaction costs that AI simply cannot eliminate.
Scaling hardware manufacturing requires supply chain integration. Pharmaceutical R&D demands long-cycle capital investment and regulatory compliance. Financial services necessitate licenses and credit backing. Defense and infrastructure involve sovereign security. The "transaction costs" in these domains are not just about information and coordination; they involve trust, compliance, capital, and physical assets—frictions that AI is far less equipped to remove than informational ones.
More importantly, massive corporations are weaponizing AI as a moat. Microsoft has armed its entire Office suite with Copilot; Google has woven Gemini into everything from search to cloud infrastructure. These behemoths are not fighting the decentralization brought by AI; they are using AI to aggressively fortify their economies of scale—doing significantly more with fewer people while maintaining organizational integrity.
What is actually happening is not the death of the giant corporation, but the collapse of the middle ground. The companies that are neither large enough to command economies of scale nor small enough to enjoy AI-driven agility—mid-sized enterprises with tens or hundreds of employees, relying on labor-intensive services for profit—will face the heaviest existential pressure. They can neither pivot iteratively like a Super Individual nor erect the ecosystem moats of a tech giant. Where the displaced workforce from this squeezed middle echelon will go may become the most profound social challenge of this revolution.
From "Hiring People" to "Orchestrating Agents"
If the essence of a company is a coordination mechanism, the core shift in the AI era is this: The object of coordination has transitioned from humans to agents.
The central tenets of traditional management—incentive design, culture building, talent acquisition, performance reviews—revolve entirely around "how to get a group of humans to collaborate efficiently." When the primary subjects of collaboration change from organic employees to AI agents, the very definition of management changes. You don’t need to pay an AI a bonus, organize team-building retreats, or navigate office politics. But you do need to master something else entirely: Workflow design.
Selecting the right AI tools, defining the data flow between them, determining where to inject human judgment, monitoring output quality, and handling edge-case anomalies—these activities constitute a brand-new "management" paradigm. It’s akin to orchestrating a distributed computing system rather than leading a human team.
This necessitates a fundamental pivot in the skill sets required of founders and executives. In the past, a great CEO excelled at reading people, motivating teams, and cultivating culture. Today—and definitively in the future—a great AI-native founder must excel at understanding AI capability boundaries, designing highly efficient human-machine workflows, and executing decisive judgment in the critical gaps where AI cannot reach.
To use an imperfect but highly intuitive metaphor: A traditional CEO is like an orchestra conductor, synchronizing dozens or hundreds of musicians. The founder of an AI-native company is a music producer in a studio, sitting alone at a workstation, using synthesizers and samplers to craft a complete album single-handedly.
In 1937, Coase answered the question of "why firms exist." Eighty-nine years later, AI is providing an amended answer: Firms still exist because coordination still incurs a cost—but the answers to "what we coordinate" and "how we coordinate it" have fundamentally changed.
As the object of coordination shifts from humans to agents, optimal business scale, organizational structures, and competitive logic are all being reshuffled. We are only in the very early innings of this Great Reshuffling—Medvi is merely the first visible prototype, not the end state.
Over the next few years, the most critical question won't be headline-grabbing curiosities like "Can a one-person company reach a billion dollars?" The deeper, far more consequential shift is this: As transaction costs and coordination costs crash simultaneously, how will the fundamental structures of human collaboration—from corporate governance and labor relations to the social contract itself—be systematically rewritten, layer by layer?
That is the question the ghost of Coase is truly compelling us to answer.
