Why Do Corporations Still Exist in the Age of AI? (Part 2)

No, AI Won't Kill the Traditional Firm (Yet)

AI makes small teams look clever, but it doesn’t erase the reasons corporations exist.

One thing I learned from eighteen years of being part of the agile community is that common sense is no match for naïve optimism. Whether it was Extreme Programming, Scrum, Holacracy, Sociocracy, Teal, or Team Topologies, the suggestion that we could simply “let people self-organize” and “keep the managers away” was as persistent and unkillable as the Dutch vision of ever winning the FIFA World Cup.

But reality is a bitch, and hope is no substitute for experience.

In my previous post, I gave five reasons why collaboration needs management (economies of scale, transaction costs, the hold-up problem, team production theory, and legal personhood). Here’s the second part of that story, with another five reasons explaining why a self-organized group of autonomous people can never fully replace a corporation.

6. The Resource Loop

If I could afford to buy myself a staff of twelve robots, I’d already done that long ago. Alas, it seems I need to give a few more talks and workshops before I ever get there.

Some things are simply too big (or too scary) for one person to own or to fund. The joint-stock company (made official in England by the Joint Stock Companies Act of 1844) was a legal gadget for piling up more capital than any single investor had, while spreading the risk thin enough that nobody got wiped out when everything went downhill.

Edith Penrose in The Theory of the Growth of the Firm (1959) made the case that a firm is really a bundle of odd, mismatched resources that together produce something none of them could alone. Their unused capacity is what drives it to grow. Penrose claimed that growth is capped by how fast the management can learn, a limit that is now known as the “Penrose effect“.

Birger Wernerfelt and Jay Barney built their Resource-Based View on top of Penrose’s work. They said a firm’s advantage comes from what it owns that rivals can’t easily copy, such as assets, skills, and processes. (We nowadays call that a competitive advantage or a “moat.”) David K. Teece, Gary Pisano, and Amy Shuen stretched this into “dynamic capabilities,” the skill of sensing what’s coming and seizing opportunities by reshuffling resources and reshaping the organization.

In other words, it’s all about how you use what you own. And it’s much easier for a corporation than for a network of individuals to own and manipulate properties.

Here, again, AI pushes in two directions.

On the one hand, frontier AI is brutally capital-intensive. The sector is pouring money into GPU clusters, data centers, and energy-grid connections. The entry fee is so steep that the firms adopting the strongest AI are overwhelmingly the biggest ones. Those early adopters then grow faster in sales and markups, resulting in a self-reinforcing resource loop: corporations need scale to afford the AI, the AI throws off better returns, and the returns fund more scale. That effect is pretty hard for a network of individuals to emulate.

On the other hand, APIs and AI agents offer world-class capabilities to anyone. Traditional moats dry up faster than the Seine in Paris under a heat dome. A solopreneur can rent frontier reasoning, image generation, voice cloning, coding, marketing, and legal drafting for the price of a streaming subscription, which makes the Resource-Based View’s competitive advantage very hard to maintain for traditional firms when most essential resources are just one API call away for anyone with an Internet connection.

Conclusion: The largest firms fare better than ever. All the others have a problem.

7. Tacit Knowledge

If I sold my one-person business now, the buyer would not be able to run it. That’s because nearly everything needed to generate revenue sits in my head, not on Google Drive.

Some knowledge refuses to fit in a contract. Michael Polanyi named it tacit knowledge in 1966:

The stuff you know in your hands and your gut, learned by doing, impossible to email. Firms are systems for moving know-how between people.

Just watch how a new medicine is made. It rests on the accumulated instincts of thousands of researchers, trial managers, and regulatory specialists marinating their expertise for years. You cannot chop that up into freelance gigs and reassemble it with a few peer-to-peer contracts. The knowledge lives within the organization.

The Knowledge-Based View says that firms beat markets at sharing and replicating knowledge and expertise, especially the tacit kind that lives in heads, hands, and habits rather than documents and that people and markets cannot easily price or transfer.

The problem with loose networks is that they are nimble and forgettable. They’re poor at holding onto tacit knowledge, enforcing standards, making relationship-specific investments, and building the routines that make next year’s work better than this year’s. When value depends on “how we do things around here” rather than generic labor you can rent off the market, the network becomes insufficient.

Now that AI can codify and spread expertise fast, it even strengthens the case for organizations that can build proprietary learning loops and real internal knowledge infrastructure. Though it remains a challenge because skills and workflows rot faster than ever because of “accelerated knowledge decay.” Keeping every capability in-house means paying to re-learn everything, all the time. When your internal expertise expires like fresh milk, renting it from the market looks clever again.

However, AI is also the first automation that can distill and replicate tacit-seeming knowledge, but only with access to proprietary data, customer interactions, internal documents, and digitized workflows. AI is quietly mastering and mimicking the expertise of your best people. That’s firm-reinforcing. And when the company owns that data, AI infrastructure becomes the most valuable asset of all, and we’re back to the Resource-Based View (see above) as the main reason for the corporation to exist.

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I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to talks, workshops, and coaching, from a single team to a multinational. Want it applied to yours? Let’s talk. And if you’re just here for the maps, they’re free, always.

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8. Identity and Reputation

Why did I pay a professional designer to create a company logo that I could put on my invoices? Why does a six-person startup hire a Chief Compliance Officer before any regulator demands it? Why do small charities build boards modeled on Fortune 500 companies they have nothing in common with? Because looking proper and professional is itself a survival skill.

Everything we’ve discussed so far is pretty rational. But what in the world ever happened for purely rational reasons? Humans are creatures who often do things for social and emotional rather than rational reasons.

An organization is a power structure shaped by social, cultural, and political forces, and only sometimes efficiency matters. There are human motives nobody writes into a business plan: prestige, ideology, the plain hunger for power. Taking this into account, we can explain why some organizations form for no good reason at all, or, the opposite: why they fail even when the rational structure looks flawless on paper. Experts refer to it as institutional theory, and it explains a lot of otherwise baffling behavior.

Paul DiMaggio and Walter Powell, in “The Iron Cage Revisited (1983),” and later Richard Scott in Institutions and Organizations: Ideas and Interests (2008), argued that organizations adopt their shapes not only to be efficient but to look legitimate. The authors identified three pressures: coercive (regulators make you), mimetic (you copy whoever’s winning), and normative (your profession says this is how it’s done). Organizations take recognizable forms because the social world expects them to. And underneath all of that sits purpose, vision, belonging, identity, status, and more. Churches, clubs, and mission-driven companies exist for reasons that are not about efficiency, liability, resources, or knowledge at all. It’s just a matter of, “They are, therefore I am.”

In a world where some of the rational reasons to organize are crumbling, the non-economic ones stand out. Trust, status, identity, professional norms, brand legitimacy: these are why clients still hire a “firm” rather than a freelancer, and why people still want to belong to something. These are organizing drivers in their own right, and they might be the ones AI finds most difficult to touch.

On the other hand, AI created a new way to look respectable: being seen as a responsible AI business. Companies now stand up AI ethics boards and “responsible AI” committees mostly because it signals trustworthiness. Some consultancies sell entire product suites to companies that want to look like grown-ups in the age of AI. “It’s what everyone else does, and so should we.”

9. Collective Action

I published this article on the last day of Pride Month, and it’s worth noting that change may come because of collective action. Sometimes, a loose network of people must turn into a united front because it’s the only way to get others to listen and take notice.

Mancur Olson (1965) explained in The Logic of Collective Action why some groups organize when no individual would bother: large groups with a shared interest do not spontaneously speak as one voice. Unions, cooperatives, mutuals, industry lobbies, and trade associations all exist because of exactly this: to prevent a Tragedy of the Commons situation by presenting a gaggle of individuals as one solid block. The job of the collective is not to make anything; it’s to bend the rules that everyone else has to play by.

Associations, foundations, and other public bodies form when the job is governing a shared resource or a public good rather than maximizing an investor’s return. People reach for a public-purpose body when the real problem isn’t capturing capital, but setting rules, sharing access, building trust, or stewarding shared infrastructure. Think industry standards groups, shared data trusts, worker-owned platforms, sector-wide safety initiatives, and open-source AI or data communities. You can’t easily do that with just a network of bilateral agreements.

Here, it’s interesting that AI hands us fresh shared resources that need governing: compute access, model-evaluation benchmarks, incident reporting, shared data infrastructure, and … the mimicking by AI of expertise developed through our professional labor.

Any single employee’s data is replaceable because a thousand colleagues generate similar data. So if each person bargains alone over the data they generate, they all individually cave, the firm assembles a brilliant AI model of their collective expertise, and the entire workforce’s leverage quietly collapses. The proposed fix is collective: workers can bargain data rights as a group, like a union. When they negotiate together, the firm still gets good AI while people keep a career ladder to climb.

Perhaps it’s time for an annual parade on Professional Month.

10. The Network Effect

The tenth and ultimate reason for corporations to exist came to prominence in the platform era. Some organizations exist purely to own and run the network itself, because its value grows with every node that joins. Metcalfe’s Law states that a network’s value scales with the square of its members. The network structure itself is the asset.

Uber, Airbnb, Booking, Spotify, and Linkedln didn’t form to save on transaction costs or to prevent the hold-up problem. They didn’t get funded because of team production theory or legal personhood challenges. They don’t exist to benefit from shared resources, tacit knowledge, reputation, or collective action. Sure, all these may contribute to the success of the firm to some extent, but they’re not the primary reason for its existence.

Networked corporations formed to capture the value created when you connect millions of strangers who’d otherwise never find each other. Rietveld and Schilling (2021) called it platform competition. Customers and suppliers generate data; this data trains better models, the better models attract more users, and round it goes. Proprietary data, customer interactions, and fine-tuned models are now relationship-specific investments that the firm captures and the market can’t copy.

Machine-learning models get better with more and cleaner data. So a company that hoards proprietary behavior or operations data builds an AI capability rivals can’t match, even when those rivals run the exact same model architecture. They may have the same algorithms; they may have a similar network, but they don’t have the same data.

So companies now form specifically to sit on a network. To be the network. When you already operate as a peer-to-peer network of individual contractors with bilateral agreements, you have a strong incentive to incorporate and own that network. Because if you don’t do it, somebody else will. And then they’ll reap the benefits of owning a network.

Conclusion

I may have been a bit naïve myself in the past regarding the viability of self-organized networks of professionals. But hey, we’ve all been young. We’ve all been optimistic and wrong about something.

I’m not here to ditch the concept of networks, nor am I criticizing the push for self-organization. Far from it! But I realize more than ever that self-organization in a peer-to-peer network is insufficient, unless this self-organization develops into something that addresses the ten reasons for corporations to exist: it has economy of scale, reduces transaction costs, solves the hold-up problem, team production problem, and legal personhood problems, benefits from shared resources, tacit knowledge, reputation, and collective action, and has a way to own the AI models built through the network.

If you do all that, you end up with what I call a Networked Agentic Organization, where the company is the network.

Jurgen, Solo Chief