Digital Economy Dispatch #272 -- Why AI Makes Firms Collapse as Expertise Becomes Cheap

In 1937, the economist Ronald Coase asked a deceptively simple question: why do firms exist? His answer has shaped how we think about organisations for nearly a century. Now AI is forcing us to revisit it.

Coase argued that a company's size and scope are determined by the relationship between internal and external costs. When it's cheaper to do something inside the firm, organisations grow. When it's cheaper to buy from outside, they shrink and outsource. The boundary of the firm sits wherever these costs balance.

A recent Harvard Business Review article by Microsoft's strategy team and Harvard's Karim Lakhani explores what happens when AI disrupts this balance. Their core insight is that we're witnessing two forces colliding. The amount of expertise required to create value keeps increasing. But the cost of accessing that expertise is plummeting.

This tension has profound implications, not just for corporate strategy, but for how we think about digital leadership and public policy.

The Expertise Paradox

Think about what it takes to build a modern digital product. You need software engineering, yes. But also user experience design, data science, cybersecurity, cloud architecture, compliance expertise, accessibility knowledge, and increasingly, AI and machine learning skills. The bar keeps rising.

At the same time, AI is making much of this expertise dramatically cheaper to access. Need a first draft of code? A security audit checklist? A compliance framework? A data analysis? Tasks that once required hiring specialists or expensive consultants can now be accomplished (at least to a functional level) by anyone with access to AI tools.

This is Coase's equation, scrambled.

What This Means for Organisations

If external costs fall faster than internal costs, Coase's logic suggests organisations should shrink. Why maintain large in-house teams when you can access expertise on demand?

We're already seeing this. Small teams are building products that once required hundreds of engineers. Startups are competing with incumbents not by matching their headcount, but by leveraging AI to punch above their weight. The Y Combinator statistic that 25% of their current batch has 95% AI-generated codebases isn't just a fluke; it's a signal of major restructuring.

But it's not that simple. Some expertise becomes more valuable as AI commoditises the rest. The ability to judge AI output, to ask the right questions, to integrate across domains, to make decisions under uncertainty are human capabilities that now command premiums precisely because the routine work around them has become cheap.

The organisations that thrive won't be the ones that simply cut costs. They'll be the ones that understand which expertise to internalise (because it's core to differentiation) and which to access externally (because AI has commoditised it).

The Policy Challenge

For policy makers, the implications are equally significant.

If AI dramatically reduces the cost of accessing expertise, what happens to the professions built around providing it? Legal services, accounting, consulting, software development are all facing versions of this question. The answer isn't mass unemployment (we've heard that prediction before), but it is structural change that policy needs to anticipate.

More subtly, if small organisations can now access expertise that was previously the preserve of large ones, what does this mean for competition policy? For industrial strategy? For how we think about supporting innovation?

The old assumption was that scale confers advantages through accumulated expertise. With AI, this is now weakening. A two-person startup with AI tools might genuinely compete with an established player in ways that weren't possible even three years ago. This changes the calculus for regulators and for government investment in innovation.

The Leadership Question

For digital leaders, the practical question revisiting Coase’s work is which expertise should you own, and which should you rent?

The answer requires honest assessment. What capabilities really differentiate your organisation? What requires deep contextual knowledge that AI can't easily replicate? What involves judgement, relationships, and trust that remain fundamentally human?

Those you invest in. Those you build. Those you protect.

Everything else AI is making it increasingly available on demand. Fighting that transition is futile. The smart play is to redirect resources from commoditised expertise toward the capabilities that still create differentiation.

Coase Updated

Coase's insight remains valid. Firms exist because sometimes it's more efficient to organise activity internally than to transact externally. What's changed is the cost curve.

AI is dramatically reducing the external cost of expertise. That pressure will reshape organisations by making some smaller and more focused, enabling others to expand into areas where they previously lacked capabilities, and forcing all of them to reconsider where their boundaries should sit.

The economists will eventually update the models. In the meantime, digital leaders and policy makers need to act on the implications now. The expertise that defined your organisation yesterday may be available to everyone tomorrow. The question is: what will you do that still matters?