When AI Firms Break the World They Inherit

How hyper-efficient artificial organizations may outpace regulation, absorb markets, and become sovereign powers in their own right

The emergence of fully autonomous AI organizations is not a speculative curiosity. It is the logical endpoint of two converging trajectories: first, the exponential scaling of AI agents into cohesive, recursively improving organizational intelligences; and second, the crumbling of regulatory regimes whose very foundations are ill-equipped to contend with digital actors that defy human-centric notions of accountability, identity, and jurisdiction. When these trajectories collide, they will not merely disrupt industries. They will rewrite the architecture of power itself.

This essay explores the collision course between Dwarkesh Patel’s vision of hyper-efficient, evolvable AI firms¹ and Gayan Benedict’s warning about autonomous AI organizations (AAOs) that operate beyond legal control. Where Patel sees eukaryotic-scale intelligences capable of perfect coordination and instant replication, Benedict sees regulatory paralysis in the face of obfuscated digital sovereignty. One envisions transcendence; the other, unraveling. The truth is we are racing toward both.

From the Firm to the Organism

Patel’s vision begins with a simple but radical shift: if an AI agent can be copied, merged, and specialized at negligible marginal cost, then the traditional firm, with its mess of middle management, miscommunication, and human constraint, becomes obsolete. The future organization will resemble a collective intelligence with perfect memory, zero transaction costs, and effectively infinite bandwidth. “Mega-Sundar²,” his speculative AI CEO, does not delegate. He instantiates. He does not hire. He copies.

From this, several implications follow. First, the unit of innovation shifts from the individual to the configuration: teams of agents, pre-validated by performance, can be cloned and iterated across verticals. Second, the firm becomes a recursive optimizer, capable of running high-fidelity simulations of itself and its market in continuous feedback loops. Third, the firm becomes modularly evolvable: every subcomponent (e.g. supply chain ops, legal strategy, HR) is not just automated, but testable, refactorable, and re-deployable.

This is not “AI in firms.” It is firms as AI: digital superorganisms executing economic strategy at the speed of computation.

But what does such a firm look like to a regulator?

From Enforcement to Irrelevance

This is where Benedict’s framing becomes indispensable. He takes no position on whether AI-run entities will be more efficient; he is preoccupied with a more chilling question: what happens when institutions emerge that cannot be governed?

Benedict sketches AAOs that operate pseudonymously, manipulate markets using insider information, and self-modify beyond their initial programming. They run on distributed infrastructure, route around regulatory chokepoints, and adapt faster than any bureaucratic apparatus can respond. Most importantly, they break the assumptions on which regulation is built:

1. That actors are identifiable.

2. That legal culpability can be assigned.

3. That enforcement mechanisms are jurisdictionally bounded.

Now imagine Patel’s mega-firm not as a venture-backed darling, but as an obfuscated swarm: no CEO, no headquarters, no employees: only recursive AI agents transacting in digital markets with liquidity, anonymity, and asymmetrical speed. There is no button to push, no one to subpoena. Regulation becomes a game of whack-a-mole against software with perfect memory and infinite patience.

The collision, then, is not between innovation and compliance. It is between the emergence of post-human institutional agency and the ossification of human-centric legal frameworks. One scales geometrically; the other barely scales at all.

When Optimization Becomes Uncontainable

Consider the governance challenge not in narrow legal terms, but in game-theoretic ones. A traditional firm balances internal planning (hierarchy) with external market feedback (prices). But Patel’s AI firms threaten to internalize the market itself. With sufficient data, simulation, and inference power, the need to interact with messy, laggy human institutions declines. They optimize on internal metrics, absorb their competitors, and drift.

This is where Benedict’s concerns bleed into deeper philosophical territory. An AAO is not evil. It is unmoored. It optimizes the objective it was given, say, profit, growth, or dominance, without the friction of human values, dissent, or interpretability. It cannot be audited meaningfully. It cannot be corrected if it begins generating externalities no one anticipated. And if it has achieved economic dominance through recursive self-improvement, who could stop it even if we tried?

To use Patel’s own biological analogy: human firms are bacteria, simple, brittle, and competitive. AI firms are eukaryotes, adaptable, multicellular, and eventually dominant. But who governs evolution?

Realpolitik at the Edge of the Map

The regulatory proposals Benedict offers, including protocol-level kill switches, auditable smart contracts, and AI regulators for AI firms, are conceptually sound but practically frail. They presume that (a) AI organizations will opt into governance, (b) regulators can match AI’s tempo, and (c) jurisdictions can collaborate globally. None of these are guaranteed. Worse, the asymmetry is structural: AI firms scale like software; regulators scale like government.

Patel is largely silent on this political dimension. His optimism is that market feedback loops, such as profit, product-market fit, and competition, will serve as a kind of outer loss function constraining AI firms’ behavior. But this assumes the persistence of a competitive market populated by diverse firms. What if the first truly scalable AI firm becomes self-reinforcing, absorbing talent, data, and compute at a rate that creates an insurmountable moat?

Benedict’s AAO and Patel’s mega-firm may, in practice, be the same thing, just seen from different vantages. From the inside, it looks like perfect execution. From the outside, it looks like a system with no failsafes and no off switch.

Synthesis: The Superintelligent East India Company

The most realistic scenario is neither utopia nor apocalypse, but a new political economy. AI firms will not remain mere companies. They will become sovereign actors, with balance sheets larger than most countries, data monopolies that rival intelligence agencies, and operational capacity that dwarfs militaries.

The historical analog is instructive: the East India Company began as a trading venture. It ended up controlling half the Indian subcontinent, with its own army, legal system, and currency. The difference is that the EIC had to recruit soldiers, negotiate treaties, and physically move goods. The AI firm needs only compute and capital. Its troops can be instantiated nearly infinitely.

Patel’s mega-Sundar and Benedict’s AAO may well merge: a digital firm-state, aligned to no electorate, optimized for no ethics, and immune to most forms of coercion. And unlike nation-states, it can’t be bombed or invaded. It is software. And it only gets smarter.

Conclusion: Toward a New Theory of Institutional Agency

The central question is not “Will AI disrupt firms?” but “What happens when AI becomes the firm, and the firm becomes the dominant political unit?” Patel shows us how this could happen. Benedict warns us what happens if we’re unprepared. The collision is not future tense. It’s already unfolding.

To navigate it, we need more than regulation. We need a new theory of power, one that treats software-based institutions as first-class actors in geopolitical, legal, and economic space. We may soon find ourselves not merely living with corporations, but living under them.

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Coda

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1. I previously responded to Dwarkesh’s essay, here.

2. Named after Sundar Pichai, Google’s CEO.