I recently re-read this post exploring the relationship between crypto networks and the theory of the firm. As AI increasingly becomes a fundamental part of the decentralized stack, I thought it would be timely to re-explore this concept and the impact crypto networks + AI will have on the future of coordination.
Firms vs. Markets as Coordination Mechanisms
Classic economic theory contrasts two primary ways to organize economic activity: within firms or through the open market. Ronald Coase’s seminal work “The Nature of the Firm” (1937) addresses why firms exist instead of relying purely on market transactions. Coase argued that using the open market incurs significant transaction costs – the myriad frictions in finding trading partners, negotiating contracts, and enforcing agreements. In a pure market, obtaining a service or input is more than just paying its price; one must also spend resources on search and information, bargaining, maintaining trade secrets, and policing and enforcing contracts. These extra costs can make market-based coordination inefficient for frequent or complex interactions.
Firms arise to minimize these transaction costs. By bringing many transactions inside an organization under hierarchical control, firms avoid the need for constant market contracting. Within a firm, an entrepreneur or manager can direct resources via authority rather than negotiation, saving cost and time. For example:
Lower negotiation overhead: Instead of bargaining a contract for every task, an employee’s duties can be assigned by managers, reducing bargaining and enforcement expenses.
Streamlined communication: Information flows internally (e.g. through memos or meetings) without costly public searches for data or partners.
Enforcement by hierarchy: Underperformance can be punished by demotion or firing, avoiding complex legal enforcement of contracts.
By internalizing these functions, firms achieve economies of coordination that open markets cannot, as long as the firm’s internal bureaucracy does not itself become too costly. Coase noted that firms grow until the cost of organizing an extra transaction internally equals the cost of using the market for that transaction. In other words, a firm expands its boundaries until internal administrative costs (e.g. manager overload, overhead) rise to parity with market transaction costs. Beyond that point, it’s not efficient to bring more activities in-house. This balance explains why firms integrate some stages of production but outsource others, and why firm size is limited. It also implies that improvements in technology that lower communication or management costs allow larger firms, as seen historically with telephones and the internet reducing internal coordination cost.
In summary, firms exist as a coordination mechanism to reduce transaction costs compared to open market exchanges. Hierarchical organizations can be viewed as a tool for efficiency when markets are too frictional. However, new technological paradigms are now emerging that challenge this traditional dichotomy of “firm vs market” by offering alternative ways to coordinate economic activity at scale.
Crypto Networks Change the Equation
Crypto networks present a novel coordination structure that blurs the line between firm and market. These networks operate through trustless protocols, smart contracts, and token-driven incentives rather than through a managerial hierarchy or price-mediated contracts.
As Qiao Wang wrote in his original post on this subject, “Cryptonetworks have the potential to bridge the gap between the market and the firm. It complements the firm by bringing the meritocracy of the market. It complements the market by offering the transaction cost benefits of the firm.”
Several features enable crypto networks to reduce coordination frictions:
Trustless transactions: Blockchains allow parties who don’t know or trust each other to cooperate using verifiable code and cryptography. This cuts out intermediaries and the need for extensive contracting or legal trust arrangements. A smart contract can automatically enforce an agreement, eliminating costly enforcement and ensuring compliance by design. By removing “unnecessary intermediaries” in execution, smart contracts significantly reduce transaction and enforcement costs.
Token incentives and governance: Many crypto networks are coordinated via native tokens that economically reward participants for contributing work (securing the network, providing a service, etc). These token incentives align participants’ interests with the success of the network, much as equity in a firm aligns employees with shareholders. The network’s rules replace layers of management by automatically allocating rewards or decision-making power to those who add value.
Distributed consensus: Distributed consensus algorithms enable a network of participants to agree on system state without a central authority. This consensus replaces the coordinating role of a firm’s central manager in certain tasks.
The impact of these innovations is especially evident in industries centered around finance and resource allocation, where coordination and trust costs are often high.
In finance, crypto networks disintermediate traditional firm-based structures (banks, exchanges, etc.) through DeFi protocols. A powerful example of this is Uniswap. Uniswap uses smart contracts to pool liquidity and execute trades automatically, allowing any user to swap assets without a centralized exchange firm. By 2023, Uniswap's DEX was processing higher trading volumes than Coinbase, one of the largest centralized crypto exchanges. In April 2023, Uniswap handled about $37 billion in volume (surpassing Coinbase’s $34 billion), marking the fourth consecutive month it outpaced the centralized exchange. This achievement underscores how a trustless DeFi protocol with open participation can coordinate traders and liquidity providers worldwide as effectively as, or even more efficiently than, a conventional firm. Not only this, but the codification of typically expensive operations that require firms or complex operations (like providing liquidity) has significantly lowered the costs of these operations. This has led to the rapid expansion of the number of assets that can be viable in a market environment—especially in long-tail sectors where limited market sizes would have typically restricted efficient market interactions.
Another domain is resource provisioning. Decentralized networks more efficiently coordinate the provisioning of different, potentially nebulous resources such as power, compute, storage, intelligence, and more. For example, Bitcoin is the most efficient energy market to ever exist. Before Bitcoin, if one had access to power/energy, the paths to capturing value from that were indirect. One would have to sell their energy to a firm, start an energy company themselves, etc. With Bitcoin, the most efficient path to capturing value from one’s access to power emerges (by using that power to secure the Bitcoin network and get compensated for doing so). A similar paradigm emerges for provisioning many other resources more efficiently across different networks.
In summary, crypto networks function as alternative coordination mechanisms that can replace or complement firms. They reduce traditional transaction costs (for trust, verification, and contracting) by using code and tokens instead of corporate hierarchy. These decentralized networks are especially powerful in domains like finance and resource allocation, where they match suppliers and users of capital or services with minimal overhead. They hint that much economic coordination will increasingly shift away from traditional firms when such coordination can be achieved more efficiently on open protocols.
AI as a Catalyst for Crypto Networks
The rise of AI is further supercharging decentralized coordination. AI can serve as an extremely efficient market participant and organizer, injecting intelligence and automation into cryptonetworks and other collaborative systems. In effect, AI reduces the cognitive and administrative burdens of coordination, making decentralized networks more viable across a larger number of sectors. A couple trends illustrate AI’s role in accelerating the decline of traditional firm hierarchies through crypto networks:
AI Agents in Decentralized Networks
Beyond automating existing workflows, AI can actively participate in decentralized crypto networks as autonomous agents. We are beginning to see AI-driven agents that can execute smart contracts, trade assets, or optimize resource allocation on their own.
The recent work around AI agents in crypto have demonstrated that even strategic decisions like automated liquidity management and portfolio optimization can be handled by AI under human-defined rules. In DeFi, algorithmic trading bots already provide continuous market making and arbitrage, essentially acting as self-directed economic agents. As AI improves, so with these agents capabilities—expanding the scope and complexity of economic actions they are able to execute. This advanced automation could organize complex economic activities without a traditional firm orchestrating the process.
Flash Organizations
AI and decentralized networks also enable the creation of ephemeral, on-demand organizations. Rather than a standing firm with long-term employees, projects can assemble talent ad hoc from global pools when needed. Research at Stanford demonstrated flash teams that algorithmically recruit and coordinate experts to prototype a product in a day. These teams can even reconfigure their organizational structure on the fly (“flash organizations”) as tasks evolve.
AI-driven flash organizations ensure the right resources are brought together temporarily to achieve a goal, then disbands the team. This fluid approach, enhanced by AI matchmaking and oversight, dramatically lowers the cost of bringing collaborators together compared to hiring within a firm. It suggests a future where many projects happen through AI-orchestrated networks of freelancers or agents, with no enduring firm needed for the collaboration to take place.
Crucially, AI not only cuts coordination costs; it also improves decision quality and speed. ML models can process vast data to inform decisions (e.g. optimizing a network’s resource allocation or detecting inefficiencies) far faster than a committee of managers. In decentralized governance, AI can assist token-holders by analyzing proposals or even proposing optimal governance changes, making collective decision-making more effective.
All these factors mean that AI acts as a force multiplier for crypto networks and other decentralized structures: it enables them to handle the complexity and scale that traditionally only large firms could manage. By lowering the information-processing and coordination costs even further – potentially by orders of magnitude – AI accelerates the shift of coordination away from traditional firm hierarchies.
Future Trajectory: Declining Role of Traditional Firms
As crypto networks and AI systems continue to advance, we can anticipate profound economic and structural shifts in how coordination is achieved. The combined effect is a world in which traditional firms play a shrinking role as the default mode of organization. Several high-level trends and implications are worth considering:
Firms being less critical for coordination: If transaction costs of using open networks drop dramatically, many activities that once had to be done within firms can move to decentralized networks. We will see a future where an increasingly narrow set of functions require firm structures, while the majority of coordination happens via crypto networks and AI-managed market environments. Economic value creation will increasingly happen in ecosystems rather than within traditional firms. Already, we’ve seen the rise of phenomena like open-source software and Wikipedia (examples of “commons-based peer production”) that coordinate thousands of contributors outside firm boundaries. Crypto networks take this a step further by adding financial incentives and trustless coordination, allowing even more complex endeavors without a firm. The result will be an economy where firms coexist with robust non-firm networks as parallel forms of organization – and much of the growth and innovation will likely skew toward the latter.
Reimagined firm structures: The firms that do persist may themselves adopt more decentralized, network-like architectures. Forward-looking organizations are already flattening hierarchies and using AI tools for internal coordination. Even within firms, employees might not have permanent bosses; instead, they rotate through projects with guidance from mentors rather than orders from managers. This begins to resemble the way decentralized projects operate, where contributors self-organize around tasks. The boundary between an agile firm and a DAO will blur, as companies adopt crypto-flavored decision-making tools. Traditional firms will evolve – becoming smaller, more project-based, and integrating more tightly with AI and blockchain to remain competitive on transaction costs.
Global and continuous coordination: Decentralized networks and AI together enable 24/7, global coordination that outpaces many firm-centric systems. An AI-enhanced crypto network can allocate resources across a much larger set of sectors or assets more efficiently than firm-centric environments. This will lead to more efficient use of global talent and assets than the patchwork of multinational corporations we have today. Economic coordination will become more continuous and fluid – think of financial liquidity being allocated worldwide by AI agents, or compute tasks dynamically routed to wherever capacity is cheapest, all without central planners. Such fluid markets were previously theoretical due to high transaction costs and information delays; with near-zero marginal costs and instant data processing, they become practical. The upshot is an economic landscape dominated by decentralized networks – networks that achieve scale and capability far beyond what any traditional firm could with lower transaction costs than open markets on their own.
The trajectory points to a diminishing need for the classic firm as the primary coordinator of economic activity. Decentralized crypto networks have demonstrated that open protocols can organize capital and labor with an efficiency and scale that rivals large companies, especially in domains like finance and digital resource provisioning. Layering AI into these networks exponentially increases their coordination power, handling complexity and decision-making with little human overhead. As transaction costs plummet due to these technologies, Coase’s original reason for firms’ existence is being eroded.
In the coming years, a growing portion of commerce and innovation will be conducted through crypto networks, DAOs, and AI agent ecosystems, while traditional firms play a smaller, more specialized role. The economic and structural shifts will be profound: we will witness a world where human economic collaboration is primarily mediated by code and distributed consensus rather than corporate management, fulfilling the promise of a more open, efficient, and globally inclusive system of coordination. Such a transformation – from firm-centric to network-centric coordination – represents a new chapter in the theory of the firm, one where technology fundamentally rewrites the rules of how we organize ourselves to create value.
