From Bitcoin to AI Autonomy: Understanding the Three Evolutions of the Crypto Network Economy
The real challenge is not to create new tokens, but to build a strong framework for collective decision-making and oversight.Original Title: "On Network Economies"
Author: 1a35e1
Compiler: Deep Tide TechFlow
"When we try to explain and improve a world that cannot be clearly described by a simple model, we need to continuously refine our theories and methods to better understand complexity, rather than simply denying it." ------ Elinor Ostrom
In the coming years, blockchain-based network economies will develop complex and diverse operating models that will be fundamentally different from the traditional business models we are familiar with today.
When studying networks, systems, or protocols, I often think of the Kardashev Scale, which is a measure of a civilization's ability to utilize and control energy. Similarly, we can assess the operational efficiency of a network based on its ability to capture and distribute economic value.
Value Capture refers to the network's ability to generate revenue through operational activities and convert user participation into economic benefits.
Value Distribution describes how the network effectively allocates these revenues to stakeholders, including investors, developers, labor contributors, end users, and even the protocol itself.
When evaluating different blockchain networks, we primarily focus on the following key attributes:
Adaptability: Can the network flexibly adjust according to project needs and market conditions?
Transparency: Are the changes in revenue and distribution mechanisms clear and predictable?
Value-alignment: Does the revenue distribution match the actual value creation?
Inclusivity: Is the revenue distribution fairly covering all stakeholders?
Based on the concept of the Kardashev Scale, I attempt to classify the three types of network economies that have emerged during the evolution of blockchain technology using the above criteria.
Type I: Fixed Mechanic Networks
The first generation of blockchain networks and tokens is often based on the "reification principle," which mimics the design concepts of traditional economic models. For example, the preset token issuance plans simulate the mining process of rare ores or the economics of scarce goods, while staking and voting mechanisms draw from traditional public voting systems or corporate governance models.
Bitcoin is a typical representative of this type, with highly deterministic operating rules: a supply cap of 21 million, fixed mining rewards and halving cycles, and a Nakamoto consensus based on Proof of Work. This system functions well as a value storage tool.
However, these systems also face significant limitations—they lack adaptability to market changes and are prone to "economic capture," where network value is excessively appropriated by specific stakeholders.
This issue is particularly evident in Curve Finance's veLocking mechanism and other early ERC-20 tokens based on value storage narratives. Curve's fixed issuance plan effectively limits the market's judgment of the token's true value and creates opportunities for external participants like Convex to "exploit" the protocol rules, highlighting how system mechanisms can be influenced by external optimizers.
Type II: Governable Parameter Networks
The second type of network is characterized by its ability to flexibly adjust parameter values. These on-chain systems can dynamically respond through oracles (such as Chainlink, UMA's Optimistic Oracle) or algorithmic information (like automated market makers AMM), forming adaptive systems that respond to changing market conditions through governance protocols.
The economic design of these networks often introduces multi-layer game theory mechanisms aimed at aligning stakeholder incentives. The competition among stablecoins and lending protocols provides important case studies, as these products hedge risks and ensure stable protocol operation through dynamic parameter adjustments.
For example, Aave, one of the earliest on-chain lending protocols in the Ethereum ecosystem, successfully protected $21 billion of user funds during extreme market volatility. To achieve this, the protocol's underlying mechanisms need continuous monitoring and optimization.
In contrast, systems that rely on off-chain components but claim to be "protocols" are often susceptible to principal-agent problems. This issue refers to agents potentially prioritizing their own interests over the collective interest. For instance, Celsius was marketed as a decentralized protocol, but at the time of its bankruptcy, its users faced $4.7 billion in debts as unsecured creditors.
Thus, true on-chain systems provide stronger protective capabilities through algorithmic control and distributed governance, making them less susceptible to power concentration or human decision-making errors.
Type III: Autonomous Networks
The third type of network represents the theoretical direction of blockchain technology evolving into fully autonomous systems. These systems will operate with minimal human intervention, capable of highly adaptive adjustments based on environmental changes, and demonstrate high efficiency in cross-system information transmission.
While there are currently no real-world examples, it is foreseeable that such systems may possess the following characteristics:
Autonomous Parameter Optimization: Multiple AI agents will continuously optimize the protocol, learning from the market and dynamically adjusting system parameters through real-time data aggregation and evolutionary algorithms.
Algorithmic Value Orchestration: Based on predictive models and reward optimization, dynamic fee structures can automatically adjust according to network usage, ensuring the long-term sustainability of the protocol.
Governance in a Dynamical System
The complexity of blockchain network economies requires systems to possess sufficient flexibility to respond to potential survival threats while maintaining a balanced operational state. In this process, governance mechanisms play a crucial role at every stage of the network's development.
The inherent governance capacity of a system provides it with a survival advantage in a "dark forest" environment. The "dark forest" typically refers to a highly competitive and threatening environment in the blockchain space. The tension between governance flexibility and security is most intuitively reflected in how networks respond to changes in the external environment.
Type I networks (like Bitcoin) prioritize security through strict immutability, while Type II networks (like Aave) demonstrate greater adaptability through parameter adjustments. However, both fail to fully resolve the contradiction between flexibility and stability: excessive pursuit of flexibility may undermine security, while an overemphasis on stability may limit the system's adaptability.
Polycentric Systems and the Commons
In exploring best practices for blockchain governance, I discovered the pioneering research on commons management by Nobel laureate Elinor Ostrom. Although her research is not entirely aligned with token economics, her empirical studies provide a clear roadmap for achieving Type III systems.
A polycentric system is a governance model in which multiple independent decision centers possess a degree of autonomy while simultaneously collaborating as part of a cohesive system.
The main characteristics of polycentric systems include:
The existence of multiple authorities and decision centers that are formally independent;
Overlapping and interactive jurisdictions and responsibilities among the centers;
Significant autonomy for each center within a unified framework;
Coordination achieved through formal or informal mechanisms.
Ostrom's Eight Principles
Based on her study of over 800 global cases, Ostrom summarized eight principles for commons management. These principles are also of great significance in the governance of blockchain and cryptocurrencies:
Clear boundaries: Clearly define the scope of resource use and users;
Rules adapted to local conditions: Rules should be context-specific;
Participatory decision-making: Stakeholders collaboratively establish rules;
Effective monitoring: Ensure compliance with the rules;
Gradual sanctions: Implement progressively escalating penalties for violations;
Accessible conflict resolution mechanisms: Provide fair and efficient dispute resolution pathways;
Organizational rights: Allow community members to self-organize;
Nested enterprises: Include multiple levels of organizational structure within a larger governance framework.
If we believe that tokenized economies are the trend of the future, we must recognize that governance technology is key to the success of these emerging systems.
Conclusion
Despite significant investments in token economics and cryptocurrency infrastructure, we are under-invested in the core area of governance systems. The real challenge is not to create new tokens but to build robust frameworks for collective decision-making and oversight. The venture capital industry's excessive focus on tokens reflects a misalignment between short-term profit incentives and the long-term sustainability of decentralized systems. Without complex and sound governance mechanisms, even the most sophisticated token designs will struggle to achieve lasting value.
The evolution of network economies from Type I to Type III systems is not just a technological advancement but also an ongoing exploration of how to build more resilient, adaptive, and equitable digital ecosystems. The fixed mechanisms of Bitcoin, the parameterized governance of Aave, and the theoretical potential of autonomous networks all provide valuable insights for this evolutionary journey.
Ostrom's research on polycentric systems and commons management bridges traditional governance wisdom with the future of digital networks. Her principles, validated by hundreds of real-world cases, offer valuable guidance for addressing the core challenges of network governance: how to balance security and flexibility, ensure fair value distribution, and promote evolution while maintaining system integrity.
As network economies evolve towards greater complexity, success may hinge on integrating the following diverse approaches:
The "security-first" mindset of Type I networks: Ensuring system security through fixed rules;
The adaptability of Type II systems: Responding to changes through dynamic parameter adjustments;
The autonomous potential of Type III networks: Minimizing human intervention through AI and algorithms;
The empirical wisdom of polycentric governance: Achieving coordination and development through multi-layered, multi-centered governance structures.
The future of network economies will not be determined by technological capabilities or popular culture but by our ability to implement these systems in a way that serves all stakeholders while maintaining operational resilience. As networks continue to evolve, the integration of artificial intelligence, dynamic parameter optimization, and new governance structures may create economic organizational forms that we do not yet fully understand.
It is certain that the path forward requires us to embrace complexity rather than attempt to evade it. As Ostrom suggested, our task is not to simplify these systems but to develop better frameworks to understand and manage them. The next generation of network economies needs to be as complex as the problems they aim to solve while remaining friendly and equitable to all participants.
