From Bitcoin to AI Autonomy: Understanding the Three Evolutions of Cryptonetwork Economies
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From Bitcoin to AI Autonomy: Understanding the Three Evolutions of Cryptonetwork Economies
The real challenge is not creating new tokens, but building robust collective decision-making and oversight frameworks.
Navigating Web3 tides with focused insightsAuthor: 1a35e1
Compiled by: TechFlow
"When the world we try to explain and improve cannot be clearly described by a simple model, we need to continuously refine our theories and methods to better understand complexity, rather than simply dismissing it." —— Elinor Ostrom
In the coming years, blockchain-based network economies will evolve into complex and diverse operational models that are fundamentally different from the traditional business models we know today.
When studying networks, systems, or protocols, I often think of the Kardashev Scale—a metric for measuring a civilization’s ability to harness and control energy. Similarly, we can assess the efficiency of networks by their capacity to capture and distribute economic value.
Value Capture refers to a network's ability to generate revenue through operations and convert user participation into economic gains.
Value Distribution describes how these gains are effectively allocated among stakeholders—including investors, developers, contributors, end users, and even the protocol itself.
When evaluating different blockchain networks, we focus on several key attributes:
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Adaptability: Can the network flexibly adjust in response to changing project needs and market conditions?
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Transparency: Are changes in revenue and distribution mechanisms clear and predictable?
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Value-alignment: Does revenue distribution align with actual value creation?
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Inclusivity: Is revenue fairly distributed across all stakeholders?
Building on the concept of the Kardashev Scale, I attempt to classify 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 typically follows a "physical analog principle," mimicking designs from traditional economic models. For example, predetermined token issuance schedules emulate the mining process of rare minerals or the economics of scarce goods, while staking and voting mechanisms draw from conventional public voting systems or corporate governance structures.
Bitcoin is a quintessential example of this type, operating under highly deterministic rules: a 21 million supply cap, fixed mining rewards with halving cycles, and Nakamoto consensus based on Proof of Work (PoW). As a store of value, this system functions effectively.
Nevertheless, such systems face significant limitations—they lack adaptability to market changes and are prone to "economic capture," where network value is disproportionately concentrated among certain stakeholders.
This issue is particularly evident in Curve Finance’s veLocking mechanism and other early ERC-20 tokens built around value storage narratives. Curve’s fixed emission schedule effectively limits the market’s ability to price the token accurately, creating opportunities for external actors like Convex to exploit protocol rules—highlighting how system mechanics can be gamed by external optimizers.
Type II: Governable Parameter Networks
The defining feature of Type II networks is their adjustable parameters. These on-chain systems can dynamically respond via oracles (such as Chainlink or UMA’s Optimistic Oracle) or algorithmic data (like automated market makers, AMMs), forming adaptive systems capable of responding to evolving market conditions through governance.
The economic design of these networks often incorporates multi-layered game-theoretic mechanisms aimed at aligning stakeholder incentives. The competition between stablecoins and lending protocols offers instructive examples—products that dynamically tune parameters to hedge risks and ensure protocol stability.
Take Aave, one of the earliest on-chain lending protocols in the Ethereum ecosystem, which successfully protected $21 billion in user funds during periods of extreme market volatility. Achieving this required continuous monitoring and optimization of its underlying mechanisms.
In contrast, systems that rely on off-chain components but claim to be “protocols” are often vulnerable to principal-agent problems, where agents may prioritize personal interests over collective well-being. For instance, Celsius was marketed as a decentralized protocol, yet when it filed for bankruptcy, users were left as unsecured creditors owed up to $4.7 billion.
This highlights how true on-chain systems offer stronger safeguards through algorithmic control and decentralized governance, making them less susceptible to centralization of power or human error.
Type III: Autonomous Networks
Type III networks represent a theoretical direction toward fully autonomous systems in blockchain evolution. These systems would operate with minimal human intervention, exhibit high adaptability to environmental changes, and demonstrate exceptional efficiency in cross-system information exchange.
While no real-world implementations exist yet, such systems could potentially feature:
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Autonomous Parameter Optimization: Multiple AI agents continuously optimize the protocol by aggregating real-time data and applying evolutionary algorithms, learning from the market to dynamically adjust system parameters.
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Algorithmic Value Orchestration: Dynamic fee structures, driven by predictive modeling and reward optimization, automatically adapt to network usage patterns, ensuring long-term protocol sustainability.
Governance in a Dynamical System
The complexity of blockchain network economies demands sufficient flexibility to address potential existential threats while maintaining operational equilibrium. Throughout this process, governance plays a crucial role at every stage of network development.
An inherent governance capability provides survival advantages in the "dark forest" environment—a term referring to the fiercely competitive and hostile landscape of blockchain ecosystems. The tension between governance flexibility and security becomes most apparent in how networks respond to external changes.
Type I networks like Bitcoin prioritize security through strict immutability, whereas Type II networks like Aave demonstrate greater adaptability through parameter adjustments. However, neither fully resolves the trade-off between flexibility and stability: excessive flexibility may undermine security, while too much rigidity limits adaptability.
Polycentric Systems and the Commons
In exploring best practices for blockchain governance, I discovered Nobel laureate Elinor Ostrom’s pioneering research on commons management. While her work does not directly map onto token economics, her empirical findings offer a clear roadmap toward achieving Type III systems.
A polycentric system is a governance model in which multiple independent decision centers possess a degree of autonomy while also functioning as parts of an integrated whole.
Key characteristics of polycentric systems include:
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Multiple authorities and decision-making centers that are formally independent;
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Overlapping jurisdictions and interactions among centers;
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Significant autonomy for each center within a unified framework;
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Coordination achieved through formal or informal mechanisms.
Ostrom’s Eight Principles
Basing her conclusions on over 800 global case studies, Ostrom identified eight principles for effective commons governance—principles equally relevant to blockchain and cryptocurrency governance:
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Clearly defined boundaries: Clear demarcation of resource users and usage rights;
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Rules adapted to local conditions: Governance rules must reflect local context;
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Participatory decision-making: Stakeholders collectively shape the rules;
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Effective monitoring: Mechanisms to ensure rule compliance;
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Graduated sanctions: Step-by-step penalties for violations;
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Accessible conflict resolution: Fair and efficient dispute-resolution processes;
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Right to organize: Communities have the right to self-govern;
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Nested enterprises: Multiple layers of nested organizational structures within a broader governance framework.
If we believe tokenized economies represent the future, we must recognize that governance technology is the linchpin of success for these emerging systems.
Conclusion
Despite substantial investments in tokenomics and crypto infrastructure, we remain critically underinvested in governance—the core challenge. The real task is not creating new tokens, but building robust frameworks for collective decision-making and oversight. Venture capital’s obsession with tokens reflects a misalignment between short-term profit motives and the long-term sustainability of decentralized systems. Without sophisticated and sound governance mechanisms, even the most elegant token designs will struggle to sustain lasting value.
The evolution from Type I to Type III network economies represents not just technological progress, but an ongoing exploration of how to build more resilient, adaptive, and equitable digital ecosystems. Bitcoin’s fixed mechanics, Aave’s parametric governance, and the theoretical promise of autonomous networks all provide valuable lessons along this evolutionary path.
Ostrom’s research on polycentric systems and commons management bridges traditional governance wisdom with the future of digital networks. Her principles—tested across hundreds of real-world cases—offer invaluable guidance for tackling core challenges in network governance: balancing security and flexibility, ensuring fair value distribution, and enabling evolution without compromising system integrity.
As network economies grow more complex, success may lie in integrating different approaches:
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The “security-first” mindset of Type I networks: safeguarding system integrity through fixed rules;
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The adaptability of Type II systems: dynamically tuning parameters in response to change;
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The autonomous potential of Type III networks: minimizing human intervention through AI and algorithms;
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The empirical wisdom of polycentric governance: enabling coordination and development through multi-layered, decentralized structures.
The future of network economies will not be determined by technical prowess or pop culture trends, but by whether we implement these systems in ways that serve all stakeholders while maintaining operational resilience. As networks continue to evolve, the convergence of artificial intelligence, dynamic parameter optimization, and novel governance architectures may give rise to forms of economic organization we have yet to fully comprehend.
One thing is certain: moving forward requires embracing complexity, not avoiding it. As Ostrom suggested, our task is not to simplify these systems, but to develop better frameworks for understanding and managing them. The next generation of network economies must be as complex as the problems they aim to solve—and remain accessible, fair, and inclusive for all participants.
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