rules of the new economy
The idea of this page is to take Kevin Kelly’s “New Rules for the New Economy” and apply the concepts to both cryptocurrency and artificial intelligence.
1. Embrace the Swarm
This rule emphasizes the power of decentralized systems. In cryptocurrency, this is foundational—blockchains are decentralized ledgers maintained by distributed nodes, eliminating the need for central authorities.
In AI, federated learning and swarm intelligence draw directly from this idea, allowing models to be trained across multiple devices without centralizing data, enhancing privacy and resilience.
2. Increasing Returns
Network effects are crucial in both crypto and AI. The more users adopt a cryptocurrency like Bitcoin or Ethereum, the more valuable it becomes.
In AI, increasing returns show up as reinforcement cycles—more data leads to better models, which attract more users, which generates more data. Open-source AI models also benefit from community contributions that accelerate their improvement exponentially.
3. Plentitude, Not Scarcity
Digital abundance overturns traditional scarcity models. Crypto tokens can be minted in large numbers, but value comes from utility and consensus, not scarcity alone.
In AI, data and models can be replicated infinitely, and APIs scale services to global audiences. Scarcity is simulated with tokenomics, but actual systems thrive on replicable resources and open access.
4. Follow the Free
Many crypto and AI services are free at the point of use. Wallets, data tools, and basic LLMs are offered without cost, monetized through transaction fees, staking, or premium tiers.
AI startups offer free model access to gain adoption, then monetize through usage tiers or API subscriptions. “Free” becomes the lead generator and adoption driver.
5. Feed the Web First
In crypto, this manifests in protocols that are built to be composable and interoperable (e.g., DeFi platforms). Feeding the web means prioritizing open standards and APIs, ensuring your project or model contributes value back to the broader network.
AI companies open source models and open source datasets to increase visibility and encourage integration into broader ecosystems.
6. Let Go at the Top
This encourages disruption of one’s own success. In crypto, projects like Ethereum continuously evolve (e.g., shifting to proof-of-stake) even while leading.
In AI, OpenAI and Meta constantly iterate and release better models even when older ones dominate. Leaders must cannibalize their own work to stay ahead, or they risk irrelevance.
7. From Places to Spaces
Physical banks and data centers give way to digital wallets and decentralized compute networks. Cryptocurrency creates financial spaces untethered from geography.
AI creates collaboration spaces like GitHub Copilot and ChatGPT that augment cognition in virtual environments. Value creation shifts from physical real estate to digital real estate—platforms, protocols, and models.
8. No Harmony, All Flux
The pace of change in both crypto and AI means constant adaptation is essential. Hard forks, regulatory shifts, model breakthroughs, and dataset expansions all require flexibility. Teams that survive are those that iterate rapidly and accept chaos as the norm. Governance in crypto must evolve dynamically, and AI safety must continuously adjust to emerging capabilities.
9. Relationship Tech
Crypto enables peer-to-peer trustless relationships via smart contracts.
AI personalizes interactions through recommendation systems, chat interfaces, and learning companions.
Both enable deeper and more scalable human connections—blockchains through financial logic, AI through language and emotion. The most valuable platforms are those that enhance meaningful interactions.
10. Opportunities Before Efficiencies
Early-stage crypto protocols and AI models prioritize innovation. Layer 1 chains, novel consensus mechanisms, or transformer architectures all began as risky experiments. Premature optimization stifles innovation.
MVPs, hackathons, and testnets embody this principle. Capitalizing on unique capabilities precedes streamlining or cost-cutting.
11. Move from Mass to Niches
Crypto enables microeconomies through DAOs, NFTs, and tokens for small communities.
AI allows the creation of specialized agents or fine-tuned models for narrow use cases. Instead of mass products, both technologies empower personalized, long-tail solutions—crypto for community governance, AI for domain-specific assistance.
12. The Centrality of Knowledge
Crypto values open access to ledgers and code; knowledge is literally money in the form of cryptographic keys.
AI codifies knowledge in weights and embeddings, making it accessible via language. Both fields position knowledge—not physical capital—as the primary generator of wealth, reputation, and progress. The edge belongs to those who learn fastest and share most effectively.