Author: danielesesta; Source: Coinspire
Editor's Note: In today's rapid development of AI technology, it is no longer a question whether Web3 can benefit from AI. The real focus is: which Web3 track can seize the dividends of AI the fastest, and how to maximize the use of AI to achieve breakthroughs - decentralized finance (DeFi) is undoubtedly one of the most promising fields, and the intersection of the two - DeFAI (DeFi+AI), is becoming one of the fastest growing tracks in the crypto economy.
The essence of DeFAI is to make AI the "autopilot" of the on-chain world. The current complexity of DeFi has always been a barrier for ordinary users to enter, while DeFAI is expected to simplify the user experience through AI and attract more mainstream users: they can analyze on-chain data in real-time, and also help you complete cross-chain arbitrage, dynamic staking, flash loan combinations and other complex strategies, and even participate in protocol upgrades through DAO governance, just like search engines allow ordinary people to surf the internet without knowing TCP protocol, DeFAI will give every novice user the asset management capabilities of a hedge fund.
Currently, some DeFAI projects have already emerged, and the author of this article, Daniele, is the founder of the leading DeFAI project Hey Anon ($ANON). As a well-known DeFi developer, he has led the development of projects including algorithmic stablecoin Wonderland, decentralized lending AbracadabraMoney and DEX WAGMI. Now, his founded Hey Anon is focused on AI-driven DeFi automation tools, and the TypeScript-based solution it has launched aims to integrate into DeFi protocols, enabling agents to manage on-chain interactions with unprecedented security and simplicity, and its market capitalization ranks third in the CoinmarketCap DeFAI sector.
Daniele was inspired by Deepseek R1's breakthrough in open-source AI reasoning, and delved into how DeFi can benefit from AI technology. I believe everyone will gain some new insights from his perspectives.
The following is the main text:
Artificial intelligence is accelerating. Large language models (LLMs) are empowering various fields from chatbots to multi-step DeFi transaction automation. However, the cost and complexity of deploying these models at scale remains a major obstacle. The new open-source AI model Deepseek R1 emerges, providing powerful reasoning capabilities at a lower cost - paving the way for hundreds of millions of new users and use cases.
This article will explore:
▶ Deepseek R1's breakthrough in open-source AI reasoning
▶ How low-cost reasoning and flexible licensing drive widespread adoption
▶ Why Jevons' paradox suggests efficiency gains may actually increase usage (and costs) - but is still a net positive for AI developers
▶ How DeFAI can benefit from the proliferation of AI in financial applications
Deepseek R1: Redefining Open-Source AI
Deepseek R1 is a new LLM trained on a broad corpus of text, optimized for reasoning and contextual understanding. Its key features include:
• Efficient architecture: Employing a new generation of parameter structures, it can achieve near-state-of-the-art performance on complex reasoning tasks without massive GPU clusters.
• Low hardware requirements: Designed to run on modest GPU setups or even high-end CPU clusters, lowering the barrier for startups, independent developers, and open-source communities.
• Open-source licensing: Unlike most proprietary models, its permissive licensing allows enterprises to directly integrate it into products - driving rapid adoption, plugin development, and professional fine-tuning.
This AI democratization trend evokes the early stages of open-source projects like Linux, Apache, and MySQL - which ultimately drove exponential growth in the tech ecosystem.
The Value Proposition of Low-Cost AI
Accelerating Adoption
When high-quality AI models become economically viable:
• SMEs: Can deploy AI solutions without relying on expensive proprietary services.
• Developers: Can freely experiment - from chatbots to automated research assistants, realizing innovations within budget.
• Geographic diversification: Emerging market enterprises can seamlessly integrate AI solutions, bridging digital divides in finance, healthcare, education, and more.
Democratizing Reasoning
Low-cost reasoning drives not just usage, but the democratization of reasoning:
• Localized models: Small communities can train Deepseek R1 on specific languages or domain corpora (e.g., specialized medical/legal data).
• Modular extensibility: Developers and independent researchers can build advanced plugins (e.g., code analysis, supply chain optimization, on-chain transaction validation), breaking through licensing barriers.
Overall, cost savings enable more experimentation, accelerating innovation across the AI ecosystem.
Jevons' Paradox: Why Efficiency Gains May Increase Consumption
What is Jevons' Paradox?
This theory states that efficiency improvements often lead to increased resource consumption, not reduction. Initially observed in the context of coal usage, it means that when a process becomes more economical, people tend to scale up its use, offsetting (and sometimes exceeding) the efficiency gains.
In the context of Deepseek R1:
• Lower-cost models: Reducing hardware requirements makes AI operation more economical.
• Result: More enterprises, researchers, and enthusiasts spin up AI instances.
• Effect: While the cost per instance decreases, the surge in total volume may drive an increase in overall compute consumption (and costs).
Is this a negative?
Not necessarily. The widespread use of models like Deepseek R1 signifies successful proliferation and application growth, which will drive:
• Ecosystem flourishing: More developers enhancing open-source code functionality, fixing bugs, and optimizing performance.
• Hardware innovation: GPU, CPU, and specialized AI chip manufacturers responding to surging demand, competing on price and energy efficiency.
• Business opportunities: Builders of analytics tools, workflow orchestration, and specialized data preprocessing will benefit from the AI usage boom.
Therefore, while Jevons' paradox suggests underlying infrastructure costs may rise, it is an overall positive signal for the AI industry - fostering an innovative environment and catalyzing economic breakthroughs (such as advanced compression techniques or offloading tasks to specialized chips).
Impact on DeFAI
DeFAI: When AI Meets DeFi
DeFAI combines decentralized finance with AI automation, enabling agents to manage on-chain assets, execute multi-step transactions, and interact with DeFi protocols. This emerging field directly benefits from open-source, low-cost AI, because:
• 24/7 Autonomy
Agents can continuously scan DeFi markets, bridge cross-chain assets, and adjust positions. Low reasoning costs make 24/7 operation financially viable.
• Unlimited Scalability
As thousands of DeFAI agents need to simultaneously serve different users or protocols, low-cost models like Deepseek R1 can control operational expenses.
• Customization
Developers can fine-tune open-source AI using DeFi-specific data (price feeds, on-chain analytics, governance forums) without paying hefty licensing fees.
More AI Agents, Stronger Financial Automation
As Deepseek R1 lowers the AI barrier, DeFAI forms a virtuous cycle:
• Agent Explosion: Developers create specialized bots (e.g., yield farming, liquidity provisioning, NFT trading, cross-chain arbitrage)
• Efficiency Gains: Each agent optimizes capital flows, potentially boosting overall DeFi activity and liquidity
• Industry Growth: More complex DeFi products emerge, from advanced derivatives to conditional payments, all coordinated by readily available AI
The ultimate result is the entire DeFAI domain benefiting from the "user growth - agent evolution" positive feedback loop.
Outlook: Positive Signals for AI Developers
Thriving Open-Source Community
Here is the English translation:New Profit Paths
Low Threshold = Expanded Talent Pool
After the open-sourcing of Deepseek R1, the community can:
• Quickly fix vulnerabilities
• Propose optimization schemes
• Create domain forks (such as finance, law, and healthcare)
Collaborative development leads to continuous model improvements and spawns ecosystem tools (fine-tuning frameworks, model service infrastructure, etc.)
AI developers in fields like DeFAI can break through the traditional API call charging model:
• Hosting AI instances: Provide enterprise-level Deepseek R1 hosting services with a friendly dashboard
• Building service layers: Based on the open-source model, integrate advanced features such as compliance review and real-time intelligence for DeFi operators
• Agent marketplace: Host agent profiles with unique strategies or risk configurations, and provide subscription or performance-sharing services
As the underlying AI technology can scale to millions of concurrent users without causing supplier bankruptcy, such business models will thrive.
With the reduced demand for Deepseek R1, more global developers can participate in AI experiments. This influx of talent:
• Inspires innovative solutions to real-world and crypto-domain problems;
• Enriches the open-source community with fresh ideas and improvements;
• Unleashes global talent previously excluded by high computing costs.
Conclusion
The emergence of Deepseek R1 marks a critical turning point: open-source AI no longer requires expensive computing power or licensing fees. By providing powerful inference capabilities at low cost, it paves the way for widespread adoption, from small development teams to large enterprises. Although Jevons' paradox suggests that infrastructure costs may rise due to surging demand, this phenomenon ultimately benefits the AI ecosystem - driving hardware innovation, community contributions, and advanced application development.
For DeFAI, the AI agents coordinating financial operations on decentralized networks will have a significant ripple effect. Lower costs mean more complex agents, greater accessibility, and an ever-expanding array of on-chain strategies. From yield aggregators to risk management, these advanced AI solutions can run sustainably, opening new paths for crypto adoption and innovation.
Deepseek R1 demonstrates how open-source progress can catalyze an entire industry - both AI and DeFi. We stand at the threshold of the future: AI will no longer be the tool of a privileged few, but the foundational element of everyday finance, creativity, and global decision-making - driven by open models, economical infrastructure, and unstoppable community momentum.
