Editor’s Note:With the rapid development of AI technology, it is no longer a problem for everyone to believe that 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 areas, 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, and DeFAI is expected to simplify the user experience through AI and attract more mainstream users: they can parse on-chain data in real time, and can also help you complete complex strategies such as cross-chain arbitrage, dynamic staking, and flash loan combinations , and even participate in protocol upgrades through DAO governance. Just like search engines allow ordinary people to surf the Internet without understanding the TCP protocol, DeFAI will enable every novice user to have hedge fund-level asset management capabilities.
At present, some DeFAI projects have emerged. The author of this article, Daniele, is the founder of the DeFAI head project Hey Anon ($ANON). As a well-known DeFi developer, he has led the development of algorithmic stablecoin Wonderland, decentralized lending AbracadabraMoney and DEXWAGMI. Today, Hey Anon, which he founded, focuses on AI-driven DeFi automation tools. The TypeScript-based solution is designed to be integrated into the DeFi protocol, enabling agents to manage on-chain interactions with unprecedented security and simplicity . Its market value ranks third in the CoinmarketCap DeFAI sector.
Inspired by DeepseekR1's breakthrough in open source AI reasoning, Daniele explored in depth how DeFi can benefit from AI technology. I believe everyone will be able to gain some new insights from his insights.
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Artificial intelligence is accelerating. Large language models (LLMs) are enabling everything from conversational assistants to DeFi multi-step transaction automation. However, the cost and complexity of deploying these models at scale remains a significant barrier. Deepseek R1, a new open source AI model, has emerged to provide powerful reasoning capabilities at a lower cost - paving the way for millions of new users and application scenarios.
This article will explore:
- Deepseek R1’s breakthrough in open source AI reasoning
- How low-cost inference and flexible licensing can drive widespread adoption
- Why Jevons’ paradox suggests that efficiency gains may drive up usage (and costs) — but still be a net positive for AI developers
- How DeFAI benefits from the proliferation of AI in financial applications
Part.1 Deepseek R1: Redefining open source AI
Deepseek R1 is a new LLM based on extensive text training, optimized for reasoning and contextual understanding . Its outstanding features include:
• Efficient architecture: Adopts a new generation parameter structure to achieve near-top performance in complex reasoning tasks without the need for a large GPU cluster .
• Low hardware requirements: The design supports running on a small number of GPUs or even high-end CPU clusters, lowering the threshold for use by startups, independent developers and open source communities.
•Open source licensing: Unlike most proprietary models, its permissive licensing allows enterprises to integrate directly into products – driving rapid adoption, plugin development, and professional fine-tuning.
This democratization of AI is reminiscent of the early days of open source projects like Linux, Apache, and MySQL — projects that ultimately drove exponential growth in the tech ecosystem.
Part 2 Value Proposition of Low-Cost AI
Accelerating Popularization
When high-quality AI models are economical to run:
• Small and medium-sized enterprises: Deploy AI solutions without relying on expensive proprietary services.
• Developers: Freedom to experiment—from chatbots to automated research assistants, iterate on innovation within budget.
• Geographic diversification : Companies in emerging markets can seamlessly access AI solutions to bridge the digital divide in industries such as finance, healthcare, and education.
Democratizing Reasoning
Low-cost inference not only drives adoption, it democratizes inference:
- Localized models : Small communities can train Deepseek R1 with language-specific or domain-specific corpora (e.g., professional medical/legal data).
- Modular expansion : Developers and independent researchers can build advanced plug-ins (such as code analysis, supply chain optimization, and on-chain transaction verification) to break through licensing bottlenecks.
Overall, cost savings lead to more experiments, which in turn accelerates overall innovation in the AI ecosystem.
Part 3 Jevons Paradox: Why Efficiency Improvement Drives Up Consumption
What is the Jevons paradox?
The theory states that efficiency gains often lead to increased rather than reduced resource consumption . Initially discovered in the context of coal use, it means that when a process becomes more economical, people tend to expand its use, offsetting (and sometimes exceeding) the efficiency gains.
In the context of Deepseek R1:
• Low-cost model : Reduces hardware requirements, making AI more economical to run.
• Result : More companies, researchers, and enthusiasts launch AI use cases.
• Effect : Although the operating cost of a single instance decreases, the surge in total number may push up the overall computing power consumption (and cost).
Is this bad news?
Not necessarily. The widespread use of models such as Deepseek R1 signals a surge in adoption and applications, which will drive:
• Eco-prosperity : More developers improve open source code functionality, fix vulnerabilities, and optimize performance.
• Hardware innovation : GPU, CPU, and dedicated AI chip makers respond to surging demand by competing on price and energy efficiency.
• Business opportunities : Builders of analytical tools, process orchestration, professional data pre-processing and other fields will benefit from the upsurge in AI use.
Therefore, while the Jevons paradox suggests that infrastructure costs may rise, it is a positive signal for the AI industry as a whole - promoting the development of an innovative environment that can lead to breakthroughs in economic deployment (such as advanced compression technology or offloading tasks to dedicated chips).
Part 4 Impact on DeFAI
DeFAI: When AI meets DeFi
DeFAI combines decentralized finance with AI automation, enabling agents to manage on-chain assets, perform multi-step transactions, and interact with DeFi protocols. This emerging field directly benefits from open source, low-cost AI because:
• 24/7 autonomy
The agent can continuously scan the DeFi market, bridge inter-chain assets and adjust positions. The low inference cost makes 24/7 operation financially viable.
• Unlimited expansion
When thousands of DeFAI agents need to serve different users or protocols simultaneously, low-cost models such as Deepseek R1 can control operating expenses.
• Customization
Developers can fine-tune open source AI using DeFi-specific data (price feeds, on-chain analytics, governance forums) without paying high licensing fees.
More AI agents, stronger financial automation
As Deepseek R1 lowers the AI threshold, DeFAI forms a positive cycle:
• Intelligent Agent Explosion : Developers create professional robots (such as yield hunting, liquidity provision, NFT trading, cross-chain arbitrage)
• Efficiency improvement : Each agent optimizes the flow of funds, which may increase the overall activity and liquidity of DeFi
• Industry growth : More complex DeFi products emerge, from advanced derivatives to conditional payments, all coordinated by accessible AI
The end result is that the entire DeFAI field benefits from the virtuous cycle of " user growth-intelligent agent evolution ".
Part.5 Outlook: Positive signals from AI developers
Thriving open source community
After Deepseek R1 is open source, the community can:
- Quick fix for vulnerabilities
- Propose an inference optimization solution
- Create domain forks (such as finance, law, and medicine)
Collaborative development leads to continuous model improvement and the emergence of ecosystem tools (fine-tuning frameworks, model serving infrastructure, etc.)
New profit path
AI developers in areas such as DeFAI can break through the traditional API call charging model :
• Hosted AI Instance : Provides enterprise-level Deepseek R1 hosting services with a user-friendly dashboard
• Service layer construction : Based on the open source model, integrate advanced functions such as compliance review and real-time intelligence for DeFi operators
• Agent Marketplace : Hosting agent profiles with unique strategies or risk profiles, offering subscription or performance sharing services
Such business models will flourish when the underlying AI technology can scale to millions of concurrent users without bankrupting the vendor.
Low threshold = talent pool expansion
As the demand for Deepseek R1 decreases, more developers around the world can participate in AI experiments. This influx of talent:
• Inspire innovative solutions to real-world and cryptographic challenges;
• Enrich the open source community with fresh ideas and improvements;
• Unleash global talent that was once locked out by high computing costs.
Conclusion
The emergence of Deepseek R1 marks a key turning point: open source AI no longer requires expensive computing power or licensing fees . By providing powerful reasoning 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 is ultimately beneficial to the AI ecosystem - driving hardware innovation, community contributions, and advanced application development.
For DeFAI, AI agents that coordinate financial operations on decentralized networks will have a significant ripple effect. Lower costs mean more complex agents, greater accessibility, and an expanding array of on-chain strategies . From yield aggregators to risk management, these advanced AI solutions can operate sustainably, opening up new paths for crypto adoption and innovation.
Deepseek R1 demonstrates how open source progress can catalyze entire industries - both AI and DeFi. We are standing on the threshold of a future where AI is no longer a tool for a privileged few, but will become a fundamental element of everyday finance, creativity, and global decision-making - driven by open models, economic infrastructure, and unstoppable community momentum .
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