Editor's Note:

In today's rapidly advancing AI technology landscape, the question of whether Web3 can benefit from AI is no longer in doubt. The real focus is on: which Web3 track can seize the AI dividend the fastest, and how to maximize the use of AI for 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 complexity of current DeFi has always been a barrier for ordinary users, while DeFAI is expected to simplify the user experience through AI, attracting more mainstream users: it can analyze on-chain data in real-time and help you execute complex strategies such as cross-chain arbitrage, dynamic staking, and flash loan combinations, even participating in protocol upgrades through DAO governance. Just as search engines allow ordinary people to access the internet without understanding TCP protocols, DeFAI will empower every novice user with hedge fund-level asset management capabilities.

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 the algorithmic stablecoin Wonderland, decentralized lending platform AbracadabraMoney, and DEXWAGMI. Now, his founded Hey Anon focuses on AI-driven DeFi automation tools, with a TypeScript-based solution aimed at integrating into DeFi protocols, enabling agents to manage on-chain interactions with unprecedented security and simplicity, ranking third in market capitalization in the CoinmarketCap DeFAI section.

Daniele draws inspiration from the breakthroughs of DeepseekR1 in open-source AI inference, exploring how DeFi can benefit from AI technology, and believes that everyone can gain new insights from his perspectives.

Main Text

Artificial intelligence is accelerating its development. Large language models (LLMs) are empowering various fields, from conversational assistants to multi-step trading automation in DeFi. However, the cost and complexity of deploying these models at scale remain significant obstacles. The emergence of the new open-source AI model Deepseek R1 provides powerful inference capabilities at a lower cost—paving the way for millions of new users and application scenarios.

This article will explore:

• Breakthroughs of Deepseek R1 in open-source AI inference
• How low-cost inference and flexible licensing drive widespread adoption
• Why the Jevons Paradox suggests that efficiency improvements may actually increase usage (and costs)—but still represent a net benefit 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 type of LLM trained on extensive text, optimized for inference and contextual understanding. Its standout features include:

• Efficient architecture: Utilizing next-generation parameter structures, it achieves near-top performance in complex inference tasks without the need for large GPU clusters.

• Low hardware requirements: Designed to run on a small number of GPUs or even high-end CPU clusters, lowering the entry barrier for startups, independent developers, and the open-source community.

• Open-source licensing: Unlike most proprietary models, its permissive license allows businesses to integrate it directly into their products—promoting rapid adoption, plugin development, and specialized fine-tuning.

This trend of AI democratization is reminiscent of the early stages of open-source projects like Linux, Apache, and MySQL—these projects ultimately drove exponential growth in the tech ecosystem.

Part.2 The Value Proposition of Low-Cost AI

Accelerating Proliferation

When high-quality AI models become economically viable:

• Small and medium enterprises: Can deploy AI solutions without relying on expensive proprietary services.

• Developers: Can freely experiment—from chatbots to automated research assistants, achieving innovative iterations within budget.

• Geographical diversification: Emerging market enterprises can seamlessly access AI solutions, bridging the digital divide in finance, healthcare, education, and more.

Democratizing Inference

Low-cost inference not only drives usage but also democratizes inference:

• Localized models: Small communities can train Deepseek R1 using specific language or domain corpora (e.g., specialized medical/legal data).
• Modular expansion: Developers and independent researchers can build advanced plugins (e.g., code analysis, supply chain optimization, on-chain transaction validation) to break through licensing bottlenecks.

Overall, cost savings foster more experimentation, accelerating overall innovation in the AI ecosystem.

Part.3 Jevons Paradox: Why Efficiency Improvements Increase Consumption

What is Jevons Paradox?

This theory suggests that efficiency improvements often lead to increased resource consumption rather than a decrease. Initially discovered in the context of coal usage, it means that when processes become more economical, people tend to scale up usage, offsetting (and sometimes exceeding) the efficiency gains.

In the context of Deepseek R1:

• Low-cost models: Reduce hardware requirements, making AI operation more economical.

• Results: More businesses, researchers, and enthusiasts launch AI instances.

• Effect: Although the operating cost per instance decreases, the surge in total instances may increase overall computational consumption (and costs).

Is this a negative signal?

Not necessarily. The widespread use of models like Deepseek R1 signifies successful proliferation and application surges, which will drive:

• Ecosystem prosperity: More developers will enhance open-source code functionality, fix bugs, and optimize performance.

• Hardware innovation: GPU, CPU, and dedicated AI chip manufacturers will respond to the surge in demand, competing on price and energy efficiency.

• Business opportunities: Builders in areas like analytical tools, process orchestration, and specialized data preprocessing will benefit from the AI usage boom.

Thus, while Jevons Paradox suggests that infrastructure costs may rise due to increased demand, this phenomenon is ultimately a positive signal for the AI industry—driving the development of an innovative environment and fostering breakthroughs in economic deployment (such as advanced compression techniques or task offloading 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, 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 the DeFi market, bridge assets across chains, and adjust positions. Low inference costs make 24/7 operation financially viable.

• Unlimited Scalability

When thousands of DeFAI agents need to serve different users or protocols simultaneously, 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 high licensing fees.

More AI Agents, Stronger Financial Automation

As Deepseek R1 lowers the AI barrier, DeFAI forms a positive feedback loop:

• Agent Explosion: Developers create specialized bots (e.g., yield hunting, liquidity provision, NFT trading, cross-chain arbitrage).

• Efficiency Gains: Each agent optimizes capital flow, potentially increasing overall DeFi activity and liquidity.

• Industry Growth: More complex DeFi products emerge, from advanced derivatives to conditional payments, all coordinated by easily accessible AI.

The ultimate result— the entire DeFAI field benefits from a virtuous cycle of "user growth - agent evolution."

Part.5 Outlook: Positive Signals for AI Developers

Thriving Open-Source Community

After the open-sourcing of Deepseek R1, the community can:

• Quickly fix bugs
• Propose inference optimization solutions
• Create domain forks (e.g., finance, law, healthcare)

Collaborative development leads to continuous model improvements and spawns ecosystem tools (fine-tuning frameworks, model service infrastructure, etc.).

New Profit Paths

AI developers in fields like DeFAI can break through traditional API call charging models:

• Managed AI Instances: Provide enterprise-level Deepseek R1 hosting services, equipped with user-friendly dashboards.

• Service Layer Development: Integrate compliance checks, real-time intelligence, and other advanced features for DeFi operators based on open-source models.

• Agent Marketplace: Host profiles of agents with unique strategies or risk configurations, offering subscription or performance-sharing services.

When underlying AI technology can scale to millions of concurrent users without bankrupting providers, such business models will thrive.

Lower Barriers = Expanded Talent Pool

As the demand for Deepseek R1 decreases, more developers worldwide can participate in AI experimentation. This influx of talent:

• Sparks innovative solutions to real-world and crypto domain challenges;

• Enriches the open-source community with fresh ideas and improvements;

• Unlocks global talent previously shut out by high computational costs.

Conclusion

The emergence of Deepseek R1 marks a critical turning point: open-source AI no longer requires expensive computational power or licensing fees. By providing powerful inference capabilities at a 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, AI agents coordinating financial operations on decentralized networks will create significant ripple effects. 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 operate sustainably, opening new pathways for crypto adoption and innovation.

Deepseek R1 demonstrates how open-source progress can catalyze entire industries—both AI and DeFi. We stand on the threshold of the future: AI is no longer a tool for a privileged few but will become a foundational element of everyday finance, creativity, and global decision-making—driven by open models, economical infrastructure, and unstoppable community momentum.

*All content on the Coinspire platform is for reference only and does not constitute an offer or advice for any investment strategy. Any personal decisions made based on the content of this article are the sole responsibility of the investor, and Coinspire is not responsible for any profits or losses incurred. Investment carries risks; decisions should be made cautiously!

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。