DeepSeeks popularity: How AI makes DeFi mainstream

Editors 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 DEX WAGMI. 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 DeepseekR 1s 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 R 1, a new open source AI model, was born to provide powerful reasoning capabilities at a lower cost - paving the way for millions of new users and application scenarios.

This article will discuss

▶ Deepseek R 1’s breakthrough in open source AI reasoning

▶ How low-cost inference and flexible licensing can drive widespread adoption

▶ Why the 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 popularity of AI in financial applications

Part.1 Deepseek R 1: Redefining open source AI

Deepseek R 1 is a new LLM based on extensive text training, optimized for reasoning and contextual understanding . Its outstanding features include:

• Efficient architecture : Adopts a next-generation parameter structure to achieve near-top performance in complex inference 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 : Emerging market companies 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 R 1 with language- or domain-specific corpora (e.g., specialized medical/legal data).

• Modular extensibility : 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 enable more experimentation, which in turn accelerates innovation in the AI ecosystem as a whole.

Part 3 Jevons Paradox: Why Efficiency Improvement Drives Up Consumption

What is the Jevons paradox?

The theory states that efficiency gains often lead to more resource consumption rather than less . 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 R 1:

• Low-cost model : Reduces hardware requirements, making AI more economical to run.

• Result : More companies, researchers, and enthusiasts launch AI instances.

• 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 R 1 signals a surge in adoption and applications, which will drive:

• Ecosystem prosperity : More developers improve open source code functionality, fix vulnerabilities, and optimize performance.

• Hardware innovation : Makers of GPUs, CPUs, and specialized AI chips respond to surging demand by competing on price and energy efficiency.

• Business opportunities : Builders of analytical tools, process orchestration, specialized data pre-processing, and more will benefit from the surge in AI use.

So while the Jevons paradox suggests that infrastructure costs may rise, it is a positive sign for the AI industry as a whole —promoting an innovative environment that can lead to breakthroughs in economic deployment (such as advanced compression techniques or offloading tasks to specialized 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

Agents can continuously scan DeFi markets, bridge inter-chain assets, 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 such as Deepseek R 1 can control operating expenses.

• Customization

Developers can fine-tune open source AI with DeFi-specific data (price feeds, on-chain analytics, governance forums) without paying high licensing fees.

More AI agents, stronger financial automation

As Deepseek R 1 lowers the AI threshold, DeFAI forms a positive cycle:

• Agent explosion : developers create specialized 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 R 1 is open sourced, the community can:

• Quickly fix vulnerabilities

• Propose inference optimization solutions

• Create domain forks (e.g. finance, law, healthcare)

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:

• Managed AI Instance : Provides enterprise-grade Deepseek R 1 managed 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 R 1 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 R 1 marks a critical 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 good for the AI ecosystem - driving hardware innovation, community contributions, and advanced application development.

For DeFAI, AI agents coordinating financial operations on decentralized networks will have a significant ripple effect. Lower costs mean more sophisticated 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 R 1 demonstrates how open source advances can catalyze entire industries - from AI to DeFi. We are standing on the threshold of a future where AI is no longer a tool for the privileged few, but a foundational element of everyday finance, creativity, and global decision-making - driven by open models, economical infrastructure, and unstoppable community momentum.

Original link:

https://x.com/danielesesta/status/1883867695470313719?t=ibCexR2OmF1MSS3h60fhmQs=09

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