Deepseek R1: Ignite DeFAI, the next era of open source breakthrough

Author: @danielesesta

Compiled by: Vernacular Blockchain

Artificial intelligence is developing rapidly. Large language models (LLMs) are providing powerful support for a wide range of fields, from conversational assistants to multi-step transaction automation in DeFi (decentralized finance). However, the high cost and complexity of deploying these models at scale remain a major obstacle. Against this backdrop, Deepseek R1 came into being, a new open source AI model that opens the door to millions of new users and application scenarios with its powerful reasoning capabilities and lower costs.

This article will explore the following:

1) What innovations does Deepseek R1 bring to the field of open source AI reasoning?

2) How lower inference costs and flexible licensing models can drive broader adoption.

3) Why the Jevons paradox suggests that as efficiency improves, usage (and corresponding costs) may increase, but it is still an overall positive for AI developers.

4) How DeFAI can benefit from the increasing accessibility of AI in financial applications.

1. Deepseek R1: Redefining open source AI

Deepseek R1 is a newly released large-scale language model that is trained on a wide range of text corpora and focuses on optimizing reasoning capabilities and contextual understanding. Its core features include:

• Efficient architecture

By adopting a next-generation parameter structure, Deepseek R1 delivers near-state-of-the-art performance in complex reasoning tasks without relying on large GPU clusters.

• Lower hardware requirements

The model is designed to run on a smaller number of GPUs or even advanced CPU clusters, making it more suitable for startups, individual developers, and the open source community.

• Open Source License

Unlike many proprietary models, Deepseek R1 uses a permissive licensing agreement, allowing enterprises to integrate it directly into their products, thereby accelerating application and plug-in development and fine-tuning for specific needs.

This push for ease of use and openness is similar to the early trajectory of open source projects like Linux, Apache, and MySQL—projects that ultimately fueled exponential growth in tech ecosystems.

2. The significance of lower-cost AI

1) Accelerate popularization

When high-quality AI models can run at a lower cost:

Small and medium-sized enterprises: Deploy AI-driven solutions without relying on expensive proprietary services.

Developers: Free to experiment and quickly iterate on new applications, from chatbots to automated research assistants, without worrying about going over budget.

Growth in multiple regions: Companies in emerging markets are able to more smoothly introduce AI solutions to fill gaps in industries such as finance, healthcare, and education.

2) Promote the democratization of reasoning

Lowering the cost of inference will not only increase usage, but also democratize inference:

Localized models: Small communities can use Deepseek R1 to customize training for specific languages or domain corpora, such as medical or legal data.

Modular plugins: Developers and independent researchers can create advanced plugins (e.g., code analysis, supply chain optimization, or on-chain transaction verification) without being bound by licensing restrictions.

Overall, cost savings can lead to more opportunities for experimentation, thereby accelerating the pace of innovation across the AI ecosystem.

3. Jevons paradox: when efficiency is improved, it leads to more consumption

1) What is the Jevons paradox?

The Jevons paradox states that improvements in efficiency usually lead to an increase in resource consumption, not a decrease. This phenomenon was first observed in the use of coal: when a process becomes cheaper or easier, people tend to increase their use, thus offsetting or even exceeding the savings from the efficiency increase.

In the context of Deepseek R1:

Lower cost models: Reducing the burden on hardware makes running AI more affordable.

The result: More companies, researchers, and enthusiasts are running AI instances.

Ultimately: while individual instances may cost less to run, total computing resource usage (and its cost) may increase as a large number of new users join the mix.

2) Is this bad news?

This is not the case. The huge growth in the use of AI models like Deepseek R1 reflects the popularity of their success and has led to more applications. This trend is driving:

Ecosystem growth: More developers are investing in improving the functionality, fixing bugs, and optimizing performance of open source code.

Hardware innovation: Makers of GPUs, CPUs, and specialized AI chips are competing on price and efficiency to meet surging demand.

Business opportunities: Builders in areas such as analytics, process orchestration, and specialized data preprocessing will benefit from the surge in AI usage.

So while the Jevons paradox suggests that infrastructure costs may rise, it is a positive sign for the AI field as a whole, fostering an environment for innovation and driving breakthroughs in cost-effective deployment (such as more advanced compression techniques or offloading tasks to specialized chips).

4. Impact on DeFAI

1) DeFAI: The integration of AI and DeFi

DeFAI combines decentralized finance (DeFi) with AI-driven automation, enabling intelligent 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 models because:

Smart agents that run autonomously 24/7 can continuously monitor the DeFi market, operate across chains, and rebalance positions. Lower AI inference costs make it more economically feasible for these agents to run 24/7.

Unlimited Scalability If thousands of DeFAI agents need to run simultaneously for different users or protocols, a low-cost model like Deepseek R1 can effectively control the operating costs.

Customization capabilities Developers can fine-tune open source AI based on DeFi-specific data (such as price feeds, on-chain analysis, governance forums) without incurring high licensing fees.

2) More AI agents, more financial automation

As Deepseek R1 lowers the AI threshold, a positive feedback loop has emerged in the DeFAI field:

The number of agents has surged as developers create specialized bots (e.g., yield mining, liquidity provision, NFT trading, cross-chain arbitrage, etc.).

Efficiency improvements Each agent can optimize financial liquidity, potentially driving overall growth in DeFi activity and liquidity.

Industry GrowthMore complex DeFi products continue to emerge, such as advanced derivatives and conditional payment protocols, all driven by readily available AI.

The end result: a virtuous circle has been formed in the entire DeFAI field - the growth of users and the intelligence of agents promote each other, driving the further prosperity and development of the DeFI ecosystem.

5. Outlook: Positive signals for AI developers

1) Thriving open source community

With Deepseek R1 open source, the community will be able to:

Fix vulnerabilities quickly,

Propose an inference optimization solution.

Create branches in specific fields (such as finance, law, medicine, etc.). Collaborative development will lead to continuous model improvements and the emergence of related ecological tools (such as fine-tuning frameworks, model deployment infrastructure, etc.).

2) New profit path

AI developers, especially in the field of DeFAI, can break through the traditional pay-per-API call model and explore more innovative models:

Hosted AI Instance: Provides enterprise-grade Deepseek R1 hosting services with a user-friendly management panel.

Service layer: Integrate advanced functions (such as compliance checks or real-time intelligence) for DeFi operators to provide value-added services based on an open source model.

Agent Marketplace: Hosting dedicated agent profiles, each with a unique strategy or risk appetite, and charging a subscription or performance fee. These business models will flourish when the underlying AI technology can support millions of concurrent users and costs are manageable.

3) Lower barriers = larger talent pool

As the hardware requirements of Deepseek R1 decrease, more developers around the world will be able to try AI technology. This diverse influx of talent will:

Inspire innovative solutions to real-world and crypto-specific challenges,

Inject new ideas and improvements into the open source community,

Unlock a potential global developer base that has been excluded due to high computing costs.

6. Conclusion

The launch of Deepseek R1 marks an important shift: open source AI no longer requires high computing resources or licensing fees. By providing powerful reasoning capabilities at a lower cost, it paves the way for a wider range of applications - from small development teams to large enterprises. Although the Jevons paradox suggests that infrastructure costs may rise due to surging demand, this phenomenon is good for the AI ecosystem, driving hardware innovation, community contributions, and the development of next-generation applications.

In the field of DeFAI, AI agents coordinate financial operations on decentralized networks, and the impact is even more far-reaching. Lower overhead means more complex agents, wider accessibility, and expanding on-chain strategies. From yield aggregation to risk management, these advanced AI solutions can continue to operate, unlocking new paths for cryptocurrency adoption and innovation.

Ultimately, Deepseek R1 demonstrates how open source technology can catalyze the development of entire industries - including AI and DeFi. As we move towards a future where AI is no longer just a tool for a few, but a foundational element of everyday financial, creative, and global decision-making, driven by an open source model, cost-effective infrastructure, and a strong community drive.