Article author: Daniele

Article translation: Block unicorn

Artificial intelligence is advancing rapidly. Large language models (LLMs) are enabling applications ranging from conversational assistants to multi-step transaction automation such as DeFi (decentralized finance). However, the cost and complexity of deploying these models remains a significant barrier. The emergence of Deepseek R1, as a new open source AI model, promises to provide powerful reasoning capabilities at a lower cost - paving the way for millions of new users and use cases.

In this article, we will explore:

  1. What Deepseek R1 brings to open source AI reasoning.
  2. How low-cost inference and flexible licensing can enable broader adoption.
  3. Why the Jevons paradox suggests that usage (and therefore costs) may actually increase with increasing efficiency and still be a net benefit for AI developers.
  4. How DeFAI benefits from the growing popularity of AI in financial applications

1. Deepseek R1: Rethinking open source AI

Deepseek R1 is a newly released LLM that has been trained on a large text corpus to optimize reasoning and contextual understanding. Its outstanding features include:

  • Efficient architecture

Deepseek R1 leverages the next-generation parameter structure to deliver near-state-of-the-art performance in complex inference tasks without relying on large GPU clusters.

  • Low hardware requirements

Deepseek R1 is designed to run on fewer GPUs or even advanced CPU clusters, lowering the barrier to entry for startups, individual developers, and the open source community.

  • Open Source License

Unlike many proprietary models, Deepseek R1's permissive licensing regime allows enterprises to integrate it directly into their products, facilitating rapid adoption, plugin development, and professional fine-tuning.

This shift toward accessible AI is similar to early open source projects like Linux, Apache, or MySQL — projects that ultimately drove exponential growth in technology ecosystems.

2. Lowering the cost of AI: Driving widespread adoption

2.1 Accelerating Adoption

When high-quality AI models can be run at an affordable price:

  1. SMBs can deploy AI-driven solutions without relying on expensive proprietary services.

  2. Developers are free to experiment — from chatbots to automated research assistants — without worrying about breaking their budget.

  3. Global growth: Companies in emerging markets can more easily introduce AI solutions, bridging gaps in industries such as finance, healthcare, and education.

2.2 Democratized Reasoning

Lowering the cost of inference not only drives adoption, it also democratizes inference :

  • Localized Models: Small communities can train Deepseek R1 on language-specific or domain-specific corpora (e.g., specialized medical or legal data).

  • Modular plugins: Developers and independent researchers can build advanced plugins (e.g., code analysis, supply chain optimization, or on-chain transaction verification) without being restricted by permission bottlenecks.

Overall, cost savings enable more experimentation , accelerating innovation across the AI ecosystem.

3. Jevons paradox: the higher the efficiency, the greater the consumption

3.1 What is the Jevons paradox?

The Jevons paradox states that improvements in efficiency tend to lead to increases (rather than decreases) in resource consumption. Originally observed in the context of coal use, the paradox means that when a process becomes cheaper or easier, people tend to use more of it, thus offsetting (and sometimes exceeding) the savings from efficiency gains.

In the context of Deepseek R1 :

  • Low-cost model: Reduce hardware overhead to make AI cheaper to run.

  • The result: More businesses, researchers, and hobbyists launching AI instances.

  • The upshot: Although each instance costs less to operate, total compute usage (and costs) may rise due to the influx of new users.

3.2 Is this bad news?

Not necessarily. Higher overall usage of AI models like Deepseek R1 indicates a surge in successful adoption and applications. This drives:

  1. Ecosystem growth: More developers optimize new features, fix bugs, and improve the performance of open source code.

  2. Hardware Innovation: Makers of GPUs, CPUs, and specialized AI chips compete on price and efficiency in response to surging demand.

  3. Business opportunities: Builders in areas such as analytics, pipeline orchestration, or specialized data preprocessing can profit from the boom in AI usage.

So while the Jevons paradox suggests that infrastructure costs may rise, this is a positive sign for the AI industry , driving an innovative environment and spurring breakthroughs in cost-effective deployment (e.g., advanced compression or offloading tasks to specialized chips).

4. Impact on DeFAI

4.1 DeFAI: The integration of artificial intelligence and DeFi

DeFAI combines decentralized finance (DeFi) with AI-driven 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 for the following reasons:

1. 24/7 Automation

Agents can continuously scan DeFi markets, bridge across chains, and rebalance positions. Reducing the cost of AI inference makes it economically viable to run these agents 24/7.

2. Unlimited Scalability

If tens of thousands of DeFAI agents need to serve different users or protocols simultaneously, a low-cost model like Deepseek R1 can keep the volume under control.

3. Customization

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

4.2 More AI Agents, More Financial Automation

As Deepseek R1 lowers the bar for AI, DeFAI sees a positive feedback loop :

  1. Explosive growth of agents: developers create specialized bots (e.g., yield hunting, liquidity provision, NFT trading, cross-chain arbitrage).

  2. Efficiency improvements: Each agent optimizes the flow of funds, potentially driving an overall increase in DeFi activity and liquidity.

  3. Industry Growth: More and more complex DeFi products are emerging, from advanced derivatives to conditional payments, all orchestrated by readily available AI.

The end result: the entire DeFAI field benefits from a virtuous cycle—user adoption and agent sophistication reinforce each other.

5. Outlook: Positive signals for AI developers

5.1 Thriving open source community

With the open source release of Deepseek R1 , the community can:

  • Quickly fix bugs ,

  • Propose inference optimization suggestions ,

  • Create branches for specific fields (such as finance, law, medical, etc.).

Collaborative development leads to continuous improvement of models and spawns ecosystem tooling (e.g., fine-tuning frameworks, model serving infrastructure, etc.).

5.2 New profit path

AI developers, especially in the field of DeFAI, can innovate beyond the standard pay-per-API-call model:

  1. Managed AI Instance: Provides enterprise-grade Deepseek R1 hosting service with a user-friendly dashboard.

  2. Service layer: Integrate advanced functions (such as compliance checking or real-time intelligence) on top of the open source model to provide services to DeFi operators.

  3. Agent Marketplace: Provides specialized agent profiles, each with a unique strategy or risk profile, which users can access through subscription or performance fees.

This business model thrives when the underlying AI technology can scale to millions of concurrent users without bankrupting the provider.

5.3 Lower Entry Barrier = Larger Talent Pool

As Deepseek R1 reduces hardware requirements, more developers around the world can try AI.

This influx of diverse talent :

  • Inspires creative solutions to real-world and crypto-specific challenges,

  • enrich the open source community with new ideas and improvements,

  • Unlocking global population groups that were previously excluded due to high computational costs.

in conclusion

The arrival of Deepseek R1 marks a critical shift: open source AI no longer requires high computing or licensing fees. By providing powerful inference capabilities at a lower cost, it paves the way for broader adoption, benefiting everyone 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 - it drives hardware innovation, community contributions, and the development of next-generation applications.

In the DeFAI space, AI agents coordinate financial operations on decentralized networks, and the ripple effect is huge. Lower overhead means more sophisticated agents, greater accessibility, and expanding on-chain strategies. From yield aggregators to risk management, these advanced AI solutions can run continuously, opening up new avenues for cryptocurrency adoption and innovation.

Ultimately, Deepseek R1 demonstrates how open source advances can drive the development of entire industries - including AI and DeFi . We are standing on the cusp of a future where AI is no longer just a tool for a privileged few, but a foundational element of everyday finance, creativity, and global decision-making - all thanks to the synergy of open source models, cost-effective infrastructure, and unstoppable community momentum.

Ready to explore more? Stay tuned for more updates on Deepseek R1 development, open source collaboration opportunities, and the DeFAI platform—together we’ll build a more inclusive, smarter, and more powerful AI future.