DeepSeek R1: Blind the open source breakthrough of the next era of Defai

Author: @Danielessta

Compilation: Blockchain

Artificial intelligence is developing rapidly. Large -scale language models (LLMS) are providing strong support for various fields. Multi -step transactions from dialogue assistants to DEFI (decentralized finance) are automated, and the scope of application is becoming increasingly widespread. However, when these models are deployed on a large scale, the cost and high complexity are still a major obstacle. In this context, DeepSeek R1 came into being. This is a new open source AI model. With its strong reasoning capabilities and lower costs, it opened the door for millions of new users and application scenarios.

This article will discuss the following:

1. What innovations did Deepseek R1 bring in the field of open source AI reasoning.

2. How to reduce reasoning costs and flexible license models to promote wider applications.

3. Why the Jevinus paradox shows that as the efficiency is improved, the amount (and corresponding costs) may increase, but it is still overall for AI developers.

4. How DEFAI benefits from AI's increasing accessibility in financial applications.

1. DEEPSEEK R1: redefine open source AI

DeepSeek R1 is a newly released large language model. It is trained based on a widely text corpus library and focuses on the optimization of reasoning ability and context understanding. Its core features include:

• Efficient architecture

By adopting the next -generation parameter structure, the DeepSeek R1 provides performance close to the most advanced level in the complex reasoning task without relying on the huge GPU cluster.

• Lower hardware requirements

This model design supports running on a small number of GPUs and even high -end CPU clusters, making it more suitable for startups, personal developers and open source communities.

• Open source permit

Unlike many proprietary models, DeepSeek R1 uses a loose license agreement to allow enterprises to directly integrate it into the product, thereby accelerating applications, plug -in development, and fine -tuning for specific needs.

This promotion of ease of use and openness is similar to the early development trajectory of open source projects such as Linux, Apache, and MySQL -these projects eventually promoted the index growth of the scientific and technological ecosystem.

2. The significance of lower cost AI

1. Accelerate popularity

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

Small and medium -sized enterprises: No need to rely on expensive services, and can also deploy AI -drive solutions.

Developers: can be tried freely, from chat robots to automation research assistants, all kinds of new applications can be iterated quickly without worrying about budget overruns.

Growth in many places: Enterprises in emerging markets can introduce AI solutions more smoothly to fill the gaps in the financial, medical and education industries.

2. Promote the democratization of reasoning

Reducing the cost of reasoning not only increases the amount of usage, but also achieves the democratization of reasoning:

Localization model: Small communities can use DeepSeek R1 to make customized training for specific language or domain coriastic libraries (such as medical or legal data).

Modular plug -in: Developers and independent researchers can create advanced plug -in (such as code analysis, supply chain optimization or chain transaction verification) without being constrained by licensed restrictions.

In general, cost savings can bring more test opportunities, thereby accelerating the innovative pace of the entire AI ecosystem.

3. Jevez Paradox: When the efficiency is improved, it brings more

consumption

1. What is Jevez Paradox?

Jevez's paradox pointed out that the improvement of efficiency usually leads to an increase in resource consumption, not a reduction. At first, this phenomenon was observed in the use of coal: When a process becomes cheaper or easier, people often increase their usage, thereby offset or even exceed the saving of efficiency improvement.

In the background of Deepseek R1:

Low -cost models: reduce the burden on hardware and make AI more economical.

Results: More companies, researchers and enthusiasts began to run AI instances.

In the end: Although the operating cost of a single instance is lower, due to the addition of a large number of new users, the total amount of computing resources (and its cost) may increase.

2. Is this bad news?

This is not the case. The use of AI models such as Deepseek R1 has increased significantly, reflecting the popularity of its success and bringing more applications. This trend promotes:

Ecosystem growth: More developers have invested in the function of open source code, loopholes and performance optimization.

Hardware innovation: GPU, CPU, and special AI chip manufacturers to compete in terms of price and efficiency in order to meet the surge demand.

Business opportunities: Analysis, process arrangement, and special data pre -processing builders will benefit from the surge in AI usage.

Therefore, although Jevinus's paradox indicates that the cost of infrastructure may rise, for the entire AI field, this is a positive signal that can give birth to an innovative environment and promote a breakthrough in cost -effective deployment (such as more advanced compression technology Or transfer the task to the special chip).

4. The effect on DEFAI

1. Defai: The fusion of AI and DEFI

DEFAI combines decentralized finance (DEFI) and AI -driven automation, so that smart proxies can manage assets on the chain, implement multiple steps transactions, and interact with the DEFI protocol. This emerging field is directly beneficial to open source, low -cost AI model, because::

The intelligent agent independently runs all -weather can continue to monitor the DEFI market, operate cross -chain and re -balance the positions. The lower AI reasoning costs make these agents more economical all -weather operations are economically feasible.

Unlimited scalability If tens of thousands of Defai agents need to run at the same time for different users or protocols, low -cost models such as Deepseek R1 can effectively control the operating costs.

Customized capabilities developers can fine -tune the open source AI based on DEFI specific data (such as price feed, chain analysis, and governance forum) without being afforded by high license fees.

2. More AI agents, more financial automation

As Deepseek R1 decreases the AI ​​threshold, a positive feedback cycle appears in the Defai field:

The surge in the number of agents to create special robots (such as income mining, liquidity provision, NFT transactions, cross -chain arbitrage, etc.).

Each agent can optimize financial liquidity, which may promote the overall growth of DEFI activities and liquidity.

The more complicated Defi products in the industry have continued to emerge, such as high -end derivatives and conditional payment agreements, and these are driven by convenient AI.

The final result: The entire DEFAI field has formed a virtuous circle -the growth of users and the intelligence of the agent to promote each other, and promote the further prosperity and development of the Defi ecology.

5. Outlook: a good signal for AI developers

1. Prosperous open source community

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

Quickly repair the vulnerability,

Proposal for reasoning optimization scheme,

Create branches in specific areas (such as finance, law, medical care, etc.). Collaborative development will bring continuous model improvement and give birth to related ecological tools (such as fine -tuning framework, model deployment infrastructure, etc.).

2. New profit path

AI developers, especially in the field of DEFAI, can break the traditional call mode according to API and explore more innovative models:

Host AI instance: Provide an enterprise -level Deepseek R1 custody service, equipped with user -friendly management panels.

Service layer: Integrate high functions (such as compliance inspection or real -time intelligence) for DEFI operators to provide value -added services based on open source models.

Acting market: Special agency configuration files, each agent has unique strategies or risk preferences, charged through subscriptions or performance. When the underlying AI technology can support millions of concurrent users and cost control, these business models will flourish.

3. Lower threshold = larger talent pool

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

Stimulate innovative solutions for specific challenges in the real world and encryption fields,

Inject new ideas and improvements into the open source community,

Unlock the potential developer groups that are excluded due to high computing costs.

6. Conclusion

The launch of DeepSeek R1 marks an important change: open source AI no longer requires high computing resources or permit fees. By providing strong reasoning capabilities at lower costs, it has paved the way for a wider range of applications -from small development teams to large enterprises, it can benefit. Although the Jevez Paradox shows that the cost of infrastructure may rise due to surge in demand, this phenomenon is good for the AI ​​ecosystem, promoting hardware innovation, community contribution, and the development of the next generation of applications.

In the field of DEFAI, the AI ​​agent coordinating financial operations on decentralized networks is far -reaching. Lower expenses mean more complicated agents, wider videlines, and expanding chain strategies. From income aggregation to risk management, these advanced AI solutions can continue to run, unlocking new paths of cryptocurrencies and innovation.

In the end, Deepseek R1 proves how open source technology catalyzes the development of the entire industry -including AI and Defi. In the process of our future, AI will no longer be a tool for a few people, but the basic elements of daily finance, creativity and global decision -making. Essence