Deepseek R1 connects the "New Era of DeFAI", what new paths will Open Source and AI agents take?

The emergence of Deepseek R1 as a new Open Source AI model promises powerful inference capabilities at a lower cost, paving the way for wider adoption that DeFAI will also benefit from. This article originated from an article by Daniele and was compiled, organized and written by Block unicorn. (Synopsis: DeepSeek launches AI multimodal Open Source model "Janus-Pro", image generation crushes DALL-E 3, Stable Diffusion) (Background supplement: DeepSeek has formed a dimensionality reduction blow to the encryption AI track, which projects are worth following under the general fall? Artificial intelligence is developing rapidly. Large language models (LLM) are enabling applications ranging from conversational assistants to multi-step transaction automation such as Decentralized Finance. However, the cost and complexity of deploying these models remains a significant obstacle. The emergence of Deepseek R1 as a new Open Source AI model promises powerful inference capabilities at a lower cost – paving the way for millions of new users and use cases. In this article, we'll explore: What Deepseek R1 brings to the Open Source AI inference. How low-cost inference and flexible licensing enable wider adoption. Why Jevons' paradox suggests that usage (and thus cost) may actually increase as efficiency increases, but is still a net benefit for AI developers. How DeFAI can benefit from the growing popularity of AI in financial applications. Deepseek R1: Rethinking Open Source AI Deepseek R1 is a newly released LLM that is trained on an extensive text corpus to optimize reasoning and contextual understanding. Standout features include: Efficient architecture: Deepseek R1 leverages next-generation argument structures to deliver near-state-of-the-art performance in complex inference tasks without relying on huge GPU clusters. Lower hardware requirements: The Deepseek R1 design can run on fewer GPUs or even high-end CPU clusters, dropping the barrier to entry for startups, individual developers, and the Open Source community. Open Source licensing: Unlike many proprietary models, Deepseek R1's permissive licensing regime allows enterprises to integrate it directly into their products, facilitating rapid adoption, plug-in development, and specialized fine-tuning. This shift to accessible AI is similar to the early Open Source projects of Linux, Apache, or MySQL—projects that ultimately drove the exponential rise of the technology ecosystem. AI at Drop: Driving Widespread Adoption Accelerate adoption when high-quality AI models can be executed at an affordable price: SMBs can deploy AI-driven solutions without relying on expensive proprietary services. Developers are free to experiment — from chatbots to automated research assistants — without worrying about breaking their budget. Global rise: Businesses in emerging markets can more easily introduce AI solutions to bridge gaps in industries such as finance, healthcare, education, and more. Democratizing Inference The cost of drop inference not only drives usage, but also democratizes inference: Localization models: Small communities can train Deepseek R1 on a specific language or domain-specific corpus (e.g., specialized medical or legal materials). Modular plugins: Developers and independent researchers can build high-level plugins (e.g., code analysis, supply chain optimization, or on-chain transaction verification) without being constrained by licensing bottlenecks. Overall, the cost savings have led to more experimentation, accelerating innovation across the AI ecosystem. Jevons' Paradox: The More Efficient, the More Consumed What is Jevons' Paradox? Jevons' paradox states that increased efficiency tends to lead to an increase (rather than a decrease) in resource consumption. The paradox, first observed in the context of coal use, implies that when a process becomes cheaper or easier, people tend to use it more, offsetting (and sometimes even exceeding) the savings from efficiency gains. In the context of Deepseek R1: Low-cost model: Reduce hardware overhead and make AI perform cheaper. The result: more businesses, researchers, and hobbyists launched AI examples. Outcome: Although operating costs are lower per case, total compute usage (and cost) is likely to rise due to the influx of new users. Is this bad news? Not always. Higher overall usage of AI models like Deepseek R1 indicates successful adoption and application proliferation. This has driven the following: Ecosystem rise: More developers optimize new features, fix bugs, and improve the performance of Open Source Origin. Hardware innovation: GPU, CPU, and dedicated AI chip makers are competing on price and efficiency in response to soaring demand. Business opportunity: Builders in areas such as analytics, pipeline orchestration, or professional data pre-processing can profit from the boom in AI use. So while Jevons' paradox suggests that infrastructure costs may rise, this is a positive sign for the AI industry, driving an environment for innovation and spurring breakthroughs in cost-effective deployment (e.g., high-end compression or decommissioning tasks to specialized chips). DeFAI: The Convergence of Artificial Intelligence and Decentralized Finance DeFAI combines Decentralized Finance with AI-driven automation to enable agents to manage on-chain assets, perform multi-step transactions, and interact with the Decentralized Finance protocol. This emerging field directly benefits from Open Source, low-cost AI for the following reasons: 1. Round-the-clock automated agents can continuously scan the Decentralized Finance market, and Cross-Chain Interaction bridges and rebalances positions. The drop AI inference cost makes it economically viable to execute these agents around the clock. 2. Infinitely scalable suite If thousands of DeFAI agents need to serve different users or protocols at the same time, low-cost models like Deepseek R1 can be kept under control. 3. Customization Developers can fine-tune Open Source AI based on Decentralized Finance-specific data (e.g., price information, on-chain analysis, governance forums, etc.) without incurring high licensing fees. More AI agents, more financial automation With Deepseek R1 dropping the threshold for AI, DeFAI sees a positive feedback loop: Agent explosion: developers create specialized bots (e.g., revenue hunting, Liquidity offering, non-fungible token trading, Cross-Chain Interaction Arbitrage). Efficiency gains: Optimizing the flow of funds per agent has the potential to drive an increase in Decentralized Finance activity and Liquidity as a whole. Industry rise: More and more complex Decentralized Finance products are emerging, from high-end derivatives to conditional payments, all orchestrated by ready-to-use AI. The end result: The entire DeFAI space benefits from a virtuous cycle where user adoption and agent complexity reinforce each other. Outlook: Favourable Information signals for AI developers Thriving Open Source Community With Deepseek R1's Open Source, the community can: Quickly fix bugs; Propose inference optimization suggestions; Create a domain-specific branch...