Given the significant increase in efficiency it brings to this space, the latest excitement surrounding the Advanced Big Speech Model (LLM) is understandable. However, some reactions to its release seem to misunderstand the extent of its impact. DeepSeek represents a leap in the expected trajectory of LLM development, but does not mean a revolutionary shift toward artificial universal intelligence (AGI), nor does it mark a sudden shift in the gravity center of AI innovation.
On the contrary, DeepSeek’s achievements are natural development along a good path, one of the exponential growth of AI technology. This is not a destructive paradigm shift, but a powerful reminder of the acceleration of technological change.
DeepSeek’s efficiency improvement: a leap along the expected trajectory
The core of the excitement surrounding DeepSeek is its impressive efficiency improvement. Its innovation is largely about making LLMS faster and cheaper, which is of great significance to the economics and accessibility of AI models. But despite the buzz, these advancements are not fundamentally made, but improvements to existing approaches.
In the 1990s, high-end computer graphics rendering required supercomputers. Today, smartphones can perform the same tasks. Likewise, facial recognition (once a niche, high-cost technology) has now become a ubiquitous off-the-shelf feature in smartphones. DeepSeek fits this technical model: Optimization of existing features provides efficiency, but is not a new groundbreaking approach.
This rapid advancement is no surprise for those familiar with the principles of technological growth. Propose advanced theoretical predictions of technological singularity in key areas such as AI, and breakthroughs will become more frequent as we approach the Singularity. DeepSeek is just a moment of this ongoing trend, and its role is to make existing AI technologies more accessible and efficient, rather than representing sudden flights to new capabilities.
DeepSeek’s innovation: architectural adjustment, not AGI’s leap
DeepSeek’s main contribution is to optimize the efficiency of large language models, especially through the mixing of its expert (MOE) architectures. MOE is a complete ensemble learning technology that has been used in AI research for many years. DeepSeek did a particularly good job of perfecting this technology and combining other efficiency measures to minimize computational costs and make LLMS more affordable.
- Parameter efficiency: DeepSeek’s Moe design activates only 37 billion of its 671 billion parameters at any given time, reducing the computational requirements to only 1/18 of traditional LLM.
- Reinforcement learning of reasoning: DeepSeek’s R1 model uses reinforcement learning to enhance thought chain reasoning, an important aspect of the language model.
- Multi-training training:DeepSeek-V3’s ability to predict multiple texts simultaneously improves the efficiency of training.
These improvements make DeepSeek models dramatically cheap when training and running compared to competitors like Openai or Anthropic. Although this is an important step in LLM’s accessibility, it is still a perfection of engineering, not a conceptual breakthrough for AGI.
The impact of open source AI
One of DeepSeek’s most famous decisions was to open source its model – a distinctly different approach to proprietary, fenced from companies like Openai, Anthropic, and Google. This open source approach, supported by AI researchers such as Yann Lecun at Meta, cultivates a more decentralized AI ecosystem in which innovation can flourish through collective development.
The economic principles behind DeepSeek’s open source decision are also clear. Open source AI is not only a philosophical position, but also a business strategy. By delivering technology to a wide range of researchers and developers, DeepSeek positiones itself as a sales that benefits from services, enterprise integration and scalable hosting rather than relying solely on proprietary models. This approach gives global AI communities access to competitive tools and reduces the shackles of large Western tech giants in the field.
China’s growing role in AI race
For many, the fact that DeepSeek’s breakthrough came from China may be surprising. However, this development should not be viewed as shocked or as part of a geopolitical competition. Having spent years observing China’s AI landscape, it is clear that the country has invested heavily in AI research, leading to an increasing number of talent and expertise.
Rather than viewing this development as a challenge to Western dominance, it is better to see it as a sign of the increasingly global nature of AI research. Open cooperation is not nationalist competition, but the most promising way toward responsible and moral development of AGI. Dispersed, globally distributed efforts are more likely to produce an AGI that benefits all human beings rather than an AGI that is generated for the benefit of a single country or company.
The broader meaning of DeepSeek: Beyond LLM
While much of the excitement surrounding DeepSeek revolves around the efficiency of the LLM space, it is crucial to take a step back and consider the broader implications of this development.
Despite its impressive capabilities, transformer-based models such as LLM still fail to implement AGI. They lack basic qualities such as rooted compositional abstraction and self-guided reasoning, which are essential for general intelligence. Although LLMs can automate a wide range of economic tasks and integrate into various industries, they do not represent the core of AGI development.
If AGI appears in the next decade, it is unlikely to be based purely on transformer architecture. Alternative models such as Opencog HyperON or neuromorphic computing may be more fundamental to realizing true general intelligence.
Commodification of LLMS will change AI investment
DeepSeek’s efficiency improves the trend of LLM commercialization. As the costs of these models continue to decline, investors may begin to move beyond traditional LLM architectures to achieve the next major breakthrough in AI. We may see that the shift in capital toward AGI architecture goes beyond Transformers, as well as investments in alternative AI hardware, such as neuromorphic chips or associated processing units.
Decentralization will affect the future of AI
As DeepSeek’s efficiency improves making it easier to deploy AI models, they also contribute to the broader trend of decentralized AI architectures. Focusing on privacy, interoperability and user control, decentralized AI will reduce our reliance on large centralized tech companies. This trend is crucial to ensuring that AI meets the needs of the global population, rather than being controlled by a few powerful players.
DeepSeek’s position in the AI Cambrian explosion
In short, although DeepSeek is a major milestone in LLM efficiency, it is not a revolutionary shift in the AI landscape. Instead, it accelerates progress along a perfect trajectory. The wide impact of DeepSeek has been felt in several areas:
- Pressure on the incumbent: DeepSeek challenges companies like Openai and Anthropic to rethink their business models and find new ways to compete.
- Accessibility of AI: By making high-quality models more affordable, DeepSeek democratizes the number of visitors to gain cutting-edge technology.
- Global competition: China’s increasingly important role in the development of artificial intelligence shows the nature of global innovation, which is not limited to the West.
- Index progress:DeepSeek is a clear example of how advances in AI become the norm.
Most importantly, DeepSeek reminds you that despite the rapid development of AI, real AGI may emerge through new fundamental approaches rather than optimizing today’s models. As we move toward strange places, it is crucial to ensure that AI development remains fragmented, open and collaborative.
DeepSeek is not AGI, but it represents an important step in the ongoing journey of transformative AI.
