HomeBlogWhat is DeepSeek AI – A Hype or a Real AI Advancement?

What is DeepSeek AI – A Hype or a Real AI Advancement?

What is DeepSeek AI

In recent weeks, there has been a great deal of information about DeepSeek AI that significantly impacted the stock market, especially funds connected to Nvidia and OpenAI. What is China's DeepSeek? In this article, we explore if there is a real foundation to consider DeepSeek the new black in the AI industry, or if it’s just trading games we’re all involved in.

What is DeepSeek?

DeepSeek refers to a set of AI models developed by a Chinese company of the same name. Its latest models, DeepSeek-V3 and DeepSeek-R1, influenced the global AI market the most.

DeepSeek AI chat

The main reason behind their tremendous impact is their lower-cost and lower-resource development compared to OpenAI and other US-based models. This is especially prominent since DeepSeek developed its models in the midst of US sanctions against China and India for Nvidia chips. These sanctions were intended to make it impossible for these countries to develop high-performing AI tools. However, DeepSeek not only built efficient models, rivaling AI leaders but also offered its open-source versions for free, which challenges the business strategies of OpenAI and others who charge monthly fees for premium access to LLM capabilities.

The history of DeepSeek

DeepSeek was founded in 2023 in Hangzhou, China, by Liang Wenfeng, who is connected to the High-Flyer hedge fund that uses AI algorithms for trading. In April 2023, High-Flyer announced starting an artificial general intelligence lab for researching tools separate from the financial industry. The lab’s purpose was to create non-commercial AI technology with a focus on innovation and experimentation, using High-Flyer expertise and resources, including around 10,000 Nvidia chips stored until sanctions took effect. This strategy paid off.

  • On November 2, 2023, the company launched DeepSeek Coder for handling programming tasks.

  • On November 24, 2023, DeepSeek released a series of DeepSeek LLM models to compete with other LLM models available in the market.

  • In January 2024, they developed the DeepSeek MoE (Mixture-of-Experts) architecture, which became the basis for their future models.

  • In April 2024, DeepSeekMath was introduced. It has impressive capabilities in solving mathematical problems and stands out in comparable reasoning and coding.

  • In May 2024, the company released high-performing DeepSeek-V2 at a low price, which led to the AI price war in China – a competition between technology giants, such as Alibaba and Baidu, to cut prices for their AI models.

  • DeepSeek-V3, released in December 2024, gained international attention for its high efficiency and accuracy. This natural language model has 671 billion total parameters, with 37 billion parameters activated for each token.

  • In January 2025, DeepSeek released the DeepSeek-R1 reasoning model. Using the DeepSeek AI chat, an end user can see not only the result of their request but also the AI's reasoning behind it. This model showed remarkable performance, on par with the OpenAI-01-1217 model.

As a result, on January 26, the DeepSeek mobile app moved from 31st to first place in the US App Store. However, DeepSeek's success led to massive cyberattacks on its servers, forcing the company to temporarily limit new user registrations while still providing services for existing users.

How DeepSeek works

What has become the basis for the new AI tech? To create their models, DeepSeek developers could potentially use the Llama LLM open-source model by Meta as their starting point and then add some innovation and optimization. However, the major technology behind DeepSeek that led to the performance breakthrough was the Mixture-of-Experts architecture. It was first introduced in the 1990s and has since been researched, advanced, and implemented in multiple projects, including DeepSpeed by Microsoft. DeepSeek, however, took MoE architecture to the next level, scaling and refining its capabilities.

In a nutshell, MoE works as a team of experts with their own unique expertise. The MoE model divides complex tasks among these experts, aka specialized networks. Each of them handles tasks in line with their expertise, delivering more accurate results with fewer resources. The gating mechanism, aka router, decides which expert best suits each task. The system uses only experts relevant to the particular problem, saving computing resources. You can also add experts or change expert specialization, making the system more adaptable for a specific task.

DeepSeek used load balancing for MoE performance optimization. Load balancing is a novel way to optimize MoE architecture. Simply put, it identifies which expert gets selected and how much of their expertise should count for a particular task.

In the training process, DeepSeek used vast datasets of natural language and code, and its training techniques include reinforcement learning (RL) and distillation.

  • RL is based on the trial-and-error learning process that humans typically use to achieve their goals. It allows the model to complete complex tasks in dynamic environments even without large amounts of data. This technique enhances DeepSeek-R1's reasoning capabilities.

  • The distillation technique is what caused OpenAI to claim that DeepSeek copied their model. However, distillation is a common technique for training AI that uses outputs from larger models to train smaller ones. There is no direct evidence at the moment that DeepSeek violated OpenAI’s terms of service.

To speed up inference and enhance the handling of complex contexts, the company adopted multi-token prediction. With this technique, the model predicts not only the next token in the chain but multiple tokens simultaneously.

Using MoE architecture and the techniques described above, the company utilizes fewer GPU hours and less expensive hardware than previously expected.

Benefits of DeepSeek for AI developers and end users

  • Open source: Everyone can use DeepSeek models for research or commercial purposes. To get some context about AI open-sourcing, pay attention to the statement by Yann LeCun, Chief AI Scientist at Meta. He pointed out that it’s not China surpassing the US in AI, but rather open-source models are surpassing proprietary ones.

  • Cost-effectiveness: Currently, there is no fee for end users to access the DeepSeek app. To develop AI solutions using DeepSeek API, you should pay $0,014 per 1M input tokens for chat and $0,14 per 1M tokens for reasoner. OpenAI o1 charges $15 per 1M input tokens.

  • Versatility: Multilingual support allows you to use DeepSeek for global projects, from content creation to customer support. On top of that, DeepSeek performs well for specific domains, providing in-depth expertise even for narrow niches.

  • Customization: DeepSeek’s open-source nature is great for developing tailored solutions since you have no limits in creating new tools and integrating GenAI and reasoning capabilities into your existing workflows.

Before using DeepSeek, you should also be aware of some privacy concerns connected to the fact that data is stored on servers in China. Current cyberattacks could also raise questions about the model safety and security breaches. Another issue is that DeepSeek performance may vary depending on how you use the model.

If you still think the model's benefits outweigh its drawbacks, let’s see which areas it can be applied in.

DeepSeek applications

DeepSeek models can be applied according to their diverse capabilities.

  • Natural Language Processing (NLP) capabilities allow you to automatically generate new content, answer queries, translate text to multiple languages, and summarize documents for whatever field.
  • Code assistance lets you speed up development processes by generating new code based on provided context, explaining and debugging code, and offering training to improve programming skills.
  • Reasoning capabilities can help solve mathematical problems and logical puzzles or enhance decision-making processes.

Given that DeepSeek is an open-source AI tool, you can also use it for your own AI research and develop new tools for healthcare, ecommerce, legal, or any other industry.

How DeepSeek impacts the future of AI

DeepSeek’s growth forced the market to change the AI game rules. Sam Altman has already stated that OpenAI is releasing its mini version and is thinking of open sourcing. So, we can conclude that AI tools tend to become more affordable and accessible while the AI market gets even more competitive. The latter, in turn, leads to even more intensive technical innovation, faster AI development, and digital transformation.

If you want to create your custom AI solutions or integrate LLMs into your business ecosystem, don’t hesitate to contact the DigitalSuits team. We have the necessary expertise to make your project successful.