DeekSeek has disrupted the whole tech industry, with over $600+ Billion of loss to many major companies such as OpenAI, Meta, Nvidia, Microsoft, and Oracle among others. On Jan. 27, 2025, the Nasdaq Composite dropped by 3.4% (or 100+ billion dollars) at the tech stocks market opening, with Nvidia declining by 17% and losing approximately $600 billion in market capitalization. Whereas, the leading OpenAI has claimed a loss of $150 billion dollars.

Let’s go through this article, covering all about DeepSeek and how it’s revolutionizing the AI industry. 

What is DeepSeek?

DeepSeek is a Chinese artificial intelligence company, that focuses on developing open-source LLMs. Recently, they released their latest R1 AI ( i.e. includes quality reasoning performance ) model on Jan. 20, 2025. Within a few days of launch, it hit the top of Apple’s App Store chart, outranking previous top-ranking OpenAI’s ChatGPT mobile app.

The company leverages a unique approach, focusing on resource optimization while maintaining the highly competitive performance of its models. 

Who Invented DeepSeek?

DeepSeek was founded in December 2023 by Liang Wenfeng, an AI development firm based in Hangzhou, China. 

Liang Wenfeng is a Chinese entrepreneur and businessman who is the co-founder of the quantitative hedge fund High-Flyer. He is also the founder and CEO of the Chinese artificial intelligence firm DeepSeek. He started working on DeepSeek, as a side quest. Liang started buying thousands of Nvidia GPUs for his AI side project while running High-Flyer. 

He initially used those high GPU computations for trading analysis, while side-by-side training his own LLM. Later on, by the end of 2024, his AI model “DeepSeek” matured in terms of performance benchmarks and gained huge popularity overnight. DeepSeek platform is available both as an online chat website & API based use case.

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DeepSeek Benchmarks Comparison 

DeepSeek’s Pricing Structure 

DeepSeek’s advanced AI capabilities make it significantly cost-effective, exceeding that of ChatGPT. When comparing the price structure of AI models, we discuss it in either per 1M or 1K tokens (think tokens as words). 

How Does Deepseek’s Cost-Effectiveness Compare to ChatGPT’s Pricing?

DeepSeek ( parent company ) claims to operate at 27 times cheaper costs per token compared to OpenAI’s models.

DeepSeek offers a subscription model starting at just $0.50 per month. For developers using its API, the costs are even lower, charging approximately $0.14 per million input tokens and $2.19 per million output tokens. 

DeekSeep’s low GPU requirement allows the API to be accessible, especially for businesses to integrate AI capabilities at large scales, without incurring high expenses.

However, in comparison, ChatGPT’s subscription starts at $20 per month for its premium features such as building your own GPTs. While it does provide a free tier, most of the features are limited in daily usage. Heavy users like startups or big corporations usually pay the subscription to even more expensive enterprise plans, to access advanced functionalities, large-scale data processing, and faster response times.

Just like the cost-effectiveness and performance on reasoning of DeepSeek, we at Octal Digital empower businesses with budget constraints to unlock their web and app development in the USA dreams. We offer all major IT development services in different niches such as AI, FinTech, Health, Blockchain, and more. Feel free to connect with our project managers to discuss your idea- built with advanced and optimized tech stack practices for scalability. 

How DeepSeek has Revolutionalized the AI Industry?

Surprisingly, new techniques were used to develop and train DeepSeek’s R1 LLM models, leading to very low GPU requirements with fewer AI accelerators and less training compared to other big players like OpenAI, Gemini, Copilot, and more. DeepSeek-R1-Distill Models are claimed to have been developed for less than $6 million, which is quite surprising when we compare it with OpenAI’s cost of development crossing over $130 million until 2019. 

Let’s see how DeepSeek has done things differently to revolutionize the whole AI industry. 

Get DeepSeek App at PlayStore and App Store.

Mixture Of Experts ( MOE ) Technique

DeepSeek involves the “Mixture Of Experts( MOE )” technique at its core. 

The Mixture of Experts (MoE) is an ML ( machine learning ) technique where multiple specialized models (experts) work together, with systemized gateways acting at entry points that configure the best expert for each input.

DeepSeek used MoE in the rep training phase, which helped them save a lot of computations. Basically, each “expert” acts as a neural network, feed-forward NN, or a more complex NN. This neural network consisting of experts is connected with gateway routers. These routers send a token to a particular expert instead of each token passing through all the experts. DeepSeek-V3 model is a 671B model but requires only 37B active parameters due to MoE architecture. 

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Auxiliary Loss Free Load-balancing Strategy

For Mixture-of-Experts (MoE) models, an unbalanced expert load through the gateways is meant to lead to routing collapse and an overall increase in computational overhead. Existing methods commonly employ an auxiliary loss to encourage load balance, but a large auxiliary loss will introduce non-negligible interference gradients into training and thus impair the model performance. 

In order to control load balance while also not producing undesired gradients during training, DeepSeek proposes a Loss-Free Load Balancing, featured by an auxiliary-loss-free load balancing strategy. It involves applying an expert-wise bias to the routing scores of each expert first, before the top-K routing decision. By dynamically updating the bias of each expert according to its recent load, Loss-Free Balancing is able to consistently maintain a balanced distribution of expert load. In addition, since Loss-Free Balancing does not produce any interference gradients, it also elevates the upper bound of model performance gained from MoE training. 

Experimental results show that Loss-Free Balancing achieves both better performance and better load balance compared with traditional auxiliary-loss-controlled load balancing strategies.

Multi-head Latent Attention (MLA)

In AI models, Transformers enable machines to understand, interpret, and generate human language in a way that’s more accurate than ever before. Thus, they act as a crucial layer in terms of processing and computation. DeepSeek utilizes Multi-Head Latent Attention (MLA) to enhance its transformer architecture. 

Unlike traditional multi-head attention, which operates directly on token embeddings, MLA introduces additional latent variables that help capture more abstract and diverse representations of the input data. MLA dynamically attends to multiple latent spaces simultaneously.

In simpler terms, instead of predicting the upcoming token based on the previous tokens, DeepSeek predicts the subsequent n tokens. Each attention head processes different latent features, allowing the model to extract nuanced contextual information and improve reasoning capabilities. 

This leads to an overall better generalization, increased interpretability, and more efficient token handling of complex patterns in tasks like natural language model understanding and code generation.

Supervised Fine Tuning (SFT) and Reinforcement Learning (RL)

To make the model human-friendly post-training, DeepSeek uses supervised fine-tuning (SFT) and reinforcement learning (RL).

Supervised fine-tuning involves adapting a pre-trained Language Model (LLM) to a specific downstream task using labeled data. This in turn minimizes the difference between prediction and desired output, increasing response time and accuracy. 

Whereas Reinforcement learning ingles training the AI model to make decisions to achieve the most optimal results. The AI model learns how to achieve the most optimum outcome by taking action, getting feedback through reward functions, and then improving to achieve that outcome.

DeepSeek overcomes the cold start problem, which is the lack of contextual data at the beginning of the RL process, by fine-tuning the model with several thousand data points. This has improved the readability of the response while also improving the model performance.

Both these techniques, SFT and RL, are used extensively to train the R1 model using a base model. In general, a R1 model delivers enhanced reasoning abilities. DeepSeek’s benchmarks show this emergence of reflection as the ‘aha moment’ during reinforcement learning. The standout feature of DeepSeek’s R1 model is that it adds its reasoning before presenting the actual response. The approach can in turn be re-assessed and modified during the reasoning or “thinking” process before generating a response. 

Distillation Method 

The Distillation Method involves using the knowledge or reasoning capabilities of larger models like R1 to fine-tune and train smaller models. It reduces the infra requirement and thereby cost during inference while still maintaining high performance due to knowledge transfer from larger models which can be expensive during inference.

DeepSeek uses the distillation method. This technique helps in improving smaller models’ performance while reducing computing costs. Thus, the smaller output models are trained at very low costs while still maintaining the quality of computing. 

U.S. Concerns About DeepSeek

The release of the DeepSeek-R1 model has raised concerns in the U.S. stock market. It holds the potential to bring new order to companies building foundational models, including firms consuming these, service partners, and end users.  Companies based in Silicon Valley took a huge hit in the market with DeepSeek’s innovation. 

Let’s overview some of these concerning factors for the U.S.

• Low Cost: With only $6 million of investment, the low-cost development of DeepSeek raises concerns for the business model of U.S. tech companies that have invested billions in AI initially. It reveals huge miscalculated amounts and resource management inefficiencies, which were funded by big investors to develop foundational AI models. As discussed in the pricing section, DeepSeek is way cheaper than best-performing AI models such as OpenAI, Gemini, Meta AI, etc.
• Destroying U.S. Monolopy: The export of the highest-performance AI accelerator with advanced chips and GPU chips from the U.S. is restricted to China. U.S. restricted this export to maintain a monopoly in the AI race. DeepSeek showed how it can be easily destroyed by a single company anywhere, with a small group of right people. DeepSeek demonstrated that AI development is possible without access to the most advanced U.S. technology dominated by tech giants such as Nvidia ( an advanced chip company ).
• Business model threat: It needs to be pointed out that while OpenAI is a proprietary technology, DeepSeek is an open-source model and free, challenging the revenue model of U.S. companies charging high monthly fees for their limited AI services.
• Geopolitical concerns: AI is the future of tech, holding the potential to give a big boom to a country’s economy. Being based in China, DeepSeek challenges U.S. technological dominance in AI. Reportedly, Marc Andreessen, a Tech investor, called DeepSeek AI a “Sputnik moment,” while comparing it to the Soviet Union’s space race breakthrough in the 1950s. The race to dominate AI is the new possibility to shift the geopolitical balance of countries. 
• Ethical Issue: One of the concerning factors with DeepSeek is the security protocols of this Chinese AI model. China has been alleged in the past multiple times to misuse user’s data for personal use. The data safety raises a concern here since it’s controlled by a Chinese company, ruled by a communist party. 

Conclusion

DeekSeek undoubtedly showcased how foundational AI can be developed without spending hundreds of millions of dollars and that too, with low running costs and high performance. It has revolutionized the way we develop AI models, opening new gateways to adopt AI cost-effectively with its optimized performance. Both investors and developers are surprised, looking forward to developing more customized AI for internal uses, without depending on big corporations. 

Deepseek has also destroyed the dilemma of power consumption to run AI models, with its low power requirement without losing performance. On an ending note, we can say DeepSeek is one of the impressive breakthroughs in 2025 especially for the American AI market.

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