Will DeepSeek be able to disrupt US dominance in roaring AI world?

Long time domination of world AI industry by the US tech companies like Google, Microsoft and Meta has finally come into a mild shock with a new challenges that is posed by the Chinese startup DeepSeek.

A Chinese startup company led by Liang Wenfeng has suddenly made gossip in Sillicon Valley, a tech capital of the world where all US tech giants are based. Even the multibillion dollar AI models such as ChatGPT and Gemini have been shaken by the emergence of this startup which is a very powerful AI model with far less money than many AI experts thought possible.

DeepSeek logo

Well, the answer might be yes for now. But the situation is different. The AI technologies used to make DeepSeek are invented along the lines of OpenAI’s ChatGPT chatbot or Google’s Gemini. Because DeepSeek has released several large language models, which is the kind of technology that underpins chatbots like ChatGPT and Gemini.

According to the media reports, DeepSeek required about $6 million in raw computing power to train their new system while roughly 10 times less than what Meta spent on its latest AI technology.

This is also an important parameter to note that when OpenAI set off the AI boom from late 2022, the prevailing notion had been that the most powerful AI systems could not be built without investing billions of dollars in specialized AI chips. That would mean that only the biggest tech companies — such as Microsoft, Google and Meta, all of which are based in the United States — could afford to build the leading technologies.

This attention has raised the question: Whether U.S. companies are even competitive in AI anymore?

💡 Smart AI Training at a Fraction of the Cost

Unlike its Western competitors, DeepSeek has successfully trained powerful AI models at a much lower cost. For example, its latest model, DeepSeek-V3, was developed with an estimated $6 million budget, a fraction of what other AI firms spend.

🧠 "Mixture of Experts" Approach
The secret to DeepSeek's efficiency is its "Mixture of Experts" (MoE) method, which:
✔ Uses multiple specialized AI models to divide and process data efficiently
✔ Minimizes data transfer delays for faster training times
✔ Reduces overall computational costs without sacrificing performance

🏆 Competing with the Best
DeepSeek-V3 has delivered top-tier results in question-answering, problem-solving, and code generation benchmarks. Additionally, DeepSeek R1, a highly advanced reasoning model, has reinforced the company’s reputation as an AI leader.

🌍 The Global AI Race: DeepSeek’s Impact

🔹 Lowering Barriers to AI Innovation
DeepSeek’s success proves that cutting-edge AI models can be built without billion-dollar budgets. This could enable smaller companies and research institutions to enter the AI space.

🔹 Reshaping AI’s Competitive Landscape
With China’s AI ecosystem rapidly evolving, DeepSeek’s emergence adds a new dimension to global AI competition, challenging the US tech monopoly.

🔹 A New Direction for AI Development
DeepSeek’s cost-effective AI strategy may push other companies to rethink their approach, leading to more efficient and sustainable AI development.

🔮 Future of AI: What’s Next for DeepSeek?

DeepSeek’s groundbreaking AI models and efficient training techniques mark a new era of AI development. As the company continues to innovate, it could redefine industry standards and inspire a wave of cost-effective AI solutions worldwide.

This proves that with cost-effective AI models and groundbreaking innovations, DeepSeek is proving that big breakthroughs don’t always require big budgets.

ChatGPT, Gemini, and DeepSeek: A Comparative Analysis

Feature	ChatGPT (OpenAI)	Gemini (Google)	DeepSeek
Development Cost	High (Billions of dollars)	High (Billions of dollars)	Low (Millions of dollars)
Training Approach	Massive datasets, significant computational resources	Focus on multimodal capabilities (text, images, code, etc.), leveraging Google's vast resources	"Mixture of Experts" approach, optimizing for efficiency and cost-effectiveness
Key Strengths	Strong in text generation, conversation, and creative content	Multimodal capabilities, excels in image understanding and generation	High performance with significantly lower development costs, innovative training approach
Weaknesses	Can sometimes hallucinate information, limitations in complex reasoning	Still under development, some features may be limited	Relatively new, long-term performance and reliability still under evaluation
Focus	General-purpose LLM, widely used for various applications	Multimodal AI, aiming for broader applications beyond text	Cost-effective AI development, challenging the status quo
Availability	Widely available through API and various platforms	Limited availability, primarily for internal Google use and select partners	Gradually expanding availability, focusing on specific applications and partnerships