DeepSeek AI: How China's Mixture of Experts Architecture Is Reshaping the Global AI Race

Introduction

On January 20, 2025, a relatively unknown Chinese AI company called DeepSeek released its R1 model, triggering one of the most significant tech market reactions in recent history. As stock market charts revealed, the release caused what some analysts called "a bloodbath" in the U.S. stock market, wiping out over $1 trillion in value in a single day.

While most casual observers may have missed the significance, this event represents what could be a pivotal moment in global technology competition. DeepSeek's R1 model is as capable as leading U.S. models but free to use, open-source, significantly more efficient, and most shockingly, reportedly cost less than 3% of ChatGPT-1 to develop.

This episode of ColdFusion, hosted by Dagogo Altraide, explores the implications of DeepSeek's emergence, the technology behind it, and the geopolitical ramifications that could reshape the AI landscape. As artificial intelligence increasingly becomes a matter of national security, the global race between competing ideologies and technological approaches is heating up in ways reminiscent of the Cold War.

The New AI Arms Race: From Companies to Countries

What began just two years ago as a competitive race between tech companies has evolved into a high-stakes competition between nations. The United States, with its long history of technological dominance, now faces a serious challenger in China, a country with different ideological foundations and strategic goals.

Some analysts have even called DeepSeek's release "the Sputnik moment of AI," drawing parallels to the 1957 Soviet satellite launch that shocked America and sparked the Space Race. The White House has already announced it's investigating "national security implications" of China's DeepSeek AI platform.

Adding fuel to the fire, OpenAI has accused DeepSeek of stealing its intellectual property to train their model. Meanwhile, the United States is pouring approximately half a trillion dollars into the Stargate AI project, signaling that the global AI race has reached new intensity.

As Dagogo puts it: "When technology meets a national security threat from an ideological opponent, we get inventions like the computer and jet aircraft from the competition of World War II... This time around, the United States was completely unchallenged in the field of AI for the most part, but that all changed on January 20th, 2025."

What Makes DeepSeek R1 So Revolutionary?

DeepSeek's R1 model has several characteristics that have shocked industry observers:

• Performance reportedly on par with OpenAI's $200-per-month model
• Superior performance to Anthropic's Claude, Claude 2, and Google's Gemini in language reasoning, mathematics, and coding
• Free availability and open-source code
• Built in just two months
• Reportedly cost less than $5.6 million to develop (compared to industry estimates of $100 million to $1 billion for similar systems)
• More efficient architecture requiring significantly less computing power

These factors have led to users flocking to the platform, with DeepSeek quickly becoming the number one app in Apple's App Store. The efficiency and cost differences are so dramatic that they've called into question the sustainability of U.S. companies' AI business models.

As one analyst in the podcast explains: "This is all horrific news for U.S. AI companies because it means that suddenly their costs are all out of balance. DeepSeek with its 671 billion parameters can run locally on a stack of M4 Mac Pros. In contrast, investors and companies have poured billions of dollars into American AI servers."

The Technology Behind DeepSeek's Efficiency

What many observers may not realize is that DeepSeek uses a fundamentally different approach to AI architecture compared to leading U.S. models. This different approach is key to understanding its efficiency advantage.

Mixture of Experts Architecture

While models like OpenAI's GPT-4 use a single massive neural network attempting to be "Einstein, Shakespeare, and Picasso rolled into one," DeepSeek uses what's called a "Mixture of Experts" (MoE) architecture.

As explained in the podcast: "DeepSeek is more like a university broken up into expert departments. This allows the AI to decide what kind of query it's being asked and then send it to a particular part of the digital brain to be dealt with. This lets the other parts remain switched off, saving time, energy, and most importantly, the need for computing power."

Rather than activating all 671 billion parameters for every query, the system might only use 30 billion parameters for a specific math question. This approach creates enormous efficiency gains.

Model Distillation

Adding to the efficiency is a process called "distillation" – using larger models to train smaller models in targeted domains. The result is equivalent performance with significantly less computing power.

Transparent Chain of Thought Reasoning

Unlike OpenAI's closed approach, DeepSeek made an interesting choice to make its Chain of Thought reasoning completely open and visible:

"Open AI pioneered this Chain of Thought, but they don't tell you how they do it because it's all closed... so it's not Open AI at all, right? In some sense, you see a kind of pricey summary version of the Chain of Thought, but it's not the internal actual internal monologue, which is essentially a trade secret. What R1 is doing is it's doing a Chain of Thought which is similar to GPT-4, but it's fully public. They've released all the models, they've released all the code. You can talk to it, you can see the entire monologue."

The Story Behind DeepSeek

Despite being responsible for one of the biggest market downturns in U.S. stock market history, relatively little is known about DeepSeek and its founder, Liang Wenfeng.

Interestingly, Liang doesn't come from a traditional tech background. He co-founded a hedge fund called High Flyer that used AI to predict market trends, and his fund now manages $8 billion. After this initial success, he set his sights on building "human-level AI."

In 2021, he began buying thousands of NVIDIA GPUs as part of what he called an "AI side project." This happened just before the Biden Administration began limiting U.S. export of AI hardware to China. Eventually, Liang spun off this project into DeepSeek.

In an interview with Waves republished in the China Academy in mid-2024, Liang made his ambitions clear:

"For years, Chinese companies have been accustomed to leveraging technological innovations developed somewhere else and monetizing them through applications, but this isn't sustainable. This time our goal isn't quick profits, but advancing the technological frontier to drive ecosystem growth. Why is Silicon Valley so innovative? Because they dare to try. When ChatGPT debuted, China lacked confidence in frontier research. From investors to major tech firms, many felt the gap was too wide and focused instead on applications. But innovation requires confidence, and young people tend to have more of it."

The Global AI Shake-Up

The release of DeepSeek R1 has triggered a cascade of responses across the AI industry:

• OpenAI's Sam Altman announced that their GPT-3.0 Mini model will now be given away for free
• Meta is reportedly "internally panicking" about the implications
• Chinese tech giants including ByteDance (TikTok maker), Alibaba, and Tencent have slashed the prices of their AI models to compete
• Alibaba quickly released Qwen 2.5 Max, which reportedly outperforms DeepSeek and even GPT-4 in some tasks
• Another Chinese model, Kimi K 1.5, was released around the same day with multimodal capabilities and web browsing

The competition has intensified so rapidly that in the weeks following DeepSeek's launch, it had to "temporarily limit user registrations due to large-scale malicious attacks," suggesting the program might not be as stable as it first appeared.

Privacy and Security Concerns

While DeepSeek's capabilities are impressive, users should be aware of important privacy considerations. The platform collects extensive data, including:

• Chat history
• Text and audio inputs
• Uploaded files
• Keystroke patterns
• Essentially anything input into the model

While OpenAI and other U.S. companies collect similar data, the difference is that with DeepSeek, this data goes directly to servers in the People's Republic of China. As Dagogo puts it: "I guess the question is: do you want to be spied on by the U.S. or do you want to be spied on by China?"

On the positive side, DeepSeek's efficiency means it can run locally on a machine without an internet connection for those prioritizing complete privacy. The YouTube channel "Some Ordinary Gamers" demonstrated running DeepSeek locally to write code for a login page.

The Thucydides Trap: What This Means for Global Tech Competition

The emergence of DeepSeek as a serious rival to U.S. AI dominance represents what political scientists call the "Thucydides Trap" – when a rising power challenges an existing power, conflict often arises.

This technological competition could have several outcomes:

1. Accelerated innovation across the AI sector as companies are forced to become more efficient
2. Reduced costs for AI services as U.S. companies respond to competitive pressure
3. New approaches to AI architecture and training becoming mainstream
4. Potential breakthroughs in medical science, material science, mathematics, and theoretical physics
5. Geopolitical tensions as AI capability becomes linked to national security

Sam Altman's only direct reference to DeepSeek so far has been measured but clearly concerned: "DeepSeek's R1 is an impressive model, particularly around what they're able to deliver for the price. We will obviously deliver much better models... it's also legit invigorating to have a new competitor."

Cost Questions and Future Implications

There is some debate about the true cost of developing DeepSeek R1. While headlines have touted the $5.6 million figure, DeepSeek's own research paper clarifies that this "includes only the official training of DeepSeek V3 and does not include cost of prior research experiments on architectures, algorithms, or data."

Whatever the real figure, it's likely significantly less than what U.S. companies have been spending, raising questions about the sustainability of current AI business models.

The future implications of this emerging AI competition are profound. On one hand, we could see breakthroughs that benefit humanity through cheaper, longer-lasting products and more efficient production. On the other hand, there are legitimate concerns about nefarious uses, bad actors, and the geopolitical consequences of AI advancement.

As Dagogo concludes: "Although a lot of people may find consumer AI annoying these days, there's no getting around it. It's here to stay and improving with each week. It's going to be an important part of everyday life soon."

Key Points

1. DeepSeek's R1 AI model from China created market turmoil by offering performance comparable to leading U.S. models at a fraction of the cost, wiping out $1 trillion in U.S. stock market value.
2. The AI competition has evolved from a corporate race to a geopolitical contest, with the U.S. government investigating national security implications and OpenAI accusing DeepSeek of IP theft.
3. DeepSeek's efficiency comes from its "Mixture of Experts" architecture, which activates only relevant portions of the model for specific queries, drastically reducing computing power needs.
4. The model's development reportedly cost under $5.6 million (though this figure is disputed) compared to industry standards of $100 million to $1 billion, forcing U.S. companies to reconsider their approach.
5. Users should be aware that DeepSeek collects extensive data that goes to servers in China, though the model can run locally for privacy-conscious users.
6. The emergence of DeepSeek has triggered a cascade of responses, with OpenAI offering free models, Chinese competitors slashing prices, and Meta reportedly "internally panicking."
7. This technological competition exemplifies the "Thucydides Trap" – when a rising power challenges an existing one – and could lead to accelerated innovation but also increased geopolitical tensions.

For the full conversation, watch the video here.