Md. Shawkat Alam Faisal

DeepSeek has emerged as a disruptive force in artificial intelligence, threatening the dominance of established competitors such as OpenAI and Anthropic with a creative yet efficient strategy. DeepSeek has proved that cutting-edge AI can be built for a fraction of the cost, whereas AI training expenses are in the hundreds of millions, needing massive data centers filled with thousands of high-end GPUs. This discovery is more than simply an incremental improvement; it represents a radical rethinking of AI design, threatening the entire base on which companies like Nvidia have established their dominance.

The classic AI development methodology is quite resource intensive. Training cutting-edge models has become synonymous with a computational arms race in which only the wealthiest corporations can afford to compete. OpenAI, for example, reportedly spends more than $100 million on compute to train a single model, necessitating thousands of GPUs costing up to $40,000 apiece. This creates a situation in which AI progress is determined by access to finance rather than ingenuity. DeepSeek has challenged this paradigm by demonstrating that an AI system of equivalent, if not higher, quality may be constructed for as little as $5 million.

The secret to this change is a series of fundamental optimizations that call into question long-held beliefs about AI performance. One of DeepSeek's most notable contributions is its rethinking of precision in AI computing. While standard models use 32-bit precision to ensure exceptional numerical accuracy at a high computational expense, DeepSeek questioned whether this level of precision was truly required. By decreasing precision to 8 bits, they reduced memory requirements by 75% while maintaining meaningful accuracy, demonstrating that efficiency may be accomplished without losing accuracy.

In addition to precision, DeepSeek has offered a unique way to language processing. Traditional AI models parse text sequentially, reading words one at a time, which is inefficient when dealing with billions of tokens. However, DeepSeek created a multi-token architecture that enables the model to handle full sentences at once. This not only increases processing speed, but also maintains 90% of the accuracy of older methods. When applied across large datasets, this innovation results in a massive reduction in computing overhead.

Perhaps the most important innovation is DeepSeek's use of a modular expert system. Unlike standard AI models, which keep all 1.8 trillion parameters active at all times, DeepSeek takes a more selective approach. Instead of a single huge model attempting to master all areas, it employs specialized "expert" sub-models that activate only when necessary. This means that, instead of processing information with all 1.8 trillion parameters, DeepSeek's algorithm only needs 37 billion parameters at any given time. The end result is a leaner, more efficient AI that provides the same intelligence while using far fewer resources.

DeepSeek's commitment to transparency amplifies its influence. In an industry dominated by proprietary models and closed ecosystems, DeepSeek elected to make its technology open-source, allowing anybody to verify, replicate, and improve on its work. This shift directly challenges the current AI hierarchy, which is based on exclusivity and restricted access. If cutting-edge AI can be developed and deployed at a fraction of the cost, huge technological businesses' monopolies will begin to collapse, allowing smaller competitors to enter the industry and drive innovation.

This poses an existential danger to Nvidia, whose business model is based on the notion that AI development requires massive volumes of high-end GPUs. If AI models can now be trained on consumer-grade GPUs rather than pricey data center infrastructure, demand for Nvidia's flagship products may fall dramatically. The ramifications are startling. Training costs that originally totaled $100 million can now be reduced to $5 million. The number of GPUs required for training can be reduced from 100,000 to just 2,000. API expenses, a major consideration in AI deployment, might be reduced by 95%. These reductions dramatically alter the economics of AI, making it available to far more players than ever before.

This development marks a watershed point for the AI industry as a whole. The incumbents, which include OpenAI and Anthropic, will not stand still. They will surely use similar optimization strategies to preserve their dominance. But the truth is that DeepSeek has already transformed the game. The efficiency genie has been released, and there will be no return to an era in which AI development is judged solely by the amount of GPUs thrown at a problem.

When seen in the context of the larger technology landscape, this event is reminiscent of previous disruptions that completely transformed sectors. DeepSeek's inventions may usher in a new age in AI development, much like personal computers rendered mainframes obsolete and cloud computing upended traditional IT infrastructure. The barriers that once locked AI within the realm of billion-dollar enterprises are crumbling, to be replaced by a system in which efficiency and innovation are valued more highly than pure computational brute force.

Just a few days ago, in a strategic move to strengthen the United States' position in artificial intelligence, newly elected President Donald Trump unveiled the Stargate Project on January 21, 2025, with the goal of investing up to $500 billion in AI infrastructure by 2029. In recent years, the United States has enacted strict export regulations to limit China's access to modern semiconductors, attempting to stymie its progress in artificial intelligence. Despite these limitations, Chinese AI firms have made significant progress. DeepSeek, for example, created its R1 model, which competes with leading US AI systems, by using novel strategies that lessen dependency on high-end CPUs.

For the major AI companies, this development represents both a challenge and an opportunity. Those that embrace these new efficiencies will survive, while those who adhere to antiquated approaches risk falling behind. Nvidia, in particular, faces a shaky future as demand for ultra-high-end AI technology falters. Investors and market analysts will be forced to reconsider the long-term viability of a business model based on selling ever expensive GPUs, especially as smarter, leaner AI solutions prove to be equally capable.

What remains uncertain is the rate at which this disruption will occur. If history is any indication, change frequently occurs faster than predicted. What appears to be a niche breakthrough today may quickly become the industry standard, leaving others who do not adapt scurrying to catch up. DeepSeek has not only introduced a new way of thinking about AI, but it has profoundly altered the course of the entire field. The question now is not if this disruption will occur, but how rapidly it will transform the future of artificial intelligence.

The writer is a, Columnist & an Apprentice Lawyer at the Bangladesh Bar Council.