Well, it's not a great day for AI investors, and NVIDIA in particular, since the Chinese firm DeepSeek has managed to disrupt industry norms with its newest R1 AI model, which is said to change the concept of model training and the resources involved behind it.
DeepSeek's R1 AI Model Manages To Disrupt The AI Market Due To Its Training Efficiency; Will NVIDIA Survive The Drain Of Interest?
If you have been living under the rocks or still haven't understood why the "AI markets" are panicking right now, this post is definitely for you. So, China has managed to launch an AI model that is said to be trained using significantly lower financial resources, which we'll talk about later, and this has stirred the debate on the fact whether the "AI supercycle" witnessed in the past year is overhyped or rather not worth the money poured into it. DeepSeek R1 has managed to compete with some of the top-end LLMs out there, with an "alleged" training cost that might seem shocking.
Let's start with what DeepSeek R1 is, and how it differs from the others. The R1 is a one-of-a-kind open-source LLM model that is said to primarily rely on an implementation that hasn't been done by any other alternative out there. While we won't go much into technicals since that would make the post boring, but the important point to note here is that the R1 relies on a "Chain of Thought" process, which means that when a prompt is given to the AI model, it demonstrates the steps and conclusions it has made to reach to the final answer, that way, users can diagnose the part where the LLM had made a mistake in the first place.
Another interesting fact about DeepSeek R1 is the use of "Reinforcement Learning" to achieve an outcome. It is a type of machine learning where the model interacts with the environment to make its decision through a "reward-based process." When a desirable outcome is reached, the model makes sure to opt for those where the reward is maximum, and in this way, it is certain that the desirable conclusion will be achieved.
Whereas, with GPT's o1, the core focus is on supervised learning methods, which involve training the model on massive datasets of text and code, which ultimately requires more financial resources.
Speaking of financial resources, there's a lot of misconception in the markets around DeepSeek's training costs, since the rumored "$5.6 million" figure is just the cost of running the final model, not the total cost. Since China is restricted from accessing cutting-edge AI computing hardware, it won't be wise of DeepSeek to reveal its AI arsenal, which is why the expert perception is that DeepSeek has power equivalent to its competitors, but undisclosed for now.
$NVDA - MUSK SUGGESTS DEEPSEEK 'OBVIOUSLY' HAS MORE NVIDIA GPUS THAN CLAIMED
Elon Musk and Alexandr Wang suggest DeepSeek has about 50,000 NVIDIA Hopper GPUs, not the 10,000 A100s they claim, due to U.S. export controls. Musk, with experience from xAI, agrees with Wang's…
— *Walter Bloomberg (@DeItaone) January 27, 2025
Compared to OpenAI's GPT-o1, the R1 manages to be around five times cheaper for input and output tokens, which is why the market is taking this development with uncertainty and a surprise, but there's a pretty interesting touch to it, which we'll talk about next, and how people shouldn't panic around DeepSeek's accomplishment.
NVIDIA has generated gigantic revenue over the past few quarters by selling AI compute resources, and mainstream companies in the Magnificent 7, including OpenAI, have access to superior technology compared to DeepSeek. Given that DeepSeek has managed to train R1 with confined computing, imagine what the companies can bring to the markets by having potent computing power, which makes this situation much more optimistic towards the future of the AI markets.
There's no competition to NVIDIA's CUDA and the surrounding ecosystem, and it's safe to say that in the world where AI is emerging as a growing technology, we are just at the start. DeepSeek's implementation doesn't mark the end of the AI hype. Rather, it shows us the untapped potential of the technology, although the markets aren't taking this development with much optimism, as Team Green has managed to shave $300 billion+ from its market cap after DeepSeek's R1.
But, we expect the dust to settle, once people realize the positive outcome of the situation. Moreover, this will prompt companies like Meta, Google and Amazon to speed up their respective AI solutions, and as a Cantor Fitzgerald analyst says, DeepSeek's achievement should rather turn us more bullish towards NVIDIA and the future of AI.
About the author: Muhammad Zuhair is a hardware and technology reporter for Wccftech, specializing in the semiconductor industry and the complex interplay between technology, manufacturing, and geopolitics. His coverage focuses on the corporate strategies and technological roadmaps of industry giants like TSMC, NVIDIA, Samsung, and Intel. Zuhair's expertise lies in deconstructing complex topics such as fabrication nodes (e.g., 2nm process), the economic impact of policies like the CHIPS Act, and the strategic development of AI infrastructure from NVIDIA, AMD and Intel.
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