<div id="readability-page-1">DeepSeek is an artificial intelligence (AI) research lab based in China. It was spun out of the country's most successful hedge fund, High-Flyer, in 2023. The fund had been using AI for years to develop trading algorithms. The DeepSeek team found a way to develop powerful large language models (LLMs) for a tiny fraction of the money being spent by America's leading AI companies. It triggered a panic in the U.S. stock market on Monday as investors considered the impact on chip suppliers like Nvidia (NVDA 0.77%) and prominent developers like OpenAI (which is backed by Microsoft). This could be a transformational moment in the AI race. Not only are DeepSeek's methods potentially valid, but there is at least one other Chinese AI start-up that seems to have produced similar results. Here's what it could mean for Nvidia and OpenAI. <div><img src="https://imageproxy.pbkrs.com/https://g.foolcdn.com/image//query-b3A9cmVzaXplJnVybD1odHRwczovL2cuZm9vbGNkbi5jb20vZWRpdG9yaWFsL2ltYWdlcy84MDU2OTEvdHdvLWludmVzdG9ycy1sb29raW5nLWF0LWEtc2VyaWVzLW9mLWNvbXB1dGVyLXNjcmVlbnMtd2l0aC1wcmljZS1jaGFydHMtb24tdGhlbS5qcGcmdz03MDA?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" width="700" height="466" original-src="https://imageproxy.pbkrs.com/https://g.foolcdn.com/image//query-b3A9cmVzaXplJnVybD1odHRwczovL2cuZm9vbGNkbi5jb20vZWRpdG9yaWFsL2ltYWdlcy84MDU2OTEvdHdvLWludmVzdG9ycy1sb29raW5nLWF0LWEtc2VyaWVzLW9mLWNvbXB1dGVyLXNjcmVlbnMtd2l0aC1wcmljZS1jaGFydHMtb24tdGhlbS5qcGcmdz03MDA"/>Image source: Getty Images. </div> <h2>AI models are quickly becoming commoditized</h2> Ilya Sutskever is one of the co-founders of America's leading AI developer, OpenAI. He once believed data and computing power were the key ingredients to training the best AI models and producing the smartest AI software. This is known as pre-training scaling, and it meant the developers with the most financial resources could build the best data centers, buy the best chips, and win the AI race. But in November 2024, he told Reuters the results from using that method have plateaued. OpenAI has since developed models with better &#34;reasoning&#34; skills, meaning they spend more time &#34;thinking&#34; to produce the best responses from the ChatGPT chatbot. This is known as test-time scaling, and models that use it (GPT-4o1 to GPT-4o3) are better at problem solving, and bring AI closer to human intelligence on an academic level. It cost OpenAI around $20 billion to reach this point (money it mostly raised from investors since 2015). But DeepSeek recently released its V3 model, which was created for just $5.6 million, and yet it's competitive with OpenAI's GPT-4o models across several performance benchmarks. The U.S. government banned Nvidia from selling its latest graphics processors (GPUs) to Chinese AI companies, so DeepSeek developed V3 using less powerful versions like the H100 and the H800. To compensate for the lesser performance, DeepSeek had to innovate on the software side by creating more efficient algorithms and data input methods. The company also used a technique called distillation to create V3. It involves taking a small model and training it using a successful model like GPT-4o1 to produce a similar final product. This strategy supercharges the speed with which an AI company can train a competitive LLM, and it will potentially lead to commoditization. In other words, there could be hundreds of LLMs on the market in the future with similar capabilities, and they will mostly be interchangeable. That could be a real threat to OpenAI and even Nvidia. OpenAI could lose the advantage it established thanks to its considerable financial resources, and since less LLM training will be required, Nvidia could suffer from reduced demand for GPUs. <h2>DeepSeek rocked the tech sector with its low costs, but it's not alone</h2> Training is only one side of the equation. There is also the inference process, which involves the AI model turning prompts into accurate responses. But like with any business, lower overall costs can translate into lower prices for customers. As of this writing, DeepSeek charges just $0.14 per 1 million input tokens, which is 94% cheaper than OpenAI's rate of $2.50 per 1 million input tokens (input tokens are calculated based on the number of words in a user's prompt). But DeepSeek isn't the only AI lab which seems to have cracked this code. Kai-Fu Lee, who used to run Alphabet's Google operations in China, launched an AI start-up called 01.ai. According to its website, its Yi models perform well against competing models from DeepSeek. The company charges just $0.10 per 1 million input tokens, which is even cheaper than its Chinese rival -- and substantially cheaper than OpenAI. <h2>What all of this means for OpenAI and Nvidia</h2> I think OpenAI is in trouble if LLMs continue trending toward commoditization. Plus, its models are closed-source, so developers are locked into the company's ecosystem, which won't be a desirable feature once competitive open-source LLMs are widely available. By contrast, DeepSeek uses an open-source approach that gives developers more freedom to tweak its models as necessary to build AI software. Developers can also download open-source models locally so they never have to share their sensitive data with the creator. But while OpenAI faces uncertainty, Nvidia might actually benefit from plunging inference costs, which could offset some of the lost GPU demand on the training side. Think about the progression of the cellphone. When we had to pay a fee every time we sent a text message or browsed the internet, we didn't use our phones as frequently as we do now. Unlimited plans with uncapped calls, texts, and data enable us to spend hours on our phone each day for a nominal monthly fee -- simply put, when costs came down, usage skyrocketed. AI could follow a similar path, and as usage increases, companies will need more of Nvidia's GPUs to cover demand for inference. That will be especially true as reasoning capabilities evolve, because more thinking requires substantially more computational power. The short-term picture is a little less certain. Will some of Nvidia's customers reduce their data center spending as they optimize their training methods like DeepSeek did? It's hard to say, but a new quarterly earnings season just started, so we should receive an update from almost every one of them over the next few weeks. </div>

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DeepSeek, a Chinese AI research lab, has developed competitive large language models (LLMs) at a fraction of the cost of U.S. companies like OpenAI and Nvidia. Its V3 model, created for $5.6 million, challenges OpenAI's offerings, which cost around $20 billion. The U.S. ban on Nvidia's latest GPUs led DeepSeek to innovate with less powerful hardware and efficient algorithms. This trend towards commoditization could threaten OpenAI's market position, while Nvidia may benefit from lower inference costs despite reduced GPU demand for training.

- DeepSeek, an AI research lab in China, emerged from the hedge fund High-Flyer in 2023, developing competitive large language models at significantly lower costs than U.S. counterparts.  
- The company's V3 model, created for $5.6 million, challenges OpenAI's offerings, prompting concerns about the commoditization of AI models and potential impacts on chip suppliers like Nvidia.  
- While OpenAI may face challenges due to its closed-source models, Nvidia could benefit from increased demand for GPUs as AI usage rises, despite uncertainties in short-term spending.

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DeepSeek Isn't the Only Low-Cost AI Startup. Here's What It Means for OpenAI and Nvidia.