Sales of Tesla‘s cars to China fell in January, as competition from domestic rivals continued to heat up.

Tesla sold 63,238 units of its electric cars in January, down 11.5% from the 71,447 cars sold in the same month last year.

Shares of Tesla were down about 1.5% in premarket trading.

Chinese rival BYD meanwhile sold 296,446 pure electric and plug-in hybrid vehicles last month, up 47% year-on-year.

Other Chinese rivals of Tesla, including Changan Automobile and Xpeng, also posted growth in sales.

Tesla has attempted to use price cuts as an incentive to retain Chinese’ buyers interest in its car. Late last year, Tesla slashed the price of its Model Y car and also extended a zero-interest five-year loan plan until the end of January.

Last month, the U.S. giant also announced a revamped version of the Model Y — one of its best-selling EV autos — in China. This also came with a 0% interest plan.

Tesla has not introduced a new model since it began delivering the Cybertruck in late 2023, which starts at nearly $80,000. Investors have been yearning for a new mass-market model from the company to reinvigorate sales.

Tesla has said a new affordable model could be launched in the first half of 2025.

Meanwhile, the automaker is pushing to launch its driver assist system, which it markets as “Full Self Driving,” in China this year, as rivals also roll out similar features.

DeepSeek has rattled the U.S.-led AI ecosystem with its latest model, shaving hundreds of billions in chip leader Nvidia’s market cap. While the sector leaders grapple with the fallout, smaller AI companies see an opportunity to scale with the Chinese startup.

Several AI-related firms told CNBC that DeepSeek’s emergence is a “massive” opportunity for them, rather than a threat. 

“Developers are very keen to replace OpenAI’s expensive and closed models with open source models like DeepSeek R1…” said Andrew Feldman, CEO of artificial intelligence chip startup Cerebras Systems.

The company competes with Nvidia’s graphic processing units and offers cloud-based services through its own computing clusters. Feldman said the release of the R1 model generated one of Cerebras’ largest-ever spikes in demand for its services. 

“R1 shows that [AI market] growth will not be dominated by a single company — hardware and software moats do not exist for open-source models,” Feldman added. 

Open source refers to software in which the source code is made freely available on the web for possible modification and redistribution. DeepSeek’s models are open source, unlike those of competitors such as OpenAI.

DeepSeek also claims its R1 reasoning model rivals the best American tech, despite running at lower costs and being trained without cutting-edge graphic processing units, though industry watchers and competitors have questioned these assertions.

“Like in the PC and internet markets, falling prices help fuel global adoption. The AI market is on a similar secular growth path,” Feldman said. 

While Nvidia holds a dominant position in GPUs used for AI training, many competitors see room for expansion in the “inference” segment, where they promise higher efficiency for lower costs.

AI training is very compute-intensive, but inference can work with less powerful chips that are programmed to perform a narrower range of tasks, Lee added.

A number of AI chip startups told CNBC that they were seeing more demand for inference chips and computing as clients adopt and build on DeepSeek’s open source model. 

“[DeepSeek] has demonstrated that smaller open models can be trained to be as capable or more capable than larger proprietary models and this can be done at a fraction of the cost,” said Sid Sheth, CEO of AI chip start-up d-Matrix. 

“With the broad availability of small capable models, they have catalyzed the age of inference,” he told CNBC, adding that the company has recently seen a surge in interest from global customers looking to speed up their inference plans. 

Robert Wachen, co-founder and COO of AI chipmaker Etched, said dozens of companies have reached out to the startup since DeepSeek released its reasoning models.

“Companies are [now] shifting their spend from training clusters to inference clusters,” he said. 

“DeepSeek-R1 proved that inference-time compute is now the [state-of-the-art] approach for every major model vendor and thinking isn’t cheap – we’ll only need more and more compute capacity to scale these models for millions of users.”

“DeepSeek’s performance appears to be based on a series of engineering innovations that significantly reduce inference costs while also improving training cost,” according to a report from Bain & Company.

“In a bullish scenario, ongoing efficiency improvements would lead to cheaper inference, spurring greater AI adoption,” it added. 

This pattern explains Jevon’s Paradox, a theory in which cost reductions in a new technology drive increased demand.

Financial services and investment firm Wedbush said in a research note last week that it continues to expect the use of AI across enterprise and retail consumers globally to drive demand.

Speaking to CNBC’s “Fast Money” last week, Sunny Madra, COO at Groq, which develops chips for AI inference, suggested that as the overall demand for AI grows, smaller players will have more room to grow.

“As the world is going to need more tokens [a unit of data that an AI model processes] Nvidia can’t supply enough chips to everyone, so it gives opportunities for us to sell into the market even more aggressively,” Madra said.

Amazon said Thursday it plans to boost its capital expenditures to $100 billion in 2025, as it continues its investments in artificial intelligence.

The capex figure exceeds last year’s spending of roughly $83 billion. Amazon CEO Andy Jassy had predicted in October that the company’s 2025 capex would surpass last year’s figure, primarily driven by growth in generative AI.

“We spent $26.3 billion in capex in Q4, and I think that is reasonably representative of what you expect an annualized capex rate in 2025,” Jassy said on call with investors after the company released its fourth-quarter earnings report. “The vast majority of that capex spend is on AI for AWS.”

Amazon has been rushing to invest in data centers, networking gear and hardware to meet vast demand for generative AI, which has exploded in popularity since OpenAI released its ChatGPT assistant in late 2022. Amazon has introduced a flurry of AI products, including its own set of Nova models, Trainium chips, a shopping chatbot, and a marketplace for third-party models called Bedrock.

Other tech companies are also spending big on AI. Google parent Alphabet said Tuesday it expects to invest about $75 billion in capital expenditures this year. Last month, Microsoft said it planned to spend $80 billion in fiscal 2025 on the buildout of data centers to support AI workloads. Meta said it will spend as much as $65 billion on capital expenditures as it works to construct more data center and computing infrastructure.

Amazon gave an update on its spending plans after reporting mixed results for the fourth quarter. The company projected weaker-than-expected sales for the current period, which overshadowed a beat on the top and bottom lines in the fourth quarter. Shares fell more than 4% in extended trading.

Jassy tried to reassure investors on the call that the jump in spending will be worthwhile, calling it a “once-in-a-lifetime type of business opportunity.”

“I think that both our business, our customers and shareholders will be happy, medium to long-term, that we’re pursuing the capital opportunity and the business opportunity in AI,” Jassy said. “We also have capex that we’re spending this year in our stores business, really with an aim towards trying to continue to improve the delivery speed and our cost to serve.”

Tech companies are facing fresh skepticism of their AI spending plans after the early success of Chinese AI startup DeepSeek. The lab claims it only took two months and less than $6 million to develop its R1 model, which it says rivals OpenAI’s o1. Markets were roiled by the launch last week, with chipmakers Nvidia and Broadcom losing a combined $800 billion in market cap.

WATCH: Multiple drivers for Amazon long-term