China has sensed a window of opportunity in its race to usurp US dominance in AI innovation. As Washington remains focused on building regulatory walls that restrict access to AI, Beijing has resorted to fostering open-sourcing artificial intelligence models to boost AI innovation and adoption. That was clear when DeepSeek unveiled cost-effective AI models that challenged American tech dominance on large language models and data centers.

While the initial focus was on how DeepSeek came up with powerful AI models that rivaled US models, the focus has since changed to how the Chinese startup catalyzed the adoption of open-source AI models. The decision to open source AI source code comes as Chinese companies look to capitalize on growing opportunities in the artificial intelligence software market, growing at a compound annual growth rate of 35.52% and expected to be worth $223.35 billion by 2028, according to Research and Markets.

“DeepSeek’s success proves that open-source strategies can lead to faster innovation and broad adoption,” said Wei Sun, principal analyst of artificial intelligence at Counterpoint Research.

Understanding that it will require time for China to reach the level of the US in advanced AI computing, Chinese firms have concentrated on creating more efficient and cost-effective AI solutions. They are also striving to establish leading roles in open-source AI, cloud services, and worldwide data networks.

These approaches allow China to provide more affordable and unrestricted AI access to nations discontented with US policies, integrating it into developing markets in ways that will be challenging to disrupt. This is not merely a contest in AI; it is a struggle for dominance over the global digital infrastructure both now and in the future.

Since DeepSeek sent shockwaves, several Chinese companies have released open-source models that are free for individual users. The move to open-source AI models underscores a broader shift in China that no longer focuses on proprietary licensing. Instead, Chinese companies freely offer the underlying source code for modification and redistribution.

While US tech giants have always insisted on open-sourcing their models or source code, that has not always been the case in entirety, for starters, most US companies claim to use open-source code while still restricting their use and modifications. Other US companies generate revenue by charging people to access their models even on claiming they are open source.

For this article, we selected AI stocks by going through news articles, stock analysis, and press releases. These stocks are also popular among hedge funds in Q4 2024.

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11. D-Wave Quantum Inc. (NYSE:QBTS)

Number of Hedge Fund Holders: 15

D-Wave Quantum Inc. (NYSE:QBTS) is a technology company that develops and delivers quantum computing systems, software, and services worldwide. The quantum computing solutions provider achieved a significant milestone on March 31 by completing a quantum artificial intelligence drug discovery project.

In partnership with the pharmaceutical division of Japan Tobacco Inc., D-Wave Quantum Inc. (NYSE:QBTS) leveraged quantum computing technology and artificial intelligence to enhance the drug discovery process. The two companies enhanced large language models with a quantum hybrid workflow to produce a drug-like molecular structure. The project’s objective was to improve quality and speed in a number of processes while hastening the discovery of first-in-class small-molecule compounds.

“AI has made impressive advancements but faces a computational challenge due to escalating power needs and costs,” said Dr. Alan Baratz, CEO of D-Wave. “Quantum computing’s integration with AI and machine learning could offer scalable, energy-efficient solutions to address these issues and potentially offer enhanced AI capabilities. We believe that our work with JT is an important demonstration and validation of quantum’s integration with AI.”

10. Progress Software Corporation (NASDAQ:PRGS)

Number of Hedge Fund Holders: 23 

Progress Software Corporation (NASDAQ:PRGS) is a leading provider of artificial intelligence-powered applications and digital experiences that enable businesses to deploy and manage applications. Its stock rallied by about 8% after the leading provider of AI infrastructure delivered better-than-expected first-quarter fiscal 2025 results and issued an impressive outlook on April 1.

Progress Software Corporation (NASDAQ:PRGS) logged a 29% year-over-year increase in revenue to $238 million as customers’ demand for its solutions grew significantly. Earnings per share, on the other hand, totaled $1.31, better than the $1.06 that analysts expected. While annualized recurring revenue increased 48% to $836 million with a net retention rate of more than 100% Progressive Software has raised its full-year EPS guidance to between $5.25 and $5.37, up from its earlier estimate of $5.00 to $5.12. The company successfully integrated ShareFile as part of its AI-driven strategy.

“We’re off to a very strong start for FY25, as our Q1 results demonstrate. Revenues at the high end of guidance reflect steady demand; expenses remain well-controlled; cash flow was again strong; and our bottom-line results and raised EPS guidance reflect numerous positives,” said Anthony Folgers, CFO of Progress.

9. Maxlinear Inc (NASDAQ:MXL)

Number of Hedge Fund Holders: 26

MaxLinear Inc. (NASDAQ:MXL) is a technology company that designs and develops highly integrated radio-frequency (RF) and mixed-signal integrated circuits (ICs) for broadband communications and data applications. Additionally, it focuses on artificial intelligence and machine learning by providing high-speed low power connectivity solutions. On March 31, the company moved to strengthen its prospects in the high-growth AI/ML interconnect market with the launch of Rushmore DSP.

Built on Samsung’s leading-edge CMOS technology, Rushmore DSP is designed to boost performance and power efficiency, paving the way for high-speed and efficient networking solutions. Its 1.6T (200G/lane) PAM4 SERDES should address power performance bottlenecks in artificial intelligence infrastructure scaling. Additionally, it should elicit strong interest as networks transition to 51.2T and 102.4T, which require 200G/lane connectivity solutions. The switch to 200G/lane technology is becoming essential as AI/ML workloads drive exponential bandwidth requirements.