Huawei has open-sourced two of its artificial intelligence models under its Pangu series, marking a significant step in the Chinese tech giant’s bid to strengthen its global AI footprint.

The company also released some of its model reasoning technology, in a move experts say is central to Huawei’s wider plan to build out its AI ecosystem and accelerate international adoption of its products.

The announcement, made Monday, reflects an intensifying open-source drive within China’s AI industry—one that has gained momentum since DeepSeek set the precedent with the release of its DeepSeek-V2 and DeepSeek-Coder models earlier this year. The strategy is seen as a way for China’s top AI firms to work around U.S.-led chip export restrictions and geopolitical barriers while courting global developers and institutions.

Huawei said its open-source initiative supports the broader “Ascend ecosystem strategy,” designed to encourage the use of its AI models and chips in “thousands of industries.” Pangu, which had previously been applied to weather prediction, pharmaceuticals, and logistics, is now being positioned as a scalable foundation model that global partners can deploy, tweak, and integrate across critical sectors.

The move comes as the company, still under heavy U.S. sanctions, accelerates its pivot from a hardware-focused telecoms firm into a vertically integrated AI powerhouse. Analysts say Huawei is now targeting every layer of the AI value chain—from chips to data centers, to open-source software and model deployment.

With American chipmaker Nvidia barred from exporting its most advanced AI chips to China, Huawei’s Ascend chip series has become Beijing’s best hope for a domestic AI accelerator platform. The coupling of Pangu’s open-source code with Ascend chips is meant to drive demand for Huawei’s broader AI infrastructure, similar to the way Google has aligned its open-source Gemma models with its custom Tensor Processing Units (TPUs).

“Huawei is not as strong as companies like DeepSeek and Baidu at the overall software level – but it doesn’t need to be,” said Marc Einstein, research director at Counterpoint Research.

“Its objective is to ultimately use open source products to drive hardware sales, which is a completely different model from others. It also collaborates with DeepSeek, Baidu and others and will continue to do so,” he added.

DeepSeek Set the Precedent

Huawei’s open-source push builds directly on the path carved by DeepSeek, a rival Chinese AI company that has taken the global community by storm with its free and modifiable language models. DeepSeek’s decision to release its models under liberal licenses has helped it break through internationally, particularly in regions where high licensing costs or Western export restrictions have made U.S. models less viable.

Experts say DeepSeek’s success provided Huawei with a roadmap: leverage open access to build market share, gather global user feedback to improve models, and use that momentum to drive hardware and infrastructure adoption.

Pangu being available in an open-source manner allows developers and businesses to test the models and customize them for their needs, said Lian Jye Su, chief analyst at Omdia.

“The move is expected to incentivize the use of other Huawei products,” he added.

Other Chinese AI companies have adopted the strategy. Baidu has announced a decision to make its Ernie generative AI large language model open source.

Huawei appears to be taking that strategy further by targeting vertical AI applications, rather than general-purpose chatbots. Pangu models are optimized for high-stakes, high-volume tasks such as pharmaceutical simulations, financial modeling, and industrial process management—areas where transparency, customizability, and infrastructure control matter more than broad conversational capabilities.

Global Push, Local Advantage

Huawei’s announcement invited developers, researchers, and corporate partners around the world to experiment with the new open-source models. While this may be a gesture of outreach to the global open-source community, it is also a calculated geopolitical move. By distributing its models freely, Huawei sidesteps Western control over cloud infrastructure and model licensing while giving emerging markets access to advanced AI without the costs associated with American platforms.

“Open-source strategy will resonate well in developing countries where enterprises are more price-sensitive, just like Huawei’s other telecom products have,” Einstein noted.

This could help Huawei replicate the success it had with its 5G infrastructure, which gained wide adoption in Asia, Africa, and parts of the Middle East despite ongoing U.S. resistance.

This outreach is paired with the company’s effort to bring its AI data center solutions to new international markets. Countries across Southeast Asia, Africa, and Latin America—long-time clients of Huawei’s telecom division—are being targeted as key partners in the next phase of the company’s global expansion.

However, the true strength of Huawei’s open-source approach lies in its vertically integrated strategy. Unlike many AI companies that license their models through API access alone, Huawei offers an entire stack: Ascend chips, hardware accelerators, development platforms, data storage, and now the model code itself.

This integration allows the company to control the performance pipeline from silicon to application—enabling it to outperform rivals in sectors like energy management, healthcare diagnostics, and logistics optimization. Analysts say that while Huawei may lag in large-scale chatbot development compared to Baidu or DeepSeek, it is staking a leadership claim in applied, enterprise AI.

It’s a model that mirrors Google’s playbook which offers free models to promote internal chip and cloud adoption. The idea is to create lock-in not through control, but through comprehensive utility.

Google is facing another legal blow in the United States after a jury in San Jose, California, found that the tech giant misused Android smartphone users’ data without permission and must pay over $314.6 million in damages.

The class-action verdict marks a significant moment in the broader fight over consumer data rights, transparency in technology services, and the unchecked power of Big Tech.

The jury ruled in favor of approximately 14 million California Android users, agreeing that Google secretly sent and received data from idle Android devices without user consent. The plaintiffs argued that Google profited from these unauthorized data transfers—largely to fuel its advertising business—while consumers were left to bear the cost, particularly through their mobile data plans.

The lawsuit, which dates back to 2019, accused Google of placing a “mandatory and unavoidable burden” on users for its own benefit, with data being collected even when users were not actively using their phones. That behavior, the suit contended, constituted a violation of California’s privacy and consumer protection laws.

The decision represents one of the largest consumer privacy verdicts ever handed down against Google and underlines the growing legal challenges the company faces around data privacy and consumer rights.

Google to Appeal

In a statement, Google spokesperson Jose Castaneda said the company plans to appeal the verdict, arguing that the decision “misunderstands services that are critical to the security, performance, and reliability of Android devices.” Google further argued that Android users had consented to data collection in the company’s terms of service and privacy policy and that there was no actual harm done to users.

However, the jury disagreed, finding that the data collection—without clear and explicit user authorization—constituted a form of digital overreach. The damages were calculated based on the mobile data costs incurred by users, even if individually small, that collectively formed a significant financial impact.

National and Global Implications

While this class-action case is limited to California residents, it may pave the way for larger legal consequences. A separate class-action lawsuit has been filed in federal court in San Jose on behalf of Android users in the other 49 U.S. states. That case is scheduled to go to trial in April 2026 and could expose Google to billions of dollars in additional liabilities if the federal court finds similarly.

The California ruling comes as Google continues to battle a wave of antitrust lawsuits both in the United States and across Europe.

Part of a Pattern of Google’s Mounting Legal Pressure

This latest judgment adds to a growing list of antitrust and privacy lawsuits targeting Google’s core businesses. In the U.S., Google is already defending:

• A landmark antitrust case from the U.S. Department of Justice (DOJ), which accuses the company of using illegal tactics to maintain its search engine monopoly.
• A separate DOJ lawsuit, filed in 2023, targeting Google’s digital advertising business, alleging that the company engaged in anti-competitive conduct by controlling multiple sides of the ad-tech stack.
• Multiple state-led lawsuits, including from attorneys general in Texas and other states, alleging Google inflated ad prices and suppressed competition through its dominance in online advertising.

In Europe, the company is also under intense scrutiny:

• In 2022, Google was fined €4.125 billion by the European Commission for abusing its market dominance in Android, forcing manufacturers to pre-install Google services.
• The European Commission is currently probing Google’s ad tech business, part of the broader enforcement of the Digital Markets Act (DMA), which aims to curtail the power of dominant digital platforms.

The California jury’s verdict reflects a larger shift in how courts, regulators, and the public are starting to view Big Tech’s data practices. Even if users click “agree” on long privacy policies, the legal system is showing signs that blanket consent may no longer shield companies from accountability—especially when background data collection occurs without meaningful transparency or user control.

As the digital economy becomes increasingly embedded in daily life, the ruling is expected to set a precedent for how consumer data costs are measured and what constitutes informed consent in the mobile age.

Google is expected to appeal the decision, and the company’s legal team is likely to argue that its practices are industry standard and necessary for ensuring reliable service performance. But the broader issue is that Google’s core business models, particularly those reliant on targeted advertising and user data, are coming under renewed legal threat.

With federal litigation looming, and similar regulatory scrutiny mounting overseas, this case could become a key inflection point in the broader effort to redefine the digital contract between technology companies and their users.