Senior figures from Boston Consulting Group (BCG) and its tech division, BCG X, discussed the intricate dynamics of the global AI race, the dominance of superpowers like the US and China, the role of emerging “middle powers,” and the implications for multinational corporations.

AI investments expose businesses to increasingly tense geopolitics

Sylvain Duranton, Global Leader at BCG X, noted the significant geopolitical risk companies face: “For large companies, close to half of them, 44%, have teams around the world, not just in one country where their headquarters are.”

Many of these businesses operate across numerous countries, making them vulnerable to differing regulations and sovereignty issues. “They’ve built their AI teams and ecosystem far before there was such tension around the world.”

Duranton also pointed to the stark imbalance in the AI supply race, particularly in investment.

Comparing the market capitalisation of tech companies, the US dwarfs Europe by a factor of 20 and the Asia Pacific region by five. Investment figures paint a similar picture, showing a “completely disproportionate” imbalance compared to the relative sizes of the economies.

This AI race is fuelled by massive investments in compute power, frontier models, and the emergence of lighter, open-weight models changing the competitive dynamic.   

Benchmarking national AI capabilities

Nikolaus Lang, Global Leader at the BCG Henderson Institute – BCG’s think tank – detailed the extensive research undertaken to benchmark national GenAI capabilities objectively.

The team analysed the “upstream of GenAI,” focusing on large language model (LLM) development and its six key enablers: capital, computing power, intellectual property, talent, data, and energy.

Using hard data like AI researcher numbers, patents, data centre capacity, and VC investment, they created a comparative analysis. Unsurprisingly, the analysis revealed the US and China as the clear AI frontrunners and maintain leads in geopolitics.

The US boasts the largest pool of AI specialists (around half a million), immense capital power ($303bn in VC funding, $212bn in tech R&D), and leading compute power (45 GW).

Lang highlighted America’s historical dominance, noting, “the US has been the largest producer of notable AI models with 67%” since 1950, a lead reflected in today’s LLM landscape. This strength is reinforced by “outsized capital power” and strategic restrictions on advanced AI chip access through frameworks like the US AI Diffusion Framework.   

China, the second AI superpower, shows particular strength in data—ranking highly in e-governance and mobile broadband subscriptions, alongside significant data centre capacity (20 GW) and capital power. 

Despite restricted access to the latest chips, Chinese LLMs are rapidly closing the gap with US models. Lang mentioned the emergence of models like DeepSpeech as evidence of this trend, achieved with smaller teams, fewer GPU hours, and previous-generation chips.

China’s progress is also fuelled by heavy investment in AI academic institutions (hosting 45 of the world’s top 100), a leading position in AI patent applications, and significant government-backed VC funding. Lang predicts “governments will play an important role in funding AI work going forward.”

The middle powers: Europe, Middle East, and Asia

Beyond the superpowers, several “middle powers” are carving out niches.

• EU: While trailing the US and China, the EU holds the third spot with significant data centre capacity (8 GW) and the world’s second-largest AI talent pool (275,000 specialists) when capabilities are combined. Europe also leads in top AI publications. Lang stressed the need for bundled capacities, suggesting AI, defence, and renewables are key areas for future EU momentum.
• Middle East (UAE & Saudi Arabia): These nations leverage strong capital power via sovereign wealth funds and competitively low electricity prices to attract talent and build compute power, aiming to become AI drivers “from scratch”. They show positive dynamics in attracting AI specialists and are climbing the ranks in AI publications.   
• Asia (Japan & South Korea): Leveraging strong existing tech ecosystems in hardware and gaming, these countries invest heavily in R&D (around $207bn combined by top tech firms). Government support, particularly in Japan, fosters both supply and demand. Local LLMs and strategic investments by companies like Samsung and SoftBank demonstrate significant activity.   
• Singapore: Singapore is boosting its AI ecosystem by focusing on talent upskilling programmes, supporting Southeast Asia’s first LLM, ensuring data centre capacity, and fostering adoption through initiatives like establishing AI centres of excellence.   

The geopolitics of generative AI: Strategy and sovereignty

The geopolitics of generative AI is being shaped by four clear dynamics: the US retains its lead, driven by an unrivalled tech ecosystem; China is rapidly closing the gap; middle powers face a strategic choice between building supply or accelerating adoption; and government funding is set to play a pivotal role, particularly as R&D costs climb and commoditisation sets in.

As geopolitical tensions mount, businesses are likely to diversify their GenAI supply chains to spread risk. The race ahead will be defined by how nations and companies navigate the intersection of innovation, policy, and resilience.

(Photo by Markus Krisetya)

See also: OpenAI counter-sues Elon Musk for attempts to ‘take down’ AI rival

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Recent developments from companies like DeepSeek illustrate how quickly China has adapted to and overcome international restrictions through creative approaches to AI development.

According to Lee Kai-fu, CEO of Chinese startup 01.AI and former head of Google China, the gap between Chinese and American AI capabilities has narrowed dramatically.

“Previously, I think it was a six to nine-month gap and behind in everything. And now I think that’s probably three months behind in some of the core technologies, but ahead in some specific areas,” Lee toldReutersin a recent interview.

DeepSeek has emerged as the poster child for this new wave of Chinese AI innovation. On January 20, 2025, as Donald Trump was inaugurated as US President, DeepSeek quietly launched its R1 model.

The low-cost, open-source large language model reportedly rivals or surpasses OpenAI’s ChatGPT-4, yet was developed at a fraction of the cost.

Algorithmic efficiency over hardware superiority

What makes DeepSeek’s achievements particularly significant is how they’ve been accomplished despite restricted access to the latest silicon. Rather than being limited by US export controls, Chinese AI innovation has flourished by instead focusing on algorithmic efficiency and novel approaches to model architecture.

Different aspects of this innovative approach were demonstrated further when DeepSeek released an upgraded V3 model on March 25, 2025. The DeepSeek-V3-0324 features enhanced reasoning capabilities and improved performance in multiple benchmarks.

The model showed particular strength in mathematics, scoring 59.4 on the American Invitational Mathematics Examination (AIME) compared to its predecessor’s 39.6. It also improved by 10 points on LiveCodeBench to 49.2.

Häme University lecturer Kuittinen Petri noted on social media platform X that “DeepSeek is doing all this with just [roughly] 2% [of the] money resources of OpenAI.”

When he prompted the new model to create a responsive front page for an AI company, it produced a fully functional, mobile-friendly website with just 958 lines of code.

Market reactions and global impact

The financial markets have noticed the shift in the AI landscape. When DeepSeek launched its R1 model in January, America’s Nasdaq plunged 3.1%, while the S&P 500 fell 1.5% – an indication that investors recognise the potential impact of Chinese AI innovation on established Western tech companies.

The developments present opportunities and challenges for the broader global community. China’s focus on open-source, cost-effective models could democratise access to advanced AI capabilities for emerging economies.

Both China and the US are making massive investments in AI infrastructure. The Trump administration has unveiled its $500 billion Stargate Project, and China projects investments of more than 10 trillion yuan (US$1.4 trillion) in technology by 2030.

Supply chain complexities and environmental considerations

The evolving AI landscape creates new geopolitical complexities. Countries like South Korea highlight the situation. As the world’s second-largest producer of semiconductors, Korea became more dependent on China in 2023 for five of the six most important raw materials needed for chipmaking.

Companies like Toyota, SK Hynix, Samsung, and LG Chem remain vulnerable due to China’s supply chain dominance. As AI development accelerates, environmental implications also loom.

According to the think tank, the Institute for Progress, maintaining AI leadership will require the United States to build five gigawatt computing clusters in five years. By 2030, data centres could consume 10% of US electricity, more than double the 4% recorded in 2023.

Similarly, Greenpeace East Asia estimates that China’s digital infrastructure electricity consumption will surge by 289% by 2035.

The path forward in AI development

DeepSeek’s emergence has challenged assumptions about the effectiveness of technology restrictions. As Lee Kai-fu observed, Washington’s semiconductor sanctions were a “double-edged sword” that created short-term challenges but ultimately forced Chinese firms to innovate under constraints.

Jasper Zhang, a mathematics Olympiad gold medalist with a doctoral degree from the University of California, Berkeley, tested DeepSeek-V3-0324 with an AIME 2025 problem and reported that “it solved it smoothly.” Zhang expressed confidence that “open-source AI models will win in the end,” adding that his startup Hyperbolic now supports the new model on its cloud platform.

Industry experts are now speculating that DeepSeek may release its R2 model ahead of schedule. Li Bangzhu, founder of AIcpb.com, a website tracking the popularity of AI applications, noted that “the coding capabilities are much stronger, and the new version may pave the way for the launch of R2.” R2 is slated for an early May release, according toReuters.

Both nations are pushing the boundaries of what’s possible. The implications extend beyond their borders to impact global economics, security, and environmental policy.

(Image credit: engin akyurt/Unsplash)

See also: US-China tech war escalates with new AI chips export controls

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The company says that it aims to “push the boundaries of multimodal and reasoning models” by providing advanced capabilities at a more accessible price point. Baidu plans to integrate these models into its broader product ecosystem, including Baidu Search and the Wenxiaoyan app, to enhance user experiences.

ERNIE 4.5, Baidu’s “new generation native multimodal foundation model,” features collaborative optimisation across multiple modalities, resulting in improved multimodal comprehension. It enhances language understanding, generation, reasoning, and memory, while also improving “hallucination prevention, logical reasoning, and coding abilities.”

A key feature of ERNIE 4.5 is its ability to integrate and understand various content types, including text, images, audio, and video. It can also grasp complex content such as internet memes and satirical cartoons, showcasing strong contextual awareness.

Baidu claims ERNIE 4.5 outperforms GPT-4.5 in several benchmarks while being significantly more affordable, priced at “just 1% of GPT-4.5.”

The model’s advancements are attributed to technologies like ‘FlashMask’ dynamic attention masking, heterogeneous multimodal mixture-of-experts, spatiotemporal representation compression, knowledge-centric training data construction, and self-feedback enhanced post-training.

ERNIE X1, Baidu’s new deep-thinking reasoning model, focuses on enhanced understanding, planning, reflection, and evolution. As Baidu’s “first multimodal deep-thinking reasoning model capable of tool use,” X1 excels in areas like Chinese knowledge Q&A, literary creation, and complex calculations.

The model’s tool use includes features like advanced search, document Q&A, image understanding, AI image generation, and webpage reading.

ERNIE X1’s capabilities are supported by technologies such as the progressive reinforcement learning method, end-to-end training approach integrating chains of thought and action, and a unified multi-faceted reward system.

For enterprise users and developers, ERNIE 4.5 is accessible through APIs on Baidu AI Cloud’s Qianfan platform, with competitive pricing structures. ERNIE X1 will soon be available on the same platform.

Baidu anticipates that “2025 is set to be an important year for the development and iteration of large language models and technologies” and plans to continue investing in AI, data centres, and cloud infrastructure to advance its AI capabilities and develop next-generation models.

See also: OpenAI and Google call for US government action to secure AI lead

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Unlike anything seen to date, the Manus AI agent doesn’t just chat with users – it is allegedly capable of independently tackling complex multi-step tasks with minimal human guidance.

Developed by Chinese startup Butterfly Effect with financial backing from tech giant Tencent Holdings, Manus AI agent has captured global attention for its ability to bridge the gap between theoretical AI capabilities and practical, real-world applications. It uses an innovative multi-model architecture that combines the strengths of multiple leading language models.

Breakthrough autonomous task execution

In a post on X, Peak Ji Yichao, co-founder and chief scientist at Butterfly Effect, said that the agentic AI was built using existing large language models, including Anthropic’s Claude and fine-tuned versions of Alibaba’s open-source Qwen.

Its multi-model nature allows Manus to use different AI strengths according to what’s demanded of it, resulting in more sophisticated reasoning and execution capabilities.

“The Manus AI agent represents a fundamentally different approach to artificial intelligence,” CNN Business stated. According to coverage, Manus “can carry out complex, multi-step tasks like screening resumés and creating a website,” and “doesn’t only generate ideas but delivers tangible results, like producing a report recommending properties to buy based on specific criteria.”

Real-world performance assessment

In an extensive hands-on evaluation, MIT Technology Review tested the Manus AI agent in three distinct task categories: compiling comprehensive journalist lists, conducting real estate searches with complex parameters, and identifying candidates for its prestigious Innovators Under 35 program.

“Using Manus feels like collaborating with a highly intelligent and efficient intern,” wrote Caiwei Chen in the assessment. “While it occasionally lacks understanding of what it’s being asked to do, makes incorrect assumptions, or cuts corners to expedite tasks, it explains its reasoning clearly, is remarkably adaptable, and can improve substantially when provided with detailed instructions or feedback.”

The evaluation revealed one of the Manus AI agent’s most distinctive features – its “Manus’s Computer” interface, which provides unprecedented transparency into the AI’s decision-making process.

The application window lets users observe the agent’s actions in real time and intervene when necessary, creating a collaborative human-AI workflow that maintains user control while automating complex processes.

Technical implementation challenges

Despite impressive capabilities, the Manus AI agent faces significant technical hurdles in its current implementation.MIT Technology Reviewdocumented frequent system crashes and timeout errors during extended use.

The platform displayed error messages, citing “high service load,” suggesting that computational infrastructure remains a limitation.

The technical constraints have contributed to highly restricted access, with less than 1% of wait-listed users receiving invite codes – the official Manus Discord channel has already accumulated over 186,000 members.

According to reporting from Chinese technology publication36Kr, the Manus AI agent’s operational costs remain relatively competitive at approximately $2 per task.

Strategic partnership with Alibaba Cloud

The creators of the Manus AI agent have announced a partnership with Alibaba’s cloud computing division. According to a South China Morning Post report dated March 11, “Manus will engage in strategic cooperation with Alibaba’s Qwen team to meet the needs of Chinese users.”

The partnership aims to make Manus available on “domestic models and computing platforms,” although implementation timelines remain unspecified.

Parallel advancements in foundation models

The Manus-Alibaba partnership coincides with Alibaba’s advances in AI foundation model technology. On March 6, the company published its QwQ-32B reasoning model, claiming performance characteristics that surpass OpenAI’s o1-mini and rivalling DeepSeek’s R1 model, despite a lower parameter count.

CNN Businessreported, “Alibaba touted its new model, QwQ-32B, in an online statement as delivering exceptional performance, almost entirely surpassing OpenAI-o1-mini and rivalling the strongest open-source reasoning model, DeepSeek-R1.”

The claimed efficiency gains are particularly noteworthy – Alibaba says QwQ-32B achieves competitive performance with just 32 billion parameters, compared to the 671 billion parameters in DeepSeek’s R1 model. The reduced model size suggests substantially lower computational requirements for training and inference with advanced reasoning capabilities.

China’s strategic AI investments

The Manus AI agent and Alibaba’s model advancements reflect China’s broader strategic emphasis on artificial intelligence development. The Chinese government has pledged explicit support for “emerging industries and industries of the future,” with artificial intelligence receiving particular focus alongside quantum computing and robotics.

Alibaba will invest 380 billion yuan (approximately $52.4 billion) in AI and cloud computing infrastructure in the next three years, a figure the company notes exceeds its total investments in these sectors during the previous decade.

As MIT Technology Review’s Caiwei Chen said, “Chinese AI companies are not just following in the footsteps of their Western counterparts. Rather than just innovating on base models, they are actively shaping the adoption of autonomous AI agents in their way.”

The Manus AI agent also exemplifies how China’s artificial intelligence ecosystem has evolved beyond merely replicating Western advances. Government policies promoting technological self-reliance, substantial funding initiatives, and a growing pipeline of specialised AI talent from Chinese universities have created conditions for original innovation.

Rather than a single approach to artificial intelligence, we are witnessing diverse implementation philosophies likely resulting in complementary systems optimised for different uses and cultural contexts.

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DeepSeek’s mobility integration transforms the auto industry

According to the South China Morning Post, over the past two weeks, more than a dozen Chinese automakers have announced plans to integrate DeepSeek’s AI technology into their vehicles. The roster includes industry leader BYD, established manufacturers like Geely, Great Wall Motor, Chery Automobile, and SAIC Motor, and emerging players like Leapmotor.

BYD’s commitment to the technology is particularly noteworthy, with the company planning to integrate DeepSeek in its Xuanji vehicle software platform. The integration will let BYD offer preliminary self-driving capabilities on nearly all its models with no change to the sticker price, making autonomous driving accessible to more consumers.

The initiative covers around 20 models, including the highly-affordable Seagull hatchback, which is currently priced at 69,800 yuan (US$9,575).

E-scooter brands join the DeepSeek bandwagon

DeepSeek has hit China’s e-scooter sector most recently, as Xiaomi-backed Segway-Ninebot Group and Nasdaq-listed Niu Technologies work to incorporate AI into their electric two-wheelers.

Ninebot stated on Friday that it would “deeply integrate DeepSeek” into its products, promising enhanced features through its mobile app. The improvements are said to include AI-powered content creation, data analytics, personalised recommendations, and intelligent services to riders.

Niu Technologies claims to have integrated DeepSeek’s large language models (LLMs) as of February 9 this year. The company plans to use the technology for:

• Driver assistance systems
• Riding safety features
• AI-powered travel companions
• Voice interaction
• Intelligent service recommendations

Yadea Group, the world’s largest by sales electric two-wheeler manufacturer, announced on Saturday that it plans to embed DeepSeek’s technology into its ecosystem.

The rapid adoption of DeepSeek in China’s mobility sector reflects what industry observers call “DeepSeek fever.” The technology’s appeal lies in its cost-effective and cost-efficient approach to AI integration.

The Hangzhou-based company’s open-source AI models, DeepSeek-V3 and DeepSeek-R1, operate at a fraction of the cost and computing power typically required for large language model projects.

“Cars without DeepSeek will either lose market share or be edged out of the market,” said Phate Zhang, founder of Shanghai-based EV data provider CnEVPost.

The expansion of DeepSeek mobility integration comes at a time when Chinese e-scooter brands are gaining traction in overseas markets. According to customs data, the value of electric two-wheeler exports rose 27.6% to US$5.82 billion in 2024, passing the previous peak of US$5.31 billion in 2022. Export volume increased by 47% to 22.13 million units.

Research firm IDC notes that DeepSeek’s open-source model has fostered a collaborative innovation ecosystem via platforms like GitHub, letting developers participate in optimisation and security testing.

The collaborative approach is expected to improve companies’ ability to deploy, train, and utilise large language models.

The impact of DeepSeek mobility integration on China’s transport sector appears to be growing. Zhang Yongwei, general secretary of China EV100, projects that by 2025, approximately 15 million cars – representing two-thirds of national sales – will be equipped with preliminary autonomous driving systems, underscoring the transformative potential of the technology in reshaping China’s transport system.

(Photo by Kenny Leys)

See also: DeepSeek ban? China data transfer boosts security concerns

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According to multiple sources familiar with the matter, Apple is in advanced talks to use Alibaba’s Qwen AI models for its iPhone lineup in mainland China. The move would depart from Apple’s global strategy of using OpenAI’s GPT models for its AI features, highlighting the company’s willingness to adapt to local market conditions.

The technical edge of Qwen AI

Qwen AI is attractive to Apple in China because of the former’s proven capabilities in the open-source AI ecosystem. Recent benchmarks from Hugging Face, a leading collaborative machine-learning platform, position Qwen at the forefront of open-source large language models (LLMs).

The platform’s data shows Qwen-powered models dominating the top 10 positions in performance global rankings, demonstrating the technical maturity that Apple seeks for its AI integration.

“The selection of Qwen AI for iPhone integration would validate Alibaba’s AI capabilities,” explains Morningstar’s senior equity analyst Chelsey Lam. “This could be particularly important for Apple’s strategy to re-invigorate iPhone sales in China, where AI features have become increasingly important for smartphone users.”

Regulatory navigation and market impact

The potential partnership reflects an understanding of China’s AI regulatory landscape. While Apple’s global AI features remain unavailable in China due to regulatory requirements, partnering with Alibaba could provide a compliant pathway to introduce advanced AI capabilities.

Market reaction to the news has been notably positive:

• Alibaba’s stock surged 7.6% on Monday, followed by an additional 1.3% gain on Tuesday
• Apple shares responded with a 2.2% increase
• The tech sector has shown renewed interest in China-focused AI integration strategies

Development timeline and expectations

The timing of the potential collaboration aligns with Apple’s upcoming China developer conference in Shanghai, scheduled for March 25. Industry observers speculate the event could serve as a platform on which to announce the integration of Qwen AI features into the iPhone ecosystem.

“The partnership could change how international tech companies approach AI localisation in China,” noted a senior AI researcher at a leading Chinese university, speaking anonymously. “It’s not just about technology integration; it’s about creating a sustainable model for AI development in China’s regulatory framework.”

Implications for developers and users

For Chinese iOS developers, the potential integration of Qwen AI presents opportunity. The partnership could enable:

• Creation of locally optimised AI applications
• Enhanced natural language processing capabilities specific to Chinese users
• Seamless integration with local services and platforms

Prospects and industry impact

The effects of the partnership extend beyond immediate market concerns. As global tech companies navigate operating in China, the Apple-Alibaba collaboration could serve as a blueprint for future integration.

For Alibaba, securing Apple as a flagship partner could catalyse more partnerships with global technology companies seeking AI solutions for China. The collaboration would demonstrate Qwen AI’s capability to meet the stringent requirements of one of the world’s most demanding tech companies.

Looking ahead

While both companies maintain official silence on the partnership, the tech community awaits announcements at the upcoming Shanghai developer conference. The development is important when AI capabilities increasingly influence smartphone purchasing decisions. For Apple, success in China will impact its global growth trajectory, and integrating Qwen AI could provide the competitive edge it needs to maintain its premium market position against local manufacturers offering advanced AI features.

It underscores a broader trend in the tech industry: the growing importance of localised AI solutions in major markets.

See also: Has Huawei outsmarted Apple in the AI race?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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DeepSeek, practically unknown just weeks ago, took the tech world by storm—gaining global acclaim for its cutting-edge performance while sparking debates reminiscent of the TikTok saga.

Its rise has been fuelled in part by its business model: unlike many of its American counterparts, including OpenAI and Google, DeepSeek offered its advanced powers for free.

However, concerns have been raised about DeepSeek’s extensive data collection practices and a probe has been launched by Microsoft and OpenAI over a breach of the latter’s system by a group allegedly linked to the Chinese AI startup.

A threat to US AI dominance

DeepSeek’s astonishing capabilities have, within a matter of weeks, positioned it as a major competitor to American AI stalwarts like OpenAI’s ChatGPT and Google Gemini. But, alongside the app’s prowess, concerns have emerged over alleged ties to the Chinese Communist Party (CCP).  

According to security researchers, hidden code within DeepSeek’s AI has been found transmitting user data to China Mobile—a state-owned telecoms company banned in the US. DeepSeek’s own privacy policy permits the collection of data such as IP addresses, device information, and, most alarmingly, even keystroke patterns.

Such findings have led to bipartisan efforts in the US Congress to curtail DeepSeek’s influence, with lawmakers scrambling to protect sensitive data from potential CCP oversight.

Reps. Darin LaHood (R-IL) and Josh Gottheimer (D-NJ) are spearheading efforts to introduce legislation that would prohibit DeepSeek from being installed on all government-issued devices. 

Several federal agencies, among them NASA and the US Navy, have already preemptively issued a ban on DeepSeek. Similarly, the state of Texas has also introduced restrictions.

Potential ban of DeepSeek a TikTok redux?

The controversy surrounding DeepSeek bears similarities to debates over TikTok, the social video app owned by Chinese company ByteDance. TikTok remains under fire over accusations that user data is accessible to the CCP, though definitive proof has yet to materialise.

In contrast, DeepSeek’s case involves clear evidence, as revealed by cybersecurity investigators who identified the app’s unauthorised data transmissions. While some might say DeepSeek echoes the TikTok controversy, security experts argue that it represents a much starker and documented threat.

Lawmakers around the world are taking note. In addition to the US proposals, DeepSeek has already faced bans from government systems in countries including Australia, South Korea, and Italy.  

AI becomes a geopolitical battleground

The concerns over DeepSeek exemplify how AI has now become a geopolitical flashpoint between global superpowers—especially between the US and China.

American AI firms like OpenAI have enjoyed a dominant position in recent years, but Chinese companies have poured resources into catching up and, in some cases, surpassing their US competitors.  

DeepSeek’s lightning-quick growth has unsettled that balance, not only because of its AI models but also due to its pricing strategy, which undercuts competitors by offering the app free of charge. That begs the question of whether it’s truly “free” or if the cost is paid in lost privacy and security.

China Mobile’s involvement raises further eyebrows, given the state-owned telecom company’s prior sanctions and prohibition from the US market. Critics worry that data collected through platforms like DeepSeek could fill gaps in Chinese surveillance activities or even potential economic manipulations.

A nationwide DeepSeek ban is on the cards

If the proposed US legislation is passed, it could represent the first step toward nationwide restrictions or an outright ban on DeepSeek. Geopolitical tension between China and the West continues to shape policies in advanced technologies, and AI appears to be the latest arena for this ongoing chess match.  

In the meantime, calls to regulate applications like DeepSeek are likely to grow louder. Conversations about data privacy, national security, and ethical boundaries in AI development are becoming ever more urgent as individuals and organisations across the globe navigate the promises and pitfalls of next-generation tools.  

DeepSeek’s rise may have, indeed, rattled the AI hierarchy, but whether it can maintain its momentum in the face of increasing global pushback remains to be seen.

(Photo by Solen Feyissa)

See also: AVAXAI brings DeepSeek to Web3 with decentralised AI agents

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The Chinese firm’s chat app, which recently soared to the top of Apple’s App Store, issued a notice on its website stating that only users with China-based phone numbers (+86) would be permitted to register for the foreseeable future.  

In the notice, DeepSeek said: “DeepSeek’s online services have recently faced large-scale malicious attacks. To ensure continued service, registration is temporarily limited to +86 phone numbers. Existing users can log in as usual.”

As of writing, sign-ups have reopened with a warning that “registration may be busy” and to please wait and try again.

Despite the challenges, existing users remain unaffected in terms of access. DeepSeek’s status page reported ongoing issues on Monday morning, adding to a turbulent day for the breakthrough AI platform.

DeepSeek faces test after explosive growth

DeepSeek has quickly become a disruptive force in the race for AI supremacy. In addition to gaining widespread user adoption, the app’s powerful open-source AI model has sparked waves of concern among US-based Big Tech firms.

The app’s strength lies in its ability to deliver robust AI performance on less-advanced chips, creating a more cost-effective and accessible solution compared to high-profile rivals such as OpenAI’s ChatGPT.  

This key differentiation shook investor confidence on Monday, leading to a significant dip in share prices for major US technology firms. Industry watchers suggest that such shocks may become more frequent as innovative competitors like DeepSeek challenge the dominance of traditional tech players.

Nigel Green, CEO of global financial advisory firm deVere Group, commented: “China’s technological advances, particularly in AI, are eroding the US’s ability to use tariffs as a tool to maintain global supremacy.

“The balance of power is shifting, and Washington must recognise that it can’t always dictate terms to Beijing as it once did. This new reality will have far-reaching consequences for investors and policymakers.

“AI will be the defining technology of our time, and the race to dominate it will shape global markets for decades to come.”  

Green went on to highlight the potential for non-traditional players and regions to seize the AI crown.

“Traditional tech giants are no longer the guaranteed winners. The focus must now shift to sectors and regions that are driving the next wave of innovation. This includes not only AI but also the critical infrastructure needed to support and secure it.”

The latest developments come against the broader canvas of growing competition between China and the US in the domain of AI and emerging technologies.

Despite export restrictions on US hardware, Beijing’s AI sector has been powering ahead—spurred by significant government investments and a burgeoning ecosystem of innovative startups.  

The rise of apps like DeepSeek signals that the playing field is no longer tilted decisively in favour of Silicon Valley. For years, US companies have been buoyed by leading semiconductor technologies from domestic firms like NVIDIA.

China’s ability to deliver competitive AI capabilities on less-advanced hardware could mark a significant turning point. However, while AI innovation is ramping up globally, DeepSeek’s struggles highlight the growing pains that can accompany explosive growth.

(Photo by Markus Spiske)

See also: Yiannis Antoniou, Lab49: OpenAI Operator kickstarts era of browser AI agents

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Cambricon’s breakthrough into profitability signals a significant shift in the US-China AI chip race, transforming from a 2016 startup into China’s most valuable artificial intelligence company, now valued at approximately 300 billion yuan ($41 billion).

While this represents only a fraction of Nvidia’s $3 trillion market capitalisation, it marks China’s growing capability to develop sophisticated AI chips domestically.

The company’s financial turnaround is particularly noteworthy in the context of technological competition between the world’s two largest economies. After years of losses, Cambricon reported its first quarterly profit in the final quarter of 2024, with net profits ranging from 240 million yuan to 328 million yuan, despite posting a 724 million yuan loss in the first nine months.

The market’s response to this shifting dynamic in the US-China AI chip race has been remarkable. Cambricon’s shares on the Shanghai Stock Exchange’s Star Market have surged more than 470% over the past year, climbing from 120.80 yuan to 695.96 yuan.

The company projects a 70% revenue increase to 1.2 billion yuan in 2024, driven by China’s aggressive buildup of computing infrastructure to support its AI ambitions.

At the technical level, Cambricon has positioned itself as China’s answer to US chip restrictions with its 7-nanometre AI chips. The company’s flagship Cambricon-1A processor has gained significant traction in the domestic market, particularly in products from major technology companies like Huawei Technologies.

The stakes in the US-China AI chip race continue to rise, with analysts at Changjiang Securities projecting that China’s AI semiconductor market will reach 178 billion yuan by 2025. Beijing’s push for semiconductor self-sufficiency and increasing investments from domestic technology companies in AI infrastructure are fueling this growth.

Recent US regulations announced in January 2025 have intensified the race, restricting Chinese access to advanced AI technology and limiting it to American companies and their allies. In response, major Chinese technology companies are investing heavily in domestic computing infrastructure.

ByteDance, TikTok’s parent company, has committed 4.5 billion yuan to a new computing centre in Datong City, Shanxi province. This highlights the growing market opportunity for domestic chip manufacturers.

While Cambricon’s progress represents a significant advancement in the US-China AI chip race, challenges remain. The company must continue to narrow the technological gap with international competitors while maintaining its growth trajectory.

However, supportive government policies and growing domestic demand provide a favourable environment for continued development. Cambricon’s inclusion in the SSE 50 Index, which tracks the Shanghai Stock Exchange’s most valuable companies, underscores its strategic importance to China’s technology sector.

As global tensions persist and access to foreign technology becomes more restricted, developing domestic AI chip capabilities has become increasingly important for China’s technological advancement and economic security.

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AI News caught up with Nerijus Šveistys, Senior Legal Counsel at Oxylabs, to understand the state of play when it comes to AI regulation and its potential implications for industries, businesses, and innovation.

“The boom of the last few years appears to have sparked a push to establish regulatory frameworks for AI governance,” explains Šveistys.

“This is a natural development, as the rise of AI seems to pose issues in data privacy and protection, bias and discrimination, safety, intellectual property, and other legal areas, as well as ethics that need to be addressed.”

Regions diverge in regulatory strategy

The European Union’s AI Act has, unsurprisingly, positioned the region with a strict, centralised approach. The regulation, which came into force this year, is set to be fully effective by 2026.

Šveistys pointed out that the EU has acted relatively swiftly compared to other jurisdictions: “The main difference we can see is the comparative quickness with which the EU has released a uniform regulation to govern the use of all types of AI.”

Meanwhile, other regions have opted for more piecemeal approaches. China, for instance, has been implementing regulations specific to certain AI technologies in a phased-out manner. According to Šveistys, China began regulating AI models as early as 2021.

“In 2021, they introduced regulation on recommendation algorithms, which [had] increased their capabilities in digital advertising. It was followed by regulations on deep synthesis models or, in common terms, deepfakes and content generation in 2022,” he said.

“Then, in 2023, regulation on generative AI models was introduced as these models were making a splash in commercial usage.”

The US, in contrast, remains relatively uncoordinated in its approach. Federal-level regulations are yet to be enacted, with efforts mostly emerging at the state level.

“There are proposed regulations at the state level, such as the so-called California AI Act, but even if they come into power, it may still take some time before they do,” Šveistys noted.

This delay in implementing unified AI regulations in the US has raised questions about the extent to which business pushback may be contributing to the slow rollout. Šveistys said that while lobbyist pressure is a known factor, it’s not the only potential reason.

“There was pushback to the EU AI Act, too, which was nevertheless introduced. Thus, it is not clear whether the delay in the US is only due to lobbyism or other obstacles in the legislation enactment process,” explains Šveistys.

“It might also be because some still see AI as a futuristic concern, not fully appreciating the extent to which it is already a legal issue of today.”

Balancing innovation and safety

Differentiated regulatory approaches could affect the pace of innovation and business competitiveness across regions.

Europe’s regulatory framework, though more stringent, aims to ensure consumer protection and ethical adherence—something that less-regulated environments may lack.

“More rigid regulatory frameworks may impose compliance costs for businesses in the AI field and stifle competitiveness and innovation. On the other hand, they bring the benefits of protecting consumers and adhering to certain ethical norms,” comments Šveistys.

This trade-off is especially pronounced in AI-related sectors such as targeted advertising, where algorithmic bias is increasingly scrutinised.

AI governance often extends beyond laws that specifically target AI, incorporating related legal areas like those governing data collection and privacy. For example, the EU AI Act also regulates the use of AI in physical devices, such as elevators.

“Additionally, all businesses that collect data for advertisement are potentially affected as AI regulation can also cover algorithmic bias in targeted advertising,” emphasises Šveistys.

Impact on related industries

One industry that is deeply intertwined with AI developments is web scraping. Typically used for collecting publicly available data, web scraping is undergoing an AI-driven evolution.

“From data collection, validation, analysis, or overcoming anti-scraping measures, there is a lot of potential for AI to massively improve the efficiency, accuracy, and adaptability of web scraping operations,” said Šveistys. 

However, as AI regulation and related laws tighten, web scraping companies will face greater scrutiny.

“AI regulations may also bring the spotlight on certain areas of law that were always very relevant to the web scraping industry, such as privacy or copyright laws,” Šveistys added.

“At the end of the day, scraping content protected by such laws without proper authorisation could always lead to legal issues, and now so can using AI this way.”

Copyright battles and legal precedents

The implications of AI regulation are also playing out on a broader legal stage, particularly in cases involving generative AI tools.

High-profile lawsuits have been launched against AI giants like OpenAI and its primary backer, Microsoft, by authors, artists, and musicians who claim their copyrighted materials were used to train AI systems without proper permission.

“These cases are pivotal in determining the legal boundaries of using copyrighted material for AI development and establishing legal precedents for protecting intellectual property in the digital age,” said Šveistys.

While these lawsuits could take years to resolve, their outcomes may fundamentally shape the future of AI development. So, what can businesses do now as the regulatory and legal landscape continues to evolve?

“Speaking about the specific cases of using copyrighted material for AI training, businesses should approach this the same way as any web-scraping activity – that is, evaluate the specific data they wish to collect with the help of a legal expert in the field,” recommends Šveistys.

“It is important to recognise that the AI legal landscape is very new and rapidly evolving, with not many precedents in place to refer to as of yet. Hence, continuous monitoring and adaptation of your AI usage are crucial.”

Just this week, the UK Government made headlines with its announcement of a consultation on the use of copyrighted material for training AI models. Under the proposals, tech firms could be permitted to use copyrighted material unless owners have specifically opted out.

Despite the diversity of approaches globally, the AI regulatory push marks a significant moment for technological governance. Whether through the EU’s comprehensive model, China’s step-by-step strategy, or narrower, state-level initiatives like in the US, businesses worldwide must navigate a complex, evolving framework.

The challenge ahead will be striking the right balance between fostering innovation and mitigating risks, ensuring that AI remains a force for good while avoiding potential harms.

(Photo by Nathan Bingle)

See also: Anthropic urges AI regulation to avoid catastrophes

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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This so-called “reasoning” AI model is being compared to OpenAI o1, and it’s one of the few you can download under a permissive license. For developers and researchers eager to experiment, that’s a significant bonus.

Built by Alibaba’s Qwen team, QwQ-32B-Preview is anything but lightweight. It packs 32.5 billion parameters—think of these as the building blocks of its problem-solving abilities—and can handle prompts of up to 32,000 words; longer than some novels! Tests show it outperforms OpenAI o1-preview and o1-mini on benchmarks like AIME and MATH. For context, AIME uses other AI models to assess performance, while MATH is a collection of word problems.

But the model isn’t just about maths problems or logic puzzles. What sets it apart is how it approaches tasks. QwQ-32B-Preview plans ahead, fact-checks its work, and avoids common AI mistakes. Of course, it’s not flawless—Alibaba acknowledges issues like language switching, occasional loops, and difficulties with “common sense” reasoning. Even so, it represents a step toward more intelligent AI systems.

QwQ-32B-Preview is accessible: You can run or download it via Hugging Face. However, like other Chinese-developed AI, it operates within regulatory boundaries. That means it carefully avoids politically sensitive topics to comply with China’s rules, ensuring it aligns with “core socialist values.”

Alibaba isn’t alone in this space. Meta’s Llama 3.1 is another open-source option, though it takes a different approach by focusing on generative AI rather than reasoning. While both models are innovative, QwQ-32B-Preview specialises in problem-solving with what the company describes as a human-like approach, putting it in the reasoning category.

The competition in AI inside China is intensifying. Companies such as DeepSeek, Shanghai AI Lab, and Kunlun Tech have entered the reasoning AI race, releasing their models at pace. For example, DeepSeek’s r1 claims to outperform OpenAI’s o1 on half of its benchmark tests, particularly in maths and programming. Shanghai AI Lab’s InternThinker takes a structured approach to problem-solving, incorporating steps such as understanding queries, recalling knowledge, planning solutions, and reflecting on its answers.

This surge of activity highlights how quickly Chinese companies are catching up with US tech giants. Xu Liang, an AI entrepreneur from Hangzhou, summed it up: “OpenAI gave the direction; with research, Chinese tech firms are making progress.” The release of QwQ-32B-Preview and its competitors shows how much ground they’re covering.

But this goes beyond just catching up. Reasoning AI marks a change in how models are designed and used. Unlike older AI systems that relied on brute force to generate answers, reasoning models like QwQ-32B-Preview aim to mimic human problem-solving. The approach not only makes them more effective for complex tasks but also expands their potential use cases, like tackling advanced maths or providing detailed financial advice.

Whether it’s solving puzzles, reasoning through intricate problems, or expanding what open-source AI can achieve, one thing is clear: the evolution of AI is accelerating. Buckle up—this is only the beginning.

(Photo by Unsplash)

See also: Alibaba Cloud overhauls AI partner initiative

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post Why QwQ-32B-Preview is the reasoning AI to watch appeared first on AI News.

In a comprehensive investigation, Reuters revealed how Chinese cloud access to US AI chips is facilitated through intermediaries. Over 50 tender documents posted in the past year revealed that at least 11 Chinese entities have sought access to restricted US technologies or cloud services. Four of these explicitly named Amazon Web Services (AWS) as a cloud service provider, though accessed through Chinese intermediaries rather than directly from AWS.

“AWS complies with all applicable US laws, including trade laws, regarding the provision of AWS services inside and outside of China,” an AWS spokesperson told Reuters.

The report highlights that while the US government has restricted the export of high-end AI chips to China, providing access to such chips or advanced AI models through the cloud is not a violation of US regulations. This loophole has raised concerns among US officials and lawmakers.

One example cited in the report involves Shenzhen University, which spent 200,000 yuan (£21,925) on an AWS account to access cloud servers powered by Nvidia A100 and H100 chips for an unspecified project. The university obtained this service via an intermediary, Yunda Technology Ltd Co. Neither Shenzhen University nor Yunda Technology responded to Reuters’ requests for comment.

The investigation also revealed that Zhejiang Lab, a research institute developing its own large language model called GeoGPT, stated in a tender document that it intended to spend 184,000 yuan to purchase AWS cloud computing services. The institute claimed that its AI model could not get enough computing power from homegrown Alibaba cloud services.

Michael McCaul, chair of the US House of Representatives Foreign Affairs Committee, told Reuters: “This loophole has been a concern of mine for years, and we are long overdue to address it.”

In response to these concerns, the US Commerce Department is tightening rules. A government spokeswoman told Reuters that they are “seeking additional resources to strengthen our existing controls that restrict PRC companies from accessing advanced AI chips through remote access to cloud computing capability.”

The Commerce Department has also proposed a rule that would require US cloud computing firms to verify large AI model users and notify authorities when they use US cloud computing services to train large AI models capable of “malicious cyber-enabled activity.”

The study also found that Chinese companies are seeking access to Microsoft’s cloud services. For example, Sichuan University stated in a tender filing that it was developing a generative AI platform and would purchase 40 million Microsoft Azure OpenAI tokens to help with project delivery.

Reuters’ report also indicated that Amazon has provided Chinese businesses with access to modern AI chips as well as advanced AI models such as Anthropic’s Claude, which they would not otherwise have had. This was demonstrated by public postings, tenders, and marketing materials evaluated by the news organisation.

Chu Ruisong, President of AWS Greater China, stated during a generative AI-themed conference in Shanghai in May that “Bedrock provides a selection of leading LLMs, including prominent closed-source models such as Anthropic’s Claude 3.”

The report overall emphasises the difficulty of regulating access to advanced computing resources in an increasingly interconnected global technological ecosystem. It focuses on the intricate relationship between US export laws, cloud service providers, and Chinese enterprises looking to improve their AI capabilities.

As the US government works to close this gap, the scenario raises concerns about the efficacy of present export controls and the potential need for more comprehensive laws that cover cloud-based access to banned technologies.

The findings of this paper are likely to feed ongoing discussions about technology transfer, national security, and the global AI race. As politicians and industry leaders analyse these findings, they may spark fresh discussions about how to balance technological cooperation with national security concerns in an era of rapid AI growth.

See also: GlobalData: China is ahead of global rivals for AI ‘unicorns’

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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