MCP works like a universal connector that lets AI assistants interact directly with favourite apps and services – enabling them to make payments, book appointments, check maps, and access information on different platforms on users’ behalves.

As reported by the South China Morning Post, companies like Ant Group, Alibaba Cloud, and Baidu are deploying MCP-based services and positioning AI agents as the next step, after chatbots and large language models. But will China’s MCP adoption truly transform the AI landscape, or is it simply another step in the technology’s evolution?

Why China’s MCP adoption matters for AI’s evolution

The Model Context Protocol was initially introduced by Anthropic in November 2024, at the time described as a standard that connects AI agents “to the systems where data lives, including content repositories, business tools and development environments.”

MCP serves as what Ant Group calls a “USB-C port for AI applications” – a universal connector allowing AI agents to integrate with multiple systems.

The standardisation is particularly significant for AI agents like Butterfly Effect’s Manus, which are designed to autonomously perform tasks by creating plans consisting of specific subtasks using available resources.

Unlike traditional chatbots that just respond to queries, AI agents can actively interact with different systems, collect feedback, and incorporate that feedback into new actions.

Chinese tech giants lead the MCP movement

China’s MCP adoption by tech leaders highlights the importance placed on AI agents as the next evolution in artificial intelligence:

• Ant Group, Alibaba’s fintech affiliate, has unveiled its “MCP server for payment services,” that lets AI agents connect with Alipay’s payment platform. The integration allows users to “easily make payments, check payment statuses and initiate refunds using simple natural language commands,” according to Ant Group’s statement.
• Additionally, Ant Group’s AI agent development platform, Tbox, now supports deployment of more than 30 MCP services currently on the market, including those for Alipay, Amap Maps, Google MCP, and Amazon Web Services’ knowledge base retrieval server.
• Alibaba Cloud launched an MCP marketplace through its AI model hosting platform ModelScope, offering more than 1,000 services connecting to mapping tools, office collaboration platforms, online storage services, and various Google services.
• Baidu, China’s leading search and AI company, has indicated that its support for MCP would foster “abundant use cases for [AI] applications and solutions.”

Beyond chatbots: Why AI agents represent the next frontier

China’s MCP adoption signals a shift in focus from large language models and chatbots to more capable AI agents. As Red Xiao Hong, founder and CEO of Butterfly Effect, described, an AI agent is “more like a human being” compared to how chatbots perform.

The agents not only respond to questions but “interact with the environment, collect feedback and use the feedback as a new prompt.” This distinction is held to be important by companies driving progress in AI.

While chatbots and LLMs can generate text and respond to queries, AI agents can take actions on multiple platforms and services. They represent an advance from the limited capabilities of conventional AI applications toward autonomous systems capable of completing more complex tasks with less human intervention.

The rapid embrace of MCP by Chinese tech companies suggests they view AI agents as a new avenue for innovation and commercial opportunity that go beyond what’s possible with existing chatbots and language models.

China’s MCP adoption could position its tech companies at the forefront of practical AI implementation. By creating standardised ways for AI agents to interact with services, Chinese companies are building ecosystems where AI could deliver more comprehensive experiences.

Challenges and considerations of China’s MCP adoption

Despite the developments in China’s MCP adoption, several factors may influence the standard’s longer-term impact:

1. International standards competition. While Chinese tech companies are racing to implement MCP, its global success depends on widespread adoption. Originally developed by Anthropic, the protocol faces potential competition from alternative standards that might emerge from other major AI players like OpenAI, Google, or Microsoft.
2. Regulatory environments. As AI agents gain more autonomy in performing tasks, especially those involving payments and sensitive user data, regulatory scrutiny will inevitably increase. China’s regulatory landscape for AI is still evolving, and how authorities respond to these advancements will significantly impact MCP’s trajectory.
3. Security and privacy. The integration of AI agents with multiple systems via MCP creates new potential vulnerabilities. Ensuring robust security measures across all connected platforms will be important for maintaining user trust.
4. Technical integration challenges. While the concept of universal connectivity is appealing, achieving integration across diverse systems with varying architectures, data structures, and security protocols presents significant technical challenges.

The outlook for China’s AI ecosystem

China’s MCP adoption represents a strategic bet on AI agents as the next evolution in artificial intelligence. If successful, it could accelerate the practical implementation of AI in everyday applications, potentially transforming how users interact with digital services.

As Red Xiao Hong noted, AI agents are designed to interact with their environment in ways that more closely resemble human behaviour than traditional AI applications. The capacity for interaction and adaptation could be what finally bridges the gap between narrow AI tools and the more generalised assistants that tech companies have long promised.

See also: Manus AI agent: breakthrough in China’s agentic AI

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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Released on April 17, this “thinking budget” feature responds to a growing industry challenge: advanced AI models frequently overanalyse straightforward queries, consuming unnecessary computational resources and driving up operational and environmental costs.

While not revolutionary, the development represents a practical step toward addressing efficiency concerns that have emerged as reasoning capabilities become standard in commercial AI software.

The new mechanism enables precise calibration of processing resources before generating responses, potentially changing how organisations manage financial and environmental impacts of AI deployment.

“The model overthinks,” acknowledges Tulsee Doshi, Director of Product Management at Gemini. “For simple prompts, the model does think more than it needs to.”

The admission reveals the challenge facing advanced reasoning models – the equivalent of using industrial machinery to crack a walnut.

The shift toward reasoning capabilities has created unintended consequences. Where traditional large language models primarily matched patterns from training data, newer iterations attempt to work through problems logically, step by step. While this approach yields better results for complex tasks, it introduces significant inefficiency when handling simpler queries.

Balancing cost and performance

The financial implications of unchecked AI reasoning are substantial. According to Google’s technical documentation, when full reasoning is activated, generating outputs becomes approximately six times more expensive than standard processing. The cost multiplier creates a powerful incentive for fine-tuned control.

Nathan Habib, an engineer at Hugging Face who studies reasoning models, describes the problem as endemic across the industry. “In the rush to show off smarter AI, companies are reaching for reasoning models like hammers even where there’s no nail in sight,” he explained to MIT Technology Review.

The waste isn’t merely theoretical. Habib demonstrated how a leading reasoning model, when attempting to solve an organic chemistry problem, became trapped in a recursive loop, repeating “Wait, but…” hundreds of times – essentially experiencing a computational breakdown and consuming processing resources.

Kate Olszewska, who evaluates Gemini models at DeepMind, confirmed Google’s systems sometimes experience similar issues, getting stuck in loops that drain computing power without improving response quality.

Granular control mechanism

Google’s AI reasoning control provides developers with a degree of precision. The system offers a flexible spectrum ranging from zero (minimal reasoning) to 24,576 tokens of “thinking budget” – the computational units representing the model’s internal processing. The granular approach allows for customised deployment based on specific use cases.

Jack Rae, principal research scientist at DeepMind, says that defining optimal reasoning levels remains challenging: “It’s really hard to draw a boundary on, like, what’s the perfect task right now for thinking.”

Shifting development philosophy

The introduction of AI reasoning control potentially signals a change in how artificial intelligence evolves. Since 2019, companies have pursued improvements by building larger models with more parameters and training data. Google’s approach suggests an alternative path focusing on efficiency rather than scale.

“Scaling laws are being replaced,” says Habib, indicating that future advances may emerge from optimising reasoning processes rather than continuously expanding model size.

The environmental implications are equally significant. As reasoning models proliferate, their energy consumption grows proportionally. Research indicates that inferencing – generating AI responses – now contributes more to the technology’s carbon footprint than the initial training process. Google’s reasoning control mechanism offers a potential mitigating factor for this concerning trend.

Competitive dynamics

Google isn’t operating in isolation. The “open weight” DeepSeek R1 model, which emerged earlier this year, demonstrated powerful reasoning capabilities at potentially lower costs, triggering market volatility that reportedly caused nearly a trillion-dollar stock market fluctuation.

Unlike Google’s proprietary approach, DeepSeek makes its internal settings publicly available for developers to implement locally.

Despite the competition, Google DeepMind’s chief technical officer Koray Kavukcuoglu maintains that proprietary models will maintain advantages in specialised domains requiring exceptional precision: “Coding, math, and finance are cases where there’s high expectation from the model to be very accurate, to be very precise, and to be able to understand really complex situations.”

Industry maturation signs

The development of AI reasoning control reflects an industry now confronting practical limitations beyond technical benchmarks. While companies continue to push reasoning capabilities forward, Google’s approach acknowledges a important reality: efficiency matters as much as raw performance in commercial applications.

The feature also highlights tensions between technological advancement and sustainability concerns. Leaderboards tracking reasoning model performance show that single tasks can cost upwards of $200 to complete – raising questions about scaling such capabilities in production environments.

By allowing developers to dial reasoning up or down based on actual need, Google addresses both financial and environmental aspects of AI deployment.

“Reasoning is the key capability that builds up intelligence,” states Kavukcuoglu. “The moment the model starts thinking, the agency of the model has started.” The statement reveals both the promise and the challenge of reasoning models – their autonomy creates both opportunities and resource management challenges.

For organisations deploying AI solutions, the ability to fine-tune reasoning budgets could democratise access to advanced capabilities while maintaining operational discipline.

Google claims Gemini 2.5 Flash delivers “comparable metrics to other leading models for a fraction of the cost and size” – a value proposition strengthened by the ability to optimise reasoning resources for specific applications.

Practical implications

The AI reasoning control feature has immediate practical applications. Developers building commercial applications can now make informed trade-offs between processing depth and operational costs.

For simple applications like basic customer queries, minimal reasoning settings preserve resources while still using the model’s capabilities. For complex analysis requiring deep understanding, the full reasoning capacity remains available.

Google’s reasoning ‘dial’ provides a mechanism for establishing cost certainty while maintaining performance standards.

See also: Gemini 2.5: Google cooks up its ‘most intelligent’ AI model to date

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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In partnership with Tsinghua University researchers, DeepSeek has created a technique detailed in a research paper, titled “Inference-Time Scaling for Generalist Reward Modeling.” It outlines how a new approach outperforms existing methods and how the team “achieved competitive performance” compared to strong public reward models.

The innovation focuses on enhancing how AI systems learn from human preferences – a important aspect of creating more useful and aligned artificial intelligence.

What are AI reward models, and why do they matter?

AI reward models are important components in reinforcement learning for large language models. They provide feedback signals that help guide an AI’s behaviour toward preferred outcomes. In simpler terms, reward models are like digital teachers that help AI understand what humans want from their responses.

“Reward modeling is a process that guides an LLM towards human preferences,” the DeepSeek paper states. Reward modeling becomes important as AI systems get more sophisticated and are deployed in scenarios beyond simple question-answering tasks.

The innovation from DeepSeek addresses the challenge of obtaining accurate reward signals for LLMs in different domains. While current reward models work well for verifiable questions or artificial rules, they struggle in general domains where criteria are more diverse and complex.

The dual approach: How DeepSeek’s method works

DeepSeek’s approach combines two methods:

1. Generative reward modeling (GRM): This approach enables flexibility in different input types and allows for scaling during inference time. Unlike previous scalar or semi-scalar approaches, GRM provides a richer representation of rewards through language.
2. Self-principled critique tuning (SPCT): A learning method that fosters scalable reward-generation behaviours in GRMs through online reinforcement learning, one that generates principles adaptively.

One of the paper’s authors from Tsinghua University and DeepSeek-AI, Zijun Liu, explained that the combination of methods allows “principles to be generated based on the input query and responses, adaptively aligning reward generation process.”

The approach is particularly valuable for its potential for “inference-time scaling” – improving performance by increasing computational resources during inference rather than just during training.

The researchers found that their methods could achieve better results with increased sampling, letting models generate better rewards with more computing.

Implications for the AI Industry

DeepSeek’s innovation comes at an important time in AI development. The paper states “reinforcement learning (RL) has been widely adopted in post-training for large language models […] at scale,” leading to “remarkable improvements in human value alignment, long-term reasoning, and environment adaptation for LLMs.”

The new approach to reward modelling could have several implications:

1. More accurate AI feedback: By creating better reward models, AI systems can receive more precise feedback about their outputs, leading to improved responses over time.
2. Increased adaptability: The ability to scale model performance during inference means AI systems can adapt to different computational constraints and requirements.
3. Broader application: Systems can perform better in a broader range of tasks by improving reward modelling for general domains.
4. More efficient resource use: The research shows that inference-time scaling with DeepSeek’s method could outperform model size scaling in training time, potentially allowing smaller models to perform comparably to larger ones with appropriate inference-time resources.

DeepSeek’s growing influence

The latest development adds to DeepSeek’s rising profile in global AI. Founded in 2023 by entrepreneur Liang Wenfeng, the Hangzhou-based company has made waves with its V3 foundation and R1 reasoning models.

The company upgraded its V3 model (DeepSeek-V3-0324) recently, which the company said offered “enhanced reasoning capabilities, optimised front-end web development and upgraded Chinese writing proficiency.” DeepSeek has committed to open-source AI, releasing five code repositories in February that allow developers to review and contribute to development.

While speculation continues about the potential release of DeepSeek-R2 (the successor to R1) – Reuters has speculated on possible release dates – DeepSeek has not commented in its official channels.

What’s next for AI reward models?

According to the researchers, DeepSeek intends to make the GRM models open-source, although no specific timeline has been provided. Open-sourcing will accelerate progress in the field by allowing broader experimentation with reward models.

As reinforcement learning continues to play an important role in AI development, advances in reward modelling like those in DeepSeek and Tsinghua University’s work will likely have an impact on the abilities and behaviour of AI systems.

Work on AI reward models demonstrates that innovations in how and when models learn can be as important increasing their size. By focusing on feedback quality and scalability, DeepSeek addresses one of the fundamental challenges to creating AI that understands and aligns with human preferences better.

See also: DeepSeek disruption: Chinese AI innovation narrows global technology divide

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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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 new model advanced seven points in the benchmark to surpass proprietary counterparts such as Google’s Gemini 2.0 Pro, Anthropic’s Claude 3.7 Sonnet, and Meta’s Llama 3.3 70B.

While V3-0324 trails behind reasoning models, including DeepSeek’s own R1 and offerings from OpenAI and Alibaba, the achievement highlights the growing viability of open-source solutions in latency-sensitive applications where immediate responses are critical.

DeepSeek V3-0324 represents a new era for open-source AI

Non-reasoning models – which generate answers instantly without deliberative “thinking” phases – are essential for real-time use cases like chatbots, customer service automation, and live translation. DeepSeek’s latest iteration now sets the standard for these applications, eclipsing even leading proprietary tools.

“This is the first time an open weights model is the leading non-reasoning model, a milestone for open source,” states Artificial Analysis. The model’s performance edges it closer to proprietary reasoning models, though the latter remain superior for tasks requiring complex problem-solving.

DeepSeek V3-0324 retains most specifications from its December 2024 predecessor, including:  

• 128k context window (capped at 64k via DeepSeek’s API)
• 671 billion total parameters, necessitating over 700GB of GPU memory for FP8 precision
• 37 billion active parameters
• Text-only functionality (no multimodal support) 
• MIT License

“Still not something you can run at home!” Artificial Analysis quips, emphasising its enterprise-grade infrastructure requirements.

Open-source AI is bringing the heat

While proprietary reasoning models like DeepSeek R1 maintain dominance in the broader Intelligence Index, the gap is narrowing.

Three months ago, DeepSeek V3 nearly matched Anthropic’s and Google’s proprietary models but fell short of surpassing them. Today, the updated V3-0324 not only leads open-source alternatives but also outperforms all proprietary non-reasoning rivals.

“This release is arguably even more impressive than R1,” says Artificial Analysis.

DeepSeek’s progress signals a shift in the AI sector, where open-source frameworks increasingly compete with closed systems. For developers and enterprises, the MIT-licensed V3-0324 offers a powerful, adaptable tool—though its computational costs may limit accessibility.

“DeepSeek are now driving the frontier of non-reasoning open weights models,” declares Artificial Analysis.

With R2 on the horizon, the community awaits another potential leap in AI performance.

(Photo by Paul Hanaoka)

See also: Hugging Face calls for open-source focus in the AI Action Plan

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I wonder whether that may be partly a generational thing. The baby boomer generation was the first to be widely employed in IT and that cohort learned the lessons of business the hard way.  Projects had to be cost-justified because technology was expensive and needed to be attached to a robust ROI case. Projects were rolled out slowly because they were complex and had to be aligned to a specific business need, endorsed by the right stakeholders. ‘Project creep’ was feared and the relationship between IT and ‘the business’ was often fraught and complex, characterised by mutual suspicion.

Today, the situation is somewhat different. The IT industry is enormous, the Fortune 50 is replete with major tech brands and other sectors marvel at the profit margins of the software sector. That may all be very well for Silicon Valley and the venture capitalists of Sand Hill Road desperate to find The Next Big Thing. But back in the real world of corporate IT, matters should be seen with more caution, an appropriate level of pragmatism and even a raised eyebrow or two.

Which brings us back to AI. AI is far from new and has its roots all the way back in the middle of the previous century. So far, despite all the excitement, it has played only a moderate role in the business world. The success of tools like Chat-GPT has catapulted it to mainstream attention but it is still beset by familiar issues. It is costly to deploy in earnest, it requires (at least until DeepSeek) enormous compute power to develop and it delivers responses that are often questionable. There are also serious questions to be asked about legal liability and copyright.

A balancing act

We need to strike a happy balance between the boosterism and experimentation inherent in AI today and a healthy sense of pragmatism. We should begin with the business case and ask how AI helps us. What is our mission? Where are our strategic opportunities and risks? OK, now how can AI help us? Today, there is too much “AI is great, let’s see what we can do with it”.

Today, I see AI as a massive opportunity but use cases need to be worked out. AI is great at massive computation tasks that human beings are bad at. It can study patterns and detect trends faster than our feeble human brains can. It doesn’t get out of the bed on the wrong side in the morning, tire easily or require two weeks holiday in the Mediterranean each year. It is surprisingly excellent at a limited number of creative tasks such as making images, music, poems and videos. But it is bad at seeing the big picture. It lacks the human sense of caution that keeps us from danger, and it has no experience of the real world of work that is composed of an enormous range of variables, not the least of which is human mood and perception.

AI today is great at the edge: in powering bots that answer predictable questions or agents that help us achieve rote tasks faster than would otherwise be the case. Robotic process automation has been a useful aid and has changed the dynamic of how the human being interacts with computers: we can now hand off dull jobs like processing credit card applications or expense claims and focus on being creative thinkers.

There are grey areas too. Conversational AI is a work in progress, but we can expect rapid improvements based on iterative continuous learning by our binary friends. Soon we may be impressed by AI’s ability to guess our next steps and to suggest smarter ways to accomplish our work. Similarly, there is scope for AI to learn more about our vertical businesses and to understand trends that humans may miss when we fail to see the forest for the trees.

But we are some way off robot CEOs, and we need to ensure that AI ‘decisions’ are tempered by human bosses that have common sense, the ability to check, test and revert. The future is one where AI and humanity work in concert but for now we are wise to deploy with care and with sensible budgets and the appropriate level of commitment.

We need to watch carefully for the next DeepSeek hit, query it and always begin with old-fashioned questions as to applicability, costs and risk. I note that DeepSeek’s website bears the tagline “Into the Unknown”. That’s about right: we need to maintain a spirit of adventure and optimism but avoid getting lost in a new technological wilderness.

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However, this development comes against the backdrop of mounting controversies that have drawn parallels to the TikTok saga.

Today, DeepSeek shared its intentions in a tweet that outlined its vision of open collaboration: “We’re a tiny team at DeepSeek exploring AGI. Starting next week, we’ll be open-sourcing five repos, sharing our small but sincere progress with full transparency.”

The repositories – which the company describes as “documented, deployed, and battle-tested in production” – include fundamental building blocks of DeepSeek’s online service.

By open-sourcing its tools, DeepSeek hopes to contribute to the broader AI research community.

“As part of the open-source community, we believe that every line shared becomes collective momentum that accelerates the journey. No ivory towers – just pure garage-energy and community-driven innovation,” the company said.

This philosophy has drawn praise for fostering collaboration in a field that often suffers from secrecy, but DeepSeek’s rapid rise has also raised eyebrows.

Despite being a small team with a mission rooted in transparency, the company has been under intense scrutiny amid allegations of data misuse and geopolitical entanglements.

Rising fast, under fire

Practically unknown until recently, DeepSeek burst onto the scene with a business model that stood in stark contrast to more established players like OpenAI and Google.

Offering its advanced AI capabilities for free, DeepSeek quickly gained global acclaim for its cutting-edge performance. However, its exponential rise has also sparked debates about the trade-offs between innovation and privacy.

US lawmakers are now pushing for a ban on DeepSeek after security researchers found the app transferring user data to a banned state-owned company.

A probe has also been launched by Microsoft and OpenAI over a breach of the latter’s systems by a group allegedly linked to DeepSeek.

Concerns about data collection and potential misuse have triggered comparisons to the controversies surrounding TikTok, another Chinese tech success story grappling with regulatory pushback in the West.

DeepSeek continues AGI innovation amid controversy

DeepSeek’s commitment to open-source its technology appears timed to deflect criticism and reassure sceptics about its intentions.

Open-sourcing has long been heralded as a way to democratise technology and increase transparency, and DeepSeek’s “daily unlocks,” that are set to begin soon, could offer the community reassuring insight into its operations.

Nevertheless, questions remain over how much of the technology will be open for scrutiny and whether the move is an attempt to shift the narrative amid growing political and regulatory pressure.

It’s unclear whether this balancing act will be enough to satisfy lawmakers or deter critics, but one thing is certain: DeepSeek’s open-source leap marks another turn in its dramatic rise.

While the company’s motto of “garage-energy and community-driven innovation” resonates with developers eager for open collaboration, its future may rest as much on its ability to address security concerns as on its technical prowess.

(Photo by Solen Feyissa)

See also: DeepSeek’s AI dominance expands from EVs to e-scooters in China

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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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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

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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Decentralised AI agents for ownership and monetisation

Key features of AIvalanche DeFAI agents

• Create and manage AI agents: Users can build AI agents in just a few clicks. Each agent has a dedicated page outlining its capabilities.
• Co-ownership of AI agents: Anyone can invest in AI agents early by acquiring tokens before they gain mainstream attention. Users can also engage with established AI agents while trading their tokens.
• Monetising AI agents: AI agents evolve by learning from new data. They have their own wallets and can execute transactions, manage tasks, and distribute revenue.

Support from key players in the Avalanche ecosystem

Expanding access through public sales and listings

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According to Bloomberg, the investigation stems from suspicious data extraction activity detected in late 2024 via OpenAI’s application programming interface (API), sparking broader concerns over international AI competition.

Microsoft, OpenAI’s largest financial backer, first identified the large-scale data extraction and informed the ChatGPT maker of the incident. Sources believe the activity may have violated OpenAI’s terms of service, or that the group may have exploited loopholes to bypass restrictions limiting how much data they could collect.

DeepSeek has quickly risen to prominence in the competitive AI landscape, particularly with the release of its latest model, R-1, on 20 January.

Billed as a rival to OpenAI’s ChatGPT in performance but developed at a significantly lower cost, R-1 has shaken up the tech industry. Its release triggered a sharp decline in tech and AI stocks that wiped billions from US markets in a single week.

David Sacks, the White House’s newly appointed “crypto and AI czar,” alleged that DeepSeek may have employed questionable methods to achieve its AI’s capabilities. In an interview with Fox News, Sacks noted evidence suggesting that DeepSeek had used “distillation” to train its AI models using outputs from OpenAI’s systems.

“There’s substantial evidence that what DeepSeek did here is they distilled knowledge out of OpenAI’s models, and I don’t think OpenAI is very happy about this,” Sacks told the network.  

Model distillation involves training one AI system using data generated by another, potentially allowing a competitor to develop similar functionality. This method, when applied without proper authorisation, has stirred ethical and intellectual property debates as the global race for AI supremacy heats up.  

OpenAI declined to comment specifically on the accusations against DeepSeek but acknowledged the broader risk posed by model distillation, particularly by Chinese companies.  

“We know PRC-based companies — and others — are constantly trying to distill the models of leading US AI companies,” a spokesperson for OpenAI told Bloomberg.  

Geopolitical and security concerns  

Growing tensions around AI innovation now extend into national security. CNBC reported that the US Navy has banned its personnel from using DeepSeek’s products, citing fears that the Chinese government could exploit the platform to access sensitive information.

In an email dated 24 January, the Navy warned its staff against using DeepSeek AI “in any capacity” due to “potential security and ethical concerns associated with the model’s origin and usage.”

Critics have highlighted DeepSeek’s privacy policy, which permits the collection of data such as IP addresses, device information, and even keystroke patterns—a scope of data collection considered excessive by some experts.

Earlier this week, DeepSeek stated it was facing “large-scale malicious attacks” against its systems. A banner on its website informed users of a temporary sign-up restriction.

The growing competition between the US and China in particular in the AI sector has underscored wider concerns regarding technological ownership, ethical governance, and national security.  

Experts warn that as AI systems advance and become increasingly integral to global economic and strategic planning, disputes over data usage and intellectual property are only likely to intensify. Accusations such as those against DeepSeek amplify alarm over China’s rapid development in the field and its potential quest to bypass US-led safeguards through reverse engineering and other means.  

While OpenAI and Microsoft continue their investigation into the alleged misuse of OpenAI’s platform, businesses and governments alike are paying close attention. The case could set a precedent for how AI developers police model usage and enforce terms of service.

For now, the response from both US and Chinese stakeholders highlights how AI innovation has become not just a race for technological dominance, but a fraught geopolitical contest that is shaping 21st-century power dynamics.

(Image by Mohamed Hassan)

See also: Qwen 2.5-Max outperforms DeepSeek V3 in some benchmarks

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 Microsoft and OpenAI probe alleged data theft by DeepSeek appeared first on AI News.

Qwen 2.5-Max boasts pretraining on over 20 trillion tokens and fine-tuning through cutting-edge techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).

With the API now available through Alibaba Cloud and the model accessible for exploration via Qwen Chat, the Chinese tech giant is inviting developers and researchers to see its breakthroughs firsthand.

Outperforming peers  

When comparing Qwen 2.5-Max’s performance against some of the most prominent AI models on a variety of benchmarks, the results are promising.

Evaluations included popular metrics like the MMLU-Pro for college-level problem-solving, LiveCodeBench for coding expertise, LiveBench for overall capabilities, and Arena-Hard for assessing models against human preferences.

According to Alibaba, “Qwen 2.5-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.”

The instruct model – designed for downstream tasks like chat and coding – competes directly with leading models such as GPT-4o, Claude-3.5-Sonnet, and DeepSeek V3. Among these, Qwen 2.5-Max managed to outperform rivals in several key areas.

Comparisons of base models also yielded promising outcomes. While proprietary models like GPT-4o and Claude-3.5-Sonnet remained out of reach due to access restrictions, Qwen 2.5-Max was assessed against leading public options such as DeepSeek V3, Llama-3.1-405B (the largest open-weight dense model), and Qwen2.5-72B. Again, Alibaba’s newcomer demonstrated exceptional performance across the board.

“Our base models have demonstrated significant advantages across most benchmarks,” Alibaba stated, “and we are optimistic that advancements in post-training techniques will elevate the next version of Qwen 2.5-Max to new heights.”

Making Qwen 2.5-Max accessible  

To make the model more accessible to the global community, Alibaba has integrated Qwen 2.5-Max with its Qwen Chat platform, where users can interact directly with the model in various capacities—whether exploring its search capabilities or testing its understanding of complex queries.  

For developers, the Qwen 2.5-Max API is now available through Alibaba Cloud under the model name “qwen-max-2025-01-25”. Interested users can get started by registering an Alibaba Cloud account, activating the Model Studio service, and generating an API key.  

The API is even compatible with OpenAI’s ecosystem, making integration straightforward for existing projects and workflows. This compatibility lowers the barrier for those eager to test their applications with the model’s capabilities.

Alibaba has made a strong statement of intent with Qwen 2.5-Max. The company’s ongoing commitment to scaling AI models is not just about improving performance benchmarks but also about enhancing the fundamental thinking and reasoning abilities of these systems.  

“The scaling of data and model size not only showcases advancements in model intelligence but also reflects our unwavering commitment to pioneering research,” Alibaba noted.  

Looking ahead, the team aims to push the boundaries of reinforcement learning to foster even more advanced reasoning skills. This, they say, could enable their models to not only match but surpass human intelligence in solving intricate problems.  

The implications for the industry could be profound. As scaling methods improve and Qwen models break new ground, we are likely to see further ripples across AI-driven fields globally that we’ve seen in recent weeks.

(Photo by Maico Amorim)

See also: ChatGPT Gov aims to modernise US government agencies

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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