Is China Winning The AI Race? Inside Beijing’s Bet On Cheap Intelligence, And Where Does India Stand? | Tech News

Last Updated:March 01, 2026, 08:30 IST

The US is dominating headlines with frontier AI models, multi-billion-dollar investments and powerful chips, while China is making AI cheaper, widely deployable at home and abroad

China’s bet on cheap, scalable AI could reshape the terms of that race. Whether it translates into lasting technological dominance will depend on how other powers, including the US and India, respond in the years ahead. (Getty Images)

For much of the past two years, the global artificial intelligence (AI) race has been framed as a high-stakes duel between Silicon Valley and Beijing. Last week, Chinese company ByteDance released an AI video-generating tool called Seedance 2.0, which produces high-quality, film-like clips from text prompts.

This comes at a time when US company Anthropic has accused three Chinese AI labs of creating fake accounts to harvest responses from its chatbot, Claude.

On the one hand, the US is dominating headlines with frontier AI models, multibillion-dollar investments and powerful chips. On the other, China is pursuing a parallel strategy, making AI cheaper, widely deployable and deeply embedded across industries at home and abroad.

The question is no longer just about who builds the smartest AI model, but who ensures that theirs becomes the most widely used.

This strategic shift could reshape the global AI balance, with implications for countries like India, which are navigating an increasingly complex technological landscape.

US vs China: Where Do They Stand In The AI Race?

When people ask whether China is winning the AI race, they often measure success in terms of breakthrough models, computing power and scientific publications. By those metrics, the US still holds significant advantages, especially in high-end semiconductor design and frontier AI systems.

But leadership in technology is not always defined by the most advanced version of a product. Sometimes it is determined by accessibility, affordability and scale.

China’s AI ecosystem is increasingly focused on lowering costs and integrating AI into everyday applications — from manufacturing and logistics to education, healthcare and consumer services. State-backed incentives, domestic competition and an enormous user base allow Chinese firms to refine and deploy AI at scale.

Is Distillation Via Fake Accounts China’s New Strategy?

Distillation is a technique in AI where smaller models are trained using the outputs generated by more sophisticated systems. Instead of building a powerful model entirely from scratch — a process that demands vast computing power, time and capital — developers can effectively “learn” from an existing advanced system and compress that knowledge into a leaner version.

This approach allows companies to recreate capabilities similar to those offered by leading AI labs such as Anthropic or OpenAI, but with far less computing infrastructure. Because these smaller models require less advanced chips and lower operating costs, they can be delivered to businesses and consumers at more affordable prices.

“Let us be clear. Distillation at industrial scale is not academic curiosity. It is competitive acceleration. If labs are systematically extracting intelligence patterns from frontier systems, they are compressing development timelines dramatically. In AI, training from scratch is expensive and time intensive. Distillation reduces that burden. From a builder standpoint, this is about shortening cycles, not innovation purity. It reflects a scale first doctrine. Whether the world accepts this as strategy or violation will define the next phase of AI competition,” said Anil Agarwal, CEO and Co-founder of InCruiter — a Bengaluru-based AI driven hiring technology company.

Explaining further, AppSquadz Co-Founder and CEO Chandrakant Agrawal told News18 that China seems to be prioritising speed, cost efficiency, and competitive advantage, even if it pushes regulatory and ethical boundaries. “(Though) distillation itself is a legitimate AI training method, but using fake accounts to extract model intelligence crosses ethical lines. This approach can shorten development cycles significantly, but it also increases global mistrust.”

How China Uses Cheap AI As Strategic Leverage

The logic behind China’s approach is straightforward. If AI tools are affordable and easy to integrate, businesses and governments, particularly in emerging markets, are more likely to adopt them.

Chinese technology companies have already demonstrated this playbook in other sectors. In telecommunications and digital payments, Chinese firms expanded globally by offering cost-effective alternatives to Western competitors. Over time, those platforms created long-term technological dependencies.

AI could follow a similar trajectory. Lower-cost models may not match the most advanced systems in complexity, but they may be “good enough” for many commercial and administrative tasks. For countries seeking rapid digital transformation without massive budgets, that trade-off can be attractive.

“China’s approach is highly state-aligned and speed-driven, with strong coordination between government and AI labs. The US, on the other hand, relies more on private sector innovation supported by intellectual property protections and regulatory scrutiny. Europe emphasizes compliance and ethical AI frameworks. China prioritises scale and national competitiveness, while the US focuses on research leadership and market-driven innovation. The philosophical difference lies in governance, transparency, and data controls,” said Agrawal from AppSquadz.

How Do Cheap AI Chips Raise Copyright Concerns?

The democratization of AI hardware allows more entities to train models on vast, unvetted datasets scraped from the internet, often including protected text, images, and music without permission or compensation to the original creators.

“With low-cost, high-volume computing, it is easier to generate large quantities of non-original data—videos, text, computer code—on a large scale, increasing the likelihood that copyrighted material will be copied, remixed, or mass-produced without trace or licensing,” said Manish Mohta, Founder and Managing Director, Learningspiral.ai.

As more models get trained quickly, it increases the risk of AI outputs being similar. Thus, the sheer volume of AI-generated content makes it challenging for copyright holders to identify infringement and enforce their rights.

InCruiter’s Agarwal explained when chips become cheaper, the cost of generating cinema grade video, code, music, and design drops sharply. This means models trained on massive Internet data can reproduce stylistic elements at unprecedented scale. “The economic balance between creators and AI platforms shifts. As someone building AI systems, I can say this clearly. Lower compute cost multiplies output volume. When output explodes, copyright enforcement becomes nearly impossible. The disruption is economic before it is legal.”

How The US Has Tried To Stop China?

The US has attempted to slow China’s AI progress through export controls on advanced chips, particularly those needed to train large-scale models. These restrictions have complicated Beijing’s access to cutting-edge hardware.

However, constraints often drive adaptation. Chinese firms have increasingly focused on optimizing AI models to run on less powerful chips or alternative architectures. Instead of competing solely on raw computational power, they are refining efficiency and scalability.

This shift may not close the gap in frontier research overnight, but it ensures continued domestic AI growth. It also signals that the race is no longer linear. The ability to innovate under constraint may become a competitive advantage in itself.

The US builds frontier capability and debates guardrails simultaneously, while China builds capability, deploys fast, and optimizes later, said Anil Agarwal. “One system prioritises research leadership. The other prioritises scale and competitive positioning. In AI, speed compounds. Once a model is deployed across millions of users, data feedback accelerates improvement. That feedback loop is power. China understands that distribution at scale can sometimes outweigh marginal technical superiority.”

What This Means For India

For India, the implications are complex. New Delhi has ambitious plans to position itself as a global AI innovation hub. The country’s large digital population, expanding start-up ecosystem and public digital infrastructure provide strong foundations.

Yet India also sits at the intersection of competing technological influences. China remains a major trade partner, even amid geopolitical tensions. The US is a key strategic and technological partner, especially in semiconductor and digital policy cooperation.

If China’s affordable AI platforms gain traction across Asia and Africa, Indian firms and policymakers will need to weigh cost advantages against strategic considerations such as data security and technological autonomy.

“India must play to its strengths, not replicate others. We should not attempt to outspend the US or out-scale China in raw compute. Our strength lies in engineering talent, enterprise adoption, and practical AI deployment… India should build domain specific AI leadership, invest in accessible compute, and create clear regulatory frameworks. If we focus on execution excellence, we can become the world’s trusted AI implementation hub,” said Agarwal.

Agrawal of Appsquadz shared similar views. India should adopt a balanced approach that promotes indigenous innovation while strengthening intellectual property and data protection frameworks. “Policymakers should encourage AI research funding, semiconductor development, and start-up acceleration… India should not replicate China’s model but instead create a transparent, scalable, and globally trusted AI ecosystem aligned with democratic values.”

Mohta highlighted that the government “should put money into AI infrastructure, offer incentives for applied AI start-ups, protect data strategically, and create a strong IP framework to support AI in the real world, paying particular attention to AI applications for governance, education, and MSMEs”.

What Advantage Does India Have Over The US And China?

“India’s advantage is balance. We have democratic institutions, a massive technical workforce, cost efficiency, and experience building at population scale. Our digital public infrastructure proves we can execute complex technology at national level. We are not trapped in the binary rivalry of the US and China. That gives us strategic flexibility. If we align policy, capital, and talent, India can define a third path in the global AI order,” said Agarwal of InCruiter.

According to Mohta, India’s biggest competitive advantage is the size of its talent pool, its democratic digital public infrastructure, and the availability of data in English. “If the government can create smart policies and execute them well, then India could have an affordable, ethical, and globally exportable AI.”

So, Is China Winning?

The answer depends on how “winning” is defined. In frontier AI research and access to the most advanced chips, the US retains significant leverage. American firms continue to lead in large-scale models and foundational AI breakthroughs.

But in adoption, integration and cost-efficient deployment, China is advancing rapidly. Its strategy suggests that the real contest may not be over who builds the most powerful AI, but over who ensures their AI becomes indispensable.

“Winning depends on how we define success. In terms of rapid deployment, manufacturing scale, and cost efficiency, China is extremely competitive. However, leadership in foundational research, global partnerships, and trust still leans toward the US… Sustainable leadership will depend on innovation quality, ethical standards, and global acceptance,” adds Agrawal.

The global AI race is not a single sprint towards a finish line. It is a layered competition across innovation, infrastructure, affordability and influence.

Agarwal of InCruiter said, “In manufacturing depth, cost efficiency, and rapid commercialization, China is extremely formidable. AI dominance will not be decided by one breakthrough model. It will be decided by ecosystem control. Talent pipelines, compute access, supply chains, and global adoption will determine leadership. Today, the race is competitive and fluid. Anyone declaring a winner misunderstands the scale of transformation underway.”

China’s bet on cheap, scalable AI could reshape the terms of that race. Whether it translates into lasting technological dominance will depend on how other powers, including the US and India, respond in the years ahead.

For now, the contest remains open. But it is no longer only about cutting-edge breakthroughs. It is about who controls the ecosystem in which the world’s intelligence systems operate.

This comes at a time when US company Anthropic has accused three Chinese AI labs of creating fake accounts to harvest responses from its chatbot, Claude.

On the one hand, the US is dominating headlines with frontier AI models, multibillion-dollar investments and powerful chips. On the other, China is pursuing a parallel strategy, making AI cheaper, widely deployable and deeply embedded across industries at home and abroad.

The question is no longer just about who builds the smartest AI model, but who ensures that theirs becomes the most widely used.

This strategic shift could reshape the global AI balance, with implications for countries like India, which are navigating an increasingly complex technological landscape.

US vs China: Where Do They Stand In The AI Race?

When people ask whether China is winning the AI race, they often measure success in terms of breakthrough models, computing power and scientific publications. By those metrics, the US still holds significant advantages, especially in high-end semiconductor design and frontier AI systems.

But leadership in technology is not always defined by the most advanced version of a product. Sometimes it is determined by accessibility, affordability and scale.

China’s AI ecosystem is increasingly focused on lowering costs and integrating AI into everyday applications — from manufacturing and logistics to education, healthcare and consumer services. State-backed incentives, domestic competition and an enormous user base allow Chinese firms to refine and deploy AI at scale.

Is Distillation Via Fake Accounts China’s New Strategy?

Distillation is a technique in AI where smaller models are trained using the outputs generated by more sophisticated systems. Instead of building a powerful model entirely from scratch — a process that demands vast computing power, time and capital — developers can effectively “learn” from an existing advanced system and compress that knowledge into a leaner version.

This approach allows companies to recreate capabilities similar to those offered by leading AI labs such as Anthropic or OpenAI, but with far less computing infrastructure. Because these smaller models require less advanced chips and lower operating costs, they can be delivered to businesses and consumers at more affordable prices.

“Let us be clear. Distillation at industrial scale is not academic curiosity. It is competitive acceleration. If labs are systematically extracting intelligence patterns from frontier systems, they are compressing development timelines dramatically. In AI, training from scratch is expensive and time intensive. Distillation reduces that burden. From a builder standpoint, this is about shortening cycles, not innovation purity. It reflects a scale first doctrine. Whether the world accepts this as strategy or violation will define the next phase of AI competition,” said Anil Agarwal, CEO and Co-founder of InCruiter — a Bengaluru-based AI driven hiring technology company.

Explaining further, AppSquadz Co-Founder and CEO Chandrakant Agrawal told News18 that China seems to be prioritising speed, cost efficiency, and competitive advantage, even if it pushes regulatory and ethical boundaries. “(Though) distillation itself is a legitimate AI training method, but using fake accounts to extract model intelligence crosses ethical lines. This approach can shorten development cycles significantly, but it also increases global mistrust.”

How China Uses Cheap AI As Strategic Leverage

The logic behind China’s approach is straightforward. If AI tools are affordable and easy to integrate, businesses and governments, particularly in emerging markets, are more likely to adopt them.

Chinese technology companies have already demonstrated this playbook in other sectors. In telecommunications and digital payments, Chinese firms expanded globally by offering cost-effective alternatives to Western competitors. Over time, those platforms created long-term technological dependencies.

AI could follow a similar trajectory. Lower-cost models may not match the most advanced systems in complexity, but they may be “good enough” for many commercial and administrative tasks. For countries seeking rapid digital transformation without massive budgets, that trade-off can be attractive.

“China’s approach is highly state-aligned and speed-driven, with strong coordination between government and AI labs. The US, on the other hand, relies more on private sector innovation supported by intellectual property protections and regulatory scrutiny. Europe emphasizes compliance and ethical AI frameworks. China prioritises scale and national competitiveness, while the US focuses on research leadership and market-driven innovation. The philosophical difference lies in governance, transparency, and data controls,” said Agrawal from AppSquadz.

How Do Cheap AI Chips Raise Copyright Concerns?

The democratization of AI hardware allows more entities to train models on vast, unvetted datasets scraped from the internet, often including protected text, images, and music without permission or compensation to the original creators.

“With low-cost, high-volume computing, it is easier to generate large quantities of non-original data—videos, text, computer code—on a large scale, increasing the likelihood that copyrighted material will be copied, remixed, or mass-produced without trace or licensing,” said Manish Mohta, Founder and Managing Director, Learningspiral.ai.

As more models get trained quickly, it increases the risk of AI outputs being similar. Thus, the sheer volume of AI-generated content makes it challenging for copyright holders to identify infringement and enforce their rights.

 

InCruiter’s Agarwal explained when chips become cheaper, the cost of generating cinema grade video, code, music, and design drops sharply. This means models trained on massive Internet data can reproduce stylistic elements at unprecedented scale. “The economic balance between creators and AI platforms shifts. As someone building AI systems, I can say this clearly. Lower compute cost multiplies output volume. When output explodes, copyright enforcement becomes nearly impossible. The disruption is economic before it is legal.”

How The US Has Tried To Stop China?

The US has attempted to slow China’s AI progress through export controls on advanced chips, particularly those needed to train large-scale models. These restrictions have complicated Beijing’s access to cutting-edge hardware.

However, constraints often drive adaptation. Chinese firms have increasingly focused on optimizing AI models to run on less powerful chips or alternative architectures. Instead of competing solely on raw computational power, they are refining efficiency and scalability.

This shift may not close the gap in frontier research overnight, but it ensures continued domestic AI growth. It also signals that the race is no longer linear. The ability to innovate under constraint may become a competitive advantage in itself.

The US builds frontier capability and debates guardrails simultaneously, while China builds capability, deploys fast, and optimizes later, said Anil Agarwal. “One system prioritises research leadership. The other prioritises scale and competitive positioning. In AI, speed compounds. Once a model is deployed across millions of users, data feedback accelerates improvement. That feedback loop is power. China understands that distribution at scale can sometimes outweigh marginal technical superiority.”

What This Means For India

For India, the implications are complex. New Delhi has ambitious plans to position itself as a global AI innovation hub. The country’s large digital population, expanding start-up ecosystem and public digital infrastructure provide strong foundations.

Yet India also sits at the intersection of competing technological influences. China remains a major trade partner, even amid geopolitical tensions. The US is a key strategic and technological partner, especially in semiconductor and digital policy cooperation.

If China’s affordable AI platforms gain traction across Asia and Africa, Indian firms and policymakers will need to weigh cost advantages against strategic considerations such as data security and technological autonomy.

“India must play to its strengths, not replicate others. We should not attempt to outspend the US or out-scale China in raw compute. Our strength lies in engineering talent, enterprise adoption, and practical AI deployment… India should build domain specific AI leadership, invest in accessible compute, and create clear regulatory frameworks. If we focus on execution excellence, we can become the world’s trusted AI implementation hub,” said Agarwal.

Agrawal of Appsquadz shared similar views. India should adopt a balanced approach that promotes indigenous innovation while strengthening intellectual property and data protection frameworks. “Policymakers should encourage AI research funding, semiconductor development, and start-up acceleration… India should not replicate China’s model but instead create a transparent, scalable, and globally trusted AI ecosystem aligned with democratic values.”

Mohta highlighted that the government “should put money into AI infrastructure, offer incentives for applied AI start-ups, protect data strategically, and create a strong IP framework to support AI in the real world, paying particular attention to AI applications for governance, education, and MSMEs”.

What Advantage Does India Have Over The US And China?

“India’s advantage is balance. We have democratic institutions, a massive technical workforce, cost efficiency, and experience building at population scale. Our digital public infrastructure proves we can execute complex technology at national level. We are not trapped in the binary rivalry of the US and China. That gives us strategic flexibility. If we align policy, capital, and talent, India can define a third path in the global AI order,” said Agarwal of InCruiter.

According to Mohta, India’s biggest competitive advantage is the size of its talent pool, its democratic digital public infrastructure, and the availability of data in English. “If the government can create smart policies and execute them well, then India could have an affordable, ethical, and globally exportable AI.”

So, Is China Winning?

The answer depends on how “winning” is defined. In frontier AI research and access to the most advanced chips, the US retains significant leverage. American firms continue to lead in large-scale models and foundational AI breakthroughs.

But in adoption, integration and cost-efficient deployment, China is advancing rapidly. Its strategy suggests that the real contest may not be over who builds the most powerful AI, but over who ensures their AI becomes indispensable.

“Winning depends on how we define success. In terms of rapid deployment, manufacturing scale, and cost efficiency, China is extremely competitive. However, leadership in foundational research, global partnerships, and trust still leans toward the US… Sustainable leadership will depend on innovation quality, ethical standards, and global acceptance,” adds Agrawal.

The global AI race is not a single sprint towards a finish line. It is a layered competition across innovation, infrastructure, affordability and influence.

Agarwal of InCruiter said, “In manufacturing depth, cost efficiency, and rapid commercialization, China is extremely formidable. AI dominance will not be decided by one breakthrough model. It will be decided by ecosystem control. Talent pipelines, compute access, supply chains, and global adoption will determine leadership. Today, the race is competitive and fluid. Anyone declaring a winner misunderstands the scale of transformation underway.”

China’s bet on cheap, scalable AI could reshape the terms of that race. Whether it translates into lasting technological dominance will depend on how other powers, including the US and India, respond in the years ahead.

For now, the contest remains open. But it is no longer only about cutting-edge breakthroughs. It is about who controls the ecosystem in which the world’s intelligence systems operate.

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