Meta has begun dismantling its $2 billion acquisition of Manus, completing an operational separation from the Chinese-founded AI startup and halting data sharing between the two companies. This is the most concrete step yet toward complying with a divestiture order Beijing issued roughly two months ago on national security grounds.

Meta has cut Manus off from its internal systems, Bloomberg reported, preventing employees from using Manus tools for internal projects as the two companies move toward a full separation.

Meanwhile, according to May reports, the co-founders of Manus have held preliminary discussions about raising approximately $1 billion from outside investors to reclaim the startup from Meta, a move that could pave the way for a Chinese joint venture structure and an eventual listing in Hong Kong, a venue that has seen a surge in AI listings this year for Chinese AI startups like MiniMax and Zhipu.

What was supposed to be a landmark exit for Chinese AI is quickly unraveling. The move underscores Beijing’s determination to retain control over strategically sensitive technology, regardless of a company’s offshore incorporation.

In addition to the forced divestiture, Chinese authorities have since expanded travel restrictions to researchers and executives at private firms, requiring government approval before heading abroad. China is also tightening its grip on foreign capital, with reports indicating that top AI firms, including Moonshot AI, StepFun, and ByteDance, will need government sign-off before accepting U.S. investment, adding another layer to Beijing’s sweeping effort to control its AI sector.

Even as Meta moves to sever ties with Manus, the agentic AI startup has continued to ship new features, rolling out integrations with Similarweb and Shopify.

Manus drew widespread attention with a viral agent demo relocated its staff to Singapore in mid-2025 before announcing a $2 billion acquisition by Meta in December. Chinese regulators moved to scrutinize the transaction earlier this year, citing potential violations of technology export controls and foreign investment rules.

Manus investors, including California-based venture firm Benchmark, have already received their proceeds from the acquisition, while Asian backers, including Tencent, HSG, and ZhenFund, have indicated they will cooperate with the unwinding process, according to the WSJ.

Manus’ Chinese origins with parent company Butterfly Effect drew scrutiny on both sides of the Pacific, with Senator John Cornyn questioning whether American capital should flow to a Chinese-linked firm.

Meta and Manus did not immediately respond to a request for comment outside regular business hours.

Anthropic’s sudden move to suspend access to its newest AI models following a U.S. government directive has raised fresh questions across the global technology industry. In India, the decision has reignited a long-running debate over whether one of the world’s largest AI markets can afford to rely on technologies built and controlled elsewhere.

The announcement came late Friday, when Anthropic said it had received the U.S. government directive requiring it to suspend access to its recently launched Fable 5 and Mythos 5 models for all foreign nationals, including its own foreign national employees. The move came shortly after the company announced a partnership with Indian IT services giant Tata Consultancy Services to expand enterprise AI adoption in India, underlining how closely the country’s AI ambitions have become tied to technologies developed and governed in the U.S.

While the broader implications remain unclear, some reports said the initial security concerns were first reported to the government by Amazon CEO Andy Jassy. And The Information said the White House is unlikely to extend similar restrictions to other AI companies and is privately blaming Anthropic’s handling of alleged jailbreak vulnerabilities. Anthropic has disputed the government’s characterization and argued the action should not have been taken.

Regardless, the development has triggered debate among Indian founders, investors, and policy experts over whether the country should accelerate efforts to build domestic AI capabilities, deepen investment in open-source alternatives, or continue relying on a handful of U.S. frontier model providers. For some, the episode is a wake-up call on technological dependence. For others, it is a reminder that access to increasingly critical AI systems can be shaped by geopolitical decisions beyond India’s control.

India has become one of the most important markets for frontier AI companies. Anthropic and OpenAI have both described the South Asian nation as their second-largest market after the U.S., reflecting its growing importance in the global AI race. The companies have already set up their offices in India, expanded local hiring, partnerships, and enterprise initiatives in recent months, betting on India’s vast base of developers, startups, and businesses to accelerate adoption of their latest technologies.

For many in India’s technology sector, Anthropic’s Friday announcement was about more than just one AI company. It reopened questions about the country’s long-term AI strategy and whether India could afford to remain dependent on a small number of foreign frontier AI providers.

“It completely changes things,” said Aakrit Vaish, founder of Indian AI venture platform Activate, referring to Anthropic’s decision. “I think this materially changes the way all of us should be thinking about sovereign AI in India.”

Vaish told TechCrunch that he woke up on Saturday morning “shocked and confused” by the announcement and said it strengthened the case for developing domestic AI capabilities. He expects startups to increasingly turn to open-source models and plans to encourage companies in his portfolio to reduce their dependence on a small number of frontier AI providers.

For some founders, the bigger concern was what restrictions on frontier AI access could mean for competitiveness. Vijay Rayapati, co-founder and CEO of Atomicwork, told TechCrunch that the episode highlighted the risks facing startups whose teams span multiple countries if access to advanced AI systems increasingly becomes subject to geopolitical restrictions.

Atomicwork has around 25 employees in the U.S., though much of its product engineering team is based in Bengaluru, India.

“If your AI team is not made up entirely of U.S. citizens, you are at a competitive disadvantage,” Rayapati said, arguing that unequal access to frontier AI models could give some companies a significant edge over rivals.

The concern comes as parts of India’s tech sector are already grappling with questions about how AI could reshape the economics of global talent. This week, U.S. real estate technology company Opendoor shut its India office less than two years after expanding in the country, with CEO Kaz Nejatian citing a push to bring operational work closer to customers in the U.S. and a shift toward smaller AI-native teams.

While Opendoor did not specify how much of the decision was driven by AI-related efficiencies, the move added to a broader debate about how advances in AI could affect the future of global technology work and what that might mean for India’s position as an engineering talent hub.

Beyond Anthropic

In addition to startups and AI builders, the Anthropic episode also prompted a broader debate among India’s technology leaders about dependence on foreign AI infrastructure.

Sridhar Vembu, founder of Indian SaaS company Zoho, said the move showed that “technology is the ultimate weapon” and urged Indian organizations to increasingly embrace smaller and open-source models.

“What can our government do right now? Ensure that orgs in India embrace smaller models, both Indian and Chinese open source ones,” Vembu wrote on X.

Investor and former Infosys executive Mohandas Pai responded to Vembu on X, arguing that the development highlighted the need for a far more ambitious national AI strategy and calling on the government to substantially increase investments in AI, computing infrastructure, and deep technology.

“We are way behind and need a national mission to get going quickly,” Pai wrote, urging the government to create an annual ₹500 billion (about $5 billion) fund for AI and deep tech, alongside a ₹2 trillion (around $21 billion) credit guarantee program to support cloud infrastructure, hardware, and semiconductor development.

Pai’s proposal would dwarf India’s existing AI efforts. In 2024, New Delhi approved the IndiaAI Mission with an outlay of ₹103.72 billion (about $1.2 billion) over five years, aimed at expanding compute infrastructure, supporting startups, and developing indigenous AI capabilities.

Despite growing interest in AI and New Delhi’s push to develop domestic capabilities, India remains a relatively small player in frontier model development. Only a handful of startups are pursuing foundational AI models, including Sarvam, which released open-source models earlier this year. However, another high-profile AI startup, Krutrim, pivoted toward cloud and AI infrastructure services after initially positioning itself around foundational model development.

Much of India’s AI ecosystem has instead concentrated on applications and specialized models built on top of existing foundation models. Recent examples include Avataar AI, which launched a video-generation model earlier this week aimed at providing a lower-cost alternative to offerings from rivals including Google’s Veo, Kling, Luma, and Runway.

Not everyone agrees that the primary challenge is a lack of capital. Responding to Pai’s comments, Lightspeed partner Hemant Mohapatra argued that the biggest constraints to building globally competitive AI companies are talent, access to computing resources, and execution, rather than simply the size of investment commitments.

Mohapatra estimated that training a frontier AI model could cost anywhere from hundreds of millions to several billion dollars, depending on the approach, but said successful AI companies have historically scaled their capital requirements over time as adoption grew.

Yet for some policy observers, the implications extend well beyond AI startups or model providers.

Prasanto Roy, a New Delhi-based technology policy expert who advises multinational companies, said the episode would likely reinforce concerns within the Indian government about strategic autonomy, comparing it to the lesson many countries drew from Russia’s loss of access to SWIFT and other parts of the global financial system following its invasion of Ukraine.

He told TechCrunch that the move was likely to provoke a significant nationalist backlash in India and described it as a poorly considered decision by Washington, with consequences extending far beyond Anthropic itself.

“Even if this is corrected or reversed, the Anthropic episode shows there’s no such thing as a geopolitically neutral foreign LLM,” Roy said. “American AI models are bound to American geopolitics.”

India’s AI model output has been slow compared to the U.S., Europe, and China. Only a few startups are releasing models, and most of them are large language models or voice models. To encourage more development, the government launched the India AI Mission, a roughly $1.2 billion initiative that — among other things — gives selected startups access to subsidized GPU compute in exchange for releasing their models publicly. One of the 12 startups selected for the program, Avataar AI, has launched a new video model called Varya that is built to understand local context — such as identifying different festivals, food, and clothing.

The Peak XV-backed startup, which focuses on creating video tools for e-commerce, didn’t build Varya from scratch. It started with Wan 2.2, a publicly available video generation model released by Alibaba, and used a technique called distillation — essentially compressing the model’s capabilities into a leaner, faster version optimized for Avataar’s specific use cases. The result is a model that runs in four steps rather than Wan 2.2’s 50, producing video 10 times faster and at a fraction of the cost.

To put that in concrete terms: Using an Nvidia H200 GPU, Varya can generate a five-second 720p clip in 45 seconds, compared to 1,230 seconds for Wan 2.2.

The most striking aspect of Varya may be its price. The company plans to charge ₹0.48 ($0.005) per second of video on its hosted service — far cheaper than models like Veo, Kling, Luma, and Runway, which typically charge $0.10 or more per second. That’s a roughly 20x price difference.

“India is a video-first market. We see this across every large consumer internet product in India: video wins over text. Current AI video models are too expensive for population-scale use in India. If video AI is going to reach students, teachers, MSMEs, creators, enterprises, and public services, costs have to come down dramatically. Cost is the biggest unlock for AI adoption in India,” Peak XV’s managing director Rajan Anandan told TechCrunch.

Image and video generation models often miss cultural nuances and produce stereotyped or generic outputs — a problem TechCrunch has reported on before. Avataar AI says it has used curated data to train Varya to recognize cultural nuances including food, clothing, architecture, and festivals.

Varya will be released as an open-weight model on India’s AIKosh portal — the Indian government’s centralized repository for publicly available AI models and datasets — along with its training data, meaning developers can self-host or modify it for their own needs. Avataar also plans to make the model available to its enterprise customers and says it is open to partnerships with video tools, including Higgsfield and Adobe Firefly. Anyone can try it now on its website using text prompts or reference images.

Varya’s launch reflects a fundamental tradeoff in India’s AI ambitions. Industry veterans have noted that India can make its mark in AI by creating applications and a robust developer ecosystem rather than competing on foundation models. And there’s a reason for that pragmatism: Model development has been slower in India than in global rivals due to a lack of compute and limited quality data availability.

The India AI Mission is also part of a broader government push to close that gap. Last year, it selected 12 startups — Avataar AI among them — to develop AI models and provided them with cost-efficient compute. Earlier this year, IT minister Ashwini Vaishnaw said India aims to attract $200 billion in AI investment by 2028 and more than double its GPU capacity within six months.