The deal values Manus at between $2 billion and $3 billion, although Meta has chosen not to disclose financial terms. Manus, now based in Singapore, did not respond to requests for comment at the time of writing.

The acquisition gives Meta full control of Manus’s technology, which it plans to operate, sell and embed across its consumer and business products, including Meta AI. 

This will help to secure what many in the industry now see as the most valuable layer in AI development, the execution layer, where software agents go beyond conversation to carry out complex tasks on their own.

Manus rose quickly into the global spotlight earlier this year after releasing what it described as a general AI agent. Unlike standard chatbots, the system was designed to make decisions and complete tasks with minimal prompting. 

The product went viral on X and was soon compared to DeepResearch, a benchmark tool in the sector. The company has claimed its agent outperforms that system, helping to drive both attention and controversy.

Behind the attention was rapid commercial growth. Manus became the fastest startup to cross $100 million in annual recurring revenue, reaching a $125 million run rate in under eight months. 

In 2025 alone, its systems processed 147 trillion tokens and powered around 80 million virtual computers, figures that point to unusual scale for a company so young. That pace made Manus one of the most talked-about AI agent startups globally.

Meta’s interest shows a change in strategy. While the company has spent years building open-source foundation models such as Llama, this deal reveals a goal to own proprietary systems that sit on top of those models and actually do the work. 

Autonomous agents that can research, write code and analyse data are now the next battleground, and competition is increasing fast.

The acquisition comes after a year of heavy spending by Meta, including its investment in Scale AI, a deal that valued the data-labelling firm at $29 billion. 

Competitors are not standing still. Microsoft is expanding Copilot, Google is pushing Gemini, and OpenAI is developing DeepResearch.

Manus’s background also adds a geopolitical edge to the deal. Founded in China and backed by its parent company, Beijing Butterfly Effect Technology, the startup was once described as China’s next DeepSeek. 

It later relocated to Singapore, joining a growing number of Chinese tech firms seeking to reduce exposure to Sino-US tensions. Singapore’s neutral stance and trade-friendly policies have made it a preferred base for global expansion.

Beijing had shown interest in supporting Manus, and the company maintains a strategic partnership with Alibaba to collaborate on AI models. 

Meta’s acquisition changes ownership firmly to a U.S. technology giant, a development that is likely to be closely watched in both Washington and Beijing.

Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai, who co-founded OpenAI’s Zurich office and also previously worked at Google DeepMind, have officially exited OpenAI and joined Meta to build artificial general intelligence (AGI). 

Their departure is a change in the balance of power, especially in the European AI research space, where Zurich has become a strategic outpost for some of the most advanced work in machine learning.

Their recruitment is part of Meta’s massive, under-the-radar goal to take over AGI development. At the centre of this is Meta’s Superintelligence Lab, a unit granted deep access to the company’s compute infrastructure and charged with building AI models that can rival or even surpass human reasoning.

It’s a pivot that founder Mark Zuckerberg is not leaving to chance.

Multiple reports now point to Zuckerberg taking a personal lead in poaching efforts, bypassing HR and headhunting directly via WhatsApp. He’s allegedly coordinating efforts through a “Recruiting Party” group chat and following up with private dinners in his homes in Palo Alto and Lake Tahoe. The approach is unconventional but it’s starting to yield results.

One of the most headline-grabbing wins so far is the $14.3 billion investment Meta recently made in Scale AI. The deal gave Meta a 49% stake in the data-labeling company and also brought on board Alexandr Wang, Scale’s 28-year-old founder and CEO, to lead its superintelligence vision. 

The investment values Scale at $29 billion and ranks among Meta’s most expensive strategic plays since acquiring WhatsApp.

Still, the road hasn’t been smooth. Meta has failed to secure OpenAI co-founders Ilya Sutskever and John Schulman, both of whom have taken off in new directions. 

Sutskever is now heading up Safe Superintelligence Inc. (SSI), a stealth startup focused on developing safe AGI, while Schulman has joined another secretive firm led by ex-OpenAI CTO Mira Murati. Their departures from OpenAI hint at ideological rifts and the growing splintering of high-level AI talent.

Meanwhile, OpenAI CEO Sam Altman has been publicly dismissive of Zuckerberg’s charm offensive. In a recent podcast with his brother Jack, Altman quipped, “I’m really happy that, at least so far, none of our best people have decided to take him up on [those offers].”

But the facts on the ground are changing fast. The exodus of top researchers, Meta’s multibillion-dollar bets, and Zuckerberg’s visible sense of urgency all point to a conclusion that the competition is just starting and Meta has no intention of staying behind.

With compensation packages rumoured to exceed $100 million for senior hires, Analysts are bothered about market distortion and the ethical implications of consolidating so much power within a handful of firms. 

Meta’s open-source LLaMA models may have generated buzz, but many insiders acknowledge the company has lagged behind rivals like OpenAI and Google in performance and adoption.

The company seems to be correcting that, not just with money, but with a structural overhaul that places AGI development at the centre of its future.

The Meta CEO is personally building a new team aimed at artificial general intelligence (AGI), machines that can think and operate at or beyond human capability.

At the heart of this development is a covert “superintelligence” unit made up of roughly 50 top-tier engineers and researchers. 

According to multiple reports, Mark Zuckerberg is leading recruitment himself. That includes closed-door meetings with experts at his homes in Lake Tahoe and Palo Alto, strategic restructuring of Meta’s offices to keep the team close, and eye-watering compensation offers ranging from millions to tens of millions of dollars.

The motivation is the frustration over Meta’s perceived stagnation in the AI space. Meta’s flagship large language model, Llama 4, hasn’t produced the breakthrough results the company hoped for. 

In fact, a planned release of a more powerful version, nicknamed “Behemoth”, was recently delayed due to issues about its real-world capabilities. 

Meanwhile, competitors like OpenAI, Anthropic, and Elon Musk’s xAI are thriving hard to expand their influence, drawing talent and investment with growing momentum.

Zuckerberg is determined not to be left behind. Reports from Bloomberg and The New York Times confirm that Meta is in advanced talks to invest over $10 billion in Scale AI, a startup founded by Alexandr Wang. 

Once the deal is sealed, Wang is expected to join the AGI group, which operates separately from Meta’s existing AI research division.

“I heard of at least three instances last week where Meta lost out on AI talent to competitors offering over $2 million a year,” Menlo Ventures partner Deedy Das wrote on X.

Zuckerberg reportedly believes that Meta has the infrastructure, data scale, and financial muscle to match and surpass the progress of others in the AGI arms race. 

If achieved, the technology would likely be embedded into Meta’s ecosystem, impacting everything from WhatsApp and Instagram to the company’s Ray-Ban smart glasses and enterprise AI tools.

However, the plan leaves users questioning. How will this “superintelligence” group integrate with Meta’s existing AI teams? What risks are involved in placing so much responsibility, and expectation, on one internal unit? And is AGI even within reach? 

The field is divided and some experts believe we’re close. Others say we’re decades away, if not longer, with no clear path forward.

Still, for Zuckerberg, this is a mission.