Fb and Instagram dad or mum firm Meta’s settlement to amass Manus for greater than $2 billion — introduced final night time by each corporations and reported in The Wall Avenue Journal — marks one of many clearest indicators but that enormous tech platforms are not simply competing on mannequin high quality, however on who controls the execution layer of AI-powered work.
Manus, a Singapore-based startup based by Chinese language entrepreneurs that debuted earlier this 12 months, has constructed a general-purpose AI agent designed to autonomously perform multi-step duties comparable to analysis, evaluation, coding, planning, and content material era.
The corporate will proceed working from Singapore and promoting its subscription product whereas its group and expertise are built-in into Meta’s broader AI group.
Manus co-founder and CEO Xiao Hong, who goes by “Red,” is anticipated to report back to Meta COO Javier Olivan.
The deal arrives as Meta accelerates its AI investments to compete with Google, Microsoft, and OpenAI — and because the business’s focus shifts from conversational demos to methods that may reliably produce artifacts, full workflows, and function with minimal supervision.
Manus as an execution layer, not a chat interface
Manus has constantly positioned itself much less as an assistant and extra as an execution engine. Reasonably than answering remoted prompts, its agent is designed to plan duties, invoke instruments, iterate on intermediate outputs, and ship completed work.
It gained 2 million customers on its waitlist alone after unveiling itself in spring 2025. At the moment, Manus outperformed OpenAI's Deep Analysis agent (powered then by the o3 mannequin) and different state-of-the-art methods on the GAIA benchmark, which evaluates how nicely AI brokers full real-world, multi-step duties, by greater than 10% in some instances.
And within the acquisition announcement final night time, Manus mentioned its system has processed greater than 147 trillion tokens and created over 80 million digital computer systems, metrics that counsel sustained, production-level utilization quite than restricted experimentation.
Meta, in the meantime, mentioned Manus can independently execute advanced duties comparable to market analysis, coding, and information evaluation, and confirmed it is going to proceed working and promoting the Manus service whereas integrating it into Meta AI and different merchandise.
For enterprises, this distinction issues. Many early “agent” methods fail not as a result of the underlying fashions can’t purpose, however as a result of execution breaks down: instruments fail silently, intermediate steps drift, or long-running duties can’t be resumed or audited. Manus’s core worth proposition is that it manages these failure modes.
What Manus customers had been truly doing with the agent
Proof of that execution-first positioning reveals up clearly in Manus’s personal neighborhood. Within the official Manus Discord server, a “Use Case Channel” publish shared by a neighborhood member named Yesly on March 6, 2025 catalogued actual examples of how customers had been already deploying the agent.
These use instances went far past informal prompting. They included:
Producing long-form analysis stories, comparable to an in depth evaluation of local weather change impacts on Earth and human society over the following century
Producing data-driven visible artifacts, together with an NBA scoring effectivity four-quadrant chart primarily based on participant statistics
Conducting product and market analysis, comparable to evaluating each MacBook mannequin throughout Apple’s historical past
Planning and synthesizing advanced, multi-country journey itineraries, full with price range estimates, lodging, and a generated journey handbook
Tackling technical and tutorial duties, together with summarizing high-temperature superconductivity analysis, proposing PhD analysis instructions, and outlining simulation-based approaches to room-temperature superconductors
Drafting structured proposals, comparable to designs for a solar-powered, self-sufficient residence with outlined geographic coordinates and engineering constraints
Every instance was shared as a replayable Manus session, reinforcing that the system wasn’t simply producing textual content, however orchestrating multi-step work to supply completed outputs.
This sample issues as a result of it reveals Manus working within the messy center floor the place enterprise AI usually stalls: duties which are too advanced for a single immediate, however too open-ended for inflexible automation.
Manus's current updates
The tempo at which Manus shipped updates was additionally spectacular, which doubtless added to its momentum with customers and as a ripe acquisition goal for Meta.
In October, the corporate launched Manus 1.5, an replace aimed squarely at the place early agent methods tended to interrupt down: lengthy, brittle duties that misplaced context or stalled midway by means of.
Manus re-architected its core agent engine and noticed instant beneficial properties. The corporate mentioned common process completion instances dropped from roughly quarter-hour earlier within the 12 months to beneath 4 minutes, practically a fourfold speedup.
The system dynamically allotted extra reasoning time and compute to more durable issues as an alternative of treating each process the identical. Manus additionally expanded the agent’s context home windows, enabling it to trace longer conversations and extra intricate workflows with out dropping key particulars. Collectively, these adjustments diminished outright process failures and improved output high quality for research-heavy, analytical, and multi-step jobs that beforehand required frequent human intervention.
In December, Manus constructed on that basis with model 1.6, extending these execution beneficial properties into extra autonomous, inventive, and platform-spanning work.
The discharge launched a higher-performance agent tuned to finish extra duties efficiently in a single move, together with new help for cell software growth, not simply web-based initiatives. Customers might describe a cell app and have the agent deal with the end-to-end construct course of, increasing Manus’s attain past the browser. On the identical time, the agent carried inventive goals throughout a complete manufacturing arc — from analysis and ideation to drafting, visible creation, revision, and remaining supply — inside one steady session.
That included producing and modifying photos by means of a visible interface, assembling shows and stories, and constructing full-stack internet purposes the agent might launch, check, and repair by itself.
Taken collectively, the updates strengthened Manus’s positioning not as a prompt-driven assistant, however as an execution system designed to stick with a job, adapt when issues broke, and reliably ship completed work throughout analytical, inventive, internet, and cell workflows.
Software-layer traction over proprietary fashions
Notably, Manus doesn’t practice its personal frontier mannequin. Reporting on the deal says it depends on third-party AI fashions from suppliers together with Anthropic and Alibaba, focusing its differentiation on orchestration, reliability, and execution.
That hasn’t prevented business traction. Yuchen Jin, co-founder and chief expertise officer (CTO) of AI cloud GPU-as-a-service supplier Hyperbolic Labs, highlighted this dynamic in a public publish discussing the acquisition. Jin famous that Manus by its personal admission reached roughly $100 million in annual recurring income simply eight months after launch, regardless of having no proprietary massive language mannequin (LLM) of its personal, counting on the aforementioned suppliers.
“People keep assuming a small update from OpenAI or Google will wipe out a lot of AI startups,” Jin wrote. “But in reality, the AI application layer should be where most of the opportunity is.”
The same interpretation got here from Dev Shah, lead developer relations at Resemble AI, who argued that Meta didn’t purchase a mannequin firm a lot as an “environment company” and that “intelligence can’t exist in isolation."
His point? Agentic capability emerges from how models are coupled with tools, memory, and execution environments — a new concept he described as “Situated Agency.”
From that perspective, Manus’s achievement was not training a proprietary foundation model, but engineering an execution layer that allows models like Claude to browse the web, write and run code, manipulate files, and complete multi-step workflows autonomously.
Shah suggested this may align more closely with Meta’s long-term strategy: rather than winning the race for state-of-the-art models, Meta could focus on owning the agentic infrastructure — the orchestration, context engineering, and interfaces — and swap in whichever model performs best over time. If that thesis holds, the Manus acquisition signals a shift toward treating foundation models as interchangeable inputs, while the execution environment becomes the primary source of durable value.
These perspectives help explain Meta’s move. Rather than buying another model team, it is acquiring a system that has already proven it can package existing models into a product users will pay for — and keep using.
What this means for your enterprise AI strategy
For enterprise technical decision-makers, the Manus acquisition is less a vendor endorsement and more a strategic signal.
First, it reinforces that orchestration layers — systems that manage planning, tools, retries, memory, and monitoring — are becoming as important as the models themselves. Enterprises building internal AI capabilities may want to invest more heavily in agent infrastructure that sits above models and can survive rapid shifts in the underlying model ecosystem.
In that sense, building an internal agent layer is not speculative or redundant. It is exactly the class of software that large platforms now view as strategically valuable — whether as acquisition targets or as internal accelerators.
A video recorded ahead of this announcement by VentureBeat founder and CEO Matt Marshall and Red Dragon co-founder Witteveen delves deeper into this subject. Watch it free below or on YouTube.
Second, the deal does not automatically mean enterprises should rush to standardize on Manus itself. Meta’s history with enterprise products gives reason for caution. Tools like Workplace by Facebook gained early adoption but ultimately failed to become deeply embedded enterprise platforms, in part due to shifting internal priorities and inconsistent long-term investment.
That history suggests a measured approach. Enterprises evaluating Manus today may want to treat it as a pilot or adjunct tool, not a foundational dependency, until Meta’s integration strategy becomes clearer.
Key questions include whether Manus remains product-led rather than ad- or data-driven, how governance and compliance evolve under Meta, and whether the roadmap continues to prioritize execution reliability over surface-level integration.
Finally, the acquisition sharpens a broader choice facing enterprises: whether to wait for vendors to define the agent layer, or to build and control it themselves. Manus’s trajectory suggests that the real leverage in AI increasingly lives not in who owns the smartest model, but in who owns the systems that turn reasoning into completed work.
In that light, Meta’s acquisition is less about Manus alone — and more about where the next durable layer of the AI stack is taking shape.
Why this deal matters beyond Meta
From the perspective of some of us here at VentureBeat, the Manus acquisition is best read as confirmation of where value is consolidating in the AI stack (and Meta’s enterprise AI agent ambitions, though the latter is far less assured.)
The defining signal is not that Manus built novel models, but that it demonstrated how quickly well-designed agents can be turned into revenue-generating products by focusing on execution, speed, and concrete outcomes.
That shift — from debating what frontier models can do to measuring what agents actually deliver — increasingly frames how AI progress is evaluated.
The deal also sharpens an important distinction for enterprise readers: this is not primarily about adopting a Meta-backed product, but about recognizing that agent orchestration has become strategically material. Manus succeeded by targeting tractable, real-world tasks and shipping agents that worked end to end, even if those use cases skewed more consumer-oriented.
The broader implication is that enterprises can apply the same approach in their own domains, building agent systems where they already possess data, expertise, and operational leverage.
At the same time, we're cautious about reading this as a direct enterprise buying signal. Meta’s history suggests that long-term enterprise trust is difficult to earn without sustained focus and specialized go-to-market muscle. Where the acquisition may make more immediate sense is on the consumer and small-business side of Meta’s own ecosystem, particularly within products already designed to manage commerce, content, and customer interaction at scale.
Manus’s agentic capabilities map cleanly onto surfaces like Meta Business Suite, where small businesses already juggle content calendars, inboxes, ads, analytics, and monetization tools across Facebook and Instagram. An execution-oriented agent could plausibly automate or coordinate many of those tasks end to end, from drafting and scheduling posts to responding to messages, optimizing ads, or assembling performance reports.
Manus's "Design View" function, which launched publicly only a week previous to the Meta acquisition announcement and permits customers to generate new imagery with editable discrete parts utilizing pure language, would appear to be tailored for a social community advert creation expertise:
Past creators and small companies, these brokers might plausibly lengthen to on a regular basis customers navigating Instagram or Fb for purchasing, discovery, or private expression. An execution-oriented agent might help common customers with duties comparable to searching and evaluating merchandise, managing purchases, assembling want lists, or coordinating returns, whereas additionally serving to them create and edit posts, reels, or tales for family and friends — not as skilled content material, however as informal, social, and entertainment-driven output.
That framing aligns carefully with Meta’s historic strengths. The corporate has been most profitable when AI capabilities are tightly built-in into high-frequency client workflows quite than positioned as standalone enterprise software program.
A Manus-powered agent that helps customers do issues — store, create, plan, or handle interactions inside Meta’s apps — would match naturally into Instagram and Fb’s evolution towards extra agentic experiences. In that state of affairs, Manus features much less as an enterprise model and extra as an invisible execution layer, powering AI assistants that function natively inside Meta’s client ecosystem, the place scale, engagement, and commerce already converge.
Because of this, the acquisition’s clearest relevance is just not whether or not enterprises ought to standardize on Manus, however that investments in inner agent frameworks, orchestration layers, and governance now seem more and more well-justified — as a result of that’s exactly the layer massive platforms at the moment are keen to pay for.
