"New Round Begins: Meta Acquires Manus VS OpenAI and Accenture Partnership"

--- Why are the giants' strategies a deadly temptation for China's AI startup scene?

1. Why is this not just an "ordinary AI acquisition"?

Regarding Meta's acquisition of Manus, the market has quickly offered several seemingly reasonable explanations:

Meta is supplementing Agent capabilities
Meta is enhancing AI application layers
Meta aims to accelerate the deployment of C-end AI products
These explanations are not wrong, but they all share a common issue: they stay at the product or business level.

If we only consider feature supplementation, we cannot answer a more critical question:
Why now? Why acquisition?

The truly necessary questions to re-ask are not:

What functions did Manus add?

But rather:

In a context where model capabilities are already significantly surplus, why is real AI usage still severely lacking?
Has the bottleneck in AI competition shifted from "capability ceiling" to "usage efficiency and interaction paradigms"?

If the latter is true, then the nature of this acquisition fundamentally changes.
Meta's acquisition of Manus is not a tactical product supplement but a structural layout targeting key bottlenecks in AI competition.
To understand this, it must be viewed within the continuity of Meta's overall AI strategy, not in isolation.

2. Continuity of Meta's AI strategy: three types of actions around the same "nonlinear bottleneck"

Looking back at Meta's key moves in AI over the past few years, they can roughly be divided into three categories:

High-paying recruitment of top AI scientists
Acquisition of Scale AI
Acquisition of Manus

On the surface, these three actions point in completely different directions: talent, data, products.

But focusing only on differences causes us to miss what is truly important.

Their commonality lies in:
They are not short-term revenue-driven
They are not passive reactions chasing hot topics
They revolve around a nonlinear bottleneck in AI competition
Meta's goal has never been to "build a Meta with stronger AI functions,"
but to build a long-term, hard-to-copy competitive advantage in the AI era.

Manus must be understood within this main line, not as an isolated product or team.

3. Phase one: high-paying recruitment of AI scientists

— Addressing "Does Meta have the qualification to participate in top-tier AI competition?"

The goal of this phase is very clear: entry qualification.
Meta needs to fill the gaps in:
Model and algorithm capability ceilings
Basic research and original capabilities
Autonomy at the foundational model level (e.g., LLaMA route)

The key questions to solve here are:
Does Meta have the research strength to compete head-to-head with OpenAI and Google?
Is it qualified to stay in the first tier?
This stage's strategic significance is more defensive and capability-building oriented.
Without this step, Meta would be excluded from top-tier AI competition.
But this step only addresses "can we develop it," not "can we evolve it long-term."

4. Phase two: acquisition of Scale AI
— Addressing "Can large models be trained continuously and at scale?"

As model sizes keep increasing, a structural problem begins to emerge:
Algorithms are no longer the only bottleneck.

The real constraints on continuous breakthroughs are:
Data supply stability
Quality control capabilities
Cost curves and engineering efficiency

Scale AI fills the entire industrial loop: data → training → feedback.
The essence of this step is not just "making models stronger once,"
but ensuring that model capabilities can evolve continuously and controllably.
From an industry chain perspective, this is a contest for control over the midstream AI infrastructure.

Without this control, breakthroughs tend to be one-off events rather than sustained capabilities.

5. Phase three: acquisition of Manus
— Addressing "Will model capabilities be genuinely used?"

The first two steps solve the issues of capability ceilings and supply sustainability.
The third step addresses a completely different level:
Will model capabilities be genuinely used?

This is a long-underestimated but increasingly decisive bottleneck.

The reality is:
Model capability ≠ user capability
AI capabilities are severely overestimated, but actual usage and penetration are extremely low
A large amount of computing power and models are in "idle" states
Not converted into stable productivity,
Nor into continuous behavioral structures.

What Manus fills is not functions but the Human-to-AI Interface:
The usage interface and behavioral bridge between humans and models.

This step signifies a change in the level of competition.

6. Key qualitative change: Manus is not just a supplement but a "lock-in of user habits"
This step is fundamentally different from the first two phases.

AI scientists & Scale AI:
Enhance the model capability ceiling

Manus:
Change the way capabilities are used and consumed
Meta's problem shifts from:

Can AI be developed?

To:

Will 3 billion users naturally use AI?

This is a terminal-related question.

Because capability ceilings can be caught up,
but once usage paradigms solidify, platform positions become locked.

7. Key analogy: OpenAI × Accenture
— Same problem, different levels of solutions

OpenAI has publicly acknowledged a structural fact:
Model capabilities far surpass user (especially enterprise) usage capabilities.

The solution of OpenAI × Accenture is:
Object-oriented: Large B
Means: Consulting, system integration, process transformation
Essentially: Using service systems to help organizations "use AI"

This is a service-oriented, outsourcing bridge.
Meta × Manus addresses the same fundamental problem,
but in a completely different way:
Object-oriented: C-end / small B
Means: Productization, endogenous embedding

Essentially: Making users "default to acting through AI"

This corresponds to a completely different adoption curve.

8. Further strategic extension: from "social network" to "AI action network"

When AI is no longer just a tool but begins to:
Participate in actions
Collaborate
Produce
It becomes a new node in social systems.

Manus's potential positioning is not just a functional module,
but the AI action layer within Meta's social ecosystem.

This may be a transitional structure for Meta from a social platform to an AI-native platform.

9. An overlooked but extremely critical judgment

— If Manus mainly targets the Chinese market, this acquisition is almost impossible.

Here, the logic must become more realistic.
Manus being acquired is not just because of its product or direction,
but because it meets an entire chain of necessary conditions:
Market internationalization
Capital internationalization
Regulatory compliance and transferability (U.S. regulatory framework)
Valuation anchored in a global comparison system

If Manus's main market is China, this chain will break at the earliest stage.
This is an uncomfortable but necessary fact to acknowledge.

10. Systematic changes in US-China capital markets are fundamentally altering startup paths

Chinese capital markets favor:
Certainty
Cash flow
Realized scale

Long-term options and paradigm value are often undervalued.
The U.S. dollar capital market is better at pricing:
Strategic scarcity
Platform potential
Mergers and acquisitions, and long-term options

Even if Manus has global users,
its valuation in China's capital market is hard to align with the U.S. system.

This is not about who is better or worse, but about different valuation functions.

11. For the Manus team:
"Capital and computing constraints are suddenly lifted"

After Meta's acquisition, the biggest change for the Manus team is not more resources, but:
No longer worrying about funding windows
No longer limited by regional capital pricing

Capital and computing constraints are both lifted

In the AI era, what is truly scarce is not ideas,
but:
Long-term stable computing power supply
Capital patience supporting high-intensity trial and error

This will fundamentally change the team's incentive structure:
From

How to survive until the next round

To

How to perfect a long-term correct but short-term uncertain project

This is especially critical for exploring high-uncertainty directions like Human-to-AI Interface.

12. Demonstration and temptation for Chinese AI startups

This is no longer just an acquisition story but a path demonstration.
For TikTok, DeepSeek, and various Chinese AI startups of all sizes, the real insight is not:
Can they sell to American companies?
But rather:

Is it worth entering the global capital and computing resource system from the start?

In the highly capital- and compute-intensive AI competition,
internationalization is not just a market issue but a question of "access to core resources."

The potential systemic consequences include:
More Chinese AI startups choosing:
Product internationalization
Company structure internationalization
Compliance pathway internationalization

The entrepreneurial goal will shift from:
Growing independently within China
To:
Becoming a key module in the global AI ecosystem

Because only then can they grow faster,
and only then can they achieve higher capital premiums (the premium of Chinese and US capital markets on startups, which will not change in the short term due to the nature of capital markets; details omitted here).

This is a deadly temptation for Chinese AI teams.
And possibly the only option for Chinese VCs.

13. From an investment perspective:
Why does this acquisition improve Meta's long-term quality?

This is not just a "bet on winning or losing" investment,
but a strategic move to increase the probability of long-term success.
For investors, the biggest concern is not failure,
but continuous investment in the wrong problems.
Whether Manus succeeds is a result variable;
whether Meta has bet on the right key issues in AI competition is the core.

During paradigm shifts:
Correct direction + continuous iteration
are often more important than one-time success.

Meta has already clearly positioned itself on:
"Completing the AI usage paradigm" as the correct direction.

This is also why:
Meta's acquisition of Manus is itself an enhancement in investment value.

Figure: Comparison of Meta's last three acquisitions