OpenClaw Ignites the "Year of Agents": In-Depth Comparison of Major Players' Strategies and the Computing Power Dilemma

At the beginning of 2026, the phenomenal product in the AI world is undoubtedly OpenClaw. This open-source agent framework has surpassed 2.5 million stars on GitHub and was hailed by Nvidia founder Jensen Huang as "the most important software release of our time." He even stated that OpenClaw has achieved a level of adoption in just three weeks that took Linux three decades to accumulate.

In the Chinese market, the explosion of OpenClaw has triggered a "beach landing battle" for future ecosystem dominance. From internet giants to model companies, and hardware suppliers, everyone is betting on OpenClaw. This is not just a technological trend-following but is seen as a key path for China's AI industry to achieve corner overtaking in the "Physical AI" era. This article will delve into the strategies of major players and explore the "computing power anxiety" hidden behind the OpenClaw explosion.

1. Cloud Providers' "Pick-and-Shovel" Logic: Ecosystem Bundling and Infrastructure Battle

Facing the torrent of OpenClaw, leading domestic cloud providers have unanimously chosen to "use it for their own purposes," packaging OpenClaw into "ready-to-use" one-click deployment products via cloud services. Their core logic is to become the "shovel sellers" in the AI gold rush, lowering the deployment barrier to capture the next-generation traffic gateway and drive consumption of underlying cloud services.

Analysis: Alibaba Cloud's approach is the most "ecosystem-oriented," with the clearest intent to use OpenClaw to funnel developers to its Bailian large model service platform, forming a closed loop from computing power, models, to applications. Tencent Cloud plays a neutral role as an "infrastructure provider," aligning with its "connector" positioning. Baidu AI Cloud attempts to integrate its mobile products (like Red Finger Operator) to explore the combination of cloud phones and agents.

2. Model Companies' "Positioning Battle": From API Suppliers to Cloud-Based Agents

If cloud providers focus on underlying resource consumption, model companies directly view OpenClaw as a "super engine" to drive API calls (Token consumption), and some have even launched their own versions of "Cloud Claw" to directly reach users.

1. Moonshot AI (Kimi): Kimi Claw
Moonshot AI transitioned from a "passive supplier" to an "active fortress builder." Initially, its Kimi K2.5 model, due to its high cost-performance and strong Agent capabilities, became the most called model for OpenClaw on the OpenRouter platform, with week-over-week growth peaking at 261% . Subsequently, Moonshot AI leveraged this momentum to launch Kimi Claw, a cloud-hosted OpenClaw service.
Advantages: Users don't need local deployment or manage API keys; they can use it directly on the webpage, with built-in 40GB of cloud storage, enabling multi-device memory synchronization. It transforms a geek-oriented tool into a membership-based SaaS service, significantly lowering the barrier to entry.

2. MiniMax: MaxClaw
MiniMax's approach is more radical. Its M2.5 model, leveraging extreme cost-performance (costing 1/10 to 1/20 of Claude Sonnet), saw its call volume on OpenRouter soar to 3.07 trillion tokens in one week, landing at the top of the leaderboard. Based on this, MiniMax launched MaxClaw, directly integrated into its Agent web interface, pre-set with numerous expert skills.
Advantages: Extreme cost-performance. For high-frequency tool calling scenarios requiring 24/7 operation, cost advantage becomes the decisive factor, quickly earning MiniMax significant attention from both capital and developer markets.

3. Zhipu AI and Others
Zhipu AI has not released a product explicitly named "Claw," but it emphasizes its advantages in localization and compliance. Driven by OpenClaw, its API packages were even temporarily sold out, indicating a huge market demand for domestic models adapted for agents.

3. OpenClaw's "Computing Power Black Hole": Exponential Token Consumption Amplification

The fundamental reason OpenClaw attracts all players is that it completely changes the "consumption scenario" of AI. Traditional conversational AI is discrete "ordering," while OpenClaw agents are "digital employees" on duty 24/7.

1. The "Amplification Effect" of Computing Power Consumption
A properly configured OpenClaw needs to decompose goals, plan steps, call tools, check results, and even engage in self-reflection and retrying. This leads to exponential growth in token consumption.

• Data Evidence: Some programmers estimate a heavy user consumes an average of 100 million tokens daily. Using Anthropic Claude Opus, the monthly cost could reach as high as 103,500 RMB; even with more economical options, the cost is substantial.

• Industry Forecast: IDC data shows that by 2030, the number of active AI agents globally will reach 2.216 billion, and annual token consumption will skyrocket from 0.0005 PetaTokens in 2025 to 152,667 PetaTokens, an increase of over 300 million times.

2. Shift in Computing Power Evaluation Metrics
The explosion of OpenClaw has rendered traditional computing power metrics (like peak FLOPs) obsolete. The industry is now focusing on two new "North Star metrics":

• Token Speed: Directly impacts the response latency of agents. In scenarios like financial anti-fraud or real-time scheduling, a few seconds of delay can mean task failure. Providers have begun pricing for "faster" speeds (e.g., Anthropic's premium mode with a 6x markup).

• Token Cost: Determines whether a business model is viable. Inspur Information points out that through architectural optimization (like HC1000 servers using decoupled D/P architecture), inference costs can be reduced to 1 RMB per million tokens, offering a nearly 47x advantage over public cloud APIs. This directly dictates whether agents can scale for implementation or remain a concept.

3. Polarization at the Hardware Level
OpenClaw's computing power demands exhibit a "two-pronged" trend:

• Cloud API Mode (Gateway Mode) : Extremely low requirements for local devices (even a Raspberry Pi can run it), with all computing power pressure concentrated on the cloud API service provider.

• Local Model Mode : Imposes high demands on GPU memory. A 7B model requires 8GB of VRAM, while a 32B model needs 24GB or more, along with stringent requirements for memory bandwidth (like Apple's Unified Memory) and disk I/O (NVMe SSD mandatory).

4. Solving the Computing Power Anxiety: The Rise of Specialization and Rental Models

Facing the "computing power black hole" triggered by OpenClaw, companies are caught in a dilemma: Relying solely on public cloud APIs risks cost spiraling out of control; building proprietary computing centers involves long construction cycles and the sunk cost risk of rapid hardware iteration.

This is the core contradiction of the "Physical AI" era proposed by Jensen Huang at CES 2026—computing power demand knows no bounds, but computing power supply must be elastic. For the vast majority of AI startups and traditional enterprises, the optimal solution is not purchasing expensive hardware, but acquiring flexible, high-performance computing power through computing power rental.

In this context, professional computing power rental platforms are becoming the "utility" supporting the development of AI across various industries. Through large-scale procurement and refined operations, they offer businesses computing power services that are more cost-effective than self-building and more flexible than big-tech APIs.

Among them, the omniyq computing power rental platform (www.omniyq.com) , with its keen market insight, is committed to becoming the "invisible engine" behind this agent revolution. The platform focuses on providing high-performance computing power at a reasonable cost. Whether it's R&D teams needing large-scale parallel training for "Physical AI" models or SMEs hoping to deploy low-cost, 24/7 OpenClaw agents, they can use omniyq's flexible rental services to avoid massive capital expenditures and depreciation traps associated with hardware purchases, channeling limited funds into core business innovation.

As the trends revealed at CES 2026 suggest, future AI competitiveness depends not only on the sophistication of algorithms but also on the ability to acquire and schedule computing power. omniyq.com , by building an efficient and accessible computing power marketplace, enables every enterprise to use AI computing power as easily as electricity, truly realizing the vision of "high-performance computing power supporting AI development across thousands of industries," providing the most solid foundation for China's AI industry to move from "breakthroughs in isolation" to "comprehensive explosion."

Conclusion

The explosion of OpenClaw reveals a "collective evolution" in China's AI industry. It's no longer just about competing on model parameters but a comprehensive competition spanning cloud services, model ecosystems, hardware architecture, and even computing power operational models. Although the exponential growth in computing power consumption brings significant cost anxiety, it has also fostered the emergence of professional and efficient computing power service platforms like omniyq. OpenClaw may not be the final form of agents, but the "Age of Execution" it has inaugurated is destined to reshape the entire value chain of the AI industry.