AI PC“伪原生”困境： 对比豆包手机看智能体落地真门槛

This article outlines the current development status of AI PCs and native agent hardware, helping ordinary consumers understand the reality of AI hardware and avoid marketing traps.

1. The agent-based AI PCs heavily promoted by Nvidia, Intel and other major players today are "pseudo-native" products. Essentially, they just add AI software to existing PC architectures rather than rebuilding the entire system from the ground up. They suffer from limited ecosystem support and poor compatibility, with marketing hype far outpacing real user experience.

2. Native agents, represented by Doubao Phone, have already proven commercial viability. Designed from the system底层 up, they allow users to issue natural language commands for AI to automatically complete full-process cross-application tasks including price comparison, order placement, photo editing and train ticket booking, eliminating the need for users to switch back and forth between multiple apps. This delivers a far better experience than pseudo-native AI products.

3. When shopping for AI hardware, ordinary consumers should not be drawn in solely by high-compute marketing. Instead, they should prioritize whether a product can truly complete end-to-end closed-loop tasks, as native agent products deliver far more practical AI experiences.

This article analyzes divergent development paths in the AI smart hardware industry, offering actionable insights for AI hardware brands on marketing, R&D and trend positioning.

1. The global PC market is currently in a broad slowdown, with IDC projecting an 11.3% decline in global PC shipments by 2026. Most PC vendors have launched agent-based AI PC concepts, which are essentially a marketing gambit to navigate weak market demand. These offerings only add incremental computing power without delivering a qualitative leap in user experience, and long-term growth cannot be sustained by marketing alone.

2. Consumers' core demand for AI hardware is reduced operational friction: they want products that can proactively understand and fulfill their needs. Native agents are a clear future consumer trend, and user adoption will rise steadily over time.

3. Product R&D needs to move away from the old "compute-stacking" mindset. Brands should follow the path of Doubao Phone, rebuilding system permissions and hardware design around the core needs of agents. By seizing the transition window from AI function overlay to native agents, brands can build truly competitive products.

This article clearly lays out the ongoing bifurcation of the AI smart hardware track, helping sellers clarify opportunities and risks, and select the right product categories to capture market share.

1. While agent-based AI PCs currently attract strong hype, these products are pseudo-native, suffering from limited application ecosystems, inability to complete closed-loop tasks and poor overall experience. The category is largely driven by vendor marketing hype, and sellers should avoid overcommitting to pseudo-native AI PCs in the short term to prevent inventory backlog.

2. Native agent hardware is a clear growth opportunity. Doubao Phone has already validated the commercial viability of this approach, solving the core pain point of existing AI hardware that requires repeated user switching and tapping. It aligns with users' core expectations for AI devices, and boasts massive long-term growth potential.

3. When selecting products, sellers should distinguish between pseudo-native and true native AI hardware, and not be misled by high-compute marketing. The core evaluation metric should be whether a product supports cross-application closed-loop task execution. Native agent categories better meet user demand and are more likely to drive sales growth; sellers can also prepare inventory in advance for native agent-related peripherals.

The directional shift in AI smart hardware development brings new requirements and business opportunities for hardware manufacturing factories, while clarifying the direction for transformation.

1. Product design and manufacturing requirements have undergone a fundamental change. In the past, AI hardware competed on hardware specifications such as computing power and memory, following the model of adding AI functions to existing architectures. Today, native agents require hardware tradeoffs starting from chip selection, tailored to support continuous agent operation and cross-application access, with sufficient system resources reserved. Factories need to adjust their design and manufacturing thinking to adapt to these new requirements.

2. In terms of business opportunities, native agents have already found product-market fit in the smartphone segment, while no true native agent product has yet launched on the PC side, creating a supply gap in the market. Factories can proactively partner with brands to lay out R&D and manufacturing for native agent hardware, capturing early share in the new track.

3. Key takeaways for digital transformation and e-commerce: Factories need to keep pace with changes in the AI industry, adjust their R&D and manufacturing systems, shift from standardized mass production to demand-driven customization, build hardware tailored specifically for AI-native scenarios, and strengthen their core competitiveness in the AI era.

This article reveals pain points and trends amid the AI smart hardware industry transition, helping service providers align with industry direction and capture new service opportunities.

1. The industry development trajectory is clear: AI smart hardware will inevitably move from the pseudo-AI phase of simple function overlay to the native agent phase. The core of industry competition is also shifting from competing on computing specifications to competing on system architecture design and closed-loop task capability. This transition will generate strong demand for specialized professional services.

2. Core industry pain points are clear: PC vendors looking to capitalize on the agent trend and build competitive products face constraints from incompatible legacy system architectures, lack of adaptive底层 permission design, and incomplete application ecosystems. No mature solution exists for native agent deployment, and the proven path from the smartphone segment cannot be directly replicated to PCs.

3. Service providers can develop targeted solutions: for example, building native agent system architecture adapted for PCs to help PC vendors solve core problems of cross-application permission access and closed-loop task execution. They can also develop supporting ecosystem building services centered on native agents to meet new market demand.

New developments in the AI smart hardware industry have brought new requirements for platform operations, merchant recruitment, and risk management, requiring platforms to adjust their strategies to align with industry growth.

1. Brands and users have put forward new demands for smart hardware platforms: native agents require platforms to open底层 permissions to support AI cross-application access for closed-loop task completion. Existing permission architectures on PC platforms cannot meet this requirement, so platforms need to adjust their permission design rules to adapt to the growth of native agents.

2. Platforms need to adjust their merchant recruitment and traffic allocation priorities: pseudo-native AI PCs are largely hype with limited long-term growth potential, while native agent hardware aligns with real user demand and will be the future growth engine. Platforms can proactively onboard relevant brands and products to lay early groundwork in the new track and capture first-mover advantage.

3. In terms of risk mitigation, platforms should avoid overfollowing the hype around pseudo-native AI PCs, and avoid allocating massive resources to support an immature category. They should instead focus on real breakthroughs in underlying technology, guide the industry to prioritize authentic user experience, and avoid platform traffic loss and user trust erosion caused by industry bubbles.

This article reveals the real-world challenges of current AI agent deployment, compares two industry development paths, and offers new samples and directions for industry research.

1. Two distinct industry paths for AI agent deployment have emerged: the hardware upgrade + software overlay path adopted by PC vendors, and the native agent path of rebuilding from the operating system底层 up represented by Doubao Phone. Existing real-world deployments have proven that the native path better meets the requirements for AI agent deployment, clarifying the direction for the industry.

2. New problems have been exposed during industry development: the overall PC industry is facing stagnant growth, and players have turned to new concept marketing to break through the slowdown, with few substantive technological breakthroughs. The core contradiction is the constraint that legacy PC system architectures impose on AI agent deployment; simply stacking more computing power cannot solve the fundamental problem of enabling closed-loop agent task execution.

3. Future research can expand in multiple directions: investigating the requirements of native agent deployment for different hardware architectures, exploring methods for the PC segment to break through existing system constraints to achieve native agent transformation, studying adaptive business models for AI-native hardware, and analyzing the paths and patterns for the broader smart hardware industry's transition to native agents.