AI手机生死劫：冲不破的系统围墙 逃不掉的流量纳贡

This article sorts out the core current status and major contradictions of today's AI-powered smartphone development. AI phones have moved beyond superficial large language model integration to enter the deep water stage of operating system reconstruction, and the biggest barrier to growth is not technical challenges, but interest barriers built by the existing internet ecosystem.

1. The industry has currently evolved three distinct development approaches for AI phones: the Doubao route, which adds AI-powered simulated click functions on top of the existing Android system; the Jieyue route, which builds a native AI operating system from scratch; and the A2A protocol cooperation route, which has now become the industry's commercial consensus.

2. The current status of each route is clear: the Doubao route has low R&D costs but crosses the red line of platform risk control and流量利益, ending in failure with the termination of the Nubia project. The native reconstruction route solves problems of user trust and risk control identification but has yet to break through the core bottleneck of traffic interest distribution. The A2A protocol is a phased compromise reached between AI developers and internet giants: AI completes operations on behalf of users without touching the platform's existing traffic and advertising interests, and platforms open compliant interfaces to enable cooperation.

3. All leading smartphone vendors including Huawei, Honor, and Xiaomi have entered the space, all competing to become the core smartphone entrance in the AI era. Only the player that successfully builds a closed commercial loop that balances technical trust and interest compromise will emerge as the ultimate winner.

AI phones have become the new core growth engine for the smartphone industry. The core to brand development lies in balancing technological innovation with interest distribution across the internet ecosystem, and different brands can choose a development route that matches their own resources.

1. Changes in consumer trends and user behavior are clear: users are no longer satisfied with AI that only handles chat conversations, and instead expect AI to complete complex tasks such as booking tickets or creating PPTs in one click. Simplifying operation workflows has become users' core demand, which creates clear incremental space for AI phones.

2. In product R&D, the route of simply adding AI functions on top of existing systems has been proven unviable. Building a native AI phone requires reallocating hardware resources around AI agents and reconstructing the underlying system framework, as well as building a complete trust loop covering "memory, decision execution and security" to turn AI from a novelty toy into a practical tool.

3. On the ecological cooperation front, brands cannot touch the core traffic interests of internet giants, and should achieve connection through compliant agreements, following the industry consensus that "AI does the work, the platform retains the traffic position". Leading brands have already taken different approaches: Huawei focuses on ecosystem scale, Honor pursues system reconstruction, OPPO works on cross-application collaboration, and vivo focuses on core scenarios, all with the end goal of competing for the top entrance in the AI era.

AI phones will be a growth trend for the long term, and industry players can clarify direction, avoid risks and seize opportunities based on the lessons and consensus from current industry development.

1. Industry changes and opportunities: User demand for AI to simplify operations is already very clear, and AI phones will drive a new wave of smartphone replacement cycles. Meanwhile, the emergence of the A2A agreement has created a unified cooperation standard for the industry, reducing connection costs between smartphone vendors and app developers and opening up cooperation space for small and medium-sized players.

2. Risks and lessons: Never choose the approach of overstepping boundaries by adding AI simulated click functions on top of existing Android systems. This model crosses platform risk control red lines and takes a share of internet giants' traffic, and will end quickly like the Nubia project. All players that simply add AI functions within the Android ecosystem face the risk of being removed by ecosystem owners at any time.

3. Actionable directions: To enter the AI phone track, players can either build a native AI phone by reconstructing the operating system from scratch to solve the AI identity issue at the root level, or follow the current industry consensus and cooperate with internet giants under the rules of the A2A agreement, sharing profits without touching core traffic interests, to achieve long-term growth. Currently, Jieyue has already reached in-depth cooperation with multiple leading apps including Ctrip and Alipay, covering high-frequency user scenarios, providing a valuable reference model.

The growth of AI phones has brought new commercial opportunities to smartphone manufacturing, while also putting forward new requirements for product design, production and digital transformation.

1. Demand for production and design has changed: Resources in traditional smartphone operating systems are designed around manual operation, while native AI phones require separate hardware allocation for AI agents and system capability re-scheduling. This requires factories to adapt to the new demands of AI systems during the hardware design and production stage, reserve corresponding hardware resources for AI, and meet the requirements of AI agents for fast memory and on-device computing.

2. Commercial opportunities are clear: AI phones have launched a new renewal cycle for the smartphone industry. User demand for AI functions will drive a replacement boom, and factories that lay out AI-compatible hardware in advance will win more orders and capture greater market share.

3. Insights for digital transformation and e-commerce development: Factories should not stop at superficial functional upgrades. They need to seize the trend of native AI reconstruction, adjust production and design directions at the root level, recognize the importance of ecosystem interests, help brands connect with ecological resources, follow industry consensus to avoid project failure from crossing interest red lines, and build core competitiveness through underlying technological upgrades.

The AI phone industry has now entered a new stage of ecosystem construction, with clear industry pain points and new development trends that bring many new market opportunities for service providers.

1. Industry development trends are clear: Competition in AI phones has shifted from technological competition around large model integration to operating system reconstruction and ecological rule building. The industry is moving from fragmented one-to-one cooperation to standardized agreement-based cooperation, and the A2A protocol will become the common underlying cooperation standard for the industry going forward.

2. There are multiple clear customer pain points in the current industry: There is no unified standard for connection between AI phone vendors and apps, so every cooperation requires separate negotiation and adaptation, leading to high costs and low efficiency. Existing technical routes either fail to solve risk control problems or fail to resolve interest distribution issues, so all industry players are searching for compliant and viable implementation solutions.

3. Corresponding solution directions: Service providers can lay out standardized adaptation services for the A2A protocol, helping small and medium-sized phone vendors and small and medium-sized apps complete connection quickly and reduce connection costs. They can also develop security frameworks that meet the requirements of "trustworthy, visible, controllable and reversible" around the security needs of AI phones, providing security solutions for the industry and capturing dividends from the industry's transition to standardization.

The arrival of the AI era puts forward new requirements for all types of platforms. Platforms need to adjust their rules and operation strategies to both seize AI development opportunities and protect their core interests.

1. The core industry demand from platforms is clear: AI phone vendors need platforms to provide standardized connection interfaces, eliminating the need for从零开始谈判和适配 for every cooperation to cut cooperation costs. At the same time, users need platforms to open up capabilities to AI to enable one-click completion of complex tasks and improve user experience.

2. The latest viable industry approach: The industry has already reached a consensus on the A2A protocol. Platforms can open controlled interfaces to compliant AI systems under the rules of the A2A protocol, where instructions are transmitted via encrypted protocols and executed by the platform itself in the backend, with the AI system gaining no access to interface content. This model meets the demand for AI calling capabilities while protecting data security and the platform's traffic interests.

3. Operation and risk reminders: Platforms can launch AI-focused investment promotion to attract AI phone vendors for connection and expand the coverage scenarios of their capabilities. The core principle is to hold the bottom line of front-end traffic: do not allow AI to bypass front-end pages, which would hurt the platform's daily active users and advertising revenue. Platforms should also improve a dual authorization mechanism to protect the information security of both users and the platform. The A2A model is currently the optimal solution that balances AI innovation and platform interests.

The AI phone industry is seeing clear new trends and emerging problems, and the entire sector is on the eve of standard establishment, providing important reference value for research on industrial innovation and business model evolution.

1. New industrial trends: The development of AI phones has completed two stages of evolution. The first phase focused on adding large models for superficial functional upgrades, and the industry has now entered the second phase: operating system reconstruction and ecological rule rebuilding. Competition has shifted from competing on technical capabilities to competing for ecological discourse power and interest distribution rules. All leading vendors including Huawei and Xiaomi have entered the market, and while they take different routes, all aim to compete for the core entrance in the AI era.

2. New emerging industrial problems: The rise of AI Agents has fundamentally impacted the traditional internet traffic business model built on daily active users and dwell time. If AI completes all operations for users and users no longer need to open apps to view ads, the entire trillion-dollar traffic ecosystem will be affected. Reconstructing the interest distribution mechanism is the core problem the entire industry needs to solve.

3. Business model insights: The A2A protocol is currently a phased commercial compromise reached by the industry. In essence, AI takes on execution work, core traffic interests remain with existing platforms, and AI earns a share of service fees. In the future, only players that can build a complete closed loop of "technical trust + interest compromise" will gain discourse power for the next generation of operating systems, and this battle over traffic distribution has only just begun.