Agent手机的三条路线与一场权力游戏

AI-powered smartphones have now completed technical validation, reaching the cusp of the 3.0 era defined by autonomous agents. Multiple Chinese brands including StepEngine, Huawei, and Xiaomi have launched models that meet the national L3 capability standard, moving AI phones from demonstration to commercial rollout.

1. Compared to earlier generations of AI phones, the biggest change with today's agent-based smartphones is their new capability: instead of just helping users operate their devices faster, they can now complete entire end-to-end tasks on users' behalf. For example, when a user asks to arrange a business trip, the AI agent can automatically search for flights and hotels, check for scheduling conflicts, generate a complete itinerary and complete bookings—all without requiring the user to jump between multiple apps, drastically cutting down on operation time.

2. AI phones are currently in the uphill transition phase from "functional" to "truly useful". There are not yet any killer pain point scenarios that only agent-based phones can deliver, and most existing features are incremental "itch point" innovations focused on improving efficiency. For general users, it may be worth waiting for the industry to mature further before upgrading.

3. Commercial AI phone products are already available on the market. Nubia and ByteDance have launched a commercially filed agent-based smartphone. Its second generation has 200,000 units ready for shipment, and the previous generation engineering model priced at 3,499 yuan sold out its 30,000-unit stock on the day of launch, indicating strong market acceptance for mid-priced AI phones.

The AI smartphone industry has reached a broad industry consensus that AI is redefining smartphones, entering the stage of ecological restructuring and commercial competition. Brands can clarify R&D directions and consumer trends to make early布局 in this new market.

1. There are a wide range of R&D roadmaps to choose from, which are broadly divided into two major tracks: terminal-led and model-led. The terminal-led track is further broken down into multiple subpaths including full-stack in-house closed development, open-source in-house development, open chip collaboration, platform-level scheduling, and external resource leveraging. Brands can select a path that matches their existing technical accumulation and resources.

2. Consumer trends show users want long-term AI assistants that are stable, adapt to personal habits and can handle unstructured needs. Most current features are time-saving incremental innovations, and no pain point innovation capable of driving large-scale device upgrades has emerged yet. The first brand to deliver a compelling killer use case will capture significant first-mover advantage in the market.

3. On the commercial side, brands should note that the shift to agent-based architecture will disrupt the existing profit structure reliant on app store commission and advertising distribution. Brands need to explore new mechanisms for commercial balance in advance. The mid-price segment for AI phones has already proven market demand, so it is a viable reference for new product布局.

AI smartphones have completed technical validation and entered the commercialization phase. Sellers can capture new growth opportunities from this trend while avoiding potential risks.

1. On the policy front, the first batch of on-device large model filings has been approved, clearing the barrier for compliant commercial products. The industry has entered a period of regulated development, and compliant products can be launched and sold normally, meaning policy risk has been significantly reduced.

2. In terms of opportunities, AI smartphones are the new growth driver for device upgrades in the current global handset industry. Users have clear demand for AI to replace manual operations and improve efficiency. The earlier sell-out of 30,000 Nubia engineering AI phones priced at 3,499 yuan in a single day proves strong market demand in the mid-price segment, which sellers can enter to capture market share.

3. For risk awareness, the current AI phone industry still lacks killer pain point scenarios. User habits and trust in AI have not yet been established. Automatic AI operations can easily trigger the security policies of super apps like WeChat and get blocked. Additionally, the original profit model reliant on app advertising and commission will face disruption, requiring sellers to make adjustments in advance.

The industrial upgrade of AI smartphones has brought new product demand and commercial opportunities, and also offers new insights for factories advancing digital transformation.

1. In terms of product manufacturing and design requirements, agent-based phones have higher requirements for on-device computing power and chip adaptation. Different technical routes have different hardware needs: leading brands following the full-stack in-house route need deeply customized hardware components, while brands following the open cooperation route need highly compatible standardized hardware. Factories can adjust production capacity and R&D directions accordingly.

2. In terms of commercial opportunities, all major domestic smartphone makers have completed strategic positioning in the large model track, and the entire industry is advancing AI phone product iteration, driving a new round of hardware update demand. Small and medium-sized factories can also partner with model providers to launch niche AI phones targeting specific segmented markets.

3. For digital transformation insights, the approach of native AI phones that use AI as the core system scheduling hub can be adapted for factory production. Factories can use AI agents to schedule all links of production, improve the level of automation and intelligence, and boost overall production efficiency.

AI smartphones have entered the stage of ecological restructuring, with clear industry development trends, spawning a large number of new service needs. Service providers can tap opportunities tailored to these new demands.

1. In terms of industry trends, AI phones have completed the transition from "AI mounted on phones" to "AI controlling phones". Operating systems will continue to upgrade toward agent-based architecture, where AI becomes the core scheduling hub of the OS. Traditional apps will gradually transform into skill modules that can be called by AI, which means the entire industrial chain is facing restructuring, creating broad new service space for providers.

2. The current core pain points for industry clients: Terminal handset brands hold users, hardware and ecological control, but lack incentive for self-disruption, as their existing interest structure inherently conflicts with agent-based development. Model providers have technical and architectural advantages, but lack the entry point and leverage to reshape the ecosystem. Both sides face the same challenges: super apps refuse to open up their capabilities, leading to subpar product experience; and user trust and habit formation remain to be built.

3. In terms of solution directions, service providers can develop adaptation services around the MCP protocol to help transform traditional apps into AI-callable skill modules. They can also offer services such as AI experience optimization and user behavior operation to help brands polish stable multi-task execution capabilities.

The agent-based evolution of AI smartphones puts forward new requirements for platform operation and ecosystem construction. Platforms need to adjust their strategies to adapt to new industry changes.

1. New industry demands for platforms: As AI becomes the core scheduling hub, platforms need to encourage traditional apps within their ecosystem to open up core capabilities, break down their functions into skill modules that can be called by AI agents, adapt to the new interaction logic, and meet user demand for one-stop task completion.

2. Current industry practices: Leading handset platforms have already started exploration. Huawei has upgraded its AI agent to become the system's core scheduling hub; Xiaomi has developed a bottom-level system-level agent that can directly operate the phone; Honor has enabled multi-scenario intelligent scheduling through the MCP architecture. All are reshaping the platform's interaction rules and ecological logic.

3. Risk mitigation and operation direction: Agentization will change the user habit of actively opening apps, disrupting the existing profit model of app distribution and advertising monetization. Platforms need to build new interest distribution mechanisms in advance, promote core super apps to open up their capabilities, and gradually cultivate user habits to avoid the risk of poor product experience caused by insufficient ecological openness.

The development of AI smartphones has entered a completely new stage, bringing many new industry trends and research-worthy problems that carry important value for research on industrial evolution and business model innovation.

1. New industry trends: AI smartphones have evolved into the 3.0 stage of autonomous agents, and the industry has reached a consensus that AI will upgrade from an additional feature of phones to the core scheduling hub of the operating system. Traditional apps will gradually devolve into skill modules that can be called by AI. The industry has formed two major tracks: terminal-led and model-led, which have spawned seven different segmented approaches including full-stack closed in-house development and open in-house development. All major players have completed strategic布局.

2. New research problems: The two tracks face different core challenges. For the terminal-led track, the core challenge is the reconstruction of commercial interests, as the existing ecological profit model conflicts with agent-based development. For the model-led track, the core challenge is the lack of hardware access and ecological resources, making it difficult to push core super apps to open up their capabilities. Across the entire industry, all players face common challenges including cultivating user habits, building user trust, and the absence of killer use cases.

3. There are three possible directions for future industrial evolution: terminal brands acquiring and integrating large models, layered collaboration across the industrial chain, and model providers building their own end-to-end business. The openness strategy of super apps is the core variable shaping industry trajectory, and its interest distribution mechanism and business model innovation deserve in-depth research.