A genuine transformation concerning the commercialization of AI has reached a critical juncture. On January 15th, Alibaba's AI assistant Qwen is no longer content with being just a "chat companion" robot. Following the announcement of surpassing 100 million monthly active users on the consumer side, Alibaba played a long-prepared trump card: teaching AI to "get things done." Alibaba Group Vice President Wu Jia announced at the day's event that Qwen is ushering in the era of AI handling tasks. In the latest version, Qwen is no longer a simple dialog box; it has directly integrated the underlying infrastructure of Ele.me, Fliggy, and Taobao. With a single user command, the AI can penetrate app barriers to complete closed-loop actions like placing food delivery orders, booking flight tickets, and purchasing goods. Sources close to Alibaba indicated these are only part of the new features, with more functionalities set to be unveiled during the day's launch event. This move by Alibaba signals that large language models cannot remain at the level of technological showcase; they must possess the capability for commercial closure. Previously, AI Agents were often viewed within the blueprints of tech giants as a distant technical concept, even perceived by the market as "PPT vocabulary" for storytelling in secondary markets. However, Alibaba's current action clearly aims to tell the market: the AI that helps you accomplish tasks has arrived.

The past two years have seen extraordinary fervor in the large model arena. From the "hundred-model battle" to price-cutting frenzies, all players face an awkward "false boom": no matter how high parameters are pushed, without generating transactions, it ultimately amounts to nothing. Although large models in 2025 kept the industry chain "excited for a full year," as 2026 began, primary market investors started to cool down. They discovered that after burning through computing power worth hundreds of millions, the return was often just a user request to "write me an acrostic poem" or "generate an image." This type of interaction cannot support a trillion-dollar valuation. The candlestick charts of the capital market are the most honest. Large model vendors urgently need to find a scenario that can convert massive traffic into real GMV. This sense of anxiety instantly awakened the "muscle memory" of internet giants—the iron rule from the PC era to the mobile era that whoever masters the transaction entry point reigns supreme. Alibaba understands the pain of traffic failing to monetize all too well. Consequently, it chose to deploy its killer app this year. Unlike most models on the market that can only "offer suggestions" or "search for guides," Alibaba's logic is to integrate its own app ecosystem.

Of course, in this battle for a trillion-dollar super-entry point, Alibaba is never short of competitors. Zhang Yiming's ByteDance is the "challenger" that keeps everyone awake at night. Doubao already leads in daily active user data, becoming the de facto "national-level AI." But when facing the question of how to make AI "get things done," Doubao and Alibaba are heading in two distinctly different directions. Because Alibaba owns "sons" like Ele.me, Fliggy, and Amap, it possesses control over the service end. It doesn't need to ask others; it can directly open data interfaces internally. The advantage of this approach is stability, high order success rates, real-time data synchronization, and almost no delay. Doubao, however, follows a more aggressive and geeky school of thought: "Auto-UI." Through the Doubao Phone Assistant, ByteDance attempts to give AI a pair of "eyes." It uses visual models to recognize every pixel and every button on the phone screen, then simulates human fingers for tapping and swiping. In Doubao's logic, AI doesn't need app developers to open interfaces; it directly takes over your phone at the system layer. You say "hail a ride," and it opens DiDi for you; you say "order food," and it opens Meituan for you. But the challenge for the Doubao Phone Assistant lies in overcoming the inherent barriers that phone systems place on cross-application operations. It bets on AI becoming a "super operating system" that reigns over all apps. Whoever can cultivate user habits first will hold the ticket for the next decade.

This leap from "chatting" to "handling practical matters" is not unique to China. Across the ocean, a similar commercial alliance is timely. The cooperation between Google Gemini and retail giant Walmart can be considered the global mirror of Alibaba's model. Google possesses the strongest "brain," but it still fears Amazon's dominance in e-commerce. Thus, it partnered with Walmart, which has the strongest offline network. Under their cooperation framework, based on the Universal Commerce Protocol, Google Gemini can directly access Walmart's real-time inventory data. When a US user asks "what do I need to buy for a barbecue party?", the AI can not only provide a menu but also directly identify products on Walmart's shelves, utilize Google Pay to complete the payment, and even arrange for curbside pickup. The logic behind this is identical to Alibaba's: using the certainty of service to hedge against the uncertainty of technological iteration. This also explains why OpenAI is busily preparing its browser agent product, codenamed "Operator." Because the elites in Silicon Valley have also realized that the pure SaaS subscription model is hitting its ceiling, and only by切入切入 the transaction flows of the real economy can AI tell a story bigger than the internet itself.

As competition in the consumer sector for large models enters deeper waters, the market is beginning to return to rationality. Alibaba's choice to feed its "whole suite" to AI at this juncture is precisely hoping to pioneer a viable business model amidst current traffic anxiety. However, truly integrating intelligent assistants into people's daily lives will still require some time. UBS Securities China internet analyst Xiong Wei also stated that the large-scale rollout, popularization, and monetization of AI agents still requires time. This involves not only ensuring technical accuracy and stability first, but also users spending time to accept it, integrating the upstream and downstream industry chain, establishing cooperative relationships, reallocating economic benefits, and regulatory considerations. In Xiong Wei's view, having an agent within a major company's ecosystem that integrates its internal resources is an important evolutionary stage for agents. The next stage involves an agent that operates across all different platforms. Achieving more comprehensive user assistance, practical decision-making support, and even collaboration between agents themselves are matters for the final stage. The so-called "getting AI to handle tasks" is essentially aimed at smoothing out the volatility of large models "only burning cash without earning money," seeking long-term stability. Using the universality of service to resolve the homogeneity of technology, and filling the emptiness of conversation with the tangible reality of transactions. This is what the true landing of AI applications should look like.