As AI implementation accelerates, it is appearing across an ever-widening range of scenarios and industries.

In the digital economy, AI is penetrating deep into the fine-grained operations of established internet players, driving a renewal that runs from underlying logic all the way to interaction methods. In the real economy, AI is embedding itself into every link of industry, completing a comprehensive transformation from the inside out.

The more noteworthy change, however, is happening within the supply-demand relationship itself: AI is no longer merely an efficiency tool—it is becoming the new bond that reconnects supply with demand.

Whether it is the explosion of AI hardware categories on e-commerce platforms, the rise of AI agent trading marketplaces, or the migration of consumption decisions from "people searching for goods" to "AI understanding people," they all point to the same fact: the misalignment and friction between the two ends of supply and demand in the traditional sense are being re-stitched by AI in a way fundamentally different from the internet era.

How to use AI to achieve another precise alignment between supply and demand is not just about demonstrating AI's functions and effects—it is about establishing a new business cycle.

On deeper analysis, a fundamental reason AI is sought after by so many players is that it redefines "connection" itself. Internet connection is matching at the information level; AI connection is alignment at the cognitive level. For the first time, the supply side can understand and respond to the specific context of each individual demand at extremely low marginal cost.

AI Has Spawned New Products, Giving Connection a Tradeable Carrier

The first signal that AI is becoming a supply-demand bond is that it has begun to "grow a body." In the past, AI was an invisible, intangible algorithm; now it is becoming a commodity that can be purchased, used, and reviewed on shelves.

AI companion hardware is among the most intuitive categories.

The AI pet dog self-developed by Luka Doctor under Intellifusion has officially gone on sale on major mainstream e-commerce platforms. Designed for children aged 3 and above, it features fun feeding, emotional guidance, voiceprint owner recognition, and bionic interaction, and is equipped with a visual interaction system powered by multimodal large models—turning "companionship" from a software feature into a tangible consumer product.

The explosion of the AI toy category is even more striking: in the first half of 2025, AI toy sales on the JD platform surged sixfold month-on-month and grew more than 200% year-on-year; on the Taobao platform, the annual transaction volume of AI toy products grew more than 1,600% year-on-year, and AI companion toys appeared on Taobao's annual top ten products list for the first time.

Huawei's "Smart Hanhan" sold out instantly upon launch, and Luobo Intelligence's "Fuzai" has monthly sales exceeding 20,000 units, with channel pre-orders surpassing 100,000 units.

What is more symbolic is the commercialization of the embodied intelligence category.

Unitree Technology's humanoid robot appeared in a Taobao livestream as the "world's first AI robot anchor" to promote products, achieving sales of 1.28 million yuan within 5 minutes; on the JD platform, humanoid robots such as Unitree G1 and Zhongqing PM01 have been listed one after another, and in the robot dog category, "Kelebao Robot Dog" has sold more than 3,000 units.

AI glasses have likewise become a hit product: on Double 11 in 2025, smart glasses transaction volume on the JD platform soared 346% year-on-year, becoming the fastest-growing category in 3C digital products.

At the same time, tokens themselves are also becoming "goods" on e-commerce shelves—token sales stores have begun to appear on e-commerce platforms, and the right to use AI has for the first time entered the transaction field of vision of ordinary consumers in a standardized form.

The common feature of these products is that they are not "AI empowering some old product," but new supply directly spawned by AI.

When the supply side can mass-produce categories that did not previously exist, and the demand side's acceptance of these categories in turn drives the expansion of supply, the connection between supply and demand no longer relies on the platform's forced matching—it is naturally completed by the product itself as the medium. New transactions are thereby concluded, and new dividends are thereby released. As the product spectrum derived from AI continues to enrich, the density of connection points and the diversity of connection methods will only rise further.

AI Has Spawned New Models, Moving Connection from "Matching" to "Effect-Based Settlement"

The commodification of products solves the question of "what to connect," while the reinvention of models answers "how to connect."

The classic paradigm of supply-demand matching in the internet era is platform matching: B2B and O2O essentially gather dispersed supply and demand into a centralized traffic pool, and then algorithms or operations complete the matching. This model was effective in the era of traffic growth, but when traffic entered the stock stage, the marginal decline in matching efficiency became a common dilemma.

The change brought by AI is to compress the basic unit of connection from "transaction" to "completion of a task."

The AI agent trading marketplace MuleRun, launched in September 2025, provides a clear sample. The platform brings together AI agents from different fields, and users can find the corresponding agent according to their needs, renting and using it on demand, with the cost of a single use as low as about 0.5 US dollars; creators of agents receive continuous revenue sharing through the platform's transaction mechanism. Just 10 days after launch, registered users reached 210,000, and more than 4,000 creators applied to register.

MuleRun is called "the Taobao of AI" by outsiders, but its essential difference from Taobao is that Taobao connects sellers and buyers, while MuleRun connects "people with specific problems" and "AI labor that can solve those problems." The granularity of supply and demand has been refined from "goods" to "tasks."

At the more fundamental transaction settlement level, a decentralized "payable AI" model is also taking shape.

OpenLedger's Payable AI allows users to upload datasets to a shared data network, developers use them to train models, and smart contracts automatically complete payment based on contributions; Gaia Labs' mechanism further allows data contributors such as hospitals and researchers to retain ownership of models and underlying data, and to continue receiving compensation when models are invoked.

This means that the supply side no longer merely "sells products"—it can continuously participate in value distribution. The demand side no longer prepays for "possible uses"—it pays for "actually produced effects." Connection has changed from one-time matching into a programmable, effect-based, continuous relationship.

Even in the seemingly traditional local life sector, AI is reshaping the logic of connection.

Meituan's "Super Brain" instant delivery system plans optimal routes for riders at an average speed of 0.55 milliseconds, with peak daily LBS calls reaching 76 billion and handling about 60 million orders per day during peak periods. Wang Xing once used a metaphor to pinpoint the key: "Even if Einstein were a secretary and were asked to book a restaurant, he still wouldn't know whether that restaurant has a seat."

Internet data tells AI the world "where," while Meituan's physical closed-loop data tells AI whether it "can" at this moment. Supply-demand matching no longer relies on static information—it is dynamically completed under real-time constraints such as weather, meal preparation speed, traffic restrictions, and elevator rules. This connection is not centralized traffic distribution, but decentralized, scenario-based real-time alignment.

AI Has Spawned New Demand, Moving Connection from "Satisfying the Known" to "Discovering the Unknown"

If products solve the carrier of connection and models solve the efficiency of connection, then changes on the demand side determine the depth of this bond. The fundamental premise for AI to become a medium between supply and demand is that it has spawned a large number of new demands that could not be effectively expressed—let alone effectively satisfied—under traditional frameworks.

The most intuitive change has occurred in the consumption decision-making process. Demand no longer needs to be translated into search keywords; AI directly establishes a mapping between "a person's situation" and "a product's attributes."

The deeper change is that AI has turned "personalization" from marketing rhetoric into a supply capability that can be delivered at scale.

Relying on the C2M model, Nike allows consumers to independently choose shoe upper colors and materials and add exclusive marks to the tongue. After the order is submitted, it is precisely produced by smart factories, ensuring delivery efficiency while meeting personalized demand.

This is not simply "more customization options"—it is the first time the demand side can issue unique specification instructions to the supply side at a cost close to mass production. When demand can be precisely expressed, supply can be precisely responded to, and the room for mismatch between supply and demand is systematically narrowed.

At the same time, AI itself is also creating demand types that did not previously exist.

People need to buy tokens to use AI, need to buy AI pets for companionship, need to rent agents on MuleRun so that "AI can complete a task for them," and need to replan consumption budgets for embodied intelligence to enter the home.

These demands are not "diverted" from existing consumption—they are newly generated from the collision between AI and specific life scenarios. They constitute the most active incremental surface of supply-demand connection.

Conclusion

When AI moves from algorithms to products, from matching to effect-based settlement, and from satisfying known demand to spawning unknown demand, it is no longer merely a tool for the supply side or the demand side separately—it becomes a self-reinforcing bond between the two ends.

New products give connection a tradeable carrier, new models make connection sink simultaneously in granularity and settlement methods, and new demand turns the direction of connection from "delivering what already exists to those who need it" to "establishing pathways between people and needs that have not yet been named."

The strength of this bond does not depend on how "smart" AI is at one end, but on whether it can simultaneously create things worth buying on the supply side, discover situations worth paying for on the demand side, and establish in the middle a transaction structure that makes both sides willing to participate continuously.

Starting from this point, the true mainstreaming of AI will not manifest as the popularization of a certain technology, but as supply and demand, under AI's connection, beginning to find each other in a finer, more accurate, and more continuous way than in the internet era.