Elon Musk Sells Products with AI, While This Chinese Company Manages 180,000 Shelves with AI: Unmanned Retail Enters the "Agent Era"

In 2026, the AI industry stands at a historic watershed.

Not long ago, Google predicted in its "AI Agent Trends 2026" report that artificial intelligence is undergoing a crucial transition from a "tool that only answers questions" to a "capable assistant" that can independently break down tasks and work across systems.

In this global AI wave, a highly symbolic detail comes from Elon Musk. At the Grok - 4 launch event in April 2025 and the subsequent Vending Bench test, Musk proved with facts that AI can not only chat but also sell goods. Taking the simulation operation test of the Vending - Bench vending machine as an example, Musk let the AI fully manage a vending machine, including product selection, pricing, restocking, supplier negotiation, and cash - flow management, demonstrating Grok 4's long - term business decision - making and sustainable profitability, and teasing its scalability potential. The vending machine driven by Grok - 4 even outperformed GPT - 5 in terms of revenue, making the industry exclaim that "the end of AGI is selling chips."

At this time, in the highly competitive unmanned retail circle in China, Musk's vision of "AI taking over a million vending machines" is becoming a reality. Feng e Zushi, an intelligent retail operator, uses AI agents to take over 180,000 smart cabinets, with an annual revenue of 2 billion.

While most enterprises began to embrace large language models in 2024 and 2025, Feng e Zushi planted the seed of algorithms as early as the end of 2021.

In the early stage, Feng e Zushi faced numerous difficulties. It entered the industry late, couldn't afford the rent price war, and couldn't make money in low - traffic scenarios. At that time, AI was also immature. However, the management team with a background of mathematics and algorithm doctors made a bold judgment: fake AI would eventually turn into real AI, so they set technology as the core development strategy for the company in the next few years.

In the following four years, with a research and development investment of nearly 250 million and an algorithm team of more than a hundred people composed of talents from major Internet companies, based on the extraction of large - scale commercial experience of over 100,000 smart cabinets, a comprehensive intelligent agent system called "FLOW Pilot" was born.

Different from the scattered AI applications in the industry, the "FLOW Pilot" comprehensive intelligent agent system created by Feng e Zushi consists of four major intelligent agents, covering the entire chain of sales, product operation, supply - chain planning, and fulfillment execution. Zhu Tao, the COO of Feng e Zushi, gave an analogy: this system is like "Level 4 autonomous driving" in the field of unmanned retail management.

Just as Tesla trains its autonomous driving system with hundreds of millions of miles of road - test data, Feng e Zushi's FLOW Pilot makes hundreds of millions of decisions on operations every day based on 180,000 shelves, and the number of times of manual intervention is only maintained at about 4,000, only making interventions in a small number of cases where customers actively apply for adjustments. Zhu Tao said, "This means that its automation level is close to Level 4 autonomous driving."

Zhu Tao defines 2026 as the first year of the transformation of AI from an "assistant" to an "employee." In the unmanned retail industry, when your "employee" is a system that makes hundreds of millions of decisions every day and only needs a few thousand interventions, traditional unmanned retail is undergoing a huge change.

Recently, we had a chat with Zhu Tao, the COO of Feng e Zushi, about how to turn AI - taking - over vending machines into a large - scale business.

The following is a conversation (edited) between 36Kr and Zhu Tao, the COO of Feng e Zushi:

A 250 - million - yuan AI bet over four years

36Kr: Previously, Musk said at the Grok4 launch event that he wanted to use AI to directly manage one million vending machines. What was your feeling when you saw this news?

Zhu Tao: We were very excited. The direction of Musk's simulation test was exactly the direction of our technological research at that time, which proved that our investment direction in the past three years was correct. At that time, many peers didn't understand why we had to invest in technology. Musk's prediction gave our technology team a "reassurance," making us more confident in the commercial potential of AI in retail.

Source: Bilibili

36Kr: When did Feng e Zushi start investing in the AI field, and what were the specific manifestations of the investment?

Zhu Tao: After we completed a 300 - million - yuan Series A financing led by SoftBank at the end of 2021, we determined the development strategy of the technology route and started to increase investment in the AI field firmly from then on. From 2022 to 2025, an additional investment of nearly 250 million was made in the R & D sector.

In terms of talent, we formed a R & D team mainly composed of people from Internet companies such as Meituan, ByteDance, and Tencent. In 2022, the team size expanded from 40 to about 130 people and currently remains at the level of 120 - 130 people, which is quite rare in the unmanned retail industry.

36Kr: In the past two years, everyone has been talking about the first year of AI and the technological explosion. But if we go back to 2021, AI hadn't become an industry consensus like it is now. What factors prompted Feng e Zushi to be one of the first to embrace this new trend?

Zhu Tao: In the domestic self - service retail industry, Feng e Zushi introduced smart cabinets with AI dynamic video recognition technology early on and achieved large - scale application in 2019. At that time, the mainstream technology route in the industry still preferred RFID radio - frequency identification, which was highly accurate but also expensive, and traditional mechanical cargo lanes.

We also came into contact with smart cabinets based on AI technology early on. Although the recognition technology of smart cabinets was still in the "fake AI" state at that time, mostly relying on manual background recognition. Even so, we still adopted these smart cabinets on a large scale, firmly believing that the development of AI technology could truly achieve AI recognition. Later, AI recognition was really realized, and both the equipment cost and the consumer experience were significantly optimized. As an operator that used smart cabinets on a large scale in the industry, we enjoyed the dividends of this wave of AI. It was also from this time that we laid a solid foundation for the firm layout of AI.

Next, we predicted that for the company to achieve large - scale growth, various scenarios would emerge in the future, and consumers' needs would become more and more diverse. The increase in points would inevitably lead to a continuous increase in the cost of manual management, requiring a large number of management personnel, which was obviously not a long - term solution. So we transferred the upgrade idea and model of AI cabinets to the overall business development of the company. Although there was no mature large language model at that time, technologies such as image recognition, video recognition, and deep learning had shown signs of development. Combined with my background as a doctor in algorithm development from an Internet company, I, together with the company's CEO and CTO, unanimously determined that AI and algorithms would be the core development strategy for the company in the next few years, and we started to increase investment officially from then on.

We designed the entire company's management organization and guidance system in advance around algorithms and AI architecture. This also enabled us to embrace these advanced technologies as quickly as possible when large language models like GPT became popular in 2024 and 2025. The intelligent agent we released now also integrates components such as language models and multi - modal models on the basis of the previous intelligent agent, further enhancing its capabilities and effects.

Evolution of unmanned retail technology

36Kr: Most of the industry adopts the franchise model, while you choose the heavy - investment direct - operation model. What are the main factors you considered? What advantages does this provide for the implementation of your AI?

Zhu Tao: Indeed. The biggest advantage of the direct - operation system is that the service quality is far better than that of the part - time or outsourcing model. The team won't just focus on short - term profits but will strictly follow the company's requirements and try their best to meet various needs of customers.

However, the direct - operation system also has obvious drawbacks. We have a large number of unmanned retail devices, and the affairs at each point are cumbersome. The management scope of the replenishment staff is wide, and the points are distributed in different regions. To give full play to the service advantages of the direct - operation system, we must manage the personnel and points in great detail, which brings great management difficulties.

It is precisely to solve the management problems of the direct - operation system that we chose to use AI algorithms to command the overall operation. Simply put, we first selected the direct - operation system to ensure service quality, and then implemented the full - chain application of AI algorithms to manage the direct - operation system well.

36Kr: In the eyes of the outside world, unmanned retail smart cabinets are more of a "tough business" of laying points and competing in operations. Feng e Zushi has found its own way with self - developed AI agents, seemingly breaking this traditional perception. How do your peers evaluate you?

Zhu Tao: Yes. In the past, people may have thought that the unmanned retail industry of smart cabinets was a relatively traditional operation industry, but it's not. Look at Feng e Zushi. The reason why we can reach the current scale is precisely because of our focus and investment in technology. We are a company that can really make money with AI. We are players who use AI and technology to revolutionize the retail industry. When we achieved a certain scale, our peers realized that we are not the same kind of company and not here to take their business.

"Autonomous driving" in unmanned retail

36Kr: What is the Feng e intelligent agent like?

Zhu Tao: Our comprehensive intelligent agent for full - chain automation is called FLOW Pilot. The "star" in "FLOW" is like our points, which are densely and widely distributed. And the "route" means that these points are not isolated but form a network with the interconnection of products and the movement of operation personnel between points, with the concept of a route. "Intelligent navigation" means that in this dense network of points, our intelligent agent can direct operation personnel to navigate and complete specific tasks in the network, which is the origin of the name. The name also fits the concept of a navigation chart, both having a sense of "flow," which is just a bit related to the English word "FLOW."

FLOW Pilot consists of four core intelligent agents, divided by the full - chain of business:

Field Pilot: It serves sales personnel, responsible for lead screening and cleaning, business opportunity recommendation, visit route planning, and customer conversion policy recommendation.

Link Pilot: It focuses on product operation, listens to consumers' needs, and is responsible for product configuration at points and docking with consumers' personalized needs.

Opti Pilot: It leads the supply - chain planning, responsible for inventory replenishment decisions of large warehouses, front - end warehouses, and shelves, as well as the order - placing planning of upstream suppliers.

Work Pilot: It is responsible for inventory fulfillment execution, converting supply - chain planning into specific tasks, matching logistics personnel and vehicle capacity, and completing task scheduling.

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36Kr: How do the sub - intelligent agents collaborate with each other?

Zhu Tao: This is actually a major challenge in developing intelligent agents. Although different intelligent agents have different decision - making roles, there will be decision - making conflicts in many things. For example, if Opti Pilot replenishes more goods, it is to reduce out - of - stock situations. But if its core assessment is product turnover and reducing unsold goods, it will tend to replenish less. And if Opti Pilot replenishes less, the frequency of replenishment tasks of Work Pilot will be forced to increase. If Work Pilot's goal is to control costs, there will be obvious decision - making contradictions, which is the core problem of multi - intelligent - agent collaboration.

Of course, we also spent a lot of effort on the coordination between intelligent agents. We will conduct a large number of A/B tests to weigh the pros and cons of various indicators and finally make the globally optimal decision. We will also precipitate the experience accumulated from these tests into the intelligent agents to improve the coordination of decision - making conflicts.

36Kr: Feng e Zushi's intelligent agent has been commercially used on a large scale. Why did you choose this time to make it public for the first time?

Zhu Tao: Yes. In the past, no one had heard of this kind of intelligent agent in the unmanned retail field. We've heard of coding agents and some intelligent agents that help with recommendations and life planning, but not this kind.

To make it easier for everyone to understand, we can make an analogy with autonomous driving. It's different from coding agents. Coding agents operate entirely on the computer, while our intelligent agent is applied to actual business, more like an intelligent agent for autonomous driving. For example, autonomous driving needs a perception module, which uses vision, radar, and ultrasonic sensors to fuse information to understand its own state. Our intelligent agent also has such a perception ability. It processes various structured data such as sales and inventory, fuses user dialogue information, and information from pictures collected by operation personnel on - site to support its decision - making.

2026 is regarded as the first year of the commercial use of AI intelligent agents. We hope that more top - notch AI technology talents will join the Feng e platform and also hope to open it up to the industry for discussion to jointly improve the digital and intelligent level of the industry.

36Kr: How to understand other parts of the intelligent agent?

Zhu Tao: Autonomous driving has a task - planning module. From the starting point to the destination, the road surface and traffic conditions of different roads at different times are different. The algorithm may plan a navigation route based on the shortest time. Our Work Pilot is responsible for similar task planning. For example, the tasks of a replenishment staff for each device every day may be different. FLOW Pilot will package each task according to the points for the replenishment staff and then plan the optimal replenishment route for that day. Finally, autonomous driving has an execution module that sends tasks to the steering wheel and accelerator for control and execution. We also break down complex tasks into sub - tasks and assign them to different execution personnel, and the Work Pilot sub - intelligent agent coordinates the completion. In the end, our intelligent agent is like an autonomous - driving intelligent agent in the field of unmanned retail management, driving our entire operation network to achieve our operation goals.

Actually, we can also make an analogy in terms of technical indicators. To measure the ability and automation level of an intelligent agent, there is a key indicator. In autonomous driving, it is the number of times of manual intervention per million kilometers. For example, Tesla claims that there are only a few hundred times of manual intervention per million kilometers. We don't have actual driving mileage here, but the number of decisions made by FLOW Pilot every day can reach hundreds of millions. Because we have 180,000 shelves, each shelf has about 50 - plus products, and there are also hundreds of SKUs of products in the warehouse that haven't been put on the shelves. The total number of decisions on product selection, inventory, and other aspects for these products reaches hundreds of millions.

Among the hundreds of millions of decisions we make every day, the number of times of manual intervention is about a few thousand, around 4,00