The monthly token consumption of G7 Easy Flow platform has increased from an average of 6.7 billion in 2025 to 770 billion, which is about 115 times higher than before. Based on the Purple Treasure Box and cloud platform launched by the company, AI has been used for tasks such as driving safety assistance, cargo management, document review, and voice interaction. ”On November 7th, at the 2026 Digital Logistics Conference hosted by G7 Easy Flow, Zhai Xuehun, founder and CEO of G7 Easy Flow, disclosed the investment progress of the G7 Easy Flow platform in the field of AI (artificial intelligence).

The theme of this digital logistics conference is AI startup, focusing on the application of AI in logistics sites and exploring improvement paths for safety, efficiency, and delivery quality. During the conference, Zhai Xuehun used three cases to demonstrate how AI can participate in on-site work - at manufacturing enterprise sites, AI automatically collects information, generates ledgers, and reduces duplicate reporting; In urban delivery, G7 Easy Flow's patted beans can record delivery actions such as temperature measurement and goods entering the container, making service quality traceable; In a fleet of 600 electric heavy-duty trucks, AI can combine safety scores, real-time feedback, and rewards, reducing the number of attempted accidents per week from five or six to two.

In the past two years, G7 has frequently mentioned the application of AI in the logistics industry and has released multiple hardware products related to AI. For example, in June of this year, G7 Yiliu released the first wearable AI hardware in the freight industry, "Paipai Dou". From a functional perspective, PaiPaiDou mainly solves three problems: AI real-time recognition of damages and abnormal quantities, and real-time push to managers to achieve pre intervention; The loading and unloading handover process is automatically recorded, and in case of disputes, the corresponding node screen can be accessed with one click; The entire process of goods circulation can be traced, and key nodes are automatically marked to achieve transparent management.

In Zhai Xuehun's vision, AI that can directly reach the logistics site can achieve things that digitalization has not been able to do for many years: no matter how complex or subtle the scene is, even if it is just whether the driver adjusts the temperature of the carriage in a timely manner, these behaviors can be monitored at any time; In addition, AI can also design optimal solutions in cost control and vehicle scheduling, and its professional capabilities in these projects will be exponentially improved compared to manual labor.

He stated that in the next three years, G7 Yiliu will establish a "two-way AI channel directly to the logistics site", allowing frontline workers to be seen, assisted, and rewarded. The AI that directly reaches the scene will eventually form a positive cycle, that is, with the application of AI, the boss can "see" the driver at any time, and thus cultivate good drivers; Drivers can receive assistance on the road and during loading and unloading; After improving transportation performance, drivers can receive good returns.

When it comes to autonomous driving, Zhai Xuehun once again draws a clear boundary: "I don't know how to do autonomous driving. G7 Yiliu has previously invested in autonomous driving companies, but will not do this within the G7 system in the future. ”

Regarding the discussion on autonomous driving replacing drivers, Zhai Xuehun stated that the speed at which autonomous driving replaces drivers cannot keep up with the speed at which young drivers leave the transportation industry. On the one hand, the logistics industry is an industry that requires on-site delivery, which means that someone needs to be responsible for it. This is a link that AI cannot replace; On the other hand, instead of worrying about AI replacing drivers, we should be more concerned about young drivers being unwilling to join this industry. Therefore, while promoting autonomous driving, fully utilizing AI to reduce the workload and improve safety in the transportation industry is actually beneficial for the positive development of the industry.

In addition, Zhai Xuehun believes that embodied intelligence has great market development space in the logistics industry, but it will definitely face multiple challenges, especially the need to find relevant adaptation scenarios. For example, in the loading and unloading process of delivery, many city delivery drivers may need to move several tons of goods in one transport, and these scenarios are very suitable for embodied intelligence applications.

(Author Du Tao)