--- title: "JD.com turned its supply chain into an AI lab — and quietly ran 50,000 agents" date: 2026-10-10 category: Companies & Stack site: NeuroAI canonical: https://neuroai.site/a/na-corp-jd-joyai-supply-chain language: en --- # JD.com turned its supply chain into an AI lab — and quietly ran 50,000 agents > JD.com rebuilt its Yanxi (言犀) industrial large model into JoyAI and pointed it at the one thing it actually owns: retail, logistics, and supply chain. With 1,000+ scenarios and 50,000 internal agents, the company shows a different AI path — grow it from the business, not from a chatbot. While most Chinese tech giants raced to ship a consumer chatbot, JD.com (京东) did something less photogenic and possibly more durable: it grew an AI model out of its warehouse floors, delivery routes, and customer-service logs. ## From Yanxi to JoyAI JD unveiled its Yanxi (言犀) large model on July 13, 2023, at the JD Global Tech Explorer Conference — a hundred-billion-parameter "industrial" model trained on a mix of general data and JD's own digital-intelligence supply-chain data (roughly 70% general, 30% native). In July 2025, at the World Artificial Intelligence Conference (WAIC), JD rebranded the family as JoyAI, with a full-size matrix spanning 3B to 750B parameters across language, speech, image, video, and digital-human modalities. In February 2025, all Yanxi products were connected to DeepSeek; in April 2025, an AI outbound-call service went live. The stated philosophy, from CEO Xu Ran (许冉) at the September 2025 JDDiscovery conference, was blunt: JD does not chase "performative AI" (运动式AI); it pursues AI that creates real industrial value. The implication is a deliberate contrast with peers who built a general model first and hunted for use cases second. ## Where the AI actually runs The hard proof is in logistics. JD Logistics runs a "wolf" (狼族) robot army: the "Smart Wolf" (智狼) goods-to-person system is deployed in more than 60 warehouses worldwide, including sites in the UK and Germany; the "Lone Wolf" (独狼) unmanned vehicles run routinely across more than 20 Chinese provinces; the "Flying Wolf" (飞狼) drones built China's largest village-delivery drone network in Zizhong, Sichuan. Orchestrating them is JD Logistics' SuperBrain (超脑) 2.0 model, which can complete routing calculation and simulation for a billion packages in about three minutes. The measurable gains JD cites: inbound put-away efficiency up 6x, storage density 4x that of a traditional warehouse, outbound picking efficiency up 3x, picking accuracy 99.99%, and logistics automation rate 95%. By the end of 2025, AI agents were applied across more than 1,000 supply-chain scenarios, and the count of internal AI agents exceeded 50,000. ## The consumer side AI is not only behind the scenes. JD's AI shopping assistant crossed 150 million annual active users with over 20% penetration, driving several billion yuan (≈ US$ hundreds of millions) in GMV. Across the company, Yanxi/JoyAI supports more than 1,000 business scenarios, and token-call volume grew nearly 100x year on year in 2025. JD's compute comes from its Tianqin Alpha (天琴α) supercomputing center, and it uses a self-developed vector database, Vearch, for model memory and retrieval. The loop Xu Ran describes is the interesting part: business generates data, data improves the model, the model improves operations, and bigger operations generate more data. Twenty years of self-run e-commerce and self-built logistics gave JD a data soil — real transactions, warehouse moves, deliveries — that a model lab simply does not have. ## Why it matters JD is the clearest example of "AI as operations, not AI as a product." Its model is not trying to beat GPT on a leaderboard; it is trying to shave a percentage point off fulfillment cost across a national logistics network. That is harder to demo at a launch event but harder for competitors to copy, because the moat is twenty years of supply-chain data, not a weights file. The labor question is unavoidable. JD employs a large blue-collar workforce — unusual among internet companies — and founder Liu Qiangdong (刘强东) publicly said employees displaced by robots "will not be laid off." Whether that promise holds as automation rate hits 95% in some warehouses is the social test of this strategy, and one JD has not yet been forced to answer at scale. ## What readers can do now - **If you run operations:** copy the loop, not the model. JD's edge is proprietary supply-chain data; the lesson is to point AI at your own highest-volume, most data-rich process first. - **If you are a JD merchant:** the Yanxi/JoyAI toolchain (customer service, digital humans, AIGC marketing) is already available — test it on product imagery and copy before buying external AI tools. - **If you invest:** judge JD's AI by logistics cost per order and automation rate, not by model benchmarks. Those operational ratios are where the value shows up. ## Honest limitations Scenario counts (1,000+), internal-agent count (50,000+ by end-2025), efficiency multipliers (6x, 4x, 3x, 99.99% accuracy, 95% automation), and GMV figures are JD's own disclosures reported through Chinese industry media (NetEase News, Baidu Baike); they were not independently verified. The "nearly 100x" token-growth and 150M AI-assistant-user figures are company-reported. Liu Qiangdong's no-layoff statement is quoted from internal remarks reported in media and is a commitment, not a contracted guarantee. JoyAI's model-performance claims (3B–750B matrix) come from JD and Baike entries, not third-party benchmarks. Currency conversions use an approximate RMB/USD rate of 7.3. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-corp-jd-joyai-supply-chain Free to quote with attribution and a link to the original.