我花7万token点了杯咖啡：瑞幸上线AI开放平台 野心何在？

This article covers Luckin Coffee’s launch of an AI open platform on June 9, which enables external AI Agents to complete the entire ordering process from store selection and product picking to checkout. General users can follow the steps to test the service, with key practical takeaways as follows:

1. Step-by-step process: First install a CLI tool matching your operating system (macOS or Windows), bind your personal Luckin account via terminal commands, then configure the large language model API key you prepared, and you can start ordering by typing conversational requests.

2. Experience notes: The first ordering process is complicated, with common issues including incorrect store name entry and inaccurate positioning. Repeated confirmation during initial configuration also consumes more tokens, though the total cost is less than 1 RMB when using an affordable basic model. For repeat orders, the AI Agent remembers users’ delivery addresses and flavor preferences, cutting token consumption significantly and delivering a much smoother experience. The service saw overwhelming demand right after launch, and Luckin has already increased available user slots urgently.

Luckin’s launch of its AI open platform offers valuable insights for consumer brands looking to track industry trends and capture new channels, with key takeaways as follows:

1. Consumer trend outlook: Everyday consumption will gradually shift to AI Agent-mediated interactions going forward. Consumption entry points will transform from "people search for brands" to "Agents find brands for people". Brands that fail to proactively adapt to the AI ecosystem risk being overlooked by Agents and losing access to critical traffic channels.

2. Layout reference: Luckin adopted an "internal first, external later" approach. It previously launched in-house AI ordering, in-conversation payment, and integration with major mainstream large models to validate its capabilities before opening the platform to the public. This strategy of early positioning for new entry points mirrors the early占位 logic for WeChat Mini Programs years ago.

3. Areas for improvement: Current AI ordering still has room to improve. Brands can issue exclusive coupons for AI ordering to cultivate user habits, while investing in better positioning and information matching capabilities to enhance user experience.

Luckin’s AI initiative offers clear opportunities and actionable lessons for food service and retail sellers, with key takeaways as follows:

1. New growth opportunity: AI Agent-mediated consumption is an incremental growth market for the future. Proactive布局 of Agent-compatible ordering services allows sellers to claim new consumption scenarios like automated morning ordering, and meet users’ demand for automated arrangement of daily consumption.

2. Adaptable approach: Luckin’s strategy of validating AI capabilities internally before opening up to external ecosystems effectively lowers trial-and-error risk, and also lets brands capture free traffic from the external AI ecosystem. This approach is well-suited for small and medium-sized sellers to replicate.

3. Risk warning: AI ordering is still in an early development stage, with complicated first-use processes and high token consumption. General user acceptance varies widely, and some users consider it a pseudo-demand. Sellers do not need to rush to overinvest, and should instead cultivate the user market gradually.

Luckin’s AI布局 brings multiple insights on digital transformation and new business opportunities for consumer goods manufacturers, with key takeaways as follows:

1. Changing product demand: As AI Agents gain popularity, future consumer behavior will become increasingly automated and personalized. AI can automatically record users’ preferences for flavors and product specifications, and match them to user demand. This requires manufacturers to account for personalized adaptation at the product design stage to meet differentiated demand.

2. Digital transformation lesson: Luckin adopted a gradual transformation path, first rolling out various AI applications internally to validate feasibility before gradually opening up capabilities to the public. This steady approach is ideal for traditional manufacturers to learn from, eliminating the need to pursue full end-to-end transformation in one step.

3. New business opportunities: As the open AI ecosystem develops, manufacturers can also open their customized production capabilities to AI Agents in the future, connecting directly to personalized C-end demand and unlocking new sales channels.

This article reveals multiple industry trends and untapped business opportunities for AI service providers and retail technology service providers, with key takeaways as follows:

1. Industry development trend: Commercial落地 of the AI Agent ecosystem is accelerating, and daily consumption is one of the core scenarios for Agent deployment. Leading consumer brands have already proactively opened up their capabilities to adapt to Agents, indicating that brand demand for AI ecosystem integration has already emerged, and the industry has entered a new development stage.

2. Unresolved customer pain points: AI ordering still has multiple unsolved problems, including unfriendly complicated initial configuration for general users, inaccurate store matching and positioning, and high token consumption for first-time use, none of which have been properly addressed.

3. Business opportunity directions: Service providers can develop simplified integration and configuration workflows for consumer brands, optimize information matching algorithms, reduce token consumption, and help brands launch their open AI capabilities quickly to capture market share in AI ecosystem services.

Luckin’s move signals new demand from brands and shifting industry winds for AI platforms and consumer e-commerce platforms, with key takeaways as follows:

1. New brand demand for platforms: Brands need to open their capabilities to connect with external AI ecosystems, requiring platforms to support standard protocol integration to make it easy for brands to output capabilities to all types of external Agents. Brands also require more affordable token services from large model platforms to lower usage barriers for end users.

2. Strategic布局 opportunities for platforms: Platforms can launch AI open access services tailored for consumer brands to simplify integration workflows, roll out discounted billing plans for C-end scenarios such as AI ordering to lower the barrier for first-time user experience, and jointly cultivate the AI consumption market.

3. Risk mitigation: AI-mediated consumption is still in the early stage with clear divergent user acceptance. Platforms should not blindly invest massive resources in expansion, and can instead validate the model by partnering with leading brands first before expanding gradually. They should also prioritize fixing core experience issues to avoid user churn.

For industry researchers, this article reveals new developments in the consumer industry in the AI era and new directions worthy of study, with key takeaways as follows:

1. New industry trends: A structural shift is underway in consumption entry points. From the early "people search for goods" model, to platform matchmaking in the mobile internet era, the sector is now shifting toward AI Agents searching for brands and completing transactions on behalf of users. Leading consumer brands have already started proactively positioning for this new entry point, following a development path similar to the rise of WeChat Mini Programs. Brands that claim position early will gain a first-mover advantage.

2. New business model exploration: The new model Luckin is exploring standardizes its in-house retail service capabilities, opens it to the public via standard protocols such as MCP, and captures traffic by integrating into external AI ecosystems. Unlike the traditional customer acquisition model relying on owned apps, this is a new experiment for brand traffic acquisition in the AI era.

3. New questions for further research: AI-mediated consumption is still in its early stage, with issues including rough user experience, high barriers to entry, and high token costs. User cultivation paths, cost optimization solutions, and large-scale scenario落地 are all new directions that merit in-depth research.