Baidu AI Open Day: Production-Ready AI & Monetization

During Baidu's AI Open Day and the Miaoda 2025 Creator Conference in Hangzhou, the focus shifted from showcasing AI capabilities to demonstrating practical, monetizable applications. Attendees, who filled the venue to capacity, engaged in discussions centered on the long-term viability, deployment, cost-saving potential, and revenue generation of AI tools, rather than just their technical prowess.

Shifting Focus to Practicality

A notable change in audience questions reflected this shift. Previously, inquiries often revolved around specific feature support. At this event, however, questions frequently addressed user adoption post-launch, application longevity, modification processes for evolving requirements, and monetization strategies. This indicated a move from "can AI do it" to "can it survive once it's done."

Real-World AI Implementations

Several applications presented at the event underscored this practical orientation. One example involved using AI for children's oral health management, designed to help parents monitor brushing habits and reduce cavity risks. This application generates revenue through training camp services and integrated in-app payments.

Another application, developed by an oil and gas worker, utilized AI for well production design, offering a cost-effective alternative to expensive professional software. This tool, which has been deployed in oil fields and used in academic settings, has reportedly saved over 1.4 million yuan.

Additionally, an AI entrepreneur from a rural area developed "Code Smart Blackboard," an AI-powered classroom tool for schools in smaller cities. This system provides features like classroom timing, random roll calls, AI courseware generation, and simulated experiments, aiming to enhance educational equity by offering an affordable alternative to traditional smart blackboards.

These applications, while not technologically flashy, shared a common trait: they were actively in use and addressed specific problems, suggesting that their creators were driven by practical needs rather than a desire to merely "make an AI product."

The "Production-Grade" Standard

The term "production-grade" was frequently used to distinguish these applications from earlier "toy-like AI applications." This standard implies meeting several practical requirements:

Comprehensive System Generation: Beyond front-end pages, production-grade AI tools generate complete backend systems, including databases, logic, and deployment mechanisms.
User Adoption and Adaptability: Applications must be usable by a broad audience, with easy modification, process adjustments, and error handling.
Real-World Integration: Essential elements like payment processing, distribution, and business closed-loops are critical for an AI application to move beyond a technological demonstration.

Miaoda's Ecosystem for Deployment and Monetization

Miaoda, a no-code platform, aims to facilitate the entire lifecycle from generation to monetization. Its capabilities include:

True Generation: Miaoda's AI model understands natural language requirements and, through an intelligent agent, generates complete database structures, interface logic, and backend services. It also supports automatic hosting, querying, and updating.
Ecosystem Support: The platform integrates with hundreds of Baidu AI Cloud tools and APIs, offering plugins for AI-related functions (text/image generation, speech recognition) and business-related services (payment, SMS, OCR).
One-Click Deployment: Miaoda simplifies the launch process with one-click deployment and cloud hosting, automatically allocating servers, generating databases, and publishing to platforms like WeChat mini-programs or web pages.
Distribution and Monetization: The platform integrates with the Baidu ecosystem for search indexing, provides private domain QR code distribution, and allows for the listing of templates and plugins for revenue sharing. It also supports WeChat Pay and Stripe for broader monetization.

During a live demonstration, an application for splitting travel expenses was created in minutes. The process involved defining requirements through dialogue, which generated a complete requirements document and an Agent planning chain. The application also supported visual editing and provided access to the underlying code.

This experience suggested that AI is enabling the successful delivery of complete applications, rather than just front-end demos. The low barrier to entry means that even novice programmers can create functional applications quickly. According to Shen Dou, Executive Vice President of Baidu Group and President of Baidu AI Cloud Business Group, this era fosters innovation where creativity can be readily transformed into business ventures.