Ming Chaoping on Product Management and Building AI Agents

Zhang Xiaojun interviews YouWare founder Ming Chaoping about his upbringing, product-management experience and views on AI applications and agents.

He recalls his youth and university years and how debate and engineering competitions developed communication, collaboration and observation.

From debate, he learned that different perspectives can yield different conclusions. He encourages his team to state views while actively seeking opposing interpretations.

He says product managers should adopt the perspective of users without prior context and ask whether a product can be understood and used naturally.

Reflecting on a lost debate, he realized strong individual performance does not guarantee team success: taking too much speaking time can obscure insights from teammates.

At OnePlus, he recalls observing everyday behavior on transit and in shops to understand needs rather than looking only at device specifications.

He uses phone battery life as an example: battery capacity and perceived endurance are not the same measure, and charging habits and anxiety affect experience.

At ByteDance, he encountered faster releases and experimental feedback, gradually building experience in how product changes might affect results.

He says data helps validate ideas but teams should not chase only short-term numbers; they also need judgment about long-term value and new ideas.

He recalls joining Moonshot after a long conversation with Yang Zhilin, saying shared interests and understanding of the technical direction influenced the decision.

He describes working on the music-video product Noisee, including creator-community interaction and growth associated with other tools. The numbers and causal account are his recollection.

He says product and research teams can explain their respective problems to one another and learn through collaboration.

After starting another company, he reconsidered adding too much fixed scaffolding around models: when foundation models change quickly, such designs may constrain adaptation to new capabilities.

He recalls stopping a direction before launch and then focusing more closely on compute consumption and value delivered to users.

He discusses value per token as a way to think about efficiency: consider not only compute used but whether it helps users complete valuable tasks.

He argues that the environment around an AI application matters: models need tools, context and interaction, so product design is more than providing a chat box.

He compares changes in coding tools with mobile photography: lower barriers may attract users without prior specialist skills, though their ability to complete tasks still needs testing.

He looks for creative formats users want to share but acknowledges uncertainty about whether a single viral moment will necessarily drive growth.

Discussing agent ecosystems, he compares centralized coordination with decentralized correction. The analogy raises questions but cannot directly predict industry structure.

He considers agents early-stage, using an exaggerated metaphor for their immature capabilities; demonstrations are still some distance from reliable completion of real tasks.

His product hypothesis is that foundation models will improve and application teams should focus on environments and workflows that help users use those capabilities. Neither progress nor product success is guaranteed.

He imagines future interfaces changing with tasks and context, such as different devices displaying different information. This is a personal vision, not proof of existing product capabilities.

He raises trust, identity and access-control questions among agents: when systems can use tools, permission boundaries need to be clear.

On interfaces, he says task initiation, tool coordination and presentation all affect user experience; different content may need different displays.

As a first-time CEO, he says he needs to explain uncertainty and why direction changes, avoiding persistence with a decision the team knows is wrong.

He reflects on pressure from funding and outside attention and says the team must remain cautious and test judgments through products and user feedback.

On team communication, he encourages separating ego from factual questions so different views can be discussed around evidence.

Finally, he discusses differences in how humans and models process information as material for research and product thinking, not a settled scientific conclusion.