Hi, I am Jialuo Mu, Nick, a Product Maker.
My background is fairly mixed. I studied information management, finance, and human-computer interaction across my undergraduate and graduate education; in practice, I have also worked across marketing, product management, and user experience. These broad attempts gradually shaped a more composite way of working: standing at the intersection of PM, UX, Design Engineer, and GTM, and trying to understand a problem through users, business, design, and technology at the same time.
I am not too attached to a single job label. I care more about two broader goals:
- How to use AI to reduce translation loss between market, product, design, and engineering, so a good idea can move more quickly into a perceivable, testable, and iterable state.
- How to rethink AI-native products: when Agents become part of the experience, rather than just a feature embedded in the interface, how should we design new interactions, workflows, and ways of forming product judgment?
If you are also thinking about new AI product forms, how Agent workflows can improve the efficiency of traditional teams, or if you are interested in my background, feel free to reach out: [email protected], Xiaohongshu, LinkedIn, X.com.
Product Maker in the AI Era
One important reason companies exist is to reduce external transaction costs: when the transaction costs of external collaboration are too high, companies internalize those collaborations inside the organization (Ronald Coase, "The Nature of the Firm," 1937). But in traditional software engineering, internal organizations also produce new costs: communication, collaboration, handoffs, scheduling, and repeated alignment. The larger the team, the more visible these frictions tend to become. The Mythical Man-Month discusses a similar problem: what often slows things down is not the technology itself, but the coordination cost that accumulates inside organizations.
What AI truly changes is not only production efficiency, but also the boundaries between roles and the concept of a team itself. In the past, many things required PM, design, GTM, and other roles to circulate repeatedly. In many early explorations and prototype stages, those loops can now converge into smaller and faster personal loops.
This is why I position myself as a Product Maker. Compared with only doing market research, product design, UX prototypes, or requirement documents, I care more about "solving the problem" itself: can I reduce translation loss, compress market insight, user needs, product design, and prototype experiments into one low-friction chain; can I use AI, design, and code to push a vague idea more quickly into a state that is perceivable, testable, and discussable?
The value of a Product Maker is not just finishing tasks faster. It is forming judgment faster, and turning that judgment into verifiable value faster. I understand this role shift as follows: judgment, design, and implementation that were originally scattered across multiple positions are being recompressed into smaller personal loops.
Diagram reference: @goocarlos
The New Benchmark
In the AI era, the cost of a single task is dropping quickly. What becomes truly scarce is no longer just whether something can be done, but whether we can find what is worth doing and validate it faster. I care about the speed of moving from observation, understanding, and experimentation to judgment, and then moving that judgment toward real value and continued validation.
Source: designcouncil
The value of design and research is not only producing solutions or reports. It is helping us abstract needs and form judgment faster, then pushing those judgments into verifiable product evolution. User feedback, market signals, experiment results, and technical constraints are most valuable when they can be rapidly absorbed, recombined, and tested inside the same high-density cognitive loop.
Two Practice Directions
1. Optimizing Existing Workflows
I focus on reducing repeated translation loss between product, design, engineering, and GTM. An idea often goes through many format conversions: feedback becomes a requirement, requirements become design, design becomes code, and feedback returns to documents and meetings. Closer collaboration, rapid prototyping, and real usage feedback can help those steps align earlier and validate judgment sooner.
2. Exploring AI-Native Products
I also explore product forms that are not simply old products with an AI feature added, but are regenerated from model capabilities, Agent behavior, and human-AI collaboration. Through rapid prototypes and experiments, I try to understand the boundaries, interaction patterns, and real value of AI-native products.
Taste is a subtle art.
When AI dramatically lowers execution cost and efficiency becomes the baseline, what becomes truly scarce is judgment and taste. Because when most people can produce interfaces, copy, code, and plans faster, difference no longer comes only from whether something can be done, but from knowing what is worth doing, to what degree, and with what quality of expression.
My understanding of craftsmanship is not just visual polish. It is continuous attention to information density, interaction rhythm, state feedback, edge handling, and the overall character of a product. AI can amplify output, but whether a product is ultimately worth using still depends on these subtle but crucial judgments.
Contact
If you are thinking about AI products, Agent workflows, or similar questions, I would be happy to connect.