The AGI era requires end-to-end product design
The Measurement Crisis in Traditional Product Development
In the internet era, we’ve grown accustomed to a mature product development paradigm: validating feature improvements through A/B testing, making data-driven decisions, and iteratively optimizing user experiences. This methodology is built on a core assumption—that product features are relatively static and controllable, allowing us to isolate variables and precisely measure user responses to specific changes.
However, the advent of the AGI era is fundamentally challenging this assumption.
The Measurement Challenges Brought by AGI
Dynamic Adaptation Dilemma: AGI systems learn and adjust in real-time based on each user’s interactions. The same “feature” exhibits dramatically different behavioral patterns across different users, making it impossible to maintain the controlled environments that traditional A/B testing relies upon.
Causal Relationship Blur: Traditional testing seeks clear “feature change → behavior change” causal chains. But in AGI applications, user satisfaction stems from the system’s overall intelligent performance rather than specific components. When a product’s core value is “intelligence” itself, designing meaningful control groups becomes extremely difficult.
Failure of Measurement Units: Internet products measure buttons, pages, and functional modules. What is the value unit for AGI products? Is it conversation rounds? Problem-solving quality? Or the accuracy of user intent understanding? Traditional metrics like conversion rates and click-through rates appear inadequate when facing continuously learning intelligent systems.
From Measurement Crisis to Design Transformation
These fundamental measurement challenges actually point to a deeper issue: traditional product development processes themselves may no longer be applicable.
When we can’t accurately measure and optimize products through conventional means, we need to rethink how products are “designed” in the first place. This isn’t just about adjusting measurement methods—it’s a paradigm shift in the entire product creation process.
The Inevitability of End-to-End Design
Traditional product development is a linear relay process: user research → requirements analysis → design prototyping → development implementation → testing optimization → market promotion. Each stage is handled by specialized teams, with handoffs managed through documentation and specifications.
But AGI systems change these rules entirely. An intelligent system can:
- Understand and analyze user needs in real-time
- Dynamically generate personalized interfaces and interactions
- Autonomously handle complex business logic
- Continuously learn and improve based on feedback
- Proactively communicate value and suggestions to users
This means product “design” is no longer a one-time planning activity, but a continuous, dynamic process. AGI becomes an intelligent bridge connecting user needs and product implementation, blurring the boundaries of traditional team divisions.
New Era Product Design Challenges
End-to-end product design brings unprecedented challenges:
Balancing Consistency and Adaptability: How do we maintain consistency and predictability in user experience while the system continuously learns and evolves?
Redistributing Control: When systems can make autonomous decisions, how should control be distributed among users, product managers, and AI?
Building Trust Mechanisms: How do we establish and maintain user trust when product behavior isn’t entirely predictable?
Reconstructing Value Measurement: Since traditional metrics fail, what new indicators do we need to measure the success of intelligent products?
Toward a New Product Development Paradigm
In the AGI era, the most successful products will be those that truly embrace this end-to-end nature. They won’t treat AGI as a plug-and-play functional module, but will position intelligence as the product’s foundational architecture and core capability.
This requires us to redefine how product teams are organized, their workflows, and success metrics. We need new frameworks to evaluate task completion quality, long-term user relationship development, and the system’s learning evolution capabilities.
More importantly, we need to learn to grow alongside continuously evolving products, finding new certainties within dynamic change. This isn’t just a technical challenge—it’s a fundamental restructuring of our product thinking.
AGI-era product design is essentially about learning to create value within uncertainty and maintaining direction amid constant change. This is an entirely new game that requires entirely new rules.