03. "Exquisite Elite Teams" Are Not an Ideal, But a Happening Reality

The Disappearing “Wait Time”

In traditional software R&D processes, the most frustrating moments are often not when you can’t write code, but “waiting”.

• Frontend Engineer: “I’m waiting for the backend to provide the API documentation.”
• Backend Engineer: “I’m waiting for the DBA to create the table.”
• QA Engineer: “I’m waiting for Ops to deploy the test environment.”

This Skill-based division was necessary in the past. Because the tech stack was too deep, asking one person to master React rendering principles, understand MySQL index optimization, and be familiar with Kubernetes orchestration was an almost impossible task.

But AI changed everything. It filled the “deep trenches” between tech stacks.

Recently, I observed a clear phenomenon: those keen engineers in the team started to stop “waiting”. When a frontend engineer needs a simple API, he no longer sends a ticket to the backend, but directly lets AI generate a piece of Node.js code, and writes the SQL along the way. When a backend engineer needs to build an internal admin panel, he no longer asks the frontend for help, but lets AI generate a set of Tailwind-based UI.

This is not just “Full Stack”, this is the dissolution of functional boundaries.

Full Ownership Engineering

If function is no longer the limit, how should organizations be re-divided?

The answer is: Based on Ownership.

We are moving towards a new model: Full Ownership Engineering. In this model, an engineer (or a group of 2-3 people) is no longer responsible for “frontend pages” or “backend APIs”, but for “the delivery and operation of a complete feature”.

A Real Scenario

Previously, developing a “user comment” feature required:

1. Backend designs database table structure.
2. Backend writes API.
3. Frontend writes UI and integrates.
4. QA intervenes.
5. Ops deploys.

Now, in an “Exquisite Elite Team”, the process looks like this: An engineer picks up the task of “user comment”. With the help of AI, he designed the table structure, wrote the API, drew the UI, and even wrote automated test scripts. He doesn’t need to meet with anyone to confirm API fields because he defined them himself. He doesn’t need to wait for scheduling because the entire link is in his hands.

Managers need to realize: When the cost of communication (meetings, alignment, documentation) exceeds the cost of execution, division of labor is no longer a means to improve efficiency, but a wall that hinders efficiency.

Why Couldn’t We Do It Before, But Can Now?

You might ask: “The concept of full-stack engineers has been expected by the industry for a long time, why didn’t it become mainstream before?”

Because being “Full Stack” used to be too tiring. Human memory is limited, and the brain struggles to maintain three different syntax contexts simultaneously. You just finished writing SQL, and switching back to CSS often required looking up documentation for a long time.

AI acts as that “External Brain”. It remembers all the syntax details, configuration items, and boilerplate code. It transforms engineers from “memorizers” to “decision-makers”. You don’t need to recite Dockerfile instructions; you just need to judge whether the generated Dockerfile is safe and reasonable.

The threshold for judgment is far lower than the threshold for memory. This is the reason why AI allows “standing alone” to be established on a large scale in reality for the first time.

Who Is This Model For?

Of course, I am not advocating disbanding all specialized functional teams.

“Full Ownership Engineering” mode is best suited for:

• Core Business Lines of SaaS Products: Iterations are extremely fast, and requirements change frequently.
• Innovation Incubation Projects: Need to verify PMF (Product Market Fit) with the lowest cost.
• Internal Tools & Automation Platforms: Clear functions, not extreme requirements for ultra-high concurrency.

It is temporarily NOT suitable for:

• Underlying Infrastructure: Such as self-developed database kernels, operating systems, etc., still require extremely deep technical specialization.
• Ultra-Large Scale Legacy Systems: If you are maintaining a banking core system with a 10-year history, please continue to maintain strict division of labor; that is for security, not for efficiency.

Conclusion: For That “I Own It”

I once saw a young engineer with a gleam in his eyes after independently completing a full-stack feature launch. He said to me: “I used to feel like just a ‘page slicer’, but today I feel this feature is mine.”

This is the extra bonus brought by Full Ownership: A strong sense of accomplishment and responsibility.

In the AI era, managers should no longer treat people as screws on an assembly line. We have the opportunity to build an organization like this: everyone is not a part of a huge machine, but individual special forces capable of fighting independently.

They don’t need you to tell them “how to do it”; they only need you to tell them “which hill we are going to capture”.