When Alibaba Enters the Global AI Coding Arena: 60 Days on the Battlefield | A Conversation with Shutong, Founder of Qoder

"Absolutely the right call. No doubt about it." That's what Shutong, founder of Alibaba's Qoder (an Agentic Coding product), told us when we asked how he felt about leading his team into the global AI Coding "red ocean" 60 days in. Simple, unequivocal.

His confidence comes from a strong track record: 100,000 users in five days after launch, and 500,000 developer users in just 60 days.

AI Coding may be the hottest topic in AI this year.

Cursor hit $500 million in annual revenue and soared to a $10 billion valuation, becoming Silicon Valley's most sought-after unicorn. Anthropic, with its powerful Claude Code, has seen a steeper revenue growth curve than OpenAI.

In this fiercely contested battlefield, where does a latecomer like Alibaba see its opportunity?

This week, we invited someone who spent years as Alibaba's "Singles' Day" technical lead and was among the earliest advocates for cloud-native transformation across China's tech industry. He shares exclusively what's behind Qoder's strong start — the product thinking, technical architecture, and organizational capabilities that made it possible.

🎉 This episode drops specially on 1024, Programmer's Day. Happy coding to all who change the world with code — may your hair stay thick and your bugs stay absent!

🎥 Recorded at AI Hacker House. Video podcast will be published on Koji Yang Yuancheng's Xiaohongshu, Bilibili, Channels, and YouTube.

🟢 03:11 Rapid-fire Q&A: Age, alma mater, MBTI and zodiac sign, one-line product pitch, revenue and profit, team size, exploration before Qoder

🟢 03:27 The AI Coding Landscape: From 0 to 1 vs. From 1 to 100

A typical user journey: start with AI to generate a website prototype, then switch to more professional tools for maintenance and iteration once it generates business value. What does this reveal? Three mainstream forms in AI coding tools: "idea-to-reality" tools for creators, "efficiency boost" tools for professional developers, and "digital employees" that directly replace human labor. Why do all forms of AI coding tools inevitably converge toward a "grand unification"? "AI Coding tools without self-developed models are just working for model vendors" — why will companies like Cursor inevitably build their own model capabilities? What natural advantages do tech giants have in AI Coding? Beyond cost, the critical factor is end-to-end optimization in tandem with models.

🟢 09:48 Breaking Through the Red Ocean: Qoder's Strategic Choices

While everyone went after the "low-hanging fruit," we chose to attack the "high-value ground" directly. A pivotal strategic choice: we bypassed the "flashy" scenario of generating new projects from 0 to 1, and cut straight into "real-world software" development — where developers spend 95% of their time. Why do we define Qoder as an "agentic programming platform"? Because the future of development collaboration will evolve from "human-machine pairing" to "AI autonomous programming." "I don't do it, the agent does" — this is an entirely new development paradigm. The reason for this unconventional path: as a latecomer, we wanted to quickly carve out our own niche in the red ocean through differentiated positioning.

🟢 16:55 The Requirements Doc Is Everything!

AI isn't just good at writing code — it's even better at writing requirements docs. From "prompt engineering" to "context engineering": how do we enable AI Agents to independently complete larger, more complex tasks? The answer is Spec-Driven. A typical Spec-Driven workflow: user states a one-sentence requirement → "doc agent" auto-generates a detailed design document → upon user confirmation, "code agent" executes over an extended period. This essentially mirrors the real-world flow of "boss states requirement → PM writes PRD → engineer develops," but AI-native.

🟢 21:44 Product Philosophy: From "Don't Break Flow" to "Give You Control"

In the AI era, developers are forced into "pair programming" — the uninterrupted "flow state" of the past has become nearly impossible. The new tension in AI: providing powerful tools while inevitably interrupting user flow — how to balance? Our answer: rather than futilely pursuing uninterrupted focus, give users complete control, making them managers of AI Agents. A counterintuitive product decision: why doesn't Qoder let users choose their model? Because "machine selection beats human selection," and it prevents users from becoming "model testers" mired in decision fatigue. How do we balance the "impossible triangle" of performance, efficiency, and cost? The core lies in refined operations in "context engineering."

🟢 32:26 Organization and Methods: How to Support a Lightning War?

Why could Qoder move so fast? Because we didn't start from scratch — we consolidated talent and technical assets from multiple mature teams within Alibaba. From Tongyi Lingma, serving the China market, to Qoder, going global — what organizational and strategic evolution happened behind this? A critical decision: to seize the global market window, we first serve developers with the world's best models, while "nurturing growth through battle" to buy time for our self-developed models. "It's an independently operated business unit" — how can internal startups within big tech truly "shed baggage" and achieve startup-level agility through organizational design?

🟢 40:13 Repo Wiki: The Secret Weapon for Taming "Legacy Code"

In our very first computer science class, professors told us to write good documentation and comments — yet almost no team actually does it. This is such an obvious need; why hadn't anyone addressed it before? The first principle behind Repo Wiki: "Documentation lies, but code is always current." How do we use AI to deconstruct "legacy code"? By analyzing the current code slice and all historical commits, we reconstruct the system's design philosophy and business logic. How do we keep this documentation from going stale? As the codebase changes, AI updates this "living document" in real time, incrementally. Why are we confident in building a moat? Because this isn't an atomic capability — it's an entire deeply customized stack of models, agents, and Git-integrated team collaboration workflows.

🟢 01:03:19 | 1024 Message: How Should Programmers Evolve in the AI Era?

Programmers may be the group least afraid of learning — and that is precisely our greatest advantage in the AI era. Will AI replace programmers? No. And Jevons Paradox tells us: when costs drop, demand explodes. We may end up with more programmers. What will be the core competency of future engineers? Shifting from pure coding ability to composite capabilities: requirement insight, holistic design, outcome validation. Advice for first-year CS students: embrace AI, but double down on computer architecture — because you need to know when AI is "bullshitting" you.

🟢 01:09:48 | Singles' Day Memories: A Top Architect's Two "Gaokao" Moments

The boss set a wild target: 50x traffic spike for Singles' Day, without spending a single extra yuan. We pulled it off. "Before Singles' Day, create countless Singles' Days" — how did "full-link stress testing" as aggressive medicine tame thousands of systems through repeated drills under real traffic? How do technical people always "extract dividends from technology"? From solving stability problems to extreme cost optimization through containerization and cloud-native tech — this is a complete methodology. The growth path of a top architect: from solving one technical problem, to building a technical system, to productizing and commercializing technical capabilities, and ultimately seeking a larger stage.

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👦🏻 Host Koji: I co-founded Jiepang / The Fair / Tangdao, and launched AI Hacker House, a community space for the new generation of AI entrepreneurs. I believe technology, especially AI, represents the greatest value-creation opportunity of our generation. Feel free to reach out to chat, bounce ideas, and connect on what's next. Koji on Jike, Koji's website

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