How Kuaishou Redefined AI R&D: From Efficiency Gains to an Organizational Leap

01. Review: Three Years, Three Steps to Personal AI Efficiency

Kuaishou summarizes its H1 2026 progress from 2023‑2025, highlighting a macro‑level framework, methods, and detailed practices that moved AI from a developer tool to personal productivity. In February 2026 the team published the earlier paper "Kuaishou: The Road to AI‑R&D Paradigm Shift for Ten‑Thousand‑Person Organizations," which warned that AI development tools ≠ personal efficiency ≠ organizational efficiency .

02. Problem: Scaling the AI‑Productivity Framework

After the benchmark teams proved the AI‑productivity framework, Kuaishou discovered that moving the L2 demand share one level higher required dramatically more effort, and the macro‑level L2+ demand ratio no longer grew proportionally with per‑person delivery.

Three concrete bottlenecks emerged:

People: AI development capability became highly polarized – about 30% of engineers generated >40% of AI‑written code, while 32% stayed below 10%.

Process & Division of Labor: The more developers participated in a demand, the smaller the efficiency gain; a detailed chart shows the inverse relationship.

AI‑Human Friction: Four recurring pain points were identified:

Manual bridging when AI and the development platform are not fully integrated.

Context alignment – AI lacks business background, requiring continuous context hand‑over.

Verification & correction – AI can generate code in minutes, but human validation takes hours.

Boundary awareness – mis‑estimating AI capabilities leads to rework.

03. Business Characteristics & Organizational Structure

Benchmark teams that achieved high efficiency were usually "product‑research‑development closed‑loop" units where product, development (frontend & backend), and testing co‑existed in a single organization. When the organization was split into siloed product, backend, frontend, and testing groups, scaling the same efficiency proved extremely difficult.

04. Historical Analogy: Banking Evolution as a Mirror

Kuaishou draws a parallel with six decades of banking transformation:

L0 → L1 (1960s): Introduction of mainframe computers accelerated computation but left organizational processes unchanged.

L1 → L2 (1970s): ATMs automated transaction execution, forcing banks to redesign roles, staffing, and service hours.

L2 → L3 (1990s‑2010s): Online banking and mobile payments rewrote the business model itself, turning banks from "places you go" into ubiquitous digital capabilities.

The three stages map directly to AI‑R&D evolution: tool upgrades, organizational redesign, and finally a simultaneous transformation of business and organization.

05. New Paradigm: From R&D Efficiency to AI Productivity

The core insight is that the goal shifts from "how to make R&D faster" to "how to continuously supply AI capability to every person and team, driving larger organizational change." Kuaishou defines AI Productivity as a systematic supply of AI ability that lifts individual effectiveness, team collaboration, and delivery quality.

06. Three Gaps, Three Crossing Strategies

Kuaishou identifies three distinct gaps that block industry‑wide adoption:

Tool Gap (L0 → L1): Fragmented AI‑product portfolios prevent unified impact.

Organizational Gap (L1 → L2): Even with unified tools, scaling requires new processes, cost‑sharing models, and cross‑functional restructuring.

Business Gap (L2 → L3): After organization adapts, the business itself must be AI‑native.

To cross these gaps Kuaishou proposes a five‑stage roadmap (see image) and an execution strategy that runs three lines in parallel: organization transformation, AI infrastructure, and制度 (policy) evolution.

07. Fast‑Track (Rapid Path) vs. Main‑Track

The organization adopts a dual‑track approach:

Main‑Track: All business lines continue scaling the AI‑R&D paradigm, targeting an 80% L2 demand share.

Rapid Path: About 30 "AI Pioneer" teams (10‑100 people each) explore the upper bound of AI‑enabled organization, testing L2→L3 or direct L1→L3 jumps.

08. Practice Before and After: Four Team Types, Four Practices

Kuaishou classifies teams by team type (core vs. peripheral) and delivery type (high vs. low AI‑ability), forming four quadrants. Each quadrant follows a tailored practice path (①‑⑥) that can transform into the next quadrant as capability grows.

09. Case Study 1 – Commercial Risk‑Control Team (L2 → L3)

The team faced a high‑adversarial environment where AI‑generated code needed to keep pace with evolving black‑market attacks. Their three‑step migration:

Map the value chain.

CLI‑ify SaaS tools and skill‑ify them.

Build an AI knowledge base.

After implementation, the workflow collapsed to "input demand + review output"; AI now drives code generation, deployment, self‑testing, and status flow. The team switched from the Kuaishou KATE platform (human‑centric) to the MyFlicker platform (AI‑centric).

09.2 Application Phase – AI Deeply Involved in Delivery

Using the crc-dev-flow Skill as a unified entry, developers now only need to provide demand and review AI output; the rest (code generation, deployment, testing) is fully automated.

10. Case Study 2 – Enterprise Application Team (Business + Org Transformation)

The internal admin service team (meeting‑room, shuttle‑bus management) grew from a 50‑person silo to an AI‑native unit of ~20 people. Changes included:

Business: Replaced SaaS web + mini‑program with a Skill‑based AI Agent that directly fulfills requests.

Organization: Merged product, frontend, backend, and testing into an FDE + PDE + full‑stack AI‑Native triangle.

11. Case Study 3 – Main‑Site Tech Department (Thousands of Engineers)

The department tackled the core contradiction "personal AI efficiency ≠ organizational AI efficiency" by redesigning information flow, processes, and roles. A live‑gift business pilot compressed the feature‑launch cycle from 20 days to 4 days using an end‑to‑end AI Agent.

Full details are in the original article "Towards AI‑Native: Paradigm Shift and Organizational Evolution of Technical Teams" (link provided in the source).

12. Quantitative Impact

Per‑person demand delivery ↑ 10%.

Average delivery cycle ↓ 11%+.

L2+ demand share grew from 13.23% to 64.87% (≈ 5×).

AI code‑generation rate doubled from ~30% to >60%.

Engineers with >40% AI contribution rose from 30% to 77%.

13. Lessons Learned & Future Work

The journey revealed three "walls":

Tool Gap: Assuming tools alone solve the problem ignored the need for organizational change.

Organizational Gap: Believing that reorganizing R&D suffices missed the deeper mismatch between R&D efficiency and overall organization efficiency.

Business Gap: Continuing to optimize L2 without redefining the problem (L3) leads to diminishing returns.

Key take‑away: successful AI‑native transformation starts by making the responsible team itself AI‑native. Kuaishou distilled four "AI‑Native Organization Axioms" that every team must satisfy before scaling to L3.

14. Conclusion

AI productivity will become a basic infrastructure; the ultimate goal is a world where no one talks about "using AI for efficiency" because AI is as ubiquitous as electricity. Kuaishou’s ongoing work includes token‑economy mechanisms, broader non‑technical team AI adoption, and next‑generation AI products that serve the entire enterprise.

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