How Kuaishou Crossed Three Chasms and Discovered Four Rules to Build an AI Productivity System

Data Wall and Counterintuitive Finding

After three years of AI R&D, Kuaishou hit a "data wall" in early 2026: the more developers involved in a requirement, the smaller the AI‑driven efficiency gain. Single‑person tasks saw 9‑17% improvement, while tasks with two or more developers showed a monotonic decline.

Three Layers of Friction

Human‑human friction: AI speeds up coding, but developers spend only ~30% of their day writing code; the rest is spent on alignment, communication, hand‑offs, so AI‑induced speedups are eaten by coordination delays.

Human‑process friction: Traditional role‑based workflows remain; front‑end teams may finish early while back‑end lags, causing idle time and unchanged estimation practices.

Human‑AI friction: Four typical dilemmas were observed: “manual patching” (AI not integrated with dev systems), “context alignment” (AI lacks business context), “validation & correction” (code generation is fast but verification takes hours), and “capability boundary judgment” (over‑ or under‑estimating AI leads to rework).

Three Gaps on the Path to AI‑Native

Tool gap (L0→L1): Internal AI products compete, leading to fragmented AI infrastructure, unclear ROI, and token‑cost burdens.

Organization gap (L1→L2): Even with unified tools, vertical siloed teams and differing cost‑benefit judgments block company‑wide AI asset consolidation.

Business gap (L2→L3): Moving from delivering software features to delivering AI services requires a fundamental shift in business model.

Strategic Shift: From R&D Efficiency to AI Productivity

Kuaishou re‑framed the goal: instead of making developers faster, it aims to redesign delivery processes, role division, and organization with AI at the core, defining “AI Productivity” as systematic uplift of individual effectiveness, team collaboration, and delivery quality for all staff.

Four‑Quadrant Framework and Six‑Month Pilot

Teams were placed in a 2×2 matrix (team type vs. demand type) and pursued tailored paths:

Complex business teams (Q1): AI‑assisted development, target L2, >15% gain.

AI‑friendly teams (Q2): AI‑autonomous development, target L3, >60% gain.

Product‑research loop teams (Q3): AI‑assisted demand delivery + process redesign, target L2→L3, >30% gain.

AI‑native delivery teams (Q4): AI‑native product innovation, target L3, >60% gain.

After six months, L2+ demand share rose from 13.23% to 64.87% (≈4×), AI code‑generation rate doubled to >60%, per‑person demand delivery increased 10%, and average cycle time dropped >11%.

Specific examples: live‑gift service reduced release cycle from 20 days to 4 days; the enterprise app team shrank from 50 people to ~20 and reorganized into FDE, PDE, and AI‑native triad.

Four Underlying Rules of an AI‑Native Organization

Goal: Become an AI system with self‑reflection, self‑evolution, and self‑service loops.

Causality: Only AI‑native teams can build high‑quality AI services; team AI‑maturity maps to service quality.

Composition: Every member must be an “AI super‑individual”; the team is a function of its members.

Starting point: The team leader must be an AI super‑individual, acting as the recursive trigger.

The rules form a recursive flywheel: leader deepens AI use → team AI‑matures → business delivery AI‑matures → leader further deepens AI understanding.

Full‑View AI Productivity System

Five‑stage path: AI×tools (2025) → AI×developers (Q1 2026) → AI×all staff (Q2 2026) → AI×team (Q3 2026) → AI×organization (Q4 2026‑2027). Parallel three‑line strategy covers organization transformation, AI infrastructure (CodeFlicker → MyFlicker Agent + AgentOS), and incentive制度 (cost‑sharing → token economics). Product stack includes generic Agent, vertical MyFlicker, AgentOS, skill market, and CLI tools. Measurement uses token‑based quotas and AI productivity metrics. The R&D efficiency center evolves into an AI productivity center, expanding its KPI from demand‑delivery speed to ROI = value / token cost for all teams.

Conclusion

Each level of the journey revealed a new “wall”: personal vs. organizational efficiency, R&D vs. organization efficiency, and finally solving the wrong problem. The true barrier is cognitive, not executional; acknowledging outdated mental models is the prerequisite for the next transformation.