The First Batch of Big Tech Employees Cut by AI: High Salary, High Performance, High P | Shenke AI 砍掉的第一批大厂人：高薪，高绩效，高P｜深氪

TL;DR

• AI coding tools have triggered a massive restructuring in major tech firms, leading to significant layoffs among junior and mid-level engineers despite high performance metrics.
• The traditional career ladder for programmers is collapsing, with roles like frontend and testing being automated or merged into "full-stack" positions, rendering specialized coding skills less valuable.
• Corporate culture has shifted to aggressive AI adoption mandates, including token usage tracking and mandatory skill documentation, creating intense pressure and anxiety among employees.
• While efficiency gains are real, they have not translated into business growth for many companies, resulting in increased workload for remaining staff rather than expansion, accelerating the "35-year-old crisis" and early-career job market contraction.

Why It Matters

This article highlights a pivotal shift in the software engineering industry where AI is no longer just a productivity booster but a primary driver of workforce reduction and organizational restructuring. For AI practitioners and industry leaders, it underscores the urgent need to adapt to a landscape where human coding labor is rapidly devalued, and the focus must shift toward managing AI agents and integrating them into complex business workflows.

Technical Details

• AI Coding Integration: Companies are deploying tools like Claude Code, Codex, and domestic models (e.g., Zhipu GLM-5.1) to automate routine coding tasks, with some teams achieving demo creation in hours instead of weeks.
• Workflow Automation: Specific implementations include AI-driven bug-fixing pipelines (e.g., Tencent CSIG’s 50% accuracy rate) and the transition from manual coding to maintaining AI Agents, where developers only modify agent logic rather than writing raw code.
• Performance Metrics: Organizations are using token consumption as a KPI, with some companies tracking hourly AI usage via plugins to monitor employee engagement with AI tools, although such metrics are proving difficult to manage effectively.
• Role Consolidation: There is a structural move towards merging frontend, backend, and testing roles into "full-stack" or "super-individual" positions to maximize the leverage provided by AI coding assistants.

Industry Insight

• Redefining Engineering Roles: The value proposition of software engineers is shifting from code production to system design, AI agent management, and business logic integration; professionals must upskill in these areas to remain relevant.
• Organizational Agility vs. Burnout: While AI enables faster iteration cycles, it risks creating unsustainable workloads if business demand does not grow proportionally; companies must balance efficiency gains with realistic resource planning to avoid employee burnout.
• Strategic HR Implications: Traditional hiring and promotion criteria based on coding proficiency are becoming obsolete; organizations should prioritize candidates with strong problem-solving skills, domain expertise, and the ability to leverage AI tools effectively.