2026 AI原生劳动力：工程与产品价值链中的工作与技能未来研究报告

February 2026 Table of contents Executive summary4New rules of technology talent5Evolution of software engineering roles8Evolution of product management roles17Emerging skills of the future20Organisations must be READY to embrace this change23 Executive summary The technology landscape is undergoing a structuraltransformation driven by two converging forces: The maturityof Generative AI (GenAI) into “agentic” workflows and thestabilisation of hybrid work models. Software engineering istransitioning from a discipline ofcreationto one oforchestration,and product management evolves fromcoordinationtostrategic acceleration. Current data indicate that GenAI tools can improve new productdevelopment times by 50 percent and significantly acceleratesoftware development tasks, with productivity gains of 30–40percent witnessed in engineering teams.¹ However, realisingthis value estimated at ~7 percent of global GDP7requiresorganisations to look beyond automating individual tasks andredesign entire workflows, develop skills and reimagine talentand leadership to accommodate human-agent collaboration. The macro environment: Drivers of change The era of agentic AI the available technology; the goal is augmentation rather thandisplacement.1,6AI agents are becoming “virtual coworkers”capable of planning and executing multistep workflows, such asmigrating legacy code or autonomously managing sales leads. We have moved past simple code completion. The futureworkforce will be a collaboration of people, agents and robots.It is suggested that while ~52 percent of worker tasks in the UScould be completed faster with the same level of quality with The remote work “productivity paradox” A significant disconnect exists between engineering data and leadership sentiment. Developer reality About 64 percent of developers report higher productivityworking remotely.2Data show a 4 percent increase in “focusedwork” (keystrokes per minute) and a 5 percent increase incoding during core business hours among remote workers.2 Leadership scepticism Only 12 percent of leaders express complete confidence inremote productivity.3 Implication Organisations must implement observability tools to bridge thistrust gap rather than enforcing mandates that risk attrition. We can see these trends playing out across the talent landscape and in how the roles of software engineers and productmanagers are evolving New rules oftechnology talent Global talent divergence1 AI is causing a seismic shift in tech hiring, decoupling productivity from headcount and favouring specialised, senior talent.From coder to conductor: The new rules of tech hiring The old hiring playbook: Focus on volume Goal: Increase headcount for productivity More engineers were hired to write more rawcode and manually build features The ideal candidate: “The coder”Valued for proficiency in specific languagesand ability to write code quickly Roles now in declineData analystsSoftware testers Goal: AI‑led supervision Hiring fewer senior specialists to guide,validate and orchestrate AI outputs. The ideal candidate: “The architect of intelligence”Valued for designing systems where AI agentsperform tasks securely and reliably Roles now in demandAI researchML engineers New focus on higher education 40–45 percent of roles in the Americas andEurope now demand aMasters or PhD The new AI‑driven playbook: Focus on value Hiring trends reveal a geographical split in engineering value chains. The Americas and Europe are increasingly focusing on highlyspecialised talent (PhDs, masters) for AI research and model architecture. Conversely, South and Southeast Asia are witnessing highdemand for application-based and operational engineering roles. Volume vs. value Internal build There is a strong preference for “reskilling overreplacing.” Instead of mass layoffs, companies arereskilling existing engineers for AI-driven roles. As AI automates routine tasks such as codegeneration, bug fixes and UI scaffolding, the demandfor entry-level “coders” is softening. The focus hasshifted to engineers capable of “AI-led Supervision”guiding, validating and integrating AI outputs ratherthan writing raw code. Ecosystem sourcing External hiring is increasingly focusing on non-traditional pools, such as open-source communities,hackathons and AI research collaborations, ratherthan relying solely on job portals such as LinkedIn. The “Orchestrator” profile Hiring is prioritising “Cross-disciplinary skills.”Candidates must grasp adjacent domains, blendingcore engineering with data pipelines, modelbehaviour and governance risks. Graduate expectations For new graduates (next 24–36 months), companiesare specifically looking for capstone projects withexternal sponsors, coursework that embeds AI intonon-AI subjects (e.g., AI in OS or DB courses) andevidence of “disciplined AI usage” (e.g., maintainingAI logs and model critiques). Net new roles Recruitment is opening for entirely new job titles,i