🤖 AI Summary
The author argues that agent-supported coding over the past year has finally made a “true” full‑stack developer possible: AI agents let experienced engineers translate broad software knowledge into effective work across frontend, backend and infra without years of deep specialization in each area. This creates a new archetype—often labeled forward‑deployed engineer (FDE)—who can prototype, ship and even talk to customers rapidly. That shift is less about tooling and more about mindset: curiosity, systems thinking and the discipline to delegate rote editing to agents become the core skills.
For the AI/ML community this is both opportunity and warning. Technically, widespread agent use raises demand for robust agent orchestration, prompt engineering, MLOps, observability, testing and human‑in‑the‑loop guardrails; product velocity will increase but so will expectations for reliability and ownership. Organizationally, team roles, hiring and compensation models are in flux: companies may push broader responsibilities while workers must renegotiate value and protect wellbeing. The key takeaway is adaptability—AI amplifies individual productivity and risk alike—so engineers, ML practitioners and leaders should invest in agent infrastructure, clear participation models and trustful leadership to capture the upside without amplifying exploitation.
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