智驾人最后的归宿，软件工程师？

TL;DR - A personal essay from an autonomous-driving practitioner (published via the 自动驾驶之心 WeChat account) arguing that the AD field has shifted from paradigm breakthroughs to industrialization, and that AD algorithm engineers are converging toward "intelligent systems engineers." It matters as a first-hand read on where talent and capital in embodied AI are moving.

• Frames AD's arc in four stages — dream (~2016), capital burn (2016–2022), deployment of highway/urban NOA (2023+), and the current profitability phase marked by IPOs (Momenta cited); AD stock behavior now tracks autos rather than AI.
• Argues the era of yearly paradigm shifts (BEV, Occupancy, Transformer perception, end-to-end) has given way to incremental gains in accuracy, latency, long-tail coverage, and cost; company moats are now data closed-loops ("data flywheel"), training platforms, simulation, compute, and org efficiency rather than a single model.
• Explains embodied-AI/robotics recruiting of AD talent by shared capability stack: 3D perception, multi-sensor fusion, spatiotemporal modeling, planning/control, RL, data closed-loop, and large-model training — with robotics seen as sitting where AD was 7–8 years ago in capital and error tolerance.
• Author's thesis: technology-name-bound value (BEV → Occupancy → world models → VLA) has shrinking half-lives, so engineers should build system-level competence; predicts AD becomes commodity infrastructure like ABS/ESP, splitting practitioners into "frontier migrants" and "industrialization builders." Opinion piece — no data or results presented.