The competition in artificial intelligence is shifting from who has the largest model to who can use AI to compress the scientific discovery cycle - forming a hypothesis, running simulations, designing experiments, operating equipment, and collecting results. The United States has made that shift explicit: the Department of Energy's Genesis Mission is pulling together 17 national laboratories into an integrated discovery platform, with a goal of doubling the productivity of US research within a decade.
China is moving in the same direction, but increasingly through cities rather than national programs. Beijing's 2025 plan calls for common scientific-intelligence infrastructure, at least 10 high-quality scientific databases, and more than 10 million users by 2027. Shanghai's "Hundred Teams, Hundred Projects" initiative offers support for up to 70 per cent of approved investment, capped at 50 million yuan (US$6 million).
That creates both an opportunity and a trap for Shenzhen, argues Dr Shaoshan Liu, director of embodied AI at the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). The opportunity is that AI could become a new source of scientific productivity and industrial growth. The trap is copying what other cities are doing.
Why Shenzhen shouldn't imitate Beijing or Shanghai
"Shenzhen should not try to become Beijing with more hardware, or Shanghai with a different set of industry labels; it should build around its own comparative advantage," Liu said.
That advantage starts with the structure of its innovation economy. Shenzhen invested 245.31 billion yuan in research and development in 2024, the highest R&D intensity among Chinese cities. Strikingly, companies accounted for over 93 per cent of that spending.
Those figures describe a city where research, engineering, and commercial deployment sit unusually close together. That proximity matters in the AI-for-science drive because the hard part isn't only inventing an algorithm - it's turning it into a reliable scientific tool. For professionals working in AI for Science & Research, the distinction between building a model and deploying it in a lab is where most projects succeed or fail.
Industrial depth as a scientific asset
Shenzhen's strength is not in basic research, where Beijing's universities dominate, nor in finance-driven biotech, where Shanghai excels. Its edge is in manufacturing and hardware - the physical infrastructure that scientific experimentation depends on.
That industrial base gives Shenzhen a natural position as a lab for experimentation and deployment rather than a pure research hub. The city can test AI systems in real production environments, iterate quickly, and feed results back into the discovery cycle.
For research scientists, this suggests a practical path. Rather than waiting for a national platform, researchers in Shenzhen can exploit the city's existing density of companies and engineering talent. An AI Learning Path for Research Scientists that focuses on deployment skills - not just algorithm theory - would match the city's comparative advantage.
Why this matters for science and research professionals
The practical takeaway for research scientists is that the AI-for-science race will not be won by model size alone. The cities and institutions that shorten the full discovery cycle - from hypothesis to deployed experiment - will set the pace.
For scientists, that means the skills that matter are shifting. Designing experiments that AI can assist with, integrating AI tools into laboratory workflows, and working across the boundary between software and physical equipment are becoming core competencies. The institutions that train researchers for that reality, and the cities that support them with industrial infrastructure, will be the ones that lead the next phase of scientific discovery.
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