The State of AI: China’s open-weight surge vs America’s chip moat—what executives must do next

Business Impact: AI supply chains are bifurcating-prepare for a dual-track model and talent strategy

Financial Times and MIT Technology Review’s “The State of AI” debate spotlights a split AI future: China leading in publications, patents, open-weight models, and large-scale deployment; the US maintaining advantages in frontier research, A100/H100-class chips, and top-tier talent. For business leaders, this shapes near-term access to models and hardware, total cost of ownership, regulatory exposure, and where to build teams. Strategy now means running proprietary and open-weight tracks in parallel-under tight governance-to keep costs down and options open.

Executive Summary

Supply bifurcation: China’s edge in open-weight models and deployment velocity meets America’s chip and frontier model moat—procurement and risk policies must reflect both.
Cost-performance shift: Efficient Chinese models (e.g., DeepSeek-V3, Alibaba Qwen 2.5-Max) deliver strong performance at lower compute—ideal for edge, operations, and multilingual markets.
Talent realignment: US still leads but the gap is narrowing; visa frictions and China’s AI education push require distributed hiring and new university pipelines.

Market Context: Two AI playbooks, one global market

Signals from the debate and recent indices point to divergence. Stanford’s 2025 AI Index shows China at 22.6% of AI citations (US 13%; Europe 20.9%) and 69.7% of AI patents in 2023. The US still leads the top-100 most cited papers (50 vs 34), but its share is slipping. In 2024, US institutions produced more “notable” models (40 vs 15), yet China’s open-weight ecosystem is scaling fast—Air Street Capital reports China has overtaken the US in monthly model downloads.

Hardware remains the US moat: export controls constrain China’s access to top GPUs, pushing efficiency-oriented training and deployment. DeepSeek-V3 reportedly trained on ~2.6 million GPU-hours—lean by US frontier standards. China’s advantage is rapid application at scale—in fintech, e-commerce, logistics—and a policy engine that moves models from lab to field quickly. Founders are increasingly transnational, while Sam Altman has conceded, “We have been on the wrong side of history here and need to figure out a different open-source strategy.”

Opportunity Analysis: Where to lean open, where to stay proprietary

Open-weight upside: Fine-tune for domain tasks, multilingual markets, and on-prem/privacy-sensitive workloads; optimize inference costs via distillation and quantization.
Proprietary strength: Complex multimodal tasks, safety tooling, long-context reasoning, and regulated use cases with enterprise SLAs and auditability.
Edge and embodied AI: Efficiency-first models suit robotics, drones, and industrial control—align pilots with export-control, data-localization, and safety regimes.
Hardware hedging: Combine reserved GPU capacity with AMD-class alternatives and inference-first accelerators; prioritize efficiency over sheer scale.

Action Items: Move now to lock in cost, resilience, and compliance

Adopt a dual-track model strategy: shortlist one frontier proprietary model and two open-weight families (e.g., Qwen-class, DeepSeek-class) with clear data and licensing guardrails.
Secure compute and cut inference cost: reserve GPU capacity; validate AMD and regional cloud options; implement 4-8 bit quantization, caching, and lightweight RAG.
Build distributed talent hubs: expand hiring in Canada, Singapore, UAE, and EU; establish university pipelines; use fellowships and contractor networks to reduce visa risk.
Harden governance: create an export-control and sanctions review for model and vendor choices; track model SBOMs, licenses, and data provenance; enforce data-localization where required.
Pilot operational AI: run 90-day pilots in warehouses, customer support, or logistics with efficient open-weights; measure unit economics versus proprietary baselines.
Run decoupling drills: model scenarios of reduced access to US chips or Chinese open-weights; pre-approve substitutes and quantify cost-to-serve deltas.

Bottom line: The winners won’t pick sides; they’ll architect optionality—balancing US frontier capability with China-driven efficiency and deployment scale, under disciplined compliance.