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Stanford's 2026 AI Index Report: Where Does China Actually Stand?

Industry Report, AI Investment, China AI, Stanford AI Index, HAI —

Every April, Stanford HAI (Human-Centered Artificial Intelligence Institute) releases the AI Index Report. The industry calls it the ‘AI physical exam’—no hype, just data.

This year’s edition is the 9th, over 400 pages covering R&D, technology, education, policy, industry, and more. I spent two nights going through it, and here are the China-related highlights worth discussing.

First, the conclusion: China’s overall AI strength may be stronger than you think, or weaker than you think—it depends on which dimension you’re looking at.

R&D Output: China Really Does Produce Lots of Papers and Patents

One data point from the report: In 2025, China published about 25% of global AI papers and filed about 60% of global AI patents.

Sounds impressive, right? But we all know paper and patent quality varies wildly.

A more valuable metric is top-tier conference papers. At NeurIPS, ICML, and ICLR—the ‘Big Three’—Chinese authors contributed about 20% of papers, second only to the US at 35%. This percentage has been steadily climbing over the past five years.

My take: China’s investment in AI basic research is producing results, but ‘0 to 1’ original breakthroughs remain relatively rare. What China excels at is ‘1 to 100’ engineering optimization—which is why Chinese large models can quickly catch up to American ones in performance, but architecture innovations remain less common.

Industry Investment: The US Still Dominates Funding

Global AI private investment reached $581.7 billion in 2025, with the US capturing about 70%. China accounts for roughly 15%.

That’s a significant gap. But note that much of China’s AI investment flows through government funds and state-owned enterprises, not fully captured in ‘private investment’ metrics.

The report also notes that among global AI unicorns ($1B+ valuation), the US has 80+ while China has 20+. This ratio roughly tracks with GDP proportions.

Model Capabilities: Gap Narrowing, But Top-Tier Models Still Trail

The report compares leading large models across various benchmarks. One interesting finding: on most standardized tests, Chinese top-tier models (GPT-level domestic alternatives) trail American top models by less than 5%.

But on tasks requiring deep reasoning and creative thinking, gaps persist. The report gives one example: on multi-step logical reasoning math proofs, American top models outperform China’s best by about 15 percentage points.

What does this tell us? We’re excellent at ‘training larger models,’ but ‘making models smarter’ still has room for improvement.

AI Talent: Mobility Is a Real Issue

One chapter focuses on AI talent flows. Data shows that among global top-tier AI researchers (measured by highly-cited paper authors), about 40% work in the US and about 15% in China.

But the key isn’t the static ratio—it’s the flow trends. In 2019, this was 50% US, 10% China. Five years later, China gained 5 percentage points.

Behind this trend are two factors: First, China’s AI industry is genuinely becoming more attractive. Second, US visa policies and geopolitics have started pushing some talent to ‘return’ or ‘diversify.’

Responsible AI: China Is Catching Up

The report dedicates a chapter to ‘Responsible AI’—AI safety, ethics, governance.

Frankly, China started later on this dimension. The US has the NIST AI Risk Management Framework, the EU has the AI Act. While China has the ‘Interim Measures for Generative AI Services,’ implementation details and industry self-regulation are still evolving.

One judgment from the report: Global AI safety incidents increased about 40% in 2025 compared to 2024, but responsible AI investment growth clearly lags behind model capability growth. This is a global problem, not just China’s.

My Overall Assessment

Reading this report, my biggest takeaway is that AI competition is becoming multi-dimensional.

It used to be just about model performance. Now you need:

  • Compute infrastructure (chips, data centers)
  • Data quality and access
  • Engineering deployment speed
  • Regulatory environment
  • Talent pipeline

Across these dimensions, China has advantages and disadvantages. Advantages in engineering capability, market size, policy support. Disadvantages in foundational chips, top-tier talent, global ecosystem.

The report’s closing line says it well: ‘AI leadership is no longer about single-point breakthroughs—it’s about systematic capabilities.’

This race is far from over.

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