1. Industry produced over 90% of notable AI models in 2025, but the most capable models are now the least transparent.

Training code, parameter counts, dataset sizes, and training duration are no longer disclosed for several of the most resource-intensive systems, including those from OpenAI, Anthropic, and Google.

2. China leads in research, while the U.S. leads in notable model development.

China leads in publication volume, citations, and patent grants, while the U.S. retains higher-impact patents and produced 50 notable models in 2025 to China's 30. South Korea leads in AI patents per capita, and China's share of the top 100 most-cited AI papers grew from 33 in 2021 to 41 in 2024.

3. Reported parameters held in the trillions as disclosure dropped.

Parameter counts have stayed near 1 trillion for three years, though reporting from frontier labs has stopped. Training compute, which can be estimated independently, has continued to rise.

4. Synthetic data is still not replacing real data in pre-training, but data quality and post-training techniques are showing promise.

OLMo 3.1 Think 32B, with nearly 90 times fewer parameters than Grok 4, achieves comparable results on several benchmarks through pruning, deduplication, and curation alone.

5. Global AI compute capacity grew 3.3x per year since 2022, reaching 17.1 million H100-equivalents.

Nvidia accounts for over 60% of total compute, with Google and Amazon supplying much of the remainder and Huawei holding a small but growing share. The buildout is being driven by hyperscaler data center expansion and sustained demand for frontier model training and inference.

6. The United States leads in AI data centers, and one Taiwanese foundry fabricates the majority of chips inside them.

The United States hosts 5,427 data centers, more than ten times any other country, and consuming more energy than any other region. A single company, TSMC, fabricates almost every leading AI chip and makes the global AI hardware supply chain dependent on one foundry in Taiwan, though a TSMC-U.S. expansion began to operate in 2025.

7. AI’s environmental footprint increases across power, water, and emissions.

In 2025, Grok 4’s estimated training emissions reached 72,816 tons of CO₂ equivalent. AI data center power capacity rose to 29.6 GW, comparable to New York state at peak demand, and annual GPT-4o inference water use alone may exceed the drinking water needs of 12 million people.

8. Open-source AI development continues to scale, with 5.6 million projects on GitHub and Hugging Face uploads tripling since 2023.

U.S.-based projects still attract the most engagement, with 30 million cumulative GitHub stars across projects that have crossed the 10-star threshold. 

9. The number of AI researchers and developers moving to the United States has dropped 89% since 2017.

The decline is accelerating, down 80% in the last year alone. The U.S. is still home to more AI talent than any other country, but it is attracting new talent at the lowest rate in over a decade.

10. The AI talent map is shifting, but gender gaps remain.

Switzerland and Singapore lead the world in AI researchers and developers per capita and some countries show relatively higher female representation, including Saudi Arabia (32.3%), Canada (29.6%), and Australia (30.1%), though no country approaches gender parity.

Support the Stanford Institute for Human-Centered AI (HAI) in our mission to advance ethical and impactful advancements in artificial intelligence.

Your support helps foster research, education, policy, and collaboration across diverse fields. Whether you are an individual, a corporation, a foundation, or a family office, together we can ensure AI serves humanity’s best interests.