The United States and China has the greatest number of cross-country collaborations in AI publications from 2010 to 2021 although the pace of collaboration has since slowed.

The number of AI research collaborations between the United States and China increased roughly 4 times since 2010, and was 2.5 times greater than the collaboration totals of the next nearest country pair, the United Kingdom and China. However, the total number of U.S.-China collaborations only increased by 2.1% from 2020 to 2021, the smallest year-over-year growth rate since 2010.

AI research is on the rise, across the board.

The total number of AI publications has more than doubled since 2010. The specific AI topics that continue to dominate research include pattern recognition, machine learning, and computer vision.

China continues to lead in total AI journal, conference, and repository publications.

The United States is still ahead in terms of AI conference and repository citations, but those leads are slowly eroding. Still, the majority of the world’s large language and multimodal models (54% in 2022) are produced by American institutions.

Industry races ahead of academia.

Until 2014, most significant machine learning models were released by academia. Since then, industry has taken over. In 2022, there were 32 significant industry-produced machine learning models compared to just three produced by academia. Building state-of-the-art AI systems increasingly requires large amounts of data, computer power, and money—resources that industry actors inherently possess in greater amounts compared to nonprofits and academia.

Large language models are getting bigger and more expensive.

GPT-2, released in 2019, considered by many to be the first large language model, had 1.5 billion parameters and cost an estimated $50,000 USD to train. PaLM, one of the flagship large language models launched in 2022, had 540 billion parameters and cost an estimated $8 million USD—PaLM was around 360 times larger than GPT-2 and cost 160 times more. It’s not just PaLM: Across the board, large language and multimodal models are becoming larger and pricier.