Analysis: AI's academic roots, concentration and going global
A column by Yan Deli of Tencent Research Institute, republished by Cyzone, traces AI's history: the 1943 M-P neuron paper, the 1950 Turing test and the 1955 Dartmouth proposal that named the field, which together answered how to express intelligence in computation, how to evaluate it and how to create a new discipline. The piece argues academia and industry are deeply intertwined—AlexNet (2012) and Transformer (2017) as the key papers behind deep learning and today's large language models, and OpenAI's 2020 scaling-law paper as the guide for model development—with AI firms following a chip-talent-model-paper loop. It cites third-party figures for high concentration: Stanford's AI Index Report 2026 counting 59 notable AI models from the US and 35 from China; The Information finding Anthropic and OpenAI together took 89% of annualized revenue across 34 leading generative-AI startups; and 1,112 generative AI services filed in China as of Aug. 31, with Beijing at 26.8%. It also says Chinese AI firms internationalized early, citing Manus, MiniMax and Kuaishou's Kling as overseas-revenue focused.
It offers a broad synthesis of AI's academic origins, US-China industry concentration and Chinese AI firms' overseas push, though its figures are all restated from other reports.