The article explains that much of today’s artificial intelligence (AI) research and breakthroughs come from a small, tightly connected group of scientists and institutions whose careers and collaborations have shaped the field’s development. While early foundational ideas trace back to pioneers like Alan Turing, the modern era of large language models and generative AI was sparked by a 2017 research paper on the transformer architecture — a design that underlies today’s most powerful AI systems.
A key theme is the network of relationships among AI leaders, many of whom studied under the same mentors or moved between the same labs. Stanford University, the Massachusetts Institute of Technology (MIT), and the University of Toronto are highlighted as primary hubs where many leading AI researchers trained, taught, and recruited from, strengthening the field’s intellectual and professional web.
The article also traces how early collaborations and movements of people helped found and grow major AI organisations. For example, the team that created OpenAI included figures such as Sam Altman and Ilya Sutskever, and many alumni later went on to form or lead other influential companies like Anthropic — showing how talent circulates and spawns new ventures within a close ecosystem.
Beyond individual careers, the piece details how mentorship and academic lineage — including relationships among deep‑learning pioneers like Geoffrey Hinton, Yann LeCun, and others — helped spread foundational ideas that now power most modern AI research. This “family tree” of shared training and collaboration explains why the world’s leading AI labs, founders, and technologies are connected not just by competition, but by common intellectual roots.
