This week we got the chance to listen to the first chapter of, Genius Maker, by Cade Metz. In this first chapter, Metz transports listeners back to 2012, when Geoffrey Hinton, a professor at the University of Toronto, and two of his graduate students founded a small company called DNNresearch. Their goal was to bring their breakthroughs in neural networks—what we now call “deep learning”—to the forefront of artificial intelligence. At the time, most of the AI community had been focused on symbolic AI, which relied on explicit rules and logic. Generative and learning-based systems were widely dismissed as too complex or computationally unrealistic. Hinton’s work changed that narrative, proving that machines could, in fact, learn patterns and representations from massive amounts of data.
Hinton and his students took their company to the NIPS conference in Las Vegas, where they discreetly held an auction for its acquisition. Google, Microsoft, Baidu, and DeepMind all competed—anonymously and entirely through Gmail—for the chance to acquire the team and their ideas. Google ultimately won with a $44 million bid, though both Google and Baidu reportedly would have gone much higher. Hinton accepted Google’s offer not for the money, but because he believed it was the right environment for his research to grow. His loyalty to the University of Toronto—an institution that had supported him during the “AI winter,” when the field was largely unfunded and overlooked—underscores his deep respect for academic freedom and the importance of long-term investment in innovation.
Listening to this story, it’s clear that the roots of modern AI were planted long before the explosion of generative models we see today. The rapid progress of recent years rests on decades of research, patience, and perseverance by scientists like Hinton, who pursued ideas others had abandoned. It’s a powerful reminder that true innovation often comes from those willing to nurture unconventional thinking, even when it’s out of fashion.
In today’s race to build ever-larger and more capable AI systems, the ownership, use, and curation of ideas have never mattered more. Hinton’s story shows that ideas are not just commodities to be bought and sold—they’re the seeds of progress, deserving of care and integrity. As companies race to outdo one another in scale and power, preserving the ethical stewardship of knowledge is essential. If innovation becomes driven solely by competition and profit, we risk losing the very spirit of curiosity and collaboration that made breakthroughs like deep learning possible in the first place.
This post was developed with the assistance of OpenAI’s GPT-5, an AI language model