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2025 is the year of open-endedness
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If 2016 was the year AI shocked the world by mastering Go, 2025 is shaping up to be the year they learn to innovate. This is the thesis of Ed Hughes, long-time researcher at Google DeepMind and one of the few voices charting a credible path toward AI systems capable of doing science.
In his closing talk at RAAIS this year, Hughes argued that we’re entering a new phase in the evolution of artificial intelligence: one where open-endedness becomes the central organizing principle. Not just solving problems, but defining them. Not just predicting the next token, but surfacing previously unknown unknowns. If he’s right, the next generation of AI systems won’t just be tools, they’ll be participants in the scientific process itself.
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