Tech CEOs are promising increasingly outlandish visions of the 2030s, powered by “superintelligence”, but the reality is that even the most advanced AI models can still struggle with simple puzzles
By Alex Wilkins
13 June 2025
Are machines about to become smarter than humans?
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If you take the leaders of artificial intelligence companies at their word, their products mean that the coming decade will be quite unlike any in human history: a golden era of “radical abundance”, where high-energy physics is “solved” and we see the beginning of space colonisation. But researchers working with today’s most powerful AI systems are finding a different reality, in which even the best models are failing to solve basic puzzles that most humans find trivial, while the promise of AI that can “reason” seems to be overblown. So, whom should you believe?
Sam Altman and Demis Hassabis, the CEOs of OpenAI and Google DeepMind, respectively, have both made recent claims that powerful, world-altering AI systems are just around the corner. In a blog post, Altman writes that “the 2030s are likely going to be wildly different from any time that has come before”, speculating that we might go “from a major materials science breakthrough one year to true high-bandwidth brain-computer interfaces the next year”.
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Hassabis, in an interview with Wired, also said that in the 2030s, artificial general intelligence (AGI) will start to solve problems like “curing terrible diseases”, leading to “much healthier and longer lifespans,” as well as finding new energy sources. “If that all happens,” said Hassabis in the interview, “then it should be an era of maximum human flourishing, where we travel to the stars and colonize the galaxy.”
This vision relies heavily on the assumption that large language models (LLMs) like ChatGPT get more capable the more training data and computer power we throw at them. This “scaling law” seems to have held true for the past few years, but there have been hints of it faltering. For example, OpenAI’s recent GPT-4.5 model, which likely cost hundreds of millions of dollars to train, achieved only modest improvements over its predecessor GPT-4. And that cost is nothing compared with future spending, with reports suggesting that Meta is about to announce a $15 billion investment in an attempt to achieve “superintelligence”.
Money isn’t the only attempted solution to this problem, however – AI firms have also turned to “reasoning” models, like OpenAI’s o1, which was released last year. These models use more computing time and so take longer to produce a response, feeding their own outputs back into themselves. This iterative process has been labelled “chain-of-thought”, in an effort to draw comparisons to the way a person might think through problems step by step. “There were legitimate reasons to be concerned about AI plateauing,” Noam Brown at OpenAI told New Scientist last year, but o1 and models like it meant that the “scaling law” could continue, he argued.