I often test new LLM models with the following question —

Given the following conditions, how many ways can Professor Y assign six different books to four different students?

  • The most expensive book must be assigned to student X.
  • Each student must receive at least one book.

The first model that could solve this problem was OpenAI’s o1 model. The second one was Google’s Gemini 2.0 Flash Thinking model. Then more thinking models came out, like DeepSeek’s R1, Anthropic’s Claude 3.7 (with extended thinking mode), all of which can solve this problem without any mistakes.

Unlike earlier models that mostly rely on pattern recognition, these thinking models use a “chain-of-thought” approach, working through problems step by step. This careful process helps them handle complex tasks in science, coding, and math much better, though it takes more time and computer power.

But I was surprised when DeepSeek’s new non-thinking model, DeepSeek-V3-0324, also got this problem right, which means either my test problem somehow ended up in the model’s training data, or maybe regular, non-thinking models are getting much better too.

I’m still not convinced that these models can make truly groundbreaking discoveries on their own. But I can’t ignore that most everyday intellectual work, including much of what we do in university education, might soon be handled well by these LLMs. When I think about this, I often remember this passage from the book Alan Turing: The Enigma —

Perhaps this was the most surprising thing about Alan Turing. Despite all he had done in the war, and all the struggles with stupidity, he still did not think of intellectuals or scientists as forming a superior class. The intelligent machine, taking over the role of the ‘masters’, would be a development which would cut the intellectual expert down to size. As Victorian technology had mechanised the work of the artisans, the computer of the future would automate the trade of intelligent thinking. The craft jealousy displayed by human experts only delighted him. In this way he was an anti-technocrat, subversively diminishing the authority of the new priests and magicians of the world. He wanted to make intellectuals into ordinary people. This was not at all calculated to please Sir Charles Darwin.

Turing’s view seems pretty spot-on for our current AI situation. As machines get better at intellectual tasks we used to think only humans could do, they challenge our habit of putting intellectual ability above other human qualities. Maybe Turing would see today’s AI progress not as something scary, but as his vision coming true— tools that make intellectual work available to everyone and remind us that being human is about much more than our ability to calculate or think logically. This way, AI might actually help reduce intellectual snobbery and point us toward the truly human qualities that machines can’t copy.