Human beings want to make sense of chaos. We want explanations, patterns, and some sense that we know what is coming next. However, wanting an explanation does not necessarily mean wanting what is true and factual. That was one of the ideas that stood out to me during my conversation with Oxford professor Carissa Véliz. Her latest book Prophecy, Prediction, Power, and the Fight for the Future: From Ancient Oracles to AI covers the ethics of AI, privacy, and the ways power and influence shape technology and vice versa. Her work takes a much longer view of prediction by tracing the human desire to know the future from ancient oracles to algorithms. Véliz states that as human beings, we do not necessarily prefer what is true but rather what is familiar. So, when it comes to outsourcing more of our thinking to Large Language Models (LLMs) and possibly future artificial general intelligence (AGI) – defined as a hypothetical computer system that matches or exceeds human cognitive abilities across any domain – this is an important consideration. Prediction itself is not inherently bad; it's a core part of the scientific process. A hypothesis is a type of prediction itself. Within this process, we expect to observe something, but are open to it being challenged. This kind of prediction is something we carefully test, taking care that outside factors do not confuse the results and understand that it may not prove to be accurate or true. Yet, when it comes to predicting human behaviors and social dynamics, problems emerge. People are, inherently, impacted by predictions themselves, which can have an effect on future behavior, whereas something like a molecule or even the weather would not respond the same way.

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