The future of AI is not just language models. It’s causal reasoning.

1 January 2025

The future of AI is not just language models. It’s causal reasoning.

Judea Pearl, is another true pioneering in artificial intelligence. But he is a little bit different. And not on the bandwagon if LLMs. Teach machines to understand the question why.

The legacy of Judea Pearl. The future of AI is not just language models. It’s causal reasoning.

To Build Truly Intelligent Machines, Teach Them Cause and Effect

In my own journey to better understand what AI—and especially language models—are capable of, I’ve found myself looking to the past. What can we learn from the pioneers in this field before we look forward? Turing is an obvious figure to study. Danny Hillis, though perhaps less widely known, also stands out—especially given the current pace of change.

Judea Pearl is another true pioneer in artificial intelligence. But he’s a bit different. He’s not on the LLM bandwagon. In fact, Pearl argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.

Pearl brings forward a powerful argument: artificial intelligence must move beyond pattern recognition and correlation to truly master causal reasoning. He criticizes modern AI—particularly deep learning—as being limited to what he calls “glorified curve fitting.”

He believes that true intelligence requires the ability to ask and answer “why” questions—enabling machines to reason about interventions and counterfactuals (what would happen if circumstances were different). This is the foundation of causal inference, a critical yet missing piece in most of today’s AI systems.

Modern AI—especially systems based on machine learning—is highly effective at detecting patterns in data. These systems learn statistical associations: when X happens, Y often follows. But they struggle with a more profound question: Does X cause Y?

This distinction becomes crucial when moving from passive prediction to active decision-making.

Imagine a model that notices people who carry umbrellas are more likely to get wet. A traditional AI might conclude: carrying an umbrella causes you to get wet. Without understanding that rain causes both, the model could suggest avoiding umbrellas to stay dry—a dangerous misstep. This is precisely where causal inference becomes essential.

“All the impressive achievements of deep learning amount to just curve fitting. We can’t trust a system that doesn’t understand cause and effect.” Judea Pearl.

Pearl’s Legacy : the Ladder of Causation

Judea Pearl, a Turing Award winner, reshaped the conversation around artificial intelligence. He introduced the ladder of causation, which defines three levels of reasoning. The first is association, or seeing—recognizing statistical patterns, such as the observation that people who exercise tend to be healthier. The second is intervention, or doing—asking what happens if we take a specific action, like wondering whether starting to exercise will make us healthier. The third is counterfactuals, or imagining—considering what would have happened under different circumstances, such as asking whether we would have gotten sick if we had exercised last year. Pearl’s work challenges us to move beyond curve-fitting. He shows that causal reasoning is not only possible—but necessary—for building truly intelligent systems. His ideas push AI toward understanding, not just recognizing.

The most fruitful way to think of intelligence is as the ability to answer ‘What if?’ questions.

Most AI systems today operate only at the first level of Pearl’s ladder: recognizing associations. Pearl’s work enables us to move to the second and third levels—intervention and counterfactual reasoning—which are essential for building truly intelligent systems. With this capability, AI could diagnose problems by identifying why a failure occurred, plan actions by determining what should be done to prevent it, adapt to new situations by assessing whether a model will still work in a different context, and provide explanations by justifying its decisions through causal reasoning. In sectors like healthcare, public policy, and economics, this isn’t just useful—it’s essential.

As Pearl himself puts it provocatively:

An AI system that cannot reason causally is not intelligent.

References

The Book of Why (Pearl & Mackenzie, 2018)

Causality: Models, Reasoning, and Inference (Pearl, 2009)

Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE, 109(5), 612-634.

Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: Foundations and learning algorithms. The MIT Press.

LeCun, Y. (2022). A path towards autonomous machine intelligence. Retrieved from https://openreview.net/pdf?id=BZ5a1r-kVsf