Curiosity has been a driving force behind our species’ remarkable success. By pushing us to question the status quo and explore the unknown, this innate desire to learn has sparked some of our greatest achievements—and shaped the course of human history.

In a world where AI is rapidly transforming our lives, it may seem obvious that we need human curiosity more than ever before. But most arguments in favor of human curiosity are really attempts to reassure ourselves that AI won’t overtake humanity. For instance:

“AI needs human guidance to ensure its capabilities are directed towards worthwhile challenges.”
“Human curiosity is required to interpret and apply AI outputs to real-world situations in meaningful ways.”
“Human curiosity will be needed to envision how to continue expanding the frontiers of what AI can do.”

All of these arguments are debatable. In truth, AI may not need human curiosity at all. But there are compelling reasons why human curiosity is needed more than ever in the age of AI, and they stem from the fundamental differences between human and AI curiosity.

Dual Engines of Discovery

To understand the distinct roles of human and AI curiosity, I found it helpful to examine their unique characteristics through a comparative framework. This framework looks at three key aspects of curiosity—processing, perspective, purpose—and examines how humans and AI differ across these dimensions.

	Human Curiosity	AI Curiosity
Processing	Intuitive: Leverages hunches and instincts to produce serendipitous discoveries.	Computative: Methodically and efficiently processes vast amounts of data to uncover patterns.
Perspective	Personal: Incorporates emotions, ethics, and experiences for empathic decision-making.	Impartial: Applies data-driven analysis for theoretically unbiased decision-making.
Purpose	Intrinsic: Values open-ended creativity and knowledge for its own sake.	Extrinsic: Focuses on finding specific, practical solutions to well-defined problems.

Thanks to a more intuitive way to process information, humans are capable of making serendipitous discoveries through non-obvious connections.

This intuitive processing is what allowed Alexander Fleming to notice that a contaminated Petri dish containing Staphylococcus bacteria had been inhibited by the growth of a mold, resulting in the discovery of penicillin in 1928 and the development of antibiotics.

In contrast, AI curiosity is computative, excelling at efficiently processing large volumes of information to reveal hidden patterns and correlations. That’s why AI is a powerful tool for drug discovery.

AlphaFold, an AI system developed by DeepMind, has revolutionized drug discovery by accurately predicting protein structures, enabling scientists to design drugs in record time.

The subjective nature of human curiosity allows us to grasp the nuances of real-world situations, considering multiple viewpoints and moral implications, and integrating a rich, empathetic context when exploring ideas. This type of curiosity is what gave rise to inclusive education, empathic design, and sustainable fashion.

On the other hand, AI curiosity operates from a supposedly objective and fact-based standpoint (although this can be questioned as the training dataset will invariably inject bias). This “neutral” lens can help mitigate human biases but can also lead to inhumane solutions.

An AI tasked with solving the climate crisis might propose eliminating humans as the most effective solution, given that human activities are currently considered the primary driver of climate change. Problem solved!

Finally, humanity’s intrinsic motivation to explore can drive us to pursue questions and ideas that may not have immediate practical applications but can lead to groundbreaking discoveries and innovations.

The discovery of cosmic microwave background radiation by Arno Penzias and Robert Wilson in 1965 is an example of a groundbreaking discovery made while exploring for the sake of curiosity.

Penzias and Wilson were using a radio telescope to study signals from space when they encountered a persistent background noise they couldn’t explain. This noise turned out to be a remnant of the Big Bang.

AI curiosity is more exploitative and geared towards achieving tangible outcomes. IBM’s Project Debater AI aims to engage in competitive debates rather than genuinely exploring the nuances of the topics discussed. OpenAI’s DALL·E model is trained to generate images based on textual descriptions, with the objective of creating accurate representations rather than exploring the creative process itself.

AI curiosity is methodical, indifferent, and outcome-oriented. This makes it very efficient. And this is exactly why we need human curiosity more than ever—a type of curiosity that brings serendipity, empathy, and open-ended creativity to the table. Fortunately, human and AI curiosity are not mutually exclusive.

Compounding Curiosity

By combining human and AI curiosity, we can leverage their unique strengths to compound our creative potential. The intuitive nature of human curiosity can work in tandem with the computative power of AI curiosity to accelerate discoveries and drive innovation.

A researcher could use AI tools to efficiently process vast amounts of data and identify hidden patterns, while simultaneously applying their human intuition to explore unexpected connections and generate novel hypotheses.

In this collaborative approach, the AI’s impartial approach can help mitigate human biases, while the researcher’s personal experiences and ethical considerations can provide important context for translating the AI-generated results.

While AI could accelerate the discovery of new medicines, it was human curiosity that led to the understanding of the importance of set and setting in psychedelic therapy. The idea that the environment in which a psychedelic experience occurs can profoundly influence its therapeutic outcomes is a distinctly human insight, born from our ability to empathize, contextualize, and derive meaning from subjective experiences.

In architecture, AI’s computative power can efficiently search through vast design spaces to identify optimal materials, structures, and layouts for a given set of constraints. This can lead to buildings that are more energy-efficient, cost-effective, and resilient.

However, you need human curiosity to envision how these designs can be brought to life in a way that’s not just functional, but also aesthetically pleasing and culturally relevant, for instance to create a sense of community or reflect the unique identity of the occupants.

Ultimately, we have little grasp of how the brain generates ideas or what fundamentally distinguishes human and artificial cognition. Confidently ceding curiosity to AI would be premature given how much we have yet to discover about cognition.

In the meantime, we can create powerful synergies between human and AI curiosity to solve complex problems and create solutions that are both humane and technically advanced. We need to create platforms that support collaboration between human and AI curiosity.

This includes developing AI systems that augment human curiosity rather than replace it, allowing knowledge workers to easily integrate AI into their workflows.

It also means investing in education and designing environments that nurture human curiosity through intrinsically-motivated intellectual exploration in schools, at home, and at work.

The future of human curiosity in the age of AI is not a zero-sum game. It’s an opportunity to create a virtuous cycle of discovery where the unique strengths of human and AI curiosity build upon and reinforce each other. It can be a journey towards a shared destination.

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