Opinion
What Would Leonardo da Vinci Ask AI?
If Leonardo da Vinci were alive in 2026, what would he ask an AI? 25 questions about nature, invention, art, anatomy, machines, observation, and the boundaries between disciplines.
If Leonardo da Vinci were alive in 2026, he would probably use AI less as an answer machine and more as an instrument for observation, experimentation, and connection. He would ask it to find relationships between things that appear unrelated: anatomy and engineering, water and architecture, birds and machines, mathematics and painting, perception and reality.
More than almost any other historical figure, Leonardo treated curiosity as a method. He observed nature, dissected bodies, studied motion, designed machines, painted, sketched, measured, and wrote constantly about what he saw. The surviving notebooks — thousands of pages of mirror-written observation — read less like a body of conclusions than a record of sustained attention.
An AI would give him something he never had: a tool capable of rapidly searching across enormous bodies of knowledge and connecting disciplines at a scale that would have been unimaginable in Renaissance Italy. But I suspect that would only make him more curious.
Because Leonardo's real question was rarely simply "What is this?" It was: "What else is this connected to?"
The Short Answer
What would Leonardo da Vinci ask AI?
If Leonardo da Vinci were alive today, he would likely ask AI to find hidden connections between disciplines — anatomy and engineering, water and architecture, birds and machines, mathematics and painting. We cannot know exactly what he would ask; the 25 questions below are speculative, grounded in the subjects and intellectual habits documented in his notebooks. His defining question was never "What is this?" but "What else is this connected to?"
Leonardo's notebooks reveal a mind that refused to respect the boundaries we now place between art, science, engineering, anatomy, mathematics, and natural philosophy. He could move from the structure of a human heart to the movement of water, from the mechanics of flight to the geometry of painting.
That makes him an unusually interesting hypothetical conversation partner for modern AI. We cannot know what Leonardo would actually ask a machine that did not exist in his lifetime. The questions that follow are therefore imaginative — but grounded in the subjects, methods, and intellectual habits associated with his work.
And perhaps the most interesting part is not what Leonardo would ask AI. It is what AI would allow Leonardo to ask next.
Part One
How does nature work?
Leonardo's starting point was observation. Before attempting to explain something, he wanted to look at it. He studied water, birds, rocks, plants, muscles, bones, light, clouds, flowing currents, and the movement of bodies, returning to nature as both teacher and laboratory. An AI would give him the ability to compare observations across disciplines almost instantly.
What is the simplest principle that explains the movement of water?
Not merely how water flows, but whether apparently different phenomena — eddies, waves, currents, turbulence — could be understood through deeper common principles. Leonardo studied turbulent flow centuries before the mathematics existed to describe it.
Find three phenomena in nature that appear unrelated but are governed by the same underlying pattern.
Leonardo's instinct was to look for analogies. The spiral of water. The growth of plants. The movement of air. The structure of a shell. Perhaps the same mathematical relationships appear where we least expect them.
What natural system is more elegantly designed than anything humans have built?
The answer might be the human eye. Or a leaf. Or an ecosystem. Or something humanity has barely begun to understand. Leonardo probably wouldn't accept the first answer — he'd ask why.
What does nature accomplish with almost no material that humans still struggle to reproduce?
This question leads directly toward modern biomimicry. Nature does not manufacture a bird's wing the way an engineer manufactures an aircraft, nor construct a tree according to a conventional blueprint. Its solutions emerge through processes operating over enormous periods of time.
If nature is the greatest engineer we know, what engineering principles are we still failing to learn from it?
And perhaps AI's answer would become the beginning of another question. Leonardo's curiosity was rarely satisfied by conclusions.
Part Two
Can machines become like living things?
Leonardo designed machines that could fly, move, lift, transport, defend, and perform mechanical work. Some were practical, some speculative, some centuries ahead of their time. He was fascinated by the possibility that understanding nature could allow humans to reproduce its mechanisms.
Today, engineering increasingly does exactly that. Robotics imitates biological movement. Computer vision imitates aspects of perception. Neural networks are inspired, at least loosely, by biological systems. The economics of that imitation is now its own field — see how AI agents are reshaping software for the commercial version of the same idea.
What can the structure of a bird's wing teach us about machines?
But he probably wouldn't stop at aircraft. He might ask what wings teach us about distributed forces, flexibility, energy efficiency, control, and adaptation.
Show me the deepest connection between anatomy, architecture, and engineering.
A human skeleton is a structural system. A building is a structural system. A bridge is a structural system. Different materials, different scales, potentially related principles.
If a machine could learn from nature continuously, rather than being designed once, how would we build it?
That question sounds surprisingly contemporary. It touches adaptive robotics, machine learning, evolutionary computation, and autonomous systems. But the underlying idea is deeply Leonardo-like: don't merely copy nature's appearance, understand its method.
What would a machine designed according to the principles of a living organism look like?
Not a machine shaped like an animal — a machine that behaves like one. One that adapts, repairs itself, responds to its environment, uses resources efficiently, and changes according to circumstances. That distinction matters.
At what point does a machine stop being a tool and become a new kind of organism?
AI makes that question considerably more difficult than it was during Leonardo's lifetime. A machine no longer has to have arms, gears, wheels, or wings. It can exist as an information-processing system. That may have fascinated him even more.
Part Three
What does the eye know that mathematics doesn't?
Leonardo was not simply an engineer. He was one of history's most famous observers of visual reality, studying light, shadow, perspective, anatomy, proportion, reflections, and the structure of the human eye.
For Leonardo, painting was not decoration. It was investigation. To paint a person convincingly, you needed to understand the person. To represent light, you needed to understand how light behaves. Art became another way of studying nature — which creates an extraordinary question for AI.
What does humanity understand mathematically but still fail to see?
Mathematics can describe a phenomenon without giving us an intuitive understanding of it. We can calculate orbital mechanics without intuitively experiencing spacetime, or model a cell without seeing the full complexity of its behavior. Leonardo might wonder whether understanding requires more than equations.
Can an image reveal something about reality that an equation cannot?
The answer isn't necessarily yes or no. Different representations expose different structures. An equation can reveal relationships hidden inside an image; an image can reveal patterns difficult to perceive through abstraction. Perhaps understanding comes from moving between the two.
If you could show me one visualization that would change how humanity understands the universe, what would it be?
Imagine giving Leonardo access to modern astronomical data. The cosmic microwave background. Black holes. Galaxies billions of light-years away. The microscopic structure of matter. The human brain rendered in three dimensions. He might spend years simply looking.
What can the human eye perceive that an AI cannot — and what can AI perceive that humans cannot?
This reverses the usual question about computer vision. AI systems can process enormous quantities of visual information and operate outside the ordinary limits of human attention. But perception isn't simply detection. Seeing something is not necessarily understanding it.
Can a machine learn to see beauty, or can it only learn the patterns humans associate with beauty?
That question crosses directly from computer vision into philosophy. If an AI generates a beautiful painting, where does the beauty exist? In the image? In the algorithm? In the observer? Or in the relationship between all three?
Part Four
Why are art and science separated?
Perhaps one of the strangest things Leonardo would encounter in the modern world is the way we categorize knowledge. Artist. Scientist. Engineer. Architect. Mathematician. Designer. These are now treated as distinct professional identities.
Leonardo would likely find the separation artificial. The study of anatomy improved his art. The study of geometry informed perspective. The study of mechanics informed his inventions. For him, disciplines were tools, not walls.
Why does modern civilization divide knowledge into disciplines when nature itself does not?
A tree doesn't know whether it belongs to biology, mathematics, materials science, or architecture. Those are human categories. Nature simply operates.
Find a problem that cannot be solved by one discipline alone.
The answer could be almost anything important. Climate change. Human aging. Artificial intelligence. Energy. The brain. Cities. Space exploration. The hardest problems increasingly exist at the intersections.
What is the deepest connection between art and science?
Perhaps both are attempts to construct representations of reality. Science seeks models that predict; art seeks representations that reveal. Neither simply copies the world. Both transform it into another form so that humans can perceive something differently.
If I gave an artist, an engineer, a biologist, and an AI the same problem, what would each see that the others would miss?
This might be one of the most valuable uses of AI. Not replacing perspectives — combining them.
Find the questions that exist between disciplines rather than inside them.
Those might be the questions nobody has properly claimed. And sometimes those are the most important ones.
Part Five
What has humanity failed to observe?
Leonardo lived before modern physics, biology, computers, spaceflight, genetics, and neuroscience. Yet his notebooks repeatedly demonstrate something modern civilization sometimes forgets: having more information does not necessarily mean observing better.
We can have cameras everywhere and still fail to notice what is happening around us. We can collect billions of measurements without understanding the system generating them. We can know more facts while asking fewer questions. The gap between adoption and actual impact is a measured, modern version of exactly that problem.
What are humans surrounded by every day but still fail to observe?
Perhaps the answer would be hidden in plain sight. Our own behavior. Our assumptions. The environment. The patterns of cities. The structure of social networks. The consequences of technologies we use every day.
What important phenomenon have humans observed repeatedly without understanding its cause?
AI could search across scientific literature and historical records for recurring mysteries. It might find dozens. But Leonardo would probably ask for the strangest one.
What question has humanity asked incorrectly for centuries?
This is a more dangerous question. Because sometimes progress doesn't come from finding the answer. It comes from realizing that the question itself was badly constructed.
If I could observe only one phenomenon for the rest of my life, what should it be?
AI might answer with something cosmic, or microscopic, or biological. But Leonardo might choose something much simpler. Water. A human face. A bird in flight. A growing plant. The movement of clouds. Extraordinary knowledge can emerge from ordinary things when someone observes them deeply enough.
What exists in nature that humanity has looked at for centuries without truly seeing?
That may be the question AI would struggle with most. Because answering it requires knowing what humanity has not yet noticed — and ignorance is precisely what a database has the hardest time representing.
The Question Leonardo Might Ask Last
“Show me something everyone can see, but no one has truly understood.”
Leonardo's greatest advantage was not that he knew everything. He obviously didn't. It was that he remained relentlessly curious about things he didn't understand. He looked at birds and asked how flight worked. He looked at water and asked how currents moved. He looked at machines and asked whether nature had already solved the problem better.
Leonardo is often remembered as a universal genius — painter, inventor, anatomist, engineer, architect, and scientist before those categories had hardened into separate professions. But curiosity is the better description. His notebooks weren't collections of answers. They were records of questions.
That distinction becomes especially important in the age of AI. AI can retrieve information faster than Leonardo ever could, compare millions of documents, model systems, and connect ideas across fields. But none of those capabilities automatically tells us what is worth investigating. That remains a human question.
The future may not belong to the people who can ask machines for the most answers. It may belong to the people who can ask them the most interesting questions.
More in this series on Opinion, and related coverage across artificial intelligence.
Liyam Flexer
Founder & Editor, The Best Blog Ever — writes opinion on AI, physics, and the questions we’ve stopped asking.