What Is the First AI-Generated Image? History & Facts

The Brush and the Binary: The True Story of the First AI-Generated Image

When we think of AI-generated images today, our minds instantly jump to hyper-realistic photographs, cinematic fantasy landscapes, or glossy anime illustrations whipped up by a text prompt in less than five seconds. We live in an era where anyone with an internet connection can summon a digital masterpiece by typing a few words into a tool like Midjourney or DALL-E.

But long before the phrase “generative AI” dominated headlines—decades before neural networks, diffusion models, or GPUs—a lone artist and programmer sat in front of a room-filling computer and asked a radical question: Can a machine learn what makes an image evocative?

The answer to that question gave birth to the world’s first AI-generated image.

Meet Harold Cohen and AARON

The story of the first AI image begins in the late 1960s with Harold Cohen, a celebrated British painter who had represented Great Britain at elite international showcases like the Venice Biennale. At the height of his traditional art career, Cohen did something unthinkable: he walked away from the canvas to explore computing.

Relocating to the University of California, San Diego, in 1968, Cohen taught himself how to write code. He wasn’t interested in merely using a computer as a tool to draw straight lines; he wanted to build an autonomous entity that could think and create on its own.

By 1973, Cohen’s brainchild—a pioneering software program named AARON—produced its first autonomous graphical outputs.

What Did the First AI Images Look Like?

If you are picturing sleek digital paintings, prepare to be surprised. Early AARON was not backed by billions of parameters scraped from the internet. Instead, it was built on symbolic artificial intelligence—meaning Cohen manually coded rules of art, cognition, and physical reality directly into the program.

AARON was taught:

• The fundamentals of geometry and spatial relationships.
• How overlapping objects create a sense of depth.
• Rules about human figures, flora, and abstract shapes.

Connected to a custom-built mechanical drawing machine (a flatbed plotter with a pen), AARON would autonomously scrawl primitive, black-and-white line drawings onto paper. The early results looked like minimalist, abstract, or ritualistic petroglyphs—resembling a cross between cave paintings and early modern abstract art.

Crucially, AARON made its own compositional choices. Cohen didn’t draw a sketch and ask the computer to fill it in; he set the boundaries of the system, and AARON decided where lines would fall, how shapes would interact, and what the final composition would look like in real time.

Evolution: From Lines to Color and Beyond

As computing power evolved over the decades, so did AARON.

• In the 1980s and 1990s, Cohen expanded the program’s rulebook to understand color theory, allowing AARON to paint its own creations using specialized brush-and-paint mechanisms.
• Later iterations began generating complex, organic figures, and sprawling jungle-like scenes.

AARON’s work became so culturally and historically significant that it was exhibited in prestigious venues worldwide, including the Tate Modern and the Whitney Museum of American Art. When Cohen passed away in 2016, it marked the end of a nearly 50-year partnership between a human artist and his software creation.

Why the First AI Image Matters Today

The journey from AARON’s humble 1973 pen-and-paper sketches to modern text-to-image generators is staggering. Yet, AARON fundamentally challenged the definition of human authorship decades before anyone else.

While today’s AI models rely on deep learning, massive datasets, and statistical probabilities to mimic human aesthetics, AARON operated on an understanding of rules and intent. It reminds us that generative AI is not a sudden overnight miracle of the 2020s, but the result of a half-century-long dialogue between human creativity and machine logic.