A History of the First AI-Generated Image and Art

The Ghost in the Machine: The Surprising History of the First AI-Generated Image

When we think of artificial intelligence and imagery today, our minds instantly snap to hyper-realistic, high-definition marvels conjured up by modern text-to-image models in a matter of seconds. We type a prompt, press enter, and watch a machine synthesize intricate photorealistic scenes or fantastical digital paintings out of thin air.

Yet, the story of the first AI-generated image didn’t begin in a sleek Silicon Valley tech lab with a neural network running on cloud servers. Instead, it started in the late 1960s and early 1970s, crafted not by a prompt engineer, but by a bewildered British painter trying to teach a machine how to think like an artist.

Meet AARON: The Pioneer of Machine Creativity

Long before Generative Adversarial Networks (GANs) or diffusion models, there was AARON.

Created by British-born artist and computer scientist Harold Cohen starting in 1968 at the University of California, San Diego, AARON was a pioneering computer program designed to generate original drawings. Cohen was a successful, internationally acclaimed abstract painter who represented Great Britain at the prestigious Venice Biennale. However, a profound curiosity gripped him: What actually constitutes the act of drawing? Could the cognitive rules of human creativity be codified and transferred into a machine?

To answer this, Cohen abandoned his thriving conventional art career and taught himself computer programming. By the early 1970s—with a monumental stint at the Stanford Artificial Intelligence Laboratory from 1973 to 1975—he had built AARON.

How the First “AI” Made Art

AARON was not an AI in the modern deep-learning sense. It didn’t “learn” from billions of scraped internet images. Rather, it was a symbolic, rule-based expert system (rooted in what was then called “Good Old-Fashioned AI”).

Cohen meticulously wrote code that embodied his own understanding of geometry, perspective, line weight, and spatial relationships.

• The Rules of Form: The program understood how objects overlapped, how to avoid visual collisions, and how to construct abstract and organic shapes.
• The Physical Act: Because computer screens of the early 1970s were crude, AARON didn’t just display pixels digitally. It was physically connected to a custom-built drawing machine—a robotic turtle-like arm holding a pen that moved across large sheets of paper spread out on the floor.

When AARON ran, it executed code to decide what to draw next in real-time, meaning every piece it produced was entirely unique. No human hand guided the pen once the program started; the machine made independent compositional choices within the boundaries of Cohen’s coded philosophy.

From Robotic Arms to Modern Marvels

For decades, Cohen and AARON evolved together. As computing power grew, AARON evolved from drawing simple abstract shapes into generating complex botanical forms, figures, and rich jungle environments. Cohen spent the rest of his life—he passed away in 2016—refining AARON, viewing the program not as a tool he used, but as an autonomous artistic collaborator.

The legacy of AARON fundamentally challenged long-held philosophical boundaries. It proved that the creation of art did not strictly require a biological pulse—it required a system capable of making autonomous choices based on a defined framework of rules.

Looking Back to Look Forward

Today, as we marvel at the lightning-fast output of contemporary generative tools, it’s easy to view AI art as an overnight technological explosion. But the true genesis of machine-made imagery is decades older than most realize.