Why early AI was designed to be random — and why we stopped liking it

A math-based escape from order

In the 1950s, a group of engineers and artists debated a simple question: can machines be creative? Some, like John Pierce, an electrical engineer at Bell Labs, believed the answer was yes — but only if you let machines make mistakes ¹.

Pierce argued that math should be used to remove order from art, not reinforce it. By adding calculated bursts of randomness, a computer could break out of predictable patterns and produce something unexpected — which he saw as the spark of creativity ¹.

This idea wasn't just philosophy. Researchers started building early neural networks — computer programs loosely inspired by how the human brain works — that deliberately included randomness. Psychologists in particular were excited because these models might let them study the "messy" processes inside the human mind ¹.

Strange answers were the point

When we think of AI today, we usually picture systems like ChatGPT that give useful, correct answers. But the early pioneers valued these systems for exactly the opposite reason ¹.

They already had machines that could give reasonable, expected answers. What excited them about neural networks was their ability to produce strange, surprising, even bizarre results — the kind of output that comes from creative, dream-like thinking ¹.

These systems lived mostly in university laboratories, so a wrong or odd answer had no real-world consequences ¹. That changed as AI moved from research labs into products people actually use every day.

The sanding down of strangeness

Today, companies like OpenAI and Anthropic deliberately tone down the randomness in their systems. Legal and financial risks mean a chatbot that occasionally makes up facts or gives unexpected answers is a liability ¹.

Lindquist calls this a "sanding down" of strangeness — the creative unpredictability is still there at the core, but it is now largely suppressed ¹. This is why modern AI sometimes still hallucinates: the randomness was never fully removed, just controlled.

The research appears in the journal Past and Present, and Lindquist argues that there will always be a trade-off between creativity and predictability in machines designed to mimic human thinking ¹.

Key facts

• Early AI researchers in the 1950s intentionally added randomness to mimic human creativity
• Bell Labs engineer John Pierce argued math should remove order, not add it, to enable creative output
• Modern AI companies suppress this randomness due to legal and financial risks
• The research was published in the journal Past and Present by MIT's Benjamin Lindquist

Sources

1. Q&A: How AI came to model the “messy” human mind