The Evolution of AI: From ELIZA to GPT-4 in 5 Minutes

How We Got Here: The Breakthroughs That Transformed AI from Science Fiction to Business Essential

Author

Segdae Campbell
May 07, 2025

Good morning, operators.

Yesterday I discovered a chatbot built in the 90s. Curious, I tested it with the same questions I now routinely ask ChatGPT.

The contrast was remarkable. The 90s program responded with obvious templates and basic keyword matching. Today's AI writes creative content, generates images, and helps run entire businesses—all working toward the same goal of understanding and generating human-like responses.

This incredible evolution didn't happen by chance. It's the result of specific breakthroughs that have transformed AI from academic experiment to business necessity.

In today’s Ops & Insights:

  • AI's early days: the surprisingly capable pioneers

  • The AI winter: why progress froze for decades

  • The three breakthroughs that changed everything

  • Where we're heading next (and how to prepare)

  • Why timing matters for solo founders adopting AI today

If you're old enough to remember the early 2000s, you might recall the frustration of those first-generation voice assistants.

"Call Mom," you'd say clearly. "Calling Tom," the system would respond confidently.

Those systems were primitive by today's standards, but they represented the culmination of decades of AI research. Understanding how we got from there to here helps you see what's actually possible today — and what's still just marketing hype.

The Surprisingly Capable Pioneers (1950s-1960s)

AI's story begins with machines that shocked researchers with their capabilities.

In 1956, researchers at Dartmouth College coined the term "artificial intelligence" at a summer workshop. Their goal? Create machines that could mimic human intelligence.

Just a decade later, ELIZA appeared — a program that simulated conversation using simple pattern matching and substitution. It couldn't actually understand language, but it created the illusion of understanding so convincingly that some users formed emotional connections with it.

What made ELIZA remarkable wasn't its technical sophistication but how willing humans were to attribute intelligence to it. It's a lesson many AI companies still exploit today.

The AI Winter: When Progress Froze (1970s-1990s)

Then came the deep freeze.

After early successes, AI research hit hard limitations. Computers lacked the processing power to handle complex tasks. Data was scarce and expensive to collect. And the rule-based systems of the time couldn't handle the messiness of the real world.

Funding dried up. Research stalled. The field entered what's now called the "AI winter" — a period where progress slowed to a crawl.

Read more about it here: https://en.wikipedia.org/wiki/AI_winter

The projects that survived focused on narrow, practical applications rather than the grand vision of human-like intelligence.

The Three Breakthroughs That Changed Everything (2000s-2020s)

Three seismic shifts thawed the AI winter and created today's renaissance:

1. From Rules to Learning

Early AI relied on explicit rules programmed by humans. Today's systems learn patterns from data.

The difference? Imagine teaching someone to recognize cats by writing down every possible feature of a cat versus showing them thousands of cat photos. The second approach scales; the first doesn't.

2. Computing Power Explosion

Training modern AI models requires massive computational resources. GPT-4 would have been physically impossible to create just 15 years ago — not because the ideas weren't there, but because the hardware simply couldn't handle it.

The rise of cloud computing and specialized AI chips (like Nvidia's GPUs) made these models possible.

3. Data Abundance

Before the internet, collecting training data was expensive and time-consuming. Today, companies have access to billions of text documents, images, and videos.

This abundance of data gives modern AI systems their seemingly magical abilities to understand language, recognize images, and generate creative content.

Data Abundance: The key to unlocking the next frontier of Gen AI

The next breakthrough in Gen AI will be driven by data abundance.

pervasileiadis.medium.com/data-abundance-the-key-to-unlocking-the-next-frontier-of-gen-ai-985d6731d978

Where We're Heading

Today's frontier is multimodal AI — systems that can understand and generate across different formats (text, images, audio, video) simultaneously.

We're also seeing the rise of "agentic" AI that can take actions in the world, not just provide information. Think of an AI that doesn't just tell you about market research but actually conducts it for you.

For solo founders, these developments mean the line between "requires a team" and "can do myself with AI assistance" will continue to shift dramatically.

What's Next?

programmed, with analogies that make it intuitive even if you're not technical.

If you haven’t read the 1st article in the series, you can do so here: What is AI? Breaking Down the Buzzword for Solo Founders 

Until then, try this: find a daily task you do that follows a pattern, something repetitive but not completely identical each time. These are the perfect candidates for AI assistance.

That's all for today.

Keep building smarter, Shay

P.S. If you missed yesterday's breakdown of What is AI? Breaking Down the Buzzword for Solo Founders , check it out before diving into tomorrow's article on Machine Learning 101: How Computers Learn Without Being Explicitly Programmed.