Historical Parallels · September 27, 2026
AI Didn't Start in 2022: A Brief History of How We Got Here
It’s easy to think of AI as a sudden breakthrough of recent years, but the truth is, artificial intelligence has been evolving for decades—marked by cycles of optimism, setbacks, and remarkable innovation. The journey began with the Dartmouth Conference in 1956, where early pioneers expressed bold optimism about creating machines that could think like humans. Shortly after came ELIZA, one of the first programs that mimicked human conversation. While impressive at the time, ELIZA also sparked early warnings about anthropomorphism—reminding us not to mistake sophisticated pattern matching for genuine understanding.
These initial bursts of excitement were followed by what are known as AI winters—periods when progress stalled and funding dried up due to unmet expectations. Understanding these cycles matters because they highlight how hype often outpaces reality in technological development. From expert systems in the 1980s to machine learning and now deep learning, each phase has built on lessons from its predecessors.
Recognizing this historical context allows us to approach today’s AI advancements with both enthusiasm and caution. Rather than falling into nostalgia or undue skepticism, history serves as a compass guiding us through current hype toward sustainable innovation. By appreciating how far we’ve come—and the challenges overcome—we can make more informed decisions about what AI can truly achieve now and in the future.