Most of us act like AI popped into existence when ChatGPT hit our screens. I used to think that way too, until I saw a post that reframed the whole thing for me. The original poster laid out something I keep coming back to: there are eight decades of research, breakthroughs, and dead ends behind the tools we touch every day.
What grabbed me is the argument underneath the timeline. The creator points out that most founders treat AI as a feature. A bolt-on. Something to bwith to the stack. But it’s a platform shift that took 80 years to arrive, and reading it as a bolt-on is exactly how you misjudge where it goes next.
The founders building the most durable things right now understand this evolution at a structural level. They know which era they’re operating in, and they build accordingly.
The full arc, era by era
Here’s the timeline the author put together. It’s genuinely fascinating when you see it in one sweep:
- 1943 to 1955: Logic, computation, and the first neuron models. The foundations get laid.
- 1956 to 1969: The term “Artificial Intelligence” gets coined. Symbolic AI and early chatbots follow.
- 1970 to 1979: The first AI Winter. Reality catches up with the hype and funding dries up.
- 1980 to 1987: Expert Systems arrive. AI starts showing up in finance, medicine, and industry.
- 1988 to 1993: The second AI Winter. Expert systems prove too costly to scale.
- 1994 to 2011: The Machine Learning era. AI learns from data instead of rules, and IBM Deep Blue beats Kasparov in 1997.
- 2012 to 2017: The Deep Learning revolution. AlexNet reshapes computer vision, AlphaGo defeats Lee Sedol, neural networks finally scale.
- 2018 to 2021: The Transformer era. Large Language Models emerge with massive jumps in language understanding.
- 2022 to 2024: Generative AI. ChatGPT brings AI to the mainstream and content generation takes center stage.
- 2025 to 2026: Agentic AI. Agents plan, reason, use tools, and execute tasks. Multi-agent systems become the new normal.
Where the expert says we’re headed
This is the part worth sitting with. The author sketches the near future like this:
- More autonomous agents doing real work end to end
- Better memory and reasoning across sessions
- AI-native software and businesses built from the ground up
- Human and AI collaborative workforces as the default setup
Notice the pattern. Every era in that list was defined by the people who built inside the shift while it was happening, not the ones who waited to see where it landed. Deep Blue, AlexNet, AlphaGo, ChatGPT: each one belonged to builders who committed early.
What to do with this now
You don’t need to memorize dates. You need to know which era you’re operating in and build for the next one. A few practical moves I pulled from the creator’s thinking:
- Stop treating AI as a feature you add later. Design your product assuming agents can plan and execute, because in this era they can.
- Invest in memory and context. The next wave rewards systems that remember, not just ones that respond.
- Watch the direction of travel, not the current tool. Today’s chatbot is a stepping stone, not the destination.
I was genuinely impressed by how the original poster tied 80 years of history into a single, usable lens. The infographic they shared maps every era, its focus, and what changed. If you build anything, or advise anyone who does, that context is worth having in your head.
Pass this along to a founder who still thinks AI is just a ChatGPT subscription. And go read the full LinkedIn post for the complete breakdown and the timeline graphic, it’s worth a proper look.