I used to think AI just showed up in 2022 with ChatGPT and changed everything overnight. Turns out that story is completely wrong. I came across a sharp LinkedIn post from an AI professional who maps out the full 80-year arc behind the tools we use today, and it reframed how I think about where this is all going.
The author’s core point hit me right away: most founders treat AI as a feature. A bolt-on. Something to bolt onto the existing stack. But the creator argues it’s actually a platform shift that took eight decades to arrive. Miss that, and you’ll keep misreading what comes next.
Why context about the tech itself matters
The original poster admits something I found refreshingly honest: one lesson learned too late was how much context matters. Not just context about your customers or your market. Context about the technology itself.
According to this expert, the founders building the most durable companies right now understand AI’s evolution at a structural level. They know which era they’re operating in, and they build accordingly. That’s a mindset shift, not a tooling one.
The full arc, era by era
Here’s the timeline the author laid out. I think it’s genuinely fascinating to see how many winters and comebacks shaped today’s tools.
- 1943 to 1955: Logic, computation, and the first neuron models. The foundations.
- 1956 to 1969: The term “Artificial Intelligence” gets coined. Symbolic AI and early chatbots follow.
- 1970 to 1979: First AI Winter. Reality catches up with the hype, and funding dries up.
- 1980 to 1987: Expert Systems. AI starts getting used in finance, medicine, and industry.
- 1988 to 1993: Second AI Winter. Expert systems prove too costly to scale.
- 1994 to 2011: Machine Learning Era. AI learns from data instead of rules. IBM Deep Blue beats Kasparov in 1997.
- 2012 to 2017: Deep Learning Revolution. AlexNet reshapes computer vision, AlphaGo defeats Lee Sedol, and neural networks finally scale.
- 2018 to 2021: Transformer Era. Large Language Models emerge with massive gains in language understanding.
- 2022 to 2024: Generative AI. ChatGPT and similar tools bring AI mainstream, with content generation as the focus.
- 2025 to 2026: Agentic AI. AI moves beyond chatbots. Agents plan, reason, use tools, and execute tasks. Multi-agent systems become the new normal.
Where the creator sees this heading
This is the part I keep thinking about. The post’s author projects the next few years pretty clearly, and it lines up with what a lot of builders are already noticing.
- More autonomous agents that run longer tasks without hand-holding.
- Better memory and reasoning, so AI actually remembers context across sessions.
- AI-native software and businesses built around agents from day one.
- Human and AI collaborative workforces working side by side.
The companies building inside this shift now, not waiting to see where it lands, are the ones that will define what comes after.
What you can do with this right now
I think the practical takeaway from this innovator is simple but powerful. Stop asking “how do I add AI to my product?” Start asking “which era am I building in, and what does that era reward?”
Right now we’re early in the Agentic Era. So the smart move is to design for agents that plan and execute, not just chatbots that answer. Build workflows where AI handles multi-step tasks. Plan for memory and tool use as core features, not extras. That’s how you build something durable instead of a feature that ages out in a year.
The mind behind this post also shared an infographic that maps every era, its focus, and what changed. Worth a proper look if you want the visual version.
If AI is the kind of thing you think about, check out the full LinkedIn post for the complete breakdown and that era map. And maybe pass it to a founder who still thinks AI is just a ChatGPT subscription. Where do you think the Agentic Era takes us next?