I used to think AI showed up out of nowhere in 2022. Then I saw a post that completely reframed how I look at this whole field. It comes from an AI professional who mapped the full 80-year arc behind the tools we use every day, and I was genuinely hooked reading it.
The core point from the original poster is simple but sharp: most founders treat AI as a feature. A bolt-on. Something to bolt onto the stack. But the expert argues it’s actually a platform shift that took eight decades to arrive, and if you don’t understand the arc, you’ll keep misreading where it’s headed next.
What stuck with me most was one line the author shared: context about the technology itself matters just as much as context about your customers or market. 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 creator laid out, and honestly it’s fascinating to see it all in one place:
- 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 starts learning from data instead of rules. IBM Deep Blue defeats Kasparov in 1997.
- 2012 to 2017: Deep Learning Revolution. AlexNet changes computer vision, AlphaGo defeats Lee Sedol, and neural networks finally scale.
- 2018 to 2021: Transformer Era. Large Language Models emerge with massive improvements in language understanding.
- 2022 to 2024: Generative AI. ChatGPT and similar tools bring AI to the mainstream, and content generation becomes the focus.
- 2025 to 2026: Agentic AI. Agents plan, reason, use tools, and execute tasks. Multi-agent systems become the new normal.
Where this goes next
This is the part I find most useful, because the expert projects out a few years instead of just recapping history. The direction the post points to:
- More autonomous agents doing real work end to end
- Better memory and reasoning across long tasks
- AI-native software and businesses, built that way from day one
- Human and AI collaborative workforces as a default setup
Why it matters now: the companies building inside this shift today, not waiting to see where it lands, are the ones the author believes will define what comes after.
What you can actually do with this
You don’t need to memorize dates to get value here. A few practical moves I pulled from the creator’s framing:
- Name your era: we’re in the Agentic phase, so ask if your product assumes agents or still assumes a chatbot.
- Stop bolting on: treat AI as the platform, not a checkbox feature added late.
- Build for memory and autonomy: the next 1 to 3 years reward tools that reason and execute, not just generate text.
The industry pro also included an infographic that maps every era, the focus of each period, and what changed. If you like seeing the big picture in one frame, that visual is worth a proper look.
Check out the full LinkedIn post for the complete breakdown and the timeline graphic. If you know a founder who still thinks AI is just a ChatGPT subscription, this one’s worth passing along.