Every Technological Revolution Starts as 'Hype', 'Mania', or 'Bubble'. Railways Proved It. AI Will Too.

 

The loudest critics of new technology are often right about the bubble — and completely wrong about the future.

#AIHype   #RailwayMania   #AIisHereToStay   #TechHistory

Open any news app today and you’ll hear two stories about artificial intelligence at once. One says AI is the future and will change everything. The other says it’s a giant bubble that’s about to pop. Both sides sound completely certain. So how do you know who to believe?

Here’s a trick that works surprisingly well: when you can’t tell who’s right, look at history. Because we have, in fact, been here before. Almost 180 years ago, an entire country lost its mind over a brand-new technology. People called it a mania, a bubble, and pure hype. It would create world peace. And here’s the fascinating part — they were partly right. But they were also spectacularly wrong. That technology was the railway, and the telegraph line that accompanied each line, and it went on to reshape the entire world.

The Story of “Railway Mania”

In the 1840s, Britain fell in love with the steam train. For the very first time in human history, people and goods could travel faster than a galloping horse. A trip that once took days now took hours. To people back then, it genuinely felt like magic.

Are you still prompting like it is 2025?

Background

When people talk about 4th and 5th generation models, it's worth distinguishing "agents" — the actual systems we build by wrapping a model in tools, memory, and a goal-seeking loop — from the "agentic nature" of the models themselves, which is the model's own built-in capacity to plan, choose tools, and self-correct. The newer generations are interesting precisely because that agentic ability is increasingly baked into the model rather than scaffolded by us, so a much simpler agent can now do far more on its own. 

What's struck me most is how much the craft of prompting has changed between 2025 and 2026, when these 4th generation models really landed. Back in 2025 we were still writing long, carefully engineered prompts — spelling out the role, the steps, the format, the edge cases — because the models needed that scaffolding to stay on track. Now much of that has fallen away: you increasingly just state the goal and the constraints and let the model handle its own planning, tool-selection, and self-correction. The skill has shifted from "instructing" to "delegating" — less about dictating every step and more about clearly framing intent, giving good context, and knowing when to check the model's reasoning rather than micromanage it. Ironically, the better the models get, the less you need to know about how they work, and the more it becomes about knowing how to talk with them.