AI Hype vs Reality

People keep arguing about “AI hype.” Smart people are on both sides, which is partly why the conversation is hard to ignore.

Some think the bubble will pop. Valuations got silly, demos got loud, and maybe a lot of us got sold a story that does not hold up. Others think this is the next big shift in how work gets done and how software gets built. Both sides can sound right depending on which article you read that week. That is useful. It is also a good way to freeze and never decide anything.

I used to be the skeptic

I was mostly in the first camp for a long time. Early chatbots did not help. They would invent answers and sound completely sure of themselves. Fluent nonsense is still nonsense. When a system makes up citations, or code that almost runs but fails in weird places, being skeptical is just good sense.

It felt like a party trick with a serious marketing budget. Fine at dinner. Fragile under real pressure.

Then something shifted

Seeing newer models (things like ChatGPT 5.6) do actual coding work, and do it fast, forced me to update my view. Not because the demos looked prettier. Because the output got closer to something you might ship, tidy up, and show a client.

That was when it stopped feeling like parlor magic. The systems are still messy. They still need judgment, review, and someone who knows the domain. But the gap between “interesting toy” and “this might actually speed up real work” has closed enough that ignoring it might be its own kind of risk.

Maybe we are asking the wrong question

Whether there is an AI bubble is not the most useful thing to argue about. Markets can get frothy while the tools still change how work happens. Prices can fall and the software can still be useful. Both can be true.

The better question is more boring and more practical: how are you going to use these advances to supercharge your business?

There is no going back to a world where none of this exists. Teams that treat AI as a dinner-table debate will keep writing strategy decks. Teams that treat it as a delivery lever might start moving faster while everyone else is still arguing about the bubble.

I am not a developer (and that might be the point)

I do not have deep software engineering chops. Some computer science courses in university, over two decades ago. Some basic C++, Turbo Pascal, Turing, a bit of old COBOL. I am not a developer in any serious sense of the word.

What I do have is a few decades of SDLC work, requirements, and sitting with clients long enough to know what they actually need. Those pieces turned out to matter more than I expected.

Recently it helped me vibe-code an MVP for a client in less than a week. Same week, roughly, I also put together a full site from front end to back end: UI, customer interaction, payments, authentication, and deployment. Work that might have taken a team months not that long ago.

I am not saying AI replaces craft, architecture, or governance. I am saying that domain understanding, SDLC literacy, and clear requirements can shrink the distance between “we need this” and “here is something working.” The bottleneck shifts. Framing the problem and knowing what “good enough to test” looks like might matter more now, not less.

What reality looks like from here

Reality is not “AI does everything.” It is also not “it is all hype.” What I keep seeing is more ordinary than either slogan. People who understand the business problem can move from idea to prototype, and from prototype to something deployed, faster than the old production model assumed.

If you already know how to talk to clients, write requirements, catch edge cases, and design for real workflows, you might not be behind at all. You might be sitting on the useful part of the stack. Pair that with modern models and short feedback loops, and the economics of building software can change under your feet.

So yes, be careful about big claims. Do not trust unconstrained output. Review the work. Own the outcomes. But waiting for the debate to end before you try anything might cost you more than a few imperfect experiments. Pick a real business problem, use the best tools you can get, and ship something useful.

The hype is real.