I read an article last week that called the AI boom a "rational bubble."

That's the most accurate description I've heard yet.

Not a scam. Not pure hype. But also not the revolution everyone's claiming it is right now.

A rational bubble. Real gains. Real investment. But massively overestimated timelines.

From where I sit, building automation systems and working with AI tools every day, I see both sides playing out in real time.

The Part That's Actually Working

AI is solving real problems right now.

I used to spend three hours debugging code to find one stupid error. Now I paste the error into Claude or GPT, and it points me to the exact line that's broken in about 30 seconds.

That's not hype. That's three hours back in my week.

Client work that used to take two weeks now takes one. Not because I'm working faster. Because AI handles the repetitive parts so I can focus on the actual problem-solving.

A manufacturing client needed to process hundreds of invoices manually. GPT extracts the data now. Takes 10 minutes instead of 8 hours.

These are real productivity gains. Not "maybe in the future" gains. Happening right now.

Code generation tools are making junior developers productive way faster than they used to be. Pattern recognition for debugging is legitimately impressive. Infrastructure automation that should have existed five years ago is finally here.

The tools work. The investment isn't stupid. The gains are measurable.

That's the rational part.

The Part That's Still A Bubble

Every company is slapping "AI-powered" on their marketing.

Half of them are just using basic automation with an LLM wrapper. The other half are using AI for things that don't need AI.

I had a potential client last month who wanted GPT to handle their scheduling. They didn't need AI. They needed a calendar app and 15 minutes of setup.

But "AI-powered scheduling" sounds better than "we finally set up Google Calendar correctly."

The costs are also still absurd for most use cases.

Running AI models at scale gets expensive fast. A lot of these "AI features" cost more to operate than they save in productivity.

We're also deploying tools without fully understanding how they fail.

AI is great until it's confidently wrong. And it's really hard to catch when that happens. A human making a mistake usually shows signs of uncertainty. AI just delivers incorrect information with the same confidence as correct information.

That's fine for code suggestions I can verify. It's not fine for high-stakes decisions.

The productivity gains are real. But they're not evenly distributed. If you're technical and know how to prompt these tools, you're seeing massive benefits. If you're not, you're probably getting mediocre results and wondering what the hype is about.

We're Overestimating The Timeline

This is the actual bubble part.

The tools work well enough that serious investment makes sense. But we haven't figured out what AI is actually good for versus what we wish it was good for.

Everyone's acting like we're two years away from AI that can replace entire job functions.

We're probably five to ten years away from that. Maybe longer.

Right now, AI is really good at being an incredibly fast assistant. It's not good at being autonomous.

I can use AI to write the first draft of code. I can't trust it to build and deploy an entire system without oversight.

I can use AI to summarize information. I can't trust it to make strategic decisions based on that information.

The gap between "helpful tool" and "replaces human judgment" is bigger than the marketing suggests.

But because the gains from the "helpful tool" phase are so visible, everyone's extrapolating that we'll hit the "replaces humans" phase soon.

That's where the bubble thinking comes in.

How To Build In A Rational Bubble

This is the tricky part.

You can't ignore AI. The productivity gains are too real. If you're not using these tools, you're falling behind people who are.

But you also can't bet your entire business on capabilities that might not exist yet.

Here's what I'm doing.

I use AI extensively for things it's proven to be good at. Code generation. Debugging. Content drafting. Data extraction. Pattern recognition.

I don't use AI for things that require judgment calls or have high failure costs. Strategic decisions. Client-facing communication without review. Anything where being confidently wrong would cause real problems.

I'm investing time in learning these tools because the time savings are worth it. But I'm not restructuring my entire business model around AI capabilities that might not materialize.

When clients ask me about AI, I tell them the same thing. Use it where it clearly saves time and money today. Don't rebuild your whole operation based on what AI might be able to do in three years.

The companies getting burned are the ones betting everything on the timeline being shorter than it is.

The companies winning are the ones using AI as a really good productivity tool right now, while staying flexible about what comes next.

It's Both Things At Once

The confusing part is that both sides are right.

The skeptics who say it's overhyped? They're right. The timelines are inflated. The promises are bigger than the reality.

The believers who say it's transformative? They're also right. The productivity gains are real. This is genuinely changing how work gets done.

It's a rational bubble. Real value. Real investment. But also real overestimation of how fast we'll get to the next level.

I'm seeing actual results every day. Client projects finishing faster. Code working better. Problems getting solved that used to take way longer.

But I'm also seeing companies waste money on AI features that don't make sense. Projects that would be better served by basic automation getting overcomplicated with AI. Marketing that promises capabilities that don't exist yet.

The smart move is probably somewhere in the middle.

Use the tools. Get the productivity gains. But don't bet the farm on AI solving problems it's not ready to solve yet.

We're in the phase where the technology works well enough to be useful, but not well enough to be autonomous.

That's going to last longer than most people think.