Every few months, a new article drops with the same thesis: AI is too expensive, the bubble is about to pop, nobody can sustain these compute bills.
And every time, I think the same thing. These people have never looked at an infrastructure cost curve before.
I pay for inference. I pay for compute. I watch the bills. The cost is real and I'm not here to pretend otherwise. But I've also spent over a decade self-hosting infrastructure, and I've watched this exact movie before. The plot doesn't change. Only the actors do.
The Paper Era
In the 1960s, if a researcher at one university needed data from another faculty, the process was exactly what you think it was. Someone physically carried paper across campus.
Stacks of documents. Walked down hallways. Handed over at a desk.
That was the state of the art for knowledge sharing. Slow, expensive, limited to whoever could physically show up. The cost wasn't the paper. It was everything that didn't happen because sharing knowledge required a body in a hallway.
One Cable Changed Everything
Then someone connected two computers with a cable.
A local network. The cost was high, the tech was unproven, and most people didn't see the point. They had paper. Paper worked.
But once researchers experienced instant data sharing, there was no going back.
That idea spread across campuses. Then across cities. Then someone looked at the Atlantic Ocean and said: "What if we ran a cable across that?"
Submarine cables. Chip fabrication plants. Global routing infrastructure. Trillions of dollars poured into something most people couldn't even visualize.
And now? You send virtually unlimited data to the other side of the planet. Basically for free.
AI Is at the Cable-Laying Phase
AI infrastructure is at the "running cable across the ocean floor" phase right now.
The hardware is expensive. Training runs cost millions. The compute bills look insane.
And a whole wave of people are saying the same thing they said about the internet: "This costs too much. It's not sustainable. Who's going to pay for this?"
The same people who would have looked at a submarine cable project in the 1990s and called it a waste of money.
The Cost Curve Always Wins
Here's what every "AI is too expensive" take misses: they're evaluating a moving target with a still photograph.
The return curve on transformative infrastructure always follows the same pattern: unbearable at the start, declining faster than anyone predicted, invisible once it matures.
OpenAI's GPT-4 API pricing dropped roughly 10x within 18 months of launch. The same pattern played out with cloud compute, storage, and bandwidth. The direction is always the same.
The question was never "is this expensive?" It was always "is the return worth the investment before the cost drops?"
The Builder's Bet
The developers figuring out how to use AI now, while it's expensive and messy, are in the same position as those early networked universities.
By the time the cost drops and everyone else shows up, the experience gap is already set. You've already learned what works, what fails, what your users actually need from AI-powered features. That knowledge doesn't come from waiting. It comes from building while it's still expensive.
The Real Risk
The real cost of AI isn't what you pay for compute today.
It's what you lose by deciding you can't afford to start. It's the products you don't build, the workflows you don't automate, the competitive gap that opens while you're waiting for prices to drop.
Nobody at those universities went back to carrying paper after they saw what a network could do. And nobody who integrates AI into their development workflow today is going to rip it out when the cost is 10x lower next year.
If you're watching from the sideline waiting for the "right time" to invest in AI tooling, ask yourself: would you have waited for internet costs to drop before connecting your first two computers?
This is the short version. The full article on wows.dev goes deeper into the cost curve data, the opportunity cost math behind the paper era, and why I'm betting on AI infrastructure for my own projects despite the current price tag.
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