AI has become one of the most hyped technologies in recent memory, but a lot of that excitement is starting to feel disconnected from reality. Every company now wants to be seen as an AI company, and every investor pitch somehow includes it. The tools themselves are impressive. ChatGPT, Claude, and similar systems can write, code, summarize, and automate tasks that used to take hours. For many people, that first experience creates a real sense that something big has changed. And something has. But the expectations being built on top of it are starting to go much further than what the technology can realistically support.

Look at Microsoft. It has invested heavily in AI through partnerships, infrastructure, and integration across products like Office and Azure. On paper, this looks like a perfect position. But the real question is not whether Microsoft is “using AI correctly.” The real question is whether the massive spending on data centers, compute, and model access will generate returns that justify the cost. AI is not cheap to run. Every query processed through a large model consumes compute, electricity, and hardware resources. If usage keeps scaling, costs scale with it. The assumption that more usage automatically leads to more profit is not guaranteed.

Now compare that with Uber. AI can improve Uber’s system in practical ways like route optimization, pricing efficiency, and customer support automation. But it does not solve Uber’s real problems. It does not change regulatory pressure in different countries. It does not fix the tension between drivers and pricing models. It does not guarantee long term profitability. Uber is a good reminder that adding AI into a business improves efficiency, but it does not transform the underlying economics of the company.

The same reality shows up in smaller AI driven companies working with models like Claude from Anthropic. Some products saw rapid adoption because users loved the experience. But when usage scaled, the cost structure became painful. Every interaction with a model costs money, and when pricing does not match usage intensity, companies can lose large amounts very quickly. In other words, a product can look like a success from the outside, while quietly burning cash underneath. High usage is not the same as a sustainable business.

This is where the AI narrative starts to feel overhyped. People often talk as if AI will replace entire professions quickly, but in practice it is mostly acting as a productivity tool. Lawyers use it to draft and research faster, programmers use it to write code faster, and analysts use it to process information faster. But it still makes mistakes, misreads context, and produces incorrect outputs with confidence. So instead of replacing professionals, it is currently accelerating them while still depending on human oversight. The gap between “useful assistant” and “full replacement” is still large, and ignoring that gap is where expectations start to drift away from reality.