The AI Hype Cycle Is Real — And Here’s How to Tell What’s Actually Useful

So I was at an AI demo day last month and a founder pitched me an AI tool that “uses agentic intelligence to revolutionize the way knowledge workers leverage productivity,” which translated from VC English means “it summarizes emails.” And he pitched it with the kind of religious conviction usually reserved for new church members, and I want to be clear — I’m not the cynic in this story. I have built and shipped real things with AI tools. I’m using AI right now to write a sentence I’ll edit in six minutes. The technology is real and the lever is real and also half of what’s being built with AI right now is going to be in the dustbin by 2027, and the tell for which half is consistent enough that you can actually learn to read it. Here’s how.

The tension

The thing about hype cycles is that they’re real *and* the underlying technology is real, simultaneously. Gartner‘s curve isn’t a takedown of the technology; it’s a description of how human attention to that technology gets distributed over time. A genuinely transformative technology can have a real bubble that pops while the technology continues to advance. The first dot-com bubble didn’t invalidate the internet. The 2017 crypto bubble didn’t invalidate blockchain (whatever you think of crypto today). The fact that some AI startups are in a frothy moment doesn’t invalidate AI.

What it does invalidate is most current AI startups. Roughly 60% of AI startups founded in 2022-2023 had pivoted or shut down by 2025, according to industry trackers, and the ones still standing aren’t uniformly the ones with the best technology — they’re the ones with use cases that hold up after the hype evaporates. You don’t survive the trough by being clever. You survive by being useful when nobody’s paying attention to you.

This matters because most of the things being sold to you as “AI tools” right now will fail this test. The right question isn’t “is this AI?” The right question is “is this useful in a way that would justify its existence if the AI buzzword were replaced with ‘software’?”

The framework

The test I’ve been using to evaluate AI products, both as a buyer and a writer covering them, has three filters:

1. Does it save real time on real work? Not benchmark time, not feels-faster time. Actual workflow time, measured. If a vendor can’t tell me that their tool saves 30 minutes a day on a specific task, with a specific user, I assume it doesn’t. Most can’t.

2. Does it still make sense without the word “AI” in the description? Take the AI marketing out of the pitch. Read it as “this is software that does X.” If the software is interesting without the AI framing, the AI is real. If the software is boring without the AI framing, the AI is the entire product, and the entire product is going to be commoditized into a model API in 18 months.

3. What happens when the underlying model gets ten times better and ten times cheaper? This is the killer question. If the answer is “the product is still differentiated by [thing],” the company has a moat. If the answer is “we’d be in trouble,” they don’t, and that should affect whether you build your workflow around them.

Apply these three to anything pitched as AI and you can usually predict whether it’ll be around in three years.

Apply it

Worked examples from the current market:

• **Cursor.** Saves measurable time (yes, autocomplete is real). Interesting as software without the AI framing (it’s a better-integrated VS Code). Differentiated when models commoditize (the editor UX is the moat, not the model). Verdict: durable.
• **Generic “AI for sales emails” startup.** Saves time on a specific task (yes). Interesting as software without AI (no — it’s a wrapper around an LLM). Differentiated when models get better (no — better models eat this product directly). Verdict: probably gone in 24 months.
• **Notion AI.** Saves real time on workspace Q&A (yes). Interesting as software (yes — Notion is the product, AI is a feature). Differentiated when models commoditize (yes — the workspace is the moat). Verdict: durable.
• **AI agent that “does customer service autonomously.”** Saves time when it works (sometimes). Interesting as software without AI framing (no). Differentiated when models commoditize (no — Intercom Fin, Zendesk AI, custom solutions all eat this). Verdict: most will not survive.

The filter is mechanical. You can run it on any product in five minutes. Most products that fail this test are still raising money and signing customers in 2026; that’s fine — markets are inefficient on short timescales — but the writing is on the wall for them.

What you might try

Next time you’re considering an AI tool, run the three filters before reading the marketing site. Write down your honest assessment. Then check it against the marketing claims. The gap between the two is your signal.

For your own work, the corollary: be skeptical of building durable workflows on top of AI products that fail filter 2 or 3. The product might be useful today; it might be gone or commoditized by 2027. Don’t pay annual contracts for products that don’t survive this analysis.

The hype is real. The technology is also real. Telling the two apart is a skill that pays off in the worst environment and the best one alike.