Hype or real shift? Eight tests for judging a new technology wave
Every few years a technology arrives that everyone says changes everything. Sometimes it does. I've worked through enough of these waves to have a working method for telling the difference: online communities, blogs, social media, cryptocurrency, and now AI. The tests below are the ones I use with clients who have to decide what to spend on this year, while the noise is loudest.
Two ideas worth keeping in your pocket
Roy Amara, who led the Institute for the Future, gave his name to the most useful sentence about technology forecasting, now called Amara's law:
We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
Gartner's hype cycle draws the same shape as a curve: an innovation trigger, a peak of inflated expectations, a trough of disillusionment, a slope of enlightenment, and a plateau of productivity. The model has its critics, and plenty of technologies never climb out of the trough. As a reminder that loud and lasting are different measurements, both earn their place.
What I've watched up close
- Online communities. I was on The WELL and ran MemeSpace long before "community manager" was a job. The tools changed completely. The thing people wanted, a place to talk with others who care about the same subject, never changed at all, and it's why Reddit threads now feed AI answers.
- Blogs. I ran blogs on Movable Type and helped develop blogger outreach in 2006. The format peaked and faded as a fashion, and the underlying shift, anyone can publish and be found, became permanent.
- Social media. I was on Facebook in 2006 and Twitter in 2007, and I've watched platforms rise, dominate, and lose their grip. The lasting change was in how news and reputations move.
- Cryptocurrency. I co-founded and edited a daily news site covering crypto and blockchain, based in Tallinn, and interviewed the people building it. Covering a market every day teaches you to separate press releases from working software.
- AI. I use AI tools every working day and have built real software with them. I also catch them inventing things every week.
Eight tests
- Does it work when it's boring? Demos show the best case. Ask what a normal Tuesday looks like for someone who has used it for six months.
- Who pays, and for what? Lasting shifts have customers paying for a result. Hype is often funded by investors paying for growth.
- Does it survive the subsidy? Many new tools are priced below cost to win users. Ask what the work costs at a price the vendor can sustain.
- Do practitioners keep using it? Watch the people who do the work after the novelty wears off. In Stack Overflow's 2025 survey, 84 percent of developers used or planned to use AI tools while more distrusted their accuracy than trusted it. That combination, heavy use with clear eyed doubt, is what a real tool looks like in its awkward years.
- Are the objections fixed or renamed? Real shifts answer their critics with better products. Hype answers them with new vocabulary.
- Does it change a cost line? The waves that lasted made something dramatically cheaper: publishing, distribution, communication. Find the line in your budget that it shrinks.
- Can you leave? Lock in tends to grow on the peak. Prefer tools that let you take your data with you.
- What does the skeptic in the room say? Find the most knowledgeable doubter you can and listen to the strongest version of the case against.
How to act while you're unsure
Be early and small. Try new tools on real work at low cost, keep what proves itself, and drop the rest without ceremony. Write down what you learn as you go, so the decision a year from now rests on your own evidence. Avoid both betting the company on the peak and waiting for the plateau, because by then your competitors have already learned what you're just starting to.
The gray beard in the room
Here's what I've learned about my own role. The rarest person in a meeting about new technology is someone who's genuinely excited by it and still asks for the receipts. I'm both, every time. Companies bring me in as the experienced voice for a young team, as the technical skeptic on a vendor call, as an interim or white label executive who can speak for the technology, and as a guest on shows, panels, and interviews about AI and search. If you need someone to separate the real from the loud, start with my advisory work, read how I vet an AI vendor's pitch, or get in touch.