TL;DR: The comparison to NFTs, crypto, web3 and the metaverse breaks down on one point: those technologies never delivered on the underlying capability claim. AI has, in working products millions of people use daily, and that lesson carries into how I use AI on a book.
The fad argument comes from smart people, and I understand why they make it. Plenty of them got burned on NFTs and crypto, and nobody wants to be fooled twice.
I respect the instinct. I also think it’s wrong this time, and the people who hold onto it longest are going to pay for it in their careers.
What is the strongest argument that AI is a fad?
The case against AI as a fad isn’t weak.
The doubter remembers the promises. NFTs were the future of ownership. Crypto would replace banking. Web3 would replace the internet. The metaverse was where work and play would move. Each of those claims came with breathless coverage, massive funding rounds, professional credentialing, and confident predictions that holdouts would regret their skepticism. In each case the holdouts were right. The technologies either collapsed entirely or settled into niche uses far smaller than the predictions promised.
The pattern is real, and the doubter who sees AI as the same pattern is doing reasonable pattern matching.
The case against AI as a fad has merit but is incomplete.Share on X
Pattern matching has a shape that the doubter should name explicitly. New technology appears. Excitement builds. Money pours in. Companies promise transformations. Most companies fail. The remaining ones produce something narrower than the original promise. Five years later, the narrower thing exists but isn’t the world-changer it was sold as, and the doubters who waited get to feel justified. That shape repeats often enough that it’s reasonable to assume it’ll repeat again, and the doubter who pattern-matches isn’t being lazy.
Watch: Jensen Huang: AI Is Not a Trend, It’s a New Industrial Revolution, Stocks Galore - Stock Picks & Investing Insights
Why does the fad pattern fail on AI?
That pattern fits cycles where the underlying capability never delivered. NFTs promised verified ownership of digital goods, which the technology did provide, but the goods weren’t valuable, so the verification didn’t solve a real problem. Crypto promised an alternative to banking, but the actual products were worse banking with more fraud and slower transactions, and the alternative-to-banking use case turned out to matter less than the marketing claimed.
I never had any sympathy for the crypto pitch. Anyone selling a product whose main use is getting a later buyer to pay more deserves suspicion, and the doubters were right to give it. Web3 promised decentralized internet, but the decentralization came at costs people didn’t want to pay, and the architectures that delivered on it were used mostly by speculators. The metaverse promised immersive virtual spaces, but the spaces were empty and the headsets were uncomfortable, and the demand never materialized.
In every case, the failure was that the working products didn’t deliver value users wanted. The technology functioned. The market didn’t show up. Doubters who said “this will fade” were right because nobody was getting utility from the working products.
I never had any sympathy for the crypto pitch.
AI’s working products do deliver utility, in ways that show up in working data instead of in marketing decks. Hundreds of millions of people use AI chat tools weekly for real work tasks. Working developers use code assistants because they produce real productivity gains. Working designers use image generators as part of actual production pipelines. The use isn’t concentrated among speculators waiting for prices to rise.
The use is concentrated among working professionals who have built the tools into how they earn a living, and that’s the strongest signal there is. Nobody keeps paying for a tool that wastes their working day out of loyalty to a trend.
How do you test fad versus durable technology?
Watch whether people keep using it after the novelty wears off, for ordinary work, without being told to.
From the podcast
Martin Ricketts has been reading technology trends for three decades and warns about the failure mode on the enthusiast side, on Leaders and Their Stories:
There’s tons of AI tools out there and people get shiny object syndrome around this stuff.Chasing every launch and dismissing the whole category are both ways of avoiding the work of getting good at one thing.
The cleanest test for whether a technology is a fad is whether it produces value people will pay for, repeatedly, with their own money, outside the hype cycle. NFT speculation produced trading volume but no recurring use case anyone would pay for if speculation stopped. Crypto produced trading volume but the same problem. Web3 produced a small core of true believers and almost nobody else. The metaverse produced billions in spending but no visible audience.
AI passes this test cleanly. Professionals pay monthly for AI tools the way they pay for other software, with their own money, for tasks they’ve decided are worth the price. Companies pay enterprise rates because the productivity gains are visible in their numbers. The payment is recurring, the use is sustained, and the payers aren’t waiting for someone to buy from them later at a higher price.
That signal is qualitatively different from the speculation that drove the previous hype cycles, and the difference is the reason the fad framing breaks down. A piece on what AI is actually good at covers the tasks where this value is showing up.
The “but the products are bad” objection
The doubter’s response is often that the working products are bad, hallucinate, produce flat output, and disappoint users repeatedly.
The objection is partly true. Products do hallucinate, and the output is often flat, and the disappointment is real. None of that puts AI in the fad category. The products being imperfect doesn’t mean the capability isn’t there. The capability is the question, and the capability is what determines whether the technology endures or fades.
NFTs were perfectly functional. The blockchain did exactly what it was supposed to do. The technology worked. What didn’t work was demand for the use case. AI products are imperfect but the demand for the underlying capability is enormous, and that demand is producing rapid product improvement on a quarterly basis. The bad products of today are training data for the better products of next year, and the working professionals using the tools are providing feedback that improves them.
The trajectory is the opposite of fad technologies, where the underlying use case never materialized and improvement couldn’t save the products.
The “this is different than X past technology” trap
Every technology cycle includes someone arguing “this one is different,” and the argument is sometimes used to defend things that turn out to be fads.
The doubter who notices this is doing legitimate pattern matching. But the trap is that the argument is also used to describe technologies that were different and didn’t fade. The internet was different from radio in the late nineties, even though both were communication technologies in their hype cycles.
Mobile was different from PCs in the late aughts, even though both were computing platforms. Email was different from fax in the eighties, even though both were business communication tools.
The way to tell which one applies in any specific case is to look at the working data instead of the rhetoric.
Real working data on AI shows sustained paid use by professionals integrating the tools into income-producing work. Real working data on NFTs showed speculative trading by investors waiting for liquidity. Those data patterns are different categories of phenomenon. Both came with rhetoric. Only one came with the working-professional adoption that signals durable technology, and that one is the one to take seriously.
I get irritated by commentators who keep asking the fad question in public long after the data answered it. They give cautious professionals permission to stand still, and the professionals who take that permission are the ones who pay for it.
A piece on the realistic AI-and-jobs picture covers more of how the adoption is playing out in working environments.
My take
Comparisons to NFTs and the metaverse miss the point. NFTs worked fine as technology, and nobody wanted the use case. AI has messy products and enormous demand, and professionals pay for it in work that earns them money. Messy products and paying customers are the pattern of a technology that stays. I get irritated by commentators still asking the fad question, because they give careful people permission to stand still. Look at the usage data and decide.
What this means if you are still a doubter
The real position for a doubter at this point is to acknowledge that the fad framing doesn’t fit the data and to engage on a different question. A doubter can reasonably ask whether AI is overhyped, which it is. They can ask whether AI is good for everything, which it’s not. They can ask whether many companies are adopting AI badly, which they are, with predictably bad results. All of those questions are legitimate and produce useful answers.
The data has answered the fad question, and continuing to ask it only widens the experience gap. Put that energy into the real questions: how to adopt the technology well, where it helps, where it doesn’t, and what to do about the companies adopting it badly. A doubter asking those questions is useful to everyone around them. A doubter still waiting for AI to go away is going to wait a very long time while other people take the work.
I wrote The Birth of the Augmented Human about AI used as a force multiplier for thinking. It shows how people produce more, decide better and become more capable when they use these tools properly.
