We’ve all heard of the Gartner Hype Cycle and seen it wielded like a cudgel to bash questions and club doubters. Too bad it’s wrong!

Eternity ago, on a dark, dry winter evening in early 2019, both principals of Next Mile attended an opening gala for a friendly local Minneapolis advertising agency. As we filtered through the greetings, curt smiles, hors d'oeuvres, and restrained congratulations, the night's inevitable small talk proffered lessons in big optimism.

One guest introduced herself as someone who marketed and socialed for small technology product companies. Upon mentioning our focus on IoT and smart devices, she asked a question obviously intended to tee up her own pet monologue: self-driving cars were here, real, and would shove aside chaotic human-driven cars by the end of 2020.

Coincidentally, we had been at a Texas Instruments lidar demonstration that very morning. When asked about self-driving cars, TI experts responded with a "Pfft, not until solid state lidar—and that's the easy part. Navigating space and responding to changes is very difficult for software. True self-driving the way people imagine it is way, way off."

Foolishly, we shared the morning's learnings with our new acquaintance, naively assuming she'd find the facts refreshing.

"Well why would investors pour all this money into something that's not going to work?"

Evidence has a way of threatening belief.

Technology people swat away any sentiment that doesn't support the pervasive progress/inevitability narrative. Any question from true doubt to informed indifference will be reflexively dismissed as ignorance. Haven't you heard of the Hype Cycle?

Gartner's Hype Cycle is a tech legend. Every budding technology begins life with promise: after careful tending, it'll change history and mint millionaires.

However, commercialization and refinement require far heavier lifting than initial invention. As the going gets tough, perception about the technology falls into the "trough of disillusionment", drying up initial profit-making and testing the tech's faithful. Once in the doldrums, the real work gets done, challenges are surmounted, and inevitably the technology takes its rightful place in the chronicles of human achievement.

If that reminds you of the classic fictional hero's journey, that's because it is.

Cynically, the Hype Cycle more accurately reflects market makers' use of new tech’s potential implications. Speculating masses conflate a nascent technology's present reality with its future possibility, grinding its current state, imagined impact, and hypothetical profit into the same fine powder together, which is then duly snorted by aspiring believers through investors' rolled-up term sheets.

Reality sets in with the comedown. Observers, investors, and technologists alike see the tech's challenges more clearly, then crash into the trough like the omnipresent roadside ditch. They bifurcate into the faithful who hold and the disillusioned who fold. A miniscule minority in between them knows the realities of the tech, its products, and the market viability of said products.

Turns out we're not the only ones poking at the Hype Cycle. This August, the Finance & Economics writer at The Economist dropped this little doozy:

Artificial intelligence is losing hype

Inside, a finding that deserves further dissemination:

"We find, in short, that the cycle is a rarity. Tracing breakthrough technologies over time, only a small share—perhaps a fifth—move from innovation to excitement to despondency to widespread adoption. Lots of tech becomes widely used without such a rollercoaster ride. Others go from boom to bust, but do not come back. We estimate that of all the forms of tech which fall into the trough of disillusionment, six in ten do not rise again."

The Economist's core thesis is that the Gartner trajectory applies about 20% of the time at best. Gartner's vaunted Hype Cycle always felt more convenient than true, but it does explain some tech cycles, as The Economist describes:

"Railway fever gripped 19th-century Britain. Hoping for healthy returns, everyone from Charles Darwin to John Stuart Mill plowed money into railway stocks, creating a stockmarket bubble. A crash followed. Then the railway companies, using the capital they had raised during the mania, built the track out, connecting Britain from top to bottom and transforming the economy. The hype cycle was complete."

What about the rest?

If we extrapolate from the Economist and assume that Gartner's cycle is truthy ~20% of the time, what about the other 80%? As Ethan Mollick highlights in the article, "since it's vibe-based data, it is hard to say much about it definitively." That sounds like license to take a crack at our own sample space!

We've got the first two distributions from The Economist. Let's fill in the rest:

1. Gartner cycle. If it's almost 1 in 5, let's call it 15%.
2. Parabola. Trough of disillusionment is actually just the dirt. You're dead. The end. "Of those that fall into disillusionment, six in ten do not rise again". Since not all fall into disillusionment, we'll carve off a slice. 40%.
3. Flatline. Born to fail. Probably a big cohort, let's say 15% ish.
4. Flatline or hovering and sudden rise. Techs like the steam engine were invented and reinvented numerous times until finding their application. Rare. 5%.
5. Hovering. A tech that succeeds enough to exist, but changes little and doesn't generate much profit. Water's always over the next dune! 5%.
6. Linear growth. The Economist cites cloud computing as an example. "No euphoria and no bust." Best exemplified by more iterative technologies, let's call it another. But successes are rare. 10%.
7. Binomial. Just like the Gartner hero story except with an unhappy ending, like a failed comeback. Let's add another 10%.

If you wanted to bet on an emerging tech trend, those are your baseline outcomes and probabilities. The Economist, quoting investor Michael Mullany, concludes that "an alarming number of technology trends are flashes in the pan".

Let's have a look at Gartner's Hype Cycle data and the relative position of technologies on it...from 2014. How has it held up 10 years later?

We'll begin with the techs teetering at the Peak of Inflated Expectations:

• Data science
• Smart advisors
• Autonomous vehicles
• Speech-to-speech translation
• Internet of Things (2014 was peak hype)
• Natural-language question answering
• Wearable user interfaces
• Consumer 3D printing
• Cryptocurrencies
• Complex-event processing
• Big data

One glance at that list using your own Magic Powers of Insight from 2024 and you see the immediate problem: definition. What exactly did you mean by _____?

All of these still exist in some form, but many exist under a different name. Today, you could credibly roll big data, data science, smart advisors, and especially natural-language question answering together under the "AI" flag. Did these technologies actually coalesce and get repackaged as AI? Or did the darlings of the day pivot or die off? When they did rebrand as AI, did they restart the Hype Cycle from the beginning?

The second, squirrelier challenge is the degree or scale of Gartner's claim for any given technology. If by "IoT", Gartner meant the then-prevalent fantasy that every single physical object would have a cellular connection, then IoT crashed and flatlined.

If they meant that telecommunication technology-powered condition monitoring, location tracking, and remote control applications would continue to come to fruition on a niche-by-niche basis, the cycle has largely played out.

Luckily for Gartner, they can claim victory or deflect at will. Techs in the Trough of Disillusionment may prove more interesting:

• In-memory database management systems
• Content analytics
• Hybrid cloud computing
• Gamification
• Augmented reality
• Machine-to-machine communication services
• Mobile health monitoring
• Cloud computing
• NFC
• Virtual reality

Although Gartner forecast that all should've reached the Plateau of Productivity by now, you don't hear much about any of these in 2024. Are they simply part of the background now? For cloud computing, absolutely. NFC? Sort of. VR and AR continue their karmic misadventure and will be reincarnated soon enough. The remainder either disappeared with a whimper or were subsumed into the mundane world without meaningfully changing it.

These techs were cresting the Slope of Enlightenment back in '14 and should've made their impact by now:

• Gesture control
• In-memory analytics
• Activity streams
• Enterprise 3D printing
• 3D scanners
• Consumer telematics

Enterprise 3D printing turned out to matter in 2024, but it definitely hasn't conquered other forms of manufacturing as was once predicted. The rest live on today, but have been amalgamated into other, larger technology systems offerings.

The trouble with the Hype Cycle isn't that accurate or inaccurate. It's simultaneously both: a technological horoscope where viability and ruin exist in superposition.

Developing successful IoT products requires strategists to think beyond hype. At Next Mile, we've long been inoculated against tech industry bluster. If you'd like help driving a technology implementation focused on reality, contact us. Silicon Valley fantasy cannot become your strategy.