Call it courage. Or call it blind obstinacy. But in June 2017, Google surprised the technology world by releasing an update to Google Glass. It was the first upgrade since 2014, and the addition of Bluetooth now allows the device’s users to operate their tiny wearable computers with a mouse and a keyboard.
That broadening of Glass’s features is likely to have surprised people less than the news that people still use the thing. The company stopped selling Glass to consumers and closed the website in 2015. But development has quietly continued for the business world. Companies are adapting Google Glass as a kind of visual guide for museums, as a tool for virtual assistance, and as an automated scribe for busy doctors, among other ideas. Some of those uses might well turn out to be helpful, but it’s all a long way from the hope of the product’s 2012 launch. Much of that hope soon turned out to be no more than hype fueled by the backing of a company large enough to make a difference.
Smart glasses though are not alone in being overhyped. Techcrunch recently reported on the failure of drones to have their mainstream moment. While remote-controlled aircraft have been taken up by industries including agriculture, energy and of course, defense, domestic buyers, the website argues, have been put off by poor user experience, high prices and unclear regulations. Domestic drones are now more likely to take up space in closets than take to the air. CES and other tech shows have also been filled with talk of the Internet of Things for years but other than televisions that can show YouTube, there’s little sign that households want to buy devices that are any smarter than your average dishwasher. If a refrigerator isn’t clever enough to cook you dinner, what use is it?
When Has A Technology Made It Big?
Part of the problem is defining when exactly a technology has become mainstream. Not all technologies have the potential reach of mobile phones, a product unusual in being both accessible to everyone from Nigeria to Nebraska and noticeable in their ubiquity. Other innovations can become as common as Henry Ford’s factory line without ever being seen by consumers. Few households might have bought 3D printers, for example, but the technology is now used by manufacturers and designers to build prototypes and create parts for products as varied as jet engines and shoes. Explore the coverage of 3D printing and you can browse articles asking when the technology will go mainstream, explaining why it hasn’t gone mainstream, and arguing that it has already gone mainstream.
Without a clear definition of “mainstream,” we’re often left looking at questionable metrics, such as whether companies that make the product are generating sustainable profits—an unreliable tool when companies like Amazon and Twitter can use a technology for years to serve millions of customers without ever making money. Or we’re left measuring the number of times a new technology is mentioned in the press even though broad talk of a new technology usually precedes mass take-up. By that definition artificial intelligence is much closer to the mainstream than either the internet of things or artificial intelligence.
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Understanding The Hype Cycle
In 1995, Gartner tried to identify when an innovation has gone mainstream, and predict which new technologies are likely to follow, with its Hype Cycle, a map showing the phases through which a new innovation passes on the way to mass adoption. The process begins with an Innovation Trigger. A new technology might begin in an R&D lab before start-ups begin looking for their first funding rounds, build the first products and attract the attention of early adopters. The Peak of Inflated Expectations occurs when mass media notices, suppliers grow, and activity extends beyond early adopters. It’s not long before negative press reports start to appear and the innovation starts to drop into the Trough of Disillusionment. Suppliers fail but venture capitalists offer second and third funding rounds. Take-up is still lower than 5 percent but second generation products are starting to appear.
The technology then starts to rise up the Slope of Enlightenment. Best practices develop and products are available out-of-the-box. Finally, the technology enters the Plateau of Productivity when high-growth adoption begins and 20-30 percent of the potential audience has adopted the innovation. It’s made it.
Each year Gartner plots new ideas on 90 Hype Cycles in domains ranging from social software and IT services to retail and life insurance to understand the current state of innovation. Investors should be able to use the map to assess the development of technologies and time their investments; large corporations can make sure that they adopt a new technology at the right moment, after the kinks have been worked out and the price has fallen but before their competitors are already benefitting from the greater efficiency the technology offers.
But not all technologies make it all the way through the cycle to mainstream adoption. When Michael Mullany, a General Partner at Icon Ventures, looked back over the Hype Cycle between 2000 and 2016, he found that of the 200 different technologies that had been listed in the Cycles, more than a quarter appeared for no more than a single year. Some, such as podcasting, crowdsourcing and HTML 5, had disappeared from the graph but still gone on to mainstream adoption. Others, such as Social TV, Folksonomies and Expertise Location, had sunk without a trace after a single brief appearance.
An additional 20 percent of technologies that did appear across multiple years of the Hype Cycle become obsolete before winning popularity. RSS Enterprise never followed the success of consumer RSS despite years of development. Utrawideband was at peak hype in 2004 and gone by 2008. WiMax never made it out of the Trough of Disillusionment.
“Based on the evidence here, most hyped technologies have proven not to be very valuable, and many valuable technologies have succeeded without hype,” Mullany told us.
Why Technology Fails
Writing for the World Economic Forum in January 2016, Bhaskar Chakravorti, Senior Associate Dean, International Business and Finance at Tufts University, argued that a number of factors can kill an innovation regardless of the level of hype. While the idea can be sound, the product is sometimes flawed, damaged by compromise and a failure to take risks. Chakravorti uses Windows Vista as an example, a product that won Best of CES in 2007 and was still largely ignored by consumers eighteen months later.
Pricing and the lack of an ecosystem can both block take-up, and sometimes they can do it together. Apple’s Pippin, a video game console launched during Steve Jobs’s exile from the company, was priced at $600, a tag too large for consumers wondering whether developers would create games for it. They didn’t, and the product died.
A new technology can also be overtaken by competitors before it has a chance to take off. Chakravorti describes how Microsoft’s SPOT watch used an FM radio signal to link to PDAs, MP3 players and radios. The smartphone soon put paid to that.
And, consumers don’t just want new technology; they’ll also turn it down if it’s not comfortable and easy to use. 3D television might have sounded like a good idea but users rebelled against wearing the glasses—a warning for developers of virtual reality products.
Jackie Fenn, one of the analysts responsible for creating the Hype Cycle, has conducted her own assessment of 20 years of Hype Cycles, producing four principles to predict technological progress and explain why it doesn’t happen:
- First, the path of development isn’t smooth or even, she argues, but makes “sudden jumps with imperceptible movement in between.”
- Second, the predictable obstacles to progress are “a lack of performance, infrastructure/ecosystem, user acceptance or return on investment.”
- Third, technology silos sometimes merge with other fields; technologies might fade away but come back in other forms.
- And fourth, public amazement at a new technology never survives mainstream adoption. Now that we take it for granted that we carry a small computer in our pockets, we’re back to moaning about our missing jetpacks.
Fenn offers a set of recommendations for companies trying to assess emergent technologies and cope with those principles. Her strategies include revisiting relevant technologies at least once a year to see how they’ve advanced; assessing the maturity of approaches and the applications of emerging technologies; and adopting early when technologies offer advantages in core or differentiating value propositions.
Michael Mullany is blunter. He describes Gartner’s Hype Cycle as mostly a summary of the industry conversation. “I think it’s a fun structure — it’s a nice way to array where technologies are compared to their level of hype. But looking at its track record, I don’t think you should rely on it for any kind of serious business planning.”
So how can you tell which technologies are over-hyped and likely to fade before becoming popular, and which will become a part of our daily lives?
Predicting The Future Is Only Easy In Hindsight
The future of some innovations may be relatively easy to spot. Garner notes that some consumer-driven innovations have particularly short pre-peak periods before they race away with rapid, viral adoption. If they fail to take off early they won’t take off at all.
A consumer technology that’s been discussed for a long time without being used may be unlikely to survive, especially if it could spread with a network effect. There was plenty of media coverage about Google Glass but when you never saw anyone actually wearing it, and you could have safely concluded that the hype would always be bigger than the technology.
But there are exceptions even for technology that can go viral. Anyone watching the demise of Friendster and MySpace might have assumed that the demand for social networking was much smaller than the press’s desire to talk about it. But the problem wasn’t the idea behind the technology; it was with the way the product was delivered and who it was aimed at. Once Facebook had squished the usability problems by testing its platform on restricted student audiences, it was able to meet a demand that earlier social media technology had missed.
So it’s worth looking beyond the concept itself and paying attention to the characteristics of the products currently using that idea, at the way companies are offering it and even at the way they’re rolling it out. A bad product can obscure a good idea that will take off when someone else picks it up and does it better.
That complexity helps to explain why predicting the development of technology is so difficult—and it is difficult. It’s not even easy sometimes to tell whether a technology has failed or not. Are sales of the Apple Watch a sign that wearable tech is here at last or the last nail in the coffin of an idea that nobody really wanted?
Perhaps the only way to make assessments that are even close to accurate is to forget about predicting the future of technology as a whole and focus on one area that you know well. Develop enough knowledge to understand the services that a single market needs and recognize when a company is developing them in a way that those consumers are likely to want to use.
“I think predicting the future requires an extraordinarily deep knowledge of the economics and characteristics of both the technology and its potential users as well as a good understanding of how people assess whether to use new technologies,” says Michael Mullany. “You can’t have that depth in multiple areas.”
Mullany made his own early bet on a technology in 2002 when he joined VMWare as an early employee. At that time the company had about $20 million in revenue but he was certain it would one day generate a billion dollars. “I knew the technical constellations were aligning just right and also knew that Windows-based cloud computing couldn’t be done without virtualization— and that was what IT wanted to get to,” he recalled. “But I wouldn’t have known that without first working at Loudcloud and seeing all the problems that virtualization would solve.”
Today, Mullany believes that augmented/mixed reality will happen in the next five years and will probably come from Apple and Google, companies that have the mapping technology and a large consumer platform. Enterprise taxonomy/ontology management will come through user-up data catalogs like Alation rather than CIO-down metadata managers, he predicts. And he also expects to see autonomous cars. “But if the last 20 years can be relied on, I think most of the other technologies won’t make it in the form that they’re currently envisioned.”
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