Innovation Monitor: Exploring the Hype Cycle…Part 1

NYC Media Lab
8 min readAug 28, 2020


Innovation Monitor: Exploring the Hype Cycle…Part 1

View this email in your browser

Welcome to this week’s Innovation Monitor where we cover a range of emerging technologies, each with varying degrees of hype. This week, we’re exploring the five phases of the hype cycle.

To do this, we’ll delve into the Gartner Hype Cycle, which many consider to be the most well regarded tracker of technologies and their current phase in that cycle. This year, they distilled and categorized 1,700 technologies into the 30 profiles you see below, and recently highlighted five prevailing emerging tech trends for 2020:

  • Composite Architectures
  • Algorithmic Trust
  • Beyond Silicon
  • Formative AI
  • Digital Me

Most readers of this newsletter are likely familiar with the concept, and before we delved into each of the five technology areas, we wanted to take a deep dive into the concept of hype, as it is central to the world of innovation. We’ll take readers through the ‘Trough of Disillusionment,’ through the ‘Slope of Enlightenment’ and to arrive at the ‘Plateau of Productivity.’

So in this week’s edition, we’ll look at the Gartner Hype Cycle itself and ask: What’s the history? What are the various stages? What are some of the famous predictions? How does it apply to specific business models?

Starting next week, we’ll explore each 2020 trend — starting with composite architecture. As always, we wish you and your community safety, calm and solidarity as we support each other through this unprecedented time. Thank you for reading!

All best,
Erica Matsumoto Definition & Terminology Gartner defines the Hype Cycle as a depiction of “a common pattern that arises with each new technology or other innovation.” That pattern can be summed up as overenthusiasm, then disillusionment, followed by eventual productivity. Furthermore, the graph shows the speed at which these innovations move through the Hype Cycle by indicating how long they will take to reach the Plateau of Productivity and enter mainstream adoption.

The definition continues: “Each year, Gartner creates more than 90 Hype Cycles in various domains as a way for clients to track technology maturity and future potential.” We’ll explore some practical applications of the Hype Cycle at the end of the newsletter. For now, let’s get back to the specific phases of the pattern: Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity. Let’s take a look at each of these phases.

  • Innovation Trigger: “The Hype Cycle starts when a breakthrough, public demonstration, product launch or other event generates press and industry interest in a technology innovation.”
  • Peak of Inflated Expectations: “A wave of ‘buzz’ builds and the expectations for this innovation rise above the current reality of its capabilities. In some cases, an investment bubble forms, as happened with the web and social media.”
  • Trough of Disillusionment: “Inevitably, impatience for results begins to replace the original excitement about potential value. Problems with performance, slower-than-expected adoption or a failure to deliver financial returns in the time anticipated all lead to missed expectations, and disillusionment sets in.”
  • Slope of Enlightenment: “Some early adopters overcome the initial hurdles, begin to experience benefits and recommit efforts to move forward. Organizations draw on the experience of the early adopters. Their understanding grows about where and how the innovation can be used to good effect and, just as importantly, where it brings little or no value.”
  • Plateau of Productivity: “With the real-world benefits of the innovation demonstrated and accepted, growing numbers of organizations feel comfortable with the now greatly reduced levels of risk. A sharp rise in adoption begins (resembling a hockey stick when shown graphically), and penetration accelerates rapidly as a result of productive and useful value.”

Q&A So why is it called a cycle when it’s just a curve? Does everything pass through the cycle at the same time? And do things “fall off” the cycle? Gartner has a nice Q&A that answers a number of confusions and misconceptions — definitely worth a read, but here are a few key questions:

  • Why is it called the Hype Cycle, when it’s not a true cycle, just a curve? “The actual shape of each Hype Cycle is a dampened wave, not a cycle…. This is because it is not the innovation profiles themselves that loop around. They progress inexorably toward maturity (or obsolescence), albeit at a slower pace than we want or expect. The cycle relates to the behavior of people. As individuals… we follow a cycle of enthusiasm and disillusionment with each innovation or trend.”
  • Does everything take the same time to pass through the Hype Cycle? “No. People often misunderstand this by skim reading, or seeing the Hype Cycle republished on the web without its supporting key. We show each item taking a different time to plateau. There is no fixed timeline on the Hype Cycle.”
  • Do things fall off the Hype Cycle? “Very little ‘falls off’ the Hype Cycle if innovations are tracked based on capabilities, rather than specific ways of delivering the capabilities. Failure typically occurs where there are multiple ways to deliver the same capability or benefit. For example, broadband connectivity has made its way through the Hype Cycle over the past decade, but some of the techniques to deliver it (such as ISDN and broadband over power lines) have fallen off the Hype Cycle.”

History How does the Hype Cycle measure up to history? The first report was published in 1995 — so we have enough hindsight to see how the predictions have fared. In 2016, Michael Mullany, General Partner at Icon Ventures and ex-VMware and Netscape, published an excellent roundup of insights from 17 years of Hype Cycle reports (2000–2016).

Mullany came to believe that the “median technology doesn’t obey the Hype Cycle. We only think it does because when we recollect how technologies emerge, we’re subject to cognitive biases that distort our recollection of the past.” He attributed the Hype Cycle’s accuracy to our cognitive biases:

  • Hindsight bias: “We unconsciously ‘improve’ our memory of past predictions.”
  • Survivor bias: “It’s much easier to remember the technologies that succeed (we’re surrounded by them) rather than the technologies that fail.”

Really, Mullany notes, we’re just not that good at predicting the future. We’d recommend reading all the lessons, but two that particularly solidify the need to check our biases:

The technical insight is often correct, but the implementation isn’t there

“I was often struck by how many times the Hype Cycle had an insight that was essentially correct, but the technology or the market just wasn’t ready yet.” One example is Public Authentication Services: “First appearing in 2002 as an emerging technology, this prediction was presumably based on the release of Microsoft Passport…. It took the development of Oauth in 2007 by an informally cooperating group from Google, Twitter and Magnolia to make the promise real.”

Lots of technologies make progress when no-one is looking

“Like machine learning in the aughts, many technologies are doggedly moved forward by researchers, startups and large tech companies when their previous generation is widely seen as having failed… speech generation, text to speech and speech to speech translation appear on multiple Hype Cycles…. But again, it took deep learning breakthroughs to finally generate near-human performance just in the last year.” Applications to Business Models

The above graph is from VP Platform Ecosystem at HubSpot Scott Brinker’s 2018 piece, One thing everybody forgets about Gartner’s hype cycle. Somewhere between the peak and the “Enlightenment”, there is a period where the technology is underestimated and talk shifts to the next slick new thing. It’s here where Brinker sees an opportunity “for a savvy company to manage to the reality while competitors chase the hype cycle.”

In fact, he sees the rise as an opportunity too — to be more realistic. “It’s a variation of the age-old investment advice: buy low, sell high.” Here’s what Brinker suggests during these two phases:

At the peak of inflated expectations, you want to avoid overspending on technology and overpromising results. You don’t want to ignore the movement entirely, since there is fire smoldering below the smoke. But you want to: evaluate claims carefully, run things with an experimental mindset, and focus on real learning.”

In the trough of disillusionment, that’s when you want to pour gas on the fire. Leverage what you learned from your experimental phase to scale up the things you know work, because you’ve proven them in your business…. Reinvest your experimental efforts in pushing the possibilities ahead of the slope of enlightenment.”

Coming Up Here are the 2020 emerging tech trends we’ll be covering over the coming weeks:

  • Formative AI: “Formative AI is a type of AI capable of dynamically changing to respond to a situation. There are a variety of types, ranging from AI that can dynamically adapt over time to technologies that can generate novel models to solve specific problems.”
  • Algorithmic Trust: “For example, ‘authenticated provenance’ is a way to authenticate assets on the blockchain and ensure they’re not fake or counterfeit. While blockchain can be used to authenticate goods, it can only track the information that it is given.”
  • Beyond Silicon: “DNA computing and storage use DNA and biochemistry in place of silicon or quantum architectures to perform computation or store data. The data is encoded into synthetic DNA strands for storage and enzymes provide the processing capabilities through chemical reactions.”
  • Formative AI: “Formative AI is a type of AI capable of dynamically changing to respond to a situation. There are a variety of types, ranging from AI that can dynamically adapt over time to technologies that can generate novel models to solve specific problems.”
  • Digital Me: One example is “bidirectional brain-machine interfaces (BMIs), are brain-altering wearables that enable two-way communication between a human brain and a computer or machine interface…. In the business world, potential applications include authentication, access and payment, immersive analytics and exoskeletons.”

This Week in Business History August 24th, 1995: Microsoft Windows 95 goes on sale

It was the most publicized software launch in history up to that point, with 500 reporters attending a launch event hosted by Jay Leno and the Rolling Stones performing in commercials. Microsoft managed to sell seven million units within six weeks, but more importantly, the event provided us with this video that will forever live in tech history:

This email was sent to <<Email Address>>
why did I get this? unsubscribe from this list update subscription preferences
NYC Media Lab · 370 Jay Street, 3rd floor · Brooklyn, New York 11201 · USA



NYC Media Lab

NYC Media Lab connects university researchers and NYC’s media tech companies to create a new community of digital media & tech innovators in New York City.