Experts Say Metaverse Will Not be Common in Consumer Life in 2040. Why?

Experts surveyed by Pew Research believe that augmented and mixed-reality applications will dominate full virtual reality environments in 2040. But half of the experts also believe the “metaverse” will not be common in the lives of most consumers by that point. 

source: Pew Research 

This will be unwelcome news for many metaverse proponents. But it is historically realistic. 

Major technology transitions typically take much longer than proponents expect. One common facet of new technology adoption is that change often comes with a specific pattern: a sigmoid curve such as the Gompertz model or Bass model. 

S curves explain overall market development, customer adoption, product usage by individual customers, sales productivity, developer productivity and sometimes investor interest. It often is used to describe adoption rates of new services and technologies, including the notion of non-linear change rates and inflection points in the adoption of consumer products and technologies.

In mathematics, the S curve is a sigmoid function. It is the basis for the Gompertz function which can be used to predict new technology adoption and is related to the Bass Model.

Such curves suggest a longish period of low adoption, followed by an inflection point leading to rapid adoption.

That leads supporters to overestimate early adoption and vastly underestimate later adoption. Mobile phone adoption, and smart phone adoption, illustrate the process. You might think adoption is a linear process. In fact, it tends to be non-linear.

Also, the more fundamental the change, the longer to reach mass adoption. Highly-useful “point technologies” such as telephones, electricity, mobile phones, smart phones, the internet and so forth can easily take a decade to reach 10-percent adoption. Adoption by 40 percent of people can take another decade to 15 years. And adoption by more than 40 percent of people can take another decade to 15 years. 

source: MIT Technology Review 

That suggests a 30-year adoption cycle for a specific innovation that has high value to be used by 40 percent to 70 percent of people. Something such as metaverse, which is far more complicated, could easily take 30 years to reach 40 percent of people in ordinary use. 

That might mean at least a decade before metaverse apps are in common use by 10 percent of people. Even then, use cases are likely to be dominated by gaming, business communications and video entertainment. 

source: Robert Patterson 

The sigmoid function arguably is among the most-important mathematical expressions one ever encounters in the telecom, application and device businesses. It applies to business strategy overall, new product development, strategy for legacy businesses, customer adoption rates, marketing messages and  capital deployment, for example. 

The sigmoid function applies to startups as well as incumbents; software and hardware; products and services; new and legacy lines of business. 

source: Innospective

The concept has been applied to technology adoption in the notion of crossing the chasm of value any technology represents for different users. Mainstream users have different values than early adopters, so value propositions must be adjusted as any new technology product exhausts the market of early adopters. Early adopters can tolerate bugs, workarounds or incomplete on-boarding and support experiences. They tend to be price insensitive. 

It always takes longer than one expects for a major new innovation to become ubiquitous. Metaverse, being a complicated development, might take longer than any point innovation.

What Drives Mobile Revenue Growth After M2M or Internet of Things?

One common facet of new technology adoption is that change often comes with a specific pattern, namely a longish period of low adoption, followed by an inflection point leading to rapid adoption.

That leads supporters to overestimate early adoption and vastly underestimate later adoption. Mobile phone adoption, and smart phone adoption, illustrate the process. You might think adoption is a linear process. In fact, it tends to be non-linear.

In developing regions, mobile phone adoption hit an inflection point about 2003, for example. What will happen, relatively shortly, is market saturation. That's also part of the adoption process.

In developed markets, saturation of mobile phone usage has shifted growth to mobile data. Inevitably, growth will saturate even for data, and service providers will make a transition to yet another growth mode.

In large part, that explains high interest in machine to machine or Internet of Things investments by mobile service providers. It is possible that the next wave of revenue growth will have to come from mobile devices not directly used by people.

It also is possible the following wave, after M2M, will involve some sort of shift to third party or over the top apps.

Granted, adoption rates for digital technologies have accelerated. It took 39 years for fixed line telephone adoption to grow from 10 percent to 40 percent. Electricity required 15 years to grow from 10 percent to 40 percent penetration.

In the past, 10 percent adoption of any new technology is an important milestone, as it tends to represent the inflection point, when adoption of some new innovation accelerates. Observers of technology adoption might say that happens because people adopt new technologies when somebody they know has done so.

But it also often is the case that it takes time for people to learn how to use a technology. Some would say a disjuncture between spending on new technology and measurable productivity gains can happen because the value of important new technologies often requires a redesign of business processes, not the automation of older practices.

One might also argue that technology sometimes leads to a change in consumer behavior only when a reasonable substitute product is available, and people have learned how to use the product or process.

Adopting a new technology is similar to  any other kind of investment, economists might argue. As in the case of the investment decision, the adoption of new technology entails uncertainty over future profit streams, irreversibility that creates at least some sunk costs and the opportunity to delay.

In other words, people can make a rational decision to delay adoption until it is clear of the value, and value outweighs the costs of acquiring and using the new technology.

In some ways, that is characteristic of consumer use of online video delivery, and the substitution of online video for traditional subscription TV.

In many ways, we are in a pre-adoption phase, in part because content owners will not support full online delivery of all content currently available as part of a video subscription. But what is happening is that people are learning to use the Internet, their PCs, smart phones and other devices as familiar ways to get and view entertainment video.

The Metaverse Could Easily Take 30 Years to Reach Ubiquity

Major technology transitions typically take much longer than proponents expect. One common facet of new technology adoption is that change often comes with a specific pattern, namely a longish period of low adoption, followed by an inflection point leading to rapid adoption.

That leads supporters to overestimate early adoption and vastly underestimate later adoption. Mobile phone adoption, and smart phone adoption, illustrate the process. You might think adoption is a linear process. In fact, it tends to be non-linear.

Also, the more fundamental the change, the longer to reach mass adoption. Highly-useful “point technologies” such as telephones, electricity, mobile phones, smart phones, the internet and so forth can easily take a decade to reach 10-percent adoption. Adoption by 40 percent of people can take another decade to 15 years. And adoption by more than 40 percent of people can take another decade to 15 years. 

source: MIT Technology Review 

That suggests a 30-year adoption cycle for a specific innovation that has high value to be used by 40 percent to 70 percent of people. Something such as metaverse, which is far more complicated, could easily take 30 years to reach 40 percent of people in ordinary use. 

That might mean at least a decade before metaverse apps are in common use by 10 percent of people. Even then, use cases are likely to be dominated by gaming, business communications and video entertainment. 

source: Robert Patterson 

The sigmoid function arguably is among the most-important mathematical expressions one ever encounters in the telecom, application and device businesses. It applies to business strategy overall, new product development, strategy for legacy businesses, customer adoption rates, marketing messages and  capital deployment, for example. 

The sigmoid function applies to startups as well as incumbents; software and hardware; products and services; new and legacy lines of business. 

source: Innospective

The concept has been applied to technology adoption in the notion of crossing the chasm of value any technology represents for different users. Mainstream users have different values than early adopters, so value propositions must be adjusted as any new technology product exhausts the market of early adopters. Early adopters can tolerate bugs, workarounds or incomplete on-boarding and support experiences. They tend to be price insensitive. 

It always takes longer than one expects for a major new innovation to become ubiquitous. Metaverse, being a complicated development, might take longer than any point innovation.

We Will Overestimate what Generative AI can Accomplish Near Term

For most people, it seems as though artificial intelligence has suddenly emerged as an idea and set of possibilities. Consider the explosion of interest in large language models or generative AI.

In truth, AI has been gestating for many many decades. And forms of AI already are used in consumer applicances such as smart speakers, recommendation engines and search functions.

What seems to be happening now is some inflection point in adoption. But the next thing to happen is that people will vastly overestimate the degree of change over the near term, as large language models get adopted, just as they overestimate what will happen longer term.

That is an old--but apt--story.

“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is a quote whose provenance is unknown, though some attribute it to Standord computer scientist Roy Amara. Some people call it the “Gate’s Law.”

The principle is useful for technology market forecasters, as it seems to illustrate other theorems including the S curve of product adoption. The expectation for virtually all technology forecasts is that actual adoption tends to resemble an S curve, with slow adoption at first, then eventually rapid adoption by users and finally market saturation.   

That sigmoid curve describes product life cycles, suggests how business strategy changes depending on where on any single S curve a product happens to be, and has implications for innovation and start-up strategy as well. 

source: Semantic Scholar 

Some say S curves explain overall market development, customer adoption, product usage by individual customers, sales productivity, developer productivity and sometimes investor interest. It often is used to describe adoption rates of new services and technologies, including the notion of non-linear change rates and inflection points in the adoption of consumer products and technologies.

In mathematics, the S curve is a sigmoid function. It is the basis for the Gompertz function which can be used to predict new technology adoption and is related to the Bass Model.

Another key observation is that some products or technologies can take decades to reach mass adoption.

It also can take decades before a successful innovation actually reaches commercialization. The next big thing will have first been talked about roughly 30 years ago, says technologist Greg Satell. IBM coined the term machine learning in 1959, for example, and machine learning is only now in use. 

Many times, reaping the full benefits of a major new technology can take 20 to 30 years. Alexander Fleming discovered penicillin in 1928, it didn’t arrive on the market until 1945, nearly 20 years later.

Electricity did not have a measurable impact on the economy until the early 1920s, 40 years after Edison’s plant, it can be argued.

It wasn’t until the late 1990’s, or about 30 years after 1968, that computers had a measurable effect on the US economy, many would note.

source: Wikipedia

The S curve is related to the product life cycle, as well. 

Another key principle is that successive product S curves are the pattern. A firm or an industry has to begin work on the next generation of products while existing products are still near peak levels. 

source: Strategic Thinker

There are other useful predictions one can make when using S curves. Suppliers in new markets often want to know “when” an innovation will “cross the chasm” and be adopted by the mass market. The S curve helps there as well. 

Innovations reach an adoption inflection point at around 10 percent. For those of you familiar with the notion of “crossing the chasm,” the inflection point happens when “early adopters” drive the market. The chasm is crossed at perhaps 15 percent of persons, according to technology theorist Geoffrey Moore.

source 

For most consumer technology products, the chasm gets crossed at about 10 percent household adoption. Professor Geoffrey Moore does not use a household definition, but focuses on individuals. 

source: Medium

And that is why the saying “most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is so relevant for technology products. Linear demand is not the pattern. 

One has to assume some form of exponential or non-linear growth. And we tend to underestimate the gestation time required for some innovations, such as machine learning or artificial intelligence. 

Other processes, such as computing power, bandwidth prices or end user bandwidth consumption, are more linear. But the impact of those linear functions also tends to be non-linear. 

Each deployed use case, capability or function creates a greater surface for additional innovations. Futurist Ray Kurzweil called this the law of accelerating returns. Rates of change are not linear because positive feedback loops exist.

source: Ray Kurzweil  

Each innovation leads to further innovations and the cumulative effect is exponential. 

Think about ecosystems and network effects. Each new applied innovation becomes a new participant in an ecosystem. And as the number of participants grows, so do the possible interconnections between the discrete nodes.  

source: Linked Stars Blog 

Think of that as analogous to the way people can use one particular innovation to create another adjacent innovation. When A exists, then B can be created. When A and B exist, then C and D and E and F are possible, as existing things become the basis for creating yet other new things. 

So we often find that progress is slower than we expect, at first. But later, change seems much faster. And that is because non-linear change is the norm for technology products.

Percentage of Portential Video Cord Cutters Less Important than Widespread Adoption of the Behavior

In the past, 10 percent adoption of any new technology is an important milestone, as it tends to represent the inflection point, when adoption of some new innovation accelerates. Observers of technology adoption might say that happens because people adopt new technologies when somebody they know has done so.

But it also often is the case that it takes time for people to learn how to use a technology. Some would say a disjuncture between spending on new technology and measurable productivity gains can happen because the value of important new technologies often requires a redesign of business processes, not the automation of older practices.

One might also argue that technology sometimes leads to a change in consumer behavior only when a reasonable substitute product is available, and people have learned how to use the product or process.

Adopting a new technology is similar to  any other kind of investment, economists might argue. As in the case of the investment decision, the adoption of new technology entails uncertainty over future profit streams, irreversibility that creates at least some sunk costs and the opportunity to delay.

In other words, people can make a rational decision to delay adoption until it is clear of the value, and value outweighs the costs of acquiring and using the new technology.

If adoption of a technology requires complex new skills, and if it is time-consuming or costly to acquire the required level of competence, then adoption might be slow, in other words. 

In the case of online video, one might note that the investment in terminals (smart phones, tablets, PCs) already has happened, or is happening. So is the level of user familiarity with the process of finding and consuming online video.

In some ways, that is characteristic of consumer use of online video delivery, and the substitution of online video for traditional subscription TV.

In many ways, we are in a pre-adoption phase, in part because content owners will not support full online delivery of all content currently available as part of a video subscription. But what is happening is that people are learning to use the Internet, their PCs, smart phones and other devices as familiar ways to get and view entertainment video.

The point is that the habits necessary to underpin a massive change in business model are being created, little by little.

That is why the current, slow shift of some consumers to abandon traditional subscriptions is not the most important trend. What is more important is users gradual habituation to consumption of online video.

To be sure, willingness to consider video “cord cutting” is increasing, according to an analysis by Frank Magid Associates.

Magid says 2.7 percent of subscription TV customers say they are “thinking” about cutting the cord in the next year. That’s up from 2.2 percent a year ago, and 1.9 percent in 2011.

Skeptics will not those are relatively small percentages, and that more people “thinking” about cord cutting is nearly always less than the number of people who will actually do so.

That arguably is less important than the fact that people widely are becoming accustomed to finding and viewing entertainment video on smart phones, tablets and PCs.

More than half of the might-be-cutters say they would do consider video cord cutting because they get enough video to keep them happy via outlets like Netflix, Hulu and Apple’s iTunes, Magid says.

More than half also say they would do so for economic reasons. That further suggests there is latent demand for other ways to consume more-affordable video entertainment.

Either way, there is a growing sense that the value-price relationship is growing unattractive, for more people.

As you might guess, the study suggests 4.4 percent of 18-to-34-year-olds are thinking about cutting the cord, a higher than average finding.

Sports enthusiasts, as you also might guess, are less likely to say they’ would consider abandoning their video subscription.

Other studies also suggest who might guess is the case, namely that users who now have learned to rely on Internet video are more likely to say they would consider cord cutting.

According to a Diffusion Group study, 8.8 percent of adult broadband users with an Internet-connected TV and traditional video service report being highly inclined to cut the cord in the next six months. That compares to 3.5 percent of adult broadband users with video service who don’t use a connected TV.

NFC Pessimism Grows, and Might be a Good Thing

Juniper Research has revised its forecasts for the global near field communications market, significantly scaling back its growth estimates for the North American and Western European markets. In some ways, that might be considered a "good" thing, to the extent that it follows a common pattern of technology adoption.

What is "good" about deflated hopes is that such periods seem "always" to happen, and are just a milestone on the way to eventual adoption on a fairly wide scale. So the argument is that dashed initial hopes mean the market is moving in the way one should expect: high hopes, disillusionment, and finally adoption.
The most significant change to the Juniper Research forecast is the amount of transaction activity NFC devices will drive, as the new forecast reduced the number of NFC devices in use only slightly.

By 2017, global NFC retail transaction values are now expected to reach $110 billion in 2017, significantly below the $180 billion previously forecast. 

Such revisions are not unusual in the predictions business, especially not for a brand new market that depends on many changes in the ecosystem.

Apple’s decision to omit an NFC chipset from the iPhone 5 has reduced retailer and brand confidence in the technology, leading to reduced point of sale) rollouts, for example.
This in turn will lead to lower NFC visibility amongst consumers and fewer opportunities to make payments, threatening a cycle of “NFC indifference” in the short term, Juniper Research believes.

“While many vendors have introduced NFC-enabled smartphones, Apple’s decision is a significant blow for the technology, particularly given its previous successes in educating the wider public about new mobile services” says Dr. Windsor Holden, author of the study.

The report found that Apple’s move would impact most dramatically on markets in North America and Western Europe, where transaction values would exhibit a “two year lag” on previous forecasts as retailers delay POS investments.

Conversely, retail transactions in NFC’s heartland in Japan and Korea are likely to experience little or no impact from the Apple decision.

None of that is terribly surprising. Though the 2011 KPMG Mobile Payments Outlook, based on a survey of nearly 1,000 executives primarily in the financial services, technology, telecommunications, and retail industries globally found that 83 percent of the respondents believe that mobile payments will be mainstream by 2015, even the moset astute industry observers tend to overestimate early adoption of a major new technology, while underestimating long term impact. 



Analysts at Gartner, for example, use a model of how expectations for significant new technologies running in a predictable cycle. What the cycle suggests is that expectations nearly always (always, according to the model) run ahead of marketplace acceptance.

What the Gartner hype cycle suggests is that expectations for mobile payments using near field communications are at a point where we can expect five to 10 years to elapse until NFC actually begins to make serious inroads as an adopted mainstream technology. The emphasis probably is important to note: “begins.”

In fact, Gartner's Hype Cycle now expects it will take five to 10 years before NFC is in widespread and mainstream use. Gartner's latest expectation likewise is that cloud computing and machine-to-machine applications will not be mainstream for another five to 10 years as well.

But new technologies historically take some time to reach 10 percent, then 50 percent, then virtually ubiquitous adoption. To be sure, there has been a tendency for new technologies based on digital and electronic technology to be adopted faster. But a decade period to reach perhaps 10 to 20 percent adoption is hardly unusual.

That is not much of an issue for point solutions like computers that can be used without lots of additional change in infrastructure. That is not true for highly-complex ecosystems such as payments, though.

ATM card adoption provides one example, where "decades" is a reasonable way of describing adoption of some new technologies, even those that arguably are quite useful.

Debit cards provide another example. It can take two decades for adoption to reach half of U.S. households, for example.

If Gartner analysts are right about the near field communications "hype cycle," we should continue to see "disillusionment" expressed about near term prospects for NFC. The reason is that Gartner now sees NFC at the "top" of its hype cycle, the point at which overly-optimistic projections face the reality of an extended period of development, before something "useful" actually emerges.

Internet TV, NFC payment and private cloud computing all are at what Garner calls the "Peak of Inflated Expectations," which is always followed by a period where the hype is viewed as outrunning the actual market. That suggests NFC soon will enter a phase where expectations are more measured.