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.

Amara's Law and Generative AI Outcomes: Less than You Expect Now; More than You Anticpate Later

Generative artificial intelligence is as likely to show the impact of Amara's Law as any other new technology, which is to say that initial outcomes will be less than we expect, while long-term impact will be greater than we anticipate.

Amara’s Law suggests that we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

Source

Amara’s Law seemingly is the thinking behind the Gartner Hype Cycle, for example, which suggests that initial enthusiasm wants when outcomes do not appear, leading to disillusionment and then a gradual appearance of relevant outcomes later. 

lots of other "rules" about technology adoption also testify to the asymmetrical and non-linear outcomes from new technology.  

“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 and some people call it “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. So is Amara’s Law.

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.