Is It the "Year of X"?

It’s that time of year when some feel compelled to prognosticate on “what will happen next year,” while others remind us of what did happen “last year.” And there always are a brave few who will try to capture the essence in a single phrase: “the year of X,” whatever X is said to be.

At a high level, we might well look back at such highly-distilled “year of X” predictions and note that it almost never happens. “The year of X,” whatever X is said to be, nearly always occurs (in the sense of commercial adoption or inflection point of adoption) some future year.

My simple way of describing this situation is to say that “whatever is said to be the ‘year of X’ trend almost ensures it will not be.” Of course, some will argue that is not what they mean.

Instead, they tend to mean this is the year some trend is popularized or discovered. Okay, in that sense, there is firmer--yet still tenuous--ground to stand on. Rarely does a big new thing just burst on the scene, in terms of public awareness, in a decisively-new way,

What does happen is that some arbiter “proclaims” that this has happened. It’s arbitrary.

The point is that any truly-significant new technology, platform or commercial activity takes quite some time to reach commercialization, and typically quite long after all the hype has been crushed by disillusionment.

source: Gartner

The point is that even highly-successful new technologies can take decades to reach commercial ubiquity, even if today’s software-driven products are adopted faster than innovations of the past.

It still can take a decade for widespread consumer use of any product or service to reach 50 percent to 60 percent adoption.

source: Harvard Business Review

Also, recall that most new products, and most new companies fail: they simply never succeed as commercial realities. Also, we sometimes overestimate the actual time any innovation takes to reach 10 percent or some other level of adoption on a mass level.

There is debate about how fast smartphones were adopted for example. Was it seven years or something greater than a decade for usage to reach half of consumers? Some estimate it took just seven years. Others have argued adoption never reached 50 percent after a decade.

And depending on how one defines “smartphone,” adoption levels of 50 percent took a couple of decades to nearly three decades.

source: Xapp Media

source: MIT Technology Review

For all such reasons, some of us tend to discount the notion of a “year of X.” Truly-significant innovations which achieve mass usage often take longer than expected to reach mass adoption levels. On the other hand, there arguably are points in time when public awareness seems to reach something like an inflection point.

In most cases it is difficult to measure the actual year when a shift becomes significant. Is it the point where 10 percent of people recognize a term, or say it is important? Or when 20 percent, 30 percent or 40 percent say so?

More significantly, at what point of innovation purchase or regular usage has something “arrived,” in a commercial sense?

Advanced Technology Takes Longer Than You Think to Become Mainstream

Advanced technology often does not get adopted as rapidly as the hype would have you believe. In fact, most useful advanced technologies tend not to go mainstream until adoption reaches about 10 percent. That is where the inflection point tends to occur. That essentially represents adoption by innovators and early adopters. 

source: LikeFolio

One often sees charts that suggest popular and important technology innovations are adopted quite quickly. That is almost always an exaggeration. The issue is where to start the clock running: at the point of invention or at the point of commercial introduction? Starting from invention, adoption takes quite some time to reach 10 percent adoption, even if it later seems as though it happened faster. 

source: Researchgate

Consider mobile phone use. On a global basis, it took more than 20 years for usage to reach close to 10 percent of people. 

source: Quora

That is worth keeping in mind when thinking about, or trying to predict, advanced technology adoption. It usually takes longer than one believes for any important and useful innovation to reach 10-percent adoption. 

source: MIT Technology Review

That is why some might argue 5G will hit an inflection point when about 10 percent of customers in any market have adopted it.

Measurable AI Returns; Technology J-Curve: Big Disconnect

Amara's Law suggests we will overestimate the immediate impact of artificial intelligence but also underestimate the long-term impact. 

And that is going to be a problem for financial analysts and observers who demand an immediate boost in observable firm earnings or revenue, as well as the firms deploying AI that will strive to demonstrate the benefit. 

“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.”

In fact, decades might pass before the fullest impact is measurable, even if some tangible results are already seen. 

Error rates in labeling the content of photos on ImageNet, a collection of more than 10 million images, have fallen from over 30 percent in 2010 to less than five percent in 2016 and most recently as low as 2.2 percent, according to Erik Brynjolfsson, MIT Sloan School of Management professor.

Likewise, error rates in voice recognition on the Switchboard speech recording corpus, often used to measure progress in speech recognition, have improved from 8.5 percent to 5.5 percent over the past year. The five-percent threshold is important because that is roughly the performance of humans at each of these tasks, Brynjolfsson says. 

A system using deep neural networks was tested against 21 board certified dermatologists and matched their performance in diagnosing skin cancer, a development with direct implications for medical diagnosis using AI systems.

Codified or understood as Amara's Law, the principle is that it generally takes entities some time to reorganize business processes in ways that enable wringing productive results from important new technologies. 

Source

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.

Likewise, economic historians such as Erik Brynjolfsson and Paul David have documented that transformative, general-purpose technologies tend to follow the J-curve pattern. 

Initial deployment generates negative or flat productivity returns relative to investment, often for a surprisingly long time. 

David's famous 1990 paper on the "dynamo paradox" showed that electrification of US industry began in earnest in the 1880s but didn't produce measurable aggregate productivity gains until the 1920s.

The reasons are structural: firms must reorganize workflows, retrain workers, build complementary infrastructure, and abandon legacy processes before the technology's benefits materialize. 

The productivity gains, when they finally arrive, are real and large, but they accrue after enormous sunk costs and a long gestation period.

source 

Maybe AI really will prove different. But there is ample evidence that quantifying impact could be difficult in the near term. Buckle up. 

How Much Post-Covid Change Will Businesses Achieve?

One often hears it said these days that one impact of Covid-19 on organizations and firms is that it will cause permanent changes in the ways businesses and organizations work. Most of those changes, though--agility, reaction speed, cost reduction, productivity changes, customer focus, innovation, operational resiliency, growth, financial performance, for example--were important organizational objectives before the Covid-19 pandemic. 

Even remote work on a full-time, part-time or episodic basis is a trend decades old, if many believe the difference is that a large percentage of information workers will shift to permanent remote work settings, and a larger number of observers might agree that many information workers will routinely work more often from home. 

One might note that very few of the key changes executives now say they have--because of their experience with the pandemic--can be addressed directly by better or more use of communications services. A recent survey by McKinsey found that “speed” was the driver of organizational changes related to Covid-19. 

source: McKinsey

What is not so clear is how much actual change has been accomplished, as organizational change normally is very difficult and also takes a long time. Consider the extensive changes needed to increase speed and agility.

Organizational silos, slow decision making, and lack of strategic clarity, for example, are impediments to speed. But do you really believe that big firms have been about to abolish silos, speed up decision  making and gain new strategic clarity in a few months' time?

Big barriers exist for real reasons. If they were easy problems they would have been fixed long ago. Few would doubt that executives say these long-standing issues are being addressed. But also, few of us might believe real progress is being made, fast. 

source: McKinsey

Rigid policies and formal hierarchy also are cited as impediments to speed. Have you heard of massive reductions of top level and mid-level management over the past few months?

Or consider the impressionistic claims some might make. “Higher meeting attendance and timeliness” resulted in faster decisions,” one survey respondent says. Do you really believe that? More people in meetings produced faster decisions? Much of the literature specifically argues that more people in meetings. reduces decision-making ability. 

It might be fair to hypothesize that meetings, as such, have had no discernible impact. Does anybody really believe holding more meetings improves output? In fact, there is evidence tot he contrary: more meetings mean less time for getting the actual work done. 

A team of researchers said this: “We surveyed 182 senior managers in a range of industries.  65 percent said meetings keep them from completing their own work. 71 percent said meetings are unproductive and inefficient. 64 percent said meetings come at the expense of deep thinking. 62 percent said meetings miss opportunities to bring the team closer together.”

Another leader notes that “communication between employees and executives has become more frequent and transparent, and as such, messages are traveling much more efficiently” through the organization. 

There already are signs of what might be called collaborative overload. “In most cases, 20 percent to 35 percent of value-added collaborations come from only three percent to five percent of employees. 

Colloquially, that proves the truth of the observation that “if you want something done, give it to a busy person.” The practical observation is that the highest performers are besieged with the greatest amount of demand for time spent in meetings and on work teams. At some point, the danger is that these high performers simply get asked to do too much, reducing their overall contributions and effectiveness. 

source: Harvard Business Review

Not to belabor the point, but value--assuming the insight is correct--can be gleaned by having many fewer people in meetings. 

Nor, for that matter, is it entirely clear how greater numbers of  team members working remotely actually addresses any of those aforementioned issues. Some changes could materially impact performance in a positive way. But the issue is how remote work, for example, materially abolishes silos, speeds up decision making, produces strategic clarity, abolishes rigid policies or reduces hierarchy. 

Some will argue that “employees like it.” The more accurate statement could be that “some employees prefer work from home, and some do not prefer it.” But productivity is not a matter of what people believe. It is a matter of fact, to the extent we can measure it. 

Productivity. is often hard to measure--perhaps almost impossible for information workers--and employees claiming they are productive does not make it so. Also, what we can measure might not actually correlate well with actual output. Quantity is not quality, in other words. Innovative ideas and creativity might not be measurable at all, except by reputation. 

Productivity measurements in non-industrial settings are difficult, as it often is difficult to come up with meaningful quantitative measurements that provide insight. We might all agree that not all tasks create the same value for any organization. Productivity can be described as the relationship between input and output, but “output” is tough to measure. 

And what can be measured might not be relevant. 

Even some who argue work from home productivity is just as high as “in the office” note that work from home productivity is only one percent lower than in the workplace.  

Some argue productivity now is higher or equal to productivity when most people were in offices. Some of us would argue we do not yet have enough data to evaluate such claims or evaluate the sustainable productivity gains. It is one thing when all competitors in a market are forced to have their work forces work from home.

It will be quite something else when WFH is a business choice, not an enforced requirement. As might be colloquially said, widespread WFH will last about as long as it takes for a key competitor not working that way begins to take market share. 

Not to deny that post-pandemic, some firms will find meaningful ways to restructure business processes to gain agility, productivity and speed, but the actual gains will be slower to realize than many expect, as organizational resistance to such changes will be significant. Resistance to change is a major fact of organizational life. 

Telco AI Monetization on the Revenue Front Will be Difficult

Mobile executives these days are talking about ways to monetize artificial intelligence beyond using AI to streamline internal operations. Generally speaking, these fall into three buckets:

• Personalizing existing services to drive higher revenue, acquisition and retention (quality of service tiers of service, for example)

• Creating enterprise or business services (private 5G networks with AI-optimized performance,, for example)

• AI edge computing services for autonomous vehicles, for example

Obviously, those are AI-enhanced extensions of ideas already in currency. But some of us might be quite skeptical that such “AI services” owned by telcos will get much traction. History suggests the difficulty of doing so. How many “at scale” new products beyond voice have telcos managed to create? Text messaging comes to mind. Mobile phone service also was a big success. So is home broadband. 

All those share a common characteristic: they are network services owned directly by the service providers. Generally speaking, other application efforts have not scaled well. 

Mobile service providers have been hoping and proclaiming such new revenue opportunities since at least the time of 3G. But many observers might agree there has been a disconnect between the technical leaps (faster speeds, lower latency, better efficiency) and the ability to turn those into new revenue streams beyond the basic "sell more data" model. 

That is not to say that service providers have had no other ways to add value. Bundling devices, content and other measures have helped increase perceived value beyond the core network features. 

But the core network as a driver of new products and revenue is challenging for a few reasons. 

• Open networks mostly have replaced closed networks (IP versus PSTN) 

• Applications are logically separate from network transport (layers)

• Permissionless app development is the norm (internet is the assumed network transport)

• Vertical control of the value replaced by horizontal functions (telcos had full-stack control of voice, but only horizontal transport functions for IP-based apps)

As I have argued in the past, modern telcos have a hybrid revenue model. They are full-stack “service” providers for voice and text messaging. But they are horizontal transport providers for most IP apps and services, and sometimes are app providers (owned entertainment video services, for example). 

The point is that most new apps and revenue cases can be built by third parties without telco or mobile operator permission, which also takes transport providers out of the direct revenue chain. 

So I’d argue there is a structural reason why telcos and mobile service providers do not directly benefit from most of the innovation that happens with apps. Think about all the customer engagement with internet-delivered apps and services, compared to service provider voice and messaging. 

In their role as voice and text messaging providers, telcos are “service providers” (they own and control the full stack). For the rest of their business, they are transport or access providers (capacity or internet access such as home broadband), a horizontal value and revenue stream. ISPs get paid to provide “internet access,” not the actual end user apps. 

And that has proven a business challenge for now-obvious reasons. Once upon a time, voice services were partly flat-rate and partly usage-based. In other words, telcos earned money by charging a flat fee for access to the network, and then variable usage based on number, length or distance of voice calls. 

In other words, greater usage meant greater revenue. But flat-rate voice and texting usage subverts the business model, as  most of the revenue-generating services become usage-insensitive. That is the real revolution or disruption for voice and texting. 

In their roles as internet access providers, some efforts have been made to sustain usage-based pricing. Customers can buy “buckets of usage” where there is some relationship between revenue and usage. 

Likewise, fixed network providers have used “speed-based” tiers of service, where higher speeds carry  higher prices. Still, those are largely flat-rate approaches to packaging and pricing. And the long-term issue with flat-rate pricing is that it complicates investment, as potential usage of the network is capped but usage is not.  

So as much as ISPs hate the notion that they are “dumb pipes,” that is precisely what home or business broadband access is. So internet access take rates, subscription volumes and prices are going to drive overall business results, not text messaging, voice or IoT revenues. 

To be sure, we can say that 5G is the first mobile generation that was specifically designed to support internet of things applications, devices and use cases. But that only means the capability to act as a platform for open development and ownership of IoT apps, services and value. And even if some mobile service providers have created app businesses such as auto-related services, that remains a small revenue stream for mobile service providers.  

Recall that IoT services are primarily driven by enterprises and businesses, not consumers. Also, the bulk of enterprise IoT revenue arguably comes from wholesale access connections made available to third-party app or service providers, and does not represent telco-owned apps and services (full stack rather than “access services”). 

Optimistic estimates of telco enterprise IoT revenues might range up to 18 percent, in some cases, though most would consider those ranges too high. 

Region/Group	Total Mobile Services Revenue	IoT Connectivity Revenue (Enterprises)	Automotive IoT Apps Share of IoT Revenue	% of Total Revenue from Automotive IoT Apps
Global Average	$1.5 trillion (2025 est.)	10-15% (2025, growing to 20% by 2027)	25-35%	2.5-5.25%
North America (e.g., Verizon)	$468 billion (U.S., 2023, growing 6.6% CAGR)	12-18% (2025 est.)	30-40% (high 5G adoption)	3.6-7.2%
Asia-Pacific (e.g., China Mobile)	$600 billion (2025 est.)	15-20% (strong automotive industry)	35-45% (leader in connected cars)	5.25-9%
Europe (e.g., Deutsche Telekom)	$400 billion (2025 est.)	10-15% (CEE high IoT reliance)	25-35%	2.5-5.25%
Top 10 Mobile Operators	$1 trillion (2025 est.)	12-18% (based on 2.9B IoT connections)	30-40%	3.6-7.2%

Though automotive IoT revenues (again mostly driven by access services) arguably are higher for the largest service providers, their contribution to  total business revenues is arguably close to three percent or so, and so arguably contributing no more than 1.5 percent of total revenues, as consumer services range from 44 percent to 65 percent of total mobile service provider revenues. 

Category	Percentage of Total Revenue	Example products
Services to Consumers	55-65%	Driven by mobile data (33.5% in 2023), voice, and equipment sales; 58% in 2023
Services to Businesses	35-45%	Includes enterprise, public sector, and SMBs; growing at 7.1% CAGR
Business Voice	5-10%	Declining due to VoIP adoption and mobile data preference
Business Internet Access	15-25%	Rising with 5G, IoT (e.g., automotive apps at 2.5-9%), and enterprise demand

The point is that the ability to monetize AI beyond its use for internal automation is likely limited. Changes in the main revenue drivers (consumer and business mobile phone subscriptions and prices) are going to have more impact on revenue and profit outcomes than IoT as a category or automotive IoT in particular.