How Big a Market for Telco APIs?

At least some estimates of the eventual size of the "telco API" market are likely to be unrealized, just as the earlier unified communications and collaboration market has arguably seen.

That might be true, in part, because telco APIs are intended to allow connectivity service providers a bigger role in the UCC markets, as well as supporting network slicing and monetization of some other network functions.

The unified communications and collaboration market always has been difficult to categorize, to the extent that it includes so many disparate segments. And the same will likely be true fot the telco application programming interface market as well.

UCC traditionally includes video conferencing hardware, subscriptions and service; voice messaging features; premises voice appliances and services and often the connectivity services used by cloud-based UCC apps and services. About the only thing the UCC category has not traditionally included are the value of business voice services sold by telcos. 

UCC Market Estimate By	Market Revenue (USD Billion)	Year	CAGR
Fortune Business Insights	71.26	2023	17.70%
Grand View Research	136.11	2023	17.40%
IDC	64.7 (full year forecast)	2023	8.60%
Mordor Intelligence	145.58	2024	27.80%
Markets and Markets	$76.7 billion	2022	13.00%

The telco API market should include these and other newer features such as 5G network slicing. And high hopes notwithstanding, the telco API market might well wind up being a niche, as UCC has been. 

The perception by traditional connectivity service providers that the various UCC services are niches can be seen in the market share figures for UCC providers of hardware and services. Microsoft's Skype and Cisco Webex are leaders in conferencing. 

Most suppliers of UCC hardware have relatively small share, as do independent providers of UCC services. Also, note that video conferencing represents at least 44 percent of the total UCC market.  

One reason the UCC category exists at all is because each of the constituent revenue components are relatively modest. Note also that some UCC functions (messaging, voice mail, conferencing) are provided by apps and services not generally tracked in the UCC bucket, often because they do not represent “for fee” apps (Facebook Messenger and other messaging apps such as WhatsApp, Apple Facetime, Google Meet and email apps, for example. 

Firm	Market Share	Source/Year
Microsoft	21.8%	Statista (Q4 2023)
Cisco	15.2%	Statista (Q4 2023)
Zoom	7.1%	Statista (Q4 2023)
RingCentral	5.5%	Statista (Q4 2023)
Avaya	4.2%	Mordor Intelligence (2024 est.)
Mitel	3.1%	Mordor Intelligence (2024 est.)
Polycom (Plantronics)	2.8%	Mordor Intelligence (2024 est.)
NEC	2.5%	Mordor Intelligence (2024 est.)
GoToConnect	2.3%	Mordor Intelligence (2024 est.)
8x8	1.9%	Mordor Intelligence (2024 est.)
3CX	1.5%	Mordor Intelligence (2024 est.)

So now we hear about telco application programming interfaces (APIs) as a possible growth market. And the relationship between telco APIs and UCC is that the most-logical near-term revenue sources for telco APIs are apps and services that use UCC features (messaging, voice, videoconferencing). 

To be sure, many hope for new additional API instances and markets for elements of the 5G mobile network experience; network slicing or other elements of the 5G experience that can be supplied by the 5G core and access networks. 

And that is where the more-optimistic forecasts for telco API-related revenue seem to rely. That assumes developers will see value in licensing access to network features related to bandwidth consistency, latency or other network-derived features. 

And some of us might suggest that those values and possible markets are going to develop more slowly than some seem to expect. Even allowing for some shifts of market share among current providers, and faster “core” UCC market growth than we have seen in recent decades, most of the forecast growth would necessarily have to come from new API markets related to private networks or network features, plus the spending on infrastructure to support the use of telco APIs. 

At a high level, there always are alternate ways to solve an engineering problem, such as latency performance or computing cost. Basically, one can substitute bandwidth for processing (remote data center processing, for example) or processing for bandwidth (edge computing or on-the-device computing, for example). 

Study	Overall UCC Market Size	Segments	Forecast Period
Verified Market Research	$222.99 Billion by 2030	Deployment Model, Platform, Application, Region	2024-2030 CAGR 17.7%
MarketsandMarkets	$141.6 Billion by 2027	Component, Deployment Mode, Organization Size, Vertical, Region	2022-2027 CAGR 13.0%
Mordor Intelligence	$496.30 Billion by 2029	Deployment Type, Type, End-User Industry, Region	2024-2029 CAGR 27.8%
Straits Research (cited by Mordor)	$1,171.63 Billion by 2032	Not specified	2024-2032 CAGR 20.4%
STL Partners (cited)	$20+ Billion by 2028	Mobile Network APIs	-
Market.us	$1,459.5 Billion by 2033	Not specified	2024-2033 CAGR 18.1%

And the point is that there are many ways to solve a latency, bandwidth or processing problem. Telco APIs are one way to solve such problems, but there are alternatives that do not require use of telco APIs. 

And as a way of gaining market share in the traditional UCC market, telco APIs might be one way, but not the only way, to participate. Ericsson simply bought Vonage, for example, to gain capabilities of creating voice, messaging or communication solutions for any app. 

Acquisitions might be one way to gain share faster than creating standards and products using telco APIs. And the issue of market potential (total addressable market) would still remain. The reason telcos generally did not want to be PBX suppliers was always that the market was sub-scale for them. Those same issues apply to new cloud-based UCC solutions that essentially replace business phone and conferencing systems as well. 

Approach	Description	Advantages	Disadvantages
Telco APIs	Leverage network APIs from telecom providers to access capabilities like quality of service, network slicing, location services etc.	Can guarantee bandwidth, low latency, and prioritize critical traffic over public networks	Requires integration with telco platforms and potential costs for premium services
Edge Computing	Process data and run applications closer to the source/user on edge nodes instead of centralized cloud	Reduces latency and bandwidth needs by bringing compute closer to the edge	Requires deploying and managing distributed edge infrastructure
On-Device Computing	Perform processing and decision-making directly on the user's device (e.g. mobile phone, IoT device)	Minimizes latency and bandwidth needs by avoiding network round trips	Limited by device compute capabilities and battery life constraints
Private Networks	Deploy dedicated private cellular or wireless networks for specific use cases or environments	Offers predictable performance, security, and control over the network	Requires significant upfront investment in private network infrastructure
Content Delivery Networks (CDNs)	Distribute content and services across a global network of edge servers closer to users	Improves performance by caching content at the edge and reducing latency	Primarily optimized for content delivery rather than real-time applications
Peer-to-Peer Networks	Establish direct connections between devices or nodes without relying on centralized servers	Reduces bandwidth needs and latency by avoiding server hops	Complexity in managing peer discovery, connectivity, and security
Network Optimization	Techniques like compression, caching, load balancing to improve utilization of available bandwidth	Can enhance performance without additional infrastructure	Optimizations have practical limits based on network conditions

The issue is that at least some forecasts of potential telco API markets seem excessively optimistic.   

Video Sreaming Ad Value Might be Between $8 and $10 Per Month, Per Subs on Those Accounts

How much financial value does advertising represent for popular content apps? Nearly 100 percent for ad-supported apps including Facebook and Google search or YouTube. 

A new wrinkle is the growing importance of advertising revenue for e-commerce or subscription-based streaming video products

And one way of quantifying advertising value is to compare the ad-free and ad-supported versions of streaming video services, where the price difference to the consumer is between $8 a month to $10 a month when choosing to use an ad-free version of the app or service. 

App/Service	Single-User  Ad-Free Per Month	Single-User w/Ads Per Month	Implied Ad Value
Spotify	$12.00	$0.00	$12.00
YouTube	$14.00	$0.00	$14.00
Hulu	$18.00	$8	$10.00
Netflix	$15.50	$7.00	$8.50

And that is the implied value of the advertising.

"Sequential" Versus "Random" Access of Content

Chrome for Android has developed a "listen to this page" feature for some months and now Google Chrome will feature the capability, allowing users to hear text rather than read it. 

That feature might have key benefits for individuals with visual impairments, learning disabilities like dyslexia, or those who prefer auditory learning.

Users can listen to web pages while performing other tasks, such as commuting, exercising, or doing chores, without staring at a screen.

Some will consider this a good feature; others might see it as unhelpful, for reasons related to the speeds at which humans can talk and process spoken words, compared to the speed at which text can be read. 

The human brain is capable of processing words at a much higher speed than a person is able to speak. 

Some suggest the average rate of speech for an American is about 125 words per minute but the human brain can process about 800 words per minute. 

Humans can generally process spoken information at a rate of around 150 to 180 words per minute. This rate is limited by the speed at which the brain can process auditory input. 

The average reading speed for adults is around 200-300 words per minute, with some skilled readers reaching up to 400-500 words per minute.

Also, spoken words are linear; written words are more akin to random access. At a conference, one might have to listen to a panel for many minutes before deciding whether to stay or attend a different session, as the content is linear. 

On the other hand, one often can quickly scan a bit of written content in seconds to decide whether it is worth investigating further. Listening to speakers is linear or sequential; reading text is more akin to random access.

How Does ChatGPT "Daily" Usage Compare to Other Popular Apps?

According to altindex, fewer than 10 percent of potential users use ChatGPT daily, the highest usage being nine percent daily use among users 18 to 24; the lowest usage among those 55 or older, at about one percent. 

That might represent significant usage for an app that was released commercially in 2023. 

source: altindex

But determining “active daily use” is not easy. There is no universally agreed-upon definition for "daily active users.” Most apps track user actions to determine engagement and count users who perform certain actions within a 24-hour window. But that requires a judgment call.

Does one track when an app is opened; whether a significant interaction occurred or session length? And how are interactions or session length defined?

If a user opens the app at least once within a 24-hour window, that typically is counted as a DAU.

Does active use mean watching a video for a certain duration, making a purchase, sending messages, or completing a specific task within the app?

Engagement with core app features often matters when counting active usage. On a social media app, a user viewing posts, liking content, or messaging someone might be considered an active user. 

An e-commerce app might consider adding items to a cart or making a purchase as active use.

And how long does a session have to last to be counted? A user who opens the app multiple times but only for a few seconds each time might not be counted as a DAU, while someone who has a single, extended session might be.

Consider that perhaps 39 percent of Instagram “users” are considered daily active users Likewise, perhaps 30 percent of the TikTok audience are daily users, by some estimates. 

By other estimates, about 20 percent of YouTube users are considered daily active users. 

App Name	Daily Active Users (%)
Instagram	39
TikTok	29
YouTube	20
Amazon	15

But some estimates of daily usage among active users are much higher. As with all such estimates, it matters greatly which denominator is used: all persons; all registered users; all active users; all active users who have used an app within the past week and so forth. 

Also, it makes a difference when counting “monthly” active users versus “daily.” 

Platform	Percentage of Users Daily
Facebook	68.38%
Instagram	63%
Snapchat	61%
Twitter	42%
YouTube	51%

The point is that ChatGPT usage is not out of line for an app that has been in commercial use less than two years. 

AI Job Displacement Might Resemble Prior Manufacturing, IT and Service Offshoring and Outsourcing

Will artificial intelligence lead to a new wave of “offshoring” or “outsourcing” of jobs, as happened to manufacturing and then many information technology and service jobs? Studies suggest the possibility is real, as AI seemingly enhances the performance of lower-skill workers more than highly-skilled workers, closing the performance gap between high and low. 

While much of the concern about artificial intelligence impact on jobs focuses on job displacement, perhaps a greater issue will ultimately be to “devalue” the wage premium garnered by top performers over lower performers. 

The reason is that AI might benefit lower-skilled workers more than high performers, allowing employers to rely more on lower-skilled workers to perform tasks once requiring higher-skilled workers. 

Wage compression, in other words, can happen when technology allows less-skilled workers to perform expert-level tasks. That might imply faster wage growth for lower-wage workers compared to higher-wage workers.

A study by the Congressional Research Service suggests that between 2019-2023, U.S. real wage growth was dramatically faster for low-wage workers (around three percent annualized) compared to high-wage workers (around one percent annualized). This contrasts with the prior 40 years where high-wage workers saw faster growth.

But forces other than AI might also be at work. A CRS study notes that wage rates for workers with advanced degrees rose faster than for lower-skilled or mid-skill workers between 1979 and 2019. And a study suggests wage increases were fastest for lower-skill workers between 2020 and 2024. 

In other words, we might see another unfolding of job displacement from high-wage areas to low-wage areas if AI allows lesser-skilled workers to accomplish tasks formerly conducted by higher-skilled workers. 

Study	Key Findings
Stanford/MIT (2023)	AI disseminates knowledge of high-skill workers to less-skilled workers
Congressional Research Service (2024)	Wage compression from 2019-2023 with much faster growth for low-wage workers
Oxford University (Frey et al.)	Language AI benefits less-skilled workers more than highly skilled
Brekelmans & Petropoulos (2020)	- AI likely to significantly alter low and middle-skilled jobs - High-skilled jobs relatively less at risk, but impact still non-negligible

Indeed, that is a pattern already established in the 20th century as manufacturing jobs moved to lower-wage countries. Many manufacturing jobs, particularly in labor-intensive industries like textiles and electronics assembly, were outsourced from developed nations to developing countries.

In the late 20th and early 21st centuries, a "second wave" of outsourcing emerged, targeting white-collar jobs in the information technology and services sectors. Countries such as India, with large pools of English-speaking graduates and significantly lower wages, became major destinations for outsourcing IT services, software development, and business processes.

And there already is evidence that AI benefits lower-skilled workers more than highly-skilled workers, narrowing the performance gap between the groups. That should create the possibility of substituting use of AI-enhanced formerly-lower-performing workers in place of at least some highly-skilled workers. 

The issue is how much the performance gap is narrowed and to what degree tasks can be redesigned so that AI-enhanced workers of lower skill can handle tasks once performed by higher-skilled workers.  

A study by researchers from Stanford and MIT analyzed data from 5,179 customer support agents at a Fortune 500 company. The findings showed that access to an AI conversational assistant increased productivity (measured by issues resolved per hour) by 14 percent on average. However, the impact was greatest on novice and low-skilled workers, with minimal impact on experienced and highly skilled workers.

Another study by Oxford University's Carl Benedikt Frey and others found that the introduction of language-based AI software in the workplace benefits less-skilled workers more than highly skilled ones.