Sunday, January 21, 2024

How Might AI Reshape Business and Revenue Models?

If artificial intelligence develops as a general purpose technology (we cannot be sure, yet), it might have horizontal and vertical impact on businesses, reorganizing functions and reshaping core business models, as did the internet. 


As has been the trend for software, the internet had a horizontal impact, reorganizing value chains, functions, products, value creation and revenue models across industries. 


Horizontal Impact

Example

Disintermediation: Distribution functions are compressed or eliminated

Travel agents replaced by online booking platforms.

Friction reduced: knowledge and interaction barriers crumble

Open-source software, crowdfunding platforms. Wikipedia

Rise of new business models and industries: Entirely new ways of creating and delivering value emerge.

E-commerce, social media marketing, cloud computing.

Shift from physical to digital products and services: Tangible goods give way to virtual experiences and intangible offerings.

Streaming services replacing physical media, online education platforms.

Death of distance: Businesses can operate across borders almost as easily as within a single market or country

Global e-commerce, social media, messaging, content services, platforms


The vertical impacts affecting industries have been equally dramatic. Think of the emergence of ride sharing, lodging and other “sharing” or “platform” business models built on the internet’s existence. 


Sharing platforms (peer-to-peer networks) disrupt existing industry models because they are “asset light.” Hotels are capital intensive, as are taxi services. But the use of smartphones and private residences and autos to replicate “lodging” and “local transportation” services recasts the role of capital in any industry or business where a sharing platform is possible. 


The sharing network allows a direct peer-to-peer exchange between buyers and sellers. 


The internet also enabled multimedia communications at scale, thus creating the conditions for on-demand digital media to replace all prior forms of electronic and digital media, once broadband internet access was reasonably well available. So content consumption shifts from physical form such as newspapers, magazines and discs to streaming or web delivery. 


So product formats change. So do value propositions. With the emergence of streaming and web access to music, revenue generation in the music business shifted from “selling prer-ecorded copies of music” to “live performance.” Revenue now mostly is earned by performances, not units of pre-recorded music or streaming content purchases. Value now is created by live performance. 


The internet also allowed for customization and individualized experiences, either of form or timing. Users could access “what they want, when they want it.” That is true for mass-produced content such as movies or TV shows or songs as well as for the custom, personal networks of social network “friends” and “following” topics. 


The internet also enabled firms to operate across far-larger markets, both for physical goods and intangible products as well. So distribution networks could be reshaped to support online fulfillment and logistics networks rather than in-store retailing: Amazon instead of shopping at a local retail location. 


Similarly, marketing efforts could be reshaped to use virtual mechanisms rather than physical, targeted and customized based on user search and social media behavior, rather than demographics or psychographics. 


Platform business models are perhaps the clearest example of how an industry can be reimagined. Throughout history, most businesses have operated on a “pipeline” model, where a given product is created, sold and distributed by the owner of that product.


The internet enabled “platforms” where the value is the exchange that matches buyers with sellers, the exchange itself often owning neither the assets sold nor the customer relationships with buyers. 


The revenue model is a fee for arranging the match of buyer and seller. Once the platform reaches sufficient size, other revenue sources, such as subscriptions or advertising, also become feasible. 


Ecosystems often become more important as well. The whole idea behind a “platform” is that it provides increasing value (for tenants, data center operators and retail customers) as more partners, suppliers and features are enabled on the platform. Consider data centers. 


These days, “software” or “digital services” are purchased directly from, or fulfilled by, a cloud-connected data center. 


These days, much of the value of any data center is the other networks, software suppliers, content and application providers that can be connected within any single data center. Some of the value comes from the ability to more easily (cross connect) or quickly (direct peering) exchange data between partners, such as a video streaming network reaching the backbone and local distribution providers of internet access. 


In other cases, proximity makes it easier for suppliers to bundle each others’ services in a more-transparent and simple way for customers who are buying “computing, storage or apps  as a service” from a data center. 


Convenience is another attraction of the ecosystem, as when a single customer is able to purchase multiple large language model, security or enterprise software services at a single location, or from a single computing as a service supplier. 


The point is that AI could have a similar impact, whether it becomes a GPT or not. AI could change the horizontal functions any business or process requires. AI also could reshape particular industries by changing value propositions. Advertising, content, sales, banking and finance are among the obvious areas where AI could add value in the same way that prior data mining has had. 


Consider the many new industries, roles, capabilities and products enabled by the internet, from e-commerce to cloud computing. In fact, some would argue AI itself is enabled by the internet. 


New Industry

Description

E-commerce: Online retail platforms for buying and selling goods and services

Amazon, Etsy, Shopify, Alibaba

Social media: Platforms for creating and sharing user-generated content and building online communities

Facebook, Instagram, Twitter, TikTok

Streaming services: On-demand access to audio and video content

Netflix, Spotify, Hulu, Disney+

Content creation: Blogging, online publishing, influencer marketing

YouTube, Twitch, Substack, Patreon

Cybersecurity: Services and solutions to protect data and systems from cyber attacks

Crowdstrike, Palo Alto Networks, McAfee

Cloud computing: On-demand access to computing resources like servers, storage, and databases

Amazon Web Services, Microsoft Azure, Google Cloud Platform

Fintech: Financial technology services delivered through digital platforms

PayPal, Square, Robinhood, Chime

Gig economy: Online platforms connecting temporary workers with businesses

Uber, Lyft, Airbnb, Deliveroo

E-learning: Online education and training platforms

Coursera, Udemy, Khan Academy, Edmodo

Online gaming: Multiplayer online games and virtual worlds

World of Warcraft, League of Legends, Fortnite, Roblox

Data analytics: Collecting, analyzing, and interpreting data to gain insights

Google Analytics, Tableau, Power BI, Amazon Redshift

Artificial intelligence: Developing and implementing AI models for various applications

Machine learning, natural language processing, computer vision, robotics

Are Multi-Access Edge Computing Revenue Forecasts Too High?

Mobile service providers are optimistic about their potential roles as suppliers of multi-access edge computing, where they can play a role in supporting processing cycles “at the edge of the network.” 


But the assumptions vary. Some studies include the value of hardware, software and services revenue. Others reflect a narrower definition. Estimates including the value of infrastructure (hardware and software) revenue as well as “edge computing services” will obviously be quite a bit higher than those estimates looking strictly at “edge computing as a service” revenues.


And some estimates might attribute “edge computing as a service” revenues in such a way that partners such as Amazon Web Services or Azure or Google Cloud are providing the actual “computing” but in cooperation with mobile or fixed network service providers who provide facilities or connectivity. 


Study by

Published

Projected MEC Revenue by 2030

CAGR

Grand View Research

2023

$92.4 billion

46.7%

ABI Research

2021

$84.6 billion

47.4%

STL Partners

2022

$50-70 billion

35-40%

Accenture

2020

$43.1 billion

33%


Some estimates might be more granular, breaking out  revenues earned by edge computing services that are earned by mobile service or “managed service”  providers, data center operators and cloud computing services. 


And even those estimates might be too optimistic, partly because much “edge computing” will be supported directly on devices (smartphones, internet of things sensors, vehicles) and not using some MEC processing facility.  


Study

Year of Publication

Global MEC Market Revenue by 2030 (USD Billion)

MSP Share (%)

Data Center Share (%)

CaaS Share (%)

Grand View Research

2023

55.41

35

25

40

Allied Market Research

2023

102

30

30

40

Market Research World

2023

86.7

32

28

40

Statista

2023

24.24

35

25

40

Research and Markets

2023

82.3

30

25

45

Market.us

2023

92.4

32

27

41

Mordor Intelligence

2023

59.2

33

27

40

Omdia

2023

38.5

35

25

40

ABI Research

2023

49.5

30

25

45

IDTechEx

2023

32.4

32

28

40


It might be reasonable to assume that most eventual MEC revenues earned by all the suppliers--mobile service providers, data centers and cloud computing services--will be in the business-to-business or business customer part of the market, and support use cases other than direct IoT use cases. 


The reason is that most consumer  “edge computing” operations will support imaging, speech-to-text or other operations conducted on-device. Some believe about half to 80 percent of such smartphone and consumer device “edge computing” operations will be handled directly on the device.


Healthcare wearables likewise might process as much as 60 percent to 80 percent of data right on the device. 


And even most Industrial IoT sensors (up to 70 percent) might process workloads solely on-device.Though more complicated “trend” analyses will be conducted remotely, many sensor operations will be of a simpler “open or shut,” “higher or lower change,” threshold reached” readings. 


Also, significant amounts of “edge computing” revenue booked by mobile service providers will come from supplying connectivity services and other services such as providing local data center facilities, even for estimates that are perhaps optimistic about the actual role of mobile service providers as suppliers of the actual “edge computing” function. 


Study

Year

Connectivity Revenue (%)

Edge Computing Services (%)

Supporting Functions (%)

Ericsson

2023

40-50

30-40

10-20

ABI Research

2023

35-45

35-45

10-20

Gartner

2023

30-40

40-50

10-20

STL Partners

2023

35-45

30-40

15-25

Omdia

2023

40-50

30-40

10-20


The point is that estimates of revenue to be earned by mobile service providers from MEC are likely overestimated.


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