Friday, October 4, 2024

Why Marginal Cost of Content Creation is Generative AI's Superpower

Virtually every observer might agree that artificial intelligence will automate laborious tasks and therefore increase process efficiency. AI should also accelerate decision making, as it enables rapid information processing. 


podcast of this content


AI should enable more personalization than already is possible for user interactions and experiences and as a byproduct could change the nature of work, entertainment and learning. 


Generative AI, though, might bring cost impact in different ways than did other computing innovations. Virtually all computing eras since the advent of the personal computer have led to lower marginal costs of doing things. 


PCs meant computing power itself was widely available to people. The internet attacked the cost of sharing information and communicating while cloud computing arguably reduced software distribution costs while boosting the ability to apply accumulated data and insights more widely in real time. 


The mobile era extended computing capabilities “everywhere” and untethered from desks, tables or laps. 


Era

Computing Paradigm

Marginal Cost Implications

PC

Personal Computing

- High upfront costs for hardware and software

- Relatively high marginal costs for upgrades and maintenance

- Limited scalability

Internet

Networked Computing

- Reduced costs for information sharing and communication

- Increased accessibility, but still significant infrastructure costs

- Marginal costs tied to bandwidth and server capacity

Cloud Computing

On-Demand Computing

- Significantly lower upfront costs

- Pay-as-you-go model reduces marginal costs

- Improved scalability and flexibility

- Potential for cost optimization through resource management1

Mobile

Ubiquitous Computing

- Lower device costs compared to PCs

- App-based ecosystem with low distribution costs

- Increased connectivity, but data costs can be significant

Future AI

Intelligent Computing

- Potential for near-zero marginal costs in some applications

- High initial investment in AI development and infrastructure

- Continuous learning and improvement may reduce long-term costs2


So it is reasonable to ask what the AI impact will be, especially generative AI, which seems to be driving mass market and most business AI use cases. 


Angela Strange, Andreessen Horowitz general partner and James da Costa Andreessen Horowitz partner, specialized in enterprise and business-to-business software, including financial technology. 


They believe the AI era leads to lower marginal cost of client and customer interactions, using AI agents to reduce the cost of labor involved in many customer support operations, including those involving information retrieval (files, ledger entries, past transitions, billing and account status). 


source: Andreessen Horowitz 


As applied in many areas outside of financial technology, the value of generative AI is squarely on its impact on content creation. 


Whether we look at text, image, video or audio, GenAI seems destined to have the highest impact on any process or industry built on content creation and its distribution or consumption. GenAI will be useful in any number of customer support contexts, but might be impactful in financial terms for the production of software and code; entertainment content; education and training; business communications; many types of research; marketing and sales. 


What Revenue-Per-Mile Metrics are Relevant for Fiber Transport Providers?

As Zayo Group weighs a purchase of Crown Castle fiber connectivity assets, some observers believe Zayo’s fiber assets might be worth close to $5 billion. Some might argue that is a reasonable price, assuming annual fiber revenue in the range of $1.5 billiion, representing a revenue multiple of 3.3 .


Other recent transactions have featured higher multiples.


It might be hard to compare the value of those fiber assets--which are mostly metro area connections useful for cell tower data transport--compared to assets of other fiber asset owners whose portfolios might be more heavily weighted either to long-haul transport or retail internet access service, including retail end user connections rather than business-to-business revenues. 


Of course, some might argue that the value of Crown Castle's annual transport revenue is greater than the direct revenue, either for strategic reasons or using different methodologies for estimating value and revenue.


Route miles are different from fiber miles, for example. Different fiber-basesd services also have different profit margins.


Service Type

Description

Profit Margin

Long-Haul Transport

Fiber connections over long distances.

40% - 50%

Local Access Transport

Metro fiber connections for local access.

30% - 40%

Dark Fiber

Unlit fiber available for lease.

60% - 70%

Lit Services

Operational fiber services (e.g., Ethernet, Wavelength).

40% - 55%

Data Center Interconnection

Connections between data centers.

50% - 60%

Enterprise/Business Connectivity

Services for businesses (e.g., MPLS, IP VPN).

25% - 35%


As a high-level estimate, some might argue that different service providers also have different revenue-per-route-mile profiles, with Crown Castle, whose footprint is weighted towards local access revenue and connections, might have among the highest revenue-per-mile profiles. 

Company

Local Fiber Route Miles

Local Transport Revenue (Annual)

Revenue per Mile (Annual)

Crown Castle

80,000+

~$2 billion

~$25,000

Lumen Technologies

190,000+

~$3.5 billion

~$18,400

Zayo Group

133,000+

~$2.8 billion

~$21,000

AT&T

250,000+

~$4 billion

~$16,000

Verizon

200,000+

~$3.7 billion

~$18,500


Combined revenues (long haul, metro and retail local access provide a different picture, generally suggesting that local connections generate more revenue per mile. By some estimates, Crown Castle's long-haul revenues are somewhere in the middle of suppliers.


Company

Long-Haul Fiber Miles

Long-Haul Transport Revenue (Annual)

Revenue per Mile (Annual)

Lumen Technologies

400,000+

$6 billion

~$15,000

Zayo Group

130,000+

$2.5 billion

~$19,000

Crown Castle

30,000+

$0.8 billion

~$26,700

AT&T

500,000+

$4.5 billion

~$9,000

Verizon

400,000+

$3.8 billion

~$9,500

Colt Technology

20,000+

$1 billion

~$50,000

Telia Carrier

60,000+

$1.2 billion

~$20,000

GTT Communications

80,000+

$1 billion

~$12,500

Thursday, October 3, 2024

Content Industries Will be Disrupted Early by Generative AI

To the extent generative artificial intelligence poses a disruptive threat to existing industries and firms, it will do so to the extent that GenAI is a content-generation tool, and will therefore pose the gravest strategic threat to content-producing industries.


Film and TV, music, question-and-answer sites and search are among the obvious flashpoints. To the extent that people use search to find answers to questions, GenAI could disrupt the business model by reducing advertising inventory, for example. 


There is, for example, some threat to Reddit user volume if people can use GenAI instead, the same reason GenAI is a potential threat to Google search, a replacement for some amount of code writing, acting, scriptwriting, video effects and music creation. 


“In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM,” say researchers.  “Within six months of ChatGPT’s release, activity on Stack Overflow decreased by 25 percent relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable.”


In other words, people reduced their content contributions on the question-and-answer site. The researchers believe alternative methods--using ChatGPT--were used as a substitute. 


Ironically, GenAI might also affect the amount of user-generated content that is created. That could, in principle, also affect social media business models to an extent. If people can find answers or create content  in ways that do not require user generation, there could well be less user-generated content. 


How that affects content businesses built on user-generated content is unclear, both in scope and scale. Many observers focus mostly on the threat of job substitution. That is almost certain to happen, to some extent, but might not actually be harmful for industry business models, even if it reduces the need for labor in some industries. 


Industry

Existing Business Model

Potential GenAI Threat

Content Creation

Professional writers, artists, designers

AI-generated content on demand

Software Development

Teams of programmers writing code

AI code generation and automation

Customer Service

Human-staffed call centers

AI chatbots and virtual assistants

Translation Services

Professional human translators

Real-time AI language translation

Marketing

Agencies creating campaigns and copy

AI-generated personalized marketing content

Legal Services

Lawyers drafting documents and contracts

AI-powered legal document generation

Education

Traditional classroom teaching

AI-driven personalized learning experiences

Music Production

Professional composers and musicians

AI-generated music and soundtracks

Financial Analysis

Human analysts processing data

AI-powered financial insights and predictions

Journalism

Professional reporters and editors

AI-generated news articles and reports


Reduced ad revenue is the biggest threat for many content-based apps that are reliant on advertising revenue for monetization. That seems obvious for search providers, for example. 


Decreased page views and clicks mean reduced ad impressions and clicks. Also, AI-generated answers should mean many users are less likely to click on ads or organic results.


On the other hand, an increase in ad inventory due to AI-generated content could lead to lower cost-per-click rates over time, and possibly affect profit margins.


To the extent that ad-supported apps rely on volume, organic traffic could drop if GenAI becomes a major substitute. Websites could see a significant drop in organic traffic if GenAI becomes a viable substitute for users seeking answers and content. 


As users rely more on AI-generated answers, they may visit fewer individual websites directly or through referrals.


With quick AI-generated answers, users may spend less time on individual sites, potentially impacting metrics like time on site and pages per session. So less user engagement is another potential business issue.


Providers of search engine optimization might find their algorithms are less valuable when direct answers to queries are possible, without referrals to websites that might provide answers.


Commoditization of basic information also seems likely, as websites offering basic information lose their value. 


The big shift in potential value is the change in behavior from “search and browse” to “ask a question.” The former drove traffic to content generator sites; the latter eliminates those visits. So even as search provider revenue models are potentially disrupted, so are internet monetization opportunities for virtually all content providers. 


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