AI Will Displace Some Content Creator Jobs, Reshape the Rest, Create New Roles

It seems almost pointless to argue about the impact artificial intelligence is going to have on content creators, for the simple reason that AI is going to have a wide range of effects, including displacing some human jobs; augmenting human labor and reshaping content creator functions, as well as creating wholly-new functions and jobs. 

Most likely, “all of the above” are likely outcomes, as has been the case with other new technologies applied to content creation. 

Technology	Displaced Roles	Augmented Roles	Reshaped Roles	Created New Roles
Printing Press (15th century)	Hand-copying scribes and illuminators, who manually reproduced manuscripts	Authors and scholars, by enabling mass distribution of their works to wider audiences	Writers shifted focus from rare, elite manuscripts to broader, accessible literature; encouraged standardization of texts	Printers, publishers, bookbinders, and editors to manage production and distribution
Photography (19th century)	Portrait artists and miniaturists, whose realistic depictions were largely supplanted by photos	Painters and illustrators, who used photographs as references for more accurate or complex compositions	Artists moved toward impressionism, abstraction, and conceptual art, emphasizing emotion over literal replication	Photographers, darkroom technicians, photo retouchers, and later film directors
Phonograph and Audio Recording (late 19th-20th century)	Some live performers in theaters or salons, as recordings reduced demand for repeated live shows	Musicians and composers, by allowing preservation and global sharing of performances	Performers adapted to studio techniques, focusing on perfect takes rather than live endurance	Sound engineers, music producers, record label executives, and radio DJs
Typewriter (late 19th century)	Professional hand-writers or copyists for official documents	Journalists and authors, with faster drafting and easier revisions	Writing became more iterative and professionalized, with emphasis on typing speed and clarity	Typists, secretaries, and stenographers specialized in machine operation
Word Processors and Computers (late 20th century)	Dedicated typists and manual typesetters in publishing	Writers and editors, through tools for easy editing, spell-checking, and formatting	Content creation became digital and collaborative, with focus on multimedia integration	Desktop publishers, web designers, software documentation specialists, and digital archivists
Internet and Digital Platforms (late 20th-21st century)	Traditional print journalists and classified ad writers, as online formats reduced print demand	Bloggers and independent creators, by providing free or low-cost global publishing tools	Creators emphasized interactive, real-time content like social media posts over static articles	Social media influencers, SEO content strategists, podcasters, and user-generated content moderators

And all that will happen irrespective of today’s efforts to “protect” human jobs. 

Netflix, for example, has guidelines for use of generative AI based on five main points:

• The outputs do not replicate or substantially recreate identifiable characteristics of unowned or copyrighted material, or infringe any copyright-protected works (respect for copyright)

• The generative tools used do not store, reuse or train on production data inputs or outputs (data security)

• Where possible, generative tools are used in an enterprise-secured environment to safeguard inputs 

• Generated material is temporary and not part of the final deliverables

• GenAI is not used to replace or generate new talent performances or union-covered work without consent.

The guidelines also caution against creating content that could be mistaken for real events, people, or statements. 

Of course, as a practical matter, all that will have to be monitored and verified. Perhaps the areas of greatest concern are final character designs and key visuals; talent replication and use of unowned training data. 

Proposed Use Case	Action	Rationale
Using GenAI for ideation only (moodboards, reference images)	✅	Low risk, non-final, likely not needing escalation if guiding principles are followed.
Using GenAI to generate background elements (e.g., signage, posters) that appear on camera		Use judgment: Incidental elements may be low risk, but if story-relevant, please escalate.
Using GenAI to create final character designs or key visuals		Requires escalation as it could impact legal rights, audience perception, or union roles.
Using GenAI for talent replication (re-ageing, or synthetic voices)		Requires escalation for consent and legal review.
Using unowned  training data (e.g., celebrity faces, copyrighted art)		Needs escalation due to copyright and other rights risk.
Using Netflix's proprietary material		Needs escalation for review if outside secure enterprise tools.

Some observers might liken the use of generative AI to the use of computer-generated graphics. It might be argued that CGI did not broadly automate creative work, as AI might threaten to do, in some cases. 

While CGI technology does automate certain repetitive or technical tasks, the work typically requires direct human input, creative intent, and iterative collaboration, some would argue. And while CGI shifted some jobs from traditional effects (such as practical props) to digital, it did not broadly automate creative work. 

AI, on the other hand, arguably can drastically reduce the need for human artists, writers, and designers, especially for routine or template-based tasks. A reasonable view held by creatives is that generative AI creates extensive automation threats to creative jobs, challenging the role, compensation, and rights of human creators in ways CGI never did. 

Issue	CGI	Generative AI
Labor Replacement	Redistributes labor, limited direct job loss	Automates substantial creative tasks, risks widespread job loss
Human Creativity	Essential for most tasks	Can fully automate or diminish creative input
New Job Creation	Created new specialist roles	Some new roles, but net job losses expected
Worker Rights/Ethics	Tied to work conditions, overtime	Issues of data exploitation, loss of control, IP and consent
Value Perception	Value linked to expertise and collaboration	Value eroded by commoditization, especially for freelancers
Legal Uncertainty	Relatively mature standards	Significant legal and ethical ambiguity

Content workers may not like it, but AI is going to reshape human roles and human jobs. New technology always has done so.

Are Telcos "Trapped" by Language or Operating Metrics?

Sebastian Barros, Circles managing director, might be quite right that average revenue per user and gross additions (new subscribers) no longer make sense. The “KPIs (key performance indicators) they still cling to are built for a world of selling minutes and megabytes, not for today’s digital ecosystems,” he argues.

One might agree with the sentiment, but the logic misses something important. It might be correct to argue that “consumers no longer just buy ‘minutes and megabytes,’” as Barros somewhat rightly notes. “They buy ecosystems: streaming, wallets, commerce, and cloud.” 

Perhaps more correctly, consumers use ecosystems, wallets, streaming, commerce, cloud, minutes and megabytes. What we actually buy from mobile and fixed network access providers is the use of their network resources so we can talk, text and use the internet. 

But “telcos” and “internet service providers” are not in businesses where metrics such as units shipped, book-to-bill ratio or monthly active users or advertising revenue per user make sense as operating indices that inform us about how the business is faring. 

Where ISPs sit in the value chain means they make their money selling internet access and some communication services. So ARPU, churn rates and net additions actually are the relevant operating metrics. 

Those metrics will not make sense for others in the value chain. But ARPU, gross adds, net adds and churn are very much the right numbers to track operating performance. 

Here’s the point: ARPU and gross adds do not “trap” ISPs in old ways of thinking. Those metrics are the right ones for the business they are in and where they are situated in the value chain. ARPU and gross adds might make no sense for other participants in the value chain. 

But just because “ARPU and gross adds make no sense for the business I am in” does not mean “they make no sense for the business ISPs are in.” 

Segment	Example Players	Key Operating Metrics	Typical Characteristics
Semiconductors / Chip Suppliers	Nvidia, Intel, AMD, Qualcomm, Broadcom	- Gross Margin (% of sales) - Fab Utilization Rate - R&D Intensity (% of revenue) - Average Selling Price (ASP) - Units Shipped	Capital-intensive; cyclical demand; high fixed costs; margins vary by product (leading-edge chips much higher).
Networking / Hardware Equipment	Cisco, Ericsson, Huawei, Arista	- Gross Margin - Book-to-Bill Ratio - Installed Base Growth - CapEx as % of Revenue - Service Revenues %	Dependent on telco/enterprise capex; recurring service revenues important; margin pressure from competition.
Access Providers (Telcos, ISPs)	AT&T, Verizon, Comcast, Deutsche Telekom	- ARPU (Average Revenue per User) - Churn Rate - Network CapEx (% of revenue) - EBITDA Margin - Subscriber Growth	Capital-intensive, slow growth, regulated; sticky customer base but high infrastructure costs; moderate margins.
Cloud / Hyperscalers	AWS, Microsoft Azure, Google Cloud	- Revenue Growth Rate - Gross Margin - Utilization of Datacenters - Customer Retention - Operating Income Margin	High scalability; strong growth; capex heavy but high-margin once scaled; sticky enterprise contracts.
Search Engines	Google, Bing, Baidu	- MAUs (Monthly Active Users) - ARPU (via advertising) - Ad Load (ads per page/search) - Click-through Rate - Operating Margin	Network effects; ad-driven monetization; extremely high margins; market concentration.
Social Media Platforms	Meta, TikTok, Snapchat, X (Twitter)	- DAUs / MAUs - ARPU (ad revenue per user) - Engagement Time per User - Ad Fill Rate - Operating Margin	Strong network effects; monetization via ads; margins high, but sensitive to user growth and ad demand.

It is quite true that “pipes and bundles are the only game in town,” as Barros notes. Unfortunately, most of those other businesses in the value chain are not the ones telcos and ISPs are in, for the most part. 

I find it is common in business for people to make a certain sort of mistake. They might argue something “cannot be done” because “my company, with its resources and business model, cannot do so.” That never means some other company, with different resources, cannot succeed in doing so.

Nor, in this case, do the prevailing operating metrics “trap” participants. Telcos and ISPs are access providers. It is their role in the value chain. If they were to substantially change roles, and move elsewhere in the value chain, of course the operating KPIs would change. 

But ISPs are not limited by their choice of metrics: they are “limited” by their chosen roles in the value chain.

EchoStar to Sell Mid-Band Spectrum Assets to AT&T, in Shift of Business Model

AT&T is purchasing spectrum licenses from Echostar (30 MHz of nationwide 3.45 GHz mid-band spectrum and approximately 20 MHz of nationwide 600 MHz low-band spectrum). The all-cash transaction for $23 billion represents licenses covering virtually every market across the United States, AT&T notes. 

The spectrum sale to AT&T marks a significant strategic shift for EchoStar's mobile communications business, refocusing the company from a facilities-based network operator to a mobile virtual network operator (MVNO) that also operates its own core 5G network.

After selling its 3.45 GHz and 600 MHz spectrum licenses to AT&T, EchoStar will still retain rights to spectrum assets for mobile service in the AWS-4 (2 GHz) band and S-band MSS (Mobile Satellite Service), including 40 MHz in the 2000–2200 MHz range. 

EchoStar's strategy following the sale to AT&T includes both terrestrial 5G and emerging satellite connectivity use cases, including potential direct-to-device applications. 

EchoStar’s Boost Mobile operations will primarily be powered by wholesale access to AT&T’s network, though it also uses T-Mobile radio access assets as well. 

Though EchoStar began life as a satellite services provider, it began in the 2010s to move into terrestrial mobility. Early on, the company envisioned a role as a major facilities-based retailer of mobile services. As the U.S. mobile market solidified as a market led by AT&T, Verizon and T-Mobile, the opportunity to compete effectively as a fourth provider dimmed. 

The latest thinking is to focus on more niche roles such as satellite direct to device use cases. 

Era	Focus	Strategic Action	Outcome/Impact
1980s–2000s	Satellite TV and broadband	Launch of DISH, Hughes acquisition	Dominant satellite TV provider
2010s	Diversification, spectrum acquisition	AWS-4, 5G, Open RAN initiatives	Entry into mobile and 5G market
2019–2023	Mobile network buildout, M&A activity	DISH/EchoStar merger, network expansion	Boost Mobile, 5G deployment on owned facilities
2024–2025	Hybrid MNO, satellite-terrestrial innovation	AT&T spectrum sale, Open RAN/D2D focus	Partnership  model; spectrum held for direct-to-device services

Well-Intentioned Regulations Can Backfire

Land use regulations including zoning laws, density restrictions, minimum lot sizes, height limits, rules on parking spaces and growth controls increase the cost of housing and limit its construction. 

That matters if the U.S. housing supply is 4.7 million homes short of demand. It’s just basic supply and demand economics. 

And focusing on supply matters. Such regulations often limit the amount, type, and location of new housing development, effectively constraining supply even as population growth, urbanization, and economic demand for housing rise. 

 This supply shortage pushes up prices and rents, making housing less affordable, particularly for low- and middle-income households. 

 For instance, regulations can impose lengthy permitting processes, environmental reviews, or inclusionary requirements that raise development costs, which are then passed on to buyers or renters. 

 But the bigger problem is simply that such rules are among the reasons more housing is not created. And there are several reasons, one might argue. Labor availability and costs; lumber availability and cost; tax rules and other government policies play a role. 

 

But land use planning rules matter. To be sure, the rationale often is compelling: preserving community character, protecting the environment, or preventing urban sprawl. 

 But those very same rules create disincentives to build affordable housing, as they all restrict housing density or volume. 

Study Title	Authors	Year	Methodology and Key Findings
The Effect of Land Use Regulation on Housing and Land Prices	Keith R. Ihlanfeldt	2007	Used an endogenous index of regulatory restrictiveness across over 100 Florida cities; found greater restrictiveness increases house prices, decreases land prices, and leads to larger new homes.
The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn?	John M. Quigley and Larry A. Rosenthal	2005	Reviewed empirical literature using surveys, econometric models (e.g., OLS, hedonic pricing), and regulatory indices; regulations like zoning and growth boundaries are associated with higher prices, but causality is not firmly established due to endogeneity and data limitations.
https://www.urban.org/research/publication/land-use-reforms-and-housing-costs	Christina Plerhoples Stacy et al.	2023	Analyzed a panel dataset of 180 reforms in 1,136 U.S. cities (2000–2019) using machine learning, manual coding, and fixed-effects models; loosening restrictions increases supply by 0.8% over 3–9 years (mainly high-end units), while tightening raises median rents and reduces affordable units.
How Land-Use Regulation Undermines Affordable Housing	Sanford Ikeda and Emily Washington	2015	Reviewed literature and urban policy data; regulations reduce supply relative to free-market levels, increase costs (e.g., 10%+ "regulatory tax" in major cities), and disproportionately affect low-income households, potentially lowering GDP by limiting growth in productive areas.
Regulation and Housing Supply	Joseph Gyourko and Raven Molloy	2014	Literature review with surveys, panel data, and regression analyses (e.g., OLS, instrumental variables); strong positive link between regulation and prices (17–22% increases), reduced construction (4–22%), and lower supply elasticity, leading to volatility.
Zoning, Land-Use Planning, and Housing Affordability	Randal O'Toole	2017	Regression analysis of court decisions as proxies for regulation intensity (2000–2010 data); rising land-use and zoning regulations correlate with higher home prices in 44 and 36 states, respectively, with federal aid flowing more to restrictive states.
The Impact of Zoning on Housing Affordability	Edward L. Glaeser and Joseph Gyourko	2002	Compared house prices to construction costs across U.S. markets; zoning drives prices above costs in high-regulation areas (e.g., NYC, California), suggesting supply restrictions exacerbate affordability issues more than demand alone.
Do Restrictive Land Use Regulations Make Housing More Expensive Everywhere?	John Landis and Vincent J. Reina	2021	Examined 336 metro areas with multiple stringency measures and growth variables; restrictive regulations pervasively raise home values and rents, especially in growing/prosperous economies, but effects on supply vary by market.