Where Will AI Prove an Existential Threat to Whole Industries?

Right now, we all speculate about the potential changes artificial intelligence might bring, as well. Predictions range from the existential (CEO jobs will go away) to the mundane (how we write emails will be more automated).

But it is not the technology we need to watch and understand, as such. It is what the technology enables. We might agree that spreadsheets, word processing, presentation software, personal computers or smartphones have had an impact on work and learning, without also arguing those information technologies fundamentally changed the nature of work or most business models.

But sometimes, new IT has reshape whole industries.

If you worked in any managerial position in U.S. ad-supported media back in 1996, you are well aware of the huge shifts that has taken place in use of ad venues. Print media, linear video and radio have taken huge hits, while online digital venues have skyrocketed. 

Many--perhaps most--of the business issues facing managers of ad-supported assets flow directly from those shifts in activity and venues. 

Source: Gemini

Put simply, digital now claims up to 82 percent of all U.S. ad placements and revenue. Print has declined from 42 percent to less than three percent. Linear video dropped from 38 percent to 16 percent. Radio dipped from 10 percent to half a percent. 

Channel	1996 (Billions)	1996 (%)	2023 (Billions)	2023 (%)
Print (Newspapers, Magazines)	80.0	42.1%	10.0	2.7%
Linear Video (TV Broadcast, Cable)	72.0	37.9%	60.0	16.2%
Network Radio	10.0	5.3%	2.0	0.5%
Other (Radio Spots, Out-of-Home)	28.0	14.7%	18.0	4.9%
Digital Ads (Search, Social Media, Display)	-	-	300.0	81.7%

When channels shift that much, the appellation "disruptive" certainly applies. While the role of advertising in business did not change, almost everything else about how it is used, when and where, did change. 

We can't yet know how extensive the changes brought by AI will be, in each industry, job role and functions. But existential change is conceivable. Virtually all prior general-purpose technologies (such as electricity, the internet, mass propduction, the internal combusion engine) were existential for some industries and jobs. Some things simply went away. 

And virtually all enabled importnat new possibilities based on the extending of human muscle, senses or brain power. 

The internet had an existential impact on legacy ad-suported businesses. AI could have similar impact on at least some industries. 

The Next U.S. Recession Will Test Resilience of Video, Communications Businesses

Whenever the next U.S. recession happens, we will see whether the many changes in the telecom, cable TV and video streaming markets will change the historic view of how telecom and video entertainment stocks behave during downturns. 

Traditionally, both telecom and cable TV equities have been viewed as resistant to customer defections in recessions as both are “essential” or “important” recurring services. 

But the markets and consumer tastes have been evolving: reliance on mobile phone services and abandonment of fixed network services; substitution or addition of video streaming services and reduced linear video subscription buying; increased importance of internet access and a decrease in importance of voice and linear video services. 

source: Broadband Search, Seeking Alpha 

All of which raises new questions, including the issue of whether streaming services will prove more resistant to customer churn during recessions, compared to linear video. 

Study Title/Author	Findings
"Do Consumers Cut the Cord in a Recession?" by John Beggs and Patrick/2010	Found a slight decrease in cable TV subscriptions, but not a significant decline.
"Telecom Stocks and Economic Downturns" by JPMorgan Chase (Investment Report) /2020	Indicated telecom stocks generally outperform the broader market during downturns.
"The Recession Resilience of Defensive Sectors" by Fidelity Investments (Market Commentary) /2023	Listed telecom as a sector with potential resilience, but noted the importance of specific company financials.
The Recession and Telecom, Deloitte (2009)	Revenue for telecom service providers remained relatively stable during the 2008 recession, but capital expenditures declined.
The U.S. Telecommunications Industry During Economic Downturns, The Brattle Group (2010)	While telecom revenue growth may slow during recessions, it generally holds up better than the broader economy.
Cord Cutting: What Do Past Recessions Tell Us? MoffettNathanson (2020)	Previous recessions saw limited cord-cutting, suggesting cable TV might retain some stability during downturns. However, the study acknowledges the changing media landscape.
Fama & French (1989)	Defensive sectors like telecom and utilities tend to outperform cyclical sectors.
Ang & Timmermann (1993)	Telecom and utilities exhibit lower volatility and higher risk-adjusted returns during recessions.
Blitz & Reichlin (2001)	Telecom and utility stocks are less affected by credit downgrades compared to cyclical sectors.

A recession might accelerate the secular trend of fixed network voice service abandonment, as consumers prefer mobile phone service. Likewise, a recession might also accelerate linear video abandonment rates, considering the relative expense, compared to streaming alternatives. 

To be sure, live sports will be a key issue for a portion of the buying public. Though most observers see a continuing shift of live sports to streaming services, that trend is not as developed, yet. So sports fans might still conclude they have no choice but to keep their linear video subscriptions. 

And that should continue to prop up demand during recessionary periods. 

On the other hand, perhaps a majority of consumers who are not sports fans can buy multiple streaming subscriptions at lower (or near equivalent) prices than they can buy a linear subscription, suggesting the possibility that streaming services could prove more attractive during a recession. 

Also, streaming arguably still is a growth business, while linear video is in decline. Any recession might accelerate such trends. 

source: Ryan Ang, Seeking Alpha 

The most recent recession, caused by the imposition of Covid shutdowns on the economy, might not provide much insight. With the “in person” economy largely shut down in many countries, demand for work from home or learn from home internet access was quite high. 

Take rates and usage of mobility services arguably rose for the same reason. And the value of streaming and even linear TV services arguably was boosted by the lack of other entertainment options. 

So the most-recent major downturns for which we arguably have data would be the 2008 global financial crisis and the 2000 to 2001 dotcom crash, when video streaming was not a mainstream business at scale. 

Which Edge Will Dominate AI Processing?

Edge computing advantages generally are said to revolve around use cases requiring low-latency response, and the same is generally true for artificial intelligence processing as well. 

Some use cases requiring low-latency response will be best executed “on the device” rather than at a remote data center, and often on the device rather than at an “edge” data center. 

That might especially be true as some estimate consumer apps will represent as much as 70 percent of total generative artificial intelligence compute requirements. 

So does that mean we see graphics processor units on most smartphones? Probably not, even if GPU prices fall over time. We’ll likely see lots of accelerator chips, though, including more use of tensor processing units or neural processing units and application specific integrated circuits, for reasons of cost.  

The general principle is always that the cost of computing facilities increases, while efficiency decreases, as computing moves to the network edge. In other words, centralized computing tends to be the most efficient while computing at the edge--which necessarily involves huge numbers of processors--is necessarily more capital intensive. 

For most physical networks, as much as 80 percent of cost is at the network edges. 

Beyond content delivery, many have struggled to define the business model for edge computing, however. Either from an end user experience perspective or an edge computing supplier perspective. 

Sheer infrastructure cost remains an issue, as do compelling use cases. Beyond those issues, there arguably are standardization and interoperability issues similar to multi-cloud, complexity concerns and fragmented or sub-scale revenue opportunities. 

In many cases, “edge” use cases also make more sense for “on the device” processing, something we already see with image processing, speech-to-text and real-time language translation. 

To be sure, battery drain, processors and memory (and therefore cost) will be issues, initially. 

On-Device Use Case	Benefits	Considerations
Image Processing (Basic)	Privacy: Processes images locally without sending data to servers.  Offline Functionality: Works even without internet connection. - Low Latency: Real-time effects and filters.	Limited Model Complexity: Simpler tasks like noise reduction or basic filters work well on-device. - Battery Drain: Complex processing can drain battery life.
Voice Interface (Simple Commands)	Privacy: Voice data stays on device for sensitive commands. - Low Latency: Faster response for basic commands (e.g., smart home controls).	Limited Vocabulary and Understanding: On-device models may not handle complex requests. - Limited Customization: Pre-trained models offer less user personalization.
Language Translation (Simple Phrases)	Offline Functionality: Translates basic phrases even without internet. - Privacy: Sensitive conversations remain on device.	Limited Languages and Accuracy: Fewer languages and potentially lower accuracy compared to cloud-based models.  Storage Requirements: Larger models for complex languages might not fit on all devices.
Message Autocomplete	Privacy: Keeps message content on device.  Offline Functionality: Auto-completes even without internet.	Limited Context Understanding: Relying solely on local message history might limit accuracy. - Personalized Experience: On-device models may not adapt to individual writing styles as well.
Music Playlist Generation (Offline)	Offline Functionality: Creates playlists based on downloaded music library. - Privacy: No need to send music preferences to the cloud.	Limited Music Library Size: On-device storage limits playlist diversity. - Static Recommendations: Playlists may not adapt to changing user tastes as effectively.
Maps Features (Limited Functionality)	Offline Functionality: Access basic maps and navigation even without internet. - Privacy: No user location data sent to servers for basic features.	Limited Features: Offline functionality may lack real-time traffic updates or detailed points of interest. - Outdated Maps: Requires periodic updates downloaded to the device.

Remote processing (edge or remote) will tend to favor use cases including augmented reality; advanced image processing; personalized content recommendations or predictive maintenance. 

Latency requirements for these and other apps will tend to drive the need for edge processing.

AI Clash Between Copyright and New Technology is an Old Tale

Every new technology brings with it new legal issues. Artificial intelligence, for example, raises copyright issues. 

It is not the first time new technology has clashed with established notions of copyright. 

When photocopying machines were commercialized, manufacturers tried to block the use of the machines for making copies of copyrighted work.

Sony tried to block the use of videocassette recorders to time shift video content for later viewing. Similar disputes erupted over the use of audiocassette tapes, music file sharing and video streaming as well. 

New Technology	Copyright Issues	Key Court Decisions
Photocopying Machines (1960s)	Mass reproduction of copyrighted materials without permission.	Fair Use Doctrine Established: Williams & Wilkins Co. v. United States (1964) established the four-factor fair use test: purpose and character of use, nature of copyrighted work, amount and substantiality of portion used, and effect of use upon the market. Copying for educational purposes could be fair use.
Audio Cassette Tapes (1970s)	Home recording of copyrighted music threatened record sales.	Audio Home Recording Act (1992): Established a royalty levy on blank audiotapes to compensate copyright holders for potential lost sales due to home recording.
MP3 Players and Napster (1990s)	Peer-to-peer file sharing enabled widespread music piracy.	A&M Records v. Napster (2001): Napster was found liable for contributory copyright infringement for failing to prevent users from sharing copyrighted music.
Streaming Services (2000s-Present)	Distribution model challenged traditional music licensing and revenue streams.	Negotiated Licensing Agreements: Streaming services like Spotify and Apple Music pay licensing fees to copyright holders based on user streams.
Digital Video Recorders (DVRs)	Shifting time viewing challenged broadcasters' control over programming.	Sony Corp. v. Universal City Studios (1984): Upheld the fair use of time-shifting for personal viewing using VCRs.

Similarly, conflicts have erupted over content, social media, search, open source software and user-generated content, for example. 

Content Issue	Copyright Issues	Key Court Decisions
Social Media Sharing	Sharing copyrighted content like photos, videos, and music raises questions of fair use and infringement.	Blurred Lines: Perfect 10 v. Amazon (2002) established thumbnails could be fair use for linking purposes. However, sharing entire works without permission is generally considered infringement. The specific context and amount used determine fair use.
User-Generated Content (UGC) Platforms	Platforms like YouTube or TikTok host user-uploaded content, potentially infringing on copyrights.	DMCA "Safe Harbor": The Digital Millennium Copyright Act (DMCA) provides a safe harbor for platforms if they remove infringing content upon notification from copyright holders. Platforms like YouTube have automated takedown systems based on copyright claims.
Software Sharing and Open-Source	Sharing copyrighted software raises concerns about piracy and unauthorized distribution.	Open-Source Licenses: Open-source licenses like GPL (General Public License) allow for modification and sharing of software code, as long as certain conditions are met. These licenses provide a framework for collaborative software development while protecting copyright.
Content Aggregation Services	News aggregators like Google News display headlines and snippets of copyrighted news articles.	Fair Use and Fair Reporting: Courts have generally allowed news aggregation under fair use for purposes of reporting and commentary. The amount and substantiality of content used are crucial factors.

Eventually we will figure out some balance between copyright and use of the new technology in non-infringing ways. But it may take a while.