Sunday, May 19, 2024

Will Video Content Industry Survive AI?

Virtually nobody in business ever wants to say that an industry or firm transition from an older business model to a newer model is doomed to failure. Instead, we’ll hear all sorts of recommendations for what to do and how to do it. "Hybrid" models will be talked about, but also some calls to "stop it," as though that ever works.


So we hear "creatives" who develop content for a living decrying the impact of artificial intelligence on their business models. But like it or not, disruption is coming. An earlier generation of creatives had to deal with the impact of the internet, and AI will be at least as profound.


Major technology transitions often lead to disruption on a massive scale. Eastman Kodak did not survive the shift to digital photography. Tower Records and Virgin Megastores did not survive the shift from physical media distribution of music to streaming. 


Blockbuster Video did not survive the shift from physical media for video to video streaming. AOL survived, but never regained its stature once the internet experience changed from a walled garden to open internet format. 


Most non-facilities-based internet service providers in the dial-up era did not survive the transition to broadband service. 


One does not hear industry executives or analysts claiming that today’s video distributors and content creators will not survive the shift from linear to streaming. But that cannot be categorically ruled out, either. 


As the video content industry struggles to build profitable business models for streaming services while slowing the decay of linear television, Some common answers tend to be given for how linear and broadcast content can complement streaming, as the linear business declines. 

source: PwC 


One might argue there is almost no problem the video streaming business model has that could not be fixed if advertising revenues are boosted or somehow replaced by other new sources. 


Most of the other revenue and cost elements for linear and streaming models are comparable. Streaming does impose new costs in the areas of on-demand support, but has the same marketing and technology costs as does linear delivery. 


And though original content production is generally deemed to be more important for streaming, and often means higher costs, overall content licensing content costs can be lower, in many cases.


Element

Video streaming (%)

Linear video (%)

Revenue



Subscription fees

50-60

10-20

Advertising

20-30

70-80

TVOD (episode sales)

5-10

0

PPV (live event sales)

0-5

0

Merchandising

0-5

0

Cost



Content licensing

30-40

40-50

Production

10-20

0-10

Marketing

10-20

10-20

Technology

10-20

10-20



Some television industry executives see broadcast evolving alongside streaming by focusing on live content and events; perhaps focusing on local content; sharing content libraries and using linear broadcast TV to build interest in streaming exposition. 


So live sports, awards shows, news, and reality TV with audience participation are difficult to replicate on streaming platforms where content is pre-recorded, leading some to speculate the future of broadcast TV is reality TV. 


Some argue that local news and weather are another area of strength for linear services. Local news is among the best revenue and profit generators for local broadcasters, along with local sports. Syndicated programming (talk shows, sitcoms, game shows)) are generally less profitable. Childrens’ programming tends to be the least profitable content type.


Source

Linear TV Revenue 2024 (estimate)

Linear TV Revenue 2030 (estimate)

Video Streaming Revenue 2024 (estimate)

Video Streaming Revenue 2030 (estimate)

MoffettNathanson

$70 billion (down from $86.3 billion in 2023)

$30 billion or less

$40 billion

$120 billion+

Grand View Research

Data not available for specific years

Likely decline

$50 billion+

$100 billion+


But a growing number of observers believe linear broadcasts can be a means of building interest in TV series that are offered on a multi-season basis on streaming platforms. 


Some note that big events and specials, including highly-watched sports events such as the Super Bowl, gig-budget miniseries, documentaries, and specials can offer a unique linear experience that can't be replicated as well by on-demand streaming platforms. 


Cable TV industry executives continue to explore ways to make streaming services complementary to linear subscription TV, perhaps initially by bundling linear with streaming. 


As reasonable as those approaches might be, it appears that live programming and content is becoming a feature of both linear and streaming services. 


The video streamer's focus on original and new content is driven by the need to keep customers engaged over time.


And that is why sports programming--though expensive--is favored by many streaming providers: it supplies an endless stream of original and new content. To be sure, unscripted content tends to be less expensive to produce than scripted series, but sports programming is expensive, if unscripted. 


To a lesser extent, news programming offers similar advantages. It is unscripted and changes every day. Though the “news” focused consumer is a relatively small segment of the viewing and subscribing public, its “new and original” value remains high, for those who favor it. 


Other tactics, such as bundling multiple subscriptions together is another tactic aiming to increase the amount of new content available on a recurring basis, and therefore a way of boosting the value and stickiness of a subscription. 


Content library depth might seem appealing, but new and original content seems to outweigh library extensiveness. 


The objectives of all that effort will be to create new revenue from streaming that at least matches the lost linear service revenues. 


AI Impact Should Disrupt Advice and Content Industries

Many would note that the internet impact on content media has been profound, boosting social and online media at the expense of linear formats. And it seems fair enough to argue that artificial intelligence might deepen that trend. 


One example is the possible impact on “clicking of links” as a platform for monetizing advertising. If AI summaries to direct questions are possible without the need to scroll entries and click on links, traffic volume for media content sites could--and should--drop. To the extent that traffic drives monetization opportunities, AI could be--or will be--yet another obstacle to legacy media monetizing digital content. 


At least, that is what some news content providers already believe, based on new initiatives by Alphabet to embed artificial intelligence into virtually all products and processes, including search. 


At a high level, it is possible to note that many important technology innovations, ranging from personal computers and software to cloud computing and use of the internet, arguably boosted worker productivity, but might not have disrupted whole industries. 


In other cases, widespread disruption did occur, as with social media, targeted advertising and e-commerce disrupting a range of legacy industries and business models. 


The issue now is how artificial intelligence might affect productivity and disrupt industries. The current expectation is that applied AI will boost productivity in a number of ways, across a number of business processes. 


As important as that might prove to be, those effects might be surpassed if AI allows some new business models to develop and provides substitute value for existing processes and industries. 


It is conceivable that a great amount of “advice” or “development” functions--financial, legal, marketing, sales, customer service, product development, software coding--could substantially use AI to displace human-dispensed experts. 


For example, some products now too expensive to provide to small and medium businesses might be possible using AI. Some consumer products that require substantial human labor might be automated to a greater extent, allowing new products to displace existing offers, especially for products with high intangible value. 


The forerunners are the ways media content and business models changed when content could be created and delivered digitally, rather than using physical media. Similar impact can be seen in use of internet retailing rather than “bricks and mortar” stores. 


The use of search to displace many physical operations (trips to the library, reading books, interviewing subjects, comparing products, purchasing products or any related information gathering) provides other examples. 


So the issue is determining when AI is helpful in the sense of possibly improving productivity, which might provide incremental benefits that are hard to quantify, from enabling the creation of whole new industries, firms and value, which might displace existing means of satisfying needs and wants. 

 

Category

Innovation

Impact

Example

Increased Productivity

Personal Computers

Increased efficiency in data processing, communication, and document management.

Faster report generation, improved collaboration, streamlined workflows.

Increased Productivity

The Internet

Enhanced communication, information sharing, and remote work capabilities.

Global teams, real-time updates, access to vast data resources.

Increased Productivity

Internet-based Software (e.g., Cloud Storage)

Reduced hardware costs, improved scalability and accessibility of applications.

On-demand storage, remote access to software, easier collaboration on projects.

New Industries & Business Models

Search Engines (Google)

Created a new industry for information retrieval and online advertising.

Revenue through targeted ads, user data monetization.

New Industries & Business Models

E-commerce (Amazon)

Revolutionized retail by enabling online shopping and global marketplaces.

Direct-to-consumption model, subscription services, logistics networks.

New Industries & Business Models

Social Media (Facebook)

Established new communication channels and platforms for brand marketing and social interaction.

User-generated content, targeted advertising, data-driven marketing strategies.


Higher productivity is a good thing. Creation of whole new industries, and the disruption of legacy industries, might well be the bigger change. 


Word processing was a huge and important aid to those of us working as journalists. Spreadsheets helped analysts build forecasting models faster. Databases helped people categorize knowledge. Search changed the quality, quantity and speed of research many of us could conduct. 


But search, e-commerce and social and digital media created whole new industries, some new business models and often led to the sunsetting of legacy industries as well. 


Today, we mostly are at an early stage of applying AI to every process, and the results will vary, ultimately. What we also might expect--in at least a few cases--is wholesale innovation and economic disruption at the “industry” level, eventually. Content and commerce provide the historic examples. 



Wednesday, May 15, 2024

AI Will Improve Productivity, But That is Not the Biggest Possible Change

Many would note that the internet impact on content media has been profound, boosting social and online media at the expense of linear formats. And it seems fair enough to argue that artificial intelligence might deepen that trend. 


One example is the possible impact on “clicking of links” as a platform for monetizing advertising. If AI summaries to direct questions are possible without the need to scroll entries and click on links, traffic volume for media content sites could--and should--drop. To the extent that traffic drives monetization opportunities, AI could be--or will be--yet another obstacle to legacy media monetizing digital content. 


At least, that is what some news content providers already believe, based on new initiatives by Alphabet to embed artificial intelligence into virtually all products and processes, including search. 


At a high level, it is possible to note that many important technology innovations, ranging from personal computers and software to cloud computing and use of the internet, arguably boosted worker productivity, but might not have disrupted whole industries. 


In other cases, widespread disruption did occur, as with social media, targeted advertising and e-commerce disrupting a range of legacy industries and business models. 


The issue now is how artificial intelligence might affect productivity and disrupt industries. The current expectation is that applied AI will boost productivity in a number of ways, across a number of business processes. 


As important as that might prove to be, those effects might be surpassed if AI allows some new business models to develop and provides substitute value for existing processes and industries. 


It is conceivable that a great amount of “advice” or “development” functions--financial, legal, marketing, sales, customer service, product development, software coding--could substantially use AI to displace human-dispensed experts. 


For example, some products now too expensive to provide to small and medium businesses might be possible using AI. Some consumer products that require substantial human labor might be automated to a greater extent, allowing new products to displace existing offers, especially for products with high intangible value. 


The forerunners are the ways media content and business models changed when content could be created and delivered digitally, rather than using physical media. Similar impact can be seen in use of internet retailing rather than “bricks and mortar” stores. 


The use of search to displace many physical operations (trips to the library, reading books, interviewing subjects, comparing products, purchasing products or any related information gathering) provides other examples. 


So the issue is determining when AI is helpful in the sense of possibly improving productivity, which might provide incremental benefits that are hard to quantify, from enabling the creation of whole new industries, firms and value, which might displace existing means of satisfying needs and wants. 

 

Category

Innovation

Impact

Example

Increased Productivity

Personal Computers

Increased efficiency in data processing, communication, and document management.

Faster report generation, improved collaboration, streamlined workflows.

Increased Productivity

The Internet

Enhanced communication, information sharing, and remote work capabilities.

Global teams, real-time updates, access to vast data resources.

Increased Productivity

Internet-based Software (e.g., Cloud Storage)

Reduced hardware costs, improved scalability and accessibility of applications.

On-demand storage, remote access to software, easier collaboration on projects.

New Industries & Business Models

Search Engines (Google)

Created a new industry for information retrieval and online advertising.

Revenue through targeted ads, user data monetization.

New Industries & Business Models

E-commerce (Amazon)

Revolutionized retail by enabling online shopping and global marketplaces.

Direct-to-consumption model, subscription services, logistics networks.

New Industries & Business Models

Social Media (Facebook)

Established new communication channels and platforms for brand marketing and social interaction.

User-generated content, targeted advertising, data-driven marketing strategies.


Higher productivity is a good thing. Creation of whole new industries, and the disruption of legacy industries, might well be the bigger change. 


Word processing was a huge and important aid to those of us working as journalists. Spreadsheets helped analysts build forecasting models faster. Databases helped people categorize knowledge. Search changed the quality, quantity and speed of research many of us could conduct. 


But search, e-commerce and social and digital media created whole new industries, some new business models and often led to the sunsetting of legacy industries as well. 


Today, we mostly are at an early stage of applying AI to every process, and the results will vary, ultimately. What we also might expect--in at least a few cases--is wholesale innovation and economic disruption at the “industry” level, eventually. Content and commerce provide the historic examples.


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