Will Sports Content Become More a Buyers' Market?

Whenever investors think about media, telecom and technology assets, changes in the content business are an unavoidable subject, as business models are changing.

Among the changes is the role of sports content as a driver of viewer engagement and therefore revenue potential. By some estimates, while sports programming makes up about 10 percent to 15 percent of channels in a typical cable bundle, it accounts for nearly 50 percent of content costs.

Historically, such premiums have been driven by the belief (and reality) that major league content (NFL, NBA, MLB) offers a unique value proposition: live, unscripted events that attract large, real-time audiences. 

The issue is that as the cost of gaining those rights has ballooned, new questions are raised about whether paying for those rights is possibly greater than the revenue generated by having the rights. 

For linear subscription TV services, amortizing the cost of sports programming over a shrinking subscriber base seems unsustainable. And declining viewership for major league sports seems to be happening. 

The caveat is that some viewing also has shifted to streaming platforms. 

Sports Viewership Decline on Linear TV (2020-2024)
Sport/Event	2020 Average Viewers	2022 Average Viewers	2024 Average Viewers	% Change (2020-2024)
NFL Regular Season	15.4 million	14.1 million	12.8 million	-16.90%
NBA Regular Season	1.96 million	1.6 million	1.38 million	-29.60%
MLB Regular Season	1.1 million	0.94 million	0.83 million	-24.50%
NHL Regular Season	0.47 million	0.42 million	0.38 million	-19.10%
Premier League Soccer	0.52 million	0.48 million	0.43 million	-17.30%
College Football (FBS)	1.84 million	1.57 million	1.32 million	-28.30%
NASCAR Cup Series	3.06 million	2.7 million	2.21 million	-27.80%
Olympics (Summer/Winter)*	19.8 million	11.4 million	9.9 million	-50.00%
The Masters Golf	5.6 million	4.8 million	4.2 million	-25.00%
Wimbledon (Finals)	2.2 million	1.9 million	1.7 million	-22.70%

The key observation is that all major sports properties have experienced double-digit percentage declines since 2020. Some note that younger viewers are opting for clips, highlight reels and other shorter-form content, rather than watching “whole games or matches.”

Olympic broadcasts show the steepest decline at 50 percent. 

NBA and College Football have experienced nearly 30 percent viewership drops. 

Traditional sports with older demographics (golf, tennis) have fared somewhat better but still show significant losses. 

Many, for example, would have argued that Warner Brothers Discovery losing NBA rights for TNT would be a major blow. Others might argue the avoided costs are greater than the potential revenue would have been. 

In other words, at what point will licensees begin to more-seriously question how much they can afford to pay to get broadcast rights to such content. We can assume high sports rights costs have been an issue for decades, using cable operator concern about the cost of ESPN carriage rights as a prime example. 

Sport	Total Annual Rights Fees (USD)	Linear Networks	Fees by Linear (USD)	Streaming Networks	Fees by Streaming (USD)	Contract Duration	Content and Format
NFL	$9.46 billion	CBS, Fox, NBC, ESPN/ABC	$8.46 billion	Amazon Prime Video	$1 billion	2023–2033	Amazon pays $1B/year for Thursday Night Football; rest split among linear.
NBA	$6.9 billion	ESPN/ABC, NBC	$4.9 billion	Amazon Prime Video	$2 billion	2025–2036	New deal starts 2025–26; Amazon’s 66 games estimated at $1.8–2B/year.
MLB	$1.96 billion (national)	ESPN, Fox, TBS	$1.84 billion	Apple TV+, Roku	$120 million	Varies (e.g., 2022–2028)	National deals only; RSNs add ~$2B more but are team-specific.
NHL	$625 million	ESPN/ABC, TNT	$625 million	None (ESPN+ simulcasts)	Included in linear	2021–2028	ESPN ($400M) and TNT ($225M) include streaming components.
MLS	$250 million	Fox (limited)	$30 million	Apple TV+	$220 million	2023–2032	Apple’s $2.5B deal dominates; Fox’s smaller linear package.
WNBA	$200 million	ESPN, CBS, Scripps	$150 million	Amazon Prime Video	$50 million	2026–2036	Part of NBA’s $76B deal; streaming share estimated.
NASCAR	$1.1 billion	Fox, NBC	$800 million	Amazon Prime Video, TNT	$300 million	2025–2031	New deal splits linear (Fox/NBC) and streaming (Amazon/TNT).
Big Ten	$1.07 billion	Fox, CBS, NBC	$1 billion	Peacock (via NBC)	$70 million	2023–2030	Peacock’s share is estimated; linear dominates.

Still, much of the value of sports rights is arguably intangible, as revenues (advertising, subscription revenue contribution) might not directly cover the cost of rights payments. So much of the value comes from higher ability to attract and retain customers, for example, by boosting perceived content value. 

There clearly is an argument to be made that direct revenue opportunities do not actually explain why distributors would pay so much for sports rights. Obviously there is perceived value beyond the direct revenue upside. 

When Warner Brothers Discovery lost its NBA rights, the avoided costs were about $1.4 billion, against expected revenue of about $600 million, for example. 

Looking at various sports rights costs and direct revenues, it might be argued that the value of such rights is to be found only partly in direct revenues, since no sport seems to generate direct distributor revenue in excess of rights cost. 

Sport	Annual Licensing Rights Cost (USD)	Linear Licensees	Linear Revenue Est. (USD)	Streaming Licensees	Streaming Revenue Est. (USD)
NFL	$9.46 billion	CBS, Fox, NBC, ESPN/ABC	$4.5–5B (ads)	Amazon Prime Video	$1–1.5B (ads + subs)
NBA	$6.9 billion	ESPN/ABC, NBC	$2–2.5B (ads)	Amazon Prime Video	$0.8–1.2B (ads + subs)
MLB	$1.96 billion (national) + ~$2B RSNs	ESPN, Fox, TBS	$1.5–2B (ads)	Apple TV+, Roku	$50–100M (subs)
NHL	$625 million	ESPN/ABC, TNT	$400–500M (ads)	None (ESPN+ simulcasts)	Included in linear
PGA Tour	$550 million	CBS, NBC, Golf Channel	$300–400M (ads)	None (Peacock simulcasts)	Included in linear
MLS	$250 million	Fox (limited)	$20–30M (ads)	Apple TV+	$150–200M (subs)

Sports rights have been a seller’s market for decades, but one wonders if that now is potentially changing.

How Long Until 50% Use of AI?

Most of us are familiar with the notion that newer waves of computing technology get adopted faster than older waves. Where it took almost two decades for personal computers to be adopted by half of households, it took less than a decade for internet use to reach half of U.S. households. Smartphones arguably reached half of users in about six years. 

So many of us would not be surprised if artificial intelligence use reached half of U.S. households in three years. That would be helped along by the fact that AI is expected to be used on smartphones, by consumer applications (social media, search, e-commerce, entertainment content), in autos and other machines and appliances as well. 

Of course, assumptions matter. We have to select particular products or apps to measure adoption. For purposes of the present analysis, we use the IBM PC as the tracked device. Obviously there were many hobbyist PCs available before then, but the IBM PC became the first mass market device “regular people” rather than hobbyists used. 

Likewise, with the internet, we track the multimedia World Wide Web, even if some people used bulletin boards before then. 

Smartphones likewise were available before the Apple  iPhone, perhaps most notably the Research in Motion BlackBerry, a mobile email device. But it was the iPhone that kicked off massive adoption by consumers. 

source: Pew Research Center 

The point is that adoption rates are lengthened if we use the hobbyist or early-adopter phases of each technology, rather than the point at which consumer mass adoption began. 

Technology	Year Introduced	Years to 10% Adoption	Years to 50% Adoption
Personal Computers	1981	5 (1986)	19 (2000)
Internet	1991	4 (1995)	9 (2000)
Smartphones	2007	2 (2009)	6 (2013)

If we consider advanced AI adoption starting around 2020 (with language models like GPT-3), AI  might reach 10-percent adoption in two years and 50 percent in three to four years. And that might be too conservative an assumption, given the fact that AI already has been used in making content recommendations, voice interfaces and search, even before the launch of language models. 

On the other hand, few consumers likely think of their use of search, e-commerce, social media or voice interfaces as “AI use,” whereas they probably do consider use of ChatGPT and other models as AI use. 

However, hardware embodiments (robots, autonomous vehicles) may align more with smartphone adoption timelines, as significant infrastructure, device development and cost reductions have  to occur.

The bigger question is “so what?” What impact will AI have on user experience or behavior? What new use cases will develop? What new markets could be created?

After Chatbots, Embodied AI (Robots, Vehicles, Devices, Machines)

Right now, the typical casual observer might tend to think of generative artificial intelligence as an AI chatbot or AI assistant. But physically-embodied  AI is going to be a big part of  next wave of growth for generative AI

Artificial intelligence increasingly is going to be “embodied” in devices and machines such as vehicles and robots, illustrating the way AI now is reshaping older markets such as the “internet of things.” Researchers at Goldman Sachs, for example, have estimated that the market for humanoid robots could reach $38 billion by 2035. 

Within industrial robotics, different form factors address different workloads. Robotic arms are used for tasks such as picking, packaging, material handling and assembly.

Mobile robots typically run on wheels and are commonly used for moving inventory from one place to another inside industrial facilities.

And then we are also seeing a rise in humanoid robots, which more closely resemble the structure of human bodies, enabling them to complete tasks and move around just like us.

Among the firms looking to lead in this market are Tesla, Boston Dynamics, Figure AI, Apptronik and Agility Robotics. And then there are all the other firms that will try to create leading roles in support and management, such as AWS. 

Amazon got into robotics in 2012 when it created the ‘Amazon Robotics’ arm by acquiring a company called Kiva Systems, which specialized in warehouse technology. Between 2021 and 2024, Amazon expanded the number of robots deployed in its own operations from 350,000 to 750,000. 

The point is that AI, IoT, robotics, autonomous vehicles and embodied AI are starting to overlap. Looking only at the “Magnificent seven” firms, for example, embodied AI is enmeshed with firm earnings. 

source: Seeking Alpha

Company	Robotics	Autonomous Vehicles	Generative AI	Language Models	AI as a Service	Smartphone Apps
Apple	Yes, acquisition of Robotics firm	Yes, investment in AV startup, Voyage	Yes, Core ML	Yes, Siri	No	Yes, Core ML apps
Amazon	Yes, acquisition of Kiva Systems, Robotics firm	Yes, investment in AV startup, Aurora	Yes, SageMaker	Yes, Alexa	Yes, SageMaker	Yes, Alexa apps
Alphabet	Yes, Boston Dynamics, Robotics firm	Yes, Waymo, AV subsidiary	Yes, Google AI	Yes, Google Assistant	Yes, Google Cloud AI	Yes, Google Assistant apps
Facebook	No	Yes, investment in AV startup, Cruise	Yes, FAIR	Yes, Portal	Yes, Facebook AI	Yes, Portal apps
Microsoft	Yes, Azure Machine Learning	Yes, investment in AV startup, Cruise	Yes, Azure Machine Learning	Yes, Bing	Yes, Azure Machine Learning	Yes, Bing  apps
NVIDIA	Yes, Isaac, Robotics platform	Yes, Drive, AV platform	Yes, Deep Learning	No	Yes, NVIDIA AI	Yes, Deep Learning apps
Tesla	Yes, Autopilot, Robotics platform	Yes, Full Self-Driving	Yes, Autopilot AI	No	No	Yes, Autopilot apps

So embodied AI (robots, vehicles, machines, devices) are going to be important AI products, beyond today’s AI assistants or chatbots.

Good Outcomes Beat Good Intentions: How Dumb Are We?

Good intentions clearly are not enough when designing policies to improve home broadband availability in underserved areas. In fact, since 2021, more than three years after its passage, the U.S. Broadband Equity, Access, and Deployment (BEAD) program has yet to install a single new connection.  

It seems we were determined to make the perfect the enemy of the good, preventing construction until we mostly were certain our maps were accurate. A rival approach would have proceeded on the assumption that residents and service providers pretty much know where they have facilities and where they do not; where an upgrade can be conducted fast and easily, and where it cannot. 

And perhaps (despite the clear industry participant interests that always seem to influence our decisions) we should not have insisted on the “fastest speed” platforms. Maybe we’d have prioritized “good enough” connections that could be supplied really fast and enabled the outcomes we were looking for (getting the unconnected connected; getting the underserved facilities that do not impede their use of internet apps). 

This is not, to use the phrase, “rocket science.” We have known for many decades that “good enough” home broadband can be supplied fast, and affordably, if we use satellite (geostationary or low earth orbit, but particularly now LEO) or wireless to enable the connections. 

To those who say we need to supply fiber to the home, some of us might argue the evidence suggests relatively-lower speed (such as 100 Mbps downstream) connections supply all the measurable upside we seek, for homework, shopping, telework. The touted gigabit-per-second or multi-gigabit-per-second connections are fine, but there is very little evidence consumers can even use that much bandwidth. 

Study/Source	Key Findings
Distinguishing Bandwidth and Latency in Households' Willingness to Pay for Broadband Internet Speed (2017)	Consumers value increasing bandwidth from 10 to 25 Mbps at about $24 per month, but the additional value of increasing from 100 Mbps to 1 Gbps is only $19. This suggests diminishing returns for speeds beyond 100 Mbps.
Are you overpaying for internet speeds you don't need? (2025)	Research indicates that many Australians are overspending on high-speed internet connections they don't need. Most households can manage well with a 50 Mbps plan unless they engage in high-bandwidth tasks like 4K streaming or online gaming.
Simple broadband mistake costing 9.5 million households up to £113 extra a year (2024)	Millions of UK households are overpaying for broadband by purchasing higher speeds than necessary. Smaller households often need speeds up to 15 Mbps but pay for over 150 Mbps, wasting £113 annually.
ITIF (2023)	- US broadband speeds outpace everyday demands - Only 9.1% of households choose to adopt 250/25 Mbps speeds when available - Clear inflection point past 100 Mbps where consumers no longer see value in higher speeds
ITIF (2020)	- Average existing connections comfortably handle more than typical applications require - A household with 5 people streaming 4K video simultaneously only needs 2/3 of current average tested speed - Research shows reaching a critical threshold of basic broadband penetration is more important for economic growth than faster speeds
European Research (2020)	- Full fiber networks are not worth the costs - Partial, not full end-to-end fiber-based broadband coverage entails the largest net benefits
US Broadband Data Analysis	- Compared to normal-speed broadband, faster broadband did not generate greater positive effects on employment
OpenVault Q3 2024 Report	- Average US household uses 564 Mbps downstream and 31 Mbps upstream - Speeds around 500 Mbps sufficient for most families
FCC Guidelines	- 100-500 Mbps is enough for 1-2 people to run videoconferencing, streaming, and online gaming simultaneously - 500-1000 Mbps suitable for 3 or more people with high bandwidth needs

We might all agree that, where it is feasible, fiber to home makes the most long-term sense. But we might also agree that where we want useful home broadband speeds, right now, everywhere, with performance that enables remote work, homework, online shopping and all other internet apps, then any platform delivering 100 Mbps (more for multi-user households, but likely not more than 500 Mbps even in the most-challenging use cases) will do the job, right now. 

Good intentions really are not enough. Good outcomes are what we seek. And that often means designing programs that we can implement fast, at lower cost, with wider impact, immediately or nearly so. “Better” platforms that cost more and are not built are hardly better.