Cable TV Was About Linear Programming Choice; Streaming Remains About On-Demand, "Live" Features Notwithstanding

One hears a lot of chatter these days that video streaming bundles are the new cable TV. No doubt, it is a substitute or replacement product for subscription TV, to a growing extent. And yet the allegation that streaming is “becoming cable TV,” often uttered as a negative, might miss important elements of the shift. 

Cable TV was initially a subscription-based way to watch linear broadcast TV for rural residents. Later it had appeal for metro-area residents as a “more choice” vehicle. But all those new and specialized content formats remained linear. 

On-demand consumption of movie and other pre-recorded content was not possible until the advent of the videocassette recorder and the business of video rentals. In one sense this also was an extension of the “more choice” shift of consumption patterns. 

But that arguably was secondary. The primary shift was from linear to on-demand consumption, a trend that continued with the availability of DVDs and then video streaming services. It is not insignificant that Netflix began life as a video rental retailer before making the shift to streaming delivery. 

On the other hand, streaming services are adding more exclusive “live” content as well, largely because of the customer interest in sports programming, for example. Such content aids customer acquisition and retention in the same way that other exclusive content creates differentiation and value. 

All that has financial implications for broadcast, cable TV and video streaming industries, as broadcast TV and cable TV advertising revenue shifts to other venues; cable TV subscriptions decrease and streaming subscriptions increase. 

 

Year	Broadcast TV Advertising Revenue ($B)	Cable TV Advertising Revenue	Cable TV Subscription Revenue	Video Streaming Subscription Revenue	Video Streaming Ad Revenue
2023 (Estimated)	40	60	120	18.8	4
2024	38.5	58	115	22.5	4.5
2025	37	55	110	26.5	5
2026	35.5	52	105	31	5.5
2027	34	49	100	36	6
2028	32.5	46	95	41.5	6.5
2029	31	43	90	47.5	7
2030	29.5	40	85	54	7.5

On the other hand, customers can use DVR capabilities to create a sort of on-demand capability for linear broadcast and cable TV programming as well. 

The point is that streaming video bundles are not the "new cable TV." Both cable and streaming are about content choice. But all forms of broadcast TV are essentially linear, with a DVR overlay. Streaming bundles remain an on-demand value proposition; the latest evolution of on-demand video. That remains true even as streaming services add some "live" programming as well.

Will All PCs Eventually be AI PCs?

It’s arguably too early to be confident about market share forecasts for AI PCs. Will they eventually become the standard PC, as smartphones now are the standard phones purchased by consumers? Or will AI PCs remain specialist tools, to a greater or lesser degree?

The issue is less “ability to use AI” and more the issue of the value of local processing. 

One might liken the market prospects to that of consumer-grade or work-grade general-purpose PCs and “workstations” used by some, but not most. 

High-end workstations are used for 3D rendering, video editing, and complex simulations. They prioritize raw local processing power, high-performance graphics cards and large memory capacities. 

AI PCs will feature specialized hardware components like Tensor Processing Units or Neural Processing Units alongside traditional CPUs and GPUs to accelerate AI computations. But that does not necessarily speak to “why” such machines would add value over PCs not including those elements. 

At least for the moment, forecasters see a gradual shift of buying patterns. 

Year	AI PC Market Share (%)	General-Purpose PC Market Share (%)	Sources
2024 (estimated)	2-5	95-98	Canalys, Grand View Research
2025	5-10	90-95	Canalys
2026	8-15	85-92	Gartner
2027	12-20	80-88	Gartner
2030	20-30	70-80	IDC

AI PCs might be useful for developers or users working on AI training, inference and other AI-dependent workloads. For most consumers, that might include image processing, speech-to-text,  language translation or gaming. 

Some professionals who are AI developers, researchers or data scientists might have work-related reasons that make AI PCs a good choice, if local processing adds value, compared to remote processing. 

It is not clear how much of the video editing or 3D rendering market might be affected. “Professional” use cases might not be supported, but casual and user-generated content might be. 

There arguably is more debate about the PC market than the smartphone market, though. AI already makes general sense for image processing on smartphones as well as speech-to-text. But additional use cases requiring on-board processing will have to be developed. 

The argument for local AI processing on PCs is more complex. AI could personalize software functions, optimize battery usage, or enhance security measures. But it is not certain those tasks must be handled by on-board processors.

AI-specific hardware could significantly improve device performance for tasks such as photo and video editing, gaming, or augmented reality applications, to the extent those features are deemed useful on PCs. 

Battery life might be a constraint for smartphones and laptops, though. And, for most users, additional AI device cost will have to be balanced against “new” and valued use cases. It will take some time for those use cases to develop. 

At least in principle, one might envision a new category of AI PCs half way between workstations and general-purpose PCs. One might also envision an eventual migration of local AI processing to most PCs as a regular feature, at some point. 

Are Large Language Models Investor "PIcks and Shovels" or Not?

Aside from all else, artificial reality Iis an investment theme. A study by Morgan Stanley, for example, argues that AI's materiality to investment theses has increased significantly, affecting at least 446 stocks, worth $15 trillion, in 2024. 

That is logical enough, given the importance of AI enabling technology including semiconductors, servers, cloud computing, data centers, energy sources for data centers; as well as AI implications for the functionality of enterprise and consumer software. 

In 2023 and 2024, much of that financial impact has centered on generative AI, eclipsing machine learning and natural language processing, for example, which already are used by many business and consumer applications. 

Facial recognition, for example, uses ML algorithms to unlock user smartphones, while digital voice assistants such as Siri and Alexa use AI, NLP and ML to understand commands and carry out a range of tasks. 

AI algorithms are used in e-commerce to make personalized shopping recommendations; in clinical trials to improve drug discovery and efficiency and elsewhere across an array of industries to automate a host of back-office tasks.

As often is the case, suppliers of “picks and shovels” were among early winners. Though we might quibble about what firms are in that category, many would say suppliers of graphics processor units, acceleration chips, memory, cloud computing suppliers and even electrical power companies are among firms and industries supplying “picks and shovels.”

There arguably is greater disagreement about whether large language models are enablers--and therefore in the “picks and shovels” category--or in the many other categories of beneficiaries of AI. 

Category	Industries/Firms	Description
AI Enablers (Picks & Shovels)	Chipmakers (Nvidia, AMD, Intel) Cloud Computing Providers such as Amazon Web Services, Microsoft Azure, Google Cloud Platform)   Data Labeling Companies , Labelbox, Scale AI)  OpenAI (research & development) Electrical Utilities	Foundational infrastructure and tools needed to train and develop AI models in general. They don't necessarily focus on specific applications of AI.
Generative AI Beneficiaries	Content Creation (Adobe, Unity, Unreal Engine)   Drug Discovery (Insilico Medicine, Generative Bio)   Materials Science  Marketing and Advertising (Anyword, Copy.ai) Social Media Search Business Services Law Transportation Retailing Information Technology	Industries and firms leverage generative AI for specific applications. For example, generative AI can create new marketing copy, design elements, or discover new materials.

Large language models are where large amounts of disagreement could occur. Large language models can be viewed as both beneficiaries of AI and, to a lesser extent, enablers (picks and shovels). GPUs, memory, power, data centers and cloud computing enable AI to run. 

In that sense, AI platforms and apps are beneficiaries, not picks and shovels. LLMs are a product of advanced AI techniques such as deep learning and natural language processing, but are applications in their own right. And AI applications are not generally considered to be picks and shovels. 

Still, many could argue that LLMs are enablers to the extent they support creation of software features and applications. LLMs will power search, social media, many forms of app personalization, smartphone image processing, speech to text functions, text summarization, image processing, notetaking and so forth. 

So LLMs are where the distinction between AI “picks and shovels” enablers and and AI beneficiaries is mixed. 

Further muddling exists because some beneficiaries of AI also are developers of LLMs, and are revenue generators. Think of Microsoft’s Copilot, Google’s Gemini or other GenAI apps sold as subscriptions. GenAI is available both as a feature of Microsoft and Alphabet (Google) products as well as a subscription-based application. 

In some instances the LLM is an enabler or feature; in other cases an app. For some firms, GenAI and LLMs are both enablers (picks and shovels) and beneficiaries of capabilities and features offered by most products and processes. 

That matters for equity investors as well as all sorts of firms, industries and products.

Consumer "Internet Downtime" is Hard to Assess

It is nearly impossible to measure the amount of uptime or downtime any single consumer experiences with internet-based experiences on an annual basis, but we frequently see estimates of app availability, or network or device availability that separately look fairly reasonable, usually in minutes per year when a particular app is not available, for example. 

And that includes planned downtime for major software upgrades or maintenance, for example. And, generally speaking, most of us, most of the time, might agree that our internet-based app experiences are robust. We normally expect them to be there, and to work.

Value Chain Segment	Uptime (%)	Downtime (Minutes per Year)
Network Outages (Average ISP)	99.95%	29.2
Application Downtime (Average)	99.90%	52.56
Device Issues (Estimated)	99.50%	21.9
Total Value Chain Availability	Varies	Varies
Akamai State of the Internet/Connectivity Report	-	2880 - 5760 Minutes (2-4 days)
ThousandEyes	-	4380 - 7200 Minutes (3-5 days)

There are a few caveats. Downtime for planned outages might be greater than we assume, because we are sleeping when those planned outages happen. Also, some apps, networks or devices might be down, but not in use by any single user at any single time, so the “outages” are not experienced. 

In other words, if an outage happens to an app or service I am not using, I do not notice it. A thousand apps I do not use can have lots of outages; I'd never notice. Conversely, I am quite apt to notice an outage of my most-favorite and most-used apps. 

Also, end user experience is not simply a matter of app availability, but all other sources in series, including one’s devices, the internet access and transport networks, data center servers and local power, for example. 

Outages of some magnitude from all of those sources must be combined to derive a full picture of internet experience availability, across all experiences any single user has in a year. 

During a recent planned 48-hour local power outage, I could not use the internet in any AC-powered context. The apps, networks and devices might have been available, but local AC power was not available. Uptime for all the apps, devices and networks might have been quite high, yet my experience of “outage” happened anyhow. 

“Observability” therefore matters. Outages I do not encounter do not matter. Outages caused by local power outages do matter, even when there is no problem with the apps, devices or networks enabling internet experiences. 

The point is that the end user experience of internet-enabled experiences is conditional and cumulative; a function of what one does and doesn’t do, and when, across the full accumulated range of possible failure points.