How Big a Problem are Industry Revenue Growth Rates?

In most industries, it is probably safe to argue that under-par performance is the existential problem, not in-line performance. Executives don't get fired unless their outcomes are sub-par, compared to industry averages.

Is low connectivity service provider revenue growth a problem? It might seem obvious that it is a problem, but whether it is an existential problem is probably the better way to frame the question. Different industries have different growth rates, profit margins and roles in the value chain. Noting such differences might be highly useful for firm and industry strategy.

It might simply be unreasonable to expect traditionally-slow-growing industries to alter those patterns, just as we might be skeptical about firms in traditionally fast-growing industries that do not seem to exhibit the “industry standard” growth rates. 

The exception is if a given firm in a given industry is able to deploy or acquire assets in different parts of an industry value chain that have distinctly-different growth characteristics. That is the logic behind the “move up the stack” argument. 

As a management professor once advised us, “if you have a choice, choose a fast-growing industry.” The reason is that similar amounts of effort and skill (the same effort by a single individual in different settings) will produce different outcomes when applied to declining, slow-growing or fast-growing industries and firms. 

source: KPMG 

The point is that annual growth rates are a “problem” in any industry only when the trend worsens and growth slows over time. But that is not necessarily an issue management can fix, in any one company in any single industry. Over time, profit margins or growth rates in many industries have slowed, in part because of market saturation and competition. 

Indeed, one would be hard pressed to find an industry whose revenue growth rates have not declined over time. 

Industry Sector	Historical Average Growth Rate (%)	Projected Long-Term Growth Rate (%)
Technology	8-10%	5-7%
Healthcare	5-7%	4-6%
Consumer Staples	3-4%	2-3%
Consumer Discretionary	5-6%	3-5%
Financials	6-8%	3-5%
Industrials	4-6%	2-4%
Materials	5-7%	3-5%
Energy	4-6%	2-4%
Utilities	3-5%	2-4%
Telecommunications	5-7%	2-4%
Retail (except E-commerce)	2-4%	1-2%
E-commerce	10-15%	7-10%
Education	4-6%	3-5%

And with the caveat that different segments and firms might have different growth rates, industries with utility-like characteristics show the same slower revenue growth rates as seen in most other industries. 

Industry Sector	Historical Average Growth Rate (%)	Long-Term Growth Rate (%)
Telecommunications	5-7%	2-4%
Cable	4-6%	1-3%
ISP (Internet Service Providers)	6-8%	3-5%
Satellite Communications	8-10%	4-6%
Electric Utilities	3-5%	2-4%
Water Utilities	3-4%	2-3%

The point is that slow growth rates, or slower growth rates, are not necessarily an existential problem. Expected growth rates might simply reflect the near-universal slowing of industry growth rates in virtually all industries over time. 

And to the extent that utility-type industries and connectivity businesses traditionally have growth rates in the middle of all industries, continued “slow growth” is not unexpected, nor unusual, nor an imminent threat. 

That is simply the nature of the business. To be sure, not every provider in every segment has the same growth rate. But the reasons for such divergences are hard--if not impossible--to replicate. Younger firms tend to grow faster than older firms. Non-dominant firms sometimes get help from regulators to increase competition with dominant firms. Some segments of an industry grow faster than others. 

Sure, every executive would prefer faster growth rates over slower growth. But there are rational limits to how much that is subject to managerial skill.

An 80-Year-Old Gift

80 years ago these young men began purchasing some very-expensive real estate on beaches, fields, farms and towns in Normandy, France. But not for themselves. Thank you. We do not forget the gift.

AI is Like the PC OS Business Model in Some Ways

In some key ways, the artificial intelligence business is developing in quite a different manner than did the internet, at least at the level of foundational models that might be likened to operating systems. 

Or, to use another analogy, the large language model business stack is built on the actual LLMs. And there is a possible divergence between the internet, built largely on open source or marketplace standards including TCP/IP and Ethernet, and generative AI, built more on the model of the personal computer operating system platforms. 

The internet's development--at the key application level--was largely driven by startups and entrepreneurs. GenAI is largely driven by a relatively small number of large and established firms, even if startups abound at the app layer. 

Building internet apps and services often required less initial investment compared to GenAI, such as the cost to build and train an application’s generative AI capabilities and inferences. 

Application user experience and scalability arguably were more important than access to capital, in part because capital was so abundant at that time and also because the ability to scale (users) was seen as key. 

AI models are dependent on vast access to data; the internet apps were not. So as mechanisms develop to codify “fair use” and licensed access, more capital is going to be required for access to quality data sources. 

There are other angles as well. The early internet was powered by private data centers of modest size. But AI is powered by “cloud computing” mechanisms. 

By most estimates, about 65 percent of the capacity in global data centers is owned by just three companies: Amazon, Google, and Microsoft. That matters for artificial intelligence provided “as a service,” as much of the digital infrastructure required to support AI will be provided by the handful of hyperscale “computing as a service” suppliers. 

And some might note that one value of investing in an AI startup are the agreements to use a particular cloud computing provider. 

Startups get investment, but also agree to use the investing cloud computing giant’s infrastructure. 

Also, some note that Google, Microsoft and Amazon are actively investing in hundreds of AI start-ups, as well.  In 2023, Google, Microsoft and Amazon invested as much as two-thirds of the $27 billion for AI startups, a report argues.  

Ignoring for the moment the matters of governance or competition in markets, there are possible systemic dangers related to firm revenue and profits. In the internet bubble at the turn of the century, for example, many firms exaggerated their revenues or capital bases using various forms of financial excess. 

Internet capacity providers engaged in a practice called "capacity swapping." They bought and sold unused bandwidth from each other, artificially inflating their reported capacity and network reach. This created the illusion of high demand and fueled investor confidence. But it was an illusion. Actual end user demand was not as high as it seemed. 

Many internet app startups relied heavily on vendor financing. Vendors would extend credit to these companies in exchange for stock options. This allowed startups to show reduced costs while vendors could report higher sales. 

Some companies also resorted to creative accounting practices to inflate their revenue figures or provide growth metrics. Companies might record barter agreements, where they traded services or advertising space instead of receiving cash, as actual revenue. This inflated their top line without reflecting any real cash flow.

Some companies recognized revenue from multi-year contracts  upfront,  treating the entire value of the  contract as income in the current year.  This practice distorted their current financial health and overstated immediate profitability.

Companies might capitalize expenses related to marketing, website development or customer acquisition  as assets instead of showing them as expenses.  This artificially inflated reported profits. 

Some companies recorded revenue for services even if the customer hadn't paid yet, again inflating reported revenues. 

The point is that the AI business is developing in quite a different way than the internet. At least until the spigots shut off, there was plenty of investment capital available during the internet bubble. I recall being quite shocked when told by a startup’s founders not to worry about some parts of a business plan I was working on, as there was “plenty of money.” 

AI is different, at the model or platform level.  It is extremely capital intensive at a time when capital arguably is not plentiful or affordable. So barriers to entry are quite significant for model builders. In that sense, GenAI more nearly resembles the PC operating system model than the internet.

The AI Business Stack

Most observers probably expect that artificial intelligence will produce some combination of new features for existing functions (image processing, speech to text, language translation, summarization, search, content creation, editing, shopping, research) as well some new use cases that could well produce entirely-new firms, industries and revenue streams. 

It is logical that AI will be used to improve existing products such as Apple’s Siri or Google search, by making existing functions “smarter and faster.”

But entirely-new firms and industries are more likely to be built, one might argue, in a different way, using AI agents, which arguably are better at tasks where lots of unstructured data and relationships between data are involved. 

source: Ark Invest 

To use an analogy, think of the difference between random access memory and sequential access memory. In the early days of the personal computer, that difference was between disk drives and tape drives. 

Or think of programmed processors such as application-specific integrated circuits or field-programmable gate arrays, versus general-purpose central processing units. 

In the pre-recorded music use case, think of tape drives versus compact discs or streaming delivery. While it is possible to pick a single song off a tape, there is substantial winding or rewinding time. With CD or streaming access, there is little to no navigation required, to say nothing of the “discovery of content” function. 

Other similar analogies are possible. Think of live or pre-recorded linear television (broadcast or streamed) versus on-demand video, where one experience is scheduled and linear; the other built on-demand access allowing users to skip around a catalog to make choices. 

The point is that AI agents might resemble general-purpose CPUS instead of ASICs; random access memory; on-demand audio or video: allowing unstructured operations using unstructured and structured data for unexpected tasks. 

And since investors are so focused on AI opportunities, it might be helpful to liken various AI functions to the standard software “stack.”

OSI Layer	OSI Model Functions	AI Model Functions
Physical Layer	Physical transmission medium (cables, etc.)	Hardware Layer: Physical components like CPUs, GPUs, TPUs that perform AI computations.
Data Link Layer	Packet transmission and error detection	Data Layer: Management of data used to train and operate AI models. This includes data pre-processing, cleaning, and formatting.
Network Layer	Routing data packets across networks	System Software Layer: AI frameworks like TensorFlow or PyTorch that provide the structure and tools for building and training models.
Transport Layer	Reliable data transfer between applications	Machine Learning Layer: Machine learning algorithms that power AI models. This includes algorithms for tasks like classification, regression, and natural language processing.
Session Layer	Establishes, manages, and terminates sessions between applications	Model Training and Optimization Layer: The process of training and optimizing the AI model using the chosen algorithms and data.
Presentation Layer	Data presentation and encryption	Model Deployment and Inference Layer: Deploying the trained model and using it to make predictions or decisions on new data.
Application Layer	Provides user interface and network services	AI Application Layer: The actual application where users interact with the AI, such as a virtual assistant, recommendation system, or self-driving car.

Broadly speaking, investment opportunities will occur at every level of the AI stack.

Where is the AI Edge?

Artificial intelligence processing “at the edge” most likely needs to be qualified, as most of the processing is likely to happen on devices such as smartphones and PCs. Mizuho analysts, for example, forecast one billion AI smartphones shipped from 2024 to 2027. Intel, for its part, expects to ship 40 million AI PC processors in 2024 alone. 

 

Year	On-Device AI Chip Shipments (Millions)	On-Device AI Deployments (Billions)	On-Device AI Sales ($US Billion)	Source
2024	5.2 - 6.0	12.0 - 14.4	28.5 - 34.2	IDC AIoT Market Forecast
2025	6.8 - 8.0	16.3 - 19.2	42.1 - 49.8	Gartner Forecast: Edge Computing
2026	8.7 - 10.2	21.0 - 25.2	62.4 - 74.7	Digi-Capital [On-Device AI Report]
2027	11.0 - 13.0	27.0 - 32.4	89.1 - 105.3	Yole Developpement [AI Hardware Market Report

Keep in mind that “AI capabilities” are going to be a feature of PCs and smartphones, rather than a distinct product category. Irrespective of the ultimate value of AI on such devices, we will be able to measure sales of products that are AI-capable, even if we might not easily be able to measure the incremental usefulness of AI on PCs and smartphones. 

Year	AI PC Sales Revenue	AI Smartphone Sales Revenue	Source
2024	102.3 - 127.8	214.6 - 268.3	Gartner
2025	148.5 - 185.7	287.1 - 352.4	IDC
2026	207.2 - 259.8	382.4 - 470.9	Counterpoint Research
2027	281.4 - 347.1	501.2 - 613.8	Strategy Analytics

AI “as a service” revenues might be robust as well. 

Year	Gartner	Forrester	IDC	McKinsey	Average
2024	132.5	117.2	125.8	150.4	128.9
2025	172.8	154.1	168.3	192.7	171.9
2026	223.7	202.4	221.5	247.8	221.1
2027	288.2	262.3	285.4	315.7	287.9

Use of Unstructured Data Might Give AI-Driven Financial Advice an Edge

The use of artificial intelligence to provide financial advice has far to go, at least partly for reasons of human trust in the quality of the advice. But there are some potential advantages for AI-generated algorithms that underpin the advice, when compared to current human-devised algorithms. 

It is relatively hard for human algorithms to quantify and make sense of unstructured data. That should be increasingly possible for AI, which will have the advantage of many more data points. On the other hand, AI systems might not be as well-equipped as human advisors when it comes to factoring in human emotions (fear versus greed). 

The same arguably holds true for unexpected events such as pandemics, wars or political upheavals. As a general rule, then, AI-generated advice might be more useful during times of relative market stability, while human decision-making might be more important at times of market turbulence. 

On the other hand, since AI can process so many more sources of data, faster than the human algorithms, it is possible AI also could be better at detecting shifts of trend. 

Also, to the extent that the AI algorithms can evolve organically over time, as the systems learn, AI could have an advantage over human-created algorithms that must be manually revised. For instance, “generally accepted accounting practices” and financial metrics of performance are relatively static measures. 

AI can take advantage of social media sentiment, easy measurement of cars parked in company parking lots and other non-traditional indicators that are similar to “channel checks.”

The point is that we might ultimately be surprised at how much "advice" businesses and functions will eventually be displaced by AI mechanisms. Much expert advice is driven by rules of thumb; general rules and experience or precedent.

AI is going to be pretty good at summarizing and applying such knowledge or precedent for financial guidance. Algorithms already have been driving buy-sell actions in equtiy markets, for example.

In the same way, the ability to process unstructured data might give AI systems an edge over human advice, or at least become a required part of humans giving advice.