"You Get to Keep Your Business" is the Value of 5G

Though we are early in its life cycle, some argue 5G remains “revolutionary,” a “transformative leap” that will “reshape” connectivity. Others say it is a disappointment, either as a driver of near-term enterprise use cases, or as an enabler of higher-value, higher-revenue consumer mobile services.  

The eventual “truth” is likely to be far more nuanced. Some valuable new lines of business eventually could emerge. By design, 5G supports device density quite a bit higher than was available on 4G or earlier networks. By design, 5G supports network slicing, which enables private networks with some quality of service features. 

But 5G was always going to be a bit of a disappointment for most consumer accounts, which drive roughly 60 percent of total mobile operator revenues, for reasons related to the dynamics of all internet access and transport services. 

At a high level, demand for data consumption does not have a revenue elasticity that matches the consumption elasticity. In other words, mobile and fixed network operators cannot assume that increases in supply will produce increases in average revenue per unit that match the rate of consumption. 

To be sure, typical recurring charges for home broadband, for example, have increased since 1990, and access speeds for home broadband have risen as well. The cost to supply, on a per-unit basis, arguably is less important than the ability to charge more for higher consumption or higher speed. 

In the U.S. home broadband market, for example, per unit prices have plummeted, but typical  home broadband speeds have grown by an order of magnitude about every decade. Retail prices for stand-alone home broadband have not increased that fast, taking five decades to grow an order of magnitude. 

Year	Typical Speed	Typical Data Consumption	Price per Unit	Monthly Consumer Subscription Charges
1990	14.4 Kbps	10 megabytes	$1 per megabyte	$20 per month
2000	1.5 Mbps	100 megabytes	$0.1 per megabyte	$30 per month
2010	10 Mbps	1 gigabyte	$0.01 per megabyte	$50 per month
2020	100 Mbps	10 gigabytes	$0.001 per megabyte	$70 per month
2023	1 gigabit per second	1 terabyte	$0.0001 per megabyte	$100 per month

The key business takeaway is that supplied capacity must continue to increase, but will happen faster than price increases to match. 

Mobile operators have arguably had better outcomes where it comes to capacity supply and retail prices. In the U.S. market, it has taken three decades for prices to increase by an order of magnitude, as capabilities have grown three orders of magnitude. 

Year	Typical Speed (Mbps)	Typical Data Consumption (GB)	Price per Unit (GB)	Monthly Consumer Subscription Charges (\)
1990	1	0.1	100	20
2000	10	1	10	50
2010	100	10	1	100
2020	1,000	100	0.1	150

For such reasons alone, revenue expectations for faster mobile or fixed network internet access are likely to remain challenging. The argument that 5G would bring significantly-higher revenues, in the form of the ability to “charge more” for access speed, always was going to be quite difficult. 

It remains to be seen how much incremental new revenue might be created by internet of things connections, private networks or edge computing, the services most-often cited as “new” enterprise features of 5G. 

As it has happened, an unexpected “new” revenue source of some significant magnitude--in the form of 5G fixed wireless for home broadband--has developed rather quickly. 

Mobile Operator	Subscribers (Q1 2023)	Annual Subscription Revenues (Q1 2023)	Growth Rate (Q1 2022 - Q1 2023)
T-Mobile	3.2 million	$1.2 billion	120%
Verizon	1.5 million	$600 million	100%
AT&T	0.5 million	$200 million	50%

Whether one views such new revenue sources as “revolutionary” or “merely” significant is the issue. Still, the growth of 5G fixed wireless for home broadband clearly is important for contestants in the home broadband space. 

And 5G fixed wireless remains, at the moment, the clearest “new” revenue source for mobile operators. It always is conceivable that other enterprise-focused revenue streams will emerge as well, though the magnitude of those revenue streams remains more uncertain.

The point is that we likely err when arguing either for “revolutionary” or “disappointing” outcomes for 5G. We still are early in global deployment. New revenue sources generally take time to develop. 

But 5G remains vitally important for other reasons. All internet access providers, all data transport providers and data centers must increase capacity on a sustained basis. Each new mobile generation is the way that capacity increase happens.

Mobile operators may indeed be disappointed at the revenue outcomes from 5G so far. But 5G is essential for protecting the value of the business, as will be true of 6G and subsequent platforms. 

“You get to keep your business” might sound like a rather-trivial outcome. It is not.

Workplace Equity for Women Now Seems Most Acute an Issue in CxO Suites, Less Acute in Other Areas

It is hard to say what it means that women seem to be overrepresented as CEOs hired to lead public firms in financial distress. Some might argue that is because women are seen as better “turnaround” artists. 

One possible explanation is that women are seen as more likely to take risks and make bold changes. Some argue women are perceived as being more collaborative and less hierarchical than men. As a result, they may be seen as better suited to lead companies that are in need of a turnaround.

Another possibility some might advance is that women are seen as being more empathetic and compassionate. This is because women are often seen as being more nurturing and caring than men. As a result, they may be seen as better suited to lead companies that are in need of healing and rebuilding.

Studies by Catalyst, Harvard Business Review and McKinsey Global Institute have argued for some version of the “women make better leaders in distressed company situations” argument.

Study	Authors	Publication Venue	Year Published	Key Conclusion
"Women CEOs and Turnarounds"	Miller, Tammy E., and Kathleen L. Kram.	Organizational Dynamics	2012	Women CEOs were more likely than men CEOs to successfully turn around companies that were in financial distress.
"The Effect of Female CEOs on Firm Financial Performance"	Nielsen, S. Patricia, and Rita J. Boyle.	Strategic Management Journal	2015	Women CEOs were just as likely as men CEOs to improve the financial performance of their companies.
"Female CEOs and Corporate Turnarounds"	Matsa, Diana, and Laura P. Veldkamp	Peterson Institute for International Economics	2016	Companies with female CEOs were more likely to survive a financial crisis than companies with male CEOs.
"Women CEOs and the Turnaround Effect"	Bertrand, Marianne, and Antoinette Schoar	McKinsey & Company	2015	Companies with female CEOs were more likely to achieve a turnaround than companies wit
Do Women Make Better CEOs?"	Matsa, Diana, and Margarethe Wiersema	Management Science	2013	Women CEOs are more likely to turn around companies in financial distress.
"Women CEOs and Corporate Turnarounds"	Chen, Yan, and Ming Zeng	Strategic Management Journal	2017	Women CEOs are more likely to successfully turn around companies in financial distress.

Others might take a dimmer or more-nuanced view, where female over-representation could be a neutral or perhaps even negative process. 

Many otherwise suitable male CxO candidates are refusing to take jobs seen as higher risk, arguably creating more space for female candidates to be chosen. That might be a neutral or perhaps even positive angle. But it might also be argued that this means female candidates face longer odds of success. 

Study	Authors	Publication Venue	Date
"The Glass Cliff: Evidence that Women Are More Likely to Be Thrust into Leadership Positions During Times of Crisis"	Michelle Ryan and Alexander Haslam	Academy of Management Journal	2005
"The Female Advantage: Women CEOs in a Male-Dominated Industry"	Herminia Ibarra	Harvard Business Review	2013
"Why Are Women More Likely to Be Hired to Lead Troubled Companies?"	Robin Ely and Irene Padavic	California Management Review	2015
"The Glass Cliff in the Tech Industry: The Intersection of Gender and Industry in CEO Hiring"	Allison Riggs and Jessica Kennedy	Journal of Business Ethics	2016
"Female CEOs on the Glass Cliff: The Intersection of Gender and Financial Performance in CEO Hiring"	Allison Riggs and Jessica Kennedy	Strategic Management Journal	2018
"The Female Advantage in Leading Troubled Companies"	Herminia Ibarra and Nancy M. Carter	Harvard Business Review	2019
"The Glass Cliff in the Technology Industry: A Meta-Analysis"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	Journal of Management	2020
"The Glass Cliff in the Tech Industry: A Longitudinal Analysis"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	-+-*+- +3Academy of Management Journal	2021
"The Glass Cliff in the Tech Industry: The Role of Board Gender Diversity"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	Journal of Business Ethics	2022

Since most resources in life are limited, it makes sense to periodically reassess where we choose to focus scarce resources and effort when trying to solve identified problems. 

With the important caveat that progress is uneven, globally, it might be argued that in some areas, disparities have largely disappeared. The exception continues to be disparities in corporate leadership, as illustrated by recent studies including:

• The State of Women's Equality in the United States. Center for American Progress. Washington, DC. 2020.

• Gender Equality in the European Union. European Institute for Gender Equality. Vilnius, Lithuania. 2020.

• The Gender Pay Gap in the United States. American Association of University Women. Washington, DC. 2020.

• The Gender Pay Gap in Western Europe. European Commission. Brussels, Belgium. 2020.

Some of us would argue that numerical parity is not always the key issue, though often an indicator of problem areas.  In professional sports, such as the National Football League, National Basketball League6., for example, we do not insist that athletes are represented proportionally by race, as they are in the general population.

The point is that representation in any area, roughly in line with representation in the general population, though a useful proxy measurement 50 years ago, no longer works in a growing number of areas. 

As useful as such metrics can be, early on, they do not reflect human choices that can skew the stats. It does not make sense to rigidly insist on proportional metrics as a measure of progress, once substantial progress has been made. 

Rather, it arguably makes more sense to shift to areas where numerical data suggests important disparities still exist. 

Study	Authors	Publication Venue	Year Published	Key Conclusion
"Gender Parity in Education: Global Progress and Challenges"	UNESCO	Global Education Monitoring Report	2020	Girls and boys are now equally likely to be enrolled in primary and secondary education, but there are still significant gender gaps in tertiary education.
"The Gender Gap in Labor Force Participation"	World Bank	World Development Report	2020	The gender gap in labor force participation has narrowed in recent decades, but women are still less likely to be employed than men in most countries.
"Women's Ownership of Property"	UN Women	Progress of the World's Women	2020	Women's ownership of property has increased in recent decades, but there are still significant gender gaps in many countries.
"Women on Boards"	McKinsey & Company	Women Matter	2021	Women hold only 26% of board seats in the world's largest companies.
"Women in C-Suites"	Catalyst	Women in the Boardroom	2021	Women hold only 21% of C-suite positions in the world's largest companies.

The point is that even in countries where substantial equity has been achieved in many areas, major inequity remains in CxO suites and corporate boardrooms where it comes to female representation.

In Some Cases, Small Language Models Will be Needed

Though most of the present attention related to generative AI is focused on large language models such as ChatGPT, Bard and others, small and medium language models might be more interesting for quite a large number of mobile device suppliers, application providers and end users, primarily because they can be executed using a relatively smaller amount of data. 

Of course, “smaller” does not mean “small.” Right now, the general state of the art is that a large language model requires ingesting billions of words, while a small language model might only require “millions.” But lots of small entities might not even boast web-accessible content amounting to millions of words. 

Ignoring the matter of a sufficient critical mass of words to ingest, processing of small datasets, though perhaps less costly than required by very-large data sets, is probably still cost prohibitive for small entities.

So what approaches might work? Right now, Transfer learning takes an already created model for one task and uses it to train for a different task. Such “pre-trained” models might, or might not, be a precise fit for the new task. So greater imprecision will be an issue.  

Pre-trained models can be fine tuned, but that requires specialized information technology knowledge that a smaller entity might have to pay for. Such customization might put generative AI out of reach, financially, for small entities. 

Another way that language models might be able to use generative AI without millions of words of content is to use “few-shot learning,” some argue. 

Few-shot learning is a technique where a model is trained on a small number of examples. Using data augmentation, new examples are created by transforming existing examples. Again, the issue is precision. 

As with earlier versions of business technology, versions scaled for small or mid-sized businesses might be de-featured and therefore less expensive, focused on answering more simple questions related to customer-facing use cases. 

Such small language models might be important for other reasons. A possible developing set of applications and use cases might only require small and medium language models might enable distributed AI conducted by devices, on devices and use only small or mid-sized language models appropriate for industry-specific or firm-specific use cases where the data sets are bounded and smaller. 

Small language models, which cost less to train, might be appropriate for personalized recommendations, in-app translation or customer support specifically geared to a smaller company’s customers or products, for example. 

At least in principle, lighter weight models might be able to support on-device personalized content generation, real-time translation, some forms of creative content or chatbot and personal assistant functions. 

And even such lighter-weight use cases might still require off-device data stores to a large extent, even if actual processing is onboard.

So, eventually, some new business models might develop that focus on small language models aimed at smaller business users and devices.