Mobile Services Now Drive as Much as 82% of Total Global Connectivity Revenue

These days, global connectivity provider revenue growth is driven by mobile operator performance, as mobile services generate as much as 82 percent of total connectivity provider revenues globally. So mobile revenue growth rates are tantamount to global industry growth rates.

According to the GSMA, mobile revenue growth rates have slipped into a two-percent annual growth rate pattern overall since about 2007, with developing market growth rates about double those of developed markets since that time. 

Date	Global	Developed Market	Developing Market
2000	6.7%	3.8%	10.8%
2001	5.2%	3.0%	8.1%
2002	4.2%	2.6%	6.5%
2003	3.7%	2.4%	5.7%
2004	3.2%	2.1%	5.0%
2005	2.7%	1.8%	4.3%
2006	2.2%	1.5%	3.7%
2007	2.0%	1.4%	3.3%
2008	1.7%	1.2%	2.9%
2009	0.9%	0.6%	1.8%
2010	1.2%	0.8%	1.9%
2011	1.5%	1.0%	2.4%
2012	1.8%	1.2%	2.8%
2013	1.9%	1.3%	2.9%
2014	2.0%	1.3%	3.1%
2015	2.1%	1.4%	3.2%
2016	2.2%	1.5%	3.4%
2017	2.3%	1.6%	3.5%
2018	2.4%	1.6%	3.7%
2019	2.5%	1.7%	3.8%
2020	2.6%	1.7%	3.9%
2021	2.7%	1.8%	4.0%
2022	2.8%	1.9%	4.1%
2023	2.9%	2.0%	4.2%

Mobile segment growth rates far outstrip fixed network growth rates, as well, according to GSMA figures. Sure, there are many segments within the global business. But none of those can drive overall industry revenues.  For better or worse, industry revenues and profits now hinge on mobility services.

Date	Mobile Growth Rate	Fixed Network Growth Rate
2000	10.5%	5.5%
2005	12.5%	4.5%
2010	10.5%	3.5%
2015	8.5%	2.5%
2020	6.5%	1.5%
2025	4.5%	0.5%

What is Generative AI "Expression of an Idea" and What is an "Idea?" It Will Matter Greatly

The debate over copyright as it applies to generative AI has just begun, but already we can glimpse the ways the arguments will have to be made. “Ideas” cannot be copyrighted, but their expression can be protected. So the issue is the extent to which “expression” and “idea” are opposed. Is an AI-generated bit of content a new “expression” of a generic idea, or infringement as a “derivative” work? 

It is going to be difficult to tell the difference. 

The conflict between "ownership of ideas" versus "expressions" of ideas arises because copyright law only protects the expression of ideas, not the ideas themselves. This means that anyone is free to use the same ideas as another person, as long as they do not copy the expression of those ideas.

A company might develop a new software program, but then another company releases a similar software program that does the same thing. The first company might argue that the second company has infringed their copyright, but the second company might argue that they are simply using the same idea as the first company, and that they have not copied the expression of that idea.

So consider training a generative AI program. When an AI system uses a copyrighted work to train, it is using the work's ideas, but it is also expressing those ideas in a new way. Or is it “copying the expression?”

“Fair use” might also come into play. “Fair use”  is a legal doctrine that allows for the use of copyrighted works without permission in certain limited circumstances. In the case of search engine results, courts have held that the fair use doctrine allows search engines to use snippets of copyrighted works to provide summaries of those works. It is possible that the fair use doctrine could also be relevant in the case of AI training.

The "merger doctrine" holds that copyright protection does not extend to ideas that are inseparable from their expression. For example, if there is only one way to express an idea, then that idea is considered to be merged with its expression and is not protected by copyright. 

For example, the merger doctrine does not apply to data structures, because there are many different ways to organize data, and the choice of data structure is often a creative decision. In other words, the way that a database is organized can be protected by copyright, even if the data itself is not.

Application programming interfaces are not covered by the merger doctrine, because there are many different ways to implement an API. For example, the way that a web browser interacts with a web server can be protected by copyright, even if the data that is being transferred is not.

Likewise, user interfaces can be protected by copyright. For example, the way that a word processor displays its menus and toolbars can be protected by copyright, even if the functionality of the word processor is not.

But plot elements in a story cannot be copyrighted. 

The "scenes a faire" doctrine holds that copyright protection does not extend to elements that are standard or common in a particular genre. For example, if a particular plot element is common in all mystery novels, then that plot element is considered to be a scene a faire and is not protected by copyright. 

Courts have held that a phone book publisher's white pages were not protected by copyright because they simply listed facts and were not original works of authorship. Software programs have been found to be protected as they used different expressions of an idea. 

Google's use of Java APIs in its Android operating system has been found to be “fair use,” on the other hand. So much will turn on whether courts see “expression” or “idea” in generative AI output.

"Garbage In, Garbage Out" Necessarily Applies to AI and Generative AI

The principle of "garbage in, garbage out" (GIGO) is a fundamental concept in computing that also applies to artificial intelligence and large language models and generative AI.

GIGO means that the output from a computer program is only as good as the quality of the input data. For generative AI, that means ingesting vast quantities of existing work that might hardly be considered factual, truthful, unbiased or balanced.

Training algorithms are themselves created by authors that might not always be capable of fully, or at least more-fully avoiding author bias. 

And many of the difficult issues we have encountered before. All search engine results, for example, must necessarily be ranked on a page, if “quality” of result is desired. So humans must decide what appears first, what appears on the first page of a browser, and so forth. “Choice” therefore must necessarily happen. 

And different people will disagree about what is “best,” in that regard. To a great extent, that problem exists because designers themselves have different ideas about what the “truth” of a matter happens to be. In journalism, at least as I learned it, an author’s choice of which adjectives to use with nouns can express bias. The use of an adjective can express bias. 

These days, many could argue the problem is not so much the choice of adjectives but the prior decision made about what is the “news” of the day. Some stories that are not deemed to be “news” or worthy of publishing by some are considered quite differently by others. 

So many media outlets will decide “stories about X” will not be published. Stories about “Y” must lead, not only be published. Those are expressions of bias, it would not be incorrect to say, keeping in mind the principle that humans, journalists and authors must always make choices, and choices can be examples of “bias.” 

Judgments are choices, but also can be biased. Is something a “fact” or an “opinion?” What is “objective” and what is “subjective?” What might be called “truth” and what is “your truth” or “my truth?” 

The point is that the established principle in computing that bad data leads to bad conclusions or output also applies to all AI training processes. And we have to deal with AI training the same way we deal with the quality of data to be analyzed or manipulated. 

Which is to say, data must be curated. This includes ensuring that the data is representative of the real world, that it is free of bias, and that it is of high quality. But we will always run into trouble when different people do not agree on what is “representative of the real world,” what is “free of bias” and what is of “high quality.”