Does Social Media Reflect or Create Values?

With social media firms and impact under investigation for a variety of reasons, ranging from antitrust to fairness and truthfulness to social and psychological impact, it might be reasonable to expect a debate on whether social media only reflects existing behaviors; changes and shapes them; or some combination of “reflects but also creates.” 

A new study of social media, for example, finds that social media does not have a causal effect on mental health.  The study finds “the past two decades have seen only small and inconsistent changes in global well-being and mental health that are not suggestive of the idea that the adoption of Internet and mobile broadband is consistently linked to negative psychological outcomes.”

On the other hand, the study also says “the idea that the rapid and global penetration of the Internet and technologies enabled by it is affecting psychological well-being and mental health is compelling but not adequately tested.”

Still, the researchers said “our results do not provide evidence supporting the view that the Internet and technologies enabled by it, such as smartphones with Internet access, are actively promoting or harming either well-being or mental health globally.”

One might note similar studies conducted over many decades on the subject of media influence on behavior as well, and note that results have remained inconsistent and inconclusive. 

We might tend to agree that mass media has a significant impact on our lives--both reflecting and shaping existing values-- but that it is a complex relationship with no clear and unambiguous answers, but with a rather strong suggestion that media does shape or change attitudes, and does not merely reflect attitudes. 

Study Title	Conclusion	Date of Publication	Publishing Venue
Media Effects: Advances in Theory and Research (1982) by Maxwell McCombs and Donald Shaw	Concludes that media effects are often subtle and indirect, but can still be significant.	1982	Sage
Cultivation Theory (1976) by George Gerbner and Larry Gross	Concludes that heavy exposure to television can lead to a distorted view of reality, in which the world is seen as more violent and dangerous than it actually is.	1976	Journal of Broadcasting
The Agenda-Setting Function of the Media (1965) by Maxwell McCombs and Donald Shaw	Concludes that media can influence the public agenda by focusing attention on certain issues.	1965	Public Opinion Quarterly
Framing Effects: Media, Public Opinion, and Public Policy (2004) by Robert Entman	Concludes that media can influence the way people think about issues by framing them in certain ways.	2004	Lawrence Erlbaum Associates
The Spiral of Silence: Public Opinion and Our Private Fears (1986) by Elisabeth Noelle-Neumann	Concludes that people are more likely to express their opinions in public if they believe that their views are widely shared.	1986	University of Chicago Press
"Media and Values: A Reassessment"	"The evidence suggests that media do not have a strong or consistent impact on values. Instead, values are shaped by a variety of factors, including family, friends, and education."	2009	Journal of Communication
"The Cultivation Hypothesis: A Critical Review"	"The cultivation hypothesis is a weak and inconsistent theory. There is no convincing evidence that media exposure leads to long-term changes in values."	2011	Mass Communication and Society
"The Media-Values Relationship: A Meta-Analysis"	"The media-values relationship is complex and multifaceted. There is some evidence that media exposure can influence values, but the effects are small and often indirect."	2014	Psychological Bulletin
"Media and Values: A New Look at an Old Issue"	"Media can play a role in shaping values, but the effects are complex and depend on a variety of factors, including the individual, the media content, and the social context."	2017	Journal of Personality and Social Psychology
"The Media's Impact on Values"	"Media can have a strong impact on values, especially for young people. Media exposure can lead to the adoption of new values and the rejection of old ones."	2020	Annual Review of Psychology
"Media Effects: Advances, Tensions, and Future Directions" by Oliver & Oliver (2019)	The authors argue that media effects research has come a long way in recent years, but that there are still some important tensions between different perspectives on how media influence people. They also call for more research on the long-term effects of media exposure.	2019	Annual Review of Psychology
"The Media's Influence on Society" by Potter (2014)	The author argues that media has a significant impact on society, but that this impact is complex and often indirect. He also argues that the media is not a monolithic entity, but rather a collection of different institutions with different agendas.	2014	Sage

As a practical matter, industry executives can be expected to downplay their degree of causation, as that creates business risk in the form of regulation. 

On the other hand, parents, governments and leaders have an equally vested interest in protecting society at large from any potential ill effects of media or social media, as difficult as that task might be. 

Content regulation, educational efforts and parental controls or content moderation policies are common remedies. But they also bring dangers in the form of censorship, privacy concerns or limitations on media voices and views. If the traditional areas of concern in the media business have been pornography or violence, in the social media realms those issues are joined by concerns about “fake” content, bullying and mental well being. 

Social media “solutions” are likely to be just as difficult as they have proven to be in the broader legacy media industries.

What's the Best Analogy for LLMs?

For large language model use cases, one size does not necessarily fit all, all the time. On the other hand, to the extent that LLMs are conceived of as similar to operating systems, one size arguably is much more important.

Looking for historical analogies is one way of trying to understand large language models and other forms of artificial intelligence, when assessing business model implications. Where and when is scale essential versus merely helpful?

For example, are LLMs more akin to operating systems, platforms or applications? 

Are LLMs in part “picks and shovels,” which are more like OSes, and also, in part, applications that are always designed to run in loosely-coupled ways on any compliant platforms? Are LLMs sometimes also platforms? The importance of scale or market share might well hinge on which scenario matters most to particular providers. 

Feature	LLMs	OSs
Purpose	Generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way	Manage hardware resources, provide a platform for running software applications, and facilitate user interaction with computer systems
Underlying technology	Artificial neural networks	Kernel, device drivers, and user interface software
Scalability	Highly scalable, can be trained on massive amounts of data and run on distributed computing systems	Limited by hardware resources, require specialized software and configurations for different types of devices
Applications	Chatbots, virtual assistants, machine translation, content creation, code generation, research	Personal computers, servers, mobile devices, embedded systems
Maturity	Relatively new technology, still under development	Mature technology with a long history of development
Adoption	Growing rapidly, but still not as widely used as OSs	Ubiquitous, used by billions of people worldwide
Scale	Potential to be used on a wide range of devices and platforms	Typically designed for specific types of devices and platforms
Niche AI Models	Possible, as LLMs can be trained on specialized datasets	Less likely, as OSs need to be general-purpose and compatible with a wide range of hardware and software

OSs have tended to be a scale phenomenon, with a few dominant players controlling the market. This is due to the network effects that exist in the OS market: the more people use an OS, the more valuable it becomes to other users. As a result, it has been difficult for new OSs to gain traction.

However, the landscape for AI models may be different. Maybe the model is more social media, e-commerce, search, messaging than operating system, for example. In other words, instead of resembling an operating systems or device market, LLMs could resemble application markets, where age, culture, language, tone, content and function can vary widely.

Though scale still matters, apps are far less monolithic than operating systems or consumer devices such as smartphones. In other words, LLMs can enable app use cases that are more variegated than the OS market or device markets tend to be. 

Highly specialized LLMs usable by a single company and its own applications will be possible. So too will apps targeted to different age groups, language groups, cultures and functions. 

Edge computing might also mean it is possible to deploy AI models on devices with limited resources, such as smartphones and IoT devices, creating more “niche” use cases.

So we might argue that operating systems  require scale to be successful. Without scale, an operating system would have limited reach and adoption.

In the context of LLMs, scale is crucial for models that aim to be general-purpose solutions, catering to a wide range of tasks and domains. For instance, LLMs used for machine translation or text summarization need to be trained on massive amounts of data from various sources to handle diverse language contexts and content types. Scale allows these models to perform well across a broad spectrum of applications.

Platforms like social media networks, e-commerce sites, and content sharing platforms benefit from scale but don't necessarily require it. For LLMs, scale arguably is helpful but not essential for models that target specific platforms or applications. 

For example, an LLM integrated into a customer service chatbot might not require the same level of scale as a general-purpose language model, though scale generally is helpful. 

End-user applications like productivity tools, creative software, and games can succeed without scale. Similarly, LLMs can be incorporated into end-user applications without requiring massive scale.

As often is the case, LLM scale is a fundamental requirement in some use cases, but not in others. For suppliers of wholesale, general-purpose LLMs, scale likely will matter. 

When used as a platform, maybe not. And when used to enable apps and use cases, scale might not be particularly important. Model owners must care about scale. “Computing as a service” providers or data centers can afford to “support all” stances. 

App developers might not necessarily care which LLM is used, beyond the obvious matters of supplier stability, cost, reliability, reputation, ease of use, support and other parts of a value bundle. 

How Big is the Large Language Model Industry? How Big Could it Get?

As a brand-new market, large language models are likely still in the single-digit-billions range, not counting all the other markets that are, or can be, built on the use of LLM. 

Estimate	Study Name	Date of Publication	Publisher
$2.1 billion	Allied Market Research	2022	Allied Market Research
$1.3 billion	MarketsandMarkets	2022	MarketsandMarkets
$3.2 billion	Grand View Research	2022	Grand View Research
$5.6 billion	Mordor Intelligence	2022	Mordor Intelligence
$6.9 billion	Precedence Research	2022	Precedence Research

Just how big the core LLM model business might be remains a guess, but if one believes LLM is a general-purpose technology, then like other forms of enabling infrastructure, the market could be substantial. 

What also remains unclear is where key revenue elements--especially fees for generating inferences--will be reaped. Use of an LLM includes both the license to use a model plus the recurring fees paid for deriving inferences. 

As with any other form of “computing as a service,” some parameters will vary. License fees might be one-time payment, a recurring fee, or a fee based on usage.

Inference fees are variable. Support and maintenance fees might also often be charged, to cover bug fixes, security updates, and documentation updates.

The ultimate analogy might determine market size. Are LLMs most akin to servers, operating systems or end-user software? More generally, are LLMs going to be infrastructure somewhat similar in function to electricity networks, road systems, airports or seaports, as enablers of commerce?

Underlying Technology	Market Size	Study Name	Publication Date	Publisher
Electricity	$2.3 trillion	Global Electricity Market Report 2023	March 2023	IEA
Roads	$1.2 trillion	Global Road Infrastructure Market 2023	June 2023	Grand View Research
Airports	$1.8 trillion	Global Airport Infrastructure Market 2023	July 2023	Allied Market Research
Seaports	$1.1 trillion	Global Seaport Infrastructure Market 2023	August 2023	MarketsandMarkets
Smartphone Operating Systems	$128 billion	Mobile Operating System Market by Platform 2023	September 2023	Statista
PC Operating Systems	$44 billion	PC Operating System Market by Platform 2023	October 2023	Gartner
Server Processors	$25 billion	Server Processor Market 2023	November 2023	IDC
Platforms such as Linux, Windows or IoS	$1.5 trillion	Cloud Platform Market 2023	December 2023	Synergy Research Group
Software as a Service	$167 billion	Global Software as a Service (SaaS) Market 2023	January 2024	Gartner

Beyond all that, where is the incidence of payments? Is revenue generated by direct fees charged to business and consumer end users, directly by business partners or indirectly in the form of value for third parties and end users? 

In many cases, AI is a capability that enhances the value of some product a buyer consumes, but might not be a distinct extra charge or involve a subscription for use of the product.