Will We Break Traditional Computing Era Leadership Paradigm?

What are the odds that the next Google, Meta or Amazon--big new leaders of new markets--will be one of the leaders of the present market,  breaking from historical patterns? 

Historically, we can argue that the leaders of each era of computing were different from the leaders of the prior era. The leaders in the mainframe era (1945-1980) included IBM, Honeywell and Burroughs. 

In the succeeding personal computer era, the leaders were Apple, Microsoft and Dell. 

The era that follows the “PC” period is more contested. Some might say it is the internet era. Others might say the mobile or cloud computing eras also followed since 2006 or 2007. It also is possible that mobile and cloud computing are merely evolutions of a single internet (or other name we do not universally agree upon) era. 

In the internet era, we might argue the leaders were Google, Amazon and Meta. Some might argue the internet era largely overlaps, since about 2007, with the mobile era, whose leaders might be said to include  Apple, Google (Android) and Samsung.

The cloud computing era might include Amazon Web Services, Microsoft Azure, Google Cloud. 

And that might suggest a possible outcome that reflects our current inability to define the present era (internet, mobile, cloud computing). The leaders in segments or eras tend to overlap with each other. That might suggest there are phases to a single era. 

Some believe the next era will center on artificial intelligence, perhaps led by generative AI frontier models. And one characteristic of the model business is its capital intensity. 

source: Pure AI 

And keep in mind that LLMs are updated, as are operating systems. Creating one version of a model necessarily includes t he necessity of updating that model every year to three years or so. 

Model Family	Company	Update Cycle	Notes
GPT (3, 4)	OpenAI	1-2 years	GPT-3 was released in 2020, GPT-4 in 2023
PaLM, Gemini	Google	1-2 years	PaLM released in 2022, Gemini in 2023
BERT	Google	2-3 years	Initial release in 2018, with subsequent variants
LLaMA	Meta	1-2 years	LLaMA 1 released in 2023, LLaMA 2 in 2023
Claude	Anthropic	6-12 months	Frequent iterative updates reported

And note: leaders include many of the same names we see in the internet, mobile or cloud computing “eras.” OpenAI is the most-prominent “new” name, but the others are familiar: Google, Meta, AWS and Microsoft,  for example. 

source: IoT Analytics 

And that suggests a possibility: the leaders of the generative AI era of computing might well be one or more of the firms said to lead in the internet, mobile or cloud computing eras as well.

That might break a pattern we have seen since the mainframe era. On the other hand, there is some divergence of opinion about which “era” computing now is in. But whether we focus on internet, cloud or mobility-based nomenclature, many of the leaders are the same. 

So though we might not know for some time to come, it is possible that the leaders of the internet era could be the leaders (mostly) of the next era, the exception being OpenAI. 

It might also be worth noting that since the PC arrived, eras have been defined by applications and platforms rather than hardware. But many observers might agree that a single computing era can last 30 years to 40  years. 

What "Killer App" Will Emerge from Generative AI?

 Agents are clearly a lead candidate for the artificial intelligence "killer app." Personalization of your digital experience is one thing; anticipation of your needs is something else. 

With the caveat that it is always possible there is no single and universal “killer app” in any computing era, it still is possible that one could emerge for generative artificial intelligence. 

Certainly, key or lead apps have been important in prior waves of computing development. Sometimes  the killer app is clear enough for end users and consumers. At other times it is the business or organization end users or business-to-business use cases that dominate. 

As a rule, B2B value was dominant in the mainframe and minicomputer eras. Since  then, virtually all killer apps can be identified by the consumer apps that surfaced. 

But some innovations, such as app stores or cloud computing, arguably were important as platforms and ways of doing things, rather than specific apps. 

Era/Platform	Killer App(s)	Rationale
Mainframe Era (1960s-1970s)	COBOL.  Batch Processing	Enabled large-scale business applications like payroll, banking, and insurance systems.
Minicomputer Era (1970s-1980s)	VAX/VMS, Accounting Systems	Brought computing power to smaller organizations, particularly in science, manufacturing, and finance.
Personal Computer (PC) Era (1980s-1990s)	VisiCalc (spreadsheet)	The first spreadsheet program, which revolutionized business and financial management.
PC Era (1990s)	Microsoft Office Suite (Word, Excel, etc.)	Dominated office productivity, becoming essential in business, education, and home environments.
PC Era (1990s)	Internet Browsers (Netscape, Internet Explorer)	Opened the gateway to the World Wide Web, fundamentally changing communication and information access.
Web 1.0 Era (late 1990s-2000s)	Email (e.g., AOL, Hotmail)	Email transformed personal and business communication, enabling near-instant global connectivity.
Web 1.0/2.0 Era (early 2000s)	Search Engines (Google)	Google’s search engine made finding information on the web faster and more accurate, changing web usability.
Mobile Era (2000s)	Text and Instant Messaging (WhatsApp and others)	Redefined personal communication with quick, accessible messaging on mobile phones.
Mobile App Era (late 2000s-2010s)	App Stores (Apple App Store, Google Play)	Created an ecosystem where developers could offer mobile apps, enabling smartphone adoption at scale.
Mobile App Era (2010s)	Social Media Apps (Facebook, Instagram)	Changed social interaction, media consumption, and online behavior globally.
Cloud Computing Era (2010s-present)	AWS, Microsoft Azure, Google Cloud	Enabled scalable, on-demand computing infrastructure, transforming how companies build and deploy services.
AI Era (2020s)?	Generative AI (ChatGPT, others)	Revolutionizing content creation, customer service, and automating complex cognitive tasks. Enable AI agents

We don’t yet know what killer apps could emerge in the AI era, but early on, generative AI might be a lead platform. Still, some believe AI agents could emerge as a potential killer app for GenAI.

Why Marginal Cost of Content Creation is Generative AI's Superpower

Virtually every observer might agree that artificial intelligence will automate laborious tasks and therefore increase process efficiency. AI should also accelerate decision making, as it enables rapid information processing. 

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AI should enable more personalization than already is possible for user interactions and experiences and as a byproduct could change the nature of work, entertainment and learning. 

Generative AI, though, might bring cost impact in different ways than did other computing innovations. Virtually all computing eras since the advent of the personal computer have led to lower marginal costs of doing things. 

PCs meant computing power itself was widely available to people. The internet attacked the cost of sharing information and communicating while cloud computing arguably reduced software distribution costs while boosting the ability to apply accumulated data and insights more widely in real time. 

The mobile era extended computing capabilities “everywhere” and untethered from desks, tables or laps. 

Era	Computing Paradigm	Marginal Cost Implications
PC	Personal Computing	- High upfront costs for hardware and software - Relatively high marginal costs for upgrades and maintenance - Limited scalability
Internet	Networked Computing	- Reduced costs for information sharing and communication - Increased accessibility, but still significant infrastructure costs - Marginal costs tied to bandwidth and server capacity
Cloud Computing	On-Demand Computing	- Significantly lower upfront costs - Pay-as-you-go model reduces marginal costs - Improved scalability and flexibility - Potential for cost optimization through resource management1
Mobile	Ubiquitous Computing	- Lower device costs compared to PCs - App-based ecosystem with low distribution costs - Increased connectivity, but data costs can be significant
Future AI	Intelligent Computing	- Potential for near-zero marginal costs in some applications - High initial investment in AI development and infrastructure - Continuous learning and improvement may reduce long-term costs2

So it is reasonable to ask what the AI impact will be, especially generative AI, which seems to be driving mass market and most business AI use cases. 

Angela Strange, Andreessen Horowitz general partner and James da Costa Andreessen Horowitz partner, specialized in enterprise and business-to-business software, including financial technology. 

They believe the AI era leads to lower marginal cost of client and customer interactions, using AI agents to reduce the cost of labor involved in many customer support operations, including those involving information retrieval (files, ledger entries, past transitions, billing and account status). 

source: Andreessen Horowitz 

As applied in many areas outside of financial technology, the value of generative AI is squarely on its impact on content creation. 

Whether we look at text, image, video or audio, GenAI seems destined to have the highest impact on any process or industry built on content creation and its distribution or consumption. GenAI will be useful in any number of customer support contexts, but might be impactful in financial terms for the production of software and code; entertainment content; education and training; business communications; many types of research; marketing and sales. 

"Platforms" Change Power Relationships in Value Chains

In the linear video business, there long has been a running debate over where “value and power” lie in the value chain, even if both content and distribution matter. 

At times, it has been argued that “content is king,” (while at other times it seems “distribution is king” (Netflix now; Comcast at the height of linear distribution).

But these days, across many industries, it might be the case that “platforms” have blurred the older distinctions between content and distribution as value drivers, across media, software and internet value chains. 

Era	Value	Key Characteristics
1950s-1970s	Distribution	• Limited TV channels controlled distribution • Networks dominated viewership • Content creators reliant on networks for distribution
1980s-1990s	Content	• Rise of cable TV increased channel options • Premium content providers like HBO gained power • Hit shows became more valuable
2000s	Distribution	• Cable/satellite providers consolidated • Bundling gave distributors leverage • Limited streaming options
2010-2020	Content	• Streaming services proliferated • Netflix, Amazon, etc. invested heavily in original content • Bidding wars for popular shows and creators
2021-Present	Hybrid	• Content remains crucial but very expensive1 • Distribution platforms also important as market saturates2 • Streaming services focus on profitability and subscriber retention

Those strategic differences arguably also have applied during the internet era, when at times internet access (distribution) has appeared to drive more value, and other times when apps or services have arguably driven more value. 

The twist for the internet value chain is the role of “platforms,” which do not neatly fit the “content versus distribution” dichotomy. 

Platforms have blurred the lines between content creation and distribution, making the distinction less clear-cut than in previous eras. Many platforms now act as both content creators and distributors. For example, Netflix and Amazon Prime Video produce original content while also distributing third-party content. 

Social media platforms such as YouTube, TikTok, and Instagram also raise different questions about the value of content versus distribution. Facebook’s platform both enables user-generated content, but also provides the value of distribution, simultaneously. 

In fact, the whole disruption of most value chains by the internet was precisely “disintermediation,” which removed some distributors from value chains (e-commerce disintermediated retail stores). 

And platforms (marketplaces) now essentially create new forms of distribution that are advantageous for many asset owners (owners of short-term lodging assets), compared to legacy providers that are displaced (hotel chains). 

Era	Value	Key Characteristics
1990s	Distribution	• Limited internet access • Dial-up modems and ISPs controlled access • Content creation tools were limited
Early 2000s	Content	• Broadband expansion • Rise of blogging and user-generated content • Google's focus on quality content for search rankings
Mid-2000s	Distribution	• Consolidation of ISPs • Net neutrality debates • Rise of social media platforms as content gatekeepers
2010-2015	Content	• Smartphone proliferation • App stores democratized content distribution • Netflix and streaming services invested in original content
2015-2020	Hybrid	• Content remained crucial but very expensive • Platform algorithms gained importance • Rise of influencer marketing
2020-Present	Content	• Increased demand for digital content during pandemic • Growth of creator economy • AI-powered content creation tools

The sorts of dynamics might be inferred for the software business as well, where at different times it has seemed that “apps” drive value, where at other times “distribution” arguably drives more value. 

Era	Value	Key Characteristics
1980s-Early 1990s	Distribution	• Limited distribution channels (retail stores) • Software companies reliant on publishers • High barriers to entry for independent developers
Mid 1990s-Early 2000s	Content	• Rise of the internet as a distribution channel • Emergence of shareware and freeware • Increased accessibility for independent developers
Mid 2000s-2008	Distribution	• Consolidation of major software companies • Dominance of Microsoft in operating systems and office software • Rise of enterprise software suites
2008-2015	Content	• Launch of app stores (Apple App Store, Google Play) • Explosion of mobile apps and indie developers • Democratization of software distribution
2015-2020	Hybrid	• Content remained crucial but discoverability became challenging • App store algorithms gained importance • Rise of subscription-based software models
2020-Present	Content	• Increased demand for specialized software solutions • Growth of no-code/low-code platforms • AI-powered development tools empowering creators

The key observation is that older dynamics (content versus distribution) still exist, but within a context where platforms now are increasingly important gatekeepers and value generators. At the physical layer, data centers and transmission networks and internet service providers remain “distribution.”

At the business and revenue layer, platforms have emerged as the key drivers of value (Facebook social media, Amazon e-commerce, Google search, Uber ridesharing, and all those embody value creation roles similar in some ways to “content” and also similar in fundamental ways to “distribution.” 

That explains why it is easier to say that “hybrid” models now predominate. For “hybrid,” substitute the word “platform.” 

Platforms such as YouTube, Facebook, TikTok and Spotify might be thought of as intermediaries--neither traditional content creators nor distributors--that aggregate content from various creators and curate it. But in some ways the platforms also act as distributors of that user-generated content. 

So “platformization” more accurately modifies former content and distribution functions in some ways, while displacing each of those functions in some ways, as exemplified by “direct to consumer” sales models. 

Perhaps the best example is Amazon, which, in its function as an e-tailer, connects buyers and sellers. In that role it essentially replaces the traditional distributor (retail stores). But Amazon also curates and funds the creation of actual content as the provider of the Amazon Prime streaming video service. 

In its role as the provider of Amazon Web Services “computing as a service,” Amazon acts as a software supplier in its own right as well as a distributor of those products. 

But if we really have to choose, we’d likely argue that platforms have usurped distributor roles more than app and content provider roles, even in instances where the platforms do a bit of both.