Where are Hyperscalers in Terms of Crossing the Chasm?

If the concept of crossing the chasm has validity, it also will apply to adoption of artificial intelligence. According to the concept, very-early adopters are technology enthusiasts. 

source: Ignition Framework 

One of the key insights is that mass market adoption will be driven by pragmatic end users who see value in using a technology, where the early adopters are more interested in “breakthrough” technology and mass market consumers are more interested in applied value, usefulness and cost benefits. 

As applied to AI, or large learning models, firms most likely to invest heavily at this early stage include infrastructure suppliers of graphics processor units; GPU as a service providers; data centers and developers of LLMs. 

Other early adopters will include enterprise and consumer software suppliers who want to ensure their existing products take advantage of LLMs. 

source: themarketingstudent.com 

Right at this moment, it might be fair to characterize enterprise software suppliers as being in the innovator stage, moving towards “early adopter” status when it comes to LLMs and AI in general. Where to place consumer software suppliers--including providers of search, social media or e-commerce, might be more complex. 

Obviously, Amazon has been using AI to support its recommendation engines for some time, as well as its speech-to-text functions. Google seems to have moved faster to incorporate LLMs into its core search functions, while Microsoft has moved to incorporate LLM functionality into its office suite. 

That tends to make Google, Amazon and Microsoft “early adopters” as well as innovators, to the extent that each is developing LLMs. 

Adoption by early majority customers has yet to be reached, as such customers care more about practical value and cost savings than tech prowess as such.

Netlfix: New Boss Just the Same as the Old Boss?

One sturdy storyline is “new boss just the same as the old boss.” So it is that some observers criticize video streamers such as Netflix for raising prices, just as legacy cable TV suppliers do. 

That seems unfair, as it is a charge that could be aimed at all sellers of products and services over time, especially when inflation rates are higher. And find any publicly-traded company whose basic imperative is anything other than “grow revenue and earnings” every year. 

Also, except for Netflix, virtually all other public streaming services are unprofitable, which is one reason why prices are being raised. 

Of course, one might prefer that higher prices over time are accompanied by higher value as well. 

So when others might argue that streamers such as Netflix are facing greater risks of becoming monopolists with weaker value propositions for consumers, that is a greater risk only if Netflix cannot grow its value proposition. 

That again is a general issue with all potentially market-leading firms. “New and improved” is almost a necessity, over time. 

We might agree with those who argue the “lower prices overall” advantage of streaming services only sometimes seems to be the case. Since many, perhaps most, consumers buy multiple streaming services, “saving money” is often, but not always an advantage, compared to buying a single linear subscription. 

It might be fairer to say that the key advantage is “choice” rather than “cost,” as a rule. 

Sudy	Year	Sample Size	Recurring Cost per Service (USD)	Avg. Number of Services per Household	Total Spending per Household (USD)	Key Findings
Parks Associates	2023	10,000 US households	$12.40	3.2	$40	Households with higher incomes subscribe to more services. Gen Z and Millennials lead in streaming adoption.
eMarketer	2022	1,000 US internet users	$15.00	2.7	$39.50	Increased cost-consciousness leads to subscription stacking and “churning” (switching between services).
GlobalWebIndex	2023	120,000 internet users across 25 countries	$10.80	2.4	$26	Global adoption of streaming is rising, with regional variations in preferred platforms.
Statista	2023	2,000 US adults	$14.20	3.0	$42.60	Original content and exclusive programming drive subscription decisions.
Nielsen	2022	5,000 US households with streaming subscriptions	$13.70	2.9	$39.30	Cord-cutting continues, with younger generations relying solely on streaming.

We might also argue that streaming is to linear video as many other forms of “internet” content are to legacy forms of content: more on-demand; more user-generated; more platform-oriented; pull (algorithm driven) from push (linear packaging); more consumption of “stories” or “items” rather than specific media. 

In and of itself, higher prices over time are not unusual, nor specific to Netflix or other streamers. What will draw attention is inflation rates that are higher than “background” rates of inflation. 

One might note that video streaming retail costs have risen faster than the background rate of inflation since perhaps 2024. On the other hand, it is also fair to note that almost no streaming services are profitable at such rates, either. 

Providers still are searching for the better formula for a sustainable business model. 

Big New AI Business Models, Use Cases, Industries Will Come from Solving New Problems

One question many of us are asking ourselves is where dangers and opportunities are to be found as artificial intelligence is applied to more processes, functions, products and industries. And it might be quite humbling--but accurate--to say that much remains unknown. 

And that is simply the way new technology tends to unfold. Many firms were created using core technology developed at Xerox PARC, including 3Com, Adobe and Synoptics. But “the success of some of these departing spinoffs was largely unforeseen, and unforeseeable,” said Henry Chesrough in Open Innovation. 

Consider what innovations the internet brought that likely were unexpected by most of us, such as social media; crowdsourcing, the sharing economy or search, as well as many innovations that already were in place, such as open source. 

Many other forms of disintermediation, where steps in a value chain were removed, are obvious: e-commerce; education, gaming or user-generated content. 

Other unfolding developments, such as virtual reality or cryptocurrency, are less directly-created by the internet, but generally require its use. 

Innovation	Unexpected Value	Use Cases	Revenue Models	Companies & Industries
Search Engines	Democratization of information, knowledge discovery, access to global resources	Finding anything online, researching topics, exploring new ideas	Advertising, affiliate marketing, premium features	Google, Bing, Yandex, Baidu, search engine marketing agencies
Social Media	Connection & community beyond physical limitations	Sharing experiences, building relationships, expressing oneself, marketing & branding	Advertising, subscriptions, data monetization	Facebook, Twitter, Instagram, Influencer marketing
Sharing economy (e.g., Uber, Airbnb)	Sharing resources & assets for income generation	Transportation, accommodation, peer-to-peer rentals, skills & services	Transaction fees, commissions, advertising, subscriptions	Uber, Airbnb, Lyft, Turo, TaskRabbit
Crowdsourcing	Collective intelligence and distributed problem-solving	Gathering diverse perspectives, generating content, finding solutions, conducting research	Platform fees, micro-transactions, project funding	Wikipedia, Kickstarter, Upwork, Freelancer
E-commerce	Convenient shopping beyond physical stores	Broader product access, competitive pricing, personalized recommendations, 24/7 availability	Online sales, marketplace commissions, product subscriptions	Amazon, Alibaba, Etsy, Online retail in every imaginable niche
Open-source software	Collaborative development and access to free software	Innovation through community involvement, cost-effective solutions, customization, security patches	Donations, sponsorships, enterprise support, premium features	Linux, Apache, WordPress, Open-source frameworks for various fields
Streaming services	On-demand access to vast entertainment libraries	Cord-cutting from traditional media, personalized recommendations, global content reach	Subscriptions, pay-per-view, ad-supported tiers	Netflix, Spotify, YouTube, Streaming platforms for music, games, podcasts, educational content
Online education	Accessible learning beyond geographical and financial constraints	Flexible learning pathways, personalized courses, diverse instructors, upskilling & reskilling	Course fees, subscriptions, micro-credentials, corporate training	Coursera, Udemy, Khan Academy, Online learning for academic degrees, professional development, personal interests
Cryptocurrency	Decentralized financial system and alternative store of value	Peer-to-peer transactions, global reach, inflation resistance, new investment opportunities	Blockchain technology, transaction fees, mining rewards, DeFi applications	Bitcoin, Ethereum, Stablecoins, Cryptocurrency exchanges, NFTs, Blockchain-based financial services
Blogging & personal branding	Sharing individual voice and expertise with a global audience	Building influence, establishing thought leadership, connecting with communities, potential for income generation	Advertising, sponsored content, affiliate marketing, product sales, consulting services	WordPress, Blogger, Medium, Independent creators across various fields

And perhaps one of the lessons of innovation is that big breakthroughs happen mostly when innovators try to solve new problems, not fix existing problems.

And right now, virtually everything we see and hear about AI is how it can help fix some existing process. That’s useful, to be sure. 

But the big, unexpected new use cases, revenue models and value will happen where we are perhaps least expecting it. 

New technology can create entirely new markets and value chains when it is harnessed to meet unmet needs we did not recognize. We did not “know” we needed search or social media. We did not know we needed mobile computing and connectivity devices or personal computing appliances. 

AI undoubtedly will, in context, be viewed as an app, a use case, a function or a capability. But in other cases it will be viewed as a platform to support new business business models, industries and types of firms. 

We just don’t know--yet--how all that will develop.

Is AI a Platform, an App, a Use Case, a Function or a Capability?

Is artificial intelligence a platform or an app? Is it a use case, a function, a capability or an ecosystem? Might it be any and all of those things, depending on the context? 

Though it might be most common to think about artificial intelligence as a platform rather than an app, use case or function, AI might be a platform in one sense; represent use cases in other senses; or functions or even discrete apps. 

Think about the internet, something most observers might agree is a general purpose technology and therefore a “platform.” Recall that a platform is “hardware or software that other hardware and software can run upon.” 

In other words, the platform is the infrastructure upon which apps are built and executed or run. But we commonly will encounter use cases where AI “seems” to be an app, a use case or function. 

Used on a smartphone to support camera features, AI might act like a function, allowing us to manipulate images. 

In other cases, such as speech recognition, we might consider a branded capability--such as Siri or Alexa--to be akin to an “app.” 

And sometimes--such as with automated vehicles--the use of AI might be so intertwined with a product that it is a use case. 

Online marketplaces (Amazon, Etsy) are platforms in a business sense.  Social media platforms (Facebook, LinkedIn) are platforms in that same sense. 

Perspective	Description	Example
Platform	Provides foundational tools and capabilities for building various AI applications. Think of it as the "Lego set" for AI developers.	TensorFlow, PyTorch, Azure Machine Learning
Use Case	Specific problem or task where AI is applied to deliver a solution. Focuses on the "what" and "why" of using AI.	Medical diagnosis, fraud detection, self-driving cars
Function	Individual AI capability or algorithm that can be combined with others to build applications. Think of them as building blocks with specific functionalities.	Natural language processing, image recognition, anomaly detection
App	Software program that integrates AI functions to deliver a specific service or user experience. The finished product, ready for end users.	Siri, Alexa, facial recognition software, personalized recommendation engines

And that might be a key to our general sense that sometimes AI is a platform; sometimes a use case; sometimes a function; sometimes an app. 

Think about the computing GPT. Though we might sometimes talk about “using computers,” as in “using a smartphone or a PC or tablet,” usually we are really talking about “using an app,” taking advantage of a function or invoking a use case. 

We might open a social media app to pass the time or find out what’s going on. In that instance we might consider that we are “using an app.” 

At other times we need information, such as what is next on a calendar, or how to get someplace. That might be more of a function. Yet at other times we might want to buy a plane ticket, in which case we might consider that computing instance to be a use case. 

So AI might “seem” a platform, or an app, or a use case or function, depending on the context where it is invoked or consumed or used.