How Will Generative AI App Adoption Compare to Internet App Adoption?

Many observers expect generative artificial intelligence applications to gain substantial use even faster than internet apps. That might imply very rapid adoption indeed. But it also is possible that adoption at scale might take longer than some expect.

By some estimates, it might have taken about five years for 10 percent of U.S. businesses to begin using internet apps to any significant degree, and that includes email. 

Year	Estimated % of U.S. Businesses Using Internet Apps
1995	1%
2000	10%
2005	30%
2010	50%
2015	70%
2020	85%
2023	90% (est.)

In fact, email might be the most-common internet use case embraced by most businesses. 

Year	% of U.S. Businesses Using Email
1980	1%
1990	10%
1995	30%
2000	60%
2005	80%
2010	95%
2020	99%

U.S. business use of web sites lagged email adoption by roughly five years. 

Year	% of U.S. Businesses Using Websites
1990	1%
1995	5%
2000	20%
2005	50%
2010	80%
2015	95%
2020	99%

But if AI apps and services are likely to be adopted faster than internet services were between 1996 and 2005, especially for consumer applications, there are some logical reasons. 

For starters, the whole internet infrastructure, including broadband access, multimedia, browser and so forth, had to be created. AI, on the other hand, as a “software” development, will be applied to existing applications, processes and use cases. 

That noted, we are likely looking at five to seven years before use of consumer-facing AI apps and usage is common. 

In the period from 1996 to 2005, for example, as the internet was popularized, a large number of new behaviors developed. Prior to 1996, for example, people were unlikely to be using:

• Web Browsers and Email

• Search Engines (Google was not founded until 1998)

• E-commerce

• Social Networking

• Instant Messaging

• File Sharing

• Blogging and user-generated content

Likewise, businesses were unlikely to use or have:

• Web sites

• E-commerce (PayPal was not founded until 1998

• Digital Marketing (Google AdWords was not introduced until 2000)

• Customer Relationship Management (Salesforce.com was founded in 1999)

• Enterprise Resource Planning (ERP)

• IP-based Communication and Collaboration (Skype was founded in 2003)

• Supply Chain Management

• Online Recruiting  (Job boards such as Monster.com appeared in 1999)

source: eMarketer 

If we look at consumer internet app adoption, uptake was quite rapid, suggesting that AI adoption could be quite rapid indeed. If consumers widely adopted internet apps about four years after initial introduction, then AI could well be widely used by consumers within two years. 

Company	Founded	Widespread Use
eBay	1995	1998
Amazon	1995	1999
Craigslist	1995	2000
Google	1998	2001
PayPal	1998	2002
Wikipedia	2001	2004
Facebook	2004	2007
YouTube	2005	2007
Twitter	2006	2009

80/20 Rule for TV Ratings: Why Sports Matter

According to TV ratings firm Nielsen, in 2023, the National Football League accounted for 93 of the year’s 100 most-watched TV shows in the U.S. market.

That’s a good example of the 80/20 rule, formally known as a Pareto principle, which suggests that 80 percent of consequences come from 20 percent of causes. 

More than a third of U.S. broadcaster NBC's viewing time in 2023, for example,  was attributable to NFL games and related programming, according to analysts at MoffettNathanson. CBS was even more dependent, at 40 percent. And at Fox, NFL games and other "shoulder programming" accounted for 63 percent of the time viewers spent with the network.

Beyond the NFL, sports programming drives a disproportionate share of revenue. 

Broadcaster	Top 20% of Programs (Revenue)	Remaining 80% of Programs (Revenue)
ABC	Major sporting events (e.g., NFL, NBA Finals)	Other sports programming, general entertainment
NBC	NFL, Olympics, Premier League	Other sports programming, general entertainment
CBS	NFL, NCAA March Madness, PGA Championship	Other sports programming, general entertainment
FOX	NFL, NASCAR, MLB postseason	Other sports programming, general entertainment

More significantly, National Football League revenue and profit also showed a Pareto distribution. in all cases, a relatively small percentage of programming time (five to 10 percent) is responsible for a disproportionately large percentage of profits (25 to 40 percent).

Network	Program Time %	Profit %
ABC	5	25
NBC	7	30
CBS	8	35
Fox	10	40

And that pattern has been in place for a while. 

source: Sportico 

That heavy draw of NFL content also suggests some potential churn issues for linear or video streaming firms showing NFL content, namely the danger of annual cycles of churn, as football season ends. 

Of course, there are other sports happening at other times of year than the NFL, but those sports do not have the drawing power of the NFL. Of course, once every couple of years there is an Olympics telecast that is highly viewed, but that event does not provide an annual or season-long upsurge in viewing. 

Can EU Catch up with US and China in Computing Technologies?

The EU is losing ground in research and development and in the creation of innovative technology companies with global reach, says a report commissioned by the European Commission. That is unlikely to surprise anybody familiar with the matter, as such concerns have been in place for many decades.

The report says the EU lags in artificial intelligence, cybersecurity, the internet of things (IoT), blockchain and quantum computers. 

The EU has generated fewer new lead innovators in the past decade than the United States, and that the share of EU firms in the top 2,500 global R&D companies has fallen compared to other blocs,” the study argues. 

“For instance, among leading companies in software and internet, EU firms represent only seven percent of R&D expenditure, compared with 71 percent for the U.S. and 15 percent for China; similarly, the EU only accounts for 12 percent of R&D expenditure among leading companies producing technology hardware and electronic equipment, compared with 40 percent for the U.S., and 19 percent  for China.

The study notes that the EU is home to only four of the fifty largest digital marketplaces worldwide, while the ten largest platforms serving EU citizens are owned by U.S. or Chinese companies (Alphabet, Amazon, Meta, Apple, Microsoft, X, Tencent, Alibaba, Byte Dance and Baidu). 

On the other hand, the report notes that “the EU has important capabilities, in particular, in green technologies, advanced manufacturing and advanced materials, the automotive industry and biotechnology.” 

Among the perhaps-obvious recommendations are to increase research and development spending. But many of the other recommendations are less directly related, such as increasing the quality of world-class research institutions.

“According to the Nature Index in 2022, which ranks institutions based solely on the volume of

publications in a selected list of top academic science journals, the EU has only three research institutions among the top fifty globally,” the report says. 

While praiseworthy, the effort to grow more world-class research institutions is a tough and long-term goal that many would argue is unlikely to happen in a world where leadership begets leadership. 

The computer science, semiconductor and biology industries  are typically concentrated in a small number of science and technology clusters, with leading clusters accounting for a large share of overall innovation in a country, the report notes. The EU simply has such clusters, but few in the top 10. 

According to the WIPO classification of world clusters (2023 Global Innovation Index), the EU has a similar number of clusters in the top 100 as the US and China, but only one in the top 20. None of the EU clusters appear among the top ten, while the United States has four and China has three, the report says.

But there are lots of other issues, ranging from a weaker venture capital system to the degree to which academics create private companies; overall commercialization of research and regulatory and bureaucratic obstacles. 

A slower pace of technology adoption; smaller firm size; quality of digital infra and skills also are cited as obstacles. But the list of issues is numerous, including the cost of energy; access to raw materials and digital infrastructure in general. 

For some observers, the report simply restates what critics have said for decades about EU competitiveness, 

The common litany of concerns includes the fragmentation of the Single Market impedes scale. It always is argued that the EU makes insufficient investment. Regulatory barriers are said to be too high. There are talent shortages as well.

Digital infra is not well-developed enough and competitors do better in such areas. Most of the proposed remedies, and they are numerous, would require both a huge shift of resources and significant time, something perhaps unavailable to the EU as the artificial intelligence and computing innovation cycles either appear or continue. 

Some problems might not have solutions, at least not solutions that are politically or economically feasible. And even if successful, EU “catching up” to China and the United States in many technology fields would take a long time, many would likely argue. 

90% of Enterprises are Exploring Generative AI, But 70% of Projects will Fail to Deliver

Fully 88 percent of organizations surveyed by S&P Global Market Intelligence on behalf of Weka for its 2024 Global AI Trends Report are now actively investigating generative AI, ahead of other AI applications such as prediction models (61 percent), classification (51 percent), expert systems (39 percent) and robotics (30 percent).  

Access to graphics processor unit capabilities is an issue. About 40 percent of respondents surveyed suggest access to AI accelerators is a leading consideration in their infrastructure decision-making, and 30 percent cite GPU availability among their top three most serious challenges in moving AI models into production.

That seems to be a bigger issue in the Asia-Pacific region than elsewhere, where lack of access to GPUs is restricting organizations from deploying AI. For example, 38 percent of organizations in India see accelerator access among their top three challenges to moving AI projects into production.

In other regions, access to “GPU as a service” might obviate such concerns. 

For these end user enterprises, though, the greatest proportion of respondents (35 percent) indicate storage and data management are the primary infrastructure issues hindering AI deployments, significantly greater than compute (26 percent), security (23 percent) and networking (15 percent).

That noted, it remains the case that many AI projects fail to reach “deployment at scale” status. According to one Gartner study, more than half of AI projects never are deployed at scale. The S&P Global Market Intelligence survey of about 1,500 respondents tends to confirm that finding, as few of the respondents have more than a handful of AI projects in production, at scale.  

source: Weka

And Gartner analysts suggest that about half of all IT projects fail to reach their financial goals. That might be equally true for GenAI projects as well, given the difficulty of quantifying success for the wide range of AI use cases being implemented. 

source: Weka 

And AI projects are at least as complicated as other IT initiatives and projects, all of which often fail to meet objectives, for any number of reasons. 

source: Weka

Of course, since generative AI and other machine learning applications remain relatively new, perhaps that should not come as a surprise. But Gartner research suggests as many as 85 percent of AI projects fail, and made that prediction about 2018, as nearly as I can ascertain. 

That would not be wildly out of line with the general industry rule of thumb that about 70 percent of IT projects also fail to deliver their intended results. 90% of Enterpris

Study Name	Date	Publisher	Key Conclusions
Standish Group Chaos Report	Annual	Standish Group	Consistently reports high failure rates for IT projects, often exceeding 70%, with common causes including unrealistic expectations, poor communication, and inadequate planning.
Project Management Institute (PMI)	Ongoing	PMI	While not always quoting the exact 70% figure, PMI's research frequently highlights the challenges and risks associated with IT projects, contributing to high failure rates.
Harvard Business Review	Various articles	Harvard Business Review	Numerous articles have discussed the high failure rates of IT projects, attributing them to factors such as organizational culture, lack of executive sponsorship, and poor change management.
IT Governance Institute	Various reports	IT Governance Institute	Provides insights into the factors contributing to IT project failures, such as inadequate governance, insufficient resources, and unclear objectives.