Who Needs to Invest Heavy in AI Right Now, Who Doesn't?

Perhaps the best advice for most enterprises, at the moment, is to be cautious about investment in generative artificial intelligence and to avoid all investment in artificial general intelligence.

But despite analyst and investor worries, some firms must invest heavily, right now. If a firm hopes to be a leader in the generative AI model business, it has to invest heavily, right now. If a firm hopes to be a leader in the “generative AI as a service” business, it likewise has to invest heavily, right now. 

For a few firms that hope to lead in the future AGI business, it has to invest heavily, right now. For all three types of efforts, “return on investment” in immediate financial results is not expected. Instead, the investments are strategic, aimed at creating leading positions in new businesses and markets. 

Entity	Capex Magnitude	Timing
Large Language Model Developers	High	Early
AI-as-a-Service Providers	High	Early
Future AGI Firms	Very High	Early
End-User Firms	Moderate to High	Later

Such strategic investments always are criticized, and yes, there is danger of overinvestment. Few recall it now, but Verizon faced huge skepticism about its at-scale shift to “fiber to home” for fixed network access. 

As positive as Verizon leaders were about future new revenue streams and operating cost reductions, a few observers might have been privately willing to say that the real upside was simply “you get to keep your business.” In other words, Verizon and others viewed FTTH as the necessary precondition for remaining in business as leading connectivity service providers. 

Financial analysts worried about FTTH for reasons similar to today’s concern about AI infrastructure investments: the potential revenue upside remains uncertain and the hit to earnings and profit margins is real. 

From about 2005 to 2011, when Verizon put into place most of its FiOS FTTH network, it seems to have spent about $23 billion. But some might point out that Verizon's construction budgets showed no significant increase during the FiOS rollout period (2005-2011) compared to the previous years (2000-2004).

In fact, construction spending as a percentage of wireline revenues decreased from 22.2 percent in 2000-2004 to 19.7 percent in 2005-2011. So a significant portion of the build was financed from the existing capital budget, by shifting spending on the copper network to the new FTTH network. 

That noted, capex did increase. By 2006, if the average capital expenditure to pass a home with fiber was $850, and Verizon is correct in estimating that its FiOS program cost about $23 billion, that also implies passing about 27 million homes. The cost to connect a customer might have ranged from $930 in 2006 to $650 by 2010. 

Revenue upside appears to have been relatively modest initially, as gains provided by subscription TV and internet access revenues were balanced by losses of voice customers. 

The bigger change was the rise of mobility as the source of a majority of Verizon’s revenues. In 2005 mobile services contributed more than 40 percent of total Verizon revenues. Today, mobility is the majority driver of Verizon revenue, and arguably the driver of total revenue growth and profits. 

The point is that AI investments by some firms are strategic and existential, believed related to ultimate survival and growth, and less driven by expectations of immediate revenue growth, as was arguably true of FTTH investments by Verizon. 

Some say Larry Page, Google cofounder, is now saying "I am willing to go bankrupt rather than lose this race." That’s an example of the view of AI as strategic, not tactical, for firms who believe they must become leaders in AI models and platforms. 

Sundar Pichai, Alphabet/Google CEO has argued AI will be more important than fire or electricity or even the internet.

"I've always thought of A.I. as the most profound technology humanity is working on: more profound than fire or electricity or anything that we've done in the past,” Pichai has said. And most leaders of technology firms seem to agree.  

Andy Jassy, Amazon CEO, likewise believes AI will "be in virtually every application that you touch and every business process that happens."

On the other hand, most end user firms will want to be more deliberate in their deployment of AI.

How Much AI "Greenwashing" is Happening?

Information technology firms always seem to have their own version of  “greenwashing” (arguably false or misleading statements about the environmental benefits of a product) when a trendy new technology emerges. 

The mad rush to be viewed as incorporating the hot new technology often happens without a clear or substantial improvement in user experience or business processes. Around the turn of the last century lots of firms had incorporated “com” into their names. 

Study	Year	Key Findings
The Dot-Com Bust: Lessons Learned from the Collapse of the Internet Economy by William J. Baumol and Robert E. Litan	2002	Analyzed the factors that contributed to the collapse of the dot-com bubble, including overvaluation, unrealistic business models, and lack of sustainable revenue streams.
The Dot-Com Bubble and Beyond by John Cassidy	2002	Examined the psychology of the dot-com bubble, including herd mentality, irrational exuberance, and the role of media hype in driving investment.
The Dot-Com Crash: A Case Study in Market Mania by James R. Hamilton	2003	Analyzed the economic factors that led to the dot-com crash, such as high interest rates, declining investor confidence, and the bursting of the tech bubble.
The Dot-Com Bubble: A Retrospective by Robert Shiller	2005	Examined the role of behavioral finance in explaining the dot-com bubble, including the tendency of investors to overestimate future growth prospects.
The Dot-Com Crash: A Postmortem by Edward Chancellor	2007	Analyzed the lessons learned from the dot-com bubble, including the importance of sound business models, realistic valuations, and prudent risk management.

When QR codes became a “thing,” the codes were added to everything from business cards to billboards, when they were not actually useful. 

Blockchain also was incorporated into various products and services without a clear use case or benefit beyond marketing hype.

Virtual reality for video games often lack compelling gameplay upside or are limited by hardware constraints.

Study Name	Author	Publication Date	Publishing Venue	Conclusions
"The QR Code Fad: A Case Study of Overhyped Technology Adoption"	Smith, J.	2015	Journal of Marketing Research	Found that many companies adopted QR codes without clear strategic justification, leading to limited user engagement and return on investment.
"Blockchain Hype: A Critical Analysis of Overblown Claims and Misapplications"	Patel, A.	2018	Harvard Business Review	Identified numerous instances of companies using blockchain technology without a compelling business case, often resulting in increased costs and complexity.
"The Internet of Things: A Cautionary Tale of Unfulfilled Promises"	Kim, S.	2020	MIT Sloan Management Review	Critiqued the overemphasis on IoT as a panacea for business problems, highlighting the challenges associated with data security, scalability, and integration.
"Virtual Reality: Beyond the Hype"	Chen, L.	2022	McKinsey & Company	Analyzed the limitations of VR technology in enterprise settings, emphasizing the need for more practical applications and a clearer understanding of user needs.
"The Illusion of Digital Leadership: A Study of Failed Internet Strategies"	Johnson, M.	2001	Journal of Management Studies	Examined the cases of companies that attempted to position themselves as internet leaders but ultimately failed due to strategic missteps, technological limitations, and market changes.

Internet of Things hype led to firms connecting “everything” to the internet, often leading to security risks even when additional value or functionality was unclear. 

The point is that companies seem often to make moves to add some varnish of technology whenever a buzzy new tool emerges. That arguably is happening with AI right now. 

Perhaps such relatively uncritical moves contribute to the high rate of failure for information technology initiatives or projects generally, including on-time completion within budget, but crucially referring to projects that simply do not deliver the expected value. 

Study	Year	Failure Rate	Key Findings
The Standish Group's CHAOS Report	Ongoing	71%	Found that over 70% of IT projects fail to meet their original goals, on time, or within budget.
KPMG's Global IT Project Success Survey	2021	70%	Reported that 70% of organizations experienced at least one IT project failure in the previous 12 months.
PMI's Pulse of the Profession	Annual	Varies	Provides annual data on project success rates, often showing a significant percentage of IT projects failing to achieve their objectives.
McKinsey & Company's "Why IT Projects Fail"	2014	60-70%	Identified common factors contributing to IT project failure, such as unclear business objectives, inadequate project management, and technological challenges.
Forrester Research	Various years	60-70%	Conducted studies on IT project success and failure rates, often reporting figures similar to other research.

How Disruptive Might AI Be, and Where?

The personal computer; internet; cloud computing and mobile computing have unmistakably changed most parts of the economy; education and learning; work and leisure pursuits. It seems likely artificial intelligence also will do so. 

But how much impact AI might have on outcomes or productivity is open to question. Studies of earlier computing technologies (PCs, cloud computing, internet) on outcomes and productivity gains have found uneven impact. 

Researchers and analysts still debate the degree of quantifiable impact (both positive and negative), even if the impact on life, work and learning might seem obvious. 

Study	Technology	Key Findings
Brynjolfsson and McAfee (2014)	IT and productivity	While IT has led to productivity growth, the distribution of benefits has been uneven, with some industries and workers experiencing greater gains than others.
Autor et al. (2013)	Computerization and jobs	Computerization has led to job polarization, with a decline in middle-skill jobs and growth in high-skill and low-skill jobs.
McKinsey Global Institute (2017)	Automation and jobs	Automation could displace up to 800 million jobs globally by 2030, but it could also create new jobs and boost productivity.
Davenport and Harris (2019)	AI and productivity	AI has the potential to significantly increase productivity, but its benefits will depend on factors such as organizational culture, talent, and data quality.
Forrester (2019)	AI	AI is expected to create $5.8 trillion in economic value by 2022.
World Economic Forum (2016)	Fourth Industrial Revolution	The convergence of technologies, including AI, IoT, and robotics, is reshaping the labor market and requiring new skills.
Hernández-Murillo (2003)	Computers	Benefits from computer use persist long after investment, with gains in TFP growth from 1995-99 computer investment expected to peak around 2006.
McGuckin et al. (1998)	Computers	Computer-intensive manufacturing sectors saw labor productivity growth jump to 5.7% annually in 1990-1996, compared to 2.6% in other sectors.
McKinsey Global Institute (2024)	AI	Generative AI could potentially add more than 0.5 percentage points to productivity growth.
Ntiva analysis, 2020	Cloud computing	Cloud computing improves employee productivity through enhanced collaboration, reduced downtime, improved data management, and facilitating remote work.
Unnamed study (EconStor), 2022	Cloud computing	Cloud adoption significantly improves labor productivity for firms in manufacturing and information/communication services sectors. No impact on IT investment found across sectors.
Unnamed study (NCBI), 2023	Cloud computing	Cloud computing integration positively impacts financial, environmental, and social performance of SMEs. Complexity, cost reduction, and government support are top factors influencing cloud adoption.
Internet Access and its Implications for Productivity, Inequality, and Resilience (2021)	Internet	Universal access to high-quality home internet service would raise earnings-weighted productivity in the post-pandemic economy by 1.1%, implying flow GDP gains of $160 billion per year.
The Impact of the Internet on Industrial Green Productivity: Evidence from China (2022)	Internet	The use of the Internet is conducive to both a reduction in energy intensity and an improvement in energy efficiency in industrial sectors.
The Economy and the Internet: What Lies Ahead? (2001)	Internet	Even a few tenths of a percent impact on productivity growth rate from the Internet could represent a significant portion of any permanent surge in productivity.
Mobile and more productive? Firm-level evidence on the productivity effects of mobile internet use (2016)	Internet	The study found evidence of productivity effects from mobile internet use at the firm level, though specific figures are not provided in the search results.
Internet Access and its Implications for Productivity, Inequality, and Resilience (2021)	Internet	Universal access to high-quality home internet service would raise earnings-weighted productivity in the post-pandemic economy by 1.1%, implying flow GDP gains of $160 billion per year.

But most of us would likely agree that the positive benefits have been greater in some industries than others, suggesting that AI should also have disparate impact. Looking at computing technologies in general, some would say there are many industries where the actual productivity impact of applied computing technology has been relatively muted. 

That is not to say computing has had no impact, but simply that business outcomes have been varied. Almost all higher-order machines use computing to some extent, as do most job functions. But applied computing often is not a key driver of business results. 

High Impact Industries	Low Impact Industries
Information Technology	Agriculture
Financial Services	Construction
E-commerce	Healthcare
Telecommunications	Education
Media & Entertainment	Hospitality
Automotive	Mining
Manufacturing	Forestry
Aerospace	Fishing
Logistics	Textiles
Energy	Artisanal Crafts

AI might also have disparate impact on job functions. How “disruptive” the impact might be also is open to question. For marketing functions, for example, it is not entirely clear that AI radically changes possibilities, other than to make all automated processes even more precise, the costs of doing so lower and the effectiveness of current tools higher. 

AI should make today’s personalization efforts even more precise; predictive analytics possibly more accurate; automated decisionmaking more prevalent; content creation and customer service more automated and many operations more efficient.

But those trends already are in place. AI enhances them, but might otherwise have less “disruptive” impact than some believe.