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.

AI Capex Might Not Grow as Much as Some Expect Over the Next Few Years

Is it possible some estimates of capital investment in artificial intelligence are inflated? Yes. According to Stanford University’s Human Centered AI institute, about $67 million was invested in AI in 2023. 

“To project such spending in the near term, we grossed up last year’s investments in AI by various annualized rates of growth ranging from 13 percent to 34 percent,” says Joe Davis, Vanguard global chief economist. “Those rates of growth would leave AI spending this year and next in the $76 billion to $121 billion range.”

source: Vanguard 

That’s significant, but nowhere near the “$1 trillion” some have estimated will be spent on AI capex over the next few years. 

source: Goldman Sachs

Or consider estimates of AI capex spending by a few of the big hyperscalers. To be sure, those firms are only some of the firms expected to make AI capex investments. 

Company	2024 US$ Billions		2025		2026
	Low Estimate	High Estimate	Low Estimate	High Estimate	Low Estimate	High Estimate
Alphabet	40	45	45	50	50	55
Microsoft	50	55	55	60	60	65
Amazon	35	40	40	45	45	50
Apple	20	25	25	30	30	35
Meta	35	40	40	45	45	50
Total	180	205	205	230	230	255

Also, a significant portion of the AI capex is likely to be shifted from existing information technology budgets, and might not represent incrementally-higher spending. That is virtually certain to be the case for most enterprises making AI capex investments, as most enterprise or smaller business IT budgets do not change all that much annually, with increases, if any, normally in single digits. 

Providers of “AI as a service” will have to make unusual investments, to be sure. But business users of AI will not do so. 

So the big change in AI capex is likely to be driven by a relatively few hyperscalers.

Estimating Edge Computing Capex is Tricky

It might be as difficult to forecast edge computing capital investment as it is to forecast edge computing demand. For starters, there are many different definitions of potential “edge” computing. Edge computing can happen directly on user devices; on a premises using a local data center; someplace three to five miles distant (base of cell tower); within a metro area or hundreds of miles distant. 

So a judgment has to be made about how to evaluate the edge computing capex “on device.” Is it the full cost of the appliance or the incremental cost of the AI capability? Do we include software? And is “capex” cost the relevant metric, or do we include the on-going costs of subscriptions, which might better capture the full cost of using AI capabilities? 

To be sure, if all we are measuring is capital investment, then software subscriptions are not relevant. But that full cost does matter if we are trying to compare the cost of creating and using remote and edge computing. 

The U.S. cloud computing services market was valued at approximately $216.91 billion in 2023 by Grand View Research, with projections indicating growth at a compound annual growth rate (CAGR) of 20 percent from 2024 to 2030. Meanwhile, the revenue from smartphone sales in the United States is projected to be around $109.8 billion in 2024.

If edge computing revenue grows, that should also imply that capex grows. The other issue is that we normally only measure “capex” on the producer side of a market. Consumer expenditures on computing gear, including smartphones, are not counted as “capex.”

source: Statista

AI Seems to be the Biggest Driver of Cloud Computing Spending Growth

A survey commissioned by Wipro of 500 business executives in Europe and the United States suggests continued growth of cloud computing spending, plus growing artificial intelligence spending. And the suirvey suggests AI is the single biggest growth driver.

Perhaps nobody would be surprised by that conclusion. Nor would many, if any, be surprised that cloud computing spending exceeds AI spending.

But the study does not specifically address the magnitude of the increases, so one cannot tell, from this survey, how much spending might be growing, if at all.

A majority of respondents (55 percent) report that their cloud adoption is outpacing their AI adoption, while 35 percent say they are moving at the same pace for both technologies. 

But 10 percent of respondents report that their AI adoption is outpacing cloud spending.

The “health and life sciences” sector has the highest percentage (41 percent) of organizations moving at the same pace for cloud and AI adoption. 

Retail has the highest percentage (63 percent) of industry response suggesting cloud spending ahead of AI. The energy and utilities sector has the highest percentage (18 percent) of organizations with AI adoption ahead of cloud.

The top reported drivers of cloud investment are AI/GenAI applications (54 percent), extended cloud infrastructure (47 percent), and increasing data demands (43 percent).

Separately, the Civo Cost of Cloud 2024 report suggests user organizations are unhappy with spiraling cloud computing costs.  According to the survey, 77 percent of the 500 industry professionals surveyed are using one of the Big 3 hyperscalers and many (37 percent, at least) believe cloud computing cost effectiveness has not been seen. 

source: Civo