How Much AI Investment is Producing Revenue for Users of the Technology?

Though estimates of enterprise and other spending on artificial intelligence often are substantial, we sometimes do not stop to evaluate the magnitude of spending by customers, as opposed to suppliers. 

That matters as customer spending  represents the revenue upside from investing in generative AI infrastructure. Spending on AI infra  by cloud computing giants to serve those customer needs arguably represents a “cost.” So does AI infra created by businesses. 

To put it another way, if AI infrastructure investments (graphical processing units, tensor processing units, other acceleration chips) do not ultimately produce sufficient revenue to justify the investments, AI spending will fall. 

Which is just another way of saying that, ultimately, customers pay for everything. 

For example, analysts at IDC estimate $632 billion in 2028 AI spending. AI spending in the United States will reach $336 billion in 2028, IDC predicts. But AI is a large field, and generative AI investments in the U.S. market are forecast to be $108 billion in 2028, representing less than half of total AI investments. 

The issue is what percentage of that investment will represent enterprise spending on AI software, hardware and services, compared to investments by suppliers to meet that need. 

Consider that Amazon, Google, Meta, and Microsoft are expected to invest about $300 billion in capital expenditures in 2025 alone, reaching $336.5 billion for these four companies combined, in 2026. So something like half of all AI investments arguably will be made by a handful of “AI as a service” suppliers, rather than “buyers.” 

And much of the other AI infra investment by businesses likewise is made in hopes of earning a financial return from customer purchases. 

With the caveat that all spending anywhere in a value chain is cost to one participant and revenue to another participant, most AI spending today is arguably for creating AI capabilities that contribute in some way to product value, sales and revenue, or reduce cost in some way. 

Relatively little direct AI end user customer revenue yet is obvious. Right now, perhaps 10 percent of “AI revenue” is generated by mostly-direct end user subscriptions or purchases. If we include indirect contributions (products that use AI), revenue could reach 20 percent. 

Category of AI Spending	Purpose	Percentage of AI Spending	Examples
AI Infrastructure	Investments in foundational AI tech, including computing power, cloud storage, and AI model training.	20-30%	Cloud AI platforms (AWS, Google Cloud AI), GPUs, data storage solutions.
AI Enterprise Software Apps and Features	Spending on AI-driven software for enterprise use cases in operations, marketing, finance, etc.	25-35%	CRM systems with AI (Salesforce Einstein), ERP with AI, HR and analytics tools.
AI Consumer Apps and Features	Development of AI-driven consumer applications and features enhancing user experience in consumer apps.	15-25%	AI in search engines, recommendation algorithms in streaming, AI photo editing.
End-User Product Revenues	Direct revenue from products where AI is a core feature or differentiator, including subscription models.	10-20%	Autonomous vehicles, AI-driven personal assistants, generative AI tools.
R&D and Experimental Projects	Investment in long-term or experimental AI research to fuel innovation and future capabilities.	5-10%	Research in AGI, quantum computing for AI, human-AI interaction studies.

Still, it might be fair to note that, at present, most AI investment is a cost incurred to generate revenue or improve efficiency. AI model subscriptions are among the best examples of consumer end user revenues being generated, at the moment. 

AI’s value for business-to-business operations (commerce, advertising) might become the bigger revenue source soon. 

We Might Have to Accept Some Degree of AI "Not Net Zero"

An argument can be made that artificial intelligence operations will consume vast quantities of electricity and water, as well as create lots of new e-waste. It's hard to argue with that premise. After all, any increase in human activity--including computing intensity--will have that impact.

Some purists might insist we must be carbon neutral or not do AI. Others of us might say we need to make the same sorts of trade offs we must make everyday, for all our activities that have some impact on water, energy consumption or production of e-waste.

We have to balance outcomes and impacts, benefits and costs, while working over time to minimize those impacts. Compromise, in other words.

Some of us would be unwilling to accept "net zero" outcomes if it requires poor people to remain poor; hungry people to remain hungry.

And not all of the increase in e-waste, energy or water consumption is entirely attributable to AI operations. Some portion of the AI-specific investment would have been made in any case to support the growth of demand for cloud computing. 

 So there is a “gross” versus “net” assessment to be made, for data center power, water and e-waste purposes resulting from AI operations. 

By definition, all computing hardware will eventually become “e-waste.” So use of more computing hardware implies more e-waste, no matter whether the use case is “AI” or just “cloud computing.” And we will certainly see more of both. 

Also, “circular economy” measures will certainly be employed to reduce the gross amount of e-waste for all servers. So we face a dynamic problem: more servers, perhaps faster server replacement cycles, more data centers and capacity, offset by circular economy efficiencies and hardware and software improvements. 

Study Name	Date	Publishing Venue	Key Conclusions
The E-waste Challenges of Generative Artificial Intelligence	2023	ResearchGate	Quantifies server requirements and e-waste generation of generative AI (GAI). Finds that GAI will grow rapidly, with potential for 16 million tons of cumulative waste by 2030. Calls for early adoption of circular economy measures.
Circular Economy Could Tackle Big Tech Gen-AI E-Waste	2023	EM360	Introduces a computational framework to quantify and explore ways of managing e-waste generated by large language models (LLMs). Estimates annual e-waste production could increase from 2.6 thousand metric tons in 2023 to 2.5 million metric tons per year by 2030. Suggests circular economy strategies could reduce e-waste generation by 16-86%.
AI has a looming e-waste problem	2023	The Echo	Estimates generative AI technology could produce 1.2-5.0 million tonnes of e-waste by 2030 without changes to regulation. Suggests circular economy practices could reduce this waste by 16-86%.
E-waste from generative artificial intelligence"	2024	Nature Computational Science	Predicts AI could generate 1.2-5.0 million metric tons of e-waste by 2030; suggests circular economy strategies could reduce this by up to 86%1 2
"AI and Compute"	2023	OpenAI (blog)	Discusses exponential growth in computing power used for AI training, implying potential e-waste increase, but doesn't quantify net impact
"The carbon footprint of machine learning training will plateau, then shrink"	2024	MIT Technology Review	Focuses on energy use rather than e-waste, but suggests efficiency improvements may offset some hardware demand growth

Which Firm Will Use AI to Boost Revenue by an Order of Magnitude?

Ultimately, there is really only one way for huge AI infrastructure investments up by an order of magnitude over cloud computing investment to pay off: revenues will have to increase by an order of magnitude as well. 

It might be a stretch to argue that is possible for most firms investing in generative AI frontier models, for example. But it is almost certain that at least one or two of those firms will manage to do so, emerging as leaders of a new industry they lead. 

And that is the prize.  

source: Sherwood 

But generative AI, the current focus of capex, might be--if not a full-blown general-purpose technology--the sort of digital product that creates a whole new--and big--industry. Think of the past pattern of new industries built on firms and products including operating systems, e-commerce, search, social media and online advertising in general, plus still-growing businesses such as ride-hailing and peer-to-peer lodging. 

if generative AI winds up being a “winner take all” business, as most other computing segments have been, there will be no prize for third best, and limited advantage for being second best. 

We have already seen that pattern in many other computing markets. The leader in search has 91 percent market share. The browser leader has 65 percent share. The mobile operating system leader has 72 percent share. The U.S. ride-hailing leader has 68 percent share. 

Market	Dominant Player	Market Share	Runner-up	Market Share
Search Engines	Google	91.9%	Bing	3.0%
Desktop Browsers	Chrome	65.72%	Safari	18.22%
Mobile Browsers	Chrome	66.17%	Safari	23.28%
E-commerce	Amazon	37.8% (US)	Walmart	6.3% (US)
Video Streaming	YouTube	2.5B users	Netflix	231M subscribers
Music Streaming	Spotify	31%	Apple Music	15%
Ride-hailing (US)	Uber	68%	Lyft	32%
Cloud Services	AWS	32%	Azure	22%
Mobile OS	Android	71.8%	iOS	27.6%

So, whether investors like it or not, would-be leaders of the generative AI ecosystem are pouring resources into the effort to lead the new market. And that investment intensity affects investor perceptions, even as the big firms continue to post revenue growth. 

Alphabet reported a robust 15-percent revenue growth; 35-percent cloud computing revenue growth; operating income up 34 percent but also AI-focused capital investment up 72 percent. 

“And as we think into 2025, we do see an increase in AI-focused capital investment coming in 2025,” said Alphabet CFO Anat Ashkenazi. 

So does Amazon, which expects capex to be about  $75 billion in 2024 and “more than that in 2025,” according to Amazon CEO Andy Jassy. “And the majority of it is for AWS and specifically, the increased bumps here are really driven by Generative AI.

“Our AI business is a multi-billion dollar business that's growing triple-digit percentages year-over-year and is growing three times faster at its stage of evolution than AWS did itself,” said Jassy.

Generative AI “is a really unusually large, maybe once-in-a-lifetime type of opportunity,” he said.

All that is fueling investment into generative AI, which based on recent computing product precedent, will produce  a “winner take all” market. 

Company	2024 Estimated AI Capex	2025 Estimated AI Capex
Microsoft	$80 billion	Significant increase
Amazon	$75 billion	Further increase
Alphabet	$52 billion	Increase expected
Meta	$38-40 billion	Significant growth

Microsoft and Meta Platforms both beat analyst expectations with their quarterly earnings reports, but also said more AI spending is coming, pushing down share prices for both firms. 

Microsoft CEO Satya Nadella noted continued capacity constraints at data centers amid surging demand, but also continues heavy spending  on cloud and AI to scale to alleviate capacity constraints. 

Meta CEO Mark Zuckerberg also forecast a "significant acceleration" in spending on AI-related infrastructure in 2025. Zuckerberg acknowledged that this may not be what investors want to hear in the near term, but insisted that the opportunities here "are really big."

GenAI is a big gamble. Based on history, we might suggest that all but one or two of these efforts will fail, and the list of serious contenders also includes OpenAI and others. Should that pattern hold, the top two companies might have 60 percent to 80 percent share of the total market. 

Market Position	Market Share	Profit Share
Leader	70-90%	80-90%
Runner-up	10-20%	5-15%
Others (3-10)	5-10%	0-5%