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. 

High AI Capex is Worrisome, But "Winner Take All" is the Prize

It is not hard to find estimates of investment in U.S. artificial intelligence infrastructure (computing capabilities) in the range of $300 billion or more between 2023 and 2030. IDC analysts have suggested $300 billion in investments between 2023 and 2026.

Nor is it hard to find critics who worry about uncontrolled spending without a clear revenue model. On the other hand, leaders of firms attempting to become leaders in the generative AI model business are likely to keep in mind the “winner take all” dynamic we have seen in the recent internet era, where just one or a few firms emerged as leaders in new markets. 

They might point to:

• Amazon's years of heavy investment to dominate e-commerce

• Google's massive spending to establish search leadership

• Cloud providers' huge datacenter investments

• Meta's acquisition strategy in social media.

In fact, many markets show scant ability to support three providers, as the market leader has twice the share--and up to an order of magnitude more-share compared to  the number-two provider.

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%
Social Media	Facebook	2.9B users	YouTube	2.5B users
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 even if McKinsey estimates AI infrastructure spending will exceed $500 billion between 2023 and 2030, and even if many of those investments do nor produce the expected results, model suppliers have incentives to risk quite a lot, knowing that there is a small  prize for being second best. 

Gartner forecasts global AI infrastructure investments will surpass $250 billion annually by 2030. 

The OECD estimates investments in AI infrastructure across industries, will reach $1 trillion by 2030, across the OECD countries. Bloomberg predicts that the global AI infrastructure market will $700 billion by 2030.

On the other hand, most of that investment will be by end users and others in the value chain, not the generative AI model providers. 

And some estimates made in 2023 might be considered conservative in 2024. Morgan Stanley’s  "The Economics of AI” study, published in October 2023 suggested more than $200 billion in AI infrastructure investments by 2030, including:

• Data centers: $125B

• Networking infrastructure: $50B

• Chip fabrication: $25B

• Cooling systems: $10B.

Boston Consulting Group in December 2023 suggested there would be $235 billion cumulative investments in 

• Data center buildout: 45%

• Compute infrastructure: 35%

• Power infrastructure: 20%. 

The Goldman Sachs "AI Infrastructure Report," published in September 2023 estimated $275 billion in  cumulative investment, including:

• Semiconductor investment: $100B

• Data centers: $115B

• Power systems: $35B

• Network upgrades: $25B. 

The caution, though, is that early estimates of the size of new technology markets often lead to overinvestment across the value chain. 

Study/Report	Date	Publisher	Key Conclusions
The Dot-Com Bubble Burst: Causes and Implications	2001	U.S. Securities and Exchange Commission (SEC)	Overinvestment in internet startups led to a speculative bubble that burst in 2000. Many companies were overvalued despite having no profitability.
Boom and Bust: The Telecommunications Investment Bubble	2002	Federal Reserve Bank of San Francisco	Overinvestment in telecom infrastructure during the late 1990s led to a major industry downturn, with unsustainable levels of capital spending.
The Case for Less Innovation	2017	Harvard Business Review	Many companies overinvest in unproven technologies without clear demand, resulting in failed projects and wasted resources.
Lessons from the Clean Tech Bubble	2016	MIT Energy Initiative	Overinvestment in cleantech (2005-2011) led to massive failures, with many companies being too early to market and receiving excessive venture capital.
Investing in Innovation: Creating a Research and Innovation Policy That Works	2010	The NESTA Foundation (UK)	Over-investment in R&D for new technologies can create inefficiencies and fail to produce proportional economic benefits if not managed strategically.
The Nanotechnology Investment Bubble	2005	Journal of Nanoparticle Research	Speculative investments in nanotechnology during the early 2000s led to unmet expectations, as many products were not commercially viable.
Unleashing Productivity: Overinvestment in Information Technology	2005	McKinsey Global Institute	Overinvestment in IT during the late 1990s and early 2000s did not yield expected productivity gains, with firms often adopting technology prematurely.
The Illusions of Overinvestment in AI	2021	Brookings Institution	Many companies overinvest in artificial intelligence without clear applications, leading to inflated expectations and unrealized returns.
The Biotechnology Bubble: When Science and Finance Collide	2004	Nature Biotechnology	Excessive capital flow into biotech during the 1990s led to overvaluation, with many firms failing to achieve meaningful breakthroughs.

In recent years we have also seen examples of overinvestment by many platform suppliers as well. 

Technology	Company/Industry	Year	Description of Over-Investment
Artificial Intelligence	IBM Watson	2011-2022	IBM invested billions in Watson AI for healthcare, but struggled to generate significant revenue and ultimately sold off the health assets
Virtual Reality	Meta (Facebook)	2014-present	Meta has invested over $36 billion in VR/AR technology with limited returns, facing skepticism about the metaverse vision
Blockchain	Various	2017-2018	Many companies rushed to invest in blockchain during the crypto boom, only to scale back or abandon projects when the hype died down
Autonomous Vehicles	Uber	2016-2020	Uber invested heavily in self-driving technology, spending over $1 billion before selling the unit after a fatal accident and regulatory challenges
3D Printing	3D Systems	2013-2015	The company aggressively acquired 3D printing startups, leading to over $1.3 billion in losses and a stock price crash when consumer adoption didn't materialize
Cloud Computing	HP	2011-2012	HP's $11 billion acquisition of Autonomy for cloud services led to an $8.8 billion write-down

So the rationale for investing heavily to secure the leading position in the generative AI model business is a reflection of the possible “winner take all” character of application and platform markets, where the number-one provider dominates. 

And since market share and profit margin generally are related, the rewards for market leadership also are significant. In many capital-intensive markets, the profit margin of the top provider is double that of number two. 

And provider number two can have margins double that of provider number three.