AI math isn't mathing: infrastructure spending forecast to outstrip expected revenue

AI services are unlikely to deliver enough revenue to cover the expected capital expenditure going into IT infrastructure between now and 2030, and it looks like neocloud operators will largely bow out or be bought out. This is the conclusion from Gartner, which has added up the anticipated investments in AI datacenters over the next several years, estimated the revenue that can be expected from AI-driven applications over the same period, and found the numbers come up short. Gartner's data comes from a report, "Prepare for AI Market Consolidation: Capital Write-Downs Could Exceed $4 Trillion by 2032," which concludes that AI will not generate enough sales to profitably cover all of the planned AI capital spending before 2032 at the earliest. But forecasts typically focus on the end of the decade, so it ran the numbers for 2030 as well. "In 2026, AI providers are expected to spend nearly $1 trillion on AI infrastructure, and are forecast to invest an additional $6 trillion between 2027 and 2030," said Gartner senior director analyst Will Sommer. "However, our research indicates that enterprise and consumer AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures during this period." Assuming a 12 percent return on invested capital (ROIC), which many companies are said to regard as the bar for acceptability, Gartner estimates AI datacenter operators would need to generate a total of $8.5 trillion in revenue to cover capital spending and financing costs while achieving that return. Unfortunately, it forecasts that the total enterprise and consumer AI revenue that can be attributed to AI datacenters during this period will add up to no more than $3.6 trillion. Returns below expectations could force operators to write down assets whose value they can no longer recover, particularly as newer GPUs displace older hardware. The result will be a shake-out of the companies providing AI infrastructure services, and Gartner believes that neocloud operators – "GPU-as-a-service" providers specializing in AI compute – will be the hardest hit. It forecasts that 80 percent of them won't survive as independent companies beyond 2030. "Not everyone is in trouble," Sommer told The Register. "Some will outperform, some will underperform, and some will have to exit the market." Gartner expects the hyperscalers to weather the shake-out better because their businesses extend beyond AI services. Last year, McKinsey & Company warned that neocloud companies need to move up the AI stack and diversify to avoid being commoditized. There has been growing unease in the financial markets over the amount of capex being poured into AI investment, with some money lenders starting to get cold feet. Last month, SoftBank Group found itself facing higher borrowing costs as the tech investment biz sought to drum up further funding for OpenAI. Financial biz Jefferies issued a report this week saying that developers such as OpenAI and Anthropic are facing threats to their profitability from open source and Chinese models that undercut them on price. Management consultant Bain & Company also published a report last month estimating that the AI industry needs to generate $6 trillion in annual revenue by 2031 to keep up funding for the infrastructure needed to meet anticipated demand. Bain also forecast a revenue shortfall unless the industry develops products and use cases not yet identified. Both the IMF and the Bank for International Settlements have voiced concerns about the AI bubble bursting and the effects rippling out across the global economy. ®