AI Industry Faces $800 Billion Revenue Gap by 2030, Bain Report Warns

An $800 Billion Revenue Shortfall Threatens AI Future, Bain Says

Artificial intelligence companies like OpenAI have been quick to unveil plans for spending hundreds of billions of dollars on data centers, but they have been slower to show how they will pull in revenue to cover all those expenses. Now, the consulting firm Bain & Co. is estimating the shortfall could be far larger than previously understood. By 2030, AI companies will need $2 trillion in combined annual revenue to fund the computing power needed to meet projected demand, Bain said in its annual Global Technology Report released Tuesday. Yet their revenue is likely to fall $800 billion short of that mark as efforts to monetize services like ChatGPT trail the spending requirements for data centers and related infrastructure, Bain predicted.

$2 trillion in new revenue needed to meet AI demand globally by 2030: Report - The Economic Times

At least $2 trillion in annual revenue is needed to fund computing power needed to meet the anticipated AI demand globally by 2030, a new report showed on Tuesday. However, even with AI-related savings, the world is still $800 billion short of keeping pace with demand, according to new research by Bain & Company. The report shows that by 2030, global incremental AI compute requirements could reach 200 gigawatts, with the US accounting for half of the power. Even if companies in the US shifted all of their on-premise IT budgets to cloud and reinvested the savings from applying AI in sales, marketing, customer support, and R&D into capital spending on new data centres, the amount would still fall short of the revenue needed to fund the full investment, as AI's compute demand grows at more than twice the rate of Moore's Law, Bain noted. "By 2030, technology executives will be faced with the challenge of deploying about $500 billion in capital expenditures and finding about $2 trillion in new revenue to profitably meet demand. Meanwhile, because AI compute demand is outpacing semiconductor efficiency, the trends call for dramatic increases in power supply on grids that have not added capacity for decades," explained David Crawford, chairman of Bain's Global Technology Practice. Add the arms race dynamic between nations and leading providers, and the potential for overbuild and underbuild has never been more challenging to navigate. Working through the potential for innovation, infrastructure, supply shortages, and algorithmic gains is critical to navigate the next few years, Crawford added. While computational demand increases, leading companies have moved from piloting AI capabilities to profiting from AI as organisations scale the technology across core workflows, delivering 10% to 25% earnings before interest, taxes, depreciation, and amortisation (Ebitda) gains over the last two years. Yet, most companies today remain stuck in AI experimentation mode and are satisfied with modest productivity gains, the report concludes. Tariffs, export controls, and the push by governments worldwide for sovereign AI are accelerating the fragmentation of global technology supply chains, Bain found. Cutting-edge domains such as AI are no longer just catalysts for economic growth but are conduits for countries' political power and national security. "Sovereign AI capabilities are increasingly seen as a strategic advantage on par with economic and military strength," said Anne Hoecker, head of Bain's Global Technology practice. Also Read: An $800 billion revenue shortfall threatens AI future: Bain