NEW DELHI: At least $2 trillion in annual revenue is needed to fund computing power needed to meet 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 to keep 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 under-build 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 per cent to 25 per cent earnings before interest, taxes, depreciation, and amortization (EBITDA) gains over the last two years.