AI Companies Face $800 Billion Funding Shortfall, Says Bain Report
A report by Bain & Co. found that the artificial intelligence sector faces an $800 billion problem.
The consulting firm’s sixth annual Global Technology Report, released Tuesday (Sept. 23), said it will take $2 trillion in yearly revenue to fund the computing power required to meet projected AI demand by 2030. Even with AI-related savings, the world is still $800 billion short when it comes to keeping up with demand.
By 2030, global incremental AI compute requirements could reach 200 gigawatts, with the United States making up half of the power, the report said. Even if U.S. companies moved all of their on-premise IT budgets to the cloud and reinvested the savings from applying AI to various aspects of their business on new data centers, it would still not be enough, with AI’s compute demand increasing at more than double the rate of Moore’s Law.
“If the current scaling laws hold, AI will increasingly strain supply chains globally,” David Crawford, chairman of Bain’s Global Technology Practice, said in a Tuesday news release. “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 moving faster than semiconductor efficiency can keep up with, the trends require “dramatic” upticks in power supply on grids that have not added capacity for decades, Crawford added in the release.
“Add the arms race dynamic between nations and leading providers, and the potential for overbuild and underbuild has never been more challenging to navigate,” he said in the release. “Working through the potential for innovation, infrastructure, supply shortages and algorithmic gains is critical to navigate the next few years.”
Meanwhile, PYMNTS this month explored the importance of inference, the stage in which an AI model is actually used to provide predictions, responses or insights.
As far as demand goes, the shift of generative AI from research to mainstream use has created billions of inference events each day. As of July of this year, OpenAI said it was handling 2.5 billion prompts each day, including 330 million from users in the U.S. Brookfield projections indicate that three-quarters of all AI compute demand will come from inference by 2030.
“Unlike training, inference is the production phase,” PYMNTS wrote Monday (Sept. 22). “Latency, cost, scale, energy use and deployment location all determine whether an AI service works or fails.”
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