Consulting giants warn: Soaring demand for computing power may lead to an $800 billion revenue gap in the AI industry by 2030

Behind the competition among AI companies to announce hundreds of billions of dollars in data center investment plans, a more severe issue is emerging—how to generate sufficient revenue to cover these enormous expenditures.

Recently, the globally renowned consulting firm Bain & Company warned that the artificial intelligence industry is facing an unprecedented revenue gap crisis. The company's latest annual global technology report predicts that by 2030, AI companies will need $2 trillion in annual revenue to support the expected computing demand, but actual revenue may fall short by $800 billion.

This significant gap arises from the monetization capability of AI services lagging far behind the expenditure demands of data centers and related infrastructure. Although AI services represented by ChatGPT are experiencing rapid user growth, their profit models remain unclear, and leading companies like OpenAI are losing billions of dollars each year.

David Crawford, Chairman of Bain's Global Technology Practice, stated, "If the current expansion pattern continues, AI will increasingly tighten the global supply chain." The report predicts that global AI computing power demand could surge to 200 gigawatts by 2030, with the United States accounting for half of that share.

This warning further intensifies market skepticism regarding the valuation and sustainability of business models in the AI industry. Although tech giants like Microsoft, Amazon, and Meta plan to significantly increase AI investments, the vast gap between input and return could reshape the entire industry landscape.

The Huge Gap Between AI Investment Frenzy and Profit Reality

Data shows that tech giants, including Microsoft, Amazon, and Meta, will increase their annual AI spending to over $500 billion in the next decade. The release of new models by companies like OpenAI and China's DeepSeek has further stimulated demand for AI services, prompting the entire industry to ramp up investment.

However, the performance on the revenue side has fallen far short of expectations. OpenAI is currently losing billions of dollars each year, prioritizing growth over profitability, and is not expected to achieve positive cash flow until 2029. The Bain report indicates that AI companies' revenue generation through services like ChatGPT is lagging significantly behind the pace of data center investments.

This mismatch between input and output is sparking widespread discussions in the industry about the rationality of AI company valuations. Although AI services are becoming increasingly popular worldwide, the pace at which businesses are realizing cost savings and additional revenue growth from AI is clearly lagging behind the explosive growth in computing power demand.

Surge in Computing Power Demand Brings Supply Chain Challenges

Bain predicts that by 2030, the global demand for new AI computing power could reach 200 gigawatts, with the United States accounting for 100 gigawatts of that. This enormous demand will pose a severe test for global supply chains and power supplies.

The report points out that while breakthroughs in technology and algorithms may alleviate some pressure, supply chain constraints or insufficient power supply could hinder the industry's development progress. The current rapid expansion of the AI industry has already begun to put pressure on global data centers, chip manufacturing, and power infrastructure.

In addition to investments in computing power, leading AI companies are also investing heavily in product development. Autonomous AI agents capable of executing multi-step tasks with limited guidance like humans have become a key development focus. Bain estimates that in the next three to five years, companies will allocate up to 10% of their technology spending to building core AI capabilities, including agent platforms

Opportunities and Challenges in Emerging Technology Fields

In addition to AI services, Bain's annual technology report also predicts growth in areas such as quantum computing. This emerging technology is expected to unlock $250 billion in market value across industries like finance, pharmaceuticals, logistics, and materials science.

Unlike the external expectations for a singular breakthrough in quantum technology, Bain predicts that this will be a gradual process, with initial applications in narrow fields over the next decade, followed by a gradual expansion of adoption.

In the robotics field, Bain points out that humanoid robots are attracting capital and becoming more widespread, but deployment is still in the early stages and heavily reliant on human supervision. Commercial success will depend on the maturity of the ecosystem, and companies piloting robots early are expected to lead industry development