Bain & Company believes that despite the massive scale of data center investments, the ability to monetize revenue is significantly lagging, potentially raising questions about industry valuations and business models. The firm forecasts that global AI computing power demand could surge to 200 gigawatts by 2030, with AI companies requiring $2 trillion in annual revenue to support computational needs. However, actual revenues may fall short by $800 billion. Additionally, the sharp rise in computing power demand will pose severe challenges to the global supply chain and electricity supply.

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

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

This massive shortfall arises because the monetization capabilities of AI services are far from keeping up with the expenditure demands for data centers and related infrastructure. Although AI services like ChatGPT have experienced rapid user growth, their profit models remain unclear, with leading companies such as OpenAI incurring losses of tens of billions of dollars annually.

David Crawford, Chair of Bain’s Global Technology Practice, stated: ‘If the current scaling trends persist, AI will increasingly strain global supply chains.’ The report predicts that global AI computing power demand could surge to 200 gigawatts by 2030, with the United States accounting for half of this share.

This warning further intensifies market skepticism regarding the valuation and sustainability of business models within the AI industry. Despite tech giants such as Microsoft, Amazon, and Meta planning significant increases in AI investments, the vast disparity between investment and returns could reshape the entire industry landscape.

The stark contrast between the AI investment boom and the reality of profitability

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

However, revenue performance has fallen far short of expectations. OpenAI currently incurs annual losses amounting to billions of dollars, prioritizing growth over profitability, and is not expected to achieve positive cash flow until 2029. The Bain report highlights that income generation through services like ChatGPT lags significantly behind the pace of data center investments.

This mismatch between investment and output is sparking widespread discussions about the valuation rationality of AI companies. While AI services are gaining popularity worldwide, the pace of cost savings and additional revenue growth that enterprises derive from AI clearly lags behind the explosive growth in computing power demand.

Surging computing power demand brings supply chain challenges

Bain predicts that by 2030, the global additional AI computing power demand could reach 200 gigawatts, with the United States accounting for 100 gigawatts. This massive demand will pose a significant test to the global supply chain and electricity supply.

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

In addition to investments in computing power, leading AI companies are also heavily investing in product development. Autonomous AI agents capable of performing multi-step tasks with limited guidance, akin to human abilities, have become a key focus of development. Bain estimates that over the next three to five years, enterprises may allocate up to 10% of their technology spending toward 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 forecasts growth in areas such as quantum computing. This emerging technology is expected to unlock $250 billion in market value across industries such as finance, pharmaceuticals, logistics, and materials science.

Contrary to expectations of a single breakthrough in quantum technology, Bain predicts this will be a gradual process, with initial applications in narrow fields within the next decade, followed by a progressive expansion of adoption.

In the robotics sector, Bain notes that humanoid robots are attracting capital and becoming more widespread, but deployment remains in its early stages and heavily relies on human supervision. Commercial success will depend on the maturity of the ecosystem, with companies piloting early robots likely to lead the industry’s development.