Post by : Saif
Money is pouring into artificial intelligence at a pace rarely seen in the history of technology. Companies are building data centres, buying advanced chips and developing increasingly powerful AI models, but economists are questioning whether future profits will arrive quickly enough to support the enormous investment.
Global spending on data centres alone could exceed $30 trillion by 2050, according to a PwC projection cited by Reuters. That figure highlights the scale of the infrastructure being built to support AI systems.
Anthropic, one of the major companies in the sector, plans to spend about $518 billion in the coming years, according to its IPO prospectus. The amount is more than 100 times the company's 2025 revenue.
Technology companies are investing heavily in computing power because advanced AI models require huge amounts of data-centre capacity, electricity and specialised chips.
US technology giants including Google, Amazon and Microsoft are among the companies expanding infrastructure around the world. Other firms are also spending heavily as they compete to develop AI products and services.
Bain & Company has estimated that existing markets may not generate enough additional revenue to support the current level of investment. Its analysis suggests that new markets and applications could be needed to close the funding gap.
The study estimated that major technology companies involved in AI infrastructure could need more than $4.2 trillion in additional revenue over the next five years to support the planned expansion.
One of the biggest questions surrounding the AI boom is whether the technology will increase productivity quickly enough.
JPMorgan has said broad productivity gains in the United States remain difficult to see so far. That raises questions about whether current AI company valuations and infrastructure spending can be maintained.
Economists point out that earlier technological revolutions also took years to produce their full economic effects.
Railways changed transportation and trade, while the internet transformed communication and business. However, their impact on overall productivity developed over long periods rather than immediately after large investments began.
Economist Diane Coyle of Cambridge University said the productivity effects of major technologies have historically taken roughly 10 to 50 years to become fully visible.
The United States is at the centre of the current AI investment boom.
Columbia Business School economist Stijn Van Nieuwerburgh estimates that US AI-related investment could reach about $9 trillion between 2025 and 2032. That would equal roughly 3.2% of US economic output each year.
His calculations suggest the US AI sector could need about $3.55 trillion in annual revenue by 2032 to achieve a 10% return on investment.
Current AI-related revenue remains far below that level, creating pressure on companies to find new customers, applications and sources of income.
Read more: OpenAI to Launch First Applied AI Lab Outside US in Singapore
AI companies argue that the technology could eventually transform offices, research laboratories, manufacturing and many other industries.
AI systems could also be combined with robotics, advanced materials and other technologies to create markets that are difficult to predict today.
Bain has suggested that applications such as AI-powered robots and new materials for batteries and semiconductors could become part of this future expansion.
Anthropic has also modeled different scenarios for AI's effect on economic growth. Its analysis included modest, substantial and extreme levels of AI impact, with significantly different growth outcomes.
Questions about Artificial intelligence are not limited to company profits and economic growth. Researchers are also examining its effect on jobs.
Some studies in the United States and Britain have found weaker hiring among younger workers in jobs where AI can perform some tasks.
Stanford researchers reported in August that employment among US workers aged 22 to 25 in industries more exposed to AI was 19% lower than in less-exposed fields. Jobs such as accounting and paralegal work were among those examined.
Overall employment has remained strong, but economists continue to study whether AI could change hiring patterns as companies adopt the technology more widely.
Large AI infrastructure projects are also being financed through debt, adding another layer of risk.
JPMorgan has warned that even a moderate fall in demand, delays in projects or declines in asset values could create larger financial losses because of the leveraged structure used to fund some infrastructure.
Still, a slower return on AI investment would not necessarily make the technology worthless.
History shows that technology bubbles can collapse while useful infrastructure remains. Rail networks continued operating after the financial crisis of the 1870s, and the internet continued expanding after the dotcom bubble burst.
For AI, the key question is whether today's enormous spending creates enough infrastructure and useful technology to generate lasting economic benefits, even if those benefits take much longer to appear than investors currently expect.
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