- Tether CEO Paolo Ardoino warned that Big Tech’s AI spending boom faces four economic risks that could affect long-term profitability.
- He said pricing, hardware costs, delayed returns, and rising competition may challenge future AI growth.
AI has become the biggest investment theme in the technology sector, with companies committing hundreds of billions of dollars to expand computing power and data center capacity. However, Tether CEO Paolo Ardoino believes the rapid pace of spending could face serious economic challenges if current trends continue.
In a recent post on X, Ardoino outlined four structural issues that could weaken the long-term outlook for AI infrastructure. His comments come as investors increasingly debate whether the massive investments in AI will generate enough returns to justify the costs.
AI big tech subsidizes compute to increase user count building expensive infrastructure / capex subject to fast decay (3/5 years).
– Token price mismatch.
– Profitability timeline mismatch.
– Cost of capital maturity mismatch.
– Open-source AI taking growing chunks of revenues.…— Paolo Ardoino 🤖 (@paoloardoino) July 4, 2026
Big Tech May Be Charging Too Little for AI Services
According to Ardoino, one of the biggest concerns is pricing.
Many technology companies are offering AI services at prices that may not reflect the actual cost of providing them. Low prices help attract users and grow market share, but they can also hide the true economics of the business.
If companies later increase prices to improve profitability, demand could slow. On the other hand, keeping prices low could continue to squeeze profit margins, making it harder to recover the huge investments made in AI infrastructure.
Heavy AI Spending Could Delay Profits
Another concern is the gap between spending and future returns.
Major technology firms continue to invest heavily in data centers, graphics processing units (GPUs), and power infrastructure. These projects require enormous upfront capital, while the financial benefits may take years to materialize.
As that gap widens, investors may begin asking whether AI can become a reliable source of long-term revenue rather than simply driving short-term excitement.
AI Hardware Becomes Obsolete Faster Than It Pays for Itself
Ardoino also pointed to the short lifespan of AI hardware.
Modern AI chips typically remain competitive for only three to five years before newer, more powerful models replace them. However, many companies finance these investments with debt or long-term capital structures that assume much longer repayment periods.
The dot com bubble and the AI trade look similar.
The difference is that this time the earnings are showing up, compressing valuations as investors disagree on just how impactful AI will be in our lives and work.
Here are 5 dirt cheap AI stocks you can buy in 2026.
1. Nvidia -… pic.twitter.com/6RB8mouPyh
— Michael Sikand (@michaelsikand) July 2, 2026
This creates a mismatch between how quickly equipment loses value and how long companies expect it to generate returns. If demand weakens or AI prices fall, recovering those investments could become more difficult.
Open-Source AI Could Increase Pressure on Commercial Providers
Competition represents another major challenge. Open-source AI models continue to improve at a rapid pace. As free or low-cost alternatives become more capable, businesses may become less willing to pay premium prices for commercial AI platforms.
Lower pricing power would make it more difficult for AI companies to recover infrastructure costs and meet the high revenue expectations that currently support market valuations.
Bubble Concerns Continue to Grow
Ardoino’s warning reflects a broader discussion taking place across financial markets.
Chinese hedge funds, including Wealspring Asset and Shanghai Banxia Investment Management Center, have argued that global AI stocks may be approaching bubble territory. Wealspring described the current rally as a “super bubble,” while Banxia suggested that conditions for a market correction may already be emerging.
The concern is that AI has become one of the biggest drivers of stock market gains. If investors begin questioning the profitability of AI investments, the effects could extend beyond technology companies into wider financial markets.
AI Investment Remains Massive Despite the Risks
Despite these concerns, forecasts for AI spending continue to climb. JPMorgan estimates that global AI-related investment could reach $5.5 trillion by 2030. Meanwhile, Alphabet, Amazon, Meta, and Microsoft are expected to spend as much as $720 billion this year on AI infrastructure.
Morgan Stanley also projects that nearly $3 trillion in AI infrastructure investment could flow through the global economy by 2028.
Supporters of the sector argue that today’s AI boom differs from the dot-com era because the companies leading these investments already generate strong earnings and operate well-established businesses.
Still, Ardoino believes investors should pay close attention to the underlying economics. If AI pricing remains weak, profits take longer to arrive, hardware continues to age rapidly, and competition intensifies, the industry’s long-term returns may fall short of current expectations.
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