Most people belief Powell knows. Does he know there's no AI bubble?
When asked whether the current boom in artificial intelligence resembles the dot-com bubble, Federal Reserve Chair Jerome Powell offered a clear distinction.
“Yeah, this is different in the sense that these companies, the companies that are so highly valued actually have earnings and stuff like that.”
Powell avoided naming any specific companies, but the message was unmistakable. The leaders of today’s AI wave are profitable, well-capitalized, and generating real cash flow.
AI spending and profitability: the facts behind Powell’s comment
Powell did not literally say that “AI spending isn’t a bubble,” but he highlighted a major contrast with the 1990s. The companies driving AI today — Microsoft, Alphabet, Amazon, Nvidia, and Meta — are producing strong earnings while scaling up investment in AI infrastructure.
Microsoft earned more than 27 billion dollars in quarterly profit, directing a large share of spending toward data centers that power its AI business.
Alphabet posted nearly 35 billion dollars in profit last quarter and lifted its investment target to more than 90 billion dollars for 2025.
Amazon is on track to spend about 125 billion dollars this year, mostly to support AI and cloud capacity.
Nvidia generated over 26 billion dollars in quarterly profit as demand for AI chips surged.
Meta plans between 70 and 72 billion dollars in capital spending this year for its AI infrastructure.
This data supports Powell’s point. These are not speculative startups. They are high-margin businesses with measurable earnings and scale.
AI productivity and ROI: the bullish view
In a recent interview with Finance Magnates, I, Itai Levitan, Head of Strategy at investingLive, said that I believe AI is driving one of the largest productivity shifts in history. AI enables individuals, teams, and entire companies to achieve more with fewer resources. The productivity boost, in my view, is the most significant humanity has ever seen.
Studies reinforce this argument. Research on call-center agents found productivity gains above ten percent when workers used AI tools, especially among newer employees. Consulting firms such as McKinsey estimate that generative AI could add several trillion dollars to global output each year.
From a commercial standpoint, OpenAI is reportedly generating around 13 billion dollars in annual revenue, supported by an estimated 30 to 40 million paying users. Google’s Gemini app is now used by hundreds of millions of people worldwide. If Powell was referring to AI companies that “actually have earnings,” these are leading examples.
The other side of the debate: overspending and unclear returns on AI
While AI’s long-term promise is strong, critics argue that much of today’s AI spending may not yet produce consistent returns. Some analysts question whether the current capital intensity is sustainable.
Only a small percentage of companies report clear financial benefits from AI initiatives.
Microsoft, Amazon, and Alphabet together are investing more than 250 billion dollars in AI infrastructure this year, raising the hurdle for returns.
The cost of running and training large models is falling, which benefits users but could compress margins for AI service providers.
In short, the companies selling AI tools are earning, but many businesses adopting AI are still struggling to turn the technology into measurable profit.
Where AI spending shows visible returns and real ROI
Customer service automation that reduces resolution time and cost.
AI coding copilots that accelerate software development and improve quality.
Marketing and creative tools that boost output while cutting production expenses.
Enterprise search and analytics that help organizations make faster decisions.
These areas already show quantifiable productivity gains, stronger margins, and growing adoption.
Where AI spending looks excessive or uncertain
Large corporate deployments without clear business goals or data strategy.
Consumer AI products that rely on early adopters but struggle with monetization.
High infrastructure costs, power demand, and data center shortages that slow scaling.
These risks remind investors that not every AI project will deliver the expected ROI.
investingLive perspective: separating real earnings from hype
Powell’s observation is mostly accurate, in my opinion. The major AI firms truly have earnings, and their business models are far more sustainable than the speculative ventures of the dot-com era. However, selective overheating is still possible, especially in the less proven layers of the AI ecosystem.
For traders and investors, the challenge is to distinguish AI spending that creates immediate value from AI spending that depends on future returns. Chipmakers, cloud providers, and infrastructure suppliers are already monetizing their investments. Smaller application developers may still need time to prove their economics.
Timing still matters, even when the long-term trend is clear
This article, and even Powell’s comment, does not mean investors should now rush to buy Nvidia or any other AI stock at current levels. As I have mentioned before, round numbers such as 200 dollars per share in Nvidia tend to attract special attention. Market makers often use these levels for liquidity hunts, triggering stop orders on both sides.
Long-term investors also treat these round numbers as natural decision points — some take profits there, while others wait for a pullback before adding more. These psychological levels, such as 200, 150, or 100, are often more significant than arbitrary ones like 173 or 237.
So while the article argues that there is likely no broad AI bubble, that does not automatically make this price level a good entry point. Timing still matters, even for long-term investors. Nvidia could just as easily pull back to around 194 dollars or even 187 dollars, based on current chart structure.
Therefore, this is not a buy or sell signal. It is a reminder that while long-term trends and fundamentals are essential, entry timing can make a major difference in real portfolio results.
Have your say in the comments below: is AI spending justified or turning into a bubble?
Do you believe there is an AI bubble forming, or are these investments justified by long-term productivity and ROI?
Please share your thoughts in the comments below and tell us why. Your reasoning matters.
This discussion around AI spending, timing, and profitability is evolving fast, and your perspective will help shape it here on investingLive.
