A company spends $100 million developing technology. If the cost is expensed today, current profit falls sharply. If qualifying costs are capitalized, part of the expense may instead reach the income statement over future periods. The cash expenditure is real either way. What changes is when the cost affects reported earnings. Investor Takeaway: Capitalization does
If high interest rates are supposed to slow the economy, why can consumers keep spending, businesses keep investing, and economic growth remain positive? The apparent contradiction comes from treating the policy rate as if it were the interest rate everyone pays today. It is not. A homeowner with a low fixed-rate mortgage, a company with
Artificial intelligence appears to have many ingredients that could produce powerful monopolies. Training frontier models requires enormous amounts of capital, computing infrastructure, specialized chips, electricity, data, and engineering talent. Big technology companies also enter the AI race with huge existing user bases, established cloud platforms, and powerful distribution networks. At first glance, the conclusion seems
Artificial intelligence is creating new revenue opportunities for Big Tech, but it is also changing the economics behind those revenues. AI services require expensive chips, data centers, networking equipment, electricity, cooling, and continuous infrastructure investment. That raises an important question for investors: Can Big Tech profit margins remain unusually high as AI usage scales? What
A company reports higher earnings. Revenue is growing, margins look healthy, and net income reaches a new high. Then you open the cash flow statement and find something unexpected: free cash flow has fallen sharply. Is that a warning sign? Not necessarily. Free cash flow vs earnings is one of the most useful financial statement
For much of the AI boom, the investment story has centered on GPUs. That makes sense. Advanced AI models require enormous amounts of computing power, and GPUs have become one of the most visible bottlenecks in the AI infrastructure buildout. But as more GPUs enter data centers, another constraint is becoming increasingly important: electricity. The
