The AI industry's most awkward moment is here: revenue keeps climbing, so why doesn't that necessarily mean...

The AI industry is putting on a remarkable business performance.
On one side, revenue is growing rapidly, enterprise customers are multiplying, and the user base keeps expanding; on the other, GPU purchases, data center construction, power consumption, and R&D spending are all increasing.
It looks like a company whose business is getting better and better, but a look at its financial statements reveals that making money is far from as simple as it seems.
Why?
Because AI has one important difference from traditional software: AI services often require ongoing computing resources.
Every time a user makes a complex request, it may involve model inference and infrastructure costs behind the scenes. Especially in compute-intensive inference scenarios, revenue growth doesn't mean costs can be ignored.
This raises a key question: can AI companies gradually reduce the cost of each service through model optimization, hardware upgrades, economies of scale, and product pricing?
I believe this will become the core competitive advantage of AI companies in the future.
The market won't pay for grand visions forever.
Revenue growth can excite investors for a while, but profit margins and free cash flow are what let them sleep soundly.
After all, no matter how smart AI is, it can't pay the company's electricity bill automatically.$MSFT

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