$OPENAI

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OpenAI has grown its quarterly revenue to $10 billion—a 50% increase on a quarterly basis—with corporate revenue doubling. At first glance, one might conclude that artificial intelligence is finally becoming a truly profitable business.

However, the story changes when you add one more line to the picture: the company’s operating loss for the same quarter stands at approximately $10.9 billion. In other words, for every dollar earned, more than a dollar has been spent. This isn’t growth; it is capital being "burned" to fuel growth—and the two are not the same. While revenue is easy to spot, identifying losses requires a closer look. When you place both side by side, the question becomes clear: when will this growth translate into profit?

To understand this landscape, one must look at the broader macroeconomic context. In an environment of high interest rates, the era of "free money" has come to an end. The cost of capital has risen, and investors are no longer looking solely for a growth narrative; they are now demanding profitability and positive cash flow.

The primary drivers of OpenAI’s losses are computational costs and R&D. Leasing chips, operating data centers, and training models are expensive endeavors. This situation is reminiscent of the dot-com infrastructure investments of the early 2000s. While the internet revolution was real, billions were poured into infrastructure, yet many companies went bankrupt. The fact that the revolution itself was genuine did not guarantee the survival of every firm. Back then, too, both revenue and losses were growing; ultimately, those that survived were the ones that managed their costs effectively.

This is because, in the realm of artificial intelligence, as the scale increases, so do the costs. A larger model requires more chips and consumes more energy. As enterprise demand doubles, revenue grows—but the demand for infrastructure rises just as rapidly. Consequently, as a company expands, it may find itself sinking deeper into losses rather than moving toward profitability.

The critical question here is: will computing costs decrease? If chip efficiency improves, next-generation hardware is deployed, and the same tasks can be performed more affordably, losses could shrink rapidly. However, if costs remain high, securing capital will become increasingly difficult. In that scenario, we might see industry consolidation—mergers and acquisitions—where smaller players are absorbed by larger ones, or major firms acquire each other.

The first scenario is optimistic: computing costs fall, enterprise demand continues to surge, losses narrow quickly, and the narrative shifts toward profitability. Should this unfold, a new chapter would begin for companies like OpenAI, where growth is financed by earned revenue rather than by burning through capital.

The second scenario is more cautious: costs remain high, capital stays expensive, and investors grow impatient. In this case, companies would be forced to raise prices, cut expenditures, or pursue mergers—all of which would increase volatility within the sector. A single quarter cannot reveal which path will be taken; one must observe the trends in costs and revenue over several consecutive quarters.

As for where to look from an investment perspective, an old adage provides the answer: during a gold rush, it’s the shovel sellers who make the money. While model developers offer high risk alongside high potential, infrastructure and hardware providers are the ones generating actual cash flow today.

In short, companies like OpenAI are writing the story of the future, but for now, they are burning through cash. The hardware and cloud segments are currently generating profit. While both are parts of the same chain, they carry different risks. Focusing on one while ignoring the other leaves an incomplete picture. One must examine the entire value chain—models, chips, cloud infrastructure, software, and enterprise applications—to distinguish which links are generating profit and which are burning through capital.

The artificial intelligence trend represents a long-term macro-transformation; that much is clear. However, at the individual company level, it is crucial to monitor the "burn rate" (the speed at which a company consumes its cash). It is easy to get excited by revenue figures, but it takes a steady hand to look at the burn rate—yet, in the current climate, it is this second perspective that serves as a safeguard.

In practical terms, this means you should not fixate on a single company. A company generating $10 billion in revenue won’t save the entire sector, just as a company posting a $10.9 billion loss won’t bring about its collapse. Look at the broader chain, maintain a measured approach, and manage your risks. Do not conflate a growth narrative with a profitability narrative. If the two ever converge, the story will truly change. Until that day arrives, one must balance enthusiasm with caution.

This content is for informational purposes only and does not constitute investment advice.

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