The Economics of AI: Rapid Growth, Falling Prices, and OpenAI’s Strategic Shift

OpenAI has clarified its financial targets for investors, and the new data turned out to be noticeably more modest than those previously discussed in the market. The company’s projected annual revenue as of the end of September was about $50 billion, not $70 billion, as some sources had suggested at the end of last month. Nevertheless, this still represents nearly 70% growth compared to July, when the figure was around $30 billion.

The reason for the discrepancy turned out to be methodological. For example, Anthropic includes sales through cloud partners like AWS and Google Cloud in the calculation of its projected annual revenue, whereas OpenAI does not take this component into account. Some investors previously tried to bring the metrics of the two companies to a single baseline, which led to inflated valuations. Now, OpenAI’s own data provides a more conservative picture, while at the same time making the business dynamics more transparent.

If the September level is maintained, the company is already operating on a scale comparable to the largest public technology businesses. At the same time, OpenAI expects to end the year with annual revenue of around $70 billion and to increase this figure to $350 billion by 2030.

The problem is that revenue growth is currently accompanied by even more aggressive growth in expenses. OpenAI remains unprofitable and, according to its own forecasts, will spend nearly $280 billion more than it earns by the end of the decade. Investors’ concerns regarding rising debt of tech companies and massive AI spending are already weighing on the market, moving Nasdaq index futures. This makes the company’s future valuation and OpenAI stock performance following its market debut dependent not only on the pace of customer base expansion, but also on its ability to gradually improve the economics of each unit of computing.

Here, one of the main risks for the entire industry emerges. The cost of working with AI is decreasing by approximately 50% each quarter and by a factor of 13 over the course of a year. For users, this accelerates the adoption of technologies, but for model developers, it creates constant pressure on prices.

A good example is the comparison of OpenAI o3 and GPT-5.6 Luna. The former showed a result of 75% in GPQA Diamond in early 2025, with a cost of about $0.30 per question. A year and a half later, a similar result could be achieved for approximately $0.0004. In fact, the consumer gets a comparable level of capabilities at a cost hundreds of times lower.

For OpenAI, this means that high revenue growth cannot automatically be translated into future profits. If models are rapidly becoming cheaper and competitors are able to offer similar quality for less money, the company is forced to constantly add new features, improve quality, and integrate more deeply into customers’ workflows.

That is precisely why the competition between OpenAI and Anthropic is gradually shifting from the quality of individual models to full-fledged product ecosystems. Anthropic, for example, has launched Claude Dashboards and Claude Motion for business users. The first tool allows you to create analytical dashboards based on connected corporate data, while the second enables you to create animations and visual content. OpenAI is moving in the same direction, adding charts, diagrams, and interactive elements to ChatGPT.

From a financial perspective, such features are especially important because they increase the cost of switching between providers. If AI becomes part of a company’s internal processes and is integrated with CRM, analytics, documents, and visualization, the client is no longer choosing just the model with the best performance in the benchmark, but an entire infrastructure.

Therefore, adjusting the revenue estimate from $70 billion to $50 billion does not change the main picture. OpenAI continues to grow exceptionally rapidly, but at the same time it operates in a market where technology is becoming cheaper at a record pace and competitive advantages are quickly losing value. In the coming years, the main question for investors will not be whether a company can increase its revenue to hundreds of billions of dollars, but whether it can make this growth sustainable and profitable amid the constant decline in the cost of AI itself.

The deeper issue is not the exact revenue number. It is what that number has to overcome.

OpenAI is not just competing with Anthropic or Google. It is competing with the fact that the underlying technology keeps getting dramatically cheaper. Every time inference costs drop by half, the company has to generate more usage, more features, and more lock-in just to stay in the same place financially. That is a brutal treadmill. High growth can hide the problem for a while, but it does not solve it. If customers can get 80% of the value for 10% of the price from a competitor six months from now, revenue growth alone will not protect margins.

This is why the product moves matter more than the model benchmarks. Once a company has wired Claude or ChatGPT into its reporting, its customer support, its internal tools, and its actual decision-making, switching becomes expensive even if a cheaper model appears. The model is no longer the product. The workflow is. That is the real race right now, and it is much harder to win than simply having the highest score on a public leaderboard.

For founders watching this from the outside, the lesson is practical. The companies that survive this phase of AI will not be the ones that merely grew the fastest. They will be the ones that made their product more painful to leave than it was to adopt. OpenAI still has the distribution and the brand to do that. Whether it can turn that advantage into durable economics, while costs keep collapsing underneath it, is the part the market has not priced yet.