OpenAI trails Anthropic on growth, reshaping the global AI market

OpenAI’s revenues increased by 18% in the second quarter to reach $6.7 billion, while Anthropic’s revenue doubled to more than $11.6 billion, according to the Wall Street Journal’s report on August 18.

The annualized revenue run rates demonstrate an even bigger difference in revenues between OpenAI and Anthropic. OpenAI’s revenue growth increased from about $20 billion by the end of 2025 to more than $40 billion, while Anthropic has jumped from around $9 billion in revenue to over $65 billion in late July.

Run rate is not the same as audited annual revenue, and private companies don’t publish their financials in a manner similar to public companies. However, investors are still interpreting the disparity as an indication of the movement of enterprise AI demand.

The implications reach beyond the two companies. The anticipated initial public offering of Anthropic may occur even before that of OpenAI, with investment bankers saying the first major AI IPO will help set a precedent for the entire industry, according to Cryptopolitan. This means OpenAI’s slower rate of development gives relevance to every AI company trying to justify its very high valuation.

Where the business money is actually going

The shift in the nature of business demand can be clearly seen in the valuations of the two companies by investors, with Anthropic’s last funding round resulting in a value of $965 billion, as compared to OpenAI’s $852 billion last value.

The gap could increase even more, once these firms become public, as Anthropic investors are reportedly considering the firm’s future at $2 trillion, which is a much larger figure than OpenAI’s target figure of up to $1 trillion. This allows to conclude that the value at which the companies are being traded demonstrates not only the recent growth of Anthropic but also the growing importance of the enterprise-demand gap.

The valuation gap can be better understood through Ramp’s AI Index for August 12. Anthropic succeeded in getting 43.5% of American companies to subscribe to AI services, a rise of 1.1%. OpenAI only grew by 0.23 points to get 39.7%, while xAI went up by 0.94 points to 4%.

Yet Anthropic’s newest model complicates the picture. Ramp economist Ara Kharazian called Fable 5 the best model to reach the market, but it represented only 6% of Anthropic tokens purchased by businesses in its first month and 11.4% of spending. OpenAI’s GPT-5.6 Sol accounted for 25% of its tokens and 23% of spend.

Fable 5’s cost is around $10 for every million tokens used; therefore, Fable 5 is twice as expensive as the competition. This means that effective and better performance does not automatically guarantee that companies will opt for its heavy use given the cost.

It should be also mentioned that Ramp points out that the data from models used in this case is much more technology-heavy than the general AI Index, so the outcomes obtained by Ramp must be interpreted more as partially illustrating the desire of corporations rather than as giving a complete overview of the situation.

The spending ceiling everyone is now watching

Data provided by Ramp indicates that the amount of money spent on corporate AI is still not the same for everyone. According to them, in July, the top 1% of businesses spent, on average, $7400 per employee on AI, the top 10% spent $650, while the median company spent only $11.95 per employee.

According to Kharazian, the future growth will be contingent upon the companies which are already spending a good amount of money. The companies which belong to that group also try to explore the possibility of open-source or low-price alternatives and run the risk of forcing the frontier laboratories to prove that their high price actually gives enough value to customers.

On May 11, Goldman Sachs also expressed the same worries. James Covello, who leads Global Equity Research, stated that even if semiconductor firms had reported their highest sales and profits, the majority of AI companies have not yet achieved significant profit from the sector. He regarded the situation as “unprecedented and unsustainable.”

On August 17, Gartner raised another alert, projecting that AI inference costs associated with agentic workflow will increase by more than five times by 2028. Its “Inference Paradox” explains that although tokens are getting cheaper, total costs are increasing because of the consumption of tokens by more sophisticated AI agents.

OpenAI continues to enjoy its existing advantage of scale. In April, the company indicated that ChatGPT had 900 million users per week, whereas as reported by Reuters in June, about 2 million enterprise clients of OpenAI were contributing to 40% of its revenues.

However, size by itself might not trigger the subsequent stage of competition in AI. The main focus is which corporation will be able to convert model capability into quantifiable business success at prices customers will be willing to pay. In case price discipline plays an important role in the industry, inexpensive models and platforms used for allocation of workloads among providers would gain along with frontier laboratories.