OpenAI is gearing towards a decade that would not only transform the financial size of the organization but it would also establish a technological presence. The company is also aiming to reach about $600 billion of total compute spend by 2030, as Reuters reported, which is a very large amount, illustrating the high cost of deploying and developing sophisticated artificial intelligence systems on a global scale. The valuation comes at a time when the ChatGPT developer is preparing the groundwork of the possible initial public offering which may sell the company up to $1 trillion.
The magnitude of this intention can hardly be exaggerated. Compute spending is the huge sums of money needed to build data centers, high performance chips, cloud infrastructure, and the amounts of energy needed to train and execute large AI models. In contrast to the traditional software companies, AI companies exist in the world where the physical infrastructure is directly proportional to the growth. The further the models are developed the higher the computing power they require. The projected OpenAI is estimated at $600 billion by 2030, which is not only sign, but a change in the nature of growth of the capital intensive major technology firms.
The company is also seen to be gaining strength financially. The 2025 revenue of openAI was 13 billion, exceeding its 10 billion forecast, and it incurred 8 million expenses in the year, which is less than its 9 million forecast, the individual said. Being above the revenue estimate and below the expenditure estimate is a good indication that there is a good control on costs as the industry focuses on AI use cases that are typically perceived as cash burners. It also shows a high demand of the products of OpenAI in both consumer and enterprise markets.
This trend is in line with the general investor excitement. The Nvidia, the market leader in selling AI chips is also said to be near closing a 30 billion investment in OpenAI as part of a larger fundraising round that the startup is pursuing over 100 billion dollars. The round would place the Sam Altman led company at a valuation of about $830 billion, in case it is done, which would be among the largest private capital raises in the history of technology. These valuations are based on more than just the optimism about the present performance of OpenAI, but also the prospects of its strategic niche in the AI ecosystem.
Microsoft, which is a long-time and high-profile supporter, still contributes considerably to the growth of OpenAI. It has allowed OpenAI to access large cloud infrastructure via the Azure platform of Microsoft, which has enabled it to roll out models such as ChatGPT to businesses across the globe within a short period of time. OpenAI anticipates approximately over 280 billion in total revenue by 2030, with the consumer and business segments almost equal, according to CNBC. That balance is noteworthy. There are numerous technology companies that are unable to commercialize consumer attention and at the same time, service enterprise clients to scale. OpenAI seems to be constructing a two-engine business model, with B2C subscription offerings alongside B2B AI technology integrated into business operations.
These projections are backed by a more tragic truth. AI model operation does not cease at training. The inference or the process of using trained models to produce output on-the-fly has become costly. The Information stated that OpenAI informed investors that costs for operating its AI models also known as inference would double in 2025 leading to a drop in its adjusted gross margin to 33% as compared to 40% in 2024. This decline points to a fundamental conflict in AI economics. The cost of computational needs increases with the number of users and every query that a model is run has a real-world infrastructure cost.
Sam Altman has already talked of how big the infrastructure would be to support future AI systems. Altman had previously told the press last year that OpenAI has vowed to invest 1.4 trillion dollars in the creation of 30 gigawatts of computing power (sufficient to provide power to about 25 million households in the United States). Such an analogy of domestic energy consumption provides a graphic example of the consumption of electricity by AI. It also brings concerns that are more about the sustainability, grid capacity, and environmental responsibility. The increase in capabilities of AI models is making them a policy issue of concern regarding energy use as much as a business matter.
Regarding the industry, the estimated 600 billion calculate spend is indicative of a competitive race that is not limited to open AI. Large actors in the United States as well as worldwide are putting substantial investments in AI infrastructure. The high performance GPUs, custom silicon, advanced cooling are currently strategic resources as well as the renewable energy integration. In this regard, the investment plan of OpenAI is defensive and offensive. It is trying to ensure that it has a sufficient capacity to remain leading but it does not allow its competitors to surpass it in model capability.
Market expectations are also there. An IPO worth approximately $1 trillion would make OpenAI one of the richest technology companies in the history upon the time of listing. These valuations will require innovation as well as forecasted revenue growth and rising margins. Although the 2025 performance of the company in terms of revenue is favorable, the decrease in the gross margin ratio as a result of increasing inference costs might influence the investors to examine long run profitability trajectory. Profitability in scaling AI is thus one of the challenges that characterize this century.
At the human level, the figures may be abstract. However, to those people, who have already seen how AI tools are becoming more and more of a daily necessity, the scale becomes rational. Companies are digitizing business processes, programmers are creating new apps on top of base models and users are using AI assistants as part of their daily lives. Every single interaction is run by servers vibrating in big data centers. Add hundreds of millions of users and the financial commitment is more evident.
Nevertheless, the future is not clear. Massive compute expenditures may entrench the superiority of OpenAI, although it also makes it vulnerable to the changes in chip supply chains, regulatory, and energy policy. Shareholders can rejoice in daring projections, but they will need to find that signs of infrastructure investments paying off in long-term competitive advantage as opposed to increased costs of operations.
