Sam Altman, OpenAI’s chief executive officer, ignored recurring questioning about the company’s financials, noting that revenue now stands “well more” than $13 billion annually. Furthermore, he said he was tired of anxieties about how the AI leader will finance its ambitious infrastructure plans.
During a joint interview with Microsoft’s Satya Nadella on the BG2 podcast, Altman bluntly put the framing of the debate: demand for OpenAI’s offerings is growing, and investor appetite for the business’s stock is fierce. When grilled on the reports of OpenAI’s sales being around $13 billion, Altman responded that the number is “well more” than $13 billion and maintained a posture against financial concern trolling.
The CEO argued that even though critics were afraid of the expense of computing power, they would line up to buy shares if they had the option. He also expressed his excitement about a world in which doubters can go short, yet underlined that OpenAI has no plans to go public anytime soon.
Altman admitted that while execution risks continue to exist on the compute side, revenue growth is “very, very fast,” buoyed by expanding enterprise adoption and developer use through APIs. He added that a new line of programs is growing. Nadella echoed the idea, claiming that OpenAI has outperformed “every” business plan submitted to Microsoft, with Azure underpinning broad-scale training and deductions.
Funding anxieties, compute costs, and growth strategies
Altman’s frustration is underlined by the backdrop of the industry’s mounting capex bill. Training and serving frontier models necessitates vast clusters of top-flight GPUs, custom accelerators, and bespoke data centers.
While one report suggested OpenAI could be on the hook for an extraordinary sum in long-term compute commitments, Microsoft and other hyperscalers are each investing tens of billions in AI infrastructure a year; Nvidia’s data center revenue has soared amid demand for its H100 and the next-generation of its accelerators—which it cannot make fast enough; and AMD GPUs and custom silicon from rivals are flooding in to help ease bottlenecks. The financial issue is whether AI revenue expands quickly enough to account for that expenditure.
OpenAI’s model combines subscription products like ChatGPT Enterprise, usage-based API fees for developers, and platform economics via Azure’s OpenAI Service. This mixture provides multiple opportunities for expansion: bigger deployments of enterprise seats, higher average use per customer as solution circumstances migrate from the pilot to production, and new buckets like AI-enabled gadgets and tools for scientific discovery, both of which Altman addressed as potential future expansion trends.
External indicators support a path for further expansion. According to IDC, global generative AI sales are expected to reach 141 billion dollars by 2027, growing rapidly as corporations incorporate AI into customer support, marketing, software development, and analytics. In the meantime, cloud providers claim that AI-related workloads are growing, implying that the economics are improving as hardware efficiency, model structures, and orchestration software all mature.
Speculation on $100B revenue and any public listing
During the podcast, for example, investor Brad Gerstner aired a scenario where OpenAI achieves $100 billion in revenue by 2028 or 2029. Altman responded, “How about 2027” — a playful rebuff with serious undertones.
As of some of the most recent public reports, OpenAI has fetched valuations above $80 billion in secondary transactions. Investors appear to be banking on its ability to generate cash flows in the long run, even though its near-term cash burn is high. Nonetheless, Altman attempted to downplay talks of an IPO by dismissing the claims that there is a board decision or any announced date.
Assuming the company goes public, the public market will have to assess the revenue from direct bookings and that from Azure, the gross margins on inference at scale, and the pace of model improvements critical to enabling more valuable workloads without generating spiraling costs.
Three factors that could define OpenAI’s growth path
- Availability of compute: the balance between supply and demand in GPUs and next-gen accelerators will impact costs and reliability.
- Efficiency: market developments in model compression, routing, and caching could improve margins and open up lower-cost, higher-throughput tiers.
- Product breadth: if OpenAI’s progress in “AI cloud,” gadgetry, and science automation results in high-usage sectors, the revenue curve may flatten, justifying the infrastructure investment.
For now, the most important factor is that OpenAI has made its position clear. This is probably the most important message that the company has broadcast within the past few years.
The company is competitive if it can outmatch its competitors, which is why it is so successful. The message that OpenAI is passing is that it is able to outdo its critics. How it does this will determine whether Altman was right when he said that it is enough to stop AI.