Editing Openai/69408f2e-3acc-800c-a0c5-7ab1f3f9f65c (section)

=== Assistant: Sure. ===
Sure. I’ll restate the entire argument in plain language, as one continuous explanation, keeping all the substance but removing the step-by-step scaffolding and bullet structure.

To answer the question about whether OpenAI would be profitable right now, you have to be very precise about what you mean by “right now.” The only way to do that is to imagine a hypothetical scenario where OpenAI stops expanding entirely. No new data centers, no new massive training runs, no speculative research meant for future models, no pre-buying of compute for growth that hasn’t happened yet. In that scenario, you are only looking at the business as it exists today: the money coming in from users and customers, and the money required to keep the existing services running at their current level.

Under that framing, you are essentially asking what OpenAI’s profit-and-loss statement would look like if it froze time and just operated what it already has. That means counting subscription revenue from ChatGPT users, usage-based revenue from the API, and enterprise or licensing revenue from companies that pay for customized or large-scale access. On the cost side, you only count the expenses necessary to serve those users: the GPUs that run inference, the electricity and cooling, the networking, the engineers and operators who keep the systems up, and the basic overhead required to run a large tech company. You explicitly do not count spending whose purpose is to create future capabilities or future capacity.

When you look at revenue through that lens, you can reasonably estimate that OpenAI is currently bringing in on the order of several billion dollars per year from consumers alone. Even if only a minority of users pay, subscription prices in the tens of dollars per month add up very quickly at global scale. On top of that, the API business contributes billions more, because large companies and developers generate enormous token volumes and pay usage fees continuously. Finally, enterprise and licensing deals add another meaningful chunk, even if they are fewer in number, because they tend to be high-value contracts. Put together, a conservative estimate for current annual revenue lands somewhere in the high single-digit billions, with a midpoint around eight or nine billion dollars per year.

On the cost side, the dominant expense is running inference at scale. That means paying for GPUs, power, cooling, networking, and redundancy across multiple regions. Even at today’s prices, serving global demand at OpenAI’s scale plausibly costs several billion dollars per year. Beyond that, there are salaries for engineers, researchers, site-reliability teams, security, support, and management. Even with very high compensation, this is still smaller than compute costs, likely on the order of one to one and a half billion dollars annually. Then there is general operational overhead: storage, monitoring, compliance, legal costs, payment processing, and other background expenses, which together add perhaps another half to three-quarters of a billion per year. If you sum only those running costs, you end up with total annual operating expenses in the neighborhood of seven billion dollars.

If you then compare those running costs to the revenue estimate, you get a surplus. Using conservative midpoints, revenue of about eight and a half billion dollars minus operating costs of about six and a half to seven billion dollars implies something like one to two billion dollars per year in operating profit in this frozen-expansion scenario. Depending on how optimistic or pessimistic your assumptions are about revenue growth and inference efficiency, that profit could plausibly be lower, near break-even, or higher, perhaps a few billion dollars per year. But the key point is that it is very reasonable to believe the core business clears its own costs.

This is where confusion arises, because that conclusion does not contradict the fact that OpenAI reports very large losses. Those losses come from spending that is deliberately excluded in this thought experiment: training new frontier models that do not yet generate revenue, hiring ahead of need, buying or reserving massive amounts of compute capacity for future demand, and paying large amounts of stock-based compensation as part of aggressive growth. All of that spending is real, and under normal accounting rules it counts as current expense, which is why the company appears deeply unprofitable on paper.

So the simplified argument is this: if you isolate the present-day business and ask whether it can pay for itself, the answer is likely yes. If you look at the company as a whole, including its attempt to build the next generation of AI infrastructure years in advance, the answer is no, and the losses are enormous. Both statements can be true at the same time. The current business is probably economically viable, while the company is intentionally cash-hungry because it is trying to build something much larger than what exists today.