Can AI companies become profitable?

Source: Epoch AI (Jaime Sevilla, Hannah Petrovic, Anson Ho) · 2026-01-28

Are AI models profitable? If you ask Sam Altman and Dario Amodei, the answer seems to be yes — it just doesn’t appear that way on the surface.

Here’s the idea: running each AI model generates enough revenue to cover its own R&D costs. But that surplus gets outweighed by the costs of developing the next big model. So, despite making money on each model, companies can lose money each year.

This is big if true. In fast-growing tech sectors, investors typically accept losses today in exchange for big profits down the line. So if AI models are already covering their own costs, that would paint a healthy financial outlook for AI companies.

But we can’t take Altman and Amodei at their word — you’d expect CEOs to paint a rosy picture of their company’s finances. And even if they’re right, we don’t know just how profitable models are.

To shed light on this, we looked into a notable case study: using public reporting on OpenAI’s finances,1 we made an educated guess on the profits from running GPT-5, and whether that was enough to recoup its R&D costs. Here’s what we found:

Whether GPT-5 was profitable to run depends on which profit margin you’re talking about. If we subtract the cost of compute from revenue to calculate the gross margin (on an accounting basis),2 it seems to be about 30% — lower than the norm for software companies (where 60-80% is typical) but still higher than many industries.

But if you also subtract other operating costs, including salaries and marketing, then OpenAI just barely broke even, even without including R&D. And they likely ended up at a loss after accounting for their revenue-sharing deal with Microsoft.

Moreover, OpenAI likely failed to recoup the costs of developing GPT-5 during its 4-month lifetime. Even using gross profit, GPT-5’s tenure was too short to bring in enough revenue to offset OpenAI’s R&D costs in the four months prior to launch. So if GPT-5 is at all representative, then at least for now, developing and running AI models is loss-making.

Inference compute: $4 billion.

Staff compensation: $1 billion, which we can back out from OpenAI staff counts, reports on stock compensation, and things like H1B filings. One big uncertainty with this: how much of the stock compensation goes toward running models, rather than R&D? We assume 40%, matching the fraction of compute that goes to inference.

Sales and marketing (S&M): $0.5 billion. This would include expenses such as their SuperBowl ad and business sale campaigns, promotions and discounts, and other related expenses.

Legal, office, and administrative costs: $0.2 billion, assuming this grew between 1.6× and 2× relative to their 2024 expenses. This accounts for office expansions, new office setups, and rising administrative costs with their growing workforce.

So what are the profits? One option is to look at gross profits. This only considers the direct cost of running a model, which in this case is just the inference compute cost of $4 billion. Since the revenue was $6 billion, this leads to a profit of around $2 billion, or gross profit margin of around 30%.15 This is lower than other software businesses (typically 70-80%) but high enough to eventually build a business on.

On the other hand, if we add up all four cost types, we get close to $6 billion.

And there’s one more hiccup: OpenAI signed a deal with Microsoft to hand over about 20% of their $6 billion revenue,18 making them end up at a loss.19 This doesn’t mean that the revenue deal is entirely harmful to OpenAI — for example, Microsoft also shares revenue back to OpenAI.20 And the deal probably shouldn’t significantly affect how we see model profitability — it seems more to do with OpenAI’s economic structure rather than something fundamental to AI models. But the fact that OpenAI and Microsoft have been renegotiating this deal suggests it’s a real drag on OpenAI’s path to profitability.

In short, running AI models is likely profitable in the sense of having decent gross margins. But OpenAI’s operating margin, which includes marketing and staffing, is likely close to zero. For a fast-growing company, though, operating margins can be misleading — S&M costs typically grow sublinearly with revenue, so gross margins are arguably a better proxy for long-run profitability.

Let’s say we buy the argument that we should look at gross margins. On those terms, it was profitable to run the GPT-5 bundle. But was it profitable enough to recoup the costs of developing it?

So to figure out the answer, let’s go back to the GPT-5 bundle. We’ve already figured out its gross profits to be around $2 billion. So how do these compare to its R&D costs?

So in practice, it seems like model tenures might indeed be too short to recoup R&D costs. Indeed, GPT-5’s short tenure was driven by external competition — Gemini 3 Pro had arguably surpassed the GPT-5 base model within three months.

So the finances of the GPT-5 bundle are less rosy than Altman and Amodei suggest. And while we don’t have as much direct evidence on other models from other labs, they’re plausibly in a similar boat — for instance, Anthropic has reported similar gross margins to OpenAI. So it’s worth thinking about what it means if the GPT-5 bundle is at all representative of other models.

The most crucial point is that these model lifecycle losses aren’t necessarily cause for alarm. AI models don’t need to be profitable today, as long as companies can convince investors that they will be in the future. That’s standard for fast-growing tech companies.

Early on, investors value growth over profit, believing that once a company has captured the market, they’ll eventually figure out how to make it profitable. The archetypal example of this is Uber — they accumulated a $32.5 billion deficit over 14 years of net losses, before their first profitable year in 2023. By that measure, OpenAI is thriving: revenues are tripling annually, and projections show continued growth.