OpenAI’s trillion-dollar AI bet is a study in ‘riskmaxxing’

As successful as OpenAI has been since the launch of ChatGPT, the company is operating in an extraordinarily expensive and risky corner of tech, building frontier AI models at massive scale. Its future, even its survival, is far from certain.

OpenAI is burning billions on top-tier AI research talent, carefully curated training data, and increasingly scarce computing power. Footing the bill is a growing cap table of VC and strategic partners, all betting on outsize returns within a few years.

Compute is the biggest cost. AI companies must lock in capacity years—not months—in advance. Data centers take years to build and bring online. That forces companies to forecast demand far ahead, then scramble to generate enough revenue to cover those commitments. If they underestimate demand, they leave revenue on the table. If they overestimate, the consequences can be existential.

OpenAI’s rival, Anthropic, must make a similarly precarious bet, but has bet more conservatively. Anthropic CEO Dario Amodei described the challenge on a recent podcast with Dwarkesh Patel:  “The curve I’m looking at is: We’ve had a 10x-a-year increase every year. At the beginning of this year, we’re looking at $10 billion in annualized revenue. . . . I could assume that the revenue will continue growing 10x a year [but] I can’t buy $1 trillion a year of compute in 2027. If I’m just off by a year in that rate of growth, or if the growth rate is 5x a year instead of 10x a year, then you go bankrupt.”

OpenAI, by contrast, is playing a riskier game. The company has committed more than $1 trillion to building new data centers and leasing compute from an array of partners, including Amazon Web Services, CoreWeave, MGX, Microsoft, Nvidia, Oracle, and Arm. Oracle alone locked in a $300 billion, five-year data center partnership with minimum commitments running at about $60 billion per year by 2027, according to a PitchBook analysis. OpenAI has also contracted roughly $250 billion in compute from Microsoft, and pays about $5 billion annually back to Microsoft through its Microsoft Azure revenue share, PitchBook estimates.

All of this spending hinges on how quickly OpenAI’s revenue grows. The company is generating about $25 billion in annualized revenue, according to PitchBook, a roughly 40-to-1 ratio of obligations to current revenue. If it misses key growth targets, it may struggle to cover its compute and data center bills.

The Wall Street Journal reported last week that OpenAI missed internal revenue and user targets in early 2026, with CFO Sarah Friar privately warning leaders that the company may not be able to fund its future computing contracts if growth slows. OpenAI did not dispute the reporting. Instead, CEO Sam Altman and Friar said in a joint statement that they are “totally aligned on buying as much compute as we can.”

And boy, are they. By PitchBook senior private company analyst Harrison Rolfes’s estimate, OpenAI’s cash losses could mount to nearly $74 billion in fiscal year 2028 before it has any realistic path to breaking even by 2030.

“The Wall Street Journal’s reporting that OpenAI missed multiple monthly revenue targets this year after losing enterprise and coding share to Anthropic and Gemini is exactly the scenario that makes this math dangerous,” Rolfes tells Fast Company. “Every revenue miss compounds against a fixed obligation ladder that doesn’t flex.” 

If OpenAI had locked down a product that no one else could replicate, or one far ahead of competitors, it might have a defensible moat. That could help offset the risks of such aggressive expansion. However, many analysts see that moat as somewhat limited.

“It has become clear that frontier models are rapidly commoditizing. DeepSeek repeatedly makes this clear,” says Columbia Business School professor Daniel Keum. “Switching costs are minimal. The main exceptions are firms like Google and Microsoft, which can embed AI into existing ecosystems that are very difficult to replace, such as Gmail, Google Calendar, and Microsoft Office.” 

OpenAI may have gotten an early lead in a market growing at an “exponential 10x” clip, Keum adds, but it hasn’t built much differentiation or strong lock-in, particularly with consumers. Anthropic, by contrast, could prove more durable given its focus on enterprise customers, where switching costs tend to be higher.

And yet OpenAI recently raised $122 billion at an $852 billion valuation, suggesting investors still believe in a relatively fast “AI takeoff,” where businesses broadly integrate AI into their operations. That anticipated shift is what OpenAI and its peers are spending so heavily to prepare for.

“The risk to OpenAI isn’t sudden collapse,” Rolfes tells Fast Company, “but more that because the obligation stack is this large and this locked in, every revenue miss shrinks your options faster than most people appreciate.​​​​​​​​​​​​​​​​”

Both OpenAI and Anthropic are expected to go public in the near future, which will offer a clearer view into their financial risk. For now, investor sentiment appears to be tilting toward Anthropic’s more measured approach. In early April, reports appeared that people were shunning OpenAI shares on special market investor sites and flocking to Anthropic shares. Does OpenAI know something about the “AI takeoff” that investors don’t? 

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