The company announced on Monday that it had submitted a confidential S-1 filing with the U.S. Securities and Exchange Commission (SEC), but said it had not yet decided when to go public.

In a statement, OpenAI said: “We recently submitted a confidential S-1. We expect it to leak so we’re just announcing it. We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company. But it’s a complicated set of tradeoffs and this gives us the option to go public sooner if that ends up being best.”

The filing places OpenAI alongside competing AI developers Anthropic and Perplexity, which also submitted confidential IPO documents last week and this week.

Meanwhile, Elon Musk’s SpaceX is preparing for its own stock market debut, setting up what could become one of the biggest waves of technology listings in recent years.

Although OpenAI did not disclose the size of the proposed offering or a timeline for the listing, this development will boost the company’s growth since the launch of ChatGPT in 2022.

OpenAI has grown into one of the world’s most valuable private firms, with latest valuation standing at about $852 billion, while Anthropic was recently valued at nearly $965 billion following a new funding round.

According to reports, OpenAI has been working with investment banks Goldman Sachs and Morgan Stanley on the IPO process.

The company is also expected to organise a tender offer that would allow employees to sell some of their shares, providing liquidity ahead of any public listing.

OpenAI, however, is still facing challenges to justify its valuation as it spends heavily on computing power and infrastructure needed to train and operate more advanced AI models.

The company has reportedly raised more than $180 billion from investors but is still in a phase of significant spending.

Chief Executive Officer Sam Altman said in a recent blog post that the company is entering what he described as its “third phase”.

According to Altman, OpenAI first focused on research into artificial general intelligence before becoming a product company through services such as ChatGPT.

“Now we are entering the third phase,” Altman wrote. “The economy is beginning to reshape around AI. The central question now is how to make advanced AI abundant, affordable, safe, useful, and easy enough for every person and organization to benefit from it.”

The planned IPO arrives shortly after a case involving OpenAI and Musk. An advisory jury recently ruled against claims brought by Musk, who had accused OpenAI and Altman of abandoning commitments related to the company’s original non-profit mission. A federal judge subsequently adopted the jury’s verdict.

The number of AI companies preparing for public listings is also drawing attention across the wider industry.

AI search startup Aravind Srinivas said Perplexity still intends to pursue an IPO in 2028 regardless of how the listings of OpenAI and Anthropic perform.

“Agnostic of these two companies, we were planning for something in 2028, so that still remains the case,” Srinivas told CNBC.

He acknowledged that investor reaction to upcoming AI listings could influence market sentiment, particularly the reception to SpaceX’s offering.

“I certainly think there will be ripple effects if they don’t go well, like there is no sugar coating on that. The SpaceX IPO this week will definitely be a leading indicator of how Anthropic or OpenAI will go out,” he said.

Srinivas added that strong public market performances would benefit the AI sector.

“I think it’s important for the AI industry that these IPOs go well, and I actually think they will go well, because they’re doing well.”

The agreement, which runs through 2028, is designed to speed up how ChatGPT and other OpenAI products respond to users, as competition gets stronger among the world’s largest technology firms to control the infrastructure behind advanced models. 

Cerebras will provide the capacity through its own cloud services, powered by its wafer-scale chips, with new data centres to be built or leased specifically for the contract.

Seven hundred and fifty megawatts is not incremental capacity, it shows industrial-level demand. 

OpenAI is no longer thinking only about training models but about inference, the everyday work of answering questions, reasoning through problems and serving millions of users at once. 

“Integrating Cerebras into our mix of compute solutions is all about making our AI respond much faster,” OpenAI stated. 

Talks between the two companies began last August after Cerebras showed that OpenAI’s open-source models could run more efficiently on its chips than on standard graphics processors. 

Months of negotiations followed. The result is a structure where Cerebras owns and operates the hardware, while OpenAI pays to access it as a service. Capacity will be added in stages over the next few years.

Cerebras is preparing to return to public markets after withdrawing its previous listing attempt in late 2024. It now plans to re-file for an initial public offering in the second quarter of 2026. This OpenAI contract helps address a long-standing concern among investors: dependence on a single customer. 

In 2024, UAE-based technology group G42 accounted for nearly 87% of Cerebras’ revenue. A multi-billion-dollar US client changes that picture.

The deal also fits neatly into OpenAI’s bigger strategy. The company is laying the foundation for a potential initial public offering that could value it at around $1 trillion. 

Its chief executive, Sam Altman, has publicly committed $1.4 trillion to building 30 gigawatts of computing capacity, enough to power roughly 25 million homes in the United States. 

Competition is high. Nvidia still tops the market for training chips, while Google and Microsoft are pushing their own custom hardware through cloud platforms. 

By turning to Cerebras, OpenAI is clearly trying to reduce reliance on a single supplier and gain an edge in speed, particularly for reasoning-heavy models.

But then, the spending spree is unsettling some observers, with warnings that the surge in valuations and capital commitments across the sector shows the excesses of the dot-com era. 

The counter-argument is equally strong, demand for inference compute is exploding as artificial intelligence moves from labs into daily use.