“Am I excited to be a public company CEO? 0%,” Altman said in an episode of the “Big Technology Podcast” published on Thursday. “Am I excited for OpenAI to be a public company? In some ways, I am, and in some ways I think it’d be really annoying.”

OpenAI is laying the groundwork for an IPO, with a Thursday report from The Wall Street Journal putting early talks of a valuation at $830 billion. In a more lofty estimate, the company could be valued at up to $1 trillion, Reuters reported in October, citing three sources. According to the Reuters report, chief financial officer Sarah Friar is eyeing a 2027 listing, with a potential IPO filing in late 2026.

Altman told “Big Technology” he didn’t know if his AI company would go public next year and was mum on details about fundraising, or the company’s valuation. OpenAI did not respond to Fortune’s request for comment.

Despite his hesitance to lead a public company—which are often under more scrutiny, greater regulatory oversight, and are associated with less influence from founders—OpenAI’s IPO wouldn’t be all bad, Altman noted. 

“I do think it’s cool that public markets get to participate in value creation,” he said. “And in some sense, we will be very late to go public if you look at any previous company. It’s wonderful to be a private company. We need lots of capital. We’re going to cross all of the shareholder limits and stuff at some point.”

An IPO would pave the way for OpenAI to raise the billions of dollars needed to compete in the AI race. Founded as a nonprofit in 2015, OpenAI just completed a complex restructuring in October that converted it into a more traditional for-profit company, giving the nonprofit controlling the company a $130 billion stake in it. The restructuring also gave Microsoft a reduced 27% stake in the company, as well as increased research access, while simultaneously freeing up OpenAI to make deals with other cloud-computing partners. 

More ‘code reds’ to come

OpenAI’s urgency to compete with rivals was apparent earlier this month when Altman declared a “code red” in an internal memo, following the surge of interest after Google rolled out its new Gemini 3 model in just one day, which the company said was the fastest deployment of a model into Google Search. Altman’s “code red” was an eight-week mandate to redouble OpenAI’s own efforts while temporarily postponing other initiatives, such as advertising and expanding e-commerce offerings.

The blitz appears to be paying off: Last week, OpenAI launched its new GPT-5.2 model, and earlier this week, it released a new image-generation model to compete with Google’s Nano Banana. Fidji Simo, OpenAI’s CEO of applications, said the update wasn’t in response to Google’s Gemini 3, but that the extra resources from the code red did help expedite its debut.

As OpenAI tries to address slowing user growth and retain and grow market share from its competitors, Altman conceded a code red will not be a one-off phenomenon. The all-out effort is a model that’s been employed by Google, and also Meta through Facebook’s more extreme “lockdown” periods. He downplayed the stakes of a code red, matching what sources told Fortune equated to a focused, but not panicked, office environment.

“I think that it’s good to be paranoid and act quickly when a potential competitive threat emerges,” Altman said. “This happened to us in the past. That happened earlier this year with DeepSeek. And there was a code red back then, too.”

Altman likened the urgency of a code red to the beginning of a pandemic, where action taken at the beginning, more so than actions taken later, have an outsized impact on an outcome. He expected code reds will be a norm as the company hopes to gain distance from the likes of Google and DeepSeek.

“My guess is we’ll be doing these once, maybe twice a year, for a long time, and that’s part of really just making sure that we win in our space,” Altman said. “A lot of other companies will do great too, and I’m happy for them.”

In a note this week, analysts looked at the top five publicly traded AI hyperscalers: Meta, Alphabet, Microsoft, Amazon and Oracle.

BofA pointed out that while the companies can fund their near-term capital expenditures with cash, they are tapping debt markets for balance-sheet flexibility and better cost of capital. Last month alone, Meta, Alphabet, and Amazon raised tens of billions of dollars in the bond market.

Operating cash flow for the big five hyperscalers is expected to hit $577 billion this year from $378 billion in 2023, while debt should climb from $356 billion to $433 billion.

That means their overall debt burden is actually getting lighter as the debt-to-cash ratio should dip from 0.94 to 0.75.

“Given the hyperscalers’ historically conservative capital allocation and balance sheet policies, elevated debt issuance is possible, as evident by the recent bond deals from Meta, Alphabet and Amazon,” BofA said.

And plenty of additional cash is on the way. By 2029, operating cash flow is seen jumping 95% to $1.1 trillion, while capex is forecast to grow at a much slower pace of 58% to $632 billion.

But then there’s Oracle. Unlike the other AI hyperscalers, it will have negative free cash flow until 2029, meaning its capex will exceed cash from operations, according to BofA. As a result, it doesn’t have much capacity to take on more debt.

Indeed, fears about Oracle’s debt binge have rattled the overall AI stock trade as the company isn’t a cash machine like its AI peers.

Recent earnings guidance was also weak, and the company raised its forecast for fiscal 2026 capex by another $15 billion. In addition, surging lease obligations have spooked Wall Street.

A Financial Times report on Wednesday that said alternative investments firm Blue Owl didn’t team up with Oracle on a data center after all piled on more concerns. Shares fell on the news, though the company’s development partner, Related Digital, said Blue Owl was outbid on the project and didn’t back out of it.

But even though debt may not pose a limit on hyperscalers’ ambitions, they still face physical limits, namely in building enough infrastructure fast enough to meet demand.

Data-center researcher Jonathan Koomey told Fortune’s Eva Roytburg that capital can be deployed instantly, but the equipment that capital must buy cannot. Tmelines for turbines, transformers, specialized cooling systems, and high-voltage gear have stretched into years, he explained.

“This happens every time there’s a massive shift in investment,” Koomey added. “Eventually manufacturers catch up, but not right away. Reality intervenes.”