OpenAI’s Landmark Week Reshapes the AI Arms Race

This week, OpenAI signaled its ambitions in the artificial intelligence arena.

Now, the pressure is on to deliver on CEO Sam Altman’s grand vision, one that requires considerable investment and aggressive scaling.

With a flurry of announcements, the company revealed partnerships, investments, and further cemented its position as a key player in the development of machine learning infrastructure.

The week began with the revelation that Nvidia intends to allocate up to $100 billion towards assisting OpenAI in constructing significant data center capacity, leveraging a substantial number of graphics processing units (GPUs). This commitment reflects the critical role of specialized hardware in training and deploying advanced AI models. A day later, OpenAI announced an expanded collaboration with Oracle and SoftBank, scaling its “Stargate” project to a $400 billion pledge across various phases and locations. This signals a long-term commitment to building out the physical infrastructure required to power next-generation AI. Furthermore, OpenAI expanded its enterprise reach with an integration into Databricks which signals a new phase of commercialization for its AI advancements.

“Taken together, this represents a bold strategy, leveraging the ‘fake it ’til you make it’ ethos prevalent in Silicon Valley,” observed Gil Luria, managing director at D.A. Davidson. “However, the true test lies in execution.”

The startup, known for ChatGPT, is aiming to become a hyperscaler. It’s important to note that the company is burning potentially billions in cash, relies on outside capital to fuel growth, and buildout plans require significant amounts of energy.

Altman has maintained that the next era of AI is infrastructure-intensive.

“You should expect OpenAI to spend trillions of dollars on data center construction in the not very distant future,” he stated in San Francisco. “And you should expect a bunch of economists wringing their hands, saying, ‘This is so crazy, it’s so reckless,’ and we’ll just be like, ‘You know what? Let us do our thing.'”

The premise is market demand that continues to accelerate and eventually, OpenAI believes these projects will be profitable.

Current financial projections indicate that OpenAI is forecast to generate $125 billion in revenue by 2029, according to internal forecasts.

Building 17 gigawatts of capacity would require nuclear power plants, which can take a long time to build. The OpenAI team is in discussions with infrastructure providers, but there are no certain answers.

The U.S. grid is already strained, gas turbines are sold out, nuclear is slow to deploy, and renewables are tied up in political roadblocks.

“I am extremely bullish about nuclear, advanced fission, fusion,” Altman said. “We should build more … a lot more of the current generation of fission plants, given the needs for dense, dense energy.” It will be interesting how these partnerships and projects are achieved due to the logistical challenges.

This week highlights Altman’s long-term goal, as OpenAI CEO began to implement hard numbers to his vision. 

“Unlike previous technological revolutions or previous versions of the internet, there’s so much infrastructure that’s required, and this is a small sample of it,” Altman said in Abilene, Texas.

Ambitious and nonconformist, it has defined Altman’s leadership.

Deedy Das, partner at Menlo Ventures, said the scale of OpenAI’s infrastructure partnerships with Oracle may seem extreme to some.

“I don’t see this as crazy. I see it as existential for the race to superintelligence,” he said.

Das argued that data and computing power are the levers scaling AI, and praised Altman for realizing the steep ramp in infrastructure needed.

“One of his gifts is reading the exponential and planning for it,” he added.

Progress in AI is powered by access to computing power. That’s why OpenAI, Google and Anthropic are chasing scale.

AI demand is insatiable for Alibaba and Anthropic. As these companies embed AI into workflows, the infrastructure keeps rising.

Ubiquitous intelligence requires power, land, chips, and years of planning.

“I think people who use ChatGPT every day have no idea that this is what it takes,” Altman said in Abilene. “This is 10% of what the site is going to be. We’re doing 10 of these.”

“This requires such an insane amount of physical infrastructure to deliver,” he said.

Though the buildout is flashy, the funding behind it is unclear.

Nvidia’s $100 billion investment will arrive in $10 billion tranches over the next several years. OpenAI’s buildout commitment with Oracle and SoftBank could reach $400 billion.

Microsoft, OpenAI’s largest partner and shareholder that holds a right of first refusal for cloud deals, “is not willing to write them an unlimited check for compute,” Luria said. “So they’ve turned to Oracle with a commitment considerably bigger than they can live up to.” 

As a non-investment-grade startup without positive cash flow, OpenAI faces a major financing challenge.

Executives have called equity “the most expensive” way to fund infrastructure, and the company is preparing to take on debt to cover the rest of its buildout. Nvidia’s long-term lease structure could help OpenAI secure better terms from banks, but it still needs to raise multiples of that capital in the private markets.

OpenAI CFO Sarah Friar said the company plans to build some of its own first-party infrastructure — not to replace partners like Oracle, but to become a savvier operator. Doing some of the work internally, she said, makes OpenAI “a better partner” by allowing it to challenge vendor assumptions and gain a clearer view into actual costs versus padded estimates.

That, in turn, strengthens its position in rate negotiations.

“The other tool at their disposal to reduce burn rate is to start selling ads within ChatGPT, which may also help with the fundraising,” Luria suggested.

Altman said earlier this year that he’d rather test affiliate-style fees than traditional ads, floating a 2% cut when users buy something they discovered through the tool. He said rankings wouldn’t be for sale and that while ads aren’t ruled out, other monetization models come first.

That question of how to monetize becomes even more urgent amid OpenAI’s breakneck growth.

“We are growing faster than any business I’ve ever heard of before,” Altman said, adding that demand is accelerating so quickly that even this buildout pace will “look slow” in hindsight. Usage of ChatGPT, he noted, has surged roughly tenfold over the past 18 months, particularly on the enterprise side.

And that demand isn’t slowing.

Accenture CEO Julie Sweet told CNBC’s Sara Eisen on “Money Movers” Thursday that she’s seeing an inflection point in enterprise adoption. 

“Every CEO board in the C-suite recognizes that advanced AI is critical to the future,” she said. “The challenge right now they’re facing is that they’re really excited about the technology, and they’re not yet AI-ready — for most companies.”

She said her firm signed 37 clients this quarter with bookings over $100 million, the rising investment in AI infrastructure is underscored.

“We’re still in the thick of it,” she added. “There’s a ton of work to do.”

Ali Ghodsi, CEO of Databricks, said Thursday that concerns about overbuilding miss the bigger picture.

“There’s going to be much more AI usage in the future than we have today. There’s no doubt about that,” he said. “Not every person on the planet is using at the fullest capacity these AI models. So more capacity will be needed.” 

That optimism is one reason Ghodsi struck a formal integration deal with OpenAI this week — a partnership that brings GPT-5 directly into Databricks’ data tooling and reflects growing enterprise demand for OpenAI’s models inside business software.

Still, Ghodsi said it’s important to maintain flexibility.

Databricks now hosts all three foundation models — OpenAI, Anthropic, and Alphabet’s Gemini — so customers aren’t locked into a single provider. By remaining agnostic to the specific AI model, Databricks hopes to capitalize on the widespread move of enterprises towards AI integration.

Even as infrastructure ramps up, the scale and speed of OpenAI’s spending have raised questions about execution.

Nvidia is supplying capital and chips. Oracle is building the sites. OpenAI is anchoring the demand. It’s a circular economy that could come under pressure if any one player falters.

While the headlines came fast this week, the physical buildout will take years to deliver — much dependent on energy and grid upgrades that remain uncertain. 

Friar acknowledged that challenge.

“There’s not enough compute to do all the things that AI can do, and so we need to get it started,” she said. “And we need to do it as a full ecosystem.”