The progress of Chinese tech companies across the entire stack is “remarkable,” OpenAI’s Sam Altman told CNBC.

Altman’s comments come as China races against the U.S. to develop artificial general intelligence (AGI) — where AI matches human capabilities — and roll out the technology across society.

The pace of technological advance in “many fields,” including AI, is “amazingly fast,” Altman said. In some areas Chinese tech companies are near the frontier, while in others they lag behind, he added.

The country is moving to scale homegrown chipmakers that it hopes could eventually rival the likes of Nvidia, and AI companies are seeing big rallies on stock exchanges as investors double down on their potential.

Altman’s not the only tech leader paying attention to China.

American tech companies should “worry a little bit” about the subsidies their Chinese competitors receive from their government in the AI race, Microsoft President Brad Smith told CNBC on Wednesday.

Finding revenue

OpenAI has been moving to develop revenue streams as its looks to provide a path to profitability for investors that have ploughed around $70 billion into the company, according to deal-counting platform Dealroom. The company is looking to wrap up a $100 billion fundraising round, sources told CNBC.

Adverts within ChatGPT is one avenue the company is exploring.

“I think we still have some work to do to figure out the exact ad format that’s going to work best,” Altman said, adding that plans are in their early stages.

“The ads that I have personally liked the most in recent years from tech [companies] have been sort of Instagram style ads where you discover something new that you might really like and otherwise wouldn’t have known about,” he said. “I think we’ve got a real opportunity to push in that direction with ads in ChatGPT.”

OpenAI is initially planning to test adverts in the U.S., but will eventually roll out to other markets, Altman said.

While conversations around OpenAI’s timelines on a path to profitability are swirling among market watchers, the company is still focused on growth right now, Altman told CNBC.

“We are growing at an extremely fast rate right now,” he said. “I think as long as we can have reasonable unit economics, we should focus on continuing to grow faster and faster, and we’ll get profitable when we think we when we think it makes sense.”

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Big Tech companies have seen over $1 trillion wiped from their market cap over the past week, as fears over AI spending sparked a sell-off.

Microsoft, Nvidia, Oracle, Meta, Amazon and Alphabet all saw their shares fall in the week up to market close on Thursday, as the companies’ earnings reports signaled huge continued capex spending from hyperscalers.

Amazon was down 7% in premarket trading on Friday. Alphabet was 0.7% lower, Meta was largely unchanged, while Oracle, Nvidia and Microsoft were up in the low single-digit percentages.

Plans to funnel $660 billion into AI this year were announced by Big Tech stocks, the Financial Times reported, a figure higher than the GDP of countries like the United Arab Emirates, Singapore and Israel.

Shares of companies developing hardware for the AI buildout will likely encounter continued volatility as “sentiment contagion takes hold,” Paul Markham, investment director at GAM Investments, told CNBC.

“Questions over the extent of capex as a result of LLM build-outs, the eventual return on that, and the fear of eventual over-expansion of capacity will be persistent,” he added.

‘Investors questioning every angle in AI race’

Amazon was among the firms announcing the biggest capex spending plans this earnings season.

“The key focus of [Amazon’s] results was the capex guide of $200bn, up +56% on the year, ahead of market expectations and the highest amongst the hyperscalers,” Mamta Valechha, consumer discretionary analyst at Quilter Cheviot, said Friday morning, adding that the spend was predominantly for its cloud unit, AWS.

Stock Chart IconStock chart icon

Amazon shares over the past month

While management is confident of long-term returns on investment, the lack of visibility is not sitting well with investors, she added. “We have suddenly gone from the fear that you cannot be last, to investors questioning every single angle in this AI race.”

Apple, on the other hand, which has faced pressure from Wall Street over its AI strategy and has previously committed far less on capex than other Big Tech firms, has seen its stock jump 7% since Monday on the back of what CEO Tim Cook described as “staggering” demand for the iPhone.

— CNBC’s Elsa Ohlen also contributed to this report.

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Traders work on the floor of the New York Stock Exchange during morning trading on Jan. 21, 2026.

Michael M. Santiago | Getty Images News | Getty Images

Technology stocks bounced on Wednesday as President Donald Trump ruled out military force in Greenland, easing geopolitical fears that sparked a global sell off earlier in the week.

Chip stocks led the tech sector’s gain. Intel rallied 11%, while Advanced Micro Devices and Arm Holdings surged about 9% and 8%, respectively. Micron Technology and Marvell Technology rose at least 5% each.

Shares of tech companies sold off on Tuesday after Trump pledged new tariffs on eight countries that opposed a U.S. acquisition of Greenland as leaders convened in Davos, Switzerland, for the World Economic Forum.

But Trump dialed down the rhetoric on Wednesday, ruling out military force. A potential peace deal between Russia and Ukraine, along with a takeover of Greenland, are among the agenda items for the president and his team while in Switzerland.

Elsewhere, Tesla and Nvidia gained more than 2%, while Meta Platforms added about 1.5%. Amazon, Salesforce and Apple were up slightly.

WATCH: President Trump: I won’t use force on Greenland

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Jim Farley, president and chief executive officer of Ford, speaks at the Ford Motor Co.’s Kentucky Truck Plant to launch the 2025 Ford Expedition, in Louisville, Kentucky, April 30, 2025.

Carolyn Kaster | AP

Ford Motor plans to introduce eyes-off driving technology on an upcoming $30,000 all-electric vehicle in 2028, the Detroit automaker announced Wednesday.

The target brings Ford into a race against competitors such as Tesla, General Motors and Rivian Automotive to develop and broadly launch such systems, which Wall Street views as a potential growth market to fully autonomous vehicles.

Ford’s plan is similar to those of other automakers, but the company notably plans to offer the new system on a mainstream EV first, rather than on a pricier model — defying typical technology rollouts in the automotive industry.

“It’s part of what has evolved to be a broader technology strategy of putting our best and newest technology where the volume is and where the accessibility is,” Doug Field, Ford’s chief EV, digital and design officer, told CNBC during an interview.

The first vehicle to feature the new system is expected to be built on the company’s upcoming “Universal EV platform,” which Ford has said is capable of supporting a variety of vehicles. The first is a roughly $30,000 midsize pickup truck set to hit the market in 2027.

Field, who joined Ford after stints with Apple and Tesla, said the first vehicle with the eyes-off system will come at that starting price, but did not disclose if it would be the pickup truck.

Ford has said the next-generation EV platform reduces parts by 20% versus a typical vehicle, with 25% fewer fasteners, 40% fewer workstations in the plant and 15% faster assembly time.

Field announced the eyes-off system at the CES technology show in Las Vegas alongside other plans, including a new vehicle software architecture as well as a Ford-engineered artificial intelligence assistant.

GM in October announced similar plans, including an AI assistant and an eyes-off system expected to launch in 2028 on its electric Cadillac Escalade IQ. That vehicle currently starts at more than $127,000.

Skunkworks

Ford, led by CEO Jim Farley, is under pressure to deliver on the new vehicle platform, known as UEV, and its supporting technologies, which largely fall under Field.

The company has wasted billions of dollars amid shifting EV strategies as well as quality and production issues in recent years.

Ford has significantly cut back its spending on EVs and has moved focus from large all-electric pickup trucks and SUVs to smaller, more affordable models through a special project, or “skunkworks,” team that created the UEV platform. Ford announced plans to invest about $5 billion in U.S. plants to produce the vehicles and the batteries to power them.

Field referred to the skunkworks team as a “bet” over the last couple of years that has “started to build a tremendous amount of confidence” over the “last few months.”

He said Ford now has all of the critical software and supporting hardware it needs in-house for a new generation of technologically advanced vehicles to differentiate the automaker.

“One of the things we’re seeing is just how much faster our development process works on this product and this architecture compared to what we’ve done in the past,” Field said. “So, we have a lot of confidence in our ability to get this out.”

Ford last month said it expects to record about $19.5 billion in special items through 2027 related to such restructuring efforts and its pullback in EV investments.

Eyes-off driving

Field said Ford’s planned eyes-off system, which the auto industry refers to as “Level 3 driving automation,” will utilize an array of sensors and in-house software development to lower costs compared to competitors.

Field declined to comment on the roadmap for expanding the new system to other vehicles, but said the goal is to commoditize the technology.

“The actual rollout schedule will be based on a lot of work we have to do on which customers need it in their applications, when, and which products are most ready for it now,” Field said. “It’ll take time to roll it out everywhere, but we’ll prioritize that based on where we will have the biggest impact on customers.”

Ford’s BlueCruise system displayed on the driver information cluster of an F-150 pickup truck.

Ford

SAE International, formerly known as the Society of Automotive Engineers, has characterized automated driving for vehicles from Level 0 to Level 5. The highest, Level 5, is a fully autonomous vehicle, with each stage from Level 0 adding more technologies and enabling human drivers to be more “out of the loop.”

Ford currently offers a Level 2 advanced driver assistance system, or ADAS, known as BlueCruise. While active, a vehicle can drive itself under certain circumstances without human intervention on divided highways, but drivers still need to pay attention to the roads and system in case of problems. 

AI assistant, new vehicle ‘brain’

Ford’s new AI assistant is expected to launch in early 2026 through its phone apps for Ford and Lincoln, followed by a native in-vehicle experience starting in 2027, the company said Wednesday.

Several other automakers also have announced plans for AI digital assistants, but Field said he believes Ford’s will offer unique capabilities specific to each car or truck by utilizing each vehicle’s unique identification number.

Ford said the AI assistant will be able to review a picture of a trailer to confirm whether a vehicle can properly tow it, for example, or assess how many bags of mulch the interior of a car or truck can hold.

“The AI companion is something we think we can make special for Ford, representative of what we’re trying to do on the customer experience side,” Field said.

Assisting in that better customer experience is expected to be an updated in-house software architecture that Ford’s calling an “integrated digital platform” that will debut with the UEV platform.

The company said the updated system will result in “a more unified ‘brain inside’ the vehicle — a single, powerful module that unifies infotainment, ADAS, audio, and networking.”

“For customers, that means a vehicle that feels more consistent, more reliable, and more capable year after year,” Field said in a blog post accompanying the CES announcements.

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The Stargate AI data center in Abilene, Texas, US, on Wednesday, Sept. 24, 2025.

Kyle Grillot | Bloomberg | Getty Images

West Texas dust, iron-tinged and orange-red, rides the wind and sticks like a film to everything you touch. It clings to skin and the inside of your mouth, a fine grit that turns every breath into a reminder of where you are. This is the landscape where OpenAI CEO Sam Altman is orchestrating something called Stargate — a fast-expanding constellation of data centers, backed by partners including Oracle, Nvidia, and SoftBank.

Six thousand workers’ vehicles pour into the site each morning. Tires raise a constant veil of grit over a construction footprint the size of a small city — more people working this single campus than OpenAI employs across its entire payroll.

Rain comes in flashes. One minute the roads are powder; the next they’re mud — thick, adhesive, the kind that tugs at boots and gums up machinery. Then the storm moves on, the sun returns, and the surface hardens again, cracked and chalky, as if the place is trying to erase the evidence that water ever touched it.

And at dusk, the same conditions that make living there punishing turn the sky into a blaze. Shorter wavelengths fall away and reds and oranges remain.

“This is what it takes to deliver AI,” Altman told CNBC on site in September. “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.”

A small sample: At roughly $50 billion per site, OpenAI’s Stargate projects add up to about $850 billion in spending — nearly half of the $2 trillion global AI infrastructure surge HSBC now forecasts.

The Abilene campus already has one data center online, with a second nearly complete. OpenAI CFO Sarah Friar told CNBC the site could ultimately scale past a gigawatt of capacity — enough electricity to power about 750,000 homes, roughly the size of Seattle and San Francisco combined.

“The shovels that are going in the ground here today, they’re really about compute that comes online in 2026,” she said in September. “That first Nvidia push will be for Vera Rubins, the new frontier accelerator chips. But then it’s about what gets built for ’27, ’28, and ’29. What we see today is a massive compute crunch.”

“We are growing faster than any business I’ve ever heard of before,” Altman said, squinting against the sun. “And we would be way bigger now if we had way more capacity.”

Land is cheap. Governments are willing. And the grid, for now, can be persuaded to bend.

Altman is not alone in building kingdoms.

Zuckerberg’s Hyperion and Musk’s Colossus

In the flatlands of northeast Louisiana, where soybean fields once stretched to the horizon, Meta‘s Mark Zuckerberg is erecting a four-million-square-foot monument to artificial intelligence. He calls it Hyperion, after the Greek titan. When finished, it will consume more electricity than the city of New Orleans — and cover a footprint the size of lower Manhattan.

Across the Mississippi River, in West Memphis, Arkansas, Alphabet‘s Google has broken ground on what state officials are calling the largest private capital investment in state history — a multibillion dollar campus rising from 1,100 acres of scrubland.

Thirty minutes south, on the Tennessee side of the border, Elon Musk has already begun transforming the industrial wastelands of South Memphis. His supercomputer, Colossus, was built in 122 days inside a shuttered Electrolux factory. Now he’s constructing Colossus 2, aiming for a million GPUs — and just acquired a third building to expand the complex further. To power the site, Musk bought a shuttered Duke Energy power plant across the border in Southaven, Mississippi.

In southeast Wisconsin, Microsoft is spending more than $7 billion on what CEO Satya Nadella calls “the world’s most powerful” AI data center — a facility that will house hundreds of thousands of Nvidia chips when it comes online in early 2026. And in rural Indiana, near Lake Michigan, Amazon has transformed 1,200 acres of farmland into Project Rainier, an $11 billion facility running entirely on custom silicon, built exclusively to train AI models for a startup called Anthropic.

“Cornfields to data centers, almost overnight,” Amazon Web Services CEO Matt Garman told CNBC in Seattle in October.

This is the AI boom rendered in steel and gravel — a slow carving of the country into zones of power and compute. What they’re building is not infrastructure in any conventional sense. It is the physical manifestation of a belief — that intelligence itself can be manufactured at industrial scale, and that whoever builds the biggest factory wins.

“This is the largest market in the history of mankind,” said Sameer Dholakia, a partner at Bessemer Venture Partners. “This is larger than oil, because everyone on the planet needs intelligence.”

The money

The sums involved have become difficult to comprehend.

The top five hyperscalers — including Amazon, Microsoft, Alphabet, and Meta — are on track to spend approximately $443 billion on capital expenditures this year. CreditSights projects that figure will climb to $602 billion in 2026 — a 36% year-over-year increase. Their analysts estimate that approximately 75% of that spending will go directly into AI infrastructure.

The current tech industry is among the most profitable in the history of the world, but not all of the companies necessarily have the cash on hand to cover the spend.

The debt raise has been staggering. Hyperscalers have added $121 billion in new debt this year — more than four times the average annual issuance over the previous five years, according to Bank of America. Over $90 billion of that came in just the past three months. Meta tapped the bond market for $30 billion. Alphabet raised $25 billion. Oracle just pulled off an $18 billion bond sale — and Citi says it now ranks as the largest issuer of investment-grade debt among non-financial U.S. companies.

Wall Street expects the pace of borrowing to accelerate.

Analysts at Morgan Stanley and JPMorgan estimate AI’s infrastructure push could drive up to $1.5 trillion in additional borrowing by tech companies in the coming years. UBS analysts forecast as much as $900 billion in new issuance coming in 2026 alone.

AWS site lead Josh Sallabedra with MacKenzie Sigalos

Katie Tarasov

“There is something inherently uncomfortable as a credit investor about the transformation of the sort we’re facing that is going to require an enormous amount of capital,” Daniel Sorid, head of U.S. investment grade credit strategy at Citi, told investors on a video call earlier this month.

You can see that discomfort in the derivatives market.

Credit-default swaps — insurance that pays out if a borrower can’t service its debt — have widened to multi-year highs for Oracle. Barclays and Morgan Stanley have told clients to buy protection, and in late October, a liquid CDS market tied to Meta began actively trading for the first time as investors rushed to hedge what’s becoming a hyperscaler debt boom.  

There’s precedent for debt-funded buildouts outrunning near-term demand. In the dot-com era, telecoms levered up to lay fiber fast. When conditions tightened, many had to restructure. The network survived — but the outcomes ranged from many early investors booking losses, to equity wipeouts.

OpenAI and the tangled web

At the center of this infrastructure arms race sits OpenAI — and a web of interlocking deals that has reshaped the competitive landscape for AI.

In the span of just two months this fall, the company announced partnerships totaling roughly $1.4 trillion in headline commitments — a figure that prompted skeptics to warn of an AI bubble and raised basic questions about whether the power, land, and supply chains exist to match the ambition.

The deals came in rapid succession.

In September, OpenAI announced a $100 billion equity-and-supply agreement with Nvidia — the chip giant taking an ownership stake in OpenAI in exchange for 10 gigawatts of its next-generation systems.

In October, OpenAI teamed up with AMD to deploy its Instinct GPUs, with the agreement structured to potentially give OpenAI a 10% stake in the chipmaker. Days later, Broadcom agreed to supply 10 gigawatts of custom chips co-designed with OpenAI. And in November, OpenAI signed its first cloud contract with Amazon Web Services, further loosening Microsoft’s once exclusive grip.

“We have to do this,” OpenAI President Greg Brockman told CNBC in October, referring to the company’s scramble to secure the raw computing power behind its ambitions. “This is so core to our mission if we really want to be able to scale to reach all of humanity, this is what we have to do.”

Nvidia is effectively financing demand for its own chips, Oracle is building the sites, AMD and Broadcom are positioning as alternative suppliers, and OpenAI is anchoring the demand. Critics call it a circular economy: capital, capacity, and revenue all recycling through the same small set of players. It works as long as growth holds — but if demand slips or funding tightens, the stress can propagate fast through a web of shared exposures.

Already, Nvidia has cautioned investors there was “no assurance” it would enter a definitive agreement with OpenAI, or complete the investment on expected terms, a reminder that headline AI pacts often start as frameworks.

Oracle’s view from the jobsite is simpler: the demand is real, diversified, and already spoken for.

“We see broad-based demand across a huge swath of the industry, so it’s not just from any one individual place,” Clay Magouyrk, Oracle’s newly elevated co-CEO, told CNBC in West Texas in September. “I don’t worry about a bubble, because I see committed demand for it.”

He described the appetite for compute as nearly limitless. “When I look at myself, when I look at my teams at Oracle, when I look at our customers, I see what looks like near-infinite demand for technology — if we can enable them to use it.”

At the DealBook Summit in December, Anthropic CEO Dario Amodei described the “cone of uncertainty” — a mismatch between long lead times and a market that can change in a quarter. Data centers take 18 to 24 months to build, and chip orders are placed years in advance, even as demand forecasts keep shifting.

“You don’t have $50 billion on you,” he said, so the financing often gets wrapped into partnerships with chipmakers or cloud providers, where “you can kind of pay as you go.”

Amodei insists Anthropic is trying to stay disciplined. “I think there are some players who are not managing that risk well,” he said, declining to share names.

The new gospel of scale

Critics question how much is firm, contracted demand versus aspirational headline math.

Gil Luria, who covers technology cycles at D.A. Davidson, points to Oracle as a test case.

“OpenAI made commitments that it’s highly unlikely they’ll be able to live up to,” he said. “Now they’re backtracking and saying these aren’t really commitments — these are frameworks. But talk to Oracle about that. Oracle thought they had a contract for $300 billion. They put that in their remaining performance obligations and made commitments to Wall Street based on that.”

Oracle stock dropped 23% in November — its worst month since 2001.

OpenAI’s Friar rejected the “circular economy” framing during the interview with CNBC in West Texas.

She compared it to the early days of the web. “When the internet was getting started, people kept feeling like, ‘Oh, we’re overbuilding, there’s too much.’ And look where we are today, right? The internet is ubiquitous. AI is going to be like that.”

Friar said equity is too expensive, so OpenAI is preparing to take on debt for the first time to finance expansion. The company has reviewed more than 800 potential sites across North America — weighing land, substations, and transmission capacity.

And like much of the industry, OpenAI is looking at every viable power source — renewables, gas, and even nuclear — as utilities and tech companies chase always-on power that wind and solar can’t reliably provide themselves.

“The real bottleneck isn’t money,” she said. “It’s power.”

That demand isn’t fading. In late December, SoftBank’s Masayoshi Son agreed to pay $4 billion for DigitalBridge, a firm that invests in data centers. To fund the deal — and his $40 billion commitment to OpenAI — Son sold down SoftBank’s entire stake in Nvidia. He later told a forum in Tokyo that he “was crying” over having to sell the shares.

The scarce asset now is energized real estate — and the ability to plug in at scale. Power like that is regulated and permitted, which means the buildout also depends on Washington.

OpenAI has lobbied the Trump administration to expand the CHIPS Act tax credit to cover AI data centers — though when its CFO floated the idea of a government “backstop” for infrastructure loans at a Wall Street Journal event in November, the backlash was swift enough that she walked it back within hours. Altman took to X to insist the company does not “have or want government guarantees.”

The companies aren’t waiting for Washington. They’re borrowing, building, and betting that the economics will catch up — because so far, every time they’ve scaled, the models have gotten better. That pattern is the industry’s founding conviction: more compute produces more capable systems. It’s why startups that have never turned a profit can still command valuations in the hundreds of billions.

The wager isn’t only that training ever-larger models will keep producing step-change intelligence. It’s that the payoff is now spilling out of the lab, as those models are put to work across the economy — answering customers, writing code, routing claims, drafting contracts, compressing weeks of work into hours. That’s inference: not training the model, but the everyday usage that turns models into products.

Inference is where the hype has to convert into margins, and it’s also where the compute bill never stops: each new user, workflow, or agent adds recurring demand, not a one-time training run. That’s why the buildout has started to look less like a moonshot and more like a utility race, with companies scrambling to secure the power and capacity to serve what they expect will be always-on intelligence.

“We have continued to be surprised, even as the people who pioneered this belief in scaling laws,” Daniela Amodei, Anthropic’s president and co-founder, told CNBC during a sitdown at the company’s headquarters in San Francisco. “Every year we’ve been like, ‘Well, this can’t possibly be the case that things will continue on the exponential,’ and then every year, it has.”

Anthropic’s revenue has jumped tenfold, year-over-year, for the last three years. In 2025 alone, the startup’s valuation surged from $60 billion to a funding round currently underway that could put it north of $300 billion.

The reckoning

Dario Amodei, Daniela’s brother, believes we are approaching something like “a country of geniuses in a datacenter” — AI systems that can perform at the level of Nobel laureates across every domain. He believes that threshold could come as soon as next year.

But he’s also sounding alarms.

“Look at entry level consultants, lawyers, financial professionals, many of the white collar service industries, a lot of what they do, AI models are already quite good at without intervention,” he told 60 Minutes. “And my worry is that it’ll be broad, and it’ll be faster than what we’ve seen with previous technology.”

That belief is driving the industry’s spending binge — but skeptics worry the buildout becomes a debt-fueled overreach, ending in a familiar cleanup: bankruptcies, fire sales, and shareholders wiped out.

Matt Murphy, a venture capitalist at Menlo Ventures and an early Anthropic investor, frames it differently.

“I’ve been in the venture business for 25 years,” Murphy said, “I’ve seen the cloud wave, the mobile wave, the semiconductor wave. This is the mother of all waves.”

Eerial shot of Open AI Stargate I (Abilene)

Courtesy: OpenAI

Stand back far enough and a new geography comes into focus.

Zuckerberg’s Hyperion. Musk’s Colossus. Altman’s Stargate. Amazon’s Rainier. Google’s archipelago of compute clusters. Each one a monument to a different vision of the future — and each one anchored to the same constraint: power.

Data centers are rising near plants and transmission lines, in places with cheap land, willing governments, and grids that can be pushed to expand. And the towns around them are now showing up in investor decks, earnings calls, and trillion-dollar projections.

Analysts tell CNBC the stakes are bigger than stock prices. Either this year marks the beginning of a transformation as profound as electrification and the internet, or it marks the peak of a bubble that future historians will study as a cautionary tale.

Altman hears the doubts — but he rejects the notion that the buildout has gone too far.

“People will get burned on overinvesting,” he told CNBC in September. “And people also get burned on underinvesting and not having enough capacity.”

“Smart people will get overexcited, and people will lose a lot of money. People will make a lot of money. But I am confident that long term, the value of this technology is going to be gigantic to society,” added Altman.

For now, the construction continues. The trucks kick up dust. The transformers hum. And across the American heartland, the factories of a new age take shape.

WATCH: Microsoft sees 10x return on OpenAI stake after restructure

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