Shares of Oracle have plunged 45% from their September high and lost 14% this week after a messy earnings report revealed it spent $12 billion in quarterly capital expenditures, higher than the $8.25 billion expected by analysts.

Earnings guidance was also weak, and the company raised its forecast for fiscal 2026 capex by another $15 billion. The bulk of that is going into data centers dedicated to OpenAI, Oracle’s $300 billion partner in the AI cycle. 

“We have ambitious achievable goals for capacity delivery worldwide,” Oracle co-CEO Clay Magouyrk said on an earnings call this week.

Investors worry how Oracle will pay for these massive outlays as its underlying revenue streams, cloud revenue and cloud-infrastructure sales, also fell short of Wall Street’s expectations. Analysts have described its AI buildout as debt-fueled, even though the company does not explicitly link specific debt to specific capital projects in its filings.

And by Friday, even the crown jewel of Oracle’s AI strategy—its OpenAI data centers—was showing cracks. Bloomberg disclosed that Oracle has pushed back completion of some U.S. data centers for OpenAI from 2027 to 2028 because of “labor and material shortages.” 

“It’s perfectly plausible that they’re seeing labor and materials shortages,” said data-center researcher Jonathan Koomey, who has advised utilities and hyperscalers including IBM and AMD. In his view, the AI boom is running directly into the difference between digital speed and physical speed. “The world of bits moves fast. The world of atoms doesn’t. And data centers are where those two worlds collide.”

Although Bloomberg didn’t identify which specific facilities were being delayed, Koomer said one likely candidate is Project Jupiter, Oracle’s gargantuan data-center complex proposed for a remote stretch of New Mexico. Local reporting has described Jupiter as a $160 billion-plus mega-campus, one of the most ambitious AI infrastructure projects ever attempted and a core piece of Oracle’s commitment to provide compute to OpenAI. 

Koomey describes an industry where capital can be deployed instantly, but the equipment that capital must buy cannot. The timelines for turbines, transformers, specialized cooling systems, and high-voltage gear have stretched into years, he explained. Large transformers can take four to five years to arrive. Industrial gas turbines, which companies increasingly rely on for building microgrids, can take six or seven. 

Even if a company is willing to pay a premium, the factories that produce these components cannot magically expand overnight, and the manufacturing industry trained to install them is already stretched thin. AI companies may want to move at the pace of model releases, but the construction and utility sectors operate on a fundamentally different timeline.

Koomey made it clear that the physical constraints he describes apply to all hyperscalers, but Oracle worries investors in particular because it’s getting into the AI infrastructure game late and tying much of its capex to one customer, OpenAI.  

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

That friction becomes ever clearer once the financial limit enters the picture. While Oracle’s stock slide is dramatic, the bond-market reaction may be more important. Oracle’s bond yields blew out, with some newer notes that were once investment grade now trading like junk, as its credit-risk gauge hit the highest level since 2009. It signals that investors who lend to companies, historically the most sober observers of tech cycles, are beginning to reassess the risk of lending into the AI buildout. 

For the past few decades, the norm for tech companies was to pay for growth with earnings. Now many of them, including Oracle, are turning to credit markets to fund their sprawling expansions. According to a Bank of Americaanalysis, the five biggest AI hyperscalers—Google, Meta, Amazon, Microsoft and Oracle—have collectively issued roughly $121 billion in bonds this year to fund AI data-center buildouts, a level of issuance far above historical averages and one that signals a major shift toward debt financing for infrastructure.

Oracle, however, has made some of the biggest deals out of the five, like its $18 billion September bond sale. Its total stack of debt is roughly $100 billion. The other four are also in stronger cash positions and have higher credit ratings (AA/A vs Oracle in BBB area), and are able to generate large positive free cash flow. So while Oracle isn’t the only tech giant tapping the debt markets for its AI outlays, its size, cash generation, and credit ratings make it one of the most leveraged.

Debt investors do not necessarily need blowout returns; they just need certainty that they will get their money back, with interest. If confidence wavers even a little, yields rise. 

“This feels like the 1998 moment,” Anuj Kapur, CEO of CloudBees and a former tech executive during the dot-com era, told Axios. There’s enormous promise, but also enormous uncertainty about how quickly the returns show up. 

Koomer saw a simple throughline. 

“You have a disconnect between the tech people who have lots of money and are used to moving super fast, and the people who make the equipment and build the facilities, who need years to scale up their manufacturing,” he said.

“It’s a hospitality business. People come to us not just for the network, but also for the hospitality,” he told Fortune. “You need to serve them. You have to take care of their needs, like serving the customers who are coming to look for them in the office.”

Kong and JustCo are expanding their presence in Asia even as employers and employees continue to fight a battle about flexible work and returning to the office. Globally, corporate giants ranging from Amazon to JPMorgan have called workers back to the office full-time. But employees tout the benefits of working from home and hybrid work, forcing employers and office designers to get creative in how they bring people back. 

The company is also expanding into new markets regionally, including Malaysia and India. In the longer run, they’re also looking to move into countries in North Asia and the Middle East.

“After entering all these markets, we will be truly covering all the key cities in Asia-Pacific,” says Kong. He’s even considering returning to mainland China, after JustCo exited the market in 2022 due to tight social distancing regulations during the COVID pandemic.

JustCo just entered the Vietnam market with a new office along Ho Chi Minh City’s waterfront. The Vietnamese city is the tenth urban market in Asia for JustCo. It’s also a return of sorts for Kong, who was first exposed to the idea of a flexi-office in Ho Chi Minh City several decades ago. 

JustCo’s story

Kong Wan Sing founded JustCo in Singapore in 2011. Following a regional expansion drive in 2015, it now operates 48 offices across Asia-Pacific, including in major cities like Seoul, Bangkok, Taipei, Melbourne, and Sydney. Kong himself hails from a family of entrepreneurs; his parents operate garment factories in nearby Malaysia. “There’s genes inside me to build a business,” he says. 

In the early 2000s, Kong was an employee of Singaporean real estate investment company Mapletree, working out of a flexi-office in Vietnam’s Ho Chi Minh City. (A flexi-office is a modern workspace where employees don’t have assigned desks, but instead choose from various work zones including hot desks, quiet pods, and collaborative areas.)

The experience opened his eyes to the value of flexible workspaces, and he saw a business opportunity in Asia, where such spaces were still few and far between. 

Kong notes that, just three years ago, just under 4% of all offices in Asia-Pacific were flexi-offices. It’s since risen to over 5%, but that’s still half the level seen in more developed markets in Europe and the U.S. Yet JustCo’s CEO says he’s seeing a “surge” in Asia: “The growth is definitely much faster than European or American countries.”

JustCo also leases small offices for businesses to rent. Sixty percent of JustCo’s clients are multinational corporations looking for space for a regional office, Kong said. Companies like Chinese tech giant Tencent and U.S. vaccine maker Moderna use JustCo for their local offices. 

New brands

JustCo has since broadened its offerings to potential renters, launching two new brands: “THE COLLECTIVE” and “the boring office.”

The former is a luxury co-working space, equipped with premium white-glove services like daily breakfasts and aperitif hours, and twice-a-day office cleaning. The first such space was launched in Tokyo in March.

“Japan is a very mature market, and people in Japan—they appreciate luxury stuff,” said Kong, when asked why the country was chosen to debut its premium brand. Kong and his team has since launched THE COLLECTIVE in Bangkok and Taipei; the company will bring the concept to Singapore and India in 2026.

“The boring office” sits on the other end of the spectrum, catering to firms that want a stripped-down solution. “When you go to the boring office, there’s no cleaning [of rooms] every day, only once a week,” Kong says. “And the pantry is a very basic pantry that provides only water—there’s no coffee, nothing.” The first space under that brand was launched in Singapore in July.

These three brands cater to companies’ differing needs, and are priced along a sliding scale. 

The firm’s luxury offices are 20 to 30% more costly than the classic JustCo workspace, while the boring office’s spaces are cheaper by roughly the same amount, Kong explains.

“Most of us are producers today,” Nancy Xu, vice president of AI and Agentforce at Salesforce, told the audience. “Most of what we do is we take some objective and we say, ‘Okay, my goal is now to spend the next eight hours today to figure out how to chase after this customer, or increase my CSAT score, or to close this amount of revenue.”

With AI agents handling more tasks, Xu said that workers will shift “from producers to more directors.” Instead of asking, “How do I accomplish the goal?” they’ll instead focus on, “What are the goals that I want to accomplish, and then how do I delegate those goals to AI?” she said.

Creative and sales professionals are increasingly anxious about AI automation as tools like chatbots and AI image generators have proved to be good at doing many creative tasks in sectors like marketing, customer service, and graphic design. Companies are already deploying AI agents to take on tasks like handling customer questions, generating marketing content, and assisting with sales outreach. 

Pointing to a recent project with electric-vehicle maker Rivian, Elisabeth Zornes, chief customer officer at Autodesk, said that the company’s AI-powered tools enabled Rivian to test designs through digital wind tunnels rather than clay models. “It shaved off about two years of their development cycle,” Zornes said.

As AI takes on some of these lower-level tasks, Zornes said, workers can focus on more creative projects.

“With AI, the floor has been raised, but so has the ceiling,” she added. “We have an opportunity to create more, to be more imaginative.”

The uneven impact of AI

The shift to AI-augmented work may not benefit all workers equally, however.

Salesforce’s Xu said AI’s impact won’t be evenly distributed between high and low performers. “The near-term impact of AI will largely be that we’re going to take the bottom 50 percentile performers inside a role and bring them into the top 50 percentile,” she said. “If you’re in the top 10 percentile, the superstar salespeople, creatives, the impact of AI is actually much less.”

While leaders were keen to emphasize that AI will augment, rather than replace, creative workers, the shift could reshape some traditional career ladders and impact workforce development. If AI agents handle entry-level execution work, companies may need to hire fewer people, and some learning opportunities may disappear for younger workers. 

Ami Palan, senior managing director at Accenture Song, said that to successfully implement AI agents, companies may need to change the way they think about their corporate structure and workforce.

“We can build the most robust technology solution and consider it the Ferrari,” she said. “But if the culture and the organization of people are not enabled in terms of how to use that, that Ferrari is essentially stuck in traffic.”

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