“When you introduce new models, introduce a new way of doing business, disrupt what has been a static environment, it’s going to take some people some time,” Intrator said Tuesday at Fortune’s Brainstorm AI conference in San Francisco. But, he added, more people are beginning to understand the CoreWeave’s business model.

“We came out into one of the most challenging environments,” Intrator said of CoreWeave’s March IPO, which occurred very close to President Trump’s “Liberation Day” tariffs in April. “In spite of the incredible headwinds, we’re able to launch a successful IPO.”

CoreWeave, which priced its IPO at $40 per share, has experienced frequent severe up-and-down price swings in the eight months since its public market debut. At its closing price of $90.66 on Tuesday, the stock remains well above its IPO price.

As Fortune reported last month, CoreWeave’s rapid rise has been fueled by an aggressive, debt-heavy strategy to stand up data centers at unprecedented speed for AI customers. And for now, the bet is still paying off. In its third-quarter results released in November, the company said its revenue backlog nearly doubled in a single quarter—to $55.6 billion from $30 billion—reflecting long-term commitments from marquee clients including Meta, OpenAI, and French AI startup Poolside. Both earnings and revenue came in ahead of Wall Street expectations.

But the numbers were not all celebratory. CoreWeave disclosed a further increase in the debt it has taken on to finance its expansion, and it revised its full-year revenue outlook downward—suggesting that, even with historic demand in the pipeline.

With media headlines calling CoreWeave a “ticking time bomb,” with critics calling out insider stock sales, circular financing accusations and an overreliance on Nvidia, Intrator was asked whether he felt CoreWeave was misunderstood.

“Look, we built a company that is challenging one of the most stable businesses that exist—that cloud business, these three massive players,” he said, referring to AWS, Microsoft Azure and Google Cloud.  I feel like it’s incumbent on CoreWeave to introduce a new business model on how the cloud is going to be built and run. And that’s what we’re doing.” 

He repeatedly framed CoreWeave not as a GPU reseller or traditional data-center operator but as a company purpose-built from scratch to deliver high-performance, parallelized computing for AI workloads. That focus, he said, means designing proprietary software that orchestrates GPUs, building and colocating its own infrastructure, and moving “up the stack” through acquisitions such as Weights & Biases and OpenPipe.

Intrator also defended the company’s debt strategy, saying CoreWeave is effectively inventing a new financing model for AI infrastructure. He pointed to the company’s ability to repurpose power sources, rapidly deploy capacity, and finance large-scale clusters as proof it is solving problems incumbents never had to face.

“When I look back at history of the company, it took us a year with with a company investor like Fidelity, before they were like, ‘Oh, I get it,’” he said. “So look, we’ve been public for eight months. I couldn’t be prouder of what the company has accomplished.” 

Earlier this week, the U.K. government announced a $965 million investment to create more apprenticeships and place 50,000 young people into roles.

In partnership with regional leaders, the three-year initiative will equip young hopefuls with the skills training needed for local job opportunities. A $186 million chunk of the eye-watering funding will be used for a pilot in which mayors will connect the Gen Zers, especially NEETs, with nearby employers. And to ease the financial burden on some companies, the plan will also cover the full cost of apprenticeships for talent under 25 years old at small and medium-size businesses.

U.K. Gen Zers will have access to more apprenticeship roles in high-demand industries like hospitality and retail. But the government is still paying close attention to the critical skills young professionals need in the age of AI; new short courses in engineering, digital skills, and AI will also start rolling out starting April 2026. This apprenticeship push by the U.K. is all part of Prime Minister Keir Starmer’s master plan to get two-thirds of young people active in higher-level learning and apprenticeships, after a sharp drop in 2017.

“For too long, success has been measured by how many young people go to university. That narrow view has held back opportunity and created barriers we need to break,” Starmer said. “It’s time to change the way apprenticeships are viewed and to put them on an equal footing with university.”

Gen Zers are struggling with unemployment in the U.K. and abroad

The U.K.’s ambitious billion-dollar strategy is a welcome one, as youth unemployment rates have surged all around the world.

During the first half of last year, more than 16%, or almost 460,000 of 18- to 24-year-old U.K. men, were NEETs—the highest rate in over a decade. On a global scale, about a fifth of people between ages 15 and 24 in 2023 were NEET-status. And for those actively on the job-hunt, options are scarce. In 2023 and 2024, more than 1.2 million applications were submitted for just under 17,000 open graduate roles in the U.K., according to the Institute of Student Employers (ISE). 

It marked the highest number of applications per job ever recorded since the ISE started collecting data in 1991.

But across the pond, the situation doesn’t look any better: In 2022, there were roughly 4.3 million jobless Gen Z NEETs in the United States. And as of September this year, 9.4% of men and 9% of women ages 20 to 24 were jobless—more than two times higher than the general 4.4% unemployment rate, according to a FRED analysis of U.S. Bureau of Labor Statistics data. 

What’s more, U.S. officials caution the problem could get even worse. U.S. Sen. Mark Warner (D-Va.) warned that joblessness among recent college graduates could skyrocket to as high as 25% in the next two to three years, thanks to AI. 

Similar to the U.K. government, Warner proposed a job retraining program—and the issue goes beyond party lines. In partnership with Josh Hawley (R-Mo.), they introduced a bill that would require businesses and federal agencies to report any AI-related job disruption to the Department of Labor, with results to be published to the public. 

One of the key forces pushing humanoid robots into factories is their advantage over the robotic arms and other automation machinery in use today, Haas said. Traditional factory robots are purpose-built machines designed for a single task, with both hardware and software optimized for that specific function. General purpose humanoid robots by contrast, combined with increasingly sophisticated “physical AI” that helps navigate the real world, will be able to take on different jobs on-the-fly with quick modifications to their instructions.

“I think in the next five years, you’re going to see large sections of factory work replaced by robots—and part of the reason for that is that these physical AI robots can be reprogrammed into different tasks,” Haas said at Fortune’s Brainstorm AI conference in San Francisco on Monday.

“One of the issues you’d had with factory robots in the past is that if it was a pick and place machine for a factory, they’re just optimized for one task—the software was for one task, the hardware is for one task. Now, if you design a general-purpose humanoid that the software is all AI and it learns by doing, it’s going to completely replace a large set of factory workers,” he said.

What happens to those workers and the broader job market as AI and robots proliferate in businesses is a growing concern among many policymakers and industry-observers, with ideas ranging from worker re-skilling to universal basic income among the options under debate.

Haas did not specifically address the jobs issue, but suggested that widespread physical AI adoption could reshape global manufacturing dynamics, potentially helping to level the global competitive playing field by automating a large amount of factory work. “Physical AI will be a great enabler,” he said. 

Haas also pointed to Waymo’s autonomous vehicles as an early indicator of physical AI’s potential. 

He said the next generation of autonomous systems may require even less hardware. While current self-driving cars are fitted with radar and cameras surveying their surroundings, future iterations using more advanced AI models could operate with fewer sensors—relying on artificial intelligence rather than exhaustive data collection to make decisions.

The semiconductor supply chain has ‘many single points of failure’

Arm, which does not manufacture or sell its own chips, designs and licenses the architecture used in processors made by companies including Qualcomm and Apple. Chips based on Arm’s designs are used in everything from smartphones and refrigerators to cars and servers, and most people use between 50 to 100 Arm chips on their person or in their homes, Haas said.

That widespread use and market share is a testament to the energy efficiency and performance that have made Arm’s chip design so popular. But it also raises risks to the semiconductor supply chain.

Asked about this vulnerability, Haas acknowledged the extreme market concentration within the industry, and noted that several large companies each control vital parts of the semiconductor supply chain.

“The semiconductor supply chain has many single points of failure…there’s TSMC, which is in a very obviously interesting part of the world geopolitically. There is also a very sophisticated device that has to go into these fabs that comes from one company on the planet…called the ASML.”

In the last few years, the COVID-19 pandemic exposed some of these supply chain fragilities when chip shortages left consumers unable to get key fobs for new cars for weeks. That crisis, Haas said, was “just a function of the semiconductor supply chain that has many single points of failure.”

Haas said the entire industry is “learning to live with” the concentration risk. 

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