CoreWeave said on Friday it had priced $3.7 billion in convertible bonds, more than the AI cloud provider had previously said it was targeting.
The convertible senior notes will be due in 2033 and carry a coupon of 2.875%, at the top of the pricing range marketed to investors and up from the ...
CoreWeave said on Thursday that it could sell up to 35 million shares, worth roughly $3 billion based on the prior day’s closing price, through a new at-the-market offering program. At the same time, the company announced plans to raise $3 billion in a convertible note issue, with an option for ...
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If it suddenly feels like every startup is trying to raise money to throw at some kind of AI infrastructure shortage, you’re probably onto something.
We reported last night that the startup behind AI assistant Instinct is in talks to raise $1 billion, as it faces compute capacity constraints after growing to more than 100,000 users on an invite-only basis. Meanwhile Coatue Management has been discussing a multibillion-dollar joint venture involving chip startup MatX, to finance purchases of memory and logic dies and the reservation of manufacturing capacity.
There’s more going on than simply scrounging up a few spare servers or chip components. Getting access to AI infrastructure increasingly means making big, long-term commitments—it’s cheaper for providers to finance new cloud capacity that way, while manufacturers are more willing to allocate scarce capacity to large, established customers. That’s making it tougher for young but fast-growing companies to get a place in line.
Smaller cloud companies often borrow heavily to finance data center capacity and purchases of graphics processing units, while even the biggest are increasingly using debt to supplement balance sheet cash. On top of that, borrowing costs have risen sharply across the economy, with benchmark yields on both long- and short-term debt climbing.
It’s generally cheaper to borrow money with established, long-term customers lined up, especially ones with good credit ratings, than to finance compute capacity to serve less-proven customers. Iren, for instance, said in August that roughly $3.6 billion in financing for GPUs for Microsoft carried an interest rate of around 6%. But a separate $2.4 billion in financing for GPUs for non–investment-grade customers had a rate around 3 percentage points higher.
Some cloud providers are also demanding more up-front payments, which helps lower the amount they have to raise. Iren said recent customers were paying as much as 55% of the cost of GPUs and related equipment up front. Nebius, similarly, said that the share of deals with prepayments reached an all-time high in the second quarter, with roughly 70% of newly inked deals requiring money up front.
Nebius expects to receive more than $9 billion in prepayments this year, compared with projected 2026 revenue of up to $3.4 billion, and called them the “market standard for securing capacity in a supply-constrained environment.”
Renting GPU capacity as needed from startups like Runpod is another option, while larger providers like CoreWeave are introducing flexible ways to buy inference capacity. But that approach can be expensive, and needed capacity might not always be available. Either way, scaling up can get pricey fast for AI-heavy startups, and they might need to reserve capacity before knowing exactly how usage growth will pan out.
In the pre–AI craze days, a consumer app might chase user growth without worrying about revenue, while computing needs were generally less intensive and cheaper, so didn’t require the same kind of hefty upfront payments. Photo sharing app BeReal, for instance, grew to nearly 8 million daily users within months of taking off in 2022, having raised roughly $90 million, though growth later fizzled.
On the chipmaking side, startups are also facing challenges in securing resources ahead of growth, in that manufacturers are more likely to allocate capacity to customers that can make large commitments and have the financial resources to back them up. The joint-venture financing for MatX, which doesn’t expect to have its chip design ready for trial production until next year, would be aimed partly at helping it reserve capacity like customers such as Broadcom or Marvell Technology do.
It’s not clear how much money Coatue would put into a joint venture, which remains in the early stages of discussion, and whether the venture could also pull in outside financing. But Coatue already has some experience helping finance capital-heavy infrastructure deals on the data center side, sometimes in creative setups.
Coatue’s Next Frontier venture participated in a joint venture with AI infrastructure startup Fluidstack to finance a planned Indiana data center campus. A subsidiary of that JV raised $5.7 billion of debt to help buy land and build the facilities and related power infrastructure. (The financing doesn’t cover chips.) Fluidstack will lease those planned facilities to provide AI cloud services, but there’s yet another company involved—Google, which is guaranteeing Fluidstack’s lease obligations.
The financing boom for the AI build-out is getting bigger and more complicated by the day—and AI companies have been staffing up for the challenge.
AI labs including OpenAI and Anthropic, as well as neoclouds such as Nscale, are among a growing number of AI companies building out their capital markets teams and hiring specialists in areas like structured finance. That in part reflects the sheer volume of deals these companies are doing, many of which don’t fit neatly into standard corporate debt. This in-house staff can help when it comes to negotiating with lenders and drilling down into construction, power and other key details.
Of course, tech and data center companies have long had in-house teams to handle fundraising, deals and other corporate finance needs. And structured finance is nothing new to the infrastructure world. But the scale of the AI build-out, which bankers peg at around $7.5 trillion in spending over the next five years, has pulled relatively young labs and upstart cloud firms into financing arrangements that are new territory.
That means finance professionals, from bankers to investors at private equity, private credit and infrastructure firms, have more options in the form of neoclouds and other AI infrastructure startups, some of which are offering significant pre–initial public offering equity.
“It's a new avenue for these people,” said James Howl-Newton, founder of Futura Search Partners, a specialist search firm focused on areas including digital infrastructure finance. As a result, “sponsors are having to deal with additional routes to exits for top performers,” he said.
AI companies and infrastructure providers are tapping financing frequently and across different instruments, requiring deeper in-house capabilities and expertise than young tech firms have typically needed. One executive overseeing finance hiring at a neocloud noted that leveraged and structured finance backgrounds bring expertise that can help in areas like working through project diligence and getting banks to sign off on deals. Some AI firms may also want to run their own project finance models so they can move quickly through negotiations and have something to compare to lenders’ models.
AI companies aren’t always issuing the debt themselves—that can fall to data center developers or special purpose vehicles, with firms like Blackstone and Apollo providing or arranging chip and other financing. And some of the biggest AI deals are using backstops from investment-grade companies like Nvidia or major cloud providers. Even so, commitments from AI customers often underpin much of the borrowing. And the users of the infrastructure will want to understand what they’re signing up for and their risks if a project runs into trouble.
“Hiring of people within that business, responsible for the financing of compute, could prove to be an existential decision,” said Dan McCarthy, founder and CEO of One Search, an executive search firm focused on infrastructure finance whose recent clients include OpenAI. “You want someone who knows where all the pitfalls are, where all the bodies are buried in multibillion-dollar loans.”
OpenAI, for its part, in July named Sven Semmelmann as head of compute capital markets. He previously led structured finance at Generate Capital, an investment firm that finances and owns infrastructure projects, and he has also held project finance roles at major banks. OpenAI Chief Financial Officer Sarah Friar, when announcing the hire on LinkedIn, said Semmelmann would oversee financing and partnerships to grow the company’s compute resources.
Anthropic, meanwhile, has made several finance hires recently to work on capital markets and compute deals, and also has open positions posted including a capital markets infrastructure financing role. AI infrastructure upstarts are staffing up as well. Nscale, which launched in 2024 and is gearing up for a potential IPO, has been hiring across levels for capital markets and treasury as well as legal roles, calling for experience in areas like structured finance and private credit.
SB Energy and Crusoe, which are developing major new data centers for OpenAI and other customers, are hiring across levels for jobs focused on project financings and other structured deals, recent postings show, while AI infrastructure startup Fluidstack is hiring a structured finance lead and a more junior counterpart.
The good news for AI companies is that private credit and infrastructure teams, as well as investment banking teams focused on structured or project finance, had been growing even prior to the AI boom, providing a pool of skills that could translate into new twists on structured finance, like big graphics processing unit–backed deals.
But that kind of finance talent doesn’t come cheap, especially for more senior people who have a track record of working on large transactions. And the normal tech tactic of dangling stock to lure talent won’t necessarily do the trick in all cases, especially for the most seasoned dealmakers and investors.
Financiers would have to weigh a cash-heavy Wall Street pay package, albeit one that can depend heavily on how good bonus season is, against betting a portion of their pay on stock in a private or newly public company. Managing directors in investment banking can make north of $1 million in cash a year, with the biggest rainmakers making considerably more. The part of pay they get in stock at big public banks may vest over a few years but is generally easy to sell after that.
For people at big infrastructure or private credit firms, senior employees may also receive carried interest, meaning a share of the profits on the funds or investments they work on, which can become worth millions over time.
For instance, an investor at a top infrastructure firm may have several million dollars’ worth of carried interest tied up at their current firm they’d have to leave on the table. An AI company could try to make them whole with stock, which could be tantalizing to some, though others might not want to make a bet on equity in a young company.
That might make the most experienced investors—those who’ve seen big infrastructure projects through over many years and know all the tricks of the trade—hard to pry away.
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