When Even the Shovel Sellers Borrow to Buy Shovels: The US Stock AI Sector Evaporates Trillions in a Week, as the Market Begins Pricing AI's 'Bill'

marsbitPublished on 2026-06-11Last updated on 2026-06-11

Abstract

Last week saw a sharp sell-off in the US AI sector, erasing trillions in market value despite companies reporting record financials. Key events included Alphabet's massive equity raise despite having over $1.2 trillion in cash, Broadcom's stock plummeting after its quarterly report, a 4% Nasdaq drop, and Oracle's stock falling even after posting record revenue and backlog. The market's focus shifted from stellar income statements to cash flow and balance sheets, questioning the massive and increasingly leveraged capital expenditures required to fund the AI infrastructure race. Analysts point out that while growth is strong, profitability and the visibility of AI monetization are now under scrutiny. The financing chain stretches from cash-rich giants like Alphabet raising debt and equity, to chipmakers like Broadcom facing margin pressure, to cloud providers like Oracle with negative free cash flow funding via debt, and finally to unprofitable AI labs like OpenAI and Anthropic—who are the ultimate customers for much of this infrastructure. The market is beginning to price the risk of this concentrated, leveraged bill coming due, with the upcoming SpaceX IPO serving as the next test for this financing ecosystem.

Author: Ada, Deep Tide TechFlow

Over the past week, the US stock AI sector presented a strange picture, with records being broken one after another and stocks being sold off one after another.

On June 1, Alphabet, sitting on hundreds of billions in cash, announced one of the largest equity financings in history; On June 3 after market close, Broadcom delivered its best-ever quarterly report, only for its stock to crash the next day; On June 5, the Nasdaq plunged 4% in a single day, with the semiconductor sector shedding roughly $1 trillion in market value in one session; On June 10 after market close, Oracle reported record revenue and backlog, yet its stock still fell; On June 11, the pricing moment arrived for what is set to be the largest SpaceX IPO in history. The financial numbers themselves are not the issue; the problem is the way these numbers are being achieved: more and more money is being borrowed into this AI infrastructure race through increasingly complex means. When the market began tallying up this bill, even record-breaking numbers couldn't save stock prices.

The Same Script: Record First, Then Get Sold Off

Broadcom went first. According to its earnings report and multiple media outlets, for its fiscal Q2 ended May 3, Broadcom's revenue reached $22.219 billion, up 48% year-over-year; AI chip revenue was $10.8 billion, surging 143% year-over-year; and earnings per share exceeded Wall Street expectations. But the market focused solely on one gap: the company's AI chip revenue guidance for the next quarter is $16 billion, below analyst expectations, and CEO Hock Tan did not raise the full-year AI revenue target, mentioning that Google might diversify its chip supply chain. Nonetheless, Broadcom's stock plummeted about 15% the next day, erasing nearly $280 billion in market value in a single day, making it one of the largest single-day market cap losses in Wall Street history.

Oracle's turn came a week later. According to its earnings report and CNBC, for its fiscal Q4 ended May 31, Oracle's revenue was $19.2 billion, up 21% year-over-year; cloud infrastructure revenue was $5.8 billion, up 93% year-over-year; adjusted EPS was $2.11, beating analyst expectations of $1.95. The backlog was even more staggering: Remaining Performance Obligations (RPO) reached $638 billion, skyrocketing 363% year-over-year, far exceeding analyst expectations of $595.7 billion. Yet the stock also didn't find favor, dropping about 9% in after-hours trading.

Sandwiched between these two earnings reports was a broad sell-off on June 5. According to TheStreet and CNBC, the Nasdaq Composite Index fell 4% that day, its worst single-day performance since the April 2025 tariff turmoil, triggered by Broadcom's cautious AI chip outlook, with AMD and Intel leading the decline across the entire semiconductor sector.

It is worth noting that the June 5 plunge wasn't solely about "AI skepticism." That day, US non-farm payrolls added 172,000 jobs, far exceeding expectations, pushing up rate hike expectations and triggering a rotation from high-valuation growth stocks into defensive sectors like healthcare and consumer staples. AI stocks, having the highest valuations, fell the hardest. In other words, macroeconomic interest rates and sector rotation were one driver, while concerns over AI capital expenditures were another. The two factors compounded, rather than having a single cause.

What's Being Sold Off Isn't the Income Statement, It's the Cash Flow Statement

Putting these three market segments together reveals a commonality: the income statement is still recording "record-breaking" numbers, but the market has already switched to reading the cash flow statement and the balance sheet. The pricing focus has shifted from "how much was earned" to "how much more will be burned and borrowed to earn this."

Oracle is the most straightforward case study. According to its earnings report, for the full FY2026, operating cash flow reached a record $32 billion, up 54% year-over-year, but free cash flow was negative $23.7 billion. For the year, the company has already raised $43 billion in debt and $5 billion in equity. What truly broke investor sentiment was its forward-looking language. According to CNBC, Oracle plans to raise approximately $40 billion more in FY2027 through a combination of debt and equity. A company that just raised nearly $50 billion, with negative free cash flow, and now forecasts another $40 billion financing round—when this appears alongside "record-breaking" numbers, the market chose to price the former.

Broadcom's logic is similar, but manifests elsewhere. According to Barron's, Broadcom lowered its Q3 gross margin guidance from 77% to 74%, attributing it to a higher revenue mix from lower-margin AI chips. Coupled with a retreat from "selling complete systems" to "selling only chips," and customers demanding chip leases to pass on financing pressure, the market sees a business with explosive growth, but deteriorating margins and capital intensity.

Goldman Sachs provided a framework for this shift. According to its research report, investors' tolerance for capital expenditure growth depends on earnings strength and the visibility of AI monetization; the same report noted that Alphabet's stock rose due to raising its profit guidance, while Meta's fell due to flat guidance. The market is no longer indiscriminately rewarding "growth," but differentiating winners from losers based on "the ability to monetize."

The Financing Chain Takes Center Stage: Even the Most Cash-Rich Players Are Borrowing

If the income statement is the surface, the financing chain is the true protagonist of this past week. From the very upstream to the very downstream, almost every link is paying for this same AI infrastructure build-out by leveraging up or diluting equity.

The most convincing case study is Alphabet. According to its SEC filing, on June 1, it announced an $80 billion equity financing; on June 2, it upsized and priced the deal at $84.75 billion, including a $10 billion private investment from Berkshire Hathaway. The anomaly is that this company is not short on cash at all. According to multiple media reports, Alphabet had $126.8 billion in cash as of March 2026, annual operating cash flow of $174 billion, and has issued over $55 billion in new debt since November. Despite this, Melius Research estimates Google's free cash flow will turn negative in the coming years. Investor Dan Niles' commentary on this is that capital is not infinite, and the fact that Google, with "the strongest technology stack in all of AI," still needs to raise massive amounts of financing precisely illustrates the intensity of this round of investment.

Looking downstream, every link on the chain is doing the same. New cloud vendor Oracle has negative free cash flow, relies on dual-track debt and equity financing, and asks customers to prepay for GPU costs or bring their own GPUs to reduce its own construction capital outlay; the chip "shovel sellers" like Broadcom, on June 9, partnered with Apollo and Blackstone to establish the AI XPV Platform, with an initial $35 billion targeting over 20 gigawatts of computing power by 2028, serving frontier labs including Anthropic and OpenAI. And at the very end of the chain, the labs are simultaneously employing even more aggressive tools: there were previous reports of SoftBank arranging margin loans secured by OpenAI equity; now SpaceX is rushing towards a Nasdaq IPO targeting $75 billion, Anthropic has confidentially filed for an IPO, and OpenAI is expected to follow closely.

The total scale of this investment is also rapidly inflating. According to CreditSights, the combined capital expenditure for mega-cap companies in 2026 is estimated at around $750 billion, up about 67% from 2025; Goldman Sachs' alternative metric puts the 2026 capex forecast for mega-caps at $518 billion, revised up from $314 billion at the start of the year. Whichever metric is used, the direction is consistent: expenditures are accelerating, the portion that can be covered by operating cash flow is shrinking, and the gap needs to be filled by capital markets.

The Chain's Pressure Point Rests on a Few Unprofitable Labs

Leverage itself isn't terrifying; what's terrifying is who the leverage ultimately rests upon. Pulling this financing chain to its end reveals that its pressure point is highly concentrated.

Oracle's $638 billion backlog seems impregnable, but according to Bank of America, over 50% comes from OpenAI alone; simultaneously, Oracle also disclosed that most of the RPO growth in the past two quarters came from large AI contracts, with customers either prepaying GPU costs or procuring GPUs themselves before handing them to Oracle. Broadcom's six major custom chip customers are similarly concentrated among a handful like Google, Meta, Anthropic, and OpenAI. In other words, from the financing of the mega-caps, to the orders of the "shovel sellers," to the injection of private credit and insurance funds, the ultimate payer for the entire chain converges onto a small group of unprofitable frontier labs like OpenAI and Anthropic, who are themselves queuing up for funding.

The record-breaking revenue is real, and the $638 billion backlog is real; but the buyers of these orders are highly concentrated, are themselves sustained by financing, and the leverage of the entire chain is being re-examined by the market. This past week, the market did not deny AI's growth; it simply started demanding to see clearly who will pay the bill for this growth, and how. SpaceX is set to price after market close on June 11 and list on the Nasdaq at $135 per share, with a valuation of approximately $1.77 trillion on June 12. Whether this largest-ever IPO can be digested smoothly will be the next stress test for this financing chain.

Related Questions

QWhat were the main reasons behind the significant sell-off in the AI sector of the U.S. stock market over the past week?

AThe sell-off was driven by a combination of macroeconomic factors and sector-specific concerns. The stronger-than-expected U.S. non-farm payrolls data raised interest rate expectations, triggering a rotation from high-valuation growth stocks (like AI) into defensive sectors. Simultaneously, the market began to reassess the massive capital expenditure required for AI infrastructure. Companies like Broadcom and Oracle reported record revenues but also signaled the need for continued heavy investment and financing, leading to a market focus on negative free cash flow and growing debt, which caused their stock prices to fall despite strong earnings.

QHow did the market's valuation focus shift for AI companies like Oracle and Broadcom, according to the article?

AThe market's valuation focus shifted from the income statement (profit and revenue growth) to the cash flow statement and balance sheet. Investors are no longer just rewarding 'record' earnings; they are now pricing in the significant capital required to generate those earnings. For example, Oracle's record $638 billion backlog and revenue were overshadowed by its negative free cash flow and plans for further large-scale debt and equity financing. For Broadcom, the market focused on the declining gross margin guidance and the increased capital intensity of its AI chip business, even as AI revenue soared.

QWhy did Alphabet, despite having a massive cash reserve, conduct a major equity financing round?

AAlphabet announced an $84.75 billion equity financing round even with $1268 billion in cash and strong annual operating cash flow because, according to analyst estimates (like from Melius Research), its free cash flow is projected to turn negative in the coming years due to the immense intensity of AI infrastructure investment. As noted by investor Dan Niles, this move by a company with the 'strongest technology stack in AI' highlights the enormous scale of capital required for this buildout, suggesting that even the most cash-rich players need to tap external funding to sustain the AI arms race.

QWhat is the common weak point or 'pressure point' in the AI infrastructure financing chain identified in the article?

AThe common weak point or 'pressure point' in the AI financing chain is its heavy reliance on a small group of unprofitable, frontier AI labs like OpenAI and Anthropic. A significant portion of the record orders for companies like Oracle (over 50% from OpenAI alone) and Broadcom's major custom chip customers are concentrated with these labs. These labs are themselves dependent on continued fundraising (e.g., private credit, impending IPOs) to pay their bills. This creates a concentrated risk where the entire chain's viability depends on the financial health and ability of a few non-profitable entities to keep raising capital.

QWhat upcoming event does the article suggest will be the next major test for the AI financing chain?

AThe article suggests that SpaceX's upcoming IPO, scheduled to price on June 11 and list on Nasdaq on June 12 with a target valuation of approximately $1.77 trillion, will be the next major test for the AI financing chain. As the largest IPO in history, its success in being absorbed by the market will serve as a crucial indicator of investor appetite and capacity to fund the massive capital requirements at the end of the AI infrastructure investment chain.

Related Reads

Xpeng and NIO Compete on Computing Power, Li Auto Shifts Architecture

On June 15, 2026, Li Auto unveiled details of its self-developed chip, Mahe M100, for its new L9 Livis model. CTO Xie Yan stated the goal was not just a faster chip, but a fundamentally different one, targeting the chip architecture itself. While competitors like NIO, Xpeng, and Huawei highlight TOPS (computing power) figures for their self-developed chips, Li Auto’s Mahe M100 focuses on redesigning the underlying architecture. It employs a "dynamic data flow architecture" to address memory bandwidth bottlenecks in large model inference, claiming up to 3x the effective computing power of Nvidia's Thor U for its specific workloads and a 40% reduction in latency. The chip's design was peer-reviewed and accepted at ISCA 2026. However, this performance is highly optimized for Li Auto's own VLA2.1 algorithm, meaning it may not generalize as well to other tasks. Li Auto aims to achieve full-stack in-house development with Mahe M100, covering chip, compiler, OS, AI algorithms, and domain controller—a level of vertical integration few competitors match. Beyond the chip, CEO Li Xiang introduced a new strategic narrative: the "embodied intelligent vehicle," defined as an integration of an EV, a professional driver, an AI computer, and a life assistant. This shifts competition from features like large screens to systemic AI capabilities. A key commitment was that Li Auto's Mahe VLA autonomous driving model will match Tesla's FSD V14 by Q4 2026, with specific OTA milestones set for July, September, and December. Financially, Li Auto faces pressure with declining revenue and vehicle gross margins since Q4 2025, while maintaining high R&D investment (approx. ¥12B in 2026, 50% AI-related). Its 2026 sales target is 550,000 vehicles, up from 406,000 in 2025. The new L9 Livis garnered over 10,000 pre-orders in two weeks. The effectiveness of these strategic moves—new products, OTAs, and the novel chip architecture—will begin to show in Q3 2026 financial results, with the year-end FSD V14 benchmark being the ultimate test.

marsbit37m ago

Xpeng and NIO Compete on Computing Power, Li Auto Shifts Architecture

marsbit37m ago

The Year of AI Applications: Saying 'Yes' While Ignoring Risks? A Comprehensive Open Source Log of Software Development's Journey

The Year of AI Applications: Blindly Saying "Yes" While Ignoring Risks? A Software Development Log Goes Fully Open Source. AI-generated code harbors risks hidden within seemingly correct programs, potentially leading to data leaks or asset loss. The open-source project "Narwhal AI Code Risks," from Peking University's Narwhal-Lab, compiles real-world cases, early warning signs, and typical risk pathways. Its goal is to help developers identify potential hazards early and avoid repeating past mistakes. In 2026, code is generated faster than ever but deployed with less scrutiny. The danger often lies not in glaring errors, but in code that appears normal—syntactically correct, passing all checks—yet introduces subtle but critical flaws like non-existent dependencies, excessive permissions, or exposed databases. A stark example is the Moonwell cbETH oracle incident. A configuration file error, where a cryptocurrency price was set to ~$1.12 instead of ~$2,200, slipped through 28 checks and a pull request signed by both AI (Claude, Copilot) and human developers. This "semantic deviation" resulted in a loss of $1.78 million. The risk is that AI can produce functionally valid code that is semantically wrong for the business context. As AI moves beyond simple code completion to modifying configurations, installing dependencies, and operating via autonomous agents, it traverses longer, less traceable paths within software engineering, blurring traditional boundaries and oversight points. The Narwhal AI Code Risks project structures information into three layers: `/cases` for documented real-world incidents, `/inferred` for early warning signals, and `/scenarios` for clear, generalized risk patterns not yet tied to specific events. This aims to create a lasting, public record to prevent collective amnesia about past AI-coding pitfalls. Risks are categorized into seven areas: Software Supply Chain (e.g., recommending fake packages), Code-Level Vulnerabilities (e.g., reintroducing path traversal bugs), Cloud & Infrastructure Misconfiguration (e.g., overly permissive settings), Agent Risks (from autonomous tool execution), Vertical Domain Risks (e.g., in finance, healthcare), Intellectual Property & Compliance issues, and Human Factors (like over-reliance on AI output). The project's core value is transforming isolated incidents into reusable knowledge—a foundational resource for developers to spot similar issues, for security researchers to build upon, for toolmakers to create detection rules, and for the community to contribute new findings. As AI integration accelerates, this open-source "logbook" serves as a crucial navigational aid, charting past errors to help future projects steer clear of the same traps.

marsbit37m ago

The Year of AI Applications: Saying 'Yes' While Ignoring Risks? A Comprehensive Open Source Log of Software Development's Journey

marsbit37m ago

The Foundation of SpaceX's Trillion-Dollar Valuation: Who is Dividing Up Musk's Annual Tens of Billions in Capital Expenditure?

SpaceX's trillion-dollar valuation is built on its three core businesses: Starlink (profitable, 60% of revenue), rockets (driving down launch costs), and AI (a major investment area). This creates a financial cycle: Starlink funds rocket development, which enables low-cost launches for AI hardware, generating future revenue. This cycle fuels annual capital expenditures of tens of billions, flowing to a vast supply chain. Suppliers are categorized by their replaceability. The first group includes irreplaceable players like NVIDIA (GPU/CUDA ecosystem), Eutelsat (critical radio spectrum), Filtronic (specialized amplifiers), Materion (strategic beryllium), and STMicroelectronics (antenna chips). The second group consists of hard-to-replace suppliers due to high switching costs, such as Honeywell (flight control), Carpenter Technology (specialty alloys), Hexcel (carbon fiber), Broadcom (data exchange), and Linde (industrial gases). The third group comprises high-volume, cost-critical suppliers for mass-produced items like Starlink terminals. Key names include Wistron NeWeb (primary manufacturer) and several A-share companies like Shenzhen Sunway (connectors), Pies New Materials (forgings), Western Superconducting (alloys), and Yingliu (castings). Other niche players include Trimble (timing), Astronics (power distribution), and CTS (thermal management). The article argues that investing in these suppliers, rather than SpaceX stock directly, offers an alternative opportunity. The rationale is threefold: procurement is just beginning to scale, SpaceX's IPO brings new transparency to its supply chain, and the situation mirrors early stages of past "super terminal" ecosystems like Apple or Tesla. While risks exist (commodity cycles, geopolitical factors, technology shifts), the core thesis is that SpaceX's massive, ongoing procurement will translate into reliable revenue for its key suppliers, regardless of its own stock price volatility.

marsbit1h ago

The Foundation of SpaceX's Trillion-Dollar Valuation: Who is Dividing Up Musk's Annual Tens of Billions in Capital Expenditure?

marsbit1h ago

SpaceX's Trillion-Dollar Valuation Base: Who's Sharing in Musk's Annual Tens of Billions in Capital Expenditure?

**Title: The Foundation of SpaceX's Trillion-Dollar Valuation: Who Benefits from Musk's Annual $100 Billion Capital Expenditure?** This article argues that investors seeking to benefit from SpaceX's growth might find greater opportunities in its supply chain rather than directly investing in the company itself, drawing parallels to historical successes with Apple, Tesla, and NVIDIA suppliers. **SpaceX's Business Model & Cash Flow:** SpaceX generates revenue from three main areas: 1. **Starlink:** Its profitable core, earning $11.3B in 2023 (60% of revenue), funding other ventures. 2. **Rockets (Falcon/Starship):** Requires $3B+ in annual R&D but achieves the world's lowest launch costs. 3. **AI:** Currently unprofitable (-$6B+ in 2023), investing heavily in ground-based supercomputers (220,000 GPUs) and future orbital data centers. The cycle is: Starlink profits → fund cheaper rockets → low-cost launches deploy AI hardware → AI compute rentals generate future revenue. This cycle drives annual procurement spending of tens of billions of dollars. **The Supply Chain Beneficiaries:** Suppliers are categorized by their replaceability: **1. Nearly Irreplaceable (High Barriers to Entry):** * **NVIDIA:** Powers the Colossus supercomputer; its CUDA ecosystem creates immense switching costs. * **Eutelsat (SATS):** Controls critical radio spectrum for satellite communications; holds a ~3% stake in SpaceX. * **Filtronic (FTC):** Supplies millimeter-wave signal amplifiers for Starlink satellites; SpaceX constitutes 83% of its revenue. * **Materion (MTRN):** Global leader in beryllium production, a strategic material used in Starship structures. * **STMicroelectronics (STM):** Supplies phased-array antenna chips for Starlink satellites. **2. Replaceable, but Switching Cost is Prohibitively High:** * **Honeywell (HON):** Provides flight control and inertial navigation systems with decades of certification. * **Carpenter Technology (CRS):** Manufactures ultra-pure specialty steel alloys for Raptor engines. * **Hexcel (HXL):** Supplies custom carbon fiber composites developed over a decade with SpaceX. * **Broadcom (AVGO):** Manages high-speed data switching. * **Linde Group:** Supplies industrial gases (liquid oxygen/nitrogen) from facilities built near SpaceX launch sites. **3. High-Volume, Cost-Critical Manufacturing:** Focuses on mass-producing components like Starlink user terminals (target: 30 million units). * **Key Players:** Wistron NeWeb (6285, primary terminal manufacturer), several Chinese A-share companies (e.g., Sunway Communication, PAX New Materials, Western Metal Materials, Yingliu Co.), and smaller US firms like Trimble (TRMB, timing systems). **Why Now?** Three factors make the supply chain opportunity timely: 1. **Volume Ramp-Up:** SpaceX plans 100 launches in 2026, aims for 30 million Starlink terminals, and will deploy AI data centers, meaning procurement will accelerate. 2. **Increased Transparency:** The IPO provides public financial data, allowing investors to track supplier order growth. 3. **Historical Precedent:** The current phase is likened to Tesla's early mass-production stage (circa 2018), suggesting a long growth runway for suppliers. **Conclusion:** The article posits that while investing in SpaceX stock is betting on Elon Musk's ambitious vision at a high valuation, investing in its established suppliers is a bet on the tangible, recurring revenue from its massive procurement budget, which is largely decoupled from day-to-day stock price volatility.

链捕手1h ago

SpaceX's Trillion-Dollar Valuation Base: Who's Sharing in Musk's Annual Tens of Billions in Capital Expenditure?

链捕手1h ago

Trading

Spot
Futures

Hot Articles

How to Buy BILL

Welcome to HTX.com! We've made purchasing Billions Network (BILL) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy Billions Network (BILL) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your Billions Network (BILL)After purchasing your Billions Network (BILL), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade Billions Network (BILL)Easily trade Billions Network (BILL) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

1.4k Total ViewsPublished 2026.05.07Updated 2026.06.02

How to Buy BILL

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BILL (BILL) are presented below.

活动图片