Originally published at twarx.com - read the full interactive version there.
Last Updated: June 21, 2026
OpenAI's massive losses strengthen the bull case for these 2 artificial intelligence (AI) stocks — and the reason is the opposite of what most retail investors assume. OpenAI losing $20.92 billion in 2025 is not a crisis; it is a purchase order for the stocks you can actually own today. Every dollar OpenAI burns on compute, talent, and distribution flows directly into the earnings of publicly traded companies — Nvidia (NVDA) and Microsoft (MSFT) — that the market is systematically mispricing right now.
This piece breaks down The Motley Fool's June 21, 2026 report on OpenAI's leaked financials and the two beneficiaries it names: Nvidia (NVDA) and Microsoft (MSFT). We map the exact mechanism — what I call the Loss-to-Leverage Effect.
By the end you'll know what was confirmed, how the money flows, how to invest, and where this thesis breaks. If you want the broader picture first, our overview of AI infrastructure investing sets the stage.
The two publicly traded beneficiaries of OpenAI's record losses: Nvidia and Microsoft. Source: The Motley Fool
Coined Framework
The Loss-to-Leverage Effect — the counterintuitive market dynamic where a private AI giant's mounting operating losses directly amplify revenue and margin expansion for its publicly traded infrastructure and distribution partners
When a private leader like OpenAI burns cash to stay at the frontier, most of that cash isn't destroyed — it's transferred onto the income statements of its chip and cloud suppliers. The loss is a leading indicator of someone else's revenue.
What Was Announced: OpenAI's Financial Losses and the Bull Case (Official Facts)
The single most consequential fact: OpenAI reported a $20.92 billion loss from operations in 2025, far worse than the $8.78 billion loss it recorded in 2024, according to leaked financials cited in The Motley Fool (June 21, 2026). For independent corroboration of OpenAI's cash burn trajectory, see reporting from Reuters Technology and Bloomberg Technology.
The Motley Fool Report: Key Figures and Publication Date
The article, published June 21, 2026, makes a contrarian argument: these losses strengthen the bull case for two stocks retail investors can own — Nvidia and Microsoft. The reasoning is structural, not sentimental. OpenAI is spending heavily because it has to, and that spending lands in suppliers' pockets.
OpenAI's Reported Loss and Revenue Breakdown
Per the leaked figures reported by the Fool: OpenAI posted $13.07 billion in revenue in 2025, up 253% year over year, while its operating loss more than doubled to $20.92 billion. Revenue is exploding. But the cost of staying at the frontier is exploding faster. That gap is the whole story.
$20.92B
OpenAI operating loss, 2025
[The Motley Fool, 2026](https://www.fool.com/investing/2026/06/21/openais-massive-losses-strengthen-the-bull-case-fo/)
+253%
OpenAI revenue growth YoY (to $13.07B)
[The Motley Fool, 2026](https://www.fool.com/investing/2026/06/21/openais-massive-losses-strengthen-the-bull-case-fo/)
$5.1T
Nvidia market cap at publication
[The Motley Fool, 2026](https://www.fool.com/investing/2026/06/21/openais-massive-losses-strengthen-the-bull-case-fo/)
OpenAI IPO 2026: What Is Officially Confirmed So Far
The Fool reports OpenAI is racing toward an IPO later this year. That matters because right now, retail investors have zero direct equity access to OpenAI. The only way to ride OpenAI's growth today is to own the suppliers it's paying. ChatGPT's launch — more than three years ago — was arguably the main catalyst for the entire AI boom, and OpenAI has stayed at the center of it since. For context on how this feeds through to purchasing decisions, see how enterprise AI adoption shapes supplier demand.
What This Means: Understanding the Loss-to-Leverage Effect in AI Markets
Most retail investors read '$20.92 billion loss' and flinch. That's the mistake. In capital-intensive technology buildouts, the leader's losses are the supplier's revenue. The question is never 'is OpenAI profitable?' — it's 'who is OpenAI paying?'
OpenAI's income statement is the most reliable demand forecast Nvidia and Microsoft will ever get. A loss line is a customer's order in disguise.
Why Operating Losses in AI Signal Demand, Not Failure
As the Fool puts it: 'Training and running frontier AI models is expensive.' OpenAI invests in 'the hardware and infrastructure necessary to support advanced AI models,' and 'that includes AI chips.' A loss caused by buying compute is fundamentally different from a loss caused by a broken business. One funds growth; the other signals decay. OpenAI's is clearly the former.
How OpenAI's Spending Becomes Revenue for Public Companies
The mechanism is direct. Nvidia's GPUs are, in the Fool's words, 'the workhorses of AI training' and 'the defining hardware of the AI revolution.' Every frontier training run and inference call needs them. Microsoft, meanwhile, has historically supplied OpenAI's cloud capacity — meaning compute spend routes through Azure infrastructure. The dollars don't vanish. They migrate onto two public income statements. Nvidia's own data-center revenue disclosures can be cross-checked in its SEC 10-K filings.
Nvidia's gross margin sits at 74.15% per the Fool's data. At that margin, roughly three of every four dollars OpenAI spends on Nvidia silicon converts to gross profit for shareholders you can actually buy today.
The Historical Precedent: The Cloud Spending Cycle
This pattern isn't new. During the 2010–2015 cloud buildout, every dollar Netflix and Airbnb burned on infrastructure became Amazon Web Services' margin expansion — a dynamic documented across years of Amazon 10-K filings. The startups absorbed the losses; the infrastructure provider compounded the profit. The Loss-to-Leverage Effect is the AI-era replay of that exact trade. I've watched this cycle run twice now, and the shape is identical.
The Loss-to-Leverage Effect visualized: OpenAI's operating loss is the upstream of Nvidia and Microsoft's revenue. Source
How One Dollar of OpenAI Loss Becomes Public-Company Revenue
1
**OpenAI raises capital**
Pre-IPO funding and revenue ($13.07B in 2025) fund a frontier model roadmap that demands ever-larger compute clusters.
↓
2
**Compute procurement**
OpenAI orders Nvidia GPUs and reserves cloud capacity. Sam Altman: 'We hope to be a gigantic customer for a very long time.'
↓
3
**Nvidia recognizes hardware revenue**
GPU sales convert at ~74% gross margin. CUDA lock-in makes the demand recurring, not one-off.
↓
4
**Microsoft recognizes cloud + Copilot revenue**
Azure hosts inference; Copilot redistributes OpenAI models across the Microsoft 365 install base, monetizing the same compute twice.
↓
5
**OpenAI books the loss; suppliers book the profit**
The $20.92B operating loss is the visible cost of the demand that public shareholders capture on the other side.
The sequence matters because each arrow is a place where a private loss becomes a public, ownable gain.
Full Capability Breakdown: Why These 2 Stocks Are Structurally Positioned to Win
Here's what most people get wrong about the AI trade: they hunt for the next model breakthrough. The durable money is in the layers underneath the models — the silicon and the cloud — where switching costs are brutal and demand is non-discretionary.
Stock 1 — Nvidia (NVDA): The CUDA Lock-In
Nvidia's moat isn't just performance — it's the CUDA ecosystem, which the Fool describes as 'sticky' and a reason 'it will be very difficult for competitors' to dislodge. When OpenAI's CEO Sam Altman responded to rumors that OpenAI was seeking chip alternatives, he posted on X: 'We love working with Nvidia and they make the best AI chips in the world. We hope to be a gigantic customer for a very long time.' That's the demand signal in the leader's own words. Hard to manufacture a more explicit forward commitment than that.
Nvidia's data-center economics are extraordinary: a $5.1 trillion market cap, a 52-week range of $142.03–$236.54, and a 74.15% gross margin, per the Fool's published data. For deeper context on why the chip layer underpins everything, see how AI agents ultimately resolve to GPU cycles. Nvidia's own developer stack is documented at NVIDIA CUDA.
The Fool's sharpest point: 'OpenAI is likely not an outlier here.' If OpenAI must spend this much, every frontier competitor — Anthropic, Google DeepMind, xAI — must too. That makes Nvidia a toll collector on the entire multi-agent systems arms race.
Stock 2 — Microsoft (MSFT): The Azure + Copilot Distribution Engine
Microsoft monetizes OpenAI's spend twice. First as infrastructure: Azure has historically hosted OpenAI's workloads. Second as distribution: Copilot redistributes OpenAI models across Microsoft 365, turning raw compute into per-seat recurring revenue. Per the Fool's data, Microsoft moved +0.19% on the publication day — modest on the surface, but the structural exposure underneath that number is enormous. See Microsoft's official AI roadmap at Microsoft Source: AI.
Coined Framework
The Loss-to-Leverage Effect in practice
Microsoft is the rare beneficiary that captures the Loss-to-Leverage Effect on both ends — as the cloud OpenAI rents and the channel that resells OpenAI's intelligence. That double-dip is why a private company's loss can be a public company's compounding flywheel.
Comparing Revenue Exposure: Who Benefits More Per OpenAI Dollar Spent
Nvidia captures the first and largest slice — hardware — at ~74% gross margin, but it's more cyclical; chip orders can and do pause. Microsoft captures a thinner but stickier recurring slice via Azure consumption and Copilot seats. For pure leverage to OpenAI's spend, Nvidia leads. For durability across cycles, Microsoft leads. Most balanced portfolios want both, and I wouldn't argue hard against that split. Our AI stock analysis playbook walks through sizing each layer.
How to Access and Invest: Step-by-Step Guide Including Pricing and Availability
Both stocks trade on US exchanges (NVDA and MSFT on NASDAQ) and are available on every major brokerage. Here's the practical path.
Buying NVDA and MSFT is commission-free on major US brokerages, with fractional shares lowering the entry barrier to as little as $1. Source
How to Buy Nvidia (NVDA): Platforms, Fractional Shares, and Cost Basis
Open a brokerage account on Fidelity, Charles Schwab, Robinhood, or Interactive Brokers — all offer $0 commission trades.
Search ticker NVDA. The Fool listed a current price of $210.95 with a day's range of $206.50–$211.38.
Use fractional shares to enter from as little as $1 on Fidelity and Robinhood — you don't need a full ~$211 share to start building a position.
Set a recurring buy to dollar-cost average through the volatility. The 52-week range spanned $142.03–$236.54 — that's a ~66% swing, and you will feel it if you go in all at once.
How to Buy Microsoft (MSFT): The Stable Core Holding
Same process — search ticker MSFT. Microsoft's diversified revenue across cloud, productivity, gaming, and Copilot makes it a lower-volatility way to own the AI buildout than a pure chip play. For automation-heavy investors tracking AI revenue signals, our workflow automation guides show how to build alerting around earnings dates. You can also explore our AI agent library to automate portfolio research using RAG over SEC filings.
Dollar-Cost Averaging Into AI Infrastructure: A Practical Framework
python — simple DCA scheduler concept (illustrative, not financial advice)
Illustrative DCA allocation across the two named beneficiaries
monthly_budget = 500 # USD you commit each month
allocation = {
'NVDA': 0.50, # hardware layer — highest leverage to OpenAI spend
'MSFT': 0.50, # cloud + distribution — stickier recurring revenue
}
for ticker, weight in allocation.items():
dollars = monthly_budget * weight
# place a fractional, recurring market buy via your broker API
print(f'Buy ${dollars:.2f} of {ticker} this month')
Output:
Buy $250.00 of NVDA this month
Buy $250.00 of MSFT this month
For automating research pipelines around these holdings, builders often combine n8n with LangChain and a vector store like Pinecone to summarize earnings calls automatically. See our orchestration walkthrough, or deploy a ready-made research agent from our agent library.
When to Buy These Stocks vs Waiting for the OpenAI IPO
The Fool reports retail investors are 'anxiously waiting to get a piece of' OpenAI ahead of its IPO 'later this year.' Here's the uncomfortable truth about that wait.
You don't need to wait for the OpenAI IPO to own OpenAI's growth. You can own its two biggest invoices — Nvidia and Microsoft — at market open tomorrow, with full liquidity and profitable underlying businesses.
The OpenAI IPO Risk Profile
OpenAI is, by the Fool's own figures, deeply loss-making: a $20.92 billion operating loss in 2025 with no disclosed path to profitability. At IPO, bankers price for institutions first. Retail buyers often enter after the first-day pop, at stretched multiples, holding the bag if sentiment shifts. The mechanics of IPO pricing and lock-ups are explained in plain English by Investopedia and by the SEC's Investor.gov. (Specific IPO valuation, pricing, and lock-up terms were not disclosed in the source and remain speculative until an S-1 is filed.)
NVDA and MSFT vs the OpenAI IPO: Risk Comparison
Nvidia and Microsoft already convert AI demand into profit today — Nvidia at a 74.15% gross margin. OpenAI converts AI demand into a widening loss. If your thesis is 'AI demand keeps rising,' the profitable suppliers express that thesis with far less binary risk than a freshly public, pre-profit model lab. I wouldn't size an OpenAI IPO bet the same way I'd size NVDA. Not even close.
When Waiting Makes Strategic Sense
For risk-tolerant investors who want pure OpenAI exposure and accept the loss profile, a small allocation after the post-IPO lock-up — commonly 180 days for insiders — has historically offered calmer entry than day-one buying. Size it as a speculative sliver. Not a core position.
Competitor Comparison: NVDA and MSFT vs Other AI Stock Picks
The Fool names Nvidia and Microsoft, but they sit inside a broader field. Here's how the layers stack up.
StockAI LayerOpenAI-Loss LeverageKey StrengthSource-Confirmed Data Point
Nvidia (NVDA)Hardware / GPUsHighestCUDA lock-in, 74.15% gross margin$5.1T market cap; price $210.95 (Fool, 2026)
Microsoft (MSFT)Cloud + DistributionHigh (double-dip)Azure hosting + Copilot reach+0.19% on publication day (Fool, 2026)
Alphabet (GOOGL)Models + SearchIndirectGemini competition forces industry spendNot named as a beneficiary in this report
Amazon (AMZN)Cloud (AWS)ModerateCloud scale, BedrockNot named in this report
Meta (META)Internal AI / LlamaLowInternal ad optimizationNot named in this report
Note: Only Nvidia and Microsoft are explicitly named as beneficiaries in the source report. Alphabet, Amazon, and Meta are included for comparative context, and their specific figures are not drawn from this source. For builders comparing model ecosystems, Anthropic and OpenAI publish their own technical docs, while orchestration frameworks like LangGraph and AutoGen abstract over whichever model wins. Our LLM comparison guide breaks down where each provider leads.
Industry Impact: How OpenAI's Losses Are Reshaping the AI Stock Landscape
OpenAI's spending isn't an isolated event — it's a trigger for a sector-wide arms race. As the Fool argues, OpenAI's spend is 'symptomatic of the industry's economics.' When the leader spends to defend its position, every rival must match. The suppliers collect from all of them.
The AI Infrastructure Arms Race
Because frontier capability is a moving target, no lab can stop buying compute without falling behind. That converts Nvidia and the major clouds into structural toll-takers on the whole field. The Model Context Protocol (MCP) and standardized agent tooling only deepen this dynamic — more deployed agents means more inference, which means more GPU and cloud demand, which means the toll keeps compounding. Our primer on LLM inference economics explains why.
AI Bubble Concerns vs Structural Demand
Bubble warnings are loud right now. The bull rebuttal is simple: OpenAI grew revenue 253% to $13.07 billion in a single year. That's not vaporware adoption. That's paying demand scaling faster than almost any product category in tech history. The losses come from serving that demand, not the absence of it. Those are two very different problems.
A company growing revenue 253% per year while losing $20.92B is not a bubble symptom by default — it's a land grab. The risk is timing, not whether the demand is real.
Expert and Community Reactions: What Analysts and Investors Are Saying
The clearest on-record signal in this story comes from OpenAI's own CEO. Sam Altman publicly affirmed the Nvidia relationship on X, calling Nvidia's chips 'the best AI chips in the world' and stating OpenAI hopes 'to be a gigantic customer for a very long time' — directly rebutting rumors of chip dissatisfaction. When you're burning $20 billion a year and still go on record saying that, it's not PR. It's procurement posture.
When the CEO burning $20 billion a year publicly calls you his preferred supplier 'for a very long time,' that is not a press quote — that is forward guidance for your shareholders.
What the Bull Case Anchors On
The most cited bull anchor is Nvidia's CUDA moat — the Fool emphasizes its 'sticky CUDA ecosystem that will make it very difficult for competitors' to displace. For Microsoft, the anchor is Azure exclusivity layered with Copilot distribution. Two different moats, both downstream of the same spending firehose. Broader analyst sentiment on data-center demand is tracked by outlets like CNBC Technology.
The Contrarian View
Bears argue the Loss-to-Leverage Effect can be overplayed. If OpenAI's losses force it to renegotiate compute pricing or diversify away from a single supplier, supplier margins could compress. The Fool itself notes these comments came 'in response to claims that OpenAI was not satisfied with some of Nvidia's newest chips and was seeking alternatives.' That tension is real. Track it.
❌
Mistake: Reading OpenAI's loss as a sell signal
Investors panic at a $20.92B loss and avoid the whole AI sector — missing that the loss is the suppliers' revenue.
✅
Fix: Map the cash flow. Ask 'who does OpenAI pay?' and invest in those public income statements (NVDA, MSFT).
❌
Mistake: Waiting only for the OpenAI IPO
Retail buyers hold cash for a pre-profit IPO and miss the liquid, profitable proxies trading today.
✅
Fix: Build a core in NVDA/MSFT now; reserve a small speculative sliver for post-lock-up IPO entry.
❌
Mistake: Ignoring supplier concentration risk
Assuming OpenAI will buy only Nvidia forever — the source itself references rumors of OpenAI seeking chip alternatives.
✅
Fix: Diversify across the hardware AND cloud layers (NVDA + MSFT) so no single procurement decision sinks your thesis.
❌
Mistake: Buying a full share when you could fraction in
New investors skip NVDA at ~$211 thinking it is unaffordable, missing fractional entry.
✅
Fix: Use fractional shares on Fidelity or Robinhood — enter from $1 and dollar-cost average through volatility.
[
▶
Watch on YouTube
How OpenAI's spending becomes Nvidia and Microsoft revenue
AI infrastructure investing • the Loss-to-Leverage Effect
](https://www.youtube.com/results?search_query=nvidia+microsoft+openai+ai+infrastructure+investing)
What Comes Next: OpenAI IPO Timeline, Catalysts, and Forward Outlook
The Fool confirms OpenAI is 'racing toward an IPO later this year.' Beyond that, here's the evidence-grounded forward map.
Coined Framework
The Loss-to-Leverage Effect compounds with each IPO cycle
As OpenAI nears IPO, its disclosed compute commitments will become public — turning today's leaked figures into audited demand signals for Nvidia and Microsoft. Transparency strengthens, rather than weakens, the supplier bull case.
2026 H2
**OpenAI IPO 'later this year'**
The Fool reports an IPO is imminent. An S-1 would convert leaked figures ($20.92B loss, $13.07B revenue) into audited disclosures — and likely itemize compute spend, directly validating supplier demand.
2026 H2
**Nvidia demand visibility increases**
Altman's public 'gigantic customer for a very long time' commitment, plus the industry-wide arms race the Fool describes, supports continued data-center order strength against the CUDA moat.
2027
**Distribution monetization deepens for Microsoft**
If OpenAI's 253% revenue trajectory persists, Azure consumption and Copilot seat expansion should track upward — the double-dip Loss-to-Leverage capture compounds.
2027+
**Supplier-diversification risk crystallizes or fades**
The rumored OpenAI search for chip alternatives resolves one way or the other — the single biggest swing factor for Nvidia's share of the AI toll.
The forward map: each OpenAI milestone — IPO disclosure, compute commitments, model launches — is a catalyst for its two named public beneficiaries. Source
Frequently Asked Questions
Why do OpenAI's losses make Nvidia and Microsoft better investments?
OpenAI's $20.92 billion 2025 operating loss is largely the cost of buying compute — Nvidia GPUs and cloud capacity hosted on Microsoft Azure, per The Motley Fool. That spending becomes Nvidia's hardware revenue (at a 74.15% gross margin) and Microsoft's cloud plus Copilot revenue. This is the Loss-to-Leverage Effect: a private leader's loss is its public suppliers' gain. Because OpenAI is 'likely not an outlier,' rivals must match the spend, multiplying the demand the suppliers collect. You capture the upside in liquid, profitable, ownable companies rather than a pre-profit private firm.
Which AI stocks benefit most from OpenAI's infrastructure spending?
The Motley Fool's June 21, 2026 report names two: Nvidia (NVDA) and Microsoft (MSFT). Nvidia benefits at the hardware layer — its GPUs are 'the workhorses of AI training,' protected by the sticky CUDA ecosystem, with a $5.1 trillion market cap and 74.15% gross margin. Microsoft benefits at the cloud and distribution layer — hosting workloads on Azure and reselling OpenAI's models through Copilot. Nvidia offers the highest direct leverage per OpenAI dollar; Microsoft offers a stickier, recurring double-dip. Other names like Alphabet, Amazon, and Meta have AI exposure but were not identified as beneficiaries in this specific report.
Should I wait for the OpenAI IPO instead of buying NVDA or MSFT?
For most retail investors, no. OpenAI is deeply loss-making — a $20.92 billion 2025 operating loss with no disclosed path to profitability — while Nvidia and Microsoft are already profitable and fully liquid today. At IPO, bankers typically price for institutions, often leaving retail buyers entering after a first-day pop at stretched multiples. A balanced approach: build a core position in NVDA and MSFT now to capture the same AI demand, and reserve a small speculative allocation for a post-lock-up OpenAI entry (insider lock-ups commonly run 180 days). Note that exact IPO valuation and timing beyond 'later this year' are not yet officially confirmed.
What is the Loss-to-Leverage Effect in AI investing?
The Loss-to-Leverage Effect is the counterintuitive dynamic where a private AI giant's mounting operating losses directly amplify revenue and margin expansion for its publicly traded infrastructure and distribution partners. When OpenAI burns $20.92 billion buying compute, that cash isn't destroyed — it migrates onto the income statements of Nvidia (hardware) and Microsoft (cloud and Copilot distribution). It mirrors the cloud era, when startups' AWS bills became Amazon's margin. The practical takeaway: in capital-intensive buildouts, the leader's loss line is a leading indicator of its suppliers' revenue, and those suppliers are often the more investable way to own the trend.
How much of Microsoft's revenue comes from its OpenAI partnership?
The source report doesn't break out an exact dollar figure, so any precise number would be speculative. What's structurally clear is that Microsoft monetizes the relationship on two fronts: as the cloud infrastructure historically hosting OpenAI's workloads (Azure consumption revenue), and as the distribution channel reselling OpenAI's models through Copilot across the Microsoft 365 suite (per-seat recurring revenue). This double-dip is why Microsoft is a prime Loss-to-Leverage beneficiary. For exact segment figures, investors should consult Microsoft's official quarterly filings on its Investor Relations page rather than rely on estimates.
Is the AI stock boom a bubble or a structural growth cycle?
The strongest counter-bubble evidence is demand velocity: OpenAI grew revenue 253% to $13.07 billion in 2025, per The Motley Fool. Losses of that scale stem from serving demand, not its absence. The Fool frames OpenAI's spending as 'symptomatic of the industry's economics' — meaning every frontier lab must spend, sustaining supplier revenue. That said, bubble risk is real at the timing and valuation level: Nvidia's 52-week range ($142.03–$236.54) shows ~66% volatility. The honest answer: the underlying demand is structural, but entry timing and individual stock multiples can still be frothy, which is why dollar-cost averaging matters.
What are the biggest risks to the bull case for Nvidia and Microsoft AI stocks?
Three stand out. First, supplier diversification: the source notes rumors that OpenAI was 'not satisfied with some of Nvidia's newest chips and was seeking alternatives' — though Altman publicly reaffirmed the partnership. Second, margin compression: if OpenAI's compute costs are renegotiated, supplier margins could tighten. Third, valuation and volatility: Nvidia's stock swung roughly 66% over 52 weeks, so timing risk is significant even if the long-term thesis holds. Mitigate these by owning both the hardware and cloud layers (NVDA and MSFT), using fractional shares, and dollar-cost averaging rather than making a single concentrated, all-at-once purchase.
Disclaimer: This article is for informational purposes only and is not financial advice. All figures are drawn from the cited Motley Fool report dated June 21, 2026, or labeled as comparative context. Verify all data against primary filings before investing.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
LinkedIn · Full Profile
This article was originally published on Twarx. Follow for daily deep dives on AI agents and automation.
Top comments (0)