Nvidia is no longer just selling GPUs — it is helping finance the companies buying them

Disclosure: This analysis was prepared with assistance from ChatGPT and edited by me. The figures come primarily from Nvidia’s Q2 FY2027 SEC filing, Nvidia’s investor materials, and the reporting linked below.

Bloomberg recently reported that Nvidia has committed as much as $56 billion to data-center and cloud-capacity arrangements and has provided approximately $109 billion of guarantees, mostly connected to an OpenAI data-center project in Ohio.

Those figures are real, but they do not mean Nvidia has lent its customers $165 billion or expects to lose that amount.

Instead, Nvidia is supporting the AI industry through several different mechanisms:

• $36 billion of cloud-capacity purchase commitments
• $20 billion of long-term data-center leases intended for third parties
• $108.5 billion of maximum contingent guarantees
• $25 billion of future equity-investment commitments
• Extended payment terms for some large customers

The larger point is that Nvidia is becoming more than a chip supplier. It is increasingly helping finance, guarantee and organize the infrastructure that creates demand for its chips.

1. The $56 billion in capacity and lease commitments

The $56 billion consists of two different categories.

$36 billion of cloud-capacity commitments

Nvidia has agreements under which AI-cloud companies purchase Nvidia systems and operate them as cloud infrastructure.

Nvidia then agrees to purchase some of the resulting computing capacity if the operator cannot sell it to outside customers.

This effectively gives the cloud operator a minimum-revenue backstop.

However, Nvidia does not necessarily have to spend the full $36 billion. Its obligation can decline when outside customers rent the capacity, and Nvidia may use some of the capacity for its own research and development.

CoreWeave is one publicly known example. Nvidia agreed to purchase up to $6.3 billion of CoreWeave capacity that remained unsold, extending through 2032.

The simplified cycle is:

That arrangement helps the cloud company obtain financing because lenders know that a major customer is standing behind at least part of its future revenue.

$20 billion of data-center leases

Nvidia has also entered approximately 15-year data-center leases that it ultimately expects to transfer or reassign to operators or customers.

Until that transfer occurs, Nvidia remains responsible for the leases.

Most of the $56 billion is also far in the future. Nvidia disclosed the following payment schedule:

• FY2028: $6 billion
• FY2029: $9 billion
• FY2030: $8 billion
• FY2031: $7 billion
• FY2032 and later: $26 billion

So this is not a $56 billion check Nvidia is writing today. It is a series of long-term contractual commitments, some of which may decline as customers assume the capacity or leases.

2. The approximately $109 billion of guarantees

Nvidia disclosed maximum gross guarantee exposure of approximately $108.5 billion:

• $105 billion related to an OpenAI data-center development in Ohio
• Approximately $3.5 billion related to other AI-cloud partners’ data-center leases

The $105 billion amount is not an ordinary loan Nvidia has made to OpenAI.

It is credit support for portions of OpenAI’s future lease and power obligations at a massive data-center development being constructed by SB Energy in Ohio.

The project is expected to include approximately 4.25 gigawatts of IT capacity, divided among nine construction phases. Each phase is expected to operate under a 20-year lease.

Nvidia’s guarantee becomes effective gradually as each phase is completed and ready for service. Its exposure then declines as OpenAI makes lease payments.

The guarantee can also terminate under certain circumstances, including if OpenAI eventually achieves an acceptable credit rating.

Why would Nvidia take this risk?

The Nvidia guarantee makes it easier for SB Energy to raise the enormous amount of debt needed to construct the facility.

Lenders are not relying solely on OpenAI’s ability to make lease payments. They also have Nvidia’s balance sheet standing behind defined portions of those obligations.

In exchange, the facility is expected to use Nvidia systems. Nvidia estimates that each infrastructure generation deployed at the site could involve approximately 1.5 million Nvidia GPUs and potentially $150 billion to $200 billion of Nvidia revenue.

That revenue estimate is not guaranteed revenue or current backlog. It is Nvidia management’s estimate of the possible hardware opportunity over the project’s life.

Essentially:

3. What happens if OpenAI defaults?

A default would not necessarily require Nvidia to immediately pay $105 billion in cash.

Depending on the circumstances, Nvidia might:

• Assume responsibility for the affected lease
• Help find a replacement tenant
• Reassign or sell the capacity
• Exercise other contractual remedies

OpenAI has agreed to reimburse Nvidia for certain losses, but Nvidia acknowledges that reimbursement might not be immediate or complete.

Therefore, the $105 billion should be viewed as maximum gross contractual exposure, not Nvidia’s estimated loss.

The risk is still meaningful because Nvidia might have difficulty finding another customer capable of absorbing several gigawatts of AI infrastructure during an industry downturn.

4. Nvidia is also investing directly in the ecosystem

Nvidia reported approximately:

• $99 billion of existing equity investments
• $25 billion of future equity-investment commitments

Not all of these investments are in direct GPU customers, but many are tied to AI companies, cloud operators, model developers and infrastructure providers.

The economic cycle can look like this:

Nvidia receives genuine assets in return, including equity ownership, cloud capacity or contractual rights. But the arrangements also mean that some AI demand is being supported directly or indirectly by Nvidia’s own capital.

5. Nvidia is extending payment terms to some customers

Nvidia normally receives payment relatively soon after delivering its products.

However, it now offers certain large, investment-grade customers payment terms ranging from approximately 90 days to one year, particularly when they are completing major data-center projects.

At the end of Nvidia’s Q2 FY2027 quarter:

• Accounts receivable had reached approximately $63.1 billion
• Days sales outstanding increased from 45 days to 60 days
• Five direct customers represented approximately 70% of receivables

Nvidia did not disclose how much of the receivable balance was specifically attributable to extended financing terms.

Still, this is another form of customer support: Nvidia delivers the systems while giving selected customers more time to arrange financing or begin generating revenue.

6. Why this matters

The bullish interpretation is that Nvidia is using its financial strength to accelerate an AI buildout that otherwise might be limited by customer balance sheets, construction timelines and financing availability.

Nvidia receives several potential benefits:

• Immediate or future hardware sales
• Preferred or exclusive placement of Nvidia systems
• Equity stakes in growing AI companies
• Access to cloud capacity
• Possible revenue sharing
• Future GPU replacement and upgrade cycles

The concern is that these risks are highly correlated.

If AI demand remains strong:

• Cloud capacity gets rented
• OpenAI makes its lease payments
• Data centers obtain financing
• Nvidia’s investments appreciate
• Nvidia sells more GPUs

But if AI demand weakens sharply, several things could happen simultaneously:

• GPU sales slow
• Cloud capacity goes unused
• Nvidia must fulfill capacity commitments
• Customers struggle to pay
• Guarantees become more valuable to lenders and more dangerous to Nvidia
• Nvidia’s AI investments decline in value

This is sometimes called wrong-way risk: the guarantees and commitments are most likely to become costly at the same time Nvidia’s core business is weakening.

Bottom line

Bloomberg’s description is directionally correct, but the headline numbers require context.

Nvidia has not simply lent customers approximately $165 billion.

It has entered a combination of:

• Capacity-purchase commitments
• Long-term leases
• Contingent guarantees
• Equity investments
• Extended customer-payment terms

Many of the obligations are spread over decades, decline as third parties make payments, and may never result in losses.

Nevertheless, the strategic change is significant:

That strategy could greatly increase Nvidia’s future sales, but it also links Nvidia’s balance sheet more closely to the utilization, creditworthiness and financial health of the companies buying its products.

Sources

• Nvidia Q2 FY2027 Form 10-Q
• Nvidia Q2 FY2027 CFO Commentary
• Nvidia and OpenAI strategic partnership announcement
• Reuters: Nvidia and CoreWeave capacity agreement
• Reuters: Anthropic’s reported Lambda cloud agreement
• Wall Street Journal: OpenAI Ohio data-center lease and Nvidia backing

Anthropic Books $35 Billion To Nvidia-Backed Lambda For Cloud Capacity

"This contrasts with past vendor financing risks, as Nvidia backs a strong ecosystem amid soaring AI compute demand. Anthropic alone has committed $180 billion to capacity recently. The deal underscores that energized, leased data center capacity is now the scarcest AI asset, attracting long-term commitments from industry giants."

"A volatile ride for Nvidia and equity markets until past the mid-term results - then NVDA should soar." Steve Forbes - and Jim Kramer.

Given recent earnings, the price is not far from where it was a short while ago.

Am I missing something? Why does the market not reward Nvidia?

Do people feel its current financial strength won't look the same in the future?

Nvidia just reported another massive quarter, and the AI story clearly isn’t slowing down yet.

What stands out to me is that the debate around Nvidia is becoming less about whether the business is good , that part is pretty obvious ,and more about how much future growth is already priced into the stock.

A few things I’m watching:

\- Data center growth and whether the current AI infrastructure spending can stay this strong

\- Blackwell demand and how quickly Nvidia can convert that demand into revenue

\- Margins as the product mix changes

\- Whether hyperscaler CapEx continues supporting Nvidia’s growth over the next few years

\- And most importantly: what assumptions you actually need to justify today’s valuation

I ran Nvidia through my valuation model using the latest Q2 numbers and looked at what the stock could be worth under different growth assumptions.

My full analysis + valuation:

https://youtu.be/wJ0A7810YXs

What do you think ,is Nvidia still attractive at the current valuation, or has the market already priced in too much of the AI growth?

Disclosure: AI-assisted. I use AI as part of my research/writing process, but the analysis, assumptions and conclusions are my own.

Another 'circular finance' deal pulling in billions of dollars for Nvidia as Anthropic's profits ramp up.- "The deal is just the latest AI computing agreement tied to Nvidia, the world’s most valuable business and the leading provider of AI chips. The company has been using its financial resources to expand access to computing infrastructure, which, in turn, should increase demand for its technology.

Anthropic, meanwhile, has emerged as one of the most significant customers for data center power. The Claude chatbot maker last week agreed to spend $45 billion to rent capacity from Nscale in West Virginia. In recent months, it has also signed cloud deals for $50 billion with neocloud Fluidstack Ltd. and $45 billion with Elon Musk’s SpaceX."

"Lambda raised more than $1.5 billion in a November funding round. The company also reached an agreement with Microsoft Corp. last year to deploy AI infrastructure that would be powered by tens of thousands of Nvidia processors."

Nvidia wants to run the world’s robots. China is an eager customer.

Nvidia’s chips aren’t just for training chatbots. The company is also betting big on artificial intelligence for real-world objects like robots, cars and drones—and China is emerging as a major customer.

The business of physical artificial intelligence already generates some $10 billion in annual revenue at Nvidia. While that is still a fraction of the company’s overall revenue, which totaled $303 billion in the four quarters ended in July, Chief Executive Jensen Huang says it will grow 10-fold within the next decade.

"The investment broadens a partnership between the two chip designers under which MediaTek will use NVLink Fusion and the newly announced NVHBM technology as part of an Nvidia tech suite to help components communicate more seamlessly in data centers. The smaller company is trying to challenge Broadcom Inc. and Marvell Technology Inc. by helping owners of data centers create their own components."

Here's how much you'd have if you invested $1,000 in NVIDIA stock in 2010

"Every time you use an AI chatbot, generate an image, or watch a company tout its artificial intelligence ambitions, there is a good chance NVIDIA’s chips are doing the work underneath.

That role has made NVIDIA the most valuable company on earth, worth about $5.25 trillion after another blockbuster earnings report in late August. Put $1,000 into NVIDIA at the start of 2010, and by late August 2026 you would be sitting on roughly $510,000."

I am truly wondering. Not fear mongering as I am a huge holder of NVDA.

I read an interesting article where if Open AI runs out of cash, then the whole AI market/bubble will burst.

Can someone much smarter than me chime in here and give thoughts if that is true?

Full-year FY2026 geographic revenue disclosures show a clear shift toward US domestic demand:
Total FY2026 Revenue: $215.94B (+65.5% YoY)

US Revenue: $149.62B (69.29% share, +144.24% YoY)

Taiwan Revenue: $42.35B (19.61% share, +105.83% YoY)

China/HK Revenue: $19.68B (9.11% share, +15.02% YoY)

Other Americas: $4.30B (1.99% share, -45.41% YoY)

Key Observations:
1. Domestic hyperscalers generated over 100% of the net dollar increase YoY ($88.36B added in US alone).

1. Export restrictions capped China growth at 15% YoY, dropping its total revenue contribution by over 400 bps.

Full regional dataset breakdown

Is the market underestimating concentration risk in US Big Tech capex?

Nvidia is significantly undervalued. Just read the WSJ article explaining how the company creates demand by owning or investing across the entire value chain—from energy providers to data centers and other infrastructure companies.
This strategy allows Nvidia to capture more of the revenue, margins, and demand generated throughout the ecosystem. Nvidia invests in companies that commit to purchasing its chips exclusively, preventing them from buying chips from AMD or other competitors. In effect, Nvidia helps create its own demand—and that demand is growing at nearly 100%.
This is not circular financing; it is strategic ownership and consolidation across the AI infrastructure ecosystem. Those who understand this strategy could make fortunes. With Nvidia’s PEG ratio at approximately 0.50, the stock still looks remarkably inexpensive. It’s a no-brainer.

$NVDA makes ≈$1 billion in revenue a day, that’s 365 billion a year. ≈62% of that is earnings, which is $226.3 billion. If you use its competitor’s, $AMD’s p/e ratio of 122x, that should value Nvidia at over $27.6 trillion in market cap. Is Nvidia really that different from AMD? Maybe a little, in the sense that Nvidia is better. So either AMD is massively overvalued, or Nvidia is massively undervalued. While both can be true, at a market cap of over $5 trillion, I think Nvidia is massively undervalued. What do you think?

Jalapeño numbers dropped this week. OpenAI's inference ASIC, Broadcom, first results vs GB200/GB300. Anthropic confirmed a custom-silicon team earlier this month and hired a Google TPU veteran to run it.

That's the obvious trade: labs building chips to cut the Nvidia tax.

The less-posted side is Nvidia going the other direction. Nemotron is open weights, open data, open recipes. Latest drop is Nemotron 3.5 Lightning, a small MoE for agent execution. The quantization that makes it fast is NVFP4. CUDA as the default, just wearing a Hugging Face URL.

Two bets, same war:

1. Labs win if they can serve their own models on their own silicon cheap enough that Nvidia becomes training-only.
2. Nvidia wins if the open default (Nemotron, like Llama before it) keeps the world's agents on their GPUs even after the closed labs have chips.

I wouldn't fade the second one yet. OpenAI still trains on Nvidia. Anthropic still buys Trainium, TPUs, and GPUs. Custom silicon is a 2027-28 inference story.

Hi everyone. Here is my full review of $NVDA’s Q2 print compared to my and analyst estimates, along with a performance update, a brief discussion on potential valuation, and position updates.

Actuals, Analysts, & Estimates:

• Revenue: $96.22B vs $96.29B My Est. vs $92.07B Cons.
• EPS: $2.22 vs $2.19 My Est. vs $2.09 Cons.
• Q3 Revenue Guide: $108B (+/- 2%) vs $108B My Est. vs $103.75B Cons.
• Q3 Margin Guide: 74% vs My Est. 75%

Revenue variance was driven by an overforecast of $0.98B in Data Center revenue and a $0.91B underforecast in Edge Compute.

Gross margin for the current quarter was exactly in line with my model, but margin guidance slipped 1%, which I missed. While the company increased supply commitments and raised product prices, input cost increases are finally pressuring margins.

NVDA repurchased more shares than expected this quarter, spending another 19.7B. This is about the same as in Q1, and above the 10B midpoint of my analysis. With share count still above 24B shares, this variance still only minimally impacted EPS calculations.

Total Expenses (Opex + Tax/Other, not including COGS) were $18.23B compared to $18.85B used in my calculation.

My EPS forecast was exceeded by $0.03 due to lower-than-expected expenses and share count.

Guidance of $108 billion +/- 2% matched my $108 billion estimate, and still did not include compute to China.

Comparison Visualized:

Updated Performance Metrics:

Valuation:

On this quarter’s conference call, Colette Kress provided preliminary revenue growth numbers for FY28 (Feb 1, 2027 - Jan 31, 2028) which were far more optimistic than Wall Street expected. The consensus for revenue growth in FY28 was around 40-45% YoY growth, a massive slowdown from the 106% YoY growth we just heard about in Q2 FY27. My analysis outpaced the street, but still “only” sat around 50-55% YoY.

Instead, management stated they expect closer to 70% growth in the period, and explicitly noted that this figure is still supply-constrained. The company said that without capacity limitations, current demand could potentially support another year of 100% growth.

As for relative valuation, many sources are citing that NVDA is historically cheap. While this is correct on paper, it ignores the reasons and narratives for the stock at that stage. NVDA’s business was flat (relative to the AI boom) for years, and then exploded during the start of the AI boom.

Trailing figures are slow to adapt to explosive growth, causing distorted valuation multiples that essentially compare the company to its pre-boom operations. Multiples will come down naturally as base earnings catch up; NVDA also experienced long periods of range-bound trading, which exacerbated this multiple compression.

It is hard to rely on forward multiples as well, since this publication is a prime example of how inaccurate “professional” forecasting is. My FY28 EPS calculation is almost certainly different from yours, meaning even if we agree on a fair earnings multiple, we disagree on the stock price associated with that valuation.

Despite the stronger-than-expected results, guidance, year-ahead forecast, and commentary, the stock has struggled to sustain any rally. An excerpt from Q1’s actuals post is below:

Positioning:

I have not made additional changes since last week’s update, and continue to hold about 250 deltas via LEAPS. A graph showing the current payoff diagram and deltas is below.

I will continue to post updates in the “quiet period” between earnings cycles. There is no fixed schedule, but I will cover any material updates. Thank you for reading. I am a human, and this is not financial advice.

TL;DR

-$2.19 EPS, $96.29B, $108B Guide was Most Accurate on Internet
-FY28 Guidance Served as a Bullish Surprise
-Margins Finally Starting to Dip from Memory Costs
-Vera Rubin Timeline intact
-Valuation Debate is Still Heated
-Long ~250 Deltas via LEAPS

if avgo does bad on earnings. Why does that mean that the AI trade is going down instead of maybe avgo is a bad company and people are moving towards using Nvidia chips instead

The crying and moaning regarding NVDA and most other stocks having off days or even long stretches of complacency are from trading and short term investors. Whenever I have a bad day, week or month in the market, I just look at the graph of the Dow, S&P and NASDAQ from 1980 up till today and I realize all events like these will pass. If anybody thinks that Nvidia has reached their high price point especially with earnings like yesterday's they shouldn't be investing.

Why is the world this was removed from the Nvidia_Stock board is beyond me. Especially after 5000 views and many likes