Nvidia is funding an AI boom. Will it trigger a financial crash? | The Economist

Nvidia is funding an AI boom. Will it trigger a financial crash? | The Economist — The Economist

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Key ideas

  • Nvidia's share price has surged 14-fold since the public launch of ChatGPT, backed by doubling annual revenues over three consecutive years and quarterly revenue nearing $100 billion. (0:00)
  • Nvidia now represents 8% of the S&P 500 and has generated nearly 15 cents of every dollar returned by the index since early 2023. (0:00)
  • Nvidia's market share in AI chips has declined from around 80% to 60% due to software firms developing custom silicon, with projections dropping to 50% by the end of the decade. (1:22)
  • To accelerate data center buildouts, Nvidia is deploying aggressive financial engineering, such as guaranteeing OpenAI's 20-year lease on an 8-gigawatt facility in Ohio alongside a $30 billion equity investment. (1:56)
  • While Nvidia's massive cash flow and fortress balance sheet provide downside cushion, analysts warn that a shortfall in end-user AI demand could trigger a double whammy of collapsed chip sales and triggered lease guarantees. (5:36)

Detailed notes

## Background Published on YouTube by The Economist on September 7, 2026, this segment features deputy editor Edward Carr and a panel of AI analysts examining Nvidia's rise to become the world's most valuable company. According to the video description, the discussion explores Nvidia's recent practice of investing in and arranging loans for its own customers—a strategy some worry mirrors the lead-up to the dot-com crash—and evaluates how risky this dealmaking is for the broader economy.

## TL;DR Since the release of ChatGPT, Nvidia's share price has grown 14-fold and its annual revenue has doubled for three consecutive years, making it represent 8% of the S&P 500. However, as software giants increasingly build their own custom silicon—eroding Nvidia's AI chip market share from 80% toward a projected 50% by the end of the decade—Nvidia has turned to aggressive financial engineering to accelerate AI infrastructure buildouts. For instance, Nvidia invested $30 billion in OpenAI while using its own balance sheet to guarantee a 20-year lease on OpenAI's 8-gigawatt Ohio data center, unlocking lender capital that OpenAI then uses to buy Nvidia chips. While critics see a "whiff" of circular financing, analysts note that Nvidia is cushioned by cash-rich hyperscaler customers, $100 billion in cash reserves, and $200 billion in projected annual free cash flow. Ultimately, the panel concludes that while Nvidia's dealmaking probably won't cause a recession on its own, a failure in AI demand could amplify a downturn by hitting Nvidia with lost chip sales and called-in guarantees simultaneously.

## Atmosphere & style The discussion carries the crisp, measured, and analytical tone of a classic editorial roundtable, balancing complex financial mechanics with accessible journalism. Host Edward Carr guides the conversation with a collegial lightness—even joking that he'll ask for help if he gets lost in the financial engineering—while the panelists dissect high-stakes macroeconomic risks without descending into alarmism. Watching the segment feels like listening in on a calm, lucid briefing where seasoned observers weigh the genuine dangers of a tech bubble against the unprecedented scale of Nvidia's balance sheet.

## Bulletin - [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) Nvidia's share price has surged 14-fold since the public launch of ChatGPT, backed by doubling annual revenues over three consecutive years and quarterly revenue nearing $100 billion. - [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) Nvidia now represents 8% of the S&P 500 and has generated nearly 15 cents of every dollar returned by the index since early 2023. - [01:22](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=82s) Nvidia's market share in AI chips has declined from around 80% to 60% due to software firms developing custom silicon, with projections dropping to 50% by the end of the decade. - [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) To accelerate data center buildouts, Nvidia is deploying aggressive financial engineering, such as guaranteeing OpenAI's 20-year lease on an 8-gigawatt facility in Ohio alongside a $30 billion equity investment. - [05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) While Nvidia's massive cash flow and fortress balance sheet provide downside cushion, analysts warn that a shortfall in end-user AI demand could trigger a double whammy of collapsed chip sales and triggered lease guarantees.

## Key takeaways - [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) Nvidia's extraordinary financial run is anchored in real revenue expansion, transforming from a PC gaming hardware vendor into an AI computing titan. - [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) Given that Nvidia accounts for 8% of the S&P 500, broad US stock market investors carry substantial passive exposure to the company's performance. - [01:22](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=82s) Nvidia is steadily losing its near-monopoly on AI chips as software hyperscalers develop internal custom silicon to run their own workloads. - [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) Nvidia has adopted the role of an infrastructure financier because private capital markets are reluctant to fund AI buildouts at Nvidia's anticipated scale. - [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) In a prominent circular deal, Nvidia guaranteed a 20-year lease for OpenAI's 8GW Ohio data center and invested $30 billion in the lab, which in turn purchases Nvidia chips. - [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) Nvidia vigorously rejects the label of 'circular financing', highlighting that it offers guarantees rather than direct vendor loans to buy hardware. - [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) Unlike dot-com era vendors, Nvidia recognizes chip sales upfront while contingent guarantee obligations only materialize far in the future if partners default. - [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) Nvidia's core customer base consists of cash-flow-positive hyperscalers, unlike the speculative startups of the late 1990s. - [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) With roughly $100 billion in liquid assets and an estimated $200 billion in annual free cash flow, Nvidia possesses a balance sheet capable of absorbing substantial risk. - [05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) Panelists argue that while Nvidia's financing may not spark a recession alone, it risks amplifying a downturn by funding marginal or subpar infrastructure deals. - [05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) A severe downturn at Nvidia would produce significant macroeconomic wealth effects across the US stock market, though losses would predominantly fall on equity holders rather than the banking system. - [05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) If commercial AI adoption disappoints, Nvidia risks simultaneous collapse in chip sales and exposure to liabilities from default guarantees on massive data center projects.

## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) Why Nvidia is so important to the economy

Deputy editor Edward Carr opens by asking Henry to describe Nvidia's remarkable financial performance. Henry calls it "astonishing," noting that in the era of AI—specifically since ChatGPT was released to the public—Nvidia's share price has gone up 14 times. Crucially, this is not merely a speculative effect of people punting on the company; it is backed up by skyrocketing underlying revenues. Looked at on an annual basis, Nvidia's revenue has doubled in each of the past three years, and its quarterly revenue is now not far off $100 billion. This represents a massive transformation from 2015, when Nvidia was primarily a company for gamers and enthusiasts "screwing in their graphics cards into their computers."

Because Nvidia has grown so massive on the back of chip demand, its stock performance now shapes America's broader stock market. Anyone invested in the US stock market is significantly exposed to Nvidia simply because of its sheer size.

- **Share price growth:** Up 14x since ChatGPT's public release. - **Revenue growth:** Annual revenue has doubled in each of the past three years, approaching $100 billion in quarterly revenue. - **S&P 500 weight:** Nvidia now makes up 8% of the S&P 500. - **Market return contribution:** Since the start of 2023, Nvidia accounts for almost 15 cents of every dollar that the S&P 500 has returned to investors.

[01:22](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=82s) How Nvidia is losing its monopoly on chips

Turning to another panelist, Shailesh, Edward asks how Nvidia's market share is being eroded. Shailesh explains that while Nvidia once held a near-monopoly on AI chips, software companies are increasingly designing their own "custom silicon" to meet their internal needs, steadily eating into Nvidia's dominance.

| Timeframe | Nvidia's AI Chip Market Share | Custom Silicon (Internal Software Co. Chips) | |:--- |:--- |:--- | | **2–3 years ago** | ~80% | ~20% | | **Present** | ~60% | ~40% | | **End of the decade (projected)** | 50% | 50% |

[01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) What financial engineering is Nvidia doing?

This shifting market share leads naturally into the question of Nvidia's financial engineering. Edward asks Shailesh to walk through one concrete example—joking that "if I get lost, I'll sort of ask for help." Shailesh chooses one of the most public deals: OpenAI, a large AI lab that is currently building a massive 8-gigawatt data center in Ohio—an expensive project requiring power on the scale of a couple of midsize nuclear reactors.

Although OpenAI is very well-funded, it is also losing a lot of cash. To help OpenAI afford the project, Nvidia is using its own balance sheet to guarantee the lease on the land and power for that data center for 20 years. Because Nvidia guarantees the lease, lenders are much more willing to lend to OpenAI. Meanwhile, OpenAI buys chips from Nvidia, and Nvidia has also invested $30 billion directly into OpenAI itself. If OpenAI defaults and cannot make its payments, Nvidia can step in and find another tenant for the facility. As Shailesh points out, "you can right away see that there are a lot of kind of circularities here."

```mermaid flowchart LR accTitle: Nvidia and OpenAI Ohio Data Center Deal Structure accDescr: Diagram showing how Nvidia invests $30B in OpenAI, guarantees its 20-year data center lease to unlock lender capital, and sells chips back to OpenAI.

subgraph Financing["Capital & Guarantees"] NV["Nvidia"] -->|"$30B equity investment"| OAI["OpenAI"] NV -->|"Guarantees 20-year lease"| DC["8GW Ohio Data Center"] Lend["Lenders"] -->|"More likely to lend"| OAI end

subgraph Operations["Hardware & Fallback"] OAI -->|"Buys AI chips"| NV OAI -->|"Leases land & power"| DC NV -.->|"Finds new tenant (if default)"| DC end ```

Another panelist chimes in on why Nvidia has to act as a financier rather than letting the capital markets fund this vision directly. Essentially, Nvidia is taking an even more bullish view than the broader market about how much infrastructure investment is required. Concluding that outside investors will not finance at the necessary scale, Nvidia leverages its massive resources and enormous revenue stream to reinvest and do the financing itself—making a "twin bet on itself, but also on the economy at large."

[04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) What’s Nvidia’s rationale?

Edward asks Shailesh how people inside Nvidia defend these practices and whether he agrees with them. Shailesh notes that the company is "very sensitive to the word circular financing and anytime you use it, they push back really hard."

In evaluating Nvidia's defense, Shailesh admits there is no getting away from the basic fact that Nvidia makes equity investments in companies that then turn around and buy Nvidia chips—leaving an undeniable "whiff of that" circularity. However, he highlights three major differences that separate Nvidia's current strategy from classic dot-com-style vendor financing:

- **Guarantees vs. direct loans, and upfront revenue recognition:** Nvidia is not loaning money directly to these companies to buy its chips; rather, it is guaranteeing future supply or a future price. When Nvidia sells a chip, it recognizes the revenue right up front, whereas the guarantees (if they ever come due) would only hit much later. - **Cash-rich core customers:** Unlike the dot-com boom, Nvidia's largest customers are cash-flow-positive hyperscalers that are very well cushioned financially. - **A fortress balance sheet:** Nvidia holds around $100 billion in cash and liquid investments, and it is set to generate around $200 billion in free cash flow this year—a number that continues to grow, giving it plenty of cushion to sustain these guarantees.

[05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) Will Nvidia trigger a financial crash?

To wrap up, Edward asks the panel whether Nvidia's financial engineering is likely to trigger a recession:

- **Shailesh's view:** It probably will not *cause* a recession by itself, but it could definitely *amplify* the effects of a downturn because this financing is enabling some subpar deals that "do not need to happen." - **Henry's view:** Because Nvidia is so central to the AI boom—which in turn drives the US economy via the stock market's wealth effect—serious problems at Nvidia would be macroeconomically significant and hurt the economy. However, Nvidia's own shareholders would bear the brunt of the losses. Drawing a parallel to the historic dot-com bust, Henry reminds the panel that the post-dot-com recession was quite shallow, unlike the deep systemic banking crisis of the Global Financial Crisis. Overall, he believes Nvidia's strategy is more likely to accelerate the AI boom than cause a crash, though in such an uncertain environment, both outcomes remain possible. - **Final panelist's view:** If end-user demand for AI fails to materialize, Nvidia's position at the center of the ecosystem could sharply accelerate the downward pull. Nvidia would face a painful double whammy: losing chip demand from its own customers at the exact same time that the companies it invested in start calling due on the guarantees it provided.

## Notable quotes > [00:00](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=0s) screwing in their graphics cards into their computers — Henry

> [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) if I get lost, I'll sort of ask for help — Edward Carr

> [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) you can right away see that there are a lot of kind of circularities here — Shailesh

> [01:56](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=116s) twin bet on itself, but also on the economy at large — Panelist

> [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) very sensitive to the word circular financing and anytime you use it, they push back really hard — Shailesh

> [04:06](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=246s) whiff of that — Shailesh

> [05:36](https://www.youtube.com/watch?v=VF1JES8Sv5U&t=336s) do not need to happen — Shailesh

## Mentioned Nvidia, OpenAI, ChatGPT, Edward Carr, Henry, Shailesh, The Economist, S&P 500, Ohio

## Video description Nvidia is synonymous with the AI boom. Global demand for its chips has made it the world’s most valuable company. Next year it may also become the most profitable.

But in recent months Nvidia has been busy investing in and arranging loans for its customers. Some worry this mirrors the dotcom crash, when hardware giants paid startups with poor prospects to buy their kit. Nvidia retorts that such schemes could not be more different from its plans to accelerate the emergence of AI.

Edward Carr, our deputy editor, is joined by a panel of our top AI watchers to discuss the company’s remarkable rise, what its dealmaking might mean for the future of AI and how risky it all is.

#Nvidia #artificialintelligence #ai #technology #finance #economy

00:00 - Why is Nvidia so important to the economy? 01:22 - How Nvidia is losing its monopoly on chips 01:56 - What financial engineering is Nvidia doing? 04:06 - What’s Nvidia’s rationale? 05:36 - Will Nvidia trigger a financial crash?

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[Source](https://www.youtube.com/watch?v=VF1JES8Sv5U)

中文简报

英伟达正在资助AI繁荣，这会引发金融崩盘吗？ 自ChatGPT发布以来，英伟达股价上涨了14倍，年收入连续三年翻番，在标普500指数中占比达到8%。然而，随着软件巨头越来越多地自主研发定制芯片——英伟达在AI芯片市场的份额预计将从80%被蚕食至本年代末的50%左右——英伟达已转向激进的金融运作来加速AI基础设施建设。例如，英伟达向OpenAI投资300亿美元，同时利用自身资产负债表为OpenAI位于俄亥俄州的8吉瓦数据中心提供20年租约担保，从而撬动贷款方资金，让OpenAI反过来用于购买英伟达芯片。尽管批评者从中看出了循环融资的“苗头”，但分析师指出，英伟达拥有现金充裕的超大规模数据中心客户、1000亿美元现金储备以及预计每年2000亿美元的自由现金流作为缓冲。最终专家小组得出结论，虽然英伟达的交易运作本身可能不会直接引发经济衰退，但若AI需求不及预期，可能会因芯片销售额损失和担保被要求履约的双重打击而加剧下行趋势。 自ChatGPT公开上线以来，在年收入连续三年翻番以及季度营收逼近1000亿美元的支撑下，英伟达股价飙升了14倍。 (0:00) 英伟达目前占标普500指数的8%，自2023年初以来，该指数每产生1美元的回报，就有近15美分来自英伟达。 (0:00) 由于软件公司自主研发定制芯片，英伟达在AI芯片市场的份额已从约80%下滑至60%，预计到本年代末将降至50%。 (1:22) 为加速数据中心建设，英伟达正在运用激进的金融运作手段，例如在进行300亿美元股权投资的同时，为OpenAI在俄亥俄州的一座8吉瓦设施提供20年租约担保。 (1:56) 尽管英伟达庞大的现金流和堡垒般的资产负债表提供了下行缓冲，但分析师警告称，终端用户AI需求不足可能会引发芯片销量崩塌和租约担保被触发的双重重创。 (5:36)

Reading summary

Nvidia's rapid expansion and heavy weighting in US stock indexes have made it central to the broader economy, while its use of balance-sheet guarantees to fund artificial intelligence infrastructure draws scrutiny over potential circular financing risks. Outsized Influence on the Stock Market (0:00) Nvidia's financial ascent since the public debut of ChatGPT has been accompanied by surging operational revenue rather than speculative pricing alone. Henry notes that the company's share price has climbed 14 times, supported by annual revenue doubling in each of the past three years to approach nearly $100 billion per quarter. This scale marks a sharp transformation from 2015, when the company focused primarily on graphics cards for PC gamers. Due to this growth, Nvidia exerts significant leverage over the performance of US equity markets. The company accounts for 8% of the S&P 500 and has contributed roughly 15 cents of every dollar returned by the index to investors since the start of 2023, leaving broad market investors exposed to its operational trajectory. Market Share Erosion From Custom Silicon (1:22) Despite rapid revenue growth, Nvidia's dominance in AI processors is being challenged by software companies developing in-house hardware. Shailesh explains that tech firms are building custom silicon tailored to their internal applications to reduce reliance on merchant suppliers. According to Shailesh, Nvidia's share of the AI chip market has dropped from approximately 80% two to three years ago to roughly 60% today. He projects that internal silicon will continue to gain ground, forecasting an even 50–50 market split between Nvidia and proprietary chip designers by the end of the decade. Balance Sheet Guarantees and Financing Arrangements (1:56) To sustain capital deployment beyond the risk tolerance of public markets, Nvidia has begun directly facilitating infrastructure financing. In an 8-gigawatt data center venture in Ohio for OpenAI, Nvidia guaranteed a 20-year lease on land and power to reassure lenders, while simultaneously making a $30 billion equity investment in OpenAI. OpenAI buys chips from Nvidia, and Nvidia retains the obligation to secure a substitute tenant if OpenAI defaults. Addressing criticism of circular financing, Shailesh highlights three distinctions from dot-com era vendor financing: Nvidia backs leases and pricing guarantees rather than extending direct purchase loans; it recognizes chip sales immediately while deferring guarantee risks; and its core hyperscaler clients are cash-flow positive. Furthermore, Nvidia holds around $100 billion in liquid reserves and is projected to generate roughly $200 billion in free cash flow this year. Macroeconomic Contagion and Downside Risks (5:36) Panelists hold diverging perspectives on whether Nvidia's aggressive commitments could precipitate a broader recession. Shailesh suggests the financing will not trigger a crash on its own, but warns it could amplify a downturn by enabling unviable infrastructure projects. Henry argues that while market wealth effects would carry macroeconomic consequences, equity investors would bear the primary impact, comparing the risk to the comparatively shallow post-dot-com downturn rather than a systemic banking collapse. A third panelist warns of a compounding contraction if sustained end-user demand for AI fails to materialize. In that event, Nvidia could face an abrupt drop in processor orders alongside obligations to honor the long-term facility guarantees it provided to partners. Whether Nvidia's financing initiatives sustain the AI buildout or intensify a future market contraction depends on end-user demand validating massive capital expenditures. While Nvidia's $100 billion cash cushion and profitable customer base offer structural protection, its dual exposure to chip demand and 20-year infrastructure liabilities leaves it vulnerable to a simultaneous industry reversal.

Source quotes

[0:00] Henry: screwing in their graphics cards into their computers [1:56] Edward Carr: if I get lost, I'll sort of ask for help [1:56] Shailesh: you can right away see that there are a lot of kind of circularities here [1:56] Panelist: twin bet on itself, but also on the economy at large [4:06] Shailesh: very sensitive to the word circular financing and anytime you use it, they push back really hard [4:06] Shailesh: whiff of that [5:36] Shailesh: do not need to happen

Original transcript

[0:00] Henry, describe to me the kind of remarkable  financial performance of this company. Yes, [0:06] it's astonishing. So, in the era of AI,  since ChatGPT was released to the public, [0:12] Nvidia's share price has gone up 14 times.  Uh and that's not just a speculative effect, [0:17] you know, people punting on the company.  It's justified by underlying revenues, [0:22] which have also shot up. And we've got a chart of  this. It shows Nvidia's quarterly revenue. If you [0:27] took it on an annual basis, it would show doubling  in each of the past three years. And now we're not [0:33] far off a hundred billion dollars in quarterly uh  revenue. And that's just completely shot up from, [0:39] you know, 2015 when this was much more of a a  a company for uh uh for gamers and enthusiasts [0:47] screwing in their graphics cards into their uh  computers. And this has been reflected uh in the [0:53] stock price. And then Nvidia has become big enough  that that's reflected in the performance overall [0:57] of America's stock market. Nvidia is now 8% of the  S&P 500. If I if I go back to the start of 2023, [1:05] it accounts for almost 15 cents of every dollar  that the S&P 500 has returned to investors. So, [1:12] you know, if you're invested in the US stock  market, uh you're significantly exposed to this [1:16] company simply because it is so uh so large now  because the demand for its chips is so great. in [1:23] your pieces suite you look at look at its market  share tell us a little about how its market share [1:26] is being eroded I think maybe two to three years  back you would say that almost 80% of the AI chips [1:32] that were sold in the market were from Nvidia  now what has happened is roughly around 60% of [1:37] the chips are from Nvidia but 40% of the chips  are coming from what is known as custom silicon [1:42] these are your um chips that are made by software  companies for their own internal needs and by the [1:48] end of uh the decade the the number is going to  be 50/50 so half the chips will come from Nvidia. [1:53] Half of it will come mostly from these software  companies. I see. Okay. So that that leads us very [1:58] naturally I think into this question about the  financial engineering and and how it works. So I I [2:04] think I think we do need though to go into an one  example just to get an idea of how how this works. [2:09] So pick one of the many examples Shailesh and and  tell us how it works and if I get lost I I'll sort [2:15] of ask for help. Let's pick one of the most public  deals which is OpenAI which is a large AI lab. [2:20] Um it is building an 8-gigawatt data center in  Ohio. Um 8 gigawatts is a very massive data center [2:26] uh the size of couple of different uh midsize  nuclear reactors because it's very expensive. [2:31] Now OpenAI is a very well-funded company but  it's also losing a lot of cash. So to be able to [2:38] uh for OpenAI to be able to afford this what  Nvidia is doing is it is guaranteeing the lease [2:43] on the land and power for that data center for 20  years. OpenAI will buy chips from Nvidia and the [2:51] fact that Open uh Nvidia is using its balance  sheet to guarantee that particular lease means [2:57] that lenders are more likely to lend to  OpenAI for that lease. If for some reason [3:03] um it defaults, OpenAI defaults and cannot make  the payment then OpenAI then Nvidia sorry can [3:08] find another tenant for it. So you can right away  see that there are a lot of kind of circularities [3:13] here and I didn't mention Nvidia has also  invested $30 billion into open AI itself. Yes, [3:18] it's interesting because of course why does Nvidia  have to act as a financier here? uh why can't the [3:25] markets provide capital in line with the vision of  the company of what is necessary and essentially [3:34] um the way to view it I think is Nvidia taking  the view an even more bullish view than the [3:40] market at large about the amount of investment and  infrastructure that is necessary saying investors [3:44] aren't going to finance at the scale that we think  is necessary so you know we're massive we've got a [3:50] ton of resources and there's enormous revenue  stream we can reinvest we can do it ourselves [3:55] and in in in in that sense it's making the  twin bet on itself but also on the economy [4:02] at large and it's able to do both uh because of  the scale of its underlying business shvidia's [4:09] defense against it what do people say at the  company and do you agree with them yeah so [4:13] the company is very sensitive to the word  circular financing and anytime you use it [4:17] they push back really hard uh now my view is  is two things so first and foremost there's no [4:22] getting away from the fact that Nvidia invests  in companies uh puts equity investment and then [4:27] those companies end up buying chips from there. So  there's no getting away from that fact. However, [4:31] I think there are also some real  differences. First and foremost is [4:34] um Nvidia is not um loaning the money to these  companies to buy the chips. It's essentially [4:39] guaranteeing some supply or some price that is  out there in the future. When Nvidia does this, [4:45] it sells the chip and recognizes the revenue  right up front where versus these loans coming [4:50] these guarantees if ever coming due later. So  that that's that's number one. Number two is [4:55] um the fact that Nvidia's largest customers  are really cash flow positive hyperscalers [5:02] and are are quite well cushioned. That is also  important when kind of drawing parallels to [5:06] the com boom because that is very different  position from where Nvidia is in. And lastly, [5:11] I think is this balance sheet which we can I can  just go on and on about because it it holds around [5:16] hundred billion in cash and liquid investments  and this year it's going to generate around $200 [5:21] billion in free cash flow and that's just number  is going to increase. So it's quite well cushioned [5:26] to be able to kind of sustain these kind of  guarantees. Um but yeah, I mean there is a whiff [5:32] of that particularly when you invest in a company  and they turn around and buy your chips. I I don't [5:35] think there's any way you get around it. I want to  finish up just by asking you all whether we think [5:39] Nvidia's practices of this financial engineering  is more likely to make a recession happen. Shal, [5:45] let's start with you. I don't know if it'll make  it happen, but it it could definitely amplify some [5:48] of the effects that happen because of it is quite  possible that some deals that are happening do not [5:53] need to happen. They are not that great. I don't  think it'll cause it in itself. Henry, Nvidia is [5:58] such a large part of the AI boom and the AI boom  is so important, especially via the wealth effect [6:03] in the US stock market to the US economy that I do  think it's the case that if it's wrong and faces [6:07] serious problems, the economy will do worse on  account of that it's macroeconomically significant [6:14] owing to the role it is playing. However, it's  still the case that the number one people who lose [6:19] out in the event that this happens are Nvidia's  shareholders. They lose more than anybody else. [6:24] And uh you know in the dot-com bust which is the  historic analog people draw the recession that [6:30] followed that dot-com crash was quite shallow.  It wasn't like the global financial crisis or [6:35] something where you truly had this deep systemic  effect of the banking system on the economy. I I [6:40] think it's more likely to accelerate the AI uh  boom than it is to cause a financial crash. But [6:46] you know it's a sufficiently uncertain environment  that both outcomes definitely possible. and yeah I [6:51] think one of the difficulties is that if the if  the demand does not materialize for AI it could [6:56] Nvidia's role in the center of this ecosystem  could really accelerate uh the downpull it would [7:02] both be losing demand from its own customers as  it's as it's um the the companies it has invested [7:08] in are starting to call due on those guarantees  it's provided so it could be really problematic