When Nvidia announced a $100 billion investment commitment to OpenAI1 in September 2025 , analysts immediately drew comparisons to the telecom bubble. The concern : is this vendor financing , where a supplier lends money to customers so they can buy the supplier’s products , a harbinger of another spectacular collapse?
American tech companies will spend $300-400 billion on AI infrastructure in 20252,3 , exceeding any prior single-year corporate infrastructure investment in nominal dollars.3 David Cahn estimates the revenue gap has grown to $600 billion4.
I analyzed the numbers. The similarities are striking , but the differences matter.
The Lucent Playbook
Lucent’s revenue peaked at $37.92B in 1999 , crashed 69% to $11.80B by 2002 , never recovered. Merged with Alcatel in 2006.
In 1999 , Lucent Technologies reached $37.92 billion in revenue at the peak of the dot-com bubble. 5 Lucent was the #1 North American telecommunications equipment manufacturer with 157,000 employees & dominated markets alongside Nortel Networks (combined 53% optical transport market share). 6 Behind the scenes , equipment makers extended billions in vendor financing to telecom customers. Lucent committed $8.1B7 , Nortel extended $3.1B with $1.4B outstanding , & Cisco promised $2.4B in customer loans.8
The strategy seemed brilliant : lend money to cash-strapped telecom companies so they could buy your equipment. Everyone wins—until the merry-go-round stops.
When the bubble burst :
- 47 Competitive Local Exchange Carriers (CLECs) bankrupted 2000-2003 , including Covad , Focal Communications , McLeod , Northpoint , Winstar 9,10
- Why they failed : $60B overbuild 1996-2001 , market saturation from identical business models , sudden funding collapse (Jan 2001 : billions available , Apr 2001 : zero)11
- 33-80% of vendor loan portfolios went uncollected as customers failed & equipment became worthless12
- Fiber networks were using less than 0.002% of available capacity , with potential for 60,000x speed increases. 13 It was just too early.
Nvidia’s Playbook
Fast forward to 2025. Nvidia’s vendor financing strategy totals $110 billion in direct investments plus another $15+ billion in GPU-backed debt. The largest commitment is $100B to OpenAI (September 2025)1,14 , structured as 10 tranches of $10B each tied to infrastructure deployment milestones. The first $10B was valued at a $500B OpenAI valuation , with subsequent tranches priced at prevailing valuations. Payment comes via lease arrangements , not upfront GPU purchases. OpenAI CFO Sarah Friar confirmed : “Most of the money will go back to Nvidia”14
Beyond OpenAI , Nvidia holds a $3B stake in CoreWeave15 , a company that has spent $7.5B on Nvidia GPUs , & $3.7B in other AI startup investments16 through NVentures.
The GPU-backed debt market adds another layer. CoreWeave alone carries $10.45B in debt using GPUs as collateral17. An additional $10B+ in GPU-backed debt has emerged for “Neoclouds” including Lambda Labs ($500M GPU-backed loan)18,19.
Lucent in 1999-2000 had vendor financing commitments of $8.1B (24% of $33.6B revenue). Nvidia’s direct investments total 67% of annual revenue ($110B against $165B LTM). Nvidia’s exposure is 2.8x larger relative to revenue than Lucent’s official outstanding loans , though Lucent’s off-balance-sheet guarantees masked the true exposure.
The Numbers Side-by-Side (2024 Dollars)
| Metric | Lucent (FY2000, inflation-adj.) | Nvidia (2025) |
|---|---|---|
| Vendor financing | $15B | $110B |
| Operating cash flow | $304M20 | $15.4B (Q2 FY26) |
| Revenue | $61B | $165B (LTM) |
| Top 2 Customers represent | 23%21 | 39% |
The Reasons to be Wary
1. The AI Customer Base is More Concentrated
Lucent’s top 2 customers—AT&T at 10% & Verizon at 13%—accounted for 23% of revenue in FY2000.21 The Regional Bell Operating Companies , or RBOCs , the seven “Baby Bells” created from AT&T’s 1984 breakup , were also major customers. Nvidia has 39% of revenue from just 2 customers & 46% from 4 customers , nearly double Lucent’s concentration. 88% of Nvidia’s revenue comes from data centers.
2. GPU-Backed Debt Is New
The new $10B+ GPU-backed debt market is built on the assumption that GPUs will hold their value over 4-6 years. GPU-backed loans carry ~14% interest rates22 , triple investment-grade corporate debt.23
How Depreciation Schedules Changed :
| Company | Pre-2020 | 2020-2021 | 2022-2023 | 2024-2025 | Change |
|---|---|---|---|---|---|
| Amazon24 | 3 years | 4 years (2020) → 5 years (2021) | 5 years | 6 years (2024) → 5 years (2025) | First reversal |
| Microsoft25 | ~3 years | 4 years | 6 years | 6 years | +100% |
| Google26 | ~3 years | 4 years | 6 years | 6 years | +100% |
| Meta27 | ~3 years | 4 years | 4.5 years → 5 years | 5.5 years | +83% |
| CoreWeave28 | N/A | N/A | 4 years → 6 years (Jan 2023) | 6 years | +50% (GPUs) |
| Nebius29 | N/A | N/A | 4 years | 4 years | Industry standard |
Amazon’s 2025 reversal (6 → 5 years) is the first major pullback.
CPUs historically have 5-10 years of useful life , while GPUs in AI datacenters last 1-3 years in practice , despite 6-year accounting assumptions.30,31 Evidence from Google architects shows GPUs at 60-70% utilization survive 1-2 years , with 3 years maximum.31 Meta’s Llama 3 training experienced 9% annual GPU failure rates , suggesting 27% failure over 3 years.31
Cerno Capital raises the question : “Are these policies a reflection of genuine economic & technological realities? Or are these policies a lever by which hyperscalers are enhancing the optics of their investment programs amid rising investor concerns?”32
4. The Use of SPVs
Tech companies use Special Purpose Vehicles (SPVs) to finance AI datacenter construction. A hyperscaler like Meta partners with a private equity firm like Apollo , contributing capital to a separate legal entity that builds & owns the datacenter.
As investor Paul Kedrosky explains : “I have a stake in it as Meta. Some giant private debt provider has a stake in it. The datacenter is under my control. But I don’t own it, so you don’t get to roll it back into my balance sheet.”2*
The Structure
- Entity Creation : Hyperscaler & PE firm form separate legal entity (SPV)
- Capital Structure : Typically 10-30% equity, 70-90% debt from private credit markets
- Lease Agreement : SPV leases capacity back to hyperscaler
- Balance Sheet Treatment : SPV debt doesn’t appear on hyperscaler’s balance sheet
The hyperscaler maintains operational control through long-term lease agreements. Because it doesn’t directly own the SPV , the debt remains off its balance sheet under current accounting standards.
The appeal is straightforward. “I don’t want the credit rating agencies to look at what I’m spending. I don’t want investors to roll it up into my income statement.”2*
Market Scale
American tech companies are projected to spend $300-400 billion on AI infrastructure in 2025. Hyperscaler capital expenditures have reached approximately 50% of operating income2, levels historically associated with government infrastructure buildouts rather than technology companies.
Where the Risk Sits
Datacenter assets now represent 10-22% of major REIT portfolios2 , up from near zero two years ago. The thin equity layer (10-30%) means if datacenter utilization falls short of projections or if GPUs depreciate faster than projected , equity holders face losses before debt holders experience impairment.
*Quotes lightly edited for clarity & brevity
5. Custom Silicon Threat
Hyperscalers are building their own AI accelerators to reduce Nvidia dependence. Microsoft aims to use “mainly Microsoft silicon” , specifically Maia accelerators , in datacenters.33 Google deploys TPUs , Amazon builds Trainium & Inferentia chips , & Meta develops MTIA processors. If customers shift to in-house silicon , CoreWeave’s GPU collateral value & Nvidia’s vendor financing become exposure to customers building competitive alternatives.
Nvidia Isn’t Lucent & 2025 Isn’t 2000
- Accounting : Lucent manipulated $1.148B in revenue , SEC charged 10 executives with fraud5 ; Nvidia shows no evidence of manipulation , audited by PwC , Aa3 rated34
- Cash flow : Lucent lent $8.1B while cash flow lagged profitability & receivables exploded $5.4B (1998-1999)20 ; Nvidia lends with $50B+ annual operating cash flow & $46.2B net cash35
- Credit rating : Lucent downgraded to A3 (December 2000)36 ; Nvidia upgraded to Aa3 (March 2024)34
- Customer base : Lucent’s customers were leveraged CLECs burning capital ; Nvidia’s top 4 customers generated $451B in operating cash flow in 2024 (Microsoft $119B , Alphabet $125B , Amazon $116B , Meta $91.3B)37
- Capacity : Fiber networks used <0.002% of capacity in 200013 ; Microsoft & AWS report AI capacity constraints in 202538,39
What I’m Watching
Is AI demand real (like cloud computing) or speculative (like dot-com fiber)?
Here’s what I’m watching :
- GPU utilization rates : Are data centers actually using the chips or just stockpiling?
- OpenAI’s monetization : Can they generate enough revenue to justify the buildout?
- Debt defaults : Any cracks in the $15B GPU-backed debt market?
- AR trends : AR improved from 68% (FY24) to 30% (Q2 FY26) , but still watch for deterioration
- Customer adds : Are new customers emerging , or is Nvidia dependent on the same 2-4 hyperscalers?
- Custom silicon threat : Microsoft developing Maia accelerators , aiming to use “mainly Microsoft silicon in the data center.”33 If hyperscalers shift to in-house chips , Nvidia’s vendor financing becomes exposure to customers building competitive alternatives.
- Vendor consolidation : Many companies are in a period of experimentation trying 2-3 competing vendors. Those experimental budgets may thin with time , reducing overall spend.
AI is already broadly deployed—40% of US employees used AI at work by September 2025 , double the 20% rate in 2023.40 Questions persist about effectiveness : the oft cited MIT study found 95% of AI pilots failed to deliver measurable P&L impact , primarily due to poor integration rather than technical failures.41
Yet the pace of improvement is tremendous. Labor market data shows wages rising twice as fast in AI-exposed industries , & workers using AI boost performance up to 40%.40 Many of Nvidia’s customers are profitable & sophisticated hyperscalers—Microsoft , Google , Amazon , Meta—generating $451B in operating cash flow in 2024 , with tremendous pull from their own enterprise customers demanding AI. OpenAI is not profitable , reporting a $4.7B loss in H1 2025 on $4.3B revenue , though nearly half the loss is stock-based compensation.42
Unlike the telecom bubble , where demand was speculative & customers burned cash , this merry-go-round has paying riders.
Coda : Lucent’s Accounting Fraud
Behind the vendor financing disaster was systematic accounting fraud. The SEC charged Lucent with manipulating $1.148 billion in revenue & $470 million in pre-tax income during fiscal year 2000. 5 The fraud involved multiple schemes :
Channel Stuffing : Lucent sent $452 million in equipment to distributors but counted it as revenue before the distributors sold to end customers.5 This created phantom sales.
Side Agreements : Lucent executives entered secret agreements with distributors granting them return rights & privileges beyond their distribution contracts , making it improper to recognize revenue.5 These side deals were hidden from auditors.
Reserve Manipulation : Lucent improperly established & maintained excess reserves to smooth earnings , violating GAAP.5
The SEC charged 10 Lucent executives with securities fraud.5 The company paid a $25 million fine—the largest ever for failing to cooperate with an SEC investigation.5 The accounting manipulation masked deteriorating fundamentals until too late.
The WinStar Collapse : Lucent committed $2 billion in vendor financing to WinStar Communications , a CLEC. When WinStar struggled , Lucent refused a final $90 million loan extension. WinStar filed bankruptcy. Lucent wrote off $700 million in bad debts.43 This pattern repeated across customer defaults : Lucent made provisions for bad debts of $2.2 billion (2001) & $1.3 billion (2002)—a total of $3.5 billion in customer loan losses.43