The panelists agreed that NVDA's valuation at 29x forward earnings leaves little cushion for growth slowdown or margin compression, but they disagreed on the timing and extent of these risks.
Risk: Multiple compression due to demand normalization and potential revenue cliff when hyperscaler capex normalizes
Opportunity: NVDA's software moat and the potential shift to lower-margin software services
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
Key Points
- Prices to rent an Nvidia H100 are up 22% in the last month.
- The rebound in price, which comes even as Nvidia is launching the new Rubin platform, shows demand for its older chips is still strong.
- Nvidia continues to look undervalued because of on an overwrought bearish thesis.
- These 10 stocks could …
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Key Points
- Prices to rent an Nvidia H100 are up 22% in the last month.
- The rebound in price, which comes even as Nvidia is launching the new Rubin platform, shows demand for its older chips is still strong.
- Nvidia continues to look undervalued because of on an overwrought bearish thesis.
- These 10 stocks could mint the next wave of millionaires ›
Nvidia's (NASDAQ:NVDA) accomplishments speak for themselves, but despite the stock having grown to a market cap of more than $5 trillion and it becoming the most profitable company in the world, there's still a lot of skepticism facing it.
Even as revenue nearly doubled in its most recent quarter, the stock trades at a price-to-earnings ratio of just 29, roughly in line with the S&P 500, indicating that investors expect its long-term earnings growth to generally resemble the broad-market index, even though it more than doubled net income in its latest quarter and expects strong growth to continue at least through 2027.
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There are a number of reasons why Nvidia doesn't get the premium you might expect for a company growing this fast. First, the semiconductor industry is historically cyclical, and investors are expecting the momentum in the AI boom to eventually fade. At that point, Nvidia's revenue and earnings growth could turn negative as it has in past cycles. Second, competitors, including Nvidia's hyperscaler customers, are building their own chips to substitute for Nvidia components. While they're unlikely to replace them entirely, it could signal that Nvidia's competitive advantage is likely to erode over time. Finally, some investors think that depreciation in Nvidia's chips is an outsize risk facing the company and the broader AI boom. If its chips lose their value quickly, that is likely to hurt their selling price and the broader sustainability of AI, as eventually, Nvidia's customers will need to sell enough services to pay for its chips.
This theory, advanced by Michael Burry of "The Big Short" fame, has been used to criticize hyperscalers and neocloud companies, as well as Nvidia.
However, there's some evidence that Nvidia chips are retaining their value much better than the skeptics would expect.
Image source: Nvidia.
Jensen Huang weighs in
The comments and chart below, taken from X, show the market average for live cloud GPU rental costs based on the Ornn H100 SXM Index.
NVIDIA compute is fungible, durable and highly rentable. It is a productive, revenue-generating asset. https://t.co/cvmjaNoiK8
— Jensen Huang (@JensenHuang) September 8, 2026
The H100 is a three-year-old training chip. Its rental price is up 22 percent on the month, to $3.28 an hour.
— Ornn (@OrnnExchange) September 7, 2026
Every depreciation schedule assumes a chip this old only loses value. The market is paying up for it instead. pic.twitter.com/TNSqgys3vx
Image source: Ornn. Via X.
As you can see, rental prices per hour for an H100 GPU, which were first launched nearly four years ago, are up 22% over the last month, even as Nvidia is now launching the new Rubin platform. the Rubin GPU, or R100, will make the H100 two generations old.
Rental and purchase prices for the H100 have indeed come down substantially from their peak in 2023, when generative AI was just starting to take off, but it's noteworthy that they were still able to increase their value at this point, even as newer options on the market emerge. The R100 will have several times as much memory as the H100 and use a superior, updated architecture.
Even the A100, the generation before the H100, remains in high demand, as some customers prefer the cheaper per-hour rental costs of the A100. Rather than disrupting itself with newer chips, Nvidia seems to be benefiting from a multi-tiered pricing model in which premium customers can pay premium prices for the newest chips and budget customers can pay lower prices for older chips.
What it means for Nvidia
The durability of H100 pricing offers yet another reason to be skeptical of the bearish thesis on the stock and to bet on the company's continued leadership in AI chips.
It's a reminder also that Nvidia, if anything, has been underestimated by Wall Street over the last few years, as the analyst consensus has been woefully short. Similarly, bearish predictions for the stock have fallen flat. Given the fact that the business just doubled in size in the most recent quarter, prices for chips that are now two generations old are holding up, and the stock is trading on par with the S&P 500, Nvidia continues to look like an excellent buy.
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Jeremy Bowman has positions in Nvidia. The Motley Fool has positions in and recommends Nvidia. The Motley Fool has a disclosure policy.
AI Talk Show
Four leading AI models discuss this article
Opening Takes
“Nvidia's upside hinges on sustained AI compute demand and pricing power, but a sharp slowdown in AI capex or a faster-than-expected shift by hyperscalers to in-house chips could trigger meaningful multiple compression.”
The strongest case for NVDA being overvalued is that the 22% month-on-month rise in H100 rental prices may reflect temporary supply/demand frictions rather than durable pricing power. Rubin/R100 could erode premium arbitrage, as multigeneration pricing compresses, and hyperscalers race to in-house accelerators. The article omits how cyclical AI capex can turn; export controls on chips to China and potential demand deceleration as the AI hype cools could cap upside. Even with solid 2026-27 guidance, the stock's valuation at roughly 29x forward earnings and a $5T market cap leaves little cushion if growth slows or margins compress.
The uptick in H100 rental prices may be episodic; once supply tightness eases or Rubin gains scale, prices fall, eroding the thesis. Additionally, if hyperscalers shift more to in-house chips, Nvidia's pricing power could erode faster than expected.
“The uptick in H100 rental prices signals temporary supply-side friction rather than a permanent expansion of Nvidia's long-term earnings durability.”
Focusing on H100 rental price spikes as a 'bear thesis destroyer' is a dangerous misreading of market dynamics. While a 22% monthly increase in rental costs suggests persistent demand, it likely reflects a supply-side bottleneck or a 'rental arbitrage' effect rather than long-term structural health. Investors must distinguish between scarcity-driven pricing and sustainable enterprise ROI. With NVDA trading at a 29x P/E, the market is pricing in a 'soft landing' for growth, but any deceleration in hyperscaler CapEx—which currently accounts for the bulk of revenue—would lead to a rapid compression of those multiples. We are seeing a classic late-cycle squeeze, not a fundamental re-rating of durable long-term demand.
If H100 rental prices are rising despite the launch of the Rubin platform, it proves that Nvidia's ecosystem has achieved 'utility status' where older hardware remains profitable enough to justify continued deployment.
“One month of GPU rental price appreciation on a single exchange, off a depressed base, is insufficient to invalidate the depreciation and cyclicality risks that justify NVDA's 29x P/E parity with the S&P 500.”
The H100 rental price uptick is real but narrow evidence. A 22% monthly bounce in a single metric—GPU rental rates on one exchange—doesn't overturn structural bear cases. Yes, older chips holding value is positive for Nvidia's moat. But the article conflates 'prices up this month' with 'depreciation thesis destroyed.' H100 rentals are still down ~70% from 2023 peaks. The real question: are rental markets tight because demand is genuinely strong, or because supply is constrained post-launch? And does a rental price recovery signal sustainable chip pricing power, or just short-term volatility in a thin market? The P/E of 29x matching the S&P 500 despite 2x net income growth IS odd—but that could reflect justified caution about cyclicality and customer in-sourcing, not undervaluation.
If H100 rentals are spiking because customers are delaying R100 purchases to wait for price cuts (a classic pre-launch pattern), this chart proves nothing about long-term durability—it may actually signal demand weakness ahead.
“H100 rental strength does not eliminate the risk that AI infrastructure spending normalizes before Nvidia's 2027 growth assumptions are met.”
The 22% rebound in H100 hourly rentals to $3.28 signals that older Nvidia silicon retains utility even with Rubin imminent, which undercuts the depreciation scare pushed by Burry and others. Yet the piece glosses over the fact that hyperscalers are already allocating meaningful budgets to custom ASICs that bypass Nvidia entirely on inference workloads. At 29x forward earnings, the stock prices in continued 30%+ growth through 2027; any flattening of AI capex after the current build-out would trigger the classic semiconductor multiple compression the bears have long warned about. Historical cycle data shows revenue can drop 30-50% once demand peaks.
Even if custom chips gain share, the sheer scale of training clusters still requires Nvidia GPUs at the frontier, and the rental data already shows pricing power persisting longer than expected.
The Debate
Responding to Claude
“The 22% H100 rent uptick signals a fragile moat that could drive multiple compression toward the mid-20s if Rubin and in-house chips scale, not durable pricing power at 29x.”
Claude, you treat the 22% H100 rental uptick as cosmetic—but the risk isn't just demand fading; it's that the spike reveals thin liquidity and a bottleneck market. If Rubin ramps and in-house chips gain scale, price elasticity could compress margins and push Nvidia's multiple below 29x toward the mid-20s. The article's cyclical caution understates the risk of multiple compression on demand normalization.
Responding to Grok
“The software ecosystem creates a structural barrier that prevents hyperscaler ASICs from fully displacing Nvidia in training workloads.”
Grok, your focus on custom ASICs ignores the software moat. Hyperscalers aren't just buying hardware; they are buying CUDA-compatible ecosystems. Even if ASICs handle inference, training remains tethered to Nvidia. The real risk isn't just cyclicality; it's the potential for a 'utility trap' where Nvidia's margins compress as they shift from selling premium hardware to supporting commoditized cloud-compute clusters. We are over-indexing on rental rates while ignoring the long-term shift in Nvidia's revenue mix toward lower-margin software services.
Responding to Gemini
“The sequencing matters: revenue cliff from capex normalization arrives before margin compression from software commoditization.”
Gemini's 'utility trap' framing is real, but backwards on timing. If Nvidia shifts to lower-margin software services, that happens *after* the current GPU supercycle peaks—not during it. The immediate risk isn't margin compression; it's revenue cliff once hyperscaler capex normalizes. Custom ASICs eroding training workloads faster than consensus expects is the actual near-term threat, not a gradual software transition. That's a 2026-27 problem, not a 2025 one.
Responding to Gemini
“ASIC shifts and export impacts could accelerate Nvidia's revenue normalization into 2025 despite rental rebounds.”
Gemini, the CUDA moat for training doesn't insulate against Claude's revenue cliff timeline. Hyperscalers' ASIC investments target inference first, but efficiency gains there could redirect 2025 capex away from Nvidia clusters entirely. Rental data at $3.28 shows short-term tightness, yet ignores how export curbs on China already cut demand 10-15% without offsetting enterprise growth. This setup risks multiple compression hitting in H2 2025 if capex flattens earlier than 29x pricing assumes.
Panel Verdict
NEUTRAL No ConsensusThe panelists agreed that NVDA's valuation at 29x forward earnings leaves little cushion for growth slowdown or margin compression, but they disagreed on the timing and extent of these risks.
NVDA's software moat and the potential shift to lower-margin software services
Multiple compression due to demand normalization and potential revenue cliff when hyperscaler capex normalizes
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