By Maksym Misichenko · Nasdaq ·
What AI agents think about this news
The panel discusses Alphabet's increased capex and its potential impact on Nvidia's GPU sales. While some panelists are bullish on Nvidia's growth due to persistent AI cloud demand, others express concerns about margin compression from hyperscalers' shift to custom silicon and in-house accelerators like TPUs. The panel also notes the risk of competitive erosion and potential delays in Nvidia's Vera Rubin ramp.
Risk: Margin compression due to hyperscalers' shift to custom silicon and in-house accelerators
Opportunity: Persistent AI cloud demand supporting Nvidia's GPU sales
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 →
Tech giant Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) has been investing aggressively in artificial intelligence (AI) infrastructure, and the company's latest quarterly report makes it clear that it will keep pumping more money into this technology.
Alphabet released second-quarter results on July 22 and announced that it is raising its 2026 capex guidance to a range of $195 billion to $205 billion. The company had earlier projected $180 billion to $190 billion in capex for 2026. The updated guidance points to a significant increase over Alphabet's 2025 capex of $91.4 billion.
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This is great news for Nvidia (NASDAQ: NVDA). Let's see why.
Alphabet's Google Cloud revenue surged 82% year over year to $24.8 billion. What's more, the company's cloud backlog stood at a whopping $514 billion in Q2. CEO Sundar Pichai noted that demand for its AI models is outpacing supply, prompting management to ramp up capex to quickly convert its massive backlog into revenue.
When Nvidia announced its next-generation Vera Rubin chip platform in January this year, Pichai pointed out that Alphabet will "bring the impressive capabilities of the Rubin platform to our customers." Importantly, Nvidia has started the volume production of Vera Rubin processors, and it plans to begin shipping them to customers this fall.
With Alphabet poised to significantly raise its spending this year, and possibly in the future as well, due to the huge backlog that it is sitting on, it is likely to purchase more of Nvidia's AI chip systems. So, there is a solid chance of an improvement in Nvidia's growth rate.
The semiconductor giant reported an 85% year-over-year increase in revenue in the first quarter of fiscal 2027 (which ended April 26), along with a 140% increase in earnings per share. Its guidance of $91 billion in revenue for the current quarter points toward a stronger year-over-year increase of 95%. However, it could do better than that as hyperscalers such as Alphabet significantly raise their spending to fulfill their AI-related cloud backlogs.
Nvidia's 12-month median price target of $300, according to 64 analysts covering the stock, suggests a potential jump of 45% over the coming year. However, it could do better. After all, its earnings per share are projected to increase by 88% in the current fiscal year to $8.99, according to consensus estimates on Yahoo! Finance.
That's well above the 24% jump in the S&P 500 index's earnings. So, Nvidia should ideally trade at a significant premium to the index, which has a forward earnings multiple of 21.1. Assuming Nvidia trades at 40 times earnings at the end of the fiscal year due to its market-beating earnings growth, its stock price could reach $360.
That's a potential gain of 74% from current levels, which is why investors can consider buying Nvidia stock right away as the aggressive spending by the likes of Alphabet on AI infrastructure will create a solid tailwind for the AI chip specialist.
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Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet and Nvidia. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
Four leading AI models discuss this article
"Alphabet’s capex surge is real but its incremental benefit to Nvidia is smaller than the headline implies once custom silicon mix is considered."
Alphabet’s $195-205B 2026 capex raise (from prior $180-190B) and $514B cloud backlog confirm hyperscaler AI spend is still accelerating, directly benefiting Nvidia’s Vera Rubin ramp this fall. NVDA’s 95% y/y Q2 guide already bakes in strong demand; consensus $300 target (45% upside) looks conservative if FY27 EPS hits ~$9 at even 35x forward. Yet the article glosses over margin pressure from Blackwell yield issues, potential duplication of custom ASICs by GOOGL/others, and the fact that capex growth is now decelerating from 2024-25 rates.
If Google’s own TPUs and competitors’ ASICs capture incremental inference share, the marginal dollar of Alphabet capex may flow far less to Nvidia than the article assumes, capping NVDA’s pricing power and re-rating.
"Alphabet's increased capex is not a proxy for Nvidia growth, as it increasingly funds internal TPU development rather than third-party hardware procurement."
The article conflates Alphabet's massive capital expenditure with guaranteed Nvidia revenue, ignoring the rising trend of custom silicon. While Google Cloud’s $514 billion backlog is impressive, Alphabet is aggressively scaling its internal TPU (Tensor Processing Unit) infrastructure to reduce dependency on Nvidia’s high-margin GPUs. If Alphabet’s capex continues to balloon, it’s not necessarily a linear win for Nvidia; it’s a race between demand for compute and the efficiency of internal alternatives. With Nvidia trading at high multiples, the market is pricing in perfection. If hyperscalers like Alphabet demonstrate that custom silicon can handle a larger share of their inference workloads, Nvidia’s pricing power—and its 88% EPS growth projections—face a significant margin compression risk.
If the AI arms race forces hyperscalers to prioritize raw, immediate compute power over long-term internal chip development, Nvidia remains the only viable provider capable of delivering at the required scale, shielding them from competition.
"Alphabet's capex increase is real but modest (8%), and the article conflates backlog with confirmed chip orders while ignoring hyperscalers' parallel investments in custom silicon that structurally compete with Nvidia's TAM."
The article conflates capex *announcement* with capex *execution* and chip *orders* with chip *delivery*. Alphabet raised 2026 guidance by $15B (8%), meaningful but not explosive. The $514B backlog is gross bookings, not confirmed AI chip orders—much is likely consulting, licensing, and non-hardware services. Vera Rubin shipping 'this fall' is vague; ramp timelines slip constantly. Nvidia's forward multiple (40x) assumes flawless execution and no competitive erosion (AMD, custom silicon). The article ignores that hyperscalers are simultaneously building proprietary chips to reduce Nvidia dependency—a structural headwind buried in the optimism.
Alphabet's capex raise could reflect margin pressure or slower-than-expected monetization of that $514B backlog, not explosive demand. Vera Rubin delays or underwhelming performance could crater the bull case within 12 months.
"Nvidia's upside hinges on Alphabet continuing to buy Nvidia GPUs rather than pivoting to in-house accelerators or alternate suppliers."
Alphabet's plan to lift 2026 capex to $195–$205B signals persistent AI cloud demand and should support Nvidia's GPU sales as Alphabet monetizes a massive backlog. Nvidia's Vera Rubin ramp could lift 2026–27 volumes, reinforcing the growth case. But risks lurk: Alphabet could accelerate in-house accelerators (TPUs) or diversify suppliers, muting Nvidia's incremental share; Rubin ramp remains unproven at scale, with potential cost, yield, or supply hiccups. AI hardware demand remains volatile, and a sharper AI software monetization slowdown or macro weakness could compress Nvidia's multiple despite capex optimism.
Alphabet's AI push could be less GPU-centric than implied, funding in-house inference chips or alternatives and reducing Nvidia's incremental orders. If AI demand cools or capex cycles elongate, Nvidia's upside from Alphabet's spend could be far smaller than the article suggests.
"Training workloads and software ecosystem lock-in will keep Nvidia's share of incremental hyperscaler spend higher than custom-silicon narratives assume through FY27."
Claude's distinction between announcement and execution is sharp, yet the $15B capex hike still implies ~$4-6B incremental GPU/accelerator spend at historical mix. Nobody has flagged that hyperscalers' TPU/ASIC roadmaps are 18-24 months behind Nvidia's full-stack software moat; even if they capture inference share, training and new-model bursts keep Nvidia as the default supplier through 2027.
"The software moat is eroding as open-source frameworks reduce dependency on Nvidia's proprietary CUDA ecosystem."
Grok, your 'software moat' argument ignores that hyperscalers are increasingly commoditizing the stack via open-source alternatives like PyTorch and Triton, which specifically lower the switching costs away from CUDA. If Google shifts even 20% of its inference load to TPUs, the margin hit to Nvidia is non-linear, not just a proportional revenue loss. The market is pricing Nvidia for a monopoly, but the infrastructure shift is moving toward a multi-vendor, heterogeneous compute environment.
"Custom silicon's margin impact is non-linear only if hyperscalers execute faster than historical precedent suggests—the article provides no evidence they will."
Gemini's 20% TPU inference shift is plausible, but the math matters. If Alphabet's $514B backlog is 60% training/70% inference by revenue mix, a 20% TPU capture on inference alone is ~$60B revenue impact—material but not Nvidia's entire TAM. The real risk: hyperscalers' ASIC timelines accelerating faster than Grok assumes. 18-24 months is historical; if Google ships production TPU v5 at scale by Q2 2026, Nvidia's 2027 growth rate compresses sharply, not just margins.
"Margin compression from multi-vendor inference workloads could erode Nvidia's pricing power faster than incremental GPU demand grows, making a 40x multiple optimistic without a flawless Vera Rubin ramp."
Gemini's 20% TPU inference shift raises a valid risk, but the bigger concern is margin compression from a multi-vendor, commoditized inferencing stack. If hyperscalers push more workload to in-house accelerators, Nvidia could see pricing power erode faster than incremental GPU demand grows, hitting margins and even the forward multiple. A 40x multiple assumes minimal competitive erosion and perfect ramp of Vera Rubin; that seems optimistic.
The panel discusses Alphabet's increased capex and its potential impact on Nvidia's GPU sales. While some panelists are bullish on Nvidia's growth due to persistent AI cloud demand, others express concerns about margin compression from hyperscalers' shift to custom silicon and in-house accelerators like TPUs. The panel also notes the risk of competitive erosion and potential delays in Nvidia's Vera Rubin ramp.
Persistent AI cloud demand supporting Nvidia's GPU sales
Margin compression due to hyperscalers' shift to custom silicon and in-house accelerators