Key Takeaways CoreWeave deployed multi-rack Vera Rubin NVL72 clusters, advancing next-generation AI workloads.CRWV said non-GPU AI products and services exceeded $400M in annual recurring revenue in Q2.CoreWeave expects $35-$39B in 2026 capex as Q3 interest expense is projected at $860-$940M. CoreWeave, Inc. (CRWV - Free Report) deployed multi-rack NVIDIA Vera Rubin NVL72 clusters on CoreWeave Cloud on Wednesday, extending its push into next-generation AI workloads.
Rubin expands the technical breadth of the platform, but the deployment also raises the stakes around capacity delivery and financing. The investment case depends on whether CoreWeave can convert new hardware into utilization, pricing and returns while funding a very large infrastructure buildout.
CoreWeave Moves Rubin From Validation to DeploymentCoreWeave had already become the first cloud provider to bring up and validate NVIDIA’s Vera Rubin NVL72 before the multi-rack deployment. The progression moves the company from system validation toward broader availability of the architecture on its cloud.
CoreWeave won a multi-year agreement with Hudson River Trading in August to build its next-generation AI research and model-development platform using Vera Rubin NVL72 and Spectrum-X Ethernet. NVIDIA Corporation (NVDA - Free Report) says Rubin is in full production and is designed for training and inference, making cloud deployment central to the platform’s rollout.
CRWV Can Pair New GPUs With a Broader AI PlatformRubin is not a stand-alone revenue story. CoreWeave said AI products and services beyond GPUs, including storage, CPU, networking and software, exceeded $400 million of annual recurring revenue in the second quarter.
Booked annual recurring revenue for managed inference rose from $1 million to more than $100 million within months of launch. These figures describe the broader platform rather than Rubin-specific revenue and show how newer compute can sit alongside software and services.
CoreWeave Has Capacity to Support Next-Gen WorkloadsActive power exceeded 1.5 gigawatts in the second quarter, while contracted power reached about 4.2 gigawatts by Aug. 11. CoreWeave targets more than 1.85 gigawatts of active power by year-end 2026 and at least 8 gigawatts by 2030.
That power pipeline is central to bringing new GPU systems online at scale. Microsoft Corporation (MSFT - Free Report) said it expects to be among the first cloud providers to deploy next-generation rack-scale AI infrastructure based on NVIDIA Vera Rubin and continues adding capacity as AI demand exceeds available supply.
CRWV Still Faces Heavy Costs to Fund the RolloutCoreWeave expects 2026 capital expenditures of $35-$39 billion. Net interest expense rose to $640 million in the second quarter from $267 million a year earlier, and management expects third-quarter interest expense of $860-$940 million as debt supports accelerating deployments.
The offset is improving contract economics. Management said second-quarter contracts are expected to carry contribution margins 5-10 percentage points above recent deals, while July pricing increased approximately 25% across product configurations. Rubin can expand technical capability, but utilization and pricing still need to support adequate returns on the capital deployed.
CoreWeave Signals Temper the Rubin OpportunityRubin is a meaningful technology milestone because it extends CoreWeave’s next-generation infrastructure offering and supports training and inference workloads. The financial payoff still depends on disciplined capacity execution and funding.
CRWV currently carries a Zacks Rank #3 (Hold). It also has a VGM Score of F, a Value Score of D, a Growth Score of D and a Momentum Score of D. The #3 Rank provides a middle-of-the-scale short-term signal, while the D and F Style Scores are less favorable than the A and B grades Zacks generally prefers alongside top-ranked stocks. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.
Grid software unicorn Emerald AI has formed a coalition with Google and Nvidia that will use the startup’s technology to secure space on the grid for more data centers.
The AI Energy Management Alliance (AEMA) hopes to make demand response — a paradigm in which data centers temporarily reduce their electricity use — an integral part of data center development. Pausing noncritical tasks and shifting some compute loads, AEMA says, could allow an additional 100 gigawatts of data centers to connect to the grid.
The founding trio is joined by Anthropic, and a range of utilities including AES, Constellation, National Grid, and NRG Energy.
Demand response is an old concept that has been used by utilities for decades. It works because the grid is designed with peak loads in mind. For much of the year, demand is well below the maximum the grid can support.
Traditionally, demand response covered large electricity users like factories, which in times of high demand would agree to reduce their usage, typically by pausing production or switching to backup generators. In exchange, the utility would pay them for the service, often handsomely.
Today, data centers can also take advantage of those programs, usually by turning on backup generators.
Emerald AI has been proposing an alternative to dirty diesel generators. The company’s software coordinates requests from utilities with data centers, which can pause noncritical tasks or shift loads to other data centers where the grid has some headroom.
The startup isn’t the only one working on such technology. Google has been developing its own tools, and Enel X lets data centers tap their uninterruptible power supplies to round off peaks in power demand. Limiting max grid usage to 90% for a few hours at a time could let data centers free up 76 gigawatts of capacity, according to a study by Goldman Sachs published last year.
Emerald AI does have an advantage, though. Its software connects utilities directly to data centers, which should allow data centers to respond quickly to requests, much like batteries. The startup also recently raised $150 million in a Series A led by Energize Capital and DCVC, giving it the sort of funding needed to roll out the technology on a wider scale.
Given the peaky nature of AI compute loads, and data centers’ ability to quickly ramp up and down, the creation of something like AEMA is overdue. The coalition also promises to help tech companies and utilities find new sites for data centers, an effort that hasn’t been easy for either party.
Despite its promise, more sophisticated demand response won’t solve all of the grid’s problems today. Ayse Coskun, Emerald AI’s chief scientist, told TechCrunch that her company’s technology promises to blunt the industry’s need for new generating sources, but it won’t eliminate it entirely.
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Tim De Chant is a senior climate reporter at TechCrunch. He has written for a wide range of publications, including Wired magazine, the Chicago Tribune, Ars Technica, The Wire China, and NOVA Next, where he was founding editor.
De Chant is also a lecturer in MIT’s Graduate Program in Science Writing, and he was awarded a Knight Science Journalism Fellowship at MIT in 2018, during which time he studied climate technologies and explored new business models for journalism. He received his PhD in environmental science, policy, and management from the University of California, Berkeley, and his BA degree in environmental studies, English, and biology from St. Olaf College.
You can contact or verify outreach from Tim by emailing [email protected].
NVIDIA's Data Center revenue more than doubled year over year, yet the stock sits well below its 52-week high while Wall Street debates a bold price target that would require nearly 90% gains. The math to get there is tighter…
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NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) has become the operating system of the AI economy, extending its role well beyond that of the world’s most important chip company.
Data Center revenue hit $89.02 billion last quarter, up 117% year over year, and CEO Jensen Huang told investors “AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.”
Shares trade at $210.85, up just 13.32% year to date. So can NVDA hit $400 in 2027? Let’s run the math.
Why NVIDIA Shares Are Stuck Despite Record Numbers The fundamentals are accelerating while the stock lags. NVDA is down 8.36% over the past week and 5.8% over the past month. Recent coverage frames it plainly: “Nvidia Stock Drops. It’s Still the Best Chip Bet in an AI Slowdown” from Barron’s captures the mood.
Investors are worried about a hyperscaler capex pause, memory pricing (management flagged “extreme pricing conditions in memory“), zero China Data Center compute revenue in guidance, and $279 billion in supply commitments that raise eyebrows about future absorption.
With a beta of 2.22, NVDA moves violently in both directions. Right now, the market is punishing anything that smells like AI overbuild, regardless of how good the underlying numbers are.
Wall Street Sees 55% Upside. Our Model Says Something Similar. The Street is overwhelmingly bullish. Alpha Vantage shows 9 Strong Buy, 48 Buy, 2 Hold, and 1 Sell ratings, with a consensus target of $327.18. Our internal base case comes in at $306.02, implying 40.19% upside, with a bull case of $348.59 and a bear case of $258.47. Confidence: high.
Pushing back on the consensus: with 95 bullish analysts against 2 bearish and forward EPS revisions running 39 up and zero down over the past 30 days for fiscal 2028, analysts are actually behind the curve. Management guided fiscal 2028 revenue to grow approximately 70%, and that number is supply-constrained.
Path to $400 Per Share Reaching $400 from today’s price of $210.85 would require a gain of 89.7%. With forward EPS of $10.52, a price of $400 implies a forward P/E of 38x. Our base case of $306.02 already implies 31x, meaning the bold target requires additional multiple expansion.
That sounds like a lot until you consider fiscal 2028 EPS consensus is $15.52, so today’s implied forward P/E against next year’s earnings is roughly 26x, hardly stretched for a company growing 70%.
The catalysts are real: Vera Rubin is already in production with purchase orders from every major hyperscaler, hyperscaler capex is on track for $1.3 trillion in 2027, and OpenAI’s commitments alone represent approximately 12 gigawatts of NVIDIA compute.
Huang added: “The AI infrastructure buildout is at full steam. Vera Rubin, now in full production, was built to power exactly this moment.” The primary risk is a hyperscaler digestion phase that turns those $279 billion in supply commitments into stranded inventory.
Where NVIDIA Trades Today vs Its Earnings Power At $210.85 against forward EPS of $10.52, NVDA carries a forward P/E of 20x. That is remarkably reasonable for a business with 75% gross margins, 60% operating margins, and revenue that more than doubled year over year.
Shares sit between the 52-week low of $163.90 and high of $236. Zoom out and the 10-year return is 14,254.68%. Long-term holders know what compounding on this scale looks like.
Is $400 Realistic? Here’s My Verdict $400 requires an 89.7% gain and a forward P/E of 38x.
That is a stretch, but a defensible one if three things go right: Vera Rubin ramps as the fastest product in company history, fiscal 2028 EPS lands closer to the $18.75 high estimate than the average, and hyperscaler capex holds at the $1.3 trillion guidepost.
A meaningful capex pause from even one hyperscaler would derail it. Returns at this level shouldn’t be expected every year, but we’ve outlined the blueprint for how NVIDIA could reach $400 in 2027.
Contact [email protected] for any questions or corrections.
Nvidia CEO Jensen Huang comments on the sidelines of an event hosted by King Charles III in Scotland about concerns surrounding the mounting risks of artificial intelligence. "You have rigorous testing systems, the testing lab should be contained, and products should not be released to the public until it's ready to be used by the public," Huang says.
Michael Burry bought his first credit default swaps on subprime mortgage bonds for his Scion Asset Management hedge fund in 2005. He continued to buy those contracts, which offered limited downsides but huge upsides if he was right, even as the trade moved against him. In the end, he turned out to be right about the mortgage-bond crisis he saw coming, and those trades generated hundreds of millions of dollars for his investors.
Burry closed Scion last year, returning invested capital to shareholders. He now manages his own money and writes a newsletter with updates on his portfolio.
Before closing Scion, the fund held put options on Palantir (PLTR +1.14%) and Nvidia (NVDA +2.40%), two tech stocks that Burry sees as overvalued. He continues to hold those options in his personal portfolio. The trade has a similar setup to the "big short" that made him famous. Will it turn out the same way?
Image source: Getty Images.
In his final 13F filing for Scion Asset Management, Burry disclosed 50,000 put options on Palantir with a notional value of $912 million. He also held 10,000 puts on Nvidia with a notional value of $186,580. Importantly, notional value refers to the value of the underlying shares tied to the options contracts. The actual capital he put at risk is far less, as deep out-of-the-money puts cost just a few pennies per share.
The amount of capital at risk is limited to the amount paid for the puts. That makes Burry's bet against Palantir and Nvidia similar to his bets against subprime mortgages in the 2000s. His potential losses were limited to the premiums he paid on the credit default swaps. In the more recent case, it's limited to the premiums paid on the options. That said, Burry also has smaller short positions on the stocks themselves.
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Billionaire investor Howard Marks once wrote, "Being too far ahead of your time is indistinguishable from being wrong." That's especially true when dealing with options that have an expiration date. Even if Burry turns out to be right, his bets against Palantir and Nvidia could prove worthless because he bought his puts too early.
Burry was early in the subprime mortgage bond short as well. He was able to remain solvent, however, and continued to support his bet against the mortgage bond market until it paid off. I expect he'll do the same with the big AI stocks.
Burry recently sold all his puts on Nvidia and Palantir that expire in December, but maintained short positions in both stocks, and Palantir puts with 2027 expirations. The move was part of a broader portfolio adjustment in which he trimmed every position while maintaining relative sizing to increase his cash holdings and reduce risk.
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Palantir is trading at an extremely expensive premium. While it's producing impressive revenue growth and operating margin expansion, with a price-to-earnings ratio of more than 100, it will need to achieve substantial long-term growth just to justify the stock's current price. Burry has called into question Palantir's ability to keep growing and said the stock -- now priced at about $174 per share -- could be worth less than $1 per share in the long term.
As for Nvidia, Burry believes its heavy customer concentration is a significant risk. If one of the hyperscalers dials back its capital spending plans, that could produce a massive drop in revenue for Nvidia. In the case of a marketwide downturn, Nvidia could be one of the hardest-hit companies.
Burry's bearish bet against Palantir and Nvidia was early, but that doesn't necessarily mean he won't turn out to be right once again.
Trinity Legacy Partners LLC cut its holdings in NVIDIA Corporation (NASDAQ:NVDA – Free Report) by 11.3% during the second quarter, according to its most recent 13F filing with the Securities and Exchange Commission (SEC). The firm owned 74,045 shares of the computer hardware maker’s stock after selling 9,392 shares during the quarter. NVIDIA makes up about 3.1% of Trinity Legacy Partners LLC’s holdings, making the stock its 5th biggest holding. Trinity Legacy Partners LLC’s holdings in NVIDIA were worth $15,357,000 at the end of the most recent quarter.
Other large investors also recently bought and sold shares of the company. Lifetime Wealth Management P.C. purchased a new position in shares of NVIDIA during the fourth quarter valued at approximately $26,000. Longview Financial Advisors Inc. purchased a new stake in shares of NVIDIA in the 1st quarter worth approximately $27,000. Phillip James Consulting Co. purchased a new stake in shares of NVIDIA in the 1st quarter worth approximately $40,000. Syntax Research Inc. increased its position in NVIDIA by 62.5% during the 4th quarter. Syntax Research Inc. now owns 260 shares of the computer hardware maker’s stock valued at $49,000 after buying an additional 100 shares in the last quarter. Finally, Frankly Finances LLC bought a new position in NVIDIA during the 2nd quarter valued at approximately $60,000. 65.27% of the stock is owned by hedge funds and other institutional investors.
Key NVIDIA News Here are the key news stories impacting NVIDIA this week:
Positive Sentiment: Management continues to defend AI spending. CEO Jensen Huang said the AI spending “flywheel” is accelerating and argued that companies, rather than new regulations, should manage AI safety. Meta CEO Mark Zuckerberg broadly supported that view, easing fears that safety concerns could trigger a development pause. NVIDIA CEO Says AI Spending Flywheel Is Really, Really Flying Positive Sentiment: Rubin demand is reinforcing the infrastructure story. CoreWeave brought a multi-rack NVIDIA Vera Rubin NVL72 cluster online, connecting hundreds of GPUs for large-scale training and inference. The deployment supports expectations for continued demand beyond Blackwell systems. CoreWeave Stock Gains as Nvidia Rubin Cluster Boosts AI Cloud Capacity Positive Sentiment: NVIDIA is expanding beyond GPUs. An energy alliance with Google and Emerald AI aims to make data centers flexible electricity customers, potentially reducing grid constraints that could otherwise limit AI capacity. NVIDIA is also supplying networking and software for Apple’s potential future AI servers. Nvidia and Google Launch Energy Alliance to Prevent AI Grid Overload Positive Sentiment: Quantum-computing research adds a longer-term catalyst. NVIDIA’s H200 GPU, CUDA-Q platform and cuQuantum software helped researchers reduce quantum-circuit compilation from minutes to roughly 28 seconds in larger tests, strengthening its position as an infrastructure provider for hybrid computing. IonQ and NVIDIA Just Cracked a Major Quantum Computing Bottleneck Neutral Sentiment: Strategic expansion could improve future monetization. Reports describe NVIDIA’s $12.9 billion acquisition of Hugging Face as a way to extend its ecosystem into AI software and models, although the deal may be expensive relative to current revenue and is unlikely to materially affect near-term results. Forget Nvidia’s Chips: Hugging Face Is the Real Reason to Own This Stock Now Negative Sentiment: Competition and customer bargaining power remain risks. OpenAI says it has more alternatives to NVIDIA, while AMD is pursuing large-scale chip deployments with major AI developers. Greater supplier choice could pressure NVIDIA’s pricing and market share over time. OpenAI’s CFO Just Explained Why Nvidia Is No Longer the Only Option Negative Sentiment: AI slowdown fears have not disappeared. Bearish investor Michael Burry continues to warn of a potential sharp decline in AI-linked stocks, while reports of insider selling and elevated valuation could encourage profit-taking if infrastructure spending moderates. Michael Burry Predicts a Possible 1987-Style Drop Wall Street Analysts Forecast Growth NVDA has been the topic of several analyst reports. Royal Bank Of Canada boosted their target price on NVIDIA from $300.00 to $330.00 and gave the stock an “outperform” rating in a report on Thursday, August 27th. Robert W. Baird set a $500.00 price target on NVIDIA and gave the company an “outperform” rating in a report on Thursday, May 21st. Melius Research set a $420.00 price target on shares of NVIDIA in a research note on Thursday, August 27th. Tigress Financial reissued a “strong-buy” rating and set a $425.00 price objective (up from $360.00) on shares of NVIDIA in a research report on Wednesday, May 27th. Finally, Rothschild & Co Redburn raised their price objective on shares of NVIDIA from $300.00 to $325.00 in a research note on Thursday, August 27th. Three research analysts have rated the stock with a Strong Buy rating, fifty have issued a Buy rating and two have issued a Hold rating to the company’s stock. According to MarketBeat, the stock presently has an average rating of “Buy” and an average price target of $324.34. View Our Latest Analysis on NVIDIA
Insider Activity at NVIDIA In related news, Director Mark Stevens sold 622,239 shares of the firm’s stock in a transaction that occurred on Friday, September 4th. The shares were sold at an average price of $231.62, for a total value of $144,122,997.18. Following the sale, the director owned 2,336,531 shares in the company, valued at approximately $541,187,310.22. This trade represents a 21.03% decrease in their ownership of the stock. The sale was disclosed in a document filed with the SEC, which can be accessed through the SEC website. Also, EVP Timothy Teter sold 30,000 shares of NVIDIA stock in a transaction that occurred on Monday, August 31st. The stock was sold at an average price of $217.88, for a total transaction of $6,536,400.00. Following the completion of the sale, the executive vice president directly owned 2,687,660 shares of the company’s stock, valued at approximately $585,587,360.80. This trade represents a 1.10% decrease in their position. Additional details regarding this sale are available in the official SEC disclosure. The transaction was executed under a pre-arranged Rule 10b5-1 trading plan. Insiders sold 1,700,740 shares of company stock worth $385,015,077 in the last 90 days. 3.94% of the stock is currently owned by corporate insiders.
NVIDIA Price Performance NASDAQ NVDA opened at $213.90 on Thursday. NVIDIA Corporation has a one year low of $164.27 and a one year high of $236.54. The stock has a 50-day moving average of $214.10 and a 200 day moving average of $203.86. The company has a debt-to-equity ratio of 0.14, a quick ratio of 3.85 and a current ratio of 4.59. The company has a market capitalization of $5.15 trillion, a price-to-earnings ratio of 27.04, a PEG ratio of 1.66 and a beta of 2.22.
NVIDIA (NASDAQ:NVDA – Get Free Report) last posted its earnings results on Wednesday, August 26th. The computer hardware maker reported $2.22 earnings per share (EPS) for the quarter, beating the consensus estimate of $2.09 by $0.13. The business had revenue of $96.22 billion for the quarter, compared to analysts’ expectations of $92.27 billion. NVIDIA had a net margin of 63.66% and a return on equity of 96.04%. The business’s revenue for the quarter was up 105.9% compared to the same quarter last year. During the same quarter in the prior year, the business earned $1.05 EPS. On average, sell-side analysts forecast that NVIDIA Corporation will post 9.12 EPS for the current year.
NVIDIA Announces Dividend The business also recently disclosed a quarterly dividend, which will be paid on Thursday, October 1st. Investors of record on Thursday, September 10th will be given a dividend of $0.25 per share. The ex-dividend date is Thursday, September 10th. This represents a $1.00 dividend on an annualized basis and a yield of 0.5%. NVIDIA’s dividend payout ratio is 12.64%.
NVIDIA announced that its board has initiated a share buyback plan on Wednesday, May 20th that permits the company to buyback $80.00 billion in shares. This buyback authorization permits the computer hardware maker to repurchase up to 1.5% of its stock through open market purchases. Stock buyback plans are often an indication that the company’s leadership believes its stock is undervalued.
NVIDIA Profile (Free Report)
NVIDIA Corporation is a technology company that designs accelerated computing platforms, graphics processors and related software. Its products are used for artificial intelligence, machine learning, high-performance computing, computer graphics, data-center applications and other workloads that benefit from parallel processing.
The company’s offerings include GeForce graphics processing units (GPUs) and software for gaming and personal computers; data-center GPUs, systems and networking products; and professional visualization solutions for design, engineering, media and scientific applications.
See Also Five stocks we like better than NVIDIA 3 Luxury Consumer Brands to Watch in a Beaten-Down Sector Jackson’s Record Quarter Powers the Bull Case Holiday Shopping Is Almost Here—And Target May Be Ready to Win Big AI Panic Hit Tech Stocks—But NVIDIA’s Growth Engine Is Intact Want to see what other hedge funds are holding NVDA? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for NVIDIA Corporation (NASDAQ:NVDA – Free Report).
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Nvidia Corporation offers a compelling growth and value hybrid, trading at a significant discount to its historical P/E multiples. NVDA's revenue surged 105.9% YoY to $96.2B in Q2 2027, with broadening AI demand beyond hyperscalers fueling robust growth. Each new product generation delivers higher value per gigawatt, supporting management's 70% FY 2028 revenue growth outlook and a 53.2% EPS CAGR through FY 2029.
Semiconductors remain one of the equity market’s most vital and volatile sectors, sitting at the center of several major investment themes, from artificial intelligence (AI) and data center spending to smartphones, autos, and broader technology demand. With that said, the stock category’s importance also comes with plenty of swings, as investors weigh factors like earnings, valuations, interest rates, and rapidly shifting chip demand.
Key Takeaways: Single-stock ETFs let investors isolate specific chipmakers (like Nvidia or AMD) instead of betting on the entire volatile sector. Leveraged and inverse ETFs allow for highly customized bullish or bearish bets based on individual company catalysts. Customization sacrifices diversification, and daily leverage means long-term returns can deviate significantly from the underlying stock. The Direxion Daily Semiconductor Bull 3X Shares (SOXL), for example, is designed to provide 3X daily exposure to the semiconductor sector, while the Direxion Daily Semiconductor Bear 3X Shares (SOXS) offers the inverse side of that trade.
Following warnings about the dangers of AI becoming too intelligent too fast from leading voices in the space — including Elon Musk, Dario Amodei, and Sam Altman — shares of SOXL’s three biggest holdings dropped on September 14.
The fund as a whole saw its prices fall roughly 17% between September 11 and September 14, 2026.
According to Direxion, holdings of the fund’s Micron (MU), Advanced Micro Devices (AMD), and Nvidia (NVDA) shares dropped 5.7%, 4.5%, and 2.8%, respectively.
A More Targeted Approach For investors looking to take a more precise approach to the sector, single-stock leveraged and inverse ETFs offer a way to customize exposure. Rather than making a call on the entire sector, investors can focus on individual companies they have a particular interest in.
The tradeoff, however, is that the more targeted the exposure approach becomes, the more concentrated the position can be. A leveraged single-stock ETF, for example, will see much larger moves than the underlying stock.
This can make a difference because stocks in the semiconductor industry don’t necessarily respond to the same catalysts at the same time or with the same magnitude. A company tied closely to AI infrastructure may react differently to changing data center expectations than a chipmaker with more exposure to PCs and smartphones.
From the Sector to Individual Stocks Targeted exposure matters when evaluating core semiconductor holdings because the customization becomes more interesting when looking at some of the companies driving the semiconductor trade.
Nvidia is probably the clearest example. The company has become a major part of the AI infrastructure story, so investors looking for more direct exposure to that theme can use single-stock products such as the Direxion Daily NVDA Bull 2X ETF (NVDU) or the Direxion Daily NVDA Bear 1X ETF (NVDD).
The funds seek 2X daily bullish exposure and 1X daily inverse exposure to Nvidia, respectively. AMD receives a similar treatment, with the Direxion Daily AMD Bull 2X ETF (AMUU) and the Direxion Daily AMD Bear 1X ETF (AMDD).
AMD’s position in high-performance computing and AI gives investors another way to express a view on the chip industry without necessarily taking the same position on Nvidia.
Broadcom (AVGO) is also another option for investors who want to be more surgical about their exposure to the industry. The Direxion Daily AVGO Bull 2X ETF (AVL) and Direxion Daily AVGO Bear 1X ETF (AVS) is another pair of products for investors who want to focus solely on one company.
Precision Comes With Risk The broader point is that investors don’t necessarily have to choose between a traditional semiconductor ETF and a single stock. Products such as SOXL and SOXS provide a leveraged or inverse view of the sector, while single-stock ETFs can narrow that exposure to a particular company.
Of course, that additional precision also comes with additional risk. Single-stock leveraged and inverse ETFs don’t provide the diversification of a sector fund, which amplifies the potential risks already inherent to leveraged strategies.
Direxion also notes that because these products are designed around daily objectives, their returns over periods longer than one day can differ from simply applying the stated leverage to the stock’s cumulative return.
For more news, information, and analysis, visit the Leveraged & Inverse Content Hub
For years, quantum computing hit a stubborn software wall. While quantum hardware promised to resolve complex computational bottlenecks, the time and cost of calibrating quantum circuits created a commercial barrier. When setting up a calculation takes longer than the calculation itself, the technology becomes much less practical.
A joint milestone achieved by Oak Ridge National Laboratory, IonQ, Inc. NYSE: IONQ, and NVIDIA Corporation NASDAQ: NVDA tackles this operational dilemma. By training generative artificial intelligence models to design quantum circuits directly, researchers showed compilation runtimes can drop from minutes to seconds.
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This engineering leap pulls quantum computing out of isolated academic testing and steps it closer to regular commercial use, offering technology investors two distinct ways to position for the growth of advanced computing.
Cracking the Code: A Quantum Compilation BreakthroughOn Sept. 16, 2026, researchers presented a study at IEEE Quantum Week in Toronto detailing how a generative AI model can write quantum optimization circuits directly.
Hybrid quantum optimization breaks an intricate problem down into smaller pieces, solves each on quantum processors, and reassembles the results.
Historically, finding the correct circuit instructions for each segment required a repetitive trial-and-error method known as variational parameter tuning. Researchers had to run, measure, adjust, and rerun circuits dozens or hundreds of times. As quantum subproblems grew larger to yield higher-quality answers, the tuning tax escalated rapidly, driving up cloud computing costs and lengthening execution times.
To eliminate this roadblock, the research team trained a transformer model on near-optimal circuit configurations. Rather than relying on repetitive guess-and-check loops, the AI model generated candidate circuits directly. Running on a single NVIDIA H200 graphics processing unit within the Oak Ridge Leadership Computing Facility's Defiant2 supercomputer, the framework evaluated a fixed set of candidate circuits, sidestepping the iterative bottleneck entirely.
Time Is Money: Slicing 11 Minutes Down to 28 SecondsThe results show how much this approach could improve quantum computing at larger scales. When tested against a dense benchmark problem involving 100 decision variables, the traditional compilation method suffered severe latency. The circuit-finding runtime jumped from approximately 34 seconds on four qubits to more than 11 minutes on 12 qubits.
The generative AI model, by comparison, maintained an almost flat runtime of roughly 28 seconds across every qubit scale tested. At the same time, the quality of the generated answers roughly doubled as the number of quantum subproblems increased.
For enterprise clients, computational efficiency can directly translate to lower costs. Classical high-performance computing clusters consume substantial electricity and capital during endless compilation cycles.
Reducing the runtime to 28 seconds could make it easier for companies in logistics, financial modeling, and materials science to run optimization routines within more predictable budgets. This improvement could also support broader adoption across commercial cloud platforms, improving the economics of quantum-as-a-service (QCaaS).
IonQ's Cash Runway Keeps Expansion RollingThis milestone arrives at an opportune moment for IonQ, Inc. IonQ continues expanding commercial access to its trapped-ion systems through major cloud partners, including Amazon's NASDAQ: AMZN AWS, Microsoft's NASDAQ: MSFT Azure, and Alphabet's NASDAQ: GOOGL Google Cloud.
IonQ Today
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Financial results highlight growing commercial traction. IonQ reported second-quarter revenue growth of roughly 286.7% year-over-year (YOY), with annual revenue reaching about $130.02 million. For the quarter ending in June, IonQ posted an earnings per share (EPS) loss of 33 cents, beating consensus expectations by 23 cents.
Operating in deep technology requires careful capital management. IonQ generated an annual net loss of approximately $510.38 million, reflecting ongoing research investments. Additionally, the federal government recently awarded $100 million grants under the CHIPS and Science Act to competitors D-Wave Quantum NASDAQ: QBTS, Rigetti Computing NASDAQ: RGTI, and Quantinuum NASDAQ: QNT, while bypassing IonQ.
IonQ carries zero long-term debt and holds a current ratio of roughly 10.66, providing a multi-year cash cushion. While peer D-Wave posted second-quarter revenue of just $3.1 million, IonQ continues to scale its top line. IonQ currently trades near $40 and sits well below its 52-week peak of $84.64, while Wall Street maintains an average price target just over $67.
CUDA-Q: NVIDIA's Quantum TollboothWhile pure-play quantum developers navigate long-term hardware roadmaps, NVIDIA is establishing itself as the infrastructure bridge connecting classical data centers to quantum processing units.
NVIDIA Today
$213.90 +1.73 (+0.82%)
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The Oak Ridge benchmark relied on NVIDIA's CUDA-Q open platform and cuQuantum software development kit running on an H200 accelerator. NVIDIA also introduced its CUDA-Q Logical software, designed to assist developers with multi-qubit error correction. By embedding its software layer across both trapped-ion and superconducting systems, NVIDIA captures enterprise compute demand regardless of which hardware architecture leads the market.
NVIDIA pairs this strategic positioning with reliable profitability. The company generates annual revenue of around $215.94 billion, with second-quarter sales climbing approximately 105.9% YOY. With net margins hovering near 63.66% and annual net income of about $120.07 billion, NVIDIA trades at a forward price-to-earnings (P/E) ratio of roughly 24. A board-authorized $80 billion share repurchase program provides additional support for long-term NVIDIA shareholders.
Balancing High-Beta Growth With Tech GiantsCommercial quantum computing is moving from theoretical ambition to engineered reality. Resolving the compilation bottleneck proves that combining accelerated classical computing with quantum algorithms can unlock productivity gains for complex logistics and industrial challenges.
Investors assessing the space face two distinct asset profiles. IonQ represents a high-beta opportunity with operational upside, supported by triple-digit top-line growth and a debt-free balance sheet. Potential risks remain centered on elevated net losses and the reality that these recent benchmarks were conducted via classical GPU simulation rather than live physical hardware.
NVIDIA offers a lower-volatility infrastructure vehicle that monetizes the hybrid quantum shift while generating free cash flow from its core artificial intelligence hardware footprint.
Growth-focused investors seeking early-stage disruption might consider tracking IonQ for stabilization after its recent consolidation, while conservative investors may prefer NVIDIA as a diversified anchor to capture the ongoing expansion of advanced computing.
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Benjamin Edwards Inc. increased its position in shares of NVIDIA Corporation (NASDAQ:NVDA – Free Report) by 32.7% in the 2nd quarter, according to the company in its most recent 13F filing with the Securities and Exchange Commission. The fund owned 849,108 shares of the computer hardware maker’s stock after purchasing an additional 209,467 shares during the period. NVIDIA makes up about 1.5% of Benjamin Edwards Inc.’s portfolio, making the stock its 11th biggest holding. Benjamin Edwards Inc.’s holdings in NVIDIA were worth $169,896,000 at the end of the most recent reporting period.
Other institutional investors have also added to or reduced their stakes in the company. Boyd Wealth Management LLC grew its holdings in shares of NVIDIA by 273.4% during the second quarter. Boyd Wealth Management LLC now owns 9,145 shares of the computer hardware maker’s stock worth $1,830,000 after purchasing an additional 6,696 shares during the last quarter. Altshuler Shaham Ltd increased its stake in shares of NVIDIA by 6,451.9% in the first quarter. Altshuler Shaham Ltd now owns 637,236 shares of the computer hardware maker’s stock valued at $111,134,000 after buying an additional 627,510 shares during the period. ASR Vermogensbeheer N.V. raised its holdings in NVIDIA by 1.8% in the 4th quarter. ASR Vermogensbeheer N.V. now owns 3,169,377 shares of the computer hardware maker’s stock valued at $591,086,000 after buying an additional 54,877 shares during the last quarter. Storen Legacy Partners LLC bought a new position in NVIDIA in the 4th quarter valued at approximately $1,350,000. Finally, Baker Tilly Wealth Management LLC boosted its position in NVIDIA by 7.4% during the 2nd quarter. Baker Tilly Wealth Management LLC now owns 71,898 shares of the computer hardware maker’s stock worth $14,386,000 after buying an additional 4,958 shares during the period. 65.27% of the stock is currently owned by institutional investors.
NVIDIA News Summary Here are the key news stories impacting NVIDIA this week:
Positive Sentiment: Management continues to defend AI spending. CEO Jensen Huang said the AI spending “flywheel” is accelerating and argued that companies, rather than new regulations, should manage AI safety. Meta CEO Mark Zuckerberg broadly supported that view, easing fears that safety concerns could trigger a development pause. NVIDIA CEO Says AI Spending Flywheel Is Really, Really Flying Positive Sentiment: Rubin demand is reinforcing the infrastructure story. CoreWeave brought a multi-rack NVIDIA Vera Rubin NVL72 cluster online, connecting hundreds of GPUs for large-scale training and inference. The deployment supports expectations for continued demand beyond Blackwell systems. CoreWeave Stock Gains as Nvidia Rubin Cluster Boosts AI Cloud Capacity Positive Sentiment: NVIDIA is expanding beyond GPUs. An energy alliance with Google and Emerald AI aims to make data centers flexible electricity customers, potentially reducing grid constraints that could otherwise limit AI capacity. NVIDIA is also supplying networking and software for Apple’s potential future AI servers. Nvidia and Google Launch Energy Alliance to Prevent AI Grid Overload Positive Sentiment: Quantum-computing research adds a longer-term catalyst. NVIDIA’s H200 GPU, CUDA-Q platform and cuQuantum software helped researchers reduce quantum-circuit compilation from minutes to roughly 28 seconds in larger tests, strengthening its position as an infrastructure provider for hybrid computing. IonQ and NVIDIA Just Cracked a Major Quantum Computing Bottleneck Neutral Sentiment: Strategic expansion could improve future monetization. Reports describe NVIDIA’s $12.9 billion acquisition of Hugging Face as a way to extend its ecosystem into AI software and models, although the deal may be expensive relative to current revenue and is unlikely to materially affect near-term results. Forget Nvidia’s Chips: Hugging Face Is the Real Reason to Own This Stock Now Negative Sentiment: Competition and customer bargaining power remain risks. OpenAI says it has more alternatives to NVIDIA, while AMD is pursuing large-scale chip deployments with major AI developers. Greater supplier choice could pressure NVIDIA’s pricing and market share over time. OpenAI’s CFO Just Explained Why Nvidia Is No Longer the Only Option Negative Sentiment: AI slowdown fears have not disappeared. Bearish investor Michael Burry continues to warn of a potential sharp decline in AI-linked stocks, while reports of insider selling and elevated valuation could encourage profit-taking if infrastructure spending moderates. Michael Burry Predicts a Possible 1987-Style Drop Analyst Upgrades and Downgrades Several equities analysts have issued reports on NVDA shares. Oppenheimer set a $315.00 price target on shares of NVIDIA and gave the company an “outperform” rating in a research report on Thursday, August 27th. Wells Fargo & Company reiterated an “overweight” rating and set a $315.00 price target on shares of NVIDIA in a report on Tuesday, August 11th. Mizuho set a $315.00 price objective on shares of NVIDIA and gave the stock an “outperform” rating in a research note on Thursday, August 27th. KeyCorp reaffirmed an “overweight” rating and set a $330.00 target price on shares of NVIDIA in a research report on Monday, August 24th. Finally, China Renaissance upped their price target on NVIDIA from $319.00 to $330.00 and gave the company a “buy” rating in a research report on Monday, August 31st. Three analysts have rated the stock with a Strong Buy rating, fifty have issued a Buy rating and two have given a Hold rating to the stock. Based on data from MarketBeat, the stock presently has an average rating of “Buy” and an average price target of $324.34. View Our Latest Report on NVDA
NVIDIA Stock Up 0.8% Shares of NVIDIA stock opened at $213.90 on Thursday. The stock’s 50-day moving average is $214.10 and its 200 day moving average is $203.86. NVIDIA Corporation has a one year low of $164.27 and a one year high of $236.54. The firm has a market cap of $5.15 trillion, a price-to-earnings ratio of 27.04, a PEG ratio of 1.66 and a beta of 2.22. The company has a debt-to-equity ratio of 0.14, a current ratio of 4.59 and a quick ratio of 3.85.
NVIDIA (NASDAQ:NVDA – Get Free Report) last issued its earnings results on Wednesday, August 26th. The computer hardware maker reported $2.22 earnings per share for the quarter, topping analysts’ consensus estimates of $2.09 by $0.13. The business had revenue of $96.22 billion during the quarter, compared to the consensus estimate of $92.27 billion. NVIDIA had a return on equity of 96.04% and a net margin of 63.66%.The firm’s revenue for the quarter was up 105.9% on a year-over-year basis. During the same period in the previous year, the company posted $1.05 earnings per share. Equities research analysts anticipate that NVIDIA Corporation will post 9.12 EPS for the current year.
NVIDIA Announces Dividend The business also recently announced a quarterly dividend, which will be paid on Thursday, October 1st. Investors of record on Thursday, September 10th will be given a dividend of $0.25 per share. This represents a $1.00 dividend on an annualized basis and a dividend yield of 0.5%. The ex-dividend date is Thursday, September 10th. NVIDIA’s payout ratio is presently 12.64%.
NVIDIA declared that its board has authorized a share repurchase program on Wednesday, May 20th that permits the company to buyback $80.00 billion in outstanding shares. This buyback authorization permits the computer hardware maker to repurchase up to 1.5% of its shares through open market purchases. Shares buyback programs are generally an indication that the company’s board believes its shares are undervalued.
Insider Buying and Selling at NVIDIA In other news, EVP Timothy S. Teter sold 30,000 shares of the company’s stock in a transaction dated Monday, August 31st. The shares were sold at an average price of $217.88, for a total transaction of $6,536,400.00. Following the transaction, the executive vice president owned 2,687,660 shares in the company, valued at approximately $585,587,360.80. This represents a 1.10% decrease in their ownership of the stock. The sale was disclosed in a document filed with the Securities & Exchange Commission, which is accessible through this link. The transaction was executed under a pre-arranged Rule 10b5-1 trading plan. Also, Director Mark A. Stevens sold 622,239 shares of the stock in a transaction dated Friday, September 4th. The stock was sold at an average price of $231.62, for a total transaction of $144,122,997.18. Following the transaction, the director directly owned 2,336,531 shares in the company, valued at $541,187,310.22. This trade represents a 21.03% decrease in their position. The SEC filing for this sale provides additional information. Over the last three months, insiders have sold 1,700,740 shares of company stock worth $385,015,077. 3.94% of the stock is owned by insiders.
About NVIDIA (Free Report)
NVIDIA Corporation is a technology company that designs accelerated computing platforms, graphics processors and related software. Its products are used for artificial intelligence, machine learning, high-performance computing, computer graphics, data-center applications and other workloads that benefit from parallel processing.
The company’s offerings include GeForce graphics processing units (GPUs) and software for gaming and personal computers; data-center GPUs, systems and networking products; and professional visualization solutions for design, engineering, media and scientific applications.
Featured Stories Five stocks we like better than NVIDIA 3 Luxury Consumer Brands to Watch in a Beaten-Down Sector Jackson’s Record Quarter Powers the Bull Case Holiday Shopping Is Almost Here—And Target May Be Ready to Win Big AI Panic Hit Tech Stocks—But NVIDIA’s Growth Engine Is Intact Want to see what other hedge funds are holding NVDA? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for NVIDIA Corporation (NASDAQ:NVDA – Free Report).
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Nvidia (NVDA +0.81%) is currently the world's most valuable company, with a market cap of over $5.1 trillion as of Sept. 15. As the central chip designer of the AI boom, Nvidia's business has seen unprecedented demand, and as a result, huge amounts of cash keep rolling in. Now, it's beginning to ramp up shareholder returns instead of focusing solely on reinvesting for growth.
Through the first two quarters of its current fiscal year, Nvidia has returned around $46 billion to shareholders through dividends and stock buybacks, and much more is likely on the way.
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Why Nvidia gave its dividend a major boost Nvidia first began paying a dividend in November 2012. After its 4-for-1 (2021) and 10-for-1 (2024) stock splits, the initial dividend was $0.001875 per share quarterly. Nearly 14 years later, Nvidia's quarterly dividend is $0.25 per share after it gave the payout a 2,400% boost in June from the $0.01 per share it previously paid.
The reason for the huge dividend boost was simple: Nvidia is making tons of money. In just the past three years, its revenue and net income have increased by 431% and 546%, respectively. It now also has $99.4 billion in cash, cash equivalents, and short-term securities on its books.
NVDA Revenue (Quarterly) data by YCharts.
With Nvidia rolling in cash, it was getting harder and harder to justify paying out a dividend of a penny per quarter.
Why another hike might be coming Nvidia's business still has plenty of runway. Even if (or when, rather) its growth rate slows down from its current rapid pace, it has a chance to remain one of the faster-growing tech stocks in the world. So it's at a point where it can aggressively reinvest in its business and also reward shareholders more handsomely.
Image source: The Motley Fool.
Much of the $46 billion Nvidia has returned to shareholders has come through stock buybacks -- it spend $20 billion and $19.7 billion on them in the first and second quarter, respectively -- but a dividend hike lets Nvidia reward investors directly. It still has $99 billion in buybacks planned under its current authorization, but it's unclear how it will execute them.
Another dividend hike could also put Nvidia on the same tier as more mature big tech dividend payers like Microsoft and Apple. Neither has a yield that would get most to consider them as income stocks, but their payouts have been reliable and growing consistently over the years.
No matter how big Nvidia's next dividend hike is, the stock is a growth stock at heart. The dividend is simply a nice-to-have perk that should continue to compound over time.
When investors think about who buys Nvidia's (NVDA +0.81%) chips, the names that come to mind tend to be American: the giant cloud platforms and the labs building artificial intelligence (AI) models. The company's latest quarterly filing offers a different answer.
During the fiscal second quarter of 2027 (the period ended July 26), $27.0 billion of Nvidia's revenue was billed to customers headquartered in Taiwan. That's 28% of the quarter's $96.2 billion total.
The line is climbing fast, too. A year earlier, it was $8.9 billion, or about 19% of revenue. Three months before the latest quarter, it was about $12 billion, or roughly 15%. So the dollar figure tripled in a year and more than doubled in a quarter -- far faster than the business as a whole grew.
An investor could see that and worry the growth story has quietly moved somewhere unexpected. The stock sits near $212 as of this writing, down about 10% from its 52-week high.
But I think the disclosure is more useful than alarming. It shows who Nvidia bills -- and why that group isn't the same thing as demand.
Image source: Nvidia.
A $27 billion line itemNvidia sorts revenue by geography using the headquarters location of the direct customers it bills. On that basis, the United States led the fiscal second quarter at $60.1 billion, or about 62% of revenue. Taiwan came second at $27.0 billion. China, including Hong Kong, contributed $7.9 billion, and the rest of the world combined for just $1.3 billion. Altogether, customers headquartered outside the U.S. accounted for 38% of the quarter's revenue, against 30% a year earlier.
The quarter those numbers sit inside was itself enormous. Companywide revenue totaled $96.2 billion, up 106% year over year, and about 92% of it came from the data center business.
In other words, Taiwan wasn't just growing with the company. It was gaining share of a total that itself doubled.
Who is actually buying?Nvidia's direct customers (the buyers it invoices) include distributors, companies that build graphics cards and servers, cloud service providers, AI model makers, and system integrators. And the company notes that the end customer and the shipping location may be different from the direct customer's headquarters.
Taiwan happens to be the home base of much of the industry that turns Nvidia's chips into finished AI servers. Nvidia's own annual filing names Taiwan-based Hon Hai Precision Industry, better known as Foxconn, among the contract manufacturers that assemble, test, and package its final products.
Much of the revenue billed to Taiwan, then, is likely hardware passing through intermediaries on its way to data centers somewhere else.
In other words, the Taiwan line doesn't say Taiwanese demand for AI chips tripled. It more likely says that as Nvidia's newest products (full server racks, not just chips) ramped up, more orders routed through the island's system builders before reaching the cloud providers, AI model makers, and other end users Nvidia calls its indirect customers.
Concentration is the real disclosureRead the table that way, and it becomes a measure of how much of Nvidia's revenue lands with just a few direct customers. One direct customer accounted for 16% of Nvidia's total revenue in the quarter -- about $15 billion billed to a single buyer.
That shape isn't new, either. In the same quarter a year earlier, two direct customers represented 23% and 16% of revenue. Of course, the names behind the percentages can shift as orders route differently. But a few enormous buyers of record have carried the company for a while.
Further, the same island shows up on the other side of the business. Nvidia's annual filing names Taiwan Semiconductor Manufacturing among the foundries that produce its semiconductor wafers.
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So Taiwan sits on both sides of Nvidia's business. Much of the manufacturing happens there, and more than a quarter of revenue is billed there. Any disruption to the island's role (geopolitical or otherwise) would touch both sides at once.
Demand itself is better judged elsewhere. Management's forecast for the fiscal third quarter, now underway, is $108.0 billion of revenue -- and growth like that is ultimately a claim about what customers keep spending.
The geography table can't tell you whether they will. Paired with the concentration disclosure, it tells you how few names of substantial size are on the invoices. That concentration, I'd argue, is the finding worth taking from the filing.
Nvidia (NVDA +0.81%) remains the dominant supplier of graphics processing units (GPUs) used to train and run artificial intelligence (AI) models. However, its latest acquisition suggests the company also wants a stronger position in how developers choose and deploy those models.
On Sept. 2, Nvidia agreed to acquire Hugging Face, paying $11.9 billion to Hugging Face's shareholders and up to $1 billion in equity awards for employees joining Nvidia. The deal is expected to close in the first half of 2027, subject to regulatory approvals.
Image source: Getty Images
The price looks steep given Hugging Face's current business model. The company was recently generating more than $150 million in annualized revenue (the latest monthly revenue was multiplied by 12). Using $150 million as a benchmark, the $11.9 billion shareholder purchase price equals roughly 79 times the revenue run rate. Including the potential employee awards takes the total deal value to around $12.9 billion, or about 86 times the annualized revenue.
The employee awards are also notable. The potential $1 billion retention package equals more than 8% of the amount being paid to shareholders. Hence, Nvidia is not simply paying for Hugging Face's current revenue or technology. Retaining the people behind its platform also appears to be an important part of the deal.
Hugging Face deal expands Nvidia's role beyond AI chips Hugging Face platform hosts over 3 million models and is used by more than 18 million developers, researchers, and creators. More than 200,000 companies use Hugging Face to work with AI models, from evaluating and adapting them to putting them into production.
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Hugging Face's extensive reach among developers and enterprises could become increasingly important to Nvidia as competition in AI chips intensifies. Meta Platforms, Microsoft, OpenAI, and other large technology companies are developing custom AI chips. Hence, AI demand can continue to grow even if a larger share of workloads eventually runs on chips made by other companies.
Hugging Face can give Nvidia another way to participate in the growing AI ecosystem. Developers can use the platform to find, customize, and deploy AI models while choosing how those models will ultimately run.
Every Hugging Face workload will not use Nvidia hardware. But the platform brings Nvidia closer to developers as they choose models, tools, and computing platforms, which could become more valuable as competition in AI hardware increases.
Hugging Face needs to remain open source Nvidia has said Hugging Face will remain open across different models, frameworks, cloud providers, and computing platforms. Nvidia hardware will also not be required to build or deploy AI through the platform. Keeping that flexibility could be important to preserving Hugging Face's broad developer appeal, especially as Nvidia ownership has already raised concerns about possible favoritism toward its own hardware.
If Nvidia starts favoring its own GPUs on Hugging Face, developers using Advanced Micro Devices' chips, custom accelerators, or other hardware could have more reason to look elsewhere. Reuters has already reported concerns that Nvidia's ownership could eventually lead to preferential treatment for its own hardware, despite the company's commitment to keep Hugging Face open and interoperable.
Keeping Hugging Face open across different models and computing platforms could help preserve its broad developer network. More models and AI applications can create more workloads that require computing power. Nvidia can then compete for those workloads through its GPUs, CUDA software, networking products, and broader AI infrastructure offerings. This gives the company another way to remain relevant even if customers increasingly use competing or custom AI chips.
Open models can help Nvidia sell chips Public model repositories on Hugging Face increased from 2.43 million to 2.96 million during the first seven months of 2026. AMD and Nvidia were also the two most active organizations publishing new open models, with each releasing more than 200 model repositories.
Hugging Face says hardware companies are increasingly using open models to demonstrate their chips and encourage adoption. Nvidia also makes a similar point in its regulatory filing for the acquisition. The company says demand for open-source foundation models and applications promotes the use of its products worldwide. However, it also warns that these models could reduce demand for Nvidia's products if they are primarily deployed on competing platforms.
Hence, growth in open models can create both opportunities and risks for Nvidia.
Risks cannot be ignored Hugging Face's financial contribution will initially be small compared with Nvidia's existing business. Nvidia generated $96.2 billion in revenue in its latest quarter (the second quarter of fiscal 2027, ending July 26, 2026), including $89 billion from the Data Center segment alone.
Additionally, government restrictions on open-source AI models could reduce the number of models and data sets available through Hugging Face or increase the platform's compliance costs. This risk is relevant because many popular open-source models originate in China before being downloaded, modified, and tested by developers elsewhere. Nvidia says restrictions based on where models originate could materially affect both Hugging Face and its own business.
There is also considerable execution risk associated with the purchase price. Hugging Face was valued at $4.5 billion in its last disclosed funding round in 2023. Nvidia's roughly $12.9 billion deal value is nearly three times that valuation.
Despite the risks, Hugging Face can strengthen the long-term Nvidia investment thesis. While Nvidia's GPUs will continue to generate most of the revenue and profits supporting its valuation, Hugging Face can give the company a strong competitive advantage.
The artificial intelligence (AI)-fueled surge in shares of Nvidia (NVDA +0.68%) has made it the largest company in the world with a market cap of over $5 trillion.
It has reached this position following a stunning jump of almost 10x in the stock price over the past five years. Investors may now be wondering whether Nvidia can achieve another 10x jump over the next five years, especially given its enormous market cap.
We will take a closer look at Nvidia's prospects and see how much upside investors can expect from this AI stock over the next five years.
Image source: The Motley Fool.
Can Nvidia really jump another 10x? A 10x jump in Nvidia stock over the next five years will take its market share to $50 trillion. The global gross domestic product (GDP) is anticipated to hit $150 trillion in 2030, according to data from Visual Capitalist, up from $125 trillion last year.
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A 10x jump in Nvidia stock means that it will account for a third of the global GDP after five years, as compared to just 4% right now. That seems quite unlikely. However, Nvidia still has significant room for growth over the next five years, and it trades at an attractive valuation. As a result, there is a good chance the company will deliver solid gains.
For instance, AI infrastructure spending isn't slowing down. Nvidia points out that the annual capital spending by the top five hyperscalers is on track to reach $800 billion this year. The figure is expected to increase to $1.3 trillion in 2027. Importantly, annual data center spending could cross $3 trillion in 2030, according to market research firm Dell'Oro.
The firm points out that a third of this spending, or $1 trillion, will go toward AI accelerators. The networking components to connect those accelerators will add to this addressable market. Nvidia is the biggest player in AI accelerators with an 80% market share. The company is bolstering its presence in this market by entering into lucrative areas, such as server processors.
As such, I won't be surprised to see Nvidia maintaining its dominance in the AI accelerator market after five years. Assuming it can sustain its impressive share of this space, its AI accelerator revenue alone could hit $800 billion (based on the $1 trillion estimate noted in the previous paragraph). That would be a big jump over Nvidia's 2026 data center revenue of $194 billion. It is worth noting that the company also includes sales of networking components in the data center segment, so Nvidia's addressable opportunity could be much larger.
The stock could still become a multibagger Nvidia's earnings per share could increase at an annual rate of 52% over the long run.
Data by YCharts
What's worth noting is that this estimate has increased substantially this year. Let's assume Nvidia indeed manages to clock 52% annual earnings growth over the next five years; its earnings per share could reach $75.54 (using fiscal 2027's projected earnings per share of $9.31 as the base).
If this AI stock trades at 21 times earnings after five years, in line with the S&P 500 index's forward earnings multiple, its price could jump to $1,586. That's 7.5x Nvidia's current stock price.
Of course, this may seem like a very ambitious estimate. Still, it is evident that Nvidia has the potential to jump significantly over the next five years, even if the stock doesn't multiply by 7x. This makes Nvidia stock an attractive buy right now as it trades at just 24 times forward earnings.
CoreWeave Inc. CRWV stock gained on Wednesday after the AI cloud-computing company said it had brought up a multi-rack Nvidia Vera Rubin NVL72 cluster on CoreWeave Cloud.
The setup connects hundreds of Nvidia Rubin graphics processing units (GPUs) into a single system, allowing customers to run training and inference workloads across multiple racks.
CoreWeave said the infrastructure is designed to provide additional capacity for larger AI models, more demanding inference workloads and reinforcement learning at scale.
The stock had gained as much as 4% before trimming the advance to around 3% after the US Federal Reserve raised interest rates to 4% in a unanimous decision and signalled further rate increases.
A single Nvidia Vera Rubin NVL72 rack combines 72 Rubin GPUs with 36 Vera CPUs, along with Nvidia networking and data-processing technology.
CoreWeave said multiple racks can be connected through Nvidia Spectrum-X Ethernet to operate as one scale-out cluster.
The company said it was the first AI cloud provider to validate and bring up a Vera Rubin NVL72.
Chen Goldberg, Executive Vice President of Product and Engineering at CoreWeave, said the multi-rack configuration connects hundreds of Rubin GPUs as a single scale-out cluster.
Goldberg said the system provides customers developing agentic AI with greater scale and faster iteration.
CoreWeave said its infrastructure automates rack setup, firmware updates, validation, power and cooling processes. It also tests complete racks before they are moved into production.
However, Bernstein said in a September 14 report that CoreWeave could be among the companies most exposed if artificial intelligence firms reduce spending on model training.
The research firm said CoreWeave’s heavy exposure to AI infrastructure could leave the company particularly vulnerable to a shift away from large-scale training workloads.
Bernstein also pointed to CoreWeave’s concentration in rural and lower-tier data-centre markets as a potential risk if AI demand increasingly favours metro-focused operators.
Bernstein maintained its Sell rating on CoreWeave while raising its price target to $74.
Alongside the new compute infrastructure, CoreWeave introduced several additions to its AI Object Storage platform.
Cross-region write acceleration allows AI workloads to write data in the region where they are running while CoreWeave copies the data to another region in the background.
The company said this can reduce pauses for customers conducting training workloads across multiple regions.
CoreWeave also introduced an Archive tier designed as a lower-cost option for customers retaining data for longer periods.
The company said the tier has no retrieval fee, early-deletion fee or fee for reading data from the Archive tier.
Its Local Object Transport Accelerator, or LOTA, can cache data closer to AI workloads.
CoreWeave said the technology reduces read latency by eight times compared with reading from a traditional storage cluster.
The company also said its expanded AI Object Storage system delivers reads at local NVMe speeds while reducing latency compared with traditional storage clusters.
The company also received a boost from a commercial development involving Blockfusion USA’s subsidiary North East Data.
The subsidiary converted a previously non-binding letter of intent into a definitive 15-year anchor lease with CoreWeave for capacity at its Niagara Falls, New York campus.
The agreement includes two five-year renewal options and a companion expansion agreement.
CoreWeave’s stock later pared some of its gains after the Federal Reserve raised its interest rate to 4% in a unanimous vote.
The central bank said inflation remained elevated and that the policy action would support a more timely return to its 2% objective.
Nearly every bull market has a signature stock, a company that seems to contain that era's dominant story, investing trend, or valuation in miniature. A list of these avatars would have to include Microsoft in the personal computer revolution of the 1990s, Cisco in the dot-com era, Apple in the smartphone boom of the early 2010s, Tesla in the post-COVID-19 years before artificial intelligence (AI), and Nvidia (NVDA +0.66%) in the years since.
Three of the previous five are among the world's most valuable companies by market cap today, with Nvidia in first place. Despite Nvidia's remarkable ascent to power, with a roughly 146-fold gain over the last decade, I don't believe it will be the market's top performer over the next 10 years. Indeed, one utility stock that will probably surpass it in performance is one whose innovations could help power Nvidia -- and AI companies more broadly: Oklo (OKLO -2.07%).
Image source: Oklo.
Small reactors, massive opportunity Oklo, in a nutshell, is designing nuclear reactors. And not just any nuclear reactor: small nuclear reactors. Oklo's reactors are compact enough to fit on a few acres, roughly two or three football fields, while conventional nuclear power plants can span hundreds and hundreds of acres.
Why should investors care about a reactor that can fit on a few gridirons? Because of the potential for on-site power generation. If a company can get power from its own little power plant, that would limit how much it needs to draw from the grid. Less dependence on the grid means less exposure to grid outages and more certainty over power supply. And for power-hungry operations like AI data centers, that could be enormously valuable.
Oklo, to be sure, doesn't aim to sell reactors; like a traditional electric utility company, it will sell the power generated by the reactors it deploys. This adds another attractive layer to its business: Companies don't have to worry about owning or maintaining a reactor fleet themselves. Oklo would handle the complicated part and simply sell them electricity under long-term contracts.
Investors have already started to recognize Oklo's future value, which is a problem for the stock today but not one the company can't grow into over time. Oklo carries a hefty valuation of roughly $7 billion, which is more than 5,500 times its present-day sales. That doesn't scream value, and it makes Oklo's near-term growth seem unlikely to match, much less outperform, Nvidia or other established AI winners.
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But if we look over the next 10 years, Oklo's valuation being smaller than Nvidia's suggests more room for upside. Just consider this: A tenfold gain in Nvidia would make it a $53 trillion-dollar company. On the other hand, a tenfold gain in Oklo would imply a $67 billion valuation, which is roughly $30 billion less than the market cap of Constellation Energy, currently America's leader in nuclear energy generation.
A $67 billion valuation in 2036 is entirely possible if Oklo's reactors can operate commercially. Since Oklo doesn't have a commercial reactor, however, any investment in it will be a gamble. I would recommend this nuclear stock only to those who can take that risk and wait a decade for the long-term hypothesis to play out.
Steven Porrello has positions in Nvidia and Oklo. The Motley Fool has positions in and recommends Apple, Cisco Systems, Constellation Energy, Microsoft, Nvidia, and Tesla. The Motley Fool has a disclosure policy.
The five largest companies in the world by market capitalization are:
Nvidia (NVDA +1.62%) at $5.27 trillion Apple (AAPL +0.43%) at $4.77 trillion Alphabet (GOOG +0.12%) (GOOGL +0.04%) at $4.04 trillion Microsoft (MSFT -0.69%) at $3.66 trillion Amazon (AMZN -0.39%) at $2.72 trillion These companies have risen to the top after years of incredible work. History tells us that the company at the top doesn't stay there forever, but some can reign for a long time, as Apple was on top for over a decade.
So, will Nvidia be able to fend off these other four over the next few years? Could there be another that rises and steals the spotlight? I think Nvidia is nearly locked in as the largest company by 2028, assuming nothing collapses in the artificial intelligence (AI) build-out.
Image source: The Motley Fool.
Nvidia is well-positioned to take advantage of the AI arms race It's hard to debate that anything else is more important to our economic future than AI development. This emerging technology will dramatically shift how business is done over the next few years, so pinpointing companies well-positioned to capitalize on that shift is key to investment success.
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Nvidia is by far the biggest pure play on this list, as it sells computing equipment to AI hyperscalers so they can continue training and developing AI models with greater impact. With demand for computing power growing again and again each year, it's well-positioned to continue expanding its lead in the rankings of the world's largest companies for several years.
But can any of these four challenge it? I don't think so, based on today's business trajectory.
Apple is the closest at No. 2, but it hasn't embraced AI openly as several others have. While a monthly subscription to AI features on Apple's platforms could be a huge moneymaker that pushes Apple's stock higher, I don't think that will happen in the next year or so because this wasn't mentioned during their recent event. Of the five, Apple likely has the most to lose going forward.
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The next three are all very similar, with one small caveat. Microsoft, Amazon, and Alphabet are all involved in the AI arms race in several ways, but most notably through their cloud computing platforms. AI firms can come to these three to rent out computing power to train and run AI models, and this is a booming business for all of them. However, none of these companies (except Alphabet) have their own AI model, so they let clients use whatever AI data sets they want on their platforms.
While this isn't a bad thing, it may not pan out if one of the major AI firms (like OpenAI or Anthropic) decides to launch its own cloud services as the only place where you can use its model. I don't see that happening, but that is a major risk.
Regardless, one thing is for sure: We don't have enough AI computing power. And that will push Nvidia to new heights, helping it maintain its crown by 2028.
Nvidia forecasts huge growth Next year, Nvidia expects to grow its revenue at a 70% clip, further extending the fastest growth rate we've ever seen from a company of Nvidia's size. This will substantially widen the gap between Nvidia and its peers, which is why its earnings before interest, taxes, depreciation, and amortization (EBITDA) estimates for the next fiscal year are much higher.
NVDA EBITDA Estimates for Next Fiscal Year data by YCharts
This gap will likely widen again in 2028, and I think that's all the information investors need to know; Nvidia will maintain its status as the world's largest company through 2028.
Keithen Drury has positions in Alphabet, Amazon, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Alphabet, Amazon, Apple, Microsoft, and Nvidia. The Motley Fool has a disclosure policy.
OpenAI's CFO just revealed how $122 billion in cash fundamentally changes the power dynamic between the world's most important AI lab and its chip suppliers, and the implications for Nvidia's premium pricing are only beginning to surface.
Sarah Friar, chief financial officer of OpenAI, sat with CNBC’s Jim Cramer and described her chip procurement in the language a corporate treasurer would use. “We also have a strategy for diversifying our supply chain. And that is just good CFO risk mitigation. You never know when someone’s supply chain is going to get gummed up. So you need to have multiple providers.”
That framing is the clearest confirmation yet that NVIDIA‘s (NASDAQ:NVDA | NVDA Price Prediction) most important private customer now treats the company as one qualified supplier among several rather than the default option.
For shareholders, the consequence hits price and margin long before it hits revenue. OpenAI is still buying enormous quantities of NVIDIA silicon and has committed to roughly 12 gigawatts of NVIDIA compute through 2030.
What has changed is negotiating posture, and posture is what compresses a premium multiple.
Training Versus Inference Is Splitting Into Two Markets Friar drew a clean line between two workloads that used to be treated as one. “Nvidia is still an incredible platform of accelerators for training. But in jalapeno’s case, that is a chip very focused on inferencing because it is set up exactly for our models. So therefore it is very efficient for you.”
Training a frontier model happens episodically and rewards general-purpose silicon. Inference runs on every user prompt afterward, and its economics reward chips built around one model’s operations.
A chip designed around one lab’s transformer stack can remove unused circuits, dedicate more die area to the operations that matter, and reduce memory movement. Lower cost per token at comparable throughput follows.
Friar’s point is that NVIDIA owns the half of the workload that ends when training ends, while purpose-built silicon owns the half that compounds with every query.
Balance Sheet Is Where the Leverage Sits OpenAI is a private company, and every figure Friar cites about it is her own and has not been independently verified. That said, the number she volunteered is the point.
Sarah Friar said OpenAI raised $122 billion in the first quarter of this year, describing the balance as “cold, hard cash sitting there.”
Cash of that magnitude removes the two levers a supplier normally uses to hold pricing: urgency and dependency. A buyer without a funding cliff will wait a quarter for better terms.
She added that “An IPO is just a milestone in the journey. I’m going to keep reiterating that.” No listing deadline forces OpenAI to lock in supply at any price it can get.
Five Sessions Have Already Sorted the Three Names Trailing five sessions tell three different stories. NVIDIA is down 5.9%, AMD (NASDAQ:AMD) is off 0.3%, and Broadcom (NASDAQ:AVGO) has dropped 7.95%. A single week is a small sample.
Broadcom fell hardest because its custom-accelerator revenue is the most concentrated by customer. Q3 AI semiconductor revenue reached $16.7 billion, up 221% year over year, and any pause in one lab’s ramp lands directly on the forward guide.
NVIDIA’s decline matters more because it follows a quarter in which revenue reached $96.22 billion, up 105.8% year over year, with a guide of $108 billion for the current quarter. Pricing power inside that growth is now the question.
AMD held roughly flat as its Instinct roadmap and gigawatt-scale deals with Anthropic and Meta get validated one contract at a time.
Bull and Bear Case for NVDA Stock The bull case rests on the platform argument the NVIDIA chief executive made on the most recent earnings call. NVIDIA is the only architecture that runs every frontier model in every cloud; its opportunity per gigawatt has expanded from roughly $18 billion with Hopper to $40 billion with Vera Rubin, and demand exceeds supply through at least fiscal 2028. On those inputs, a forward earnings multiple of 24x is defensible, and the underlying figures are laid out in the most recent 8-K.
The bear case is what Friar described. Large AI customers are commissioning inference-specific silicon while still buying training capacity from NVIDIA and AMD, compressing NVIDIA’s share of the fastest-growing workload category even as unit volumes rise. Gross margin, guided to bottom in the 71% to 72% range in the fourth quarter, is where that pressure surfaces first.
The variable that decides between them is inference mix. If custom accelerators prove out at roughly half the cost of a GPU across more than one lab, NVIDIA’s premium contracts before revenue growth ever slows.
Contact [email protected] for any questions or corrections.
Meta CEO Mark Zuckerberg and Nvidia boss Jensen Huang shrugged off AI gurus’ recent spate of red alarms calling for government regulations, saying the matter should be left to companies themselves.
Zuck said AI labs have a natural incentive to create safer models because “people won’t want to use agents that are misaligned with them and that don’t do what they ask,” and they’ll face “significant liability if their models cause harm.”
“Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens,” he wrote late Tuesday on X.
Nvidia CEO Jensen Huang said companies don’t have to choose between safety and speed. REUTERS “My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn’t focus on alignment will fall behind,” he added.
For his part, Huang said Tuesday that speedy development and product safety don’t have to be opposed to each other, arguing it should be each company’s responsibility to ensure safeguards – not regulators.
“It’s a false choice,” Huang said Tuesday at the annual Salesforce Dreamforce event, referring to the debate about speed versus safety. “You could definitely have both at the same time.”
“Run as fast as you can. But if you feel at any given point in time the company’s out of control or the product’s not going to be safe, take a pause and make sure you get it right,” he told Salesforce CEO Marc Benioff in front of an audience.
On CNBC’s “Mad Money” on Tuesday, he called the push for more government intervention “just completely unnecessary,” saying there are already “plenty of laws.”
Their comments made for a big contrast with warnings from Anthropic CEO Dario Amodei, who published a lengthy essay over the weekend calling on rival companies to slow their development of AI models — fearful of the risks if the tech becomes too powerful.
SpaceX founder Elon Musk and OpenAI CEO Sam Altman also urged global cooperation among regulators, while Microsoft billionaire Bill Gates warned that no government in the world is properly prepared for a looming AI crisis.
Meta CEO Mark Zuckerberg sided with Huang’s argument. BACKGRID At the Dreamforce conference, Altman doubled down on the need for global cooperation between leading tech firms during a fireside chat with Benioff.
“I think it’s great for our industry to say we want to come together and we want to be able to coordinate and make sure we have enough time to do this safely,” Altman said.
“But when there’s any implication that because of the commercial pressures and the race, some company or between countries, some countries might not do the right thing, I think that’s when people get very scared.”
President Trump has dismissed the safety warnings, claiming they sound like a “conspiracy” that could help China win the global AI race.
He specifically took aim at Amodei for “pretending to be a ‘perfect little angel.’”
Industry leaders have sounded the alarms over concerns AI models could grow too powerful. Everett Collection China has already signaled an unwillingness to cooperate, with a state-backed newspaper comparing the warnings to a “Cold War” tactic.
Guo Jiakun, a spokesperson for China’s Foreign Ministry, chalked the warnings up to “fear mongering” while Chinese President Xi Jinping said his country will take the lead when it comes to fostering AI collaboration among developing countries.
In his essay, Amodei predicted that foreign rivals may not be willing to coordinate the pacing of AI development.
“I think that’s going to be very difficult because the incentives to pull ahead and the military advantage that you get from that are so large. And honestly, I don’t know if it’s possible, but we should try,” he told CBS News’ “Sunday Morning” over the weekend.
At a private gathering in Washington this week, dozens of business leaders cited the AI safety threats as real, urging President Trump to work with China's Xi Jinping.
The biggest threat to Nvidia Corp’s (NASDAQ:NVDA) AI boom may not be a slowdown in demand. It may be whether the power grid can keep up. As Nvidia CEO Jensen Huang pushes back against calls to slow AI development, Nvidia is simultaneously working with Alphabet Inc‘s (NASDAQ:GOOG) (NASDAQ:GOOG) Google and energy companies to make AI data centers flexible enough to respond to grid conditions — revealing the infrastructure constraint behind the next phase of the boom.
Nvidia Rejects AI SlowdownThe debate over AI’s pace intensified this week after Anthropic CEO Dario Amodei renewed calls for the industry to slow frontier AI development and strengthen safety measures. Huang took the opposite view at Salesforce, Inc.‘s (NYSE:CRM) Dreamforce conference, arguing that AI safety should be treated primarily as an engineering challenge rather than a reason to slow development.
That matters for Nvidia because its business depends on the continued expansion of AI computing infrastructure. Every new generation of models and AI agents requires more computing, and that computing ultimately requires electricity.
Huang has increasingly framed energy as part of the AI infrastructure equation. Nvidia’s recent work focuses on getting more AI output from each unit of electricity, rather than treating power availability as a fixed constraint. Its DSX platform is designed to coordinate compute, networking, cooling and power so AI factories can produce more tokens per watt.
AI Data Centers Meet The GridThat strategy took a bigger step Wednesday.
Nvidia, Google and Emerald AI launched the AI Energy Management Alliance, Axios reported. The alliance brings together 18 companies across AI, utilities and energy. The group wants to make data centers flexible enough to adjust electricity consumption in response to grid conditions, potentially allowing more AI capacity to connect without overwhelming local power systems.
The idea is not to make AI use less power in absolute terms. It is to make the demand more flexible.
Nvidia says Emerald AI has demonstrated data centers reducing power demand by as much as 40% in under a minute while maintaining critical workloads. The company says that flexibility could help unlock up to 100 gigawatts of existing U.S. grid capacity for AI factories.
That could become increasingly important as AI data centers grow larger and compete with households, manufacturers and other industries for scarce grid capacity.
Power Becomes The BottleneckFor Nvidia investors, this creates an unusual tension.
The company is arguing that AI development should continue rapidly, while simultaneously investing in technology designed to manage one of the physical limits to that expansion.
That doesn’t mean electricity demand will slow Nvidia’s growth. Instead, it suggests the company’s opportunity is expanding into another layer of the AI stack: helping data centers manage the power needed to run Nvidia’s own GPUs.
The next phase of the AI infrastructure race may therefore be measured not just in GPUs, racks or data centers, but in megawatts that can actually reach them. If Nvidia can help customers turn power-hungry AI factories into flexible grid assets, electricity will no longer be just a constraint on AI growth and will become another infrastructure market Nvidia can help optimize.
Nvidia, Google and Emerald AI launched an alliance dedicated to promoting flexible artificial intelligence data centers, Nvidia said in a Wednesday (Sept. 16) blog post.
Meta CEO Zuckerberg Sends Fresh Signal for Nvidia Stock and AI Spending Summary
Meta CEO Mark Zuckerberg said AI companies have incentives to manage safety without a coordinated slowdown, easing concerns around Nvidia’s chip demand
Nvidia NVDA shares climbed 1.5% on Wednesday as Meta CEO Mark Zuckerberg pushed back against proposals for a broad slowdown in artificial intelligence development, easing some concerns about potential restrictions on the technology sector.
Zuckerberg said existing market incentives and legal exposure can encourage AI companies to address safety concerns without requiring a mandated reduction in development, according to a Wednesday report.
His comments put Meta alongside Nvidia CEO Jensen Huang, who has also opposed measures that could restrict the pace of AI progress. The debate follows calls from executives at OpenAI and Anthropic for greater caution as AI capabilities advance.
For Nvidia, the discussion matters because tighter rules could affect demand for advanced chips and the infrastructure needed to train and operate AI models. Investors have been monitoring whether technology companies will maintain large-scale spending on data centers and computing capacity.
The latest market reaction suggests Zuckerberg's position may have reduced some near-term concern over regulatory constraints, although the broader debate around AI safety and development remains unresolved.
Disclosures I/we have no positions in any stocks mentioned, and have no plans to buy any new positions in the stocks mentioned within the next 72 hours.
AI leaders shocked the world by calling for a slowdown in AI’s advancement and for regulation. The news triggered a sector-wide drawdown, raising the risk of a deeper correction, but short sellers shouldn’t get their hopes up.
As scary as it may seem to AI investors, the slowdown in AI’s advancement isn't likely to affect the infrastructure buildout. For the market, more advanced AI models don't matter, since labs such as OpenAI and Anthropic already have highly capable (and potentially dangerous) versions; what matters is the infrastructure to deploy utilitarian AI across the enterprise landscape.
The market gets this wrong: safety and capacity aren’t the same thing. AI demand is not collapsing. Q2 results from NVIDIA NASDAQ: NVDA to Salesforce NYSE: CRM, and nearly every AI-capable company in between, point to accelerating infrastructure demand. In this scenario, September price weakness is a good time to buy these stocks, as catalysts for their price action lie ahead.
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The AI Boom Hasn’t Really Boomed (Yet)Key details for investors to watch include backlogs, utilization rates, persistent pricing power, and the potential for capital expenditure (CapEx) relief. Q2 backlogs expanded at a historic pace, increasing by triple digits at Alphabet NASDAQ: GOOGL, Dell NYSE: DELL, and Super Micro Computer NASDAQ: SMCI.
Critical players like Oracle NASDAQ: ORCL, now ubiquitous across cloud instances regardless of hyperscaler, saw backlogs grow to more than $650 billion, while neoclouds like Nebius NASDAQ: NBIS reported far larger increases, with Nebius's backlog up 4x. CapEx plans among the leading hyperscalers total over $700 billion, underscoring the spend.
Utilization rates matter because they run near 100%, even for older legacy technology. The takeaway is that technology transitions from model training and advanced computing to inference as it ages out, providing a long runway for cash flow and capitalization. This is the foundational factor in NVIDIA’s ability to securitize its GPUs, enabling institutional investors such as retirement funds to invest in the cash flow.
The trigger for stock price gains will be the monetization of existing assets, which, coincidentally, aligns with profitability. Hyperscalers dialing back ultra-expensive frontier modeling will suddenly free up cash flow, removing the primary hurdle for stock prices today. The upfront cost of AI infrastructure is debilitating, impairing cash flow and profitability for most AI-related companies.
AI CapEx Fears Haven’t Derailed the Infrastructure TradeQ2 reporting was spectacular, with results outpacing consensus estimates across the board by a significant margin.
However, analysts were skeptical of CapEx plans and a rapidly differentiating market, causing stocks to move in different directions. As it stands, AI infrastructure names, including NVIDIA, remain the big winners, as they are the focus of current spending.
Wedbush pointed out that the buildout is moving slightly faster than adoption, which is a root cause of concern. The takeaway for investors is that slowing AI model spend not only improves profitability but also frees up cash to meet existing CapEx plans.
Wall Street Keeps Raising the Bar for NVIDIANVIDIA Today
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NVIDIA is the most important stock for AI today, providing the core infrastructure and software that enable it, and its analyst trends are as strong as they could be.
MarketBeat shows coverage increasing month over month and a firm Buy consensus rating with nearly 97% Buy-side bias. Currently, the company carries no Sell ratings, and the consensus price target implies more than 50% upside.
The price target trend is significant, as August and September revisions have pushed the consensus higher.
The high-end range pegs this stock at $515, more than 100% upside, and even that forecast is likely to be low.
Valuation metrics suggest NVIDIA is 50% undervalued today simply based on its price-to-earnings (P/E) multiple, trading in the low 20s versus the historical mid-30s.
Longer-term, this stock is trading at pennies on the dollar, approximately 5x the five-year outlook, suggesting it can rise by 400% to 600% over the coming years.
The Inference Boom Is About to HappenInference is accelerating today, heading toward a boom that Advanced Micro Devices NASDAQ: AMD will unleash.
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Its Helios racks provide hyperscalers with numerous benefits, including inference speed, cost, and profitability. Investors need to remember that model training is the “upfront” cost of AI, while inference is the back-end monetization. With this in mind, we can expect the data center buildout to continue at full steam, if not accelerate, over the coming quarters as AI inference ramps to critical mass.
The biggest risks for AI are bottlenecks in GPU and memory supply, as well as in energy and water. These factors are slowing the build, but also provide opportunities. Companies such as Vertiv NYSE: VRT, Bloom Energy NASDAQ: BE, and AirJoule NASDAQ: AIRJ provide hurdle-jumping technologies that enable data centers to exist and operate with minimal impact on locals.
Among the risks are the upcoming elections, which are likely to be a referendum on AI. The outcome will have far-reaching ramifications but is unlikely to end the buildout. Potentially, established AI companies could benefit from increased regulatory oversight, as it could widen their competitive moats—making it harder for new AI labs to start, limiting the risk of disruption. If increased regulations don't come about, these companies will have free rein to continue building their city-sized supercomputers.
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The AI boom is creating opportunities across semiconductors, cloud computing, enterprise software, infrastructure, cybersecurity, and automation.
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Nokia stock is surging while rivals Ericsson and NVIDIA barely budge, and the reason comes down to a single announcement that reframes who actually wins when AI moves into mobile networks.
Nokia (NYSE:NOK | NOK Price Prediction) stock is rising in early Wednesday trading, up 6% to $10.39 after the company said its artificial intelligence-powered radio access network (AI-RAN) platform is moving from evaluation to live field trials at eight global operators. The list spans North America, Europe, Asia-Pacific and the Middle East, and it reframes AI-RAN as a shared industry priority rather than a single-vendor pilot. That geographic reach is what today’s move is paying up for.
The move is Nokia-specific. The iShares U.S. Technology ETF (NYSEARCA:IYW) is unchanged at $250.08, and the SPDR S&P 500 ETF Trust (NYSEARCA:SPY) is up 0.4% to $760.33. Nokia stock is climbing while U.S. technology sits still and the broad benchmark is only marginally higher, which frames today as a name-specific catalyst.
Peer positioning tightens the read. Ericsson (NASDAQ:ERIC) stock is up 0.7% to $10.11, and NVIDIA (NASDAQ:NVDA) stock is up 0.9% to $214.04. Both are close to flat while Nokia leads, and that divergence gives the announcement its shape as a Nokia story.
AI-RAN Trials Reach Eight Operators Nokia announced growing global momentum for its AI-RAN business, with operators moving from early evaluation into lab and live field trials. The company named eight operators advancing proofs of concept and trials, including A1 Group, du, e&, Mobily, stc and TPG Telecom. Nokia is presenting AI-RAN as an adoption curve rather than a launch headline.
The platform pairs Nokia’s AI-native anyRAN software with NVIDIA’s Aerial RAN Computer, and Nokia says it’s built for 5G networks with a software upgrade path toward future generations. Nokia says the platform has already delivered improvements in spectral efficiency of more than 20%, letting operators wring more capacity from spectrum they already hold rather than funding a hardware refresh. That software-led capacity story is Nokia’s commercial pitch.
The commercial argument here is straightforward for Nokia. Operators can convert existing spectrum licenses into more capacity, a resonant offer in a period when radio access budgets are tight and greenfield buildout has slowed (the same non-chipmaker AI infrastructure angle we broke down in a free report on seven suppliers powering the buildout, here). Nokia’s stack promises additional headroom without the capex event of a hardware refresh.
Emma Falck, President of Mobile Infrastructure at Nokia, stated the growing ecosystem of partners across regions demonstrates that AI-RAN “is becoming a shared industry priority, enabling more intelligent, efficient and software-driven networks.” Ronnie Vasishta, Senior Vice President of Telecom at NVIDIA, declared AI-RAN “will transform the mobile network into one of the world’s largest distributed AI infrastructures.”
Why Ericsson and NVIDIA Aren’t Following The gain at Nokia rests on an ecosystem announcement. Trials and proofs of concept carry no disclosed revenue, and Nokia has put no figure on what the AI-RAN pipeline is worth. What Nokia is selling here is software that pulls capacity from spectrum operators already own, a cheaper argument than the hardware cycle Nokia would otherwise be asking them to fund.
Ericsson is the peer that tests the read. Ericsson competes directly with Nokia in radio access, and a genuine industry rerating around AI-RAN would lift both names together rather than Nokia alone. Ericsson stock’s 0.7% tick suggests today’s news isn’t yet being priced as a category-wide event.
NVIDIA sits on the supply side of the same announcement through its Aerial RAN Computer contribution. A nearly flat NVIDIA against a sharply higher Nokia signals today’s news is more material to Nokia’s positioning than to NVIDIA’s scale. NVIDIA’s market capitalization is measured in trillions, so AI-RAN attaches to a small slice of its revenue base.
The pattern here has logic. AI-RAN is a rerating catalyst for Nokia’s software mix, and for NVIDIA it’s another workload attached to a compute platform already priced for AI at scale. Ericsson’s near-flat move would likely flip if a second RAN vendor announced a comparable pipeline of trials.
What to Watch Separately, Nokia said Nokia Defense signed a memorandum of understanding with C3IA, a UK systems engineering and security services company, to support digital transformation across UK defense operations. The two plan to co-develop capabilities in tactical and operational communications, private mobile networks, command and control, sensing, autonomous systems and edge computing. Both companies participate in the UK Ministry of Defence’s TacSys framework.
As background info rather than a same-day driver, Reuters reported earlier this month that Nokia is set to rejoin the Euro Stoxx 50 index, which brings index-tracking flows on a schedule of their own. Investors in Nokia stock can watch for whether today’s operator list converts into disclosed commercial commitments in coming quarters, since that is the step that turns AI-RAN from a positioning story into a revenue line. The company’s next earnings update will be the natural checkpoint for that translation, and investors should size their exposure to reflect that pipeline conversion still needs to happen.
Contact [email protected] for any questions or corrections.
WASHINGTON--(BUSINESS WIRE)-- #AI--Global Technology Pioneers Emerald AI, Google, and NVIDIA Launch the AI Energy Management Alliance to Advance Flexible AI Data Centers.
AMD just handed OpenAI, Meta, and Anthropic gigawatt-scale reasons to abandon NVIDIA, and Lisa Su is making a bold promise about 2027 that Wall Street has not fully priced in yet.
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AMD (NASDAQ:AMD | AMD Price Prediction) is having the year NVIDIA (NASDAQ:NVDA) had in 2023. Shares are up 128.8% year-to-date, Data Center revenue more than doubled to a record $6.7 billion last quarter, and Lisa Su has locked in gigawatt-scale commitments from OpenAI, Meta, and Anthropic. NVIDIA, by contrast, is up just 13.63% YTD.
The question I want to answer: can AMD ride this second-half Helios ramp and its 2027 MI500 launch to $750 per share by 2027, decisively out-returning NVIDIA along the way?
Why AMD Shares Stalled in the Last Month Before I make the bull case, the recent weakness deserves attention. AMD is down 4.74% over the past month even after climbing 2.6% in the past week.
Two things are weighing on the stock. First, a beta of 2.48 means every macro wobble hits this name twice as hard as the market. Second, investors are digesting the reality that Gaming revenue fell 31% year-over-year and U.S. export controls on the MI308 to China still hang over the accelerator story. After a 209% one-year run, some digestion is healthy.
Wall Street Sees 25% Upside. I Think That Undersells 2027 The consensus target is $615.07, backed by 4 strong buys, 39 buys, 11 holds, and zero sells. Our own model is pricier: a base case of $480.90, an optimistic case of $610.14, and a bear case of $377.09, at 0.9 confidence.
Both frameworks look correct for the next 12 months. Both look too conservative for 2027. With 33 upward FY2027 EPS revisions in the last 30 days against only 3 downward, and 80% analyst bullishness, the consensus is still catching up to the story.
Path to $750 Per Share Reaching $750 from today’s price of $490 would require a gain of 53.1%. With forward EPS of $8.64, a price of $750 implies a forward P/E of 87x. Our base case of $480.90 already implies 85x, meaning the bold target requires only 1.5x of additional multiple expansion. The compression story is what makes this achievable.
FY2027 EPS consensus is $15.51, up from $13.10 ninety days ago. On that number, $750 is just 48x forward earnings. Su told investors AMD expects Data Center segment revenue to “more than double year-over-year in 2027,” and that “customer pull for Helios is very strong and tracking ahead of our initial forecasts.”
Anthropic alone will deploy up to 2 GW of MI450 Series GPUs, on top of 6 GW committed by Meta and 6 GW from OpenAI. The primary risk: any delay to the MI500 ramp or a fresh round of export controls resets the model.
Where AMD Trades Today vs Its Earnings Power At $490 against $8.64 forward EPS, AMD trades at roughly 57x next-twelve-months earnings. Optically rich. But shares sit only fractionally above the 52-week high of $584.73 on an intraday basis and well off the low of $149.85.
Zoom out and the setup is stark: AMD has returned 7,999% over the last decade. If FY2027 EPS lands anywhere near the $20.25 high estimate, today’s multiple looks cheap in retrospect.
Is $750 Realistic? My Verdict Getting to $750 by 2027 requires a 53.1% gain from here. It is a stretch, but achievable.
Three things must go right: Helios must ramp on schedule through Q4 2026 and Q1 2027, MI500 customer engagement has to convert to firm orders, and the server CPU business needs to grow the 70%+ Su guided for 2027.
What derails it? A macro-driven multiple contraction across all high-beta semis. Returns at this level shouldn’t be expected every year, but we’ve outlined the blueprint for how AMD could reach $750 in 2027.
AMD fits a pattern we’ve seen before: a chipmaker whose data-center franchise inflects years ahead of the consensus model. We reverse-engineered what the biggest tech winners looked like at exactly this stage in a free playbook you can grab here.
Contact [email protected] for any questions or corrections.
Nvidia logo is seen in this illustration taken June 11, 2026. REUTERS/Dado Ruvic/Illustration/File Photo Purchase Licensing Rights, opens new tab
Sept 16 (Reuters) - Apple is considering returning to the enterprise server market, combining its own chips with Nvidia’s networking technology, The Information reported on Wednesday, citing people familiar with the matter.
Cupertino, California-based Apple (AAPL.O), opens new tab is developing an AI server that could be offered with either two or four planned M8 Ultra chips, its highest-performance processors, according to the report.
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Such a move would signal Apple's push into AI infrastructure — targeting the fast-growing inference market as enterprise workloads shift from training AI models to running them — while expanding its strategy of using in-house silicon beyond consumer devices.
The server is not expected to reach the market until 2029 and could still be canceled or launched without Nvidia technology, The Information reported. It added that the initiative was backed by the then-hardware chief, now CEO, John Ternus when it began about a year ago.
Apple is considering Nvidia's (NVDA.O), opens new tab NVLink Fusion technology to connect the M8 chips, with some staffers believing Nvidia offers the best available connectivity solution for the project, according to the report.
NVLink Fusion gives chipmakers ready-made connectors and specialized memory so custom AI chips can plug into Nvidia's larger data-center systems.
Apple and Nvidia did not immediately respond to Reuters requests for comment. Reuters could not independently verify the report.
Apple last month refreshed its Mac lineup with M6, M5 Pro, M5 Max and M5 Ultra chips, saying the 2-nanometer M6 delivers up to four times faster AI performance, while the higher-end chips target developers and professional AI workloads.
The project would represent Apple's first dedicated server hardware offering since it discontinued its Xserve line in 2011 after shifting its focus toward standard Mac hardware and consumer devices.
The report also suggests a thaw in relations between Apple and Nvidia. The companies have had a strained relationship for nearly two decades, following disputes that culminated in Apple severing ties after widespread MacBook failures linked to Nvidia graphics chips.
Reporting by Anhata Rooprai and Akash Sriram in Bengaluru; Editing by Joyjeet Das
Our Standards: The Thomson Reuters Trust Principles., opens new tab
Every time the overpriced chorus grows louder on Nvidia, one investor adds more shares, and the reasoning behind that move cuts against almost everything the skeptics keep quoting.
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I keep hitting the buy button on NVIDIA (NASDAQ:NVDA | NVDA Price Prediction), and the louder the “it’s overpriced” chorus gets, the more shares I add. That sounds reckless until you look at what the multiple is actually paying for.
Why the Expensive Label Misses the Point At $212.17 and a $5.12 trillion market cap, NVIDIA looks like the definition of a stretched stock. The trailing P/E of 43 is the number every skeptic quotes. The one they skip is the forward P/E of 24 and a PEG ratio of 0.46. Growth at a reasonable price rarely arrives in mega-cap wrapping, and that mismatch is what keeps me buying. The multi-trillion dollar valuation looks intimidating at face value, yet under the hood NVIDIA behaves like a GARP stock.
Three Reasons the Numbers Keep Winning the Argument Start with growth. Q2 FY2027 revenue landed at $96.22 billion, up 105.8% year over year, with Data Center alone at $89.02 billion, up 117%. Networking inside that segment printed $40.31 billion, up 138%. Management then guided Q3 to $108.0 billion, plus or minus 2%, and told analysts on the call to expect roughly 70% revenue growth in fiscal 2028. That is a company still accelerating at a scale most large caps stopped attempting a decade ago.
Quality of earnings is the second pull. Non-GAAP gross margin ran 75.0%, return on equity is 101.49%, and return on invested capital is 92.21%. Debt-to-equity sits at 0.073 with interest coverage of 503.4x. Fortress balance sheet, operator-level margins.
Third, capital return shows up. Q2 free cash flow was $21.34 billion, and NVIDIA returned roughly $26.0 billion to shareholders through buybacks and dividends, with about $99.0 billion remaining under the repurchase authorization. The dividend barely registers, and the buyback carries real weight.
Why I Pass on the Obvious Alternatives Every time I check Advanced Micro Devices (NASDAQ:AMD) or Broadcom (NASDAQ:AVGO), I come back to the same wall. NVIDIA’s Networking business by itself did $40.31 billion in a single quarter, up 138%. That is a networking company living inside an accelerator company, at a scale neither the pure GPU challenger nor the custom-silicon challenger has produced in a public quarterly earnings report. When I can own a platform earning a 75.0% non-GAAP gross margin and a 92.21% ROIC, I stop shopping for a proxy.
Risk I Refuse to Wave Away The scary line in the filings is $279 billion in supply commitments, most of it memory for Vera Rubin, alongside $108.5 billion in guarantee obligations tied to AI cloud partners. Add customer concentration among a handful of hyperscalers and DSO stretching from 45 to 60 days, and the shape of a demand shock becomes visible. Management calls the arrangement durable and fungible: “We recognize the scale of this support, and we know some will call this circular financing. We see it differently.” I take the risk seriously. I also weigh it against top-five hyperscaler CapEx management pegged at nearly $800 billion in 2026 and $1.3 trillion in 2027. The demand pool is wider than any one customer.
What Keeps the Buy Button Live Vera Rubin is shipping, and Huang says it delivers 30X higher throughput per megawatt and 35X lower token costs versus Grace Blackwell Ultra. Analyst EPS for the fiscal year ending January 2028 has climbed from 12.67 ninety days ago to 15.57 today. When earnings power keeps racing ahead of the multiple, the overpriced label starts to look like a mispricing I get to keep exploiting. That is why my next buy is already scheduled.
Contact [email protected] for any questions or corrections.
CoreWeave Advances AI Expansion With Nvidia Rubin Rollout, Shares Jump Summary
The AI cloud provider moved a larger Nvidia Rubin deployment into production, marking another step in its infrastructure expansion
CoreWeave (CRWV) shares climbed about 3% on Wednesday trading after the AI cloud company said it had brought Nvidia's (NVDA) multi-rack Vera Rubin NVL72 platform online.
The new deployment connects hundreds of Rubin GPUs into a unified computing cluster designed to support large-scale artificial intelligence workloads. The rollout builds on CoreWeave's June launch of a single Vera Rubin NVL72 rack.
CoreWeave said the expanded system combines multiple Vera Rubin racks into one scalable environment using Nvidia's Spectrum-X networking technology. A single Vera Rubin NVL72 rack includes 72 Rubin GPUs and 36 Vera CPUs, along with networking and data-processing hardware intended for AI applications.
The company also introduced new capabilities for its AI Object Storage platform, including cross-region write acceleration and an archive storage tier. CoreWeave said the additions are designed to keep data closer to AI workloads and may reduce latency through managed caching across its Kubernetes-based infrastructure.
Disclosures I/we have no positions in any stocks mentioned, and have no plans to buy any new positions in the stocks mentioned within the next 72 hours.
Nvidia Corp’s (NASDAQ:NVDA) AI expansion is reaching a part of the technology stack investors rarely associate with GPUs: the radio network. Nokia Corp (NYSE:NOK) says eight global telecom operators are now advancing AI-RAN trials using its platform and Nvidia technology, suggesting the AI infrastructure boom could eventually extend beyond data centers and into the cell towers connecting billions of devices.
Nokia said Wednesday that A1 Group, Chunghwa Telecom, du, e&, Mobily, stc, TPG Telecom and Zain Saudi are advancing proofs of concept and live field trials using Nokia’s AI-native radio access network (AI-RAN) platform powered by Nvidia’s Aerial RAN Computer. The trials span Europe, Asia-Pacific and the Middle East.
The underlying idea is relatively simple: use AI and accelerated computing to make wireless networks more efficient. Nokia says its AI-RAN platform has already demonstrated more than 20% spectral-efficiency gains, with advanced AI models targeting more than 2x efficiency over time. Higher spectral efficiency means operators can move more data through the spectrum they already own, potentially increasing network capacity without relying solely on additional spectrum or hardware.
That is where Nvidia enters the picture. Nokia combines its anyRAN software with Nvidia accelerated computing to run AI models at radio-network timescales. Nvidia CEO Jensen Huang has described the radio access network as "the next AI infrastructure," framing the opportunity as a shift from using AI to simply optimize networks toward making the network itself an AI computing platform.
Nokia Wants AI at the EdgeFor Nokia, AI-RAN could turn its existing network footprint into a software-driven growth opportunity.
The company’s AI-native RAN platform, powered by Nvidia computing, is expected to begin pilots later this year and reach commercial availability in 2027.
Nokia also sees the technology as a path from 5G and 5G-Advanced toward AI-native 6G.
Nvidia’s AI Footprint ExpandsThe bigger investor implication may be for Nvidia.
The company has built its AI dominance around data-center computing, but AI-RAN opens another potential layer of demand: the telecommunications network itself. Nokia brings the radio expertise and operator relationships, while NVIDIA supplies accelerated computing and AI software.
The commercial opportunity is still developing. Today’s operator trials must ultimately demonstrate that the additional computing produces enough network capacity and economic value to justify deployment at scale.
But the direction is becoming clearer. As AI workloads spread from centralized data centers toward the network edge, Nokia is positioning its radio business to become part of Nvidia’s expanding AI infrastructure ecosystem — and cell towers could become another place where GPUs matter.
Kevin Hincks tells investors to keep an eye on the vote between an interest rate hike or hold in Wednesday's rate decision. He walks investors through his expectations on what he's calling an "incredible" day as Wall Street braces for what could be the beginning of a rate hiking cycle.
Ignite Planners LLC increased its holdings in NVIDIA Corporation (NASDAQ:NVDA – Free Report) by 3.7% in the 2nd quarter, according to the company in its most recent filing with the Securities and Exchange Commission. The firm owned 113,546 shares of the computer hardware maker’s stock after buying an additional 4,008 shares during the period. NVIDIA accounts for 3.1% of Ignite Planners LLC’s investment portfolio, making the stock its 8th biggest position. Ignite Planners LLC’s holdings in NVIDIA were worth $22,722,000 as of its most recent SEC filing.
A number of other hedge funds have also bought and sold shares of NVDA. Lifetime Wealth Management P.C. purchased a new stake in NVIDIA during the 4th quarter worth about $26,000. Longview Financial Advisors Inc. purchased a new position in NVIDIA during the 1st quarter valued at about $27,000. Phillip James Consulting Co. bought a new stake in shares of NVIDIA during the first quarter valued at about $40,000. Syntax Research Inc. lifted its holdings in shares of NVIDIA by 62.5% in the fourth quarter. Syntax Research Inc. now owns 260 shares of the computer hardware maker’s stock worth $49,000 after buying an additional 100 shares in the last quarter. Finally, Frankly Finances LLC bought a new position in shares of NVIDIA in the second quarter valued at approximately $60,000. 65.27% of the stock is currently owned by hedge funds and other institutional investors.
Key NVIDIA News
Here are the key news stories impacting NVIDIA this week:
Positive Sentiment: Management pushed back against AI slowdown concerns. CEO Jensen Huang said AI safety risks should be addressed through engineering and testing rather than new regulations, while also arguing that demand for data-center infrastructure remains strong. His planned attendance at a U.S.-China state dinner could also support investor hopes for constructive technology discussions. Nvidia’s Huang on AI safety and antitrust
Positive Sentiment: New products and partnerships reinforce ecosystem demand. Salesforce launched Koa, a CRM reasoning model built on NVIDIA’s Nemotron platform. NVIDIA is also expanding CUDA-Q Logical, which links GPUs with quantum processors and could create longer-term demand for GPUs, networking equipment and DGX systems. Salesforce launches Koa
Positive Sentiment: Supply-chain developments point to sustained demand. MediaTek launched a flagship chip using TSMC’s 2-nanometer process, while its broader partnership with NVIDIA includes custom AI chips, PCs and automotive applications. Separate reports highlight demand for NVIDIA’s GB300 systems and the high GPU content of upcoming Vera Rubin racks. TSMC 2-nanometer technology and NVIDIA partnership
Neutral Sentiment: Analysts remain broadly constructive but acknowledge execution risks. Commentary points to NVIDIA’s strong profitability, broad AI ecosystem and potential growth in CPUs, networking, inference and quantum computing. However, much of this optimism depends on successfully deploying new products and maintaining hyperscaler capital spending. NVIDIA revenue guidance outlook
Negative Sentiment: AI spending and valuation concerns continue to cap upside. Calls from AI executives to slow frontier-model development have raised questions about whether enormous data-center investments can continue at the same pace. Higher Treasury yields and oil prices are also pressuring high-growth technology valuations, while rising memory costs may weigh on NVIDIA’s gross margins. AI development slowdown concerns
Insider Buying and Selling
In other NVIDIA news, Director Mark Stevens sold 622,239 shares of the company’s stock in a transaction that occurred on Friday, September 4th. The stock was sold at an average price of $231.62, for a total value of $144,122,997.18. Following the completion of the sale, the director owned 2,336,531 shares of the company’s stock, valued at $541,187,310.22. This represents a 21.03% decrease in their ownership of the stock. The sale was disclosed in a filing with the Securities & Exchange Commission, which is available through the SEC website. Also, EVP Timothy Teter sold 30,000 shares of the stock in a transaction that occurred on Monday, August 31st. The shares were sold at an average price of $217.88, for a total transaction of $6,536,400.00. Following the transaction, the executive vice president owned 2,687,660 shares in the company, valued at $585,587,360.80. The trade was a 1.10% decrease in their ownership of the stock. The disclosure for this sale is available in the SEC filing. The transaction was executed under a pre-arranged Rule 10b5-1 trading plan. Insiders sold a total of 2,585,740 shares of company stock valued at $571,015,527 in the last 90 days. 3.94% of the stock is currently owned by corporate insiders.
Analysts Set New Price Targets
A number of analysts recently weighed in on NVDA shares. Rothschild & Co Redburn upped their target price on shares of NVIDIA from $300.00 to $325.00 in a report on Thursday, August 27th. Zacks Research downgraded shares of NVIDIA from a “strong-buy” rating to a “hold” rating in a report on Friday, August 21st. Wedbush boosted their price target on shares of NVIDIA from $330.00 to $345.00 and gave the company an “outperform” rating in a research report on Thursday, August 27th. Morgan Stanley set a $300.00 price target on shares of NVIDIA and gave the stock an “overweight” rating in a report on Thursday, August 27th. Finally, Wolfe Research reissued an “outperform” rating and set a $275.00 price objective on shares of NVIDIA in a research note on Thursday, May 21st. Three equities research analysts have rated the stock with a Strong Buy rating, fifty have assigned a Buy rating and two have issued a Hold rating to the stock. Based on data from MarketBeat, NVIDIA currently has an average rating of “Buy” and a consensus price target of $324.34.
Read Our Latest Stock Analysis on NVDA
NVIDIA Stock Performance
NASDAQ NVDA opened at $212.17 on Wednesday. NVIDIA Corporation has a 52-week low of $164.27 and a 52-week high of $236.54. The company has a fifty day moving average of $213.90 and a 200-day moving average of $203.66. The company has a market capitalization of $5.11 trillion, a P/E ratio of 26.82, a P/E/G ratio of 1.65 and a beta of 2.22. The company has a debt-to-equity ratio of 0.14, a quick ratio of 3.85 and a current ratio of 4.59.
NVIDIA (NASDAQ:NVDA – Get Free Report) last released its quarterly earnings results on Wednesday, August 26th. The computer hardware maker reported $2.22 earnings per share for the quarter, beating analysts’ consensus estimates of $2.09 by $0.13. NVIDIA had a return on equity of 96.04% and a net margin of 63.66%.The business had revenue of $96.22 billion for the quarter, compared to the consensus estimate of $92.27 billion. During the same quarter last year, the business posted $1.05 earnings per share. The company’s revenue for the quarter was up 105.9% on a year-over-year basis. As a group, equities research analysts predict that NVIDIA Corporation will post 9.12 EPS for the current fiscal year.
NVIDIA announced that its board has initiated a stock repurchase plan on Wednesday, May 20th that allows the company to buyback $80.00 billion in shares. This buyback authorization allows the computer hardware maker to repurchase up to 1.5% of its stock through open market purchases. Stock buyback plans are typically an indication that the company’s leadership believes its stock is undervalued.
NVIDIA Dividend Announcement
The company also recently disclosed a quarterly dividend, which will be paid on Thursday, October 1st. Investors of record on Thursday, September 10th will be issued a dividend of $0.25 per share. This represents a $1.00 dividend on an annualized basis and a dividend yield of 0.5%. The ex-dividend date of this dividend is Thursday, September 10th. NVIDIA’s dividend payout ratio (DPR) is currently 12.64%.
NVIDIA Profile
(Free Report)
NVIDIA Corporation is a technology company that designs accelerated computing platforms, graphics processors and related software. Its products are used for artificial intelligence, machine learning, high-performance computing, computer graphics, data-center applications and other workloads that benefit from parallel processing.
The company’s offerings include GeForce graphics processing units (GPUs) and software for gaming and personal computers; data-center GPUs, systems and networking products; and professional visualization solutions for design, engineering, media and scientific applications.
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Meta CEO Mark Zuckerberg said artificial intelligence labs should rely on independent evaluators and advisers to ensure that models are safe, while Nvidia CEO Jensen Huang dismissed the need for new AI security regulations, arguing that market forces will help companies safely innovate. Though leaders from Anthropic and other major labs have discussed an orchestrated slowdown of tech development, others have called for alternative approaches.
Although it briefly lost its crown to Apple (AAPL -0.52%) in July, chipmaker Nvidia (NVDA +0.57%) is once again reigning supreme as the largest company in the world by market capitalization.
It's also the only company in the world currently worth over $5 trillion.
But Nvidia has had a bit of a rocky year. It's only barely keeping pace with the S&P 500 in terms of growth. Both are up 17.6% over the past year. Is that a sign that Nvidia's high-growth days are behind it?
In other words, is it too late to buy Nvidia stock? Here's what investors need to know.
Image source: Nvidia.
Massive companies can still grow further Just because a company is the biggest of the big doesn't necessarily mean it's done growing, or even done growing quickly.
Apple, the only current challenger for Nvidia's largest-company crown, is a great example of this. In 2018, it became the first U.S. company to reach a $1 trillion valuation, and investors questioned how much higher its price could possibly go.
As it turns out, quite a bit higher. It took just two years for Apple to double in size and reach a $2 trillion valuation in 2020. Since then, it's more than doubled again, with a $4.9 trillion valuation today.
Imagine if you'd sold your Apple shares just eight years ago because you thought it couldn't go much higher. You'd have missed out on massive, market-crushing gains.
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Fundamentals are still important Of course, for every Apple and Nvidia that have kept growing, even at a massive size, there's a company that's seen its growth stall. And the larger a company gets, the harder it can be for it to maintain strong growth. Eventually, it can simply run out of addressable market.
The good news is that this doesn't seem to be happening to Nvidia. Over the last five years, Nvidia's revenue has grown 1,150%, and its net income has soared 2,250%. Meanwhile, its stock is up "just" 821%.
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That means that from a valuation perspective, Nvidia's shares are much cheaper than they were five years ago. Its current trailing price-to-earnings (P/E) ratio of 26.7 is at a five-year low. It's also much lower than Apple's trailing P/E of 38.2 (lower is better).
Of course, if Apple were expected to grow faster than Nvidia, it's not surprising that investors would be willing to pay more of a premium for Apple shares. But Apple's revenue is only up 27.4% over the past five years, and its net income has only gone up 36.2%.
That probably means investors are worried about Nvidia's current revenue and profit streams, which are reliant on sustained AI infrastructure spending.
There are no signs that the current AI spending boom is fizzling, but even if it did, Nvidia's top-of-the-line GPUs would likely find a market powering the next big technological advance.
All things considered, Nvidia still looks very much like a company worth buying for the long term.
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Wall Street’s AI panic trade is already facing its first test.
Nvidia, AMD and SanDisk stocks are trying to recover after Monday’s sharp sell-off, when calls from industry leaders to slow frontier-model development triggered a rush out of AI-linked hardware.
Nvidia fell 3.4%, while semiconductor shares broadly sold off as investors quickly translated “slower AI” into weaker data-centre demand.
That assumption is now being challenged.
AMD says demand still exceeds supply, while infrastructure investors argue that inference, enterprise adoption and competition between model developers could keep spending elevated even if frontier research slows.
Nvidia remains the clearest proxy for the AI infrastructure trade.
The bearish logic was straightforward: if OpenAI, Anthropic and other frontier developers slow model development, hyperscalers may need fewer accelerators and less data-centre capacity.
Hendi Susanto, portfolio manager at Gabelli Funds, challenged that assumption in comments reported by MarketWatch.
He argued that if leading developers slow down, “second and third tier players will seize the opening to catch up.”
Susanto also pointed to inference, enterprise AI and physical AI as demand engines that do not depend solely on ever-larger frontier models.
That matters for Nvidia because its opportunity is no longer limited to training the next generation of foundation models.
Inference, robotics and enterprise deployment can continue consuming compute even if the frontier race becomes more cautious.
Slower model progress and slower infrastructure consumption are not necessarily the same trade.
AMD provides the clearest company-level reality check, as the company's commentary offers little support for that fear.
Piper Sandler analyst David O’Connor reiterated an Overweight rating and $600 price target after speaking with AMD, describing business momentum as “full-steam ahead.”
The firm said demand remains well ahead of available supply across AMD’s portfolio, while CPU and GPU ramps remain on track.
That matters because Monday’s selloff effectively priced in the possibility that AI customers could delay infrastructure spending.
AMD also has another route to growth. Even if overall AI capital expenditure rises more slowly, customers are still trying to diversify accelerator suppliers and reduce dependence on one vendor.
That gives AMD room to gain market share without requiring the entire infrastructure market to grow at the breakneck pace investors previously assumed.
SanDisk is the useful counterweight because not every semiconductor subsector depends on the same demand drivers.
AI has lifted enterprise SSD demand as data centres require more flash storage for training datasets, inference workloads and model updates.
GF Securities analyst Jeff Pu said that the firm maintains a “high-teen QoQ NAND price expectation for 3Q26,” but expects growth to soften to low single digits in the fourth quarter as weaker mobile demand and elevated PC and handset inventories weigh on pricing.
That makes SanDisk a reminder that the AI panic trade can be overdone while the memory cycle still cools independently.
AI storage demand can remain healthy without NAND prices rising at peak rates indefinitely.
A two-day rebound cannot settle the spending debate. The real evidence will come from hyperscaler capex guidance, accelerator order books and memory pricing over the next few quarters.
Because Nvidia holds investments worth more than $100 million, it has to submit a 13F filing each quarter that details its holdings. This publicly available filing gives investors a clear look at what investment moves the company made in a given quarter, and the market pays close attention to the information due to Nvidia's highly influential position in the artificial intelligence (AI) space.
At the end of the second quarter of Nvidia's current fiscal year, which ended July 26, the company's investment portfolio was worth roughly $99 billion -- and one very high-profile tech stock became one of the AI leader's top holdings in the quarter.
Image source: Getty Images.
SpaceX is now Nvidia's second-largest stock holding Intel remained Nvidia's largest stock holding at the end of fiscal Q2, with the value of shares in the AI hardware leader's portfolio sitting at roughly $30 billion. Space Exploration Technologies (SPCX -3.15%), or SpaceX as it's commonly known, was a newcomer to the portfolio -- but it immediately became Nvidia's second-largest holding. Nvidia's position in SpaceX was worth roughly $21 billion at the end of fiscal Q2. Meanwhile, its CoreWeave stake was worth roughly $4.7 billion.
Nvidia's investments in public companies remain very heavily concentrated in the AI space. While most people might think of rocket launches or the Starlink satellite internet business when they hear "SpaceX," artificial intelligence is actually the most important part of the company's long-term growth strategy.
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The company also has partnerships with each of its three biggest investment holdings. SpaceX CEO Elon Musk also committed to using Nvidia's graphics processing units (GPUs) as the primary hardware for his company's data centers. While Musk's decision to stick with the best available offerings for advanced processors isn't terribly surprising, his commitment to using only the company's processors is surprising, as it suggests that Advanced Micro Devices won't even have a chance to win business in the category.
Notably, Nvidia's position in SpaceX actually came from its initial $10 billion investment in xAI, which was merged with SpaceX in February. Nvidia received private SpaceX shares upon the completion of the merger, and these converted into shares in the public company when Elon Musk's company went private in June.
With Musk centering data center build-outs at the heart of SpaceX's growth initiatives, the company is likely to maintain a very close relationship with Nvidia. In addition to leading the market in high-performance GPUs, the company is rapidly gaining ground in the central processing unit (CPU) market. With CPUs increasingly being integrated into data centers to perform AI inference tasks, SpaceX is poised to be a top purchaser of Nvidia's next-gen family of processing hardware.
Keith Noonan has positions in Intel. The Motley Fool has positions in and recommends Advanced Micro Devices, Intel, and Nvidia. The Motley Fool has a disclosure policy.
Nvidia founder and CEO Jensen Huang made his position on the dangers AI poses very clear while speaking at Salesforce’s Dreamforce conference on Tuesday. To him, AI isn’t some new form of “alien mind,” as at least one OpenAI safety researcher has described it. It’s just hardware and software, he says, built by humans. That means, in his view, it can be controlled by humans and existing laws.
“Safety is an engineering problem, not a legal one,” he said. “We’re developing software after all. We’re developing computing systems after all. It’s a complicated computing system, but it’s ultimately a computing system.”
Therefore, there’s no need for new laws or regulations to govern it, he argues. In fact, Huang sees little need for new laws at all. The free market, he argues, will be enough to pressure companies not to release unsafe products.
“If we’re not confident about the safety of the products, like all companies, like you and I, all the companies here, if you build a product or a service, and you’re not confident in its functionality, capability, or safety, then don’t release it. And so that’s a very obvious thing to do,” he said.
He continued: “You pace yourself until you are confident you’re releasing something that the market would appreciate. The market forces are already there. We don’t need any new laws. We don’t need new regulations. We just need companies to decide that when [to] run as fast as they can. I think innovation, speed, and safe products … it’s a false choice. You could definitely have both at the same time. So run as fast as you can. But if you feel at any given point in time the company’s out of control, or the product’s not going to be safe, you know, take a pause and make sure you get it right.”
In some ways, this is a comforting thought. If anyone in the world knows AI, it is Nvidia’s founder, who’s been building the hardware brains of AI since well before ChatGPT existed and now runs a company that also makes open source models, agents, harnesses, and sandboxes.
Then again, if we’re being cynical, his point of view is also unsurprising for someone who’s had his bread so well buttered by the AI boom. Why would he want regulation to come along and add in a layer of hinderance that could slow down Nvidia’s quest to sell ever more AI systems and software? As he also said in the interview: “I’m more ambitious than ever. As a result of our ambition, and with the product productivity boost that we get from AI, the sky’s the limit for us. The sky’s the limit for our company. The sky’s the limit for every industry, for every single country.”
Unfortunately, even companies with the best intentions ship faulty products with unintended consequences, even software. Remember the 2024 CrowdStrike bluescreen-of-death fiasco that grounded thousands of flights and caused other havoc for businesses? Then there are companies accused of deliberately acting with less-than-good intentions. Meta just paid $18 billion to settle a lawsuit over social media harms to children.
And AI has already caused harm, too, whatever the intentions or safety testing involved, from an OpenAI model hacking into Hugging Face to lawsuits against the AI lab over the suicides of young people who engaged in long conversations with its chatbot.
The “leave them alone” strategy, which would let these companies release products as they see fit, could be an unwise approach to AI safety as far as society is concerned. Though Huang is right that it’s possible existing product liability laws could cover AI — if AI doesn’t somehow kill us all before enough cases get through the courts to test that theory.
He didn’t discuss the other route, which seems close to taking shape: industry self-regulation. Huang’s approach has been more to champion open-weight models and companies’ use of them as a competitive counterweight to proprietary AI labs.
But right now, the industry has a short window to institute self-regulation and to encourage AI labs worldwide, even those in China, to see the wisdom in participating. As Microsoft CEO Satya Nadella said at the All-In Summit on Monday, “China should also deeply care about the same safety concerns if the United States cares about them, right? Why should it be different for them? It’s not like they won’t have the same hacking problem. It’s not as if they don’t want to make sure that their citizens are benefiting from AI, just like we would want our citizens to benefit from AI.”
For now, though, if Huang is a no on any new AI regulation, he may be influential enough to get his way. He also demonstrated this week that he, quite literally, has the ear of President Trump.
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Nvidia (NVDA +0.57%) returned approximately $26.0 billion to shareholders through share repurchases and cash dividends in its fiscal second quarter of 2027 (the period ended July 26). That's more than the $21.3 billion of free cash flow the artificial intelligence (AI) chip giant generated in the quarter, and nearly 10 times its capital spending for the period.
With guidance, management says what it expects from the coming quarter. With buybacks and dividends of this size, I think it shows how long it expects profits like today's to last. After all, repurchasing stock only works out if the earnings behind the price hold up.
Image source: Nvidia.
The returns keep stepping upA year earlier, the fiscal second-quarter total was about $10 billion -- $9.7 billion of buybacks and $244 million in dividends. In the first quarter of fiscal 2027, the figure hit about $20 billion. And the most recent quarter brought the first half's total to more than $45 billion. Zoom out, and the step-up looks even steeper: the company returned $41.1 billion in all of fiscal 2026, and this year's first half alone topped that.
The pace is speeding up, and the board keeps making room for more. On May 18, Nvidia increased its quarterly dividend from $0.01 per share to $0.25 per share and approved an extra $80 billion of share repurchase authorization, without expiration.
On top of that, after the second quarter's record returns, about $99 billion still remained under the repurchase program.
Why isn't Nvidia reinvesting the money?Because the business doesn't need it. Nvidia designs chips and systems, while manufacturing partners own the factories that produce them. Revenue grew 106% year over year to $96.2 billion in the quarter, and net income climbed 126% to $59.7 billion -- yet capital spending was just $2.7 billion.
That doesn't mean management is out of ideas. Nvidia spent more than $42 billion buying equity stakes in other companies in the first half of the fiscal year, up from about $1.2 billion a year earlier. And in early September, it agreed to buy Hugging Face, a platform for open-source AI models, in a deal valued at about $12.9 billion.
Between the returns and that investing, Nvidia used more cash in the first half than the business produced. Free cash flow was $21.3 billion for the quarter and $69.9 billion for the half, up from $39.6 billion a year earlier. The company even issued $25 billion of senior notes in June.
Chief financial officer Colette Kress said on the company's late-August earnings call that Nvidia's plan is to return 50% or more of its free cash flow. Year to date, she said, it has returned 60%.
The dividend is the louder signalA repurchase program can be slowed quietly anytime management turns cautious. A dividend, I'd argue, is a harder commitment to unwind. Reducing one tends to alarm investors like almost nothing else.
At $0.25 per share, Nvidia's payout runs about $6 billion a quarter. Increasing the dividend 25-fold in May was management saying it expects free cash flow to remain comfortably above that bill for years to come.
"[W]e intend to increase and return excess free cash flow net of strategic uses," Kress said on the call.
The repurchases point the same way. Nvidia paid average prices of about $202 to $214 per share for the stock it bought during the fiscal second quarter. That's close to the share price of about $211 as of this writing.
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Boards can misjudge cycles, of course. The chip industry has a long history of booms that ended abruptly. If AI spending slows down a couple of years from now, the money spent on buybacks at these prices could end up looking expensive.
But a management team bracing for that outcome would probably hoard cash instead of multiplying its dividend by 25.
What, then, does the $26 billion tell us about the next three years? I think it shows us that management expects the earnings funding it to hold up at least that long.
The stock trades at about 28 times earnings, calculated over the past four quarters. Arguably, that's a modest price for a company whose profits more than doubled in the latest quarter -- so long as they prove durable.
Sure, Nvidia's board could be misreading its own cycle. But it has a clearer view of demand than anyone outside the company, and it isn't acting concerned.
Meta CEO Mark Zuckerberg shared his view of artificial intelligence safety on Tuesday, taking a stance that's more akin to the perspective of Nvidia CEO Jensen Huang rather than Anthropic chief Dario Amodei.
Zuckerberg said in a post on X and other social media platforms that he believes AI labs that fail to "focus on alignment will fall behind," referring to the kind of work that developers conduct to ensure the technologies adhere to human values. This topic of AI safety has been blown into the spotlight over the past few days after Amodei published an essay over the weekend in which he called for the tech industry to "slow the pace at which we improve the capabilities of AI models," an option that Altman later endorsed and which President Donald Trump later criticized.
"There is a lot of debate about slowing progress on capabilities until alignment catches up," Zuckerberg said. "My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models."
Because AI labs risk "significant liability if their models cause harm," Zuckerberg believes that companies are incentivized to prevent potential problems, taking a more market-driven perspective of the topic that's in line with Huang's public comments on the same day. Zuckerberg said that Meta "delayed shipping" its Muse AI technologies because of "safety and security" reasons on its own volition.
"We just did it as part of our day-to-day work because it was clearly the right thing for people and for us," Zuckerberg said.
Speaking at the annual Salesforce Dreamforce conference, Huang told Salesforce CEO Marc Benioff that AI model makers should take responsibility for their products instead of seeking government regulations from lawmakers intended to limit potential harms and risks, as Amodei previously said. Huang reiterated his perspective in an interview on CNBC's "Mad Money," characterizing such AI- related laws and regulations as "just completely unnecessary."
"We have plenty of laws. We have plenty of regulations that govern the reliability and the functionality of products," Huang said.
Amodei also spoke at Dreamforce and reinforced his core belief that slowing down the pace of model development represents "the way to lead the industry forward, to set an example, to say that everyone can always be better."
Later in the day at Dreamforce, Altman joined Benioff for a fireside chat and discussed his belief that safety and monitoring should take precedent over features when it comes to AI development, and acknowledged the general sense of fear that companies may not do so because of competitive pressure.
"I think it's great for our industry to say we want to come together and we want to be able to coordinate and make sure we have enough time to do this safely," Altman said. "But when there's any implication that because of the commercial pressures and the race, some company or between countries, some countries might not do the right thing, I think that's when people get very scared."
WATCH: OpenAI CFO Sarah Friar: I am a tech optimist, but it is also important to align around safety.
Nvidia (NVDA +0.57%) has already won one of the biggest races in the technology sector.
Its graphics processing units (GPUs) provide the key computing power for the majority of the servers used to train today's most advanced artificial intelligence models. But here's the part of the company's opportunity that investors may be underestimating: Training AI is only the beginning.
Once an AI model has been trained, it can be deployed so that people can actually use it. And every time an AI is tasked with answering a question, writing computer code, generating an image, searching for information, or completing a task, the computing infrastructure built around Nvidia's hardware gets called upon again.
That's inference, and it may become Nvidia's next enormous opportunity.
Image source: Getty Images.
Think of training as a school The easiest way to understand the difference is to think about a student. Training is like going to school. The student spends enormous amounts of time learning. Inference is what happens after graduation -- the now-educated individual actually using what they learned to do a job.
AI works similarly. Companies spend huge amounts of time and computing power training models to understand language, images, code, and other information. Nvidia's GPUs have been the workhorses providing the parallel processing power behind much of that training.
But once the model is ready, the need for large quantities of computing power doesn't end. In fact, it may just be getting started. Imagine a company spending billions to build an incredibly sophisticated AI model. It doesn't build that model just to sit unused in a data center. It wants paying customers to use it.
Every use creates another computing workload. And unlike training, those inference workloads can happen millions or billions of times.
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Training creates the AI. Inference puts the AI to work. If systems that use AI become a common part of everyday life and business, the volume of inference workloads they generate could become enormous. And the expected wide use of AI agents could make this opportunity even bigger.
Today's chatbot might answer a question it's asked -- a single response to a single query. Tomorrow's AI agents could do much more complicated things. Suppose you ask an AI agent to research a company. It could take that one request, and respond to it by searching through multiple financial databases, reading corporate filings, looking for recent news, comparing it to competitors, building a valuation model, checking its calculations, and then writing a report.
That's not one AI response. It's an agent undertaking a chain of reasoning and actions. The more steps an AI agent takes in its effort to fulfill a request, the more computing power it requires.
Nvidia is actively positioning itself to benefit from this trend. For instance, its newest Vera Rubin architecture, which combines the chipmakers GPUs with its new CPUs, is specifically designed for these long-running inference workloads.
Don't assume Nvidia wins this market automatically While the opportunity for inference will likely be larger than the market for training, the competition among chipmakers in this space is likely to be intense as well.
On one hand, hyperscalers like Amazon and Alphabet are developing their own custom AI chips, partly to reduce their reliance on Nvidia. Besides, the inference market is particularly sensitive to cost, as customers will have both incentives to switch and opportunities to do so if a competitor can deliver similar results at a meaningfully lower cost.
So the investment thesis isn't simply that inference will grow, therefore Nvidia will win. The more important question is: How much market share in this growing segment of AI can Nvidia capture?
So far, the company is betting heavily that its combination of hardware, networking, software, and a robust developer ecosystem will keep it at the center of the AI infrastructure stack. That said, investors should pay close attention to how well the company sustains its market share in the coming years.
What does it mean for investors? The first phase of the AI revolution was about building increasingly powerful AI models. Nvidia was by far the most important supplier of the computing power needed to build them.
The next phase is about putting those models to work. If AI agents become widespread, they will process billions of increasingly complex tasks on our behalf. That means Nvidia's opportunity will not end when an AI model finishes training. It will begin there.
That's why, despite the fact that Nvidia has already delivered massive returns to its shareholders since the start of the AI race, the bulls believe the stock still has more excellent performances ahead of it.
Nvidia CEO Jensen Huang is set to attend U.S. President Donald Trump's state dinner for Chinese President Xi Jinping, a source familiar with the matter said on Tuesday.
Nvidia CEO Jensen Huang is expected to attend a state dinner for Xi Jinping when the Chinese leader travels to Washington to meet with President Donald Trump next week, a person familiar with the matter told CNBC on Tuesday.
The person spoke on condition of anonymity to discuss the private guest list.
Huang's planned attendance at the high-profile event is the latest sign of his alliance with Trump. Those ties appeared stronger than ever this week, as the two men pushed back against other tech CEOs who have issued dire warnings about the risks posed by rapid AI advancement.
AI is expected to be a main topic of discussion when Xi meets with Trump. Huang was one of a select group of CEOs who traveled to China with Trump earlier this year.
It was not immediately clear who else has been invited to the state dinner.
A White House official told CNBC, "President Trump has invited several great American business leaders to his dinner with President Xi because he will always prioritize making good deals for our country."
Nvidia CEO Jensen Huang pushed back against calls to give AI companies antitrust exemptions to coordinate on slowing the development of advanced models.Huang said AI safety is a real concern but argued it should be addressed through engineering and testing rather than new laws or regulations.watch now
Nvidia CEO Jensen Huang on Tuesday slammed proposals of artificial intelligence companies antitrust exemptions that could coordinate a slowdown of developing increasingly powerful models.
Huang's comments — in an interview with CNBC's Jim Cramer — come during an intense debate over AI safety fueled by an essay Anthropic CEO Dario Amodei published Saturday. Amodei proposed that leading AI labs coordinate to "pace the frontier," or deliberately slow the development of advanced models to give companies more time to test and safeguard. Amodei said that effort could require government mediation or waivers of antitrust restrictions.
Huang agreed that AI companies need to take safety seriously, but argued they should be able to solve those challenges themselves.
"The fact that we need new laws, new antitrust laws, or new regulations, so that these companies could do their fundamental engineering and do it properly before they release products, that is just completely unnecessary," Huang said on CNBC's "Mad Money." "We have plenty of laws. We have plenty of regulations that govern the reliability and the functionality of products."
In his essay, Amodei laid out a three-step approach to moderate the pace of model development, beginning with embedding third-party safety evaluators at each lab. The second and third steps are democratic and global coordination on safety standards and "limits on the rate of unchecked AI progress," Amodei wrote. "Some forms of coordination that would be impactful for pacing are legally challenging, and will require government support."
The legal issues arise because leading AI labs are competitors and their models are competing products. The Sherman Antitrust Act generally prohibits agreements among rivals that unreasonably restrain competition, while certain forms of coordination — including agreements to restrict output — can be illegal. That creates potential legal risks if competing AI developers were to jointly agree on how quickly they would develop or release new models.
Huang said model developers have the power to ensure their products are safe before releasing them.
"Safety is an engineering problem. Testing is an engineering problem," said Huang, whose company counts Anthropic and rival lab OpenAI as important customers. Nvidia also develops its own Nemotron family of open-weight models. "We should create products and properly test them. And if they're not ready to be released, just hold on to it and keep testing it and keep engineering until it's ready."
The AI boom has turned Nvidia into the world's largest chipmaker and most valuable company. Efforts to slow the development of more capable models may pose a risk to Nvidia's future growth.
Anthropic did not respond to CNBC's request for comment on Huang's remarks.
"AI safety is a real thing," Huang told Cramer. "Engineering products so that they are safe for the world to use — that's a real thing. And companies should innovate as fast as possible, but they should never innovate so fast as to release unsafe products."
Huang also pushed back against warnings that increasingly powerful AI models will pose an existential threat to humanity.
"We're not going to die in 2030," he said. "There are so many people in the world who are going to build AI properly. We're going to have all kinds of guardrails, invent all kinds of technology for safety and security."
Nvidia (NVDA +0.57%) shares have delivered a remarkable 10-year gain of about 14,090%, and a total return (with dividends reinvested) of 14,760%. So in either case, a $10,000 investment made 10 years ago would have grown into a position worth over $1.4 million today.
As eye-popping as the company's returns have been, no investment is without risks. Here's a quick look at the products and services that made Nvidia a titan of the tech world, and a few factors worth considering as you decide whether you want to add it to your portfolio now.
Image source: Getty Images.
Nvidia's claim to fame Nvidia made its name by designing powerful graphics processing units (GPUs). Those GPUs were originally used primarily to improve video game and design software performance, but over the years, their powerful parallel processing capabilities have found uses in high-performance computing, crypto mining, and, more recently, data centers, artificial intelligence, and autonomous vehicles. Nvidia also offers a powerful software platform, CUDA, that developers can use to program its chips for specific tasks.
But is Nvidia right for you? There's no doubt that Nvidia is an impressive company, and it has been a winning pick for long-term shareholders in the past. However, with any enterprise, it's a good idea to look beyond the hype and consider the risks. For Nvidia, potential risks include:
Heavy dependence on AI spending: The largest use case for Nvidia's hardware is now to power AI data centers, so its revenues and earnings hinge on big tech companies continuing to invest heavily in expanding their compute capacity. If community pushback or economic issues slow the build-out of new data centers, or if companies cut back on their AI budgets, Nvidia's growth could take a hit. Rising debt: Nvidia's quarterly filing in August warned investors that rising debt could "adversely affect" the company's financial condition and cash flows. As of July 26, 2026, the company had $33.5 billion in senior notes outstanding. In addition, $15 billion of that debt is due within one to five years. Political headwinds: Nvidia sells products globally, so its sales can be impacted by international trade rules and economic conflicts, including tariffs. Because there's no way to predict when the next set of tariffs may be implemented in this political climate or where the next war may break out, there's always the risk of it losing easy access to some foreign markets, which could cut into its revenue. Regulatory risks: It was reported earlier this month that the Department of Justice is intensifying its scrutiny of Nvidia's $17 billion deal with AI start-up Groq. The regulator's concern is whether Nvidia structured its agreement with Groq to avoid antitrust oversight. Potential fines and tighter scrutiny of upcoming deals could ultimately affect the bottom line and investors' returns. Whether you're planning for retirement or diversifying your portfolio, Nvidia could be a great addition to your portfolio. However, before making a decision to buy, weigh it all -- both the potential rewards and potential risks.
Dana George has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Nvidia. The Motley Fool has a disclosure policy.
The artificial intelligence safety debate triggered by a dire warning about the fate of humanity has united longtime enemies and opposing political foes across Washington and Silicon Valley.
Two distinct camps have emerged.
On one side is President Donald Trump, Nvidia CEO Jensen Huang and former White House crypto czar David Sacks. The other has brought together clashing frontier lab leaders Dario Amodei of Anthropic, OpenAI's Sam Altman and Elon Musk, along with a chorus of AI researchers.
AI worriesThat wave of discourse was initially set off by Anthropic researcher Jacob Coxon, who announced last week that he quit his job due to concerns that OpenAI and Anthropic are "gambling with our lives." As the discussion raged over Coxon's post, an Anthropic alignment lead responded that AI has a greater than 10% chance of 'killing all humans."
That frenzy built into the weekend, when Amodei called for the AI frontier to slow model development.
Altman quickly joined forces with Amodei in an unusual pairing between the rival AI labs. Tesla and SpaceX CEO Musk chimed in on X that "Dario is right," later adding that he's been "sounding the alarm on AI for a long time."
The discussion quickly reached Washington, amplifying growing tensions over how to rein in mounting AI risks without stifling tech innovation.
Calls for an AI slowdown have united unlikely political opponents in favor of regulation. Progressive leaders Sen. Bernie Sanders, I-Vt., Rep. Chip Roy, R-Texas, and former White House strategist Steve Bannon convened at a nonprofit event in Washington on Tuesday in support of AI action.
Read more CNBC tech newsTwo camps have emerged in the debate over AI safety and regulationAWS says it can't restore service to Bahrain, UAE facilities 6 months after Iran strikesMusk urges top AI labs, Chinese companies to test each other's models amid calls for slowdownWill AI really destroy humanity? Pioneers who created the tech weigh inTrump leads 'hoax' opposition Trump has vehemently clapped back against the push for AI regulation and warnings about extinction.
Over multiple posts to Truth Social this week, Trump called fears of runaway AI a "hoax" and a "scam" and lashed out against public data center pushback. On Monday, he phoned in to Nvidia CEO Jensen Huang's interview at the All-In Summit in Los Angeles to bring the message directly to a tech-focused audience.
Trump said in a video posted on social media by an attendee that anyone opposing data center construction is "playing right into the hands of a lot of people that don't want to see it happen," and that could be China.
"You're right," Huang replied. "We're not going to let that happen, sir."
Treasury Secretary Scott Bessent said that Trump is "completely aligned" with Huang during his testimony before the House Financial Services Committee on Tuesday.
Later in the day, Huang again backed Trump's stance at the Salesforce Dreamforce, saying, "We don't need any new laws. We don't need new regulations."
"We just need companies to decide that when it's you know run as fast as they can," he said.
Others in the administration have taken a more aggressive tone with their comments.
Sacks, who is currently co-chair of the President's Council of Advisors on Science and Technology, encouraged the tech giants to "go ahead and pace the frontier" but "stop pretending the motivation to slow down is purely altruistic."
That jab at perceived "effective altruism" has also been echoed by Emil Michael, under secretary of War for Research and Engineering.
"I think the fearmongering we're seeing now is a coordinated campaign by a set of people and ideologies, and the nonprofits behind them, that are trying to scare the American public," Michael told Fox News on Monday.
Part of the administration's message is that limiting regulation is critical to winning the AI race against China. Trump indicated that the China threat is top of mind, saying on Sunday that "whoever wins AI wins."
House Speaker Mike Johnson indicated during a press conference on Tuesday that Trump plans to unite AI leaders at the White House "within the next week."
He also dismissed AI apocalypse concerns.
"What the president is saying is you're not all going to be dead in 10 years," he said. "That's a hoax."
Nvidia (NVDA +0.57%) spent about $12.9 billion recently to acquire Hugging Face. While the name tells you absolutely nothing about what Hugging Face does, I think it's a game-changing buy for Nvidia, as it marks the chipmaker's step into a different part of the artificial intelligence (AI) sector.
Nvidia investors should be thrilled with the direction this purchase will lead the company in.
Image source: Getty Images.
Hugging Face dovetails well with Nvidia's existing businesses Hugging Face is essentially a collaborative platform and repository for hosting pre-trained AI models, datasets, and open-source machine learning libraries. Its users can test out these models in its free cloud spaces and inference environments. While it does offer some paid tiers, it's mostly a free-to-use service as of now. So, why should Nvidia investors be excited about its purchase of a company that isn't generating a ton of revenue? In this instance, the chipmaker is OK with losing money on this service -- because it will steer developers toward the Nvidia ecosystem.
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If a user develops an AI model or application using Hugging Face, and then wants to take it to the next level, redoing everything on different hardware won't make a lot of sense. So if everything developed in Hugging Face's programming environment runs on Nvidia's hardware already, it would be pretty easy to recreate it on a different Nvidia-powered platform where the program could be monetizable.
This will allow Nvidia to capture more of the business of AI developers utilizing open-source AI models (versus closed-source models like those from OpenAI or Anthropic), as they will be less likely to switch hardware platforms later on in their process.
This is a genius move, and it ensures that Nvidia will also be relevant in the next phase of AI when less hardware will need to be purchased.
However, the acquisition is not a measurable needle-mover. While Nvidia may benefit over the long term by having more developers steered in the direction of its hardware, that return on investment won't be easily discernible. That may be a problem for some investors, but I think most should be OK with it. After all, Nvidia had nearly $100 billion in cash, debt securities, and equity securities on its books as of the end of its most recent quarter. Nvidia made a smart move by acquiring Hugging Face, and while it may not be an obvious needle-mover, it will help secure the tech giant's place in the future.
