My FINAL Nvidia Stock Verdict After Q1 Earnings! 📈 | NVDA Stock Update | Investing Tutorial | NVDA

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[00:00:00]Obviously, we've seen a typical reaction in video. It doesn't typically post I should say rally post earnings because it does rally on its customers earnings earlier in the season. So, what have we learned from this company?

[00:00:16]>> Hey Sam, thanks for having me on today.

[00:00:17]Appreciate the opportunity to go through the video earnings with you.

[00:00:21]Honestly, a a blowout quarter, you know, beating on the the quarter, you know, guiding up. I mean, I think we got confirmation that we're still very much in the early innings of the AI cycle. Um I think a lot of questions got answered, right? You know, as as Reuben is ramping, we saw the margins kind of holding. That was a key sign. I think we got some interesting new news on the capital allocation front. You know, the buyback, the dividend. I think you know, really starting to think about what's going to make Nvidia attractive to that, you know, other type of investor that tends to be more, you know, value-focused. I think, you know, with the the stock a little bit stuck, I think the the management team starting to explore, you know, how they can kind of open the aperture in terms of, you know, what type of investors are interested in the company. But, you know, I think all signs pointing to, you know, us being very early in the AI revolution, you know, still really supply constrained relative demand, and you know, a really bright look outlook ahead for not just the couple quarters, but multiple years.

[00:01:22]>> So, >> [gasps] >> who is Nvidia's earnings good for? I mean, when you think of it really being at the top of the food chain here, and one man's capex being another man's revenue, and obviously the companies that work with Nvidia, I'm thinking about the picks and shovels and the broader AI uh trade here, which it was very important to hear from Nvidia to get a sense of who benefits the most.

[00:01:49]>> Well, I think there's a lot of beneficiaries, right? I mean, I think you can look at some of the companies that have gotten some, you know, prepayments and support from Nvidia, you know, as they build out the supply chain.

[00:02:00]Um I think, you know, you have to look at the the picks and shovels, like the semi cap equipment companies, like Applied Materials, Lam Research, ASML.

[00:02:08]Uh obviously, TSMC, a huge beneficiary here. Micron, Hynix, and the memory suppliers. But, you know, I I took a lot of positivity down at the application layer, as well. I mean, obviously, we're going to need to build out a lot more compute and all of the supply chain and infrastructure names are benefiting.

[00:02:24]But, I think we got a lot of positive signals that, you know, we're in the very early innings of the inference ramp. And as inference ramps up, I think you're going to start to see a lot more value reckoning.

[00:02:34]>> Well, Jensen, this is all happening at a time that the US military is increasingly in need of AI chips, as well. And I mean, here we are in the middle of a war, and I know that many of the rescues and the Operation Epic Fury is relying on AI-enabled surveillance, satellite imagery, drone signal processing, high-performance uh computing systems, where you're number one. So, tell me a little about that.

[00:03:01]Without giving us any secrets, obviously, Jensen, about this great nation, can you talk to us a little about, for example, when we were locating that missing officer in the mountainous terrain of Iran, how Nvidia chips were actually critical in that regard, in terms of surveillance and finding our man?

[00:03:21]>> Well, I we um uh we do a lot of work in imaging. And um uh most of the world's radar systems and imaging systems has Nvidia chips in it. And uh um uh And and we're we're just incredibly honored and to to be able to to uh support our nation. Uh recently, uh you might have seen an announcement where the Department of War uh uh has uh access to Nvidia's technology.

[00:03:47]Uh so that they And our technology is completely open source, so that it could be modified and enhanced for the applications of our military and and we have great partnerships across the ecosystem of contractors and and technology companies that service the national securities organizations around our in our country and so it's a great honor for us to be able to do that. And so technology matters in the future of national security and and we're delighted to be part of that.

[00:04:18]>> Don't be fooled by Nvidia's 1.7% decrease on May 21st.

[00:04:23]The chip giant's first quarter fiscal 2027 report was nothing short of spectacular with 81.6 billion dollars in quarterly revenue.

[00:04:32]Up 20% quarter over quarter and 85% year over year.

[00:04:37]Even more amazing, Nvidia converted a mind-boggling 65.6% of its revenue into operational profits during the period ending April 26th.

[00:04:48]Despite its massive size, Nvidia defies the rules of business physics by sustaining a rapid top and bottom line growth rate resulting in more cash flow than the company needs to reinvest in its operations.

[00:05:02]As a result, Nvidia's board of directors authorized an additional 80 billion dollars in share repurchases and declared a significant increase in the quarterly dividend from $0.01 to $0.25 per share.

[00:05:16]Here's why Nvidia's dividend rise makes it an even more compelling buy right now.

[00:05:20]In March, I correctly anticipated that Nvidia would increase its dividend significantly in 2026 based on comments made by CFO Colette Kress during the company's GTC 2026 conference.

[00:05:34]Kress stated that Nvidia intends to distribute at least 50% of free cash flow FCF to shareholders through dividends, particularly in the second half of the year when it completes some projects.

[00:05:47]And considering that Nvidia's repurchase program is already huge and its dividend is only $0.01 per share, a yield of 0.02%, a dividend increase was the logical next step in the company's transformation from a high-flying cyclical growth stock to a mature, industry-leading computer powerhouse.

[00:06:06]Nvidia began paying dividends in 2012, and while they were fairly relevant at the time, the payout grew absurdly little as the stock's value surged.

[00:06:15]Nvidia's latest dividend increase does not make it a high-yield company, but it will have a yield of roughly 0.4%, which is comparable to other IT behemoths such as Apple, Alphabet, and Meta Platforms.

[00:06:28]However, some investors may prefer that Nvidia invest that capital in the company rather than return it to shareholders.

[00:06:35]After all, there's a lengthy line of once-innovative IT companies that became dividend-paying stalwarts before losing market share over time.

[00:06:43]However, Nvidia is not like the others.

[00:06:46]Despite a 2,400% increase in payout, Nvidia will only pay approximately $6.08 billion in dividends per quarter or $24.3 billion annually.

[00:06:58]Nvidia recently reported $58.3 billion in net income in a single quarter.

[00:07:04]So, even if Nvidia's growth slowed, the company could simply reduce buybacks while still having plenty of cash to sustain its dividend.

[00:07:12]Nvidia can comfortably pay its enormous dividend leaving enough of cash to buy back vast amounts of stock.

[00:07:18]But the real prize for long-term investors is that Nvidia's business model may grow less cyclical over time while continuing to capitalize on new frontiers in artificial intelligence, AI.

[00:07:29]Nvidia's recent growth has been mostly driven by the sale of chips and related hardware for data center applications.

[00:07:35]Now that large language models, LLMs, have advanced, hyperscalers are shifting their attention from AI training to AI inference, which employs AI agents and tools to apply the models knowledge base.

[00:07:47]Inferencing and training have different requirements, which is why Nvidia has expanded beyond graphics processing units, GPUs, to handle a bigger portion of the hardware and software stack.

[00:07:59]On its May 20th earnings call, Nvidia stated that it expected $20 billion in revenue this year from its new Vera Rubin central processing units alone, demonstrating its capacity to generate revenue streams other than GPUs.

[00:08:13]Nvidia is developing an AI ecosystem for the era of inference.

[00:08:17]As a critical facilitator of AI, we will make the necessary investments to achieve the industry's lowest cost per token and the highest token throughput, allowing our clients and partners to scale and expand the AI frontier, Kress stated on the May 20th earnings call.

[00:08:33]Inference might generate regular revenue for Nvidia, reducing its reliance on one-time chip sales.

[00:08:39]As generative and physical AI applications grow, hyperscalers will look for solutions that can process tokens rapidly and cheaply.

[00:08:47]Tokens are discrete bits of text, code, or graphics.

[00:08:52]These data nuggets are essentially the currency of AI, similar to how kilowatts quantify electricity consumption.

[00:08:58]Because applications and agents are created on Nvidia's stack utilizing its hardware and software, Nvidia earns recurring revenue from services such as Nvidia AI Enterprise and virtual GPU support.

[00:09:11]It also expands its ecosystem, which may one day resemble Apple's model.

[00:09:16]Apple, like Nvidia, used to be cyclical, relying on increases in consumer electronics expenditure.

[00:09:23]Apple has changed dramatically since its inception.

[00:09:26]Customers can have various items in the ecosystem that complement one another and increase the payback when upgraded during a refresh cycle.

[00:09:34]Meanwhile, Apple generates revenue from subscription services.

[00:09:38]As the use cases for agents and physical AI, such as robotics and self-driving cars grow, hyperscalers will need to update their capabilities to meet consumer demands, just as the iPhone's processor and camera capabilities are light-years ahead of previous versions.

[00:09:54]A solid AI stock to anchor any portfolio.

[00:09:58]The largest danger for Nvidia is that it overestimates AI agent and physical AI demand, and that AI inferencing does not result in recurring high-margin income.

[00:10:08]For the time being, the company's earnings are still highly influenced by the cyclical increase in AI spending as hyperscalers improve infrastructure designed for basic IT needs to suit AI demands.

[00:10:20]However, doubt is likely already baked into Nvidia's value as the stock trades at only 33.7 times earnings, which is extremely low for a firm developing as swiftly and effectively as Nvidia.

[00:10:32]Nvidia's earnings have skyrocketed quarter after quarter as tech titans hurry to join in on its latest artificial intelligence AI systems.

[00:10:40]As the AI revolution heats up, stock performance has been excellent, with shares rising more than 600% in the last 3 years.

[00:10:48]However, in recent months, investors have expressed concerns about one specific issue.

[00:10:54]They are concerned about the sustainability of the current high levels of demand.

[00:10:58]Major cloud providers, such as Microsoft, Amazon, and other IT companies, have pledged to invest almost $700 billion in infrastructure this year, which benefits chip designers, such as Nvidia.

[00:11:11]However, there is concern that any slowdown in the rate of such spending could have the opposite effect, weighing on GDP.

[00:11:19]Given all of this, investors have been especially attentive to any statements from tech titans that might provide insight into what's to come.

[00:11:27]In this week's earnings call, Nvidia CEO Jensen Huang revealed major news to shareholders, which may affect your choice to buy Nvidia stock right now.

[00:11:36]First though, let's take a quick look at the AI story thus far and how Nvidia has evolved in this context.

[00:11:43]Customers concentrated on training huge language models in the early stages of the AI boom, which required computing to input massive volumes of information into these models at rapid speeds.

[00:11:55]Nvidia's graphics processing unit, GPU, provided the optimal computing power.

[00:12:00]While other AI chips may power training, Nvidia's GPUs have done so faster and more efficiently than any other.

[00:12:07]Customers hurried to acquire these powerful tools and comprehensive AI systems, accelerating Nvidia's sales growth.

[00:12:15]As previously said, after such high levels of growth and demand, investors have speculated that Nvidia's best days may be behind it.

[00:12:23]The premise is that while major IT customers are currently spending heavily on infrastructure, this may not continue indefinitely.

[00:12:31]It's important to note, however, that AI doesn't end with training. In fact, that's just the beginning.

[00:12:37]And this brings me to Huang's big announcement to shareholders this week.

[00:12:41]Demand has skyrocketed, according to Huang.

[00:12:45]The reasoning is straightforward.

[00:12:47]Agentic AI has come.

[00:12:49]AI can now perform useful and valued tasks.

[00:12:52]AI agents employ the knowledge gained via all of their training to take action and perform tasks.

[00:12:58]And the crucial point here is that AI continues to require computing in the form of GPUs and central processing units, CPUs.

[00:13:07]Nvidia has this covered with their current Blackwell system and its most recent platform, Vera Rubin.

[00:13:14]Rubin, which is tailored to the demands of AI agents, is expected to ship in the third quarter of this year.

[00:13:21]Furthermore, clients continue to flock to the current Blackwell platform, putting Blackwell and Rubin in a strong position to drive Nvidia's growth in the coming quarters.

[00:13:30]According to the firm, the number of partner data centers with capacity more than 10 megawatts has nearly doubled in the last year.

[00:13:37]What exactly does this mean for investors?

[00:13:40]Huang definitely gave encouraging news, which should allay investor concerns about future development opportunities.

[00:13:47]We're seeing that the demand for computing is continuing and may even expand as more organizations apply AI to real-world scenarios.

[00:13:55]Meanwhile, recent concerns about the sustainability of demand weighed on Nvidia stock during the first quarter, lowering its valuation.

[00:14:03]Even though the stock has subsequently recovered, the pricing remains highly intriguing.

[00:14:08]The company trades at 25x projected earnings projections, down from 40x at the beginning of the year.

[00:14:15]So, after Jensen Huang's major announcement, Nvidia stock seems like a buy.

[00:14:20]During Nvidia's earnings call, CEO Jensen Huang stated that customers are expected to spend 3 to 4 trillion dollars on AI infrastructure by the end of the decade.

[00:14:31]According to Michael Parekh, a former Goldman Sachs executive who now manages the AI Return to Zero Substack, the figure is likely conservative and a single business in Taiwan will decide whether it happens.

[00:14:44]In an exclusive StockTwits interview with Michelle Steel, Parekh stated that Taiwan Semiconductor Manufacturing Company, TSMC, is the determining factor.

[00:14:54]Jensen trails to Taiwan virtually every month, basically urging his friends at TSMC, "Please, here's another 100 billion, create me extra fabs." But the TSMC folks aren't doing it. TSMC serves as the global tech market's Federal Reserve. They need to expand their production facilities, which take 3 to 5 years to build and cost billions, tens, and hundreds of billions of dollars.

[00:15:17]And they're not increasing manufacturing to handle the entire 5-plus trillion.

[00:15:22]Michael Parekh, founder, AI Return to Zero Substack.

[00:15:27]Nvidia's stock closed roughly 2% lower on Friday and is down more than 6% this week.

[00:15:33]Retail attitude toward the tech titan on StockTwits has trended in very bullish area over the last week accompanied by extremely high levels of conversation.

[00:15:42]Why TSMC holds the cards.

[00:15:46]TSMC produces over 90% of the chips Nvidia requires.

[00:15:50]Apple and Nvidia together account for around 50% of TSMC's revenue.

[00:15:56]Jensen Huang stood outside a Taipei restaurant in February and told the reporters, "TSMC needs to work very hard this year because I need a lot of wafers." indicating a constraint rather than a negotiation.

[00:16:07]Meanwhile, Parekh believes that TSMC's purposeful pacing is not indicative of intransigence.

[00:16:13]It is the sensible action of a corporation that recognizes that creating a new cutting-edge fab costs tens of billions of dollars and takes three to five years to reach full output.

[00:16:24]According to him, TSMC is balancing the demand it sees with the capacity it can safely build.

[00:16:30]According to Parekh, the evolution of technology over the last year has had a significant impact on demand.

[00:16:37]He pointed out that chatbots were the key use case for AI computing 12 months ago.

[00:16:42]Today, it's agents and reasoning models, which consume more than 100 times the processors for inference that chatbots do for the same activity.

[00:16:52]He claimed that the increase in demand is what allows Jensen to offer figures that would have seemed ludicrous three years ago.

[00:16:59]It is also what makes the TSMC limitation worse rather than better because demand is outpacing any fab that can be built to meet it.

[00:17:07]Jensen has the confidence to talk about trillions of dollars in demand and he does in fact have access to trillions.

[00:17:15]Only a year ago, artificial intelligence was all about chatbots.

[00:17:19]Now, it's all about agents and reasoning, which require 100 times more chips for inference than chatbots did a year ago.

[00:17:27]Michael Parekh, founder of AI returned to Zero Substack.

[00:17:30]Parekh has covered the internet, mobile, cloud, and every major technological trend during the last three decades.

[00:17:36]He informed StockTwits that this was bigger than any of them.

[00:17:39]Jensen believes the three to four trillion dollars amount is definitely an underestimate. He estimated that the real value will be four to five trillion dollars over the next six years, assuming that TSMC can create the necessary capacity.

[00:17:52]This is much bigger than the internet explosion.

[00:17:55]And Jensen is correct, at least three plus trillion.

[00:17:58]In actuality, he may sell four to five trillion dollars over the next six years.

[00:18:03]However, TSMC remains the deciding factor, according to Parekh.

[00:18:08]Nvidia's stock is up more than 13% this year and more than 60% in the last 12 months.

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