Case for a super-stellar NVDA Earnings and unprecedented growth
Case for a super-stellar NVDA Earnings and unprecedented growth
TL;DR: Immediate PT 162 after earnings event on 2/26
Disclaimer: I have 10+ years of extensive AI background (before it was cool) in leading industry and academic institutions with multiple first-author top AI research papers and patents. Never worked for NVDA, and have worked with their proprietary chip-software CUDA since 2013.
All of this note is manually typed out, without the use of ChatGPT or any GenAI platform (also added in the post on why). May not respond to comments, but if something jumps out to me, may edit the post.
Not Financial Advice (NFA) - do your own DD, and evaluate your risk tolerance.
This contains a) verifiable quantitative elements, b) interpretations on continued growth, c) current GenAI limitations, fueling further innovation, d) Leading public personalities' testimonies with supporting numbers
- Will leave out comments on some research components signaling increased demand for NVDA to avoid conflict of interest by my employer and ongoing research pursuits.
- So, the following thesis contains publicly verifiable information for anyone with an internet connection and a computing device.
Thesis:
Quantitative indicator (no emotions or belief needed):
CapEx (sic) (post DeepSeek): 44% more than last fiscal year at 320B spend projected
(The graph does not include confirmed orders for xAI, OpenAI, ORCL, Perplexity AI, India)
Quantitative indicator (no emotions or belief needed):
Which top Chipmaker gets the revenue for new AI datacenters amongst NVDA, AVGO, AMD
- To be noted: AMD Datacenter revenue this earnings was a miss, despite there being increased demand. So, it should be going to the other contending chipmakers. (AMD's data center earnings was 6% lower than the consensus estimate: Actual $3.9 billion, Expected: $4.15 billion)
- More on NVIDIA's revenue increase in later section
Continued AI spend and AI growth
- MSFT: "Microsoft Azure earnings grow but disappoint, company blames data center capacity constraints"“We have been short power and space. And so, as you see those investments land that we've made over the past three years, we get closer to that balance by the end of this year.” - Amy Hood (MSFT CFO)"Azure is the infrastructure layer for AI. We continue to expand our data center capacity in line with both near-term and long-term demand signals. We have more than doubled our overall data center capacity in the last three years. And we have added more capacity last year than any other year in our history."
- AMZN: Generative AI is a “really unusually large, maybe once-in-a-lifetime type of opportunity,” - Amazon CEO Andy Jassy to analyst during ER call.In same ER call: “Our deep collaboration with NVIDIA goes back more than 13 years, when we launched the world’s first GPU cloud instance on AWS. Today we offer the widest range of GPU solutions available anywhere in the cloud, supporting the world’s most technologically advanced accelerated workloads. It's why the new NVIDIA Blackwell GPU will run so well on AWS and the reason that NVIDIA chose AWS to co-develop Project Ceiba, combining NVIDIA’s next-generation Grace Blackwell Superchips with the AWS Nitro System's advanced virtualization and ultra-fast Elastic Fabric Adapter networking, for NVIDIA's own AI research and development. Through this joint effort between AWS and NVIDIA engineers, we're continuing to innovate together to make AWS the best place for anyone to run NVIDIA GPUs in the cloud.” Will skip 'The Jevons paradox' that Jassy shared during the ER call signaling increased AI work and adoption, since everyone must be bored to death by this theory. Also, trying to avoid using emotions or wishful thinking in this research thesis.
- NVDA: Jensen Huang, Stanford Conference, 2024 (pre-deepseek or anything remotely similar. How aware he was of events like Deepseek much before it happened, and how he envisions NVDA will keep growing even more as a result of such software improvements)
- Question: "According to your projection and calculation in 5 to 10 years how much more semiconductor manufacturing capacity is needed to support the growth of AI"
- Answer: "We're going to need more Fabs however remember that we're also improving the (software) algorithms and the processing of it um tremendously over time it's not as if the efficiency of computing will be what it is today and therefore the demand is this much in the meantime I'm improving Computing by a million times every 10 years while demand is going up by a trillion times and that has to offset each other does that make sense and then there's technology diffusion and so on so forth that's just a matter of time but it doesn't change the fact that one day all of the computers in the world will be changed 100% every single data center will be all of those general purpose Computing data centers 100% of the trillion dollars worth of infrastructure will be completely changed and then there'll be new infrastructure built on even on top of that"
- Question: You make completely State of-the-art chips. Is it possible though, that you'll face competition, that claims to be good enough, not as good as Nvidia, but good enough, and and much cheaper is that a threat. See highlighted/bolded section if you don't want to read the long answer
- Answer:TL;DR: our TCO (Total Cost of Operations to the AI customer) is so good that even when the competitor's chips are free, it's not cheap enough."We have more competition than anyone on the planet has competition. Not only do we have competition from competitors, we have competition from our customers, and I'm the only competitor to a customer fully knowing they're about to design a chip to replace ours, and I show them not only what my current chip is, I show them what my next chip is, and I'll show them what my chip after that is.And the reason for that is because, look, if you don't make an attempt at explaining why you're good at something, they'll never get a chance to buy your products. And so, we're completely open book in working with just about everybody in the industry.And the reason for that—our advantage—is several. What we're about is several things. Whereas you could build a chip to be good at one particular algorithm, remember, computing is more than even Transformers. There's this idea called Transformers; there's a whole bunch of species of Transformers, and there are new Transformers being invented as we speak. And the number of different types of software is really quite rich.And so, we can accelerate that. We can accelerate quantum physics. We can accelerate Schrödinger's equations. We can accelerate just about everything—fluids, particles, lots and lots of code.And so, what NVIDIA is good at is the general field of accelerated computing. One of them is generative AI. And so, for a data center that wants to have a lot of customers—some in financial services, some in manufacturing, and so on—in the world of computing, we're a great standard. We're in every single cloud, we're in every single computer company, and so our company's architecture has become a standard, if you will, after some 30-some years. And so, that's really our advantage.If a customer can do something specifically that's more cost-effective, quite frankly, I'm even surprised by that. And the reason for that is this: Remember, our chip is only part of it. Think of when you see computers these days—it's not a computer like a laptop. It's a computer, it's a data center, and you have to operate it. And so, people who buy and sell chips think about the price of chips. People who operate data centers think about the cost of operations (TCO)—our time to deployment, our performance, our utilization, our flexibility across all these different applications in total allows our operations cost—they call it total cost of operations, TCO—our TCO is so good that even when the competitor's chips are free, it's not cheap enough. And that is our goal—to add so much value that the alternative is not about cost. And so, of course, that takes a lot of hard work, and we have to keep innovating and things like that, and we don't take anything for granted. But we have a lot of competitors."
- Perplexity AI CEO: https://x.com/AravSrinivas/status/1889668709356023942 "Need to clarify this in no ambiguous terms: We still think NVIDIA is peerless and singular and the industry leader by far. And nothing changes in our relationship with them. I like Andrew and Cerebras team and they have really done good work in helping us achieve the 1200 tok/sec. It’s primarily the quality of our post training and the value of the data we have collected as a product that’s serving so many millions of users that got us to be better than the lightweight OpenAI and Anthropic models and on-par with the bigger ones. All this was done on NVIDIA GPUs. We benefitted a lot from Llama 3.3 that was trained with a ton of NVIDIA GPUs. We still serve majority of the models we serve on production on NVIDIA GPUs, eg custom versions of Llama and DeepSeek R1. Cerebras or Grok are not robust chips that can handle both dense models and sparse MoEs yet. NVIDIA chips can do any model class really efficiently. Cerebras is still bound by capacity constraints. I wish the Cerebras team and Andrew the best and glad we’re working with them and I think competition is good for the industry and will push NVIDIA to innovate faster on Blackwell and next generation of chips. But, I do not appreciate any clickbait PR that claims Perplexity is moving away from NVIDIA or NVIDIA moats are disappearing. We did not participate in that PR ourselves."
On DeepSeek:
The viral blog was authored by Jeffrey Emanuel (BA, Reed College, 2005), who does not hail from an AI background (academic or industrial), and does not have experience at a top-firm of their field (buy-side, or sell-side, or IB). So, the infamous blog which caused a flash crash of 600B in a day, may not be well founded and may feel sloppy to say the least. Prior AI investment experience does not necessarily evoke sound understanding to identify bloated claims from research papers. All it created was a stampede in a crowded theatre of people who wanted to be the first one out of the stampede, and are too scared to go back in, after it was found to be a hoax. The Chain-of-Thought concept explored in the paper is not new, and has been there for quite a few years. The paper took a departure from regular fine-tuning and relied on CoT processes which produces more grounded responses. The total cost of creating the model was grossly understated, and the results released in their paper was selectively shared, while it underperforms on few non-reported metrics.The AI research community needs just the very best GPUs to carry out their experiments, serving models via inference is just one aspect where people may even try to customize their specialized chips (see Jensen's response in earlier point), but the superpower of NVDA chips is how far ahead they are for the competition to even catch up, that AMD CEO famously stated that NVDA chips are so far ahead, that they chose to not compete with NVDA in creating the best chips - someone can link the news piece, if they have it readily available.
On Need for more compute:
While it may be noted that GenAI is mostly task-based, and not AGI, a challenge most researchers are figuring out rather than beating another state-of-the-art performance on yet another curated dataset.
The most understated aspect is that model developers need the most state-of-the-art devices so as to not be constrained in their experiments and prefer general purpose devices for experiments to create breakthroughs. There will always be firms playing catchups and creating optimization, while industry leaders will strive to stay ahead as opposed to becoming an IBM, who stayed stuck in the older era of computing.
NVDA GPU backlogs:
The GPU orders are so backlogged, that prompted the AMZN CEO Andy Jassy to mention his investment in proprietary chips, to let users have a choice of cheaper chips, despite them not being the very best (see earlier section on his ER comments for added context): "customers that they want better price performance on their AI workloads. As customers approach higher scale in their implementations, they realize quickly that AI can get costly. It's why we've invested in our own custom silicon in Trainium for training and Inferentia for inference. The second version of Trainium, Trainium2 is starting to ramp up in the next few weeks and will be very compelling for customers on price performance."
Also refer to other chipmakers like AVGO and Cerebras. Counter: See the earlier point made by Perplexity CEO.
While there will always be competitors as Jensen stated in section 3, they are well aware of their moat, and while there is always another social media app competing with Facebook (META), there is always a leader, and leads to better products and aggressive growth, rather then devolving into an IBM. As an example, one would strive to hire a top scientist from a top-school, as opposed to hire a person self-taught in their basement and using their local-resources. As always, exceptions exist.
Moat (outside of the earlier point on competition note by Jensen):
This segment is over and above NVDA CEO Jensen's (pre Deepseek) and Perplexity's comments (post Deepseek). Keeping this additional segment short, and invite readers to read up further on it, since it is more technical than the rest of the thesis.I have been using CUDA since 2013, which many current GenAI experts (recently-minted from deepseek fiasco) may not have had a chance to explore. Some of their other propietary technologies are CUDA, nCCL, cuDNN, TesorRT, NVLink, MIG, OptiX, DOCA (Data Center-on-a-Chip Architecture).AMD & Intel don’t have a CUDA alternative (ROCm is underdeveloped, and Intel's OneAPI is not competitive). NVIDIA’s ecosystem (CUDA + TensorRT + hardware) is an end-to-end solution that competitors lack.
Quantum:
They have been invested in Quantum technologies, and are hosting the upcoming"NVIDIA GTC 2025: Quantum Day to Illuminate the Future of Quantum Computing" https://blogs.nvidia.com/blog/gtc-2025-quantum-day/, March 17-21, 2025: "At the first-of-its-kind event, NVIDIA founder and CEO Jensen Huang will host industry leaders to discuss where quantum computing is headed".
While I can not comment on the ethicality of his recent quantum jab, but they have been very heavily invested in R&D in this space.While Quantum has two main approaches (annealing, and gate-based), one of which is currently usable (annealing) and the other (gate-based) is currently experimental. NVDA may have recognized this and I suspect his comment was aimed at the gate-based quantum approach.
Conclusion:
I need to go to sleep, so have to call it a day and wrap it up, although there are some additions on how the fear generated by a half-baked viral blog by Emanuel Jeffrey, that may have been necessary, but nevertheless was unfounded. It is understandable, since the AI revolution may be seen as having three facets: a) the model creators, b) model use-case creators, and c) AI users. From my understanding of people, the people in the model-creators segment are not necessarily very vocal, except in research efforts and publications, and not as visible in the wall-street or media. As always, exceptions exist.
Not Financial Advice (NFA) - do your own DD, and evaluate your risk tolerance.
My Position: Expecting PT 162/stock post-earnings on 2/26.
