The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 142 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 4 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Gerstner: NVIDIA trades around 30 P/E compared to Cisco's 2000 peak
“Cisco, you know, 2000 and we'll show it on the pod, but you know, they peaked at like a 120 PE. Right. And yeah, you know, if you look at the fall off that occurred in revenue and in EBITDA, you know, and then it had 70% compression in the priced earnings mult…”
Gerstner: 2025 AI model gains will increasingly come from post-training and inference
“So I think at least As we look at 2025 for NVIDIA, for the supply chain, and for the model companies themselves, we think we're going to continue to see improvements in the models. It won't all come from pre-training. We think there's an increasing amount comi…”
Gerstner: Huang believes NVIDIA can triple revenue with 25% headcount growth
“He thinks they can three X, you know, the top line of the business while only adding 25% more humans because they can have a 100,000 autonomous agents doing things like building the software, doing the security, and that he becomes really a prompt agent, not o…”
Madra: NVIDIA's internal AI productivity gains for chip design exceed 100%
“I really, you know, been thinking a lot about Jensen's point in the pod about, you know, how much AI they're using internally for design, design verification for all those pieces. Right. And I think, you know, it's not 30%. I actually think sort of that's an u…”
Huang: NVIDIA's moat in inference will be greater than in training
“And I'm sure I said it would be greater.”
Huang: NVIDIA could not design Hopper, Blackwell, or Rubin chips without AI agents
“Our cyber security system today can't run without Without our own agents. We have agents helping to design chips. Hopper wouldn't be possible. Blackwell wouldn't be possible. Ruben don't even think about it.”
Huang: NVIDIA delivers 2x-3x performance and cost improvements every single year
“We're trying to build a brand new one every single year, and every single year we deliver two or three times more performance. As a result, every single year, we reduce the cost by two or three times. Every single year, we improve the energy efficiency by two …”
Huang: $1T in legacy data center infrastructure must be modernized in 4-5 years
“And we just know that we have a trillion dollars with the data centers that we have to modernize. And so right now as we speak, if we were to have a trajectory over the next four or five years to modernize that old stuff, that's not unreasonable.”
Huang: NVIDIA never tracks or discusses market share internally
“NVIDIA is a market maker, not share taker. If you look at our company slides, we don't show, not one day does this company talk about market share, not inside.”
Huang: Almost every software application will be highly machine-learned in the future
“Almost every single application, Word, Excel, PowerPoint, Photoshop, Premiere, you know, AutoCAD. You give me your favorite application. That was all hand, hand engineered. I promise you it will be highly machine learned in the future.”
Huang: Scaling large AI training jobs would have failed without Megatron
“Without Megatron. That we developed with some seven years ago now. The scaling of these large training jobs wouldn't have happened.”
Huang: The new market for AI factories will probably reach a few trillion dollars
“So there's a whole layer of computing fabric, a whole layer of what I call AI factories that the world has to make that doesn't exist today at all. So the question is how big is that? Unknowable at the moment, probably a few trillion dollars.”
Huang: NVIDIA's Nemotron is the world's best reward model
“Our model, Nemo Tron, is, is the best model in the world for reward systems. And so, it is the best critique.”
Jensen Huang: GPU makers have no clue how to build accelerated computing
“Somebody who's really, really good at building GPUs have no clue how to be an accelerated computing company.”
Gerstner: The entire Nvidia and AI infrastructure supply chain remains undersupplied
“We think the entire NVIDIA and AI infrastructure supply chain is going to continue to be under supplied.”
Gerstner: Next-Gen AI Frontier Models Will Arrive in Q2 2025
“All the rest of the frontier models are kind of in this holding pattern because they're waiting for the B 100 to get launched to this Q three, Q four out of Nvidia. Which probably means the next iteration of the frontier models will come out in Q two of next y…”
Gerstner: GCC sovereign AI compute orders will rival hyperscalers
“And I think what people still don't appreciate is they're probably dozens of sovereigns who are trying to get into the NVIDIA order book, and that they view it as one of their top three national priorities to build out AI capabilities. And I think the size you…”
Gerstner: Fears of an Impending Nvidia Chip Glut Are Inaccurate
“So everybody's just the wall of worry around NVIDIA is when does the glut come? We've pulled forward all this training demand. It's dark fiber like in the year 2000. I think those things are not accurate.”
Gerstner: Nvidia's $700 consensus price assumes market share drops to 26%
“The consensus forecast that has the stock at 700 dollars a share assumes, if you believe this TAM to be accurate, assumes that their share will go from 55% today to 26% in twenty-twenty-eight.”
Gerstner: Over 40% of NVIDIA's revenue comes from AI inference workloads
“Over 40% of your revenue today is inference.”
Huang: 100% of NVIDIA engineers and chip designers use AI agents
“Every single software engineer, every single chip designer in our company already has AIs working with them. A hundred percent coverage.”
Huang: Hyperscalers have consistently underforecasted GPU demand for years
“Now what's, what's going on, of course, is that every one of their forecasts that they provide us, it turns out to have been wrong. Because they under forecasted, and so now we're always in a scramble mode, and so we've been in the scramble mode now for, you k…”
Huang: Startups should capture entire nascent markets rather than entering mature ones
“You're supposed to create a startup before the market grows. You're not supposed to come up as a startup when the market's a trillion dollars large. You know, this fallacy, and all VCs know this fallacy that a large market, if you could just take a few percent…”
Gerstner: Nvidia and Jensen Huang are American national heroes
“I think NVIDIA and Jensen are national heroes for the work that they've done and the leadership they've given us in global AI. American success stories.”
Haas: The Base CPU for Stargate AI Data Centers Will Be ARM
“And every data center whether it's running general purpose compute, whether it's running inference or running training, needs a base CPU to run everything, end quote. And that's our role. And whether that is going to be what we are part of today, which is GB 2…”
Gerstner: Nvidia inference revenue is upwards of 50% of total revenue
“Jensen made, and maybe we'll insert the clip here on the pod, where he talks about his inference revenue is already 50% of his revenue, so already upwards of 50% of his revenue”
Gerstner: Nvidia does not intend to compete in frontier AI models
“You know, Nvidia launching a few models. I don't think they intend to be in the, you know, frontier model game where they're competing heads up with these other folks.”
Gerstner: Jensen projected $1T in new AI and CPU replacement workloads
“And for the first time, he said, not only are we going to have a trillion dollars of new AI workloads over the course of the next four years he said, but we're also going to have a trillion dollars of CPU replacement, of data center replacement workloads over …”
Patel: Apple rents Google's TPU silicon, but GCP AI rentals remain GPUs
“While they do have some customers for their internal silicon externally, such as Apple the vast majority of their external rental business for AI in terms of cloud business is still GPUs.”
Patel: Amazon Trainium 2 costs ~$5,000 per chip versus $30,000+ for NVIDIA
“You're not paying, you know, north of 30,000, you know, 40,000 dollars per chip for the server, you're paying, you know, significantly less, right? 5000 dollars per chip, right?”
Patel: Google TPU clusters scale up to 8,000 chips today
“Nvidia's talking about GB 200, NVL 72, TPUs go to 8000 today, right?”
Patel: NVIDIA holds 98% of AI workloads outside Google, 70% overall
“So I would say if you ignored Google, it would be over 98%. But then when you bring Google into the mix, it's actually more like 70 because Google is really that large a percentage of AI workloads especially production workloads.”
Patel: SK Hynix memory is NVIDIA's largest COGS item, surpassing TSMC
“When you look at the cost of goods sold of NVIDIA their highest cost of goods sold is not TSMC, which is a Thing that people don't realize. It's actually HBM memory primarily SK Hynix.”
Patel: AWS Trainium2 offers highest HBM capacity and bandwidth per dollar
“Their whole thing at reInvent, if you really talk to them when they announced Trinium II and our whole post about it and our analysis of it is, like, supply chain-wise, this is, looks, you know, you squint your eyes, this looks like an Amazon Basics TPU, right…”
Patel: Nvidia Blackwell costs over twice as much to manufacture as Hopper
“The cost to make Blackwell is north of two X that of the cost to make Hopper, right?”
Patel: Hyperscalers will significantly boost capex in 2025, lifting chip suppliers
“The plans for hyperscalers are pretty firm on, they're gonna spend a crap load more next year, right? And therefore, the ecosystem of networking players, of ASIC vendors, of systems vendors is gonna do well, whether it be NVIDIA or Marvell or Broadcom or AMD o…”
Patel: A quarter of Nvidia shipments could provide Llama-7B to humanity
“If you take one quarter of NVIDIA shipments and you said all of them are going to inference Lama-Seven-B, you can give every single person on earth a hundred tokens per minute, right? Or sorry, a hundred tokens per second. You give every single person on earth…”
Patel: Cisco was fueled by speculative debt whereas NVIDIA is cash-backed
“Cisco's revenue, a lot of it was funded through private-slash-credit investments into building out telecom infrastructure, right? When we look at NVIDIA's revenue sources, very little of it is private-slash-credit, right? And in some cases, yes, it's private-s…”
Gerstner: AMD guidance implies 3% GPU data center share vs Nvidia's 90%
“They gave some forward guidance tonight that suggests they're going to stay at about three percent of the GPU kind of data center market relative to NVIDIA at about 90%.”
Gurley: NVIDIA customer concentration may increase as AI training costs escalate
“And there may be, if that trajectory remains true, you could have an evolution where customer concentration increases for NVIDIA over time rather than going the other way, depending on how, you know, if Sam's right that they're gonna spend a hundred, Billion o…”
Gerstner: 40% of NVIDIA's revenue already comes from inference
“40% of their revenues are already inference.”
Madra: Fewer developers will touch CUDA long-term, weakening NVIDIA's software moat
“I think there's going to be fewer people touching that. And I do think that's a point where they're the moat is not as strong as a longer term, as you say, and think about like, you know, the way the analogy that I would go with is like, think about the number…”
Huang: NVIDIA aims for 50,000 employees and 100M AI assistants
“I'm hoping that NVIDIA someday will be a 50,000 employee company with a hundred million, you know, AI assistants. And they're in every single group.”
Huang: The entire data center is now the fundamental unit of computing
“The data center is now the unit of computing. To me, when I think about a computer, I'm not thinking about that chip. I'm thinking about this thing. That's my mental model, and all the software, and all the orchestration, all the machinery that's inside.”
Huang: NVIDIA Lowered Marginal Compute Costs by 100,000x Over 10 Years
“You know, a lot of this is happening because we drove the marginal cost of computing down by a 100,000 X over the course of 10 years.”
Huang: Parallel processing favors transistor volume over individual transistor speed
“Serial processing requires every transistor to be excellent. Parallel processing requires Lots and lots of transistors to be more cost effective. I'd rather have 10 times more transistors, 20% slower. Than 10 times less transistors, 20% faster.”
Huang: cuDNN revolutionized deep learning by powering frameworks like PyTorch
“So we revolutionized deep learning because of our domain specific library called QDNN. Without QDNN, nobody talks about QDNN because it's one layer underneath PyTorch and, you know, and TensorFlow and back in the old days, CAFE and Theano and now Triton, and t…”