NVIDIA, every mention
232 scenes, the whole family · ← back to NVIDIA
tap a year for its mentions
every year anyone Brad Gerstner 361Bill Gurley 110Jensen Huang 60Dylan Patel 42Sunny Madra 14Clark Tang 7Satya Nadella 5Michael Dell 3Sundeep Madra 1Gavin Baker 1
Verbatim, from the transcripts: the passages where NVIDIA comes up
Market Predictions, Rates & Inflation, DOGE, CES, AI Compute | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 23:20 Brad Gerstner Remember, I, I, I give this quote in, in two, in 2000, or in 20, 23, you had 26 analysts on Wall Street covering NVIDIA.
- ▶ 25:37 Brad Gerstner I mean, you saw NVIDIA release some models, which is the packaging of LLAMA models to make them easier for some customers of NVIDIA. 2 times in the scene
- ▶ 34:33 Bill Gurley Obviously NVIDIA is the biggest one, but who, who else is a recipient here? 11 times in the scene
- ▶ 1:13:23 Bill Gurley You've heard all of these hyperscalers talk about doing their own, um, processors of some sort, you know, both Tranium at Amazon and then the TPU at Google compete with NVIDIA, but they're buying NVIDIA's product. 4 times in the scene
AI Semiconductor Landscape feat. Dylan Patel | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 4:17 Bill Gurley Well, so I think if you're going to talk about AI and semiconductors, there's only one place to start, which is to talk about NVIDIA broadly. 5 times in the scene
- ▶ 6:59 Bill Gurley Why are they so dominant? 6 times in the scene
- ▶ 8:10 Brad Gerstner They're not infrastructure companies and, and NVIDIA. 6 times in the scene
- ▶ 9:33 Dylan Patel You know, today everyone's freaking out about, or not freaking out, but like everyone's like very excited about Nvidia's Blackwell system, right? 2 times in the scene
- ▶ 10:59 Bill Gurley Where would you say NVIDIA has been investing the most in incremental differentiation? 10 times in the scene
- ▶ 13:30 Dylan Patel So, so the main thing for NVIDIA is, you know, hey, this workload is this big, right? 7 times in the scene
- ▶ 14:16 Dylan Patel Like with Blackwell, not only is it way, way, way faster, anywhere from 10 to 15 times on really large models for inference, because they've optimized it for very large language models, they've also decided, hey, we're gonna cut our… 2 times in the scene
- ▶ 15:23 Dylan Patel So, so I think a lot of people in the investor community, you know, call CUDA, which is just like one layer for all of NVIDIA software. 3 times in the scene
- ▶ 18:20 Brad Gerstner On the data center replacement and say, there's no way people are going to, you know, rebuild a CPU data center with a bunch of NVIDIA GPUs.
- ▶ 18:42 Dylan Patel NVIDIA has been pushing non AI workloads for accelerators for a very long time.
- ▶ 22:30 Bill Gurley Well, before, before we dive into the alternatives to NVIDIA, I thought we would hit on this, uh,
- ▶ 29:46 Brad Gerstner There is a lot of tension in the world over NVIDIA as we look forward at 20, 25, and this question of pre-training, right?
- ▶ 38:12 Bill Gurley You talked a lot at the beginning about NVIDIA's differentiation around these large coherent clusters that are used in pre-training.
- ▶ 41:44 Brad Gerstner And so I said to, you know, in this dinner conversation, I said, well, NVIDIA's, you know, PE today is 30. 4 times in the scene
- ▶ 49:15 Dylan Patel I can group together over 256 users' requests on one server for Llama-Four of NVIDIA server, right?
- ▶ 50:36 Bill Gurley If you're just selling NVIDIA hardware and they remain the architecture, and this is our scaling path, you're going to consume way more of it.
- ▶ 54:34 Dylan Patel I can run Lama-seventy-b on one NVIDIA GPU, and soon enough there will be on one set of Amazon's Neutronium, right? 2 times in the scene
- ▶ 1:02:27 Dylan Patel And it, it objectively is when you look at the cost of goods sold of NVIDIA, um, their highest cost of goods sold is not TSMC, which is a, uh, 5 times in the scene
- ▶ 1:04:00 Dylan Patel It's a commodity in that sense, but because reasoning requires so much more memory, and the cost of goods sold of an H 100 to Blackwell, the percentage of cost to HBM has grown faster than the percentage of cost to leading edge silicon,…
- ▶ 1:04:00 Dylan Patel It's a commodity in that sense, but because reasoning requires so much more memory, and the cost of goods sold of an H 100 to Blackwell, the percentage of cost to HBM has grown faster than the percentage of cost to leading edge silicon,…
- ▶ 1:06:30 Dylan Patel Like, hey, we maybe can't design as well as NVIDIA, but we can put more memory on the package, right? 6 times in the scene
- ▶ 1:11:12 Dylan Patel Nvidia maybe has not focused on as much, right? 4 times in the scene
- ▶ 1:13:10 Dylan Patel But like, they'll go down because they, you know, and, and, and then you're like, oh, well, like what's the market clearing price for, uh, if I wanted an H 100 for two years or a year.
- ▶ 1:14:45 Dylan Patel And I think there's, there's a whole long story around why Apple hates NVIDIA. 4 times in the scene
- ▶ 1:17:41 Brad Gerstner For example, over the last 30 days, right, we've seen Broadcom, you know, explode higher, NVIDIA trade off a lot. 5 times in the scene
- ▶ 1:20:02 Brad Gerstner So make a, make, make, make a call for us as you think about the semis market today, you know, you've got ARM, Broadcom, you've got NVIDIA, you've got AMD, etc. 6 times in the scene
- ▶ 1:22:23 Dylan Patel The problem is, nowadays, if you look at rental prices for H-one hundreds, they're tanking, right?
- ▶ 1:22:42 Dylan Patel Three-month, six-month deals at way better pricing than even the four-month or the four-year, three-year deals that you used to have for Hopper, right?
- ▶ 1:23:39 Brad Gerstner Is there a scenario where in 2026, where you see industry volumes actually down versus 20 25 or Nvidia volumes actually down, um, meaningfully from 2025? 5 times in the scene
Satya Nadella | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 43:05 Satya Nadella And again, the fact that we've had two years of runway on it, uh, I think I like that business a lot more, and that's one of the reasons why the adverse selection problems here would have been lots of tech startups all looking for their… 2 times in the scene
BG2 Thanksgiving Roundtable: DOGE, EV Tax Credit, Tariffs, Conspiracy Theories, Invest America · Bg2 Pod
- ▶ 30:08 Brad Gerstner Um, Bill, this just made me think about, um, I was watching CNBC this week, and I hear David Faber say, um, you know, they have a chart of NVIDIA up. 2 times in the scene
- ▶ 39:34 Brad Gerstner They would seed it with a very small amount of money that would be a rounding error to the federal government, but really be a four or one K from birth where companies have already raised their hands from Uber to Dell, Oracle to Nvidia,…
Ep20. AI Scaling Laws, DOGE, FSD 13, Trump Markets | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 4:25 Bill Gurley There's a question about how big the context window will be, and that came up on the NVIDIA call today. 5 times in the scene
- ▶ 10:07 Brad Gerstner They called hopper demand exceptional. 2 times in the scene
- ▶ 10:19 Brad Gerstner Um, and interesting, they call Blackwell demand staggering.
- ▶ 12:32 Bill Gurley You know, train a model on a big NVIDIA cluster, and you can run it on, you know, a different system, and there are certainly performance tests that are in the public that show the Groks and Cerebris and other TPUs, including the ones at… 4 times in the scene
- ▶ 21:05 Brad Gerstner To the consumption on the Nvidia side, certainly it would be higher. 2 times in the scene
Ep19. State of Venture, AI Scaling, Elections | BG2 w/ Bill Gurley, Brad Gerstner, & Jamin Ball · Bg2 Pod
- ▶ 38:41 Brad Gerstner There was a lot of question whether or not NVIDIA would grow in 25, 25.
- ▶ 1:00:06 Brad Gerstner The AMD could capture maybe 10% of the data center market, and the fact of the matter is when you, 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…
Ep18. Jensen Recap - Competitive Moat, X.AI, Smart Assistant | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 1:53 Sunny Madra Cause you kind of get the, you get the tidbits and the ones that really struck with me was when he said Nvidia is on a GPU company, they're an accelerated compute company. 6 times in the scene
- ▶ 4:33 Brad Gerstner We spent about a third of the pod on NVIDIA's competitive moat, really trying to break it down, really trying to understand this idea of systems level advantages, the combinatorial advantages, um, that he has in the business. 12 times in the scene
- ▶ 14:14 Brad Gerstner Clearly, NVIDIA's got ARM embedded now in a lot of their, uh, you know, in a lot of their Grace Blackwell, etc.
- ▶ 14:40 Bill Gurley It's a systems company. 6 times in the scene
- ▶ 17:12 Brad Gerstner Yeah, and, you know, again, Bill, you touched on something that I think is, is, is really important here, and this is this question of whether their competitive mode is also as powerful in training as it is in inference, right? 8 times in the scene
- ▶ 26:59 Sunny Madra Then Nvidia is your number one choice. 8 times in the scene
- ▶ 27:50 Brad Gerstner And Larry Ellison apparently had, and he's like, you know, just, just because that dinner occurred and they ended up with a 100,000 H 100 don't necessarily, you know, connect the dots.
- ▶ 31:55 Brad Gerstner I said, you know, Elon's gonna, you know, rumor is that he's gonna get another 100,000, you know, uh, H 200, add them to this cluster.
- ▶ 48:08 Bill Gurley First of all, I think NVIDIA is a very special company, and it's a company that's, that's, even, even if it's a systems company, it's an IP company, and the demand is growing at such a rate that they don't need more designers or more… 2 times in the scene
Ep17. Welcome Jensen Huang | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 0:58 Brad Gerstner We're at the NVIDIA headquarters just down the street from Altimeter.
- ▶ 3:54 Jensen Huang Basically, everything that, that I describe about how NVIDIA does things, uh, led to,
- ▶ 6:04 Clark Tang I think to that point, you know, one of the most misunderstood things about NVIDIA is how deep the true NVIDIA moat is, right? 5 times in the scene
- ▶ 13:30 Jensen Huang Um, our company is a little different in the sense that, and we recognize this, that, that in fact, parallel processing doesn't require every transistor to be excellent. 3 times in the scene
- ▶ 16:50 Jensen Huang You could, you could still go and optimize it for other architectures, but at the very minimum, since it's already been architect, you know, built on NVIDIA, it will run on NVIDIA. 4 times in the scene
- ▶ 17:41 Jensen Huang Now, we also put a lot of energy into continuously reinventing new algorithms so that when the time comes, the Hopper architecture is two, three, four times better than when they bought it, so that, that you, that infrastructure continues… 2 times in the scene
- ▶ 18:08 Jensen Huang Ampere is better for it.
- ▶ 20:16 Jensen Huang And so that, that architecture is really hard to do, and we invented, uh, Grace Blackwell and B-Link for that.
- ▶ 20:47 Brad Gerstner And I think most people, um, again, view these as a problem for NVIDIA, but in the very next breath, he said, NVIDIA is a huge and important partner to us and will remain a huge and important partner for us. 4 times in the scene
- ▶ 27:29 Jensen Huang NVIDIA is a market maker, not share taker.
- ▶ 30:17 Brad Gerstner The forecast for NVIDIA at that dinner in January of 23 was that you would do twenty-six billion of revenue, um, for the year twenty-twenty-three. 5 times in the scene
- ▶ 38:05 Jensen Huang And so, you know, I, I think, I think the, the good fortune that our company is, has, is that since the very beginning of our company, we had to invent the market for us to go swim in. 2 times in the scene
- ▶ 45:03 Jensen Huang In fact, this was the great observation of our company 32 years ago. 2 times in the scene
- ▶ 46:57 Brad Gerstner They talked to you out of a 100,000 H-one hundreds.
- ▶ 47:22 Brad Gerstner And there's talk out there that they want another 100,000 H 200, right?
- ▶ 47:52 Jensen Huang From the moment of concept to, um, a data, data center that's ready for NVIDIA to have our gear, gear there, to the moment that we, um, uh, powered it on, had it all hooked up, and it did its first training. 5 times in the scene
- ▶ 54:05 Clark Tang So, you know, the idea that, you know, because NVIDIA is just scaling up or scaling out, it's to meet the future.
- ▶ 58:13 Brad Gerstner Right, of course, they're here today, which comes back to this, you know, over 40% of your revenue today is inference, but inference is about ready 3 times in the scene
- ▶ 59:42 Jensen Huang Hopper wouldn't be possible.
- ▶ 59:43 Jensen Huang Blackwell wouldn't be possible.
- ▶ 1:00:17 Brad Gerstner Um, you know, Nvidia is in a league of its own, really, um, you know, at about four million of revenue per employee, about two million of profits or free cash flow per employee. 5 times in the scene
- ▶ 1:05:47 Jensen Huang And the chip complexities and the computer, computer complexities we're building are going up exponentially while the company's employee base is not. 2 times in the scene
Ep16. Nuclear Update, AI Fast & Furious, State of VC | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 0:58 Brad Gerstner That there were four unbuilt nuclear reactors that are all already plotted on the site, and we have joked that NVIDIA and Microsoft and Oracle could come sponsor these reactors and that they could have a new type of public-private…
- ▶ 16:04 Brad Gerstner Now, if you look at these GB 200, the Nvidia selling the cost or the improvement in inference, I mean, I, you know, Nvidia says it's a 50 X improvement in inference. 2 times in the scene
- ▶ 31:07 Brad Gerstner And there's a real question on the table here that you've articulated well, which is, you know, is there going to be a layer, a tax here, right, that Nvidia and the cost of inference and the cost of training imposes in perpetuity on these…
- ▶ 1:03:54 Brad Gerstner I think, for example, we think the entire, uh, NVIDIA and AI infrastructure supply chain is going to continue to be under supplied. 2 times in the scene
- ▶ 1:05:14 Brad Gerstner Certainly we could see Blackwell doesn't get its production levels up.
Ep15: Inside Diablo Canyon Nuclear Power Plant | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 0:10 Brad Gerstner I could imagine if you had four more reactors sitting here, if you could land them at the right price, you could have one reactor for Meta, one for Amazon, one for Microsoft, one for NVIDIA.
- ▶ 59:08 Bill Gurley They're not acting like, say, Google and Amazon are buying Nvidia clusters where, oh, we're going to put them up and they're, they're more conservative. 3 times in the scene
Ep14. Public Market Volatility, AI Air Pocket, $GOOG Ruling | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 3:20 Brad Gerstner So, you know, I put what's happened this week and what's happened over the course of the last month in kind of run of the mill, healthy consolidation, um, of these big gains that we had in these six quarters.
- ▶ 6:25 Brad Gerstner We were at all-time highs, um, and the moves had been fairly parabolic in a lot of the names that you and I talked about, and we had talked on this pod about concerns over an AI air pocket, and people were pointing out that Nvidia was up…
- ▶ 30:51 Bill Gurley I mean, we've seen, um, most of the stocks that you would have called AI stocks, um, Nvidia being the most obvious, but, but I think 6 times in the scene
Ep13. Silicon Valley’s Political 180, META AI, COVID Postmortem | BG2 w/ Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 43:16 Brad Gerstner You're, uh, you know, Fireworks, another benchmark investment, I think, uh, you know, is inference optimized on top of, of, of NVIDIA, right?
- ▶ 1:00:15 Brad Gerstner They all started issuing dividends, and they bought Nvidia chips, right?
Ep12. Tech Stocks vs the Rest; AI VC Bubble, Calcium CT Scan | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 12:16 Brad Gerstner So if you look at it for Microsoft, NVIDIA, Amazon, Google, Meta, earnings are up 38% over the last 12 months.
- ▶ 16:21 Brad Gerstner Uh, you know, so you get the point here, and in case I, I skipped NVIDIA, 66 times was the peak.
- ▶ 23:19 Brad Gerstner I remember when we went over the two trillion dollar build out for NVIDIA over the course of the next four or five years, and you asked the question some six months ago, what the hell do you have to have in terms of revenue on the other… 3 times in the scene
Ep11. Coatue Conference Recap, Tesla & ISS Controversy | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 6:04 Brad Gerstner Remember, NVIDIA had gone from 120 dollars a share, maybe at that time, to 250 dollars a share, and people were already saying we're in a bubble. 4 times in the scene
- ▶ 17:11 Bill Gurley He said NVIDIA is not expensive on an earnings basis.
Ep9. GPT-4o, Astra, Multi Modal, China Tariffs, Tech Earnings | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 26:26 Brad Gerstner We've talked on this pod and, and others about how Inflection, who was building, right, remember Pi, which stood for personal intelligence, um, was trying to build a consumer app, built one of the largest H 100 clusters in, in the world,…
- ▶ 58:07 Brad Gerstner You know, owning some of the companies that you think are real, you're really confident are going to continue to compound, um, and benefit from AI, the Microsofts of the world, you know, the Nvidia's of the world seems to me like a…
Ep8. AI Models, Data Scaling, Enterprise & Personal AI | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 52:13 Sundeep Madra But it, it, it intersects with that really big purchase of, um, you know, H-one hundreds that they talked about as well.
Ep7. The Great IPO Debate, Tesla Robotaxi v Uber, Tech Check | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
- ▶ 1:11:11 Brad Gerstner Nvidia's beat their numbers.
Ep6. AI Demand / Supply - Models, Agents, the $2T Compute Build Out, Need for More Nuclear & More · Bg2 Pod
- ▶ 11:11 Brad Gerstner If you are trying to build your own LLM today as inflection was, and you want to get your hands on 40,000 H 100, which I think they did, that's, that's like,
- ▶ 11:52 Brad Gerstner It was just not even a year ago that, you know, perhaps those of us who owned Nvidia went from 125 to two 50 or 300 and everybody yelled and said, you got to sell your Nvidia. 2 times in the scene
- ▶ 16:33 Brad Gerstner And clearly, and clearly NVIDIA's taking, they're, they're going to be all these different models, vision models, Omniverse, models of the real world, et cetera, that tackle the other problems that you're talking about.
- ▶ 16:33 Brad Gerstner And clearly, and clearly NVIDIA's taking, they're, they're going to be all these different models, vision models, Omniverse, models of the real world, et cetera, that tackle the other problems that you're talking about.
- ▶ 37:16 Brad Gerstner Is on the front lines of the new Cold War about national economic security, national, uh, defense and offensive security, and if you think the limiting factor to pushing forward in AI is power, then all the sudden, right, you're sitting…