[@DwarkeshPatel] Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat

Link: https://youtu.be/Hrbq66XqtCo

Duration: 103 min

Short Summary

Nvidia CEO Jensen Huang discusses the company's five-layer AI ecosystem strategy, $100B+ supply chain commitments, and 70%+ margins from its CUDA moat. He argues custom chips (TPUs, ASICs) offer minimal cost savings over Nvidia's 70% margins, that export controls may have accelerated China's chip industry, and that DeepSeek first running on Huawei hardware would be "horrible" for US tech leadership. Huang outlines $30B invested in OpenAI, $10B in Anthropic, and explains his "do as much as needed" philosophy of ecosystem-building.

Key Quotes

1. "We've seen the valuations of a bunch of software companies crash because people are expecting AI to commoditize software." (00:00:00)
2. "The input is electrons, the output is tokens. In the middle is Nvidia. Our job is to do as much as necessary and as little as possible to enable that transformation to be done at incredible capabilities." (00:00:01)
3. "AI is a five-layer cake, if you will. We have ecosystems across the entire five layers. We try to do as little as possible, but the part that we have to do, as it turns out, is insanely hard. I don't think that gets commoditized." (00:00:26)
4. "Some of the doomers were telling people, 'Whatever you do, don't be a radiologist.' You might hear some of those videos still on the web saying radiology is going to be the first career to go and the world is not going to need any more radiologists. Guess what we're short of? Radiologists." (00:00:13)
5. "Nvidia is fundamentally making software that other people are manufacturing, and if software gets commoditized, does Nvidia get commoditized? In the end, something has to transform electrons to tokens." (00:00:02)

Detailed Summary

ThinkingBlock(signature='679afbc231c65e593490b0d6bc0332e5a5c57495eb5a631f52f56e5cd5549f46', thinking="The user wants me to create a detailed markdown summary for a long-form transcript about an interview with Nvidia CEO Jensen Huang. Let me analyze the requirements:\n\n1. Markdown only\n2. At least 10 section headings using '##'\n3. Each section should start with 1-2 narrative sentences\n4. Include at least 30 bullets total across the document\n5. Bullets must focus on substantive claims, disagreements, mechanisms, examples, chronology, names, numbers, and outcomes\n6. Avoid sponsor copy, banter, repeated praise, meta commentary, and filler\n7. Prefer concrete details over generic abstraction\n\nI need to use the provided structured summary and section digests without inventing facts, transitions, quotes, motives, or context.\n\nLet me structure this with at least 10 sections, using narrative opening sentences and bullet points with concrete details.\n\nSections I can identify from the content:\n1. Nvidia's Ecosystem & Business Model\n2. AI Cloud Investments & Strategy\n3. Supply Chain & CoWoS Resolution\n4. Custom Chips vs. GPU Competition\n5. Blackwell Architecture & Efficiency\n6. AI Progress & Algorithm Advances\n7. China, Export Controls & Geopolitics\n8. DeepSeek & Huawei Competition\n9. US Competitive Position\n10. Radiology AI Predictions & Industry Outlook\n11. Future Roadmap\n\nThat's 11 sections which exceeds the minimum of 10.\n\nNow I need to create narrative openers (1-2 sentences) and bullets for each section. Let me count bullets to ensure I have at least 30 total across the document. I'll aim for 3-4 bullets per section minimum.\n\nLet me write this out:", type='thinking')

Nvidia's Five-Layer AI Ecosystem

Nvidia operates as a vertically integrated AI company spanning five distinct layers—chips, CUDA software, foundation models, applications, and services—transforming raw electrons into valuable tokens through an ecosystem that competitors struggle to replicate. The company's GPU business maintains approximately 70% gross margins, supported by decades of investment in proprietary technology that creates a defensible moat around its core offerings.

• Jensen Huang describes the ecosystem as transforming "electrons into tokens" through chips, CUDA, models, applications, and services
• Nvidia's CUDA moat is supported by NVLink, CUDA-X libraries, and cuLitho for computational lithography
• Purchase commitments with foundries and suppliers approach $100 billion, with SemiAnalysis reporting potential $250 billion in commitments
• Nvidia is TSMC's largest customer on N3 and N2 nodes, with AI representing 60% of N3 capacity this year and 86% next year

AI Cloud Investment Philosophy

Nvidia has deployed significant capital into AI infrastructure companies, treating investments as ecosystem-building rather than financial speculation. The company's core philosophy is "do as much as needed, as little as possible"—focusing investments on creating infrastructure that wouldn't exist without Nvidia's involvement.

• Nvidia invested $30 billion in OpenAI, $10 billion in Anthropic, and backstopped CoreWeave up to $6.3 billion
• Direct investment in CoreWeave totals $2 billion as a neocloud provider
• Huang explicitly rejected picking winners among foundation model companies, stating "picking winners would be arrogant"
• The company created neoclouds like CoreWeave, Nscale, and Nebius that wouldn't exist without Nvidia's capital and commitment

CoWoS Packaging Resolution and Supply Chain Strategy

Advanced chip packaging became a critical bottleneck for AI compute availability, with CoWoS capacity limiting GPU shipments for approximately two years. Nvidia addressed the constraint through aggressive capital deployment, fundamentally changing the scaling dynamics of advanced packaging.

• CoWoS packaging was a two-year bottleneck that Nvidia resolved by "swarming it with investment"
• TSMC now scales packaging at the same rate as logic, having doubled multiple times to meet demand
• Huang argues no bottleneck (chip capacity, CoWoS, EUV machines) lasts longer than 2-3 years once demand signal is established
• The partnership with TSMC spans approximately 30 years without a formal legal contract, based entirely on mutual trust
• ASML can scale EUV production relatively quickly once demand signals are clear

Custom Silicon Economics and Competition

Custom AI chips have emerged as a potential alternative to Nvidia's GPUs, but Huang argues the economics don't justify the engineering investment required. The margin differential between custom ASICs and Nvidia's offerings is narrower than commonly perceived, while the complexity of building competitive silicon continues to increase.

• ASIC margins (~65%) are only marginally lower than Nvidia's (~70%), making custom chips economically marginal
• Anthropic is cited as the "sole driver" of TPU and Trainium growth—"without them there would be zero growth"
• Many custom ASIC projects have been canceled, validating Nvidia's position that "building an ASIC better than Nvidia is not easy and not sensible"
• 60% of Nvidia's revenue comes from hyperscalers: Google, Amazon, Azure, and OCI
• Google runs TPUs as the majority of their compute while OpenAI uses custom Triton kernels instead of standard CUDA libraries

Blackwell Architecture Efficiency Gains

The transition from Hopper to Blackwell represents a generational leap in AI compute efficiency that challenges conventional assumptions about hardware scaling. Despite only modest transistor improvements, architectural innovation delivered transformative performance gains that benefit both training and inference workloads.

• Hopper to Blackwell delivers 30x-50x energy efficiency improvement (Huang initially announced 35x but corrected to 50x)
• The generational improvement came three years apart with only 75% transistor improvement
• Blackwell achieved 50x overall performance through architecture improvements alone, demonstrating that architecture matters more than lithography scaling
• Nvidia's CUDA ecosystem supports every framework including Triton, vLLM, and SGLang
• Nvidia serves as the primary backend contributor to Triton's open-source kernel library

Algorithmic Progress and Efficiency Multipliers

Hardware advances alone cannot explain AI's rapid capabilities improvement—algorithmic innovations contribute substantially to overall performance gains. Jensen Huang emphasizes that "great computer science" through model architectures, attention mechanisms, and training methodologies can deliver 10x improvements that complement hardware scaling.

• Moore's Law advances approximately 25% per year, but algorithmic improvements can yield 10x performance gains
• Jensen argues most advances in AI came from algorithm advances, not just raw hardware
• Mixture of Experts (MoEs), attention mechanisms, and other innovations reduce compute requirements dramatically
• Post-training and reinforcement learning frameworks like verl and NeMo RL are described as "exploding" as important areas for AI development

Export Controls and China's Chip Industry

US export restrictions on advanced semiconductor technology have fundamentally altered the trajectory of China's AI chip development, potentially accelerating domestic capability building rather than slowing it. Huang argues that restricting chip sales may be counterproductive, noting that China has responded by developing internal ecosystems while facing severe compute limitations.

• China manufactures 60% of the world's mainstream chips
• China has 50% of AI researchers (half the world's AI developers) and represents approximately 40% of the global technology industry
• Huang claims export controls "enabled and accelerated China's chip industry" by forcing their ecosystem to focus on internal architectures
• China has "one tenth the amount of flops the US has" at 7nm without EUVs due to chip-making export controls
• China has "enormous energy and datacenters sitting completely empty" that cannot be utilized due to compute restrictions
• Jensen estimates the threshold China needs for advanced AI capabilities has already been reached, making export controls insufficient without dialogue and research engagement

DeepSeek, Huawei, and Non-American AI Stacks

Chinese AI technology has progressed significantly, with Huawei reporting its "largest single year in the history of their company" through millions of chip shipments. Jensen expresses concern that DeepSeek models running first on Huawei hardware would represent a "horrible outcome" for US technological leadership, as models optimized for non-American architecture would disadvantage the US tech stack.

• Huawei just had the "largest single year in the history of their company," shipping millions of chips with logic and HBM2 memory
• SMIC has "plenty of logic capacity and plenty of HBM2" to meet China's AI needs
• The H200 outperforms Huawei 910C by roughly 2-3x, with Huawei compensating by using twice as many chips
• DeepSeek "first releasing on Huawei hardware would be a horrible outcome" for the US
• China's limited compute has forced researchers to develop "extremely smart algorithms"—DeepSeek represents "not an inconsequential advance"

US Competitive Advantages and Limitations

The United States maintains substantial advantages in AI development through superior chip technology, but Huang emphasizes that compute alone doesn't determine outcomes. Energy availability and the application layer represent critical dependencies for sustained US leadership that go beyond pure hardware supremacy.

• Jensen argues the US has a "100x compute advantage more than anywhere else in the world"
• Nvidia ensures US labs get first access to advanced technologies through allocation prioritization
• The US is "scarce on energy," requiring continued architecture advances to maximize throughput per watt with fewer chips
• AI is described as a "five-layer cake" where abundance of energy makes up for chips and vice versa
• Every layer of the AI stack must succeed for US leadership, including the application layer where "AI diffuses into society" and benefits from the industrial revolution
• Huang explicitly rejected comparing AI chips to enriched uranium: "We're not enriched uranium. It's a chip, and it's a chip that they can make themselves."

Radiology AI Predictions and Industry Outlook

Early predictions about AI replacing radiologists have proven unfounded, with the field now facing shortages despite a decade of algorithmic improvement. The episode illustrates a broader pattern where AI excels at discrete tasks but faces challenges integrating into complex professional workflows.

• Radiology AI doomer predictions from approximately ten years ago warned careers would disappear
• Radiologists are now "in short supply" despite those predictions
• Jensen argues AI misunderstands the distinction between "tasks" (reading scans) and "jobs" (patient care), causing unnecessary fear
• CUDA enables flexibility for creating MoE, diffusion, and disaggregated systems, making AI dependent on the stack above as much as architecture below

Future Roadmap and Computational Scope

Nvidia commits to continuing its annual GPU release cadence with Vera Rubin, Vera Rubin Ultra, and Feynman architectures in the pipeline. Beyond AI, Huang emphasizes that "every important computation is not AI-related"—traditional HPC applications in molecular dynamics, seismic processing, and scientific computing remain critical workloads where CUDA acceleration delivers substantial value.

• Annual GPU releases committed: Vera Rubin, Vera Rubin Ultra, then Feynman
• Token costs decreasing by 10x each year through architecture improvements
• Nvidia can fulfill orders from "single rack or graphics card to $100 billion AI factory"—claiming to be the only company that can say that today
• "Every important computation is not AI-related" includes molecular dynamics, seismic processing for energy discovery, and image processing
• General purpose computing remains too inefficient for these workloads, requiring CUDA acceleration

Full Transcript

Show transcript

We've seen the valuations of a bunch of 
software companies crash because people
are expecting AI to commoditize software.
There's a potentially naive way of thinking
about things, which is: look, 
Nvidia sends a GDS2 file to TSMC.
TSMC builds the logic dies, it builds the 
switches, then it packages them with the HBM
that SK Hynix, Micron, and Samsung make.
Then it sends it to an ODM in Taiwan
where they assemble the racks.
Nvidia is fundamentally making software that
other people are manufacturing, and if software 
gets commoditized, does Nvidia get commoditized?
In the end, something has to 
transform electrons to tokens.
The transformation of electrons to tokens
and making those tokens more valuable over 
time is hard to completely commoditize.
The transformation from electrons to 
tokens is such an incredible journey.
Making that token is like making one molecule 
more valuable than another molecule, making
one token more valuable than another.
The amount of artistry, engineering,
science, and invention that goes 
into making that token valuable,
obviously we're watching it happen in real time.
The transformation, the manufacturing, all of the
science that goes in there is far from deeply 
understood and the journey is far from over.
I doubt that it will happen.
We're going to make it more efficient, of course.
The way that you framed the question 
is my mental model of our company.
The input is electrons, the output is 
tokens. In the middle is Nvidia. Our job
is to do as much as necessary and as little 
as possible to enable that transformation
to be done at incredible capabilities.
What I mean by "as little as possible,"
whatever I don't need to do, I partner with 
somebody and make it part of my ecosystem.
If you look at Nvidia today, we probably 
have the largest ecosystem of partners,
both in the supply chain upstream and 
downstream, all of the computer companies,
application developers, and model makers.
AI is a five-layer cake, if you will.
We have ecosystems across the entire five layers.
We try to do as little as possible,
but the part that we have to do, 
as it turns out, is insanely hard.
I don't think that gets commoditized.
In fact, I also don't think the enterprise
software companies, the tools makers… Most 
software companies today are tool makers.
Some of them are not. Some of them 
are workflow codification systems.
But for a lot of companies, they're tool makers.
For example, Excel is a tool, PowerPoint is a
tool, Cadence makes tools, Synopsys makes tools.
I actually see the opposite of what people see.
I think the number of agents is going 
to grow exponentially, and the number
of tool users is going to grow exponentially.
It's very likely that the number of instances
of all these tools is going to skyrocket.
It’s very likely that the number of instances
of Synopsys Design Compiler is going to 
skyrocket, along with the number of agents
using the floor planners, our layout 
tools, and our design rule checkers.
Today we're limited by the number of engineers.
Tomorrow, those engineers are going to be
supported by a bunch of agents.
We're going to be exploring the
design space like you've never seen before, and 
we're going to use the tools that we use today.
I think tool use is going to cause 
the software companies to skyrocket.
The reason why it hasn't happened 
yet is because the agents aren't
good enough at using their tools yet.
Either these companies are going to build
the agents themselves, or agents are going to 
get good enough to be able to use those tools.
I think it's going to be a combination of both.
I think in your latest filings, you had almost
a $100 billion in purchase commitments 
with foundries, memory, and packaging.
SemiAnalysis has reported that you will have $250 
billion of these kinds of purchase commitments.
One interpretation is that Nvidia's 
moat is really that you've locked up
many years of these scarce components.
Somebody else might have an accelerator,
but can they actually get the memory to build it?
Can they actually get the logic to build it?
Is this really Nvidia's big 
moat for the next few years?
It's one of the things that we can do that 
is hard for someone else to do. We've made
enormous commitments upstream. Some of it is 
explicit, these commitments that you mentioned.
Some of it is implicit. For example, a lot of 
the investments that are upstream are made by
our supply chain because I said to the CEOs, "Let 
me tell you how big this industry is going to be,
let me explain to you why, let me reason through 
it with you, and let me show you what I see."
As a result of that process of informing, 
inspiring, and aligning with CEOs of all
different industries upstream, they're 
willing to make the investments.
Why are they willing to make the 
investments for me and not someone else?
The reason for that is because they know 
that I have the capacity to buy their
supply and sell it through my downstream.
The fact is that Nvidia's downstream supply
chain and our downstream demand is so large, 
they're willing to make the investment upstream.
If you look at GTC, people are marveled 
by the scale of it and the people that go.
It's a full 360 degrees, the entire 
universe of AI all in one place.
They're all in one place because 
they need to see each other.
I bring them together so that the 
downstream can see the upstream,
the upstream can see the downstream, and 
all of them can see the advances in AI.
Very importantly, they can all meet the AI 
natives, all the AI startups being built,
and all the amazing things happening so they can 
see firsthand all the things that I tell them.
I spend a lot of my time informing, directly 
or indirectly, our supply chain, partners, and
ecosystem about the opportunity in front of us.
Some people always say, "Jensen, in most keynotes,
it's one announcement after another."
With our keynotes, there’s always a part
of it that's a little torturous in the sense 
that it almost comes across like education.
In fact, that's exactly on my mind.
I need to make sure the entire supply
chain, upstream and downstream, the ecosystem, 
understands what is coming at us, why it's coming,
when it's coming, how big it's going to be, 
and is able to reason about it systematically,
just like I reason about it.
Regarding the moat as you
describe it, we're able to build for a future.
If our next several years are a trillion dollars
in scale, we have the supply chain to do it.
Without our reach, the velocity of our business…
Just as there's cash flow, there's 
supply chain flow, there's churns.
Nobody is going to build a supply chain for an 
architecture if the business churns are low.
Our ability to sustain the scale is only 
because our downstream demand is so great.
And they see it, they hear about 
it, they see it all coming.
That allows us to do the things we're 
able to do at the scale we do them.
I do want to understand more concretely 
whether the upstream can keep up.
For many years now, you guys have 
been 2x-ing revenue year over year.
You've been more than tripling the amount of flops 
you're providing to the world year over year.
And 2x-ing at this scale now is really incredible.
Exactly. But then you look at logic.
You're the biggest customer on TSMC's N3 
node, and you're one of the biggest on N2.
AI as a whole this year is 
going to be sixty percent of N3.
It's going to be 86% next year, 
according to SemiAnalysis.
How do you double if you're the majority?
And how do you do that year over year?
Are we in a regime now where the growth rate 
in AI compute has to slow because of upstream?
Do you see a way to get around this?
How do we build 2x more fabs year over
year, ultimately?
At some level,
the instantaneous demand is greater than the 
supply upstream and downstream in the world.
At any instant, we could be limited by the 
number of plumbers, which actually happens.
The plumbers are invited to next year's GTC.
By the way, great idea. But that's a good
condition. You want an industry where 
the instantaneous demand is greater
than the total supply of the industry.
The opposite is obviously less good.
If we're too far apart, if one particular 
component is too far away, the industry swarms it.
For example, notice people aren't 
talking very much about CoWoS anymore.
The reason for that is because for two years 
we swarmed the living daylights out of it.
We doubled, doubled, doubled on several doubles.
Now I think we're in fairly good shape.
TSMC now knows that CoWoS supply has to 
keep up with the rest of the logic demand
and the memory demand.
They're scaling CoWoS
and future packaging technologies at 
the same level as they scale logic.
This is terrific, because for a long time, 
CoWoS and HBM memory were rather specialty.
But they're not specialties anymore. People now 
realize they're mainstream computing technology.
Of course, we're now much more able to 
influence a larger scope of our supply chain.
At the beginning of the AI 
revolution, all the things
that I say now, I was saying five years ago.
Some people believed in it and invested in it,
for example, Sanjay and the Micron team.
I still remember the meeting really well
where I was clear about exactly what was 
going to happen, why it was going to happen,
and the predictions of today.
They really doubled down on it.
We partnered with them across LPDDR and HBM 
memories, and they really invested in it.
It obviously has been tremendous for the company.
Some people came a little bit 
later, but now they're all here.
Each one of these bottlenecks 
gets a great deal of attention.
Now we're prefetching the 
bottlenecks years in advance.
For example, the investments that 
we've done with Lumentum, Coherent,
and the silicon photonics ecosystem over the last 
several years really reshaped the supply chain.
We built up an entire supply chain around TSMC.
We partnered with them on COUPE, invented a whole
bunch of technology, and licensed those patents 
to the supply chain to keep it nice and open.
We're preparing the supply chain through the 
invention of new technologies, new workflows,
new testing equipment like double-sided 
probing, investing in companies, and helping
them scale up their capacity.
You can see that we're trying to
shape the ecosystem so that the supply 
chain is ready to support the scale.
It seems like some bottlenecks 
are easier than others.
Scaling up CoWoS versus scaling up—
I went to the hardest one, by the way.
Which is?
Plumbers. Plumbers and electricians. This is
one of the concerns that I have about the doomers 
describing the end of work and killing of jobs.
If we discourage people from 
being software engineers,
we're going to run out of software engineers.
The same prediction happened ten years ago.
Some of the doomers were telling people, 
"Whatever you do, don't be a radiologist."
You might hear some of those videos still 
on the web saying radiology is going to be
the first career to go and the world is 
not going to need any more radiologists.
Guess what we're short of? Radiologists.
Going back to this point about how some things you
can scale, and other things… How do you actually 
manufacture 2x the amount of logic a year?
Ultimately, memory and logic 
are bottlenecked by EUV.
How do you get to 2x as many 
EUV machines year over year?
None of that is impossible to scale quickly.
All of that is easy to do
within two or three years.
You just need a demand signal.
Once you can build one, you can build ten, and 
once you can build ten, you can build a million.
These things are not hard to replicate.
How far down the supply chain do you go?
Do you go to ASML and say, "Hey, 
if I look out three years from now,
for Nvidia to be generating two trillion a year 
in revenue, we need way more EUV machines"?
Some of them I have to directly, some 
of them indirectly, and some of them…
If I can convince TSMC, ASML will be convinced.
We have to think about the critical pinch points.
But if TSMC is convinced, you'll have 
plenty of EUV machines in a few years.
My point is that none of the bottlenecks last 
longer than a couple of years, two, three years,
none of them.
Meanwhile, we're
improving computing efficiency by 10x 20x, and 
in the case of Hopper to Blackwell, 30x to 50x.
We're coming up with new algorithms 
because CUDA is so flexible.
We're developing all kinds of new 
techniques so that we drive efficiency
in addition to increasing capacity.
None of those things worry me.
It's the stuff that's downstream from us.
Energy policies that prevent energy from… You
can't create an industry without energy.
You can't create a whole new
manufacturing industry without energy.
We want to reindustrialize the United States.
We want to bring back chip manufacturing, 
computer manufacturing, and packaging.
We want to build new things like EVs and robots.
We want to build AI factories.
You can't build any of these things without 
energy, and those things take a long time.
More chip capacity, that's a 2-3 year problem.
More CoWoS capacity, 2-3 year problem.
Interesting. I feel like I have guests 
tell me the exact opposite thing sometimes.
In this case, I just don't have the 
technical knowledge to adjudicate.
The beautiful thing is 
you're talking to the expert.
True. I want to ask about your competitors.
If you look at the TPU, arguably two out
of the top three models in the world, 
Claude and Gemini, were trained on TPU.
What does that mean for Nvidia going forward?
We build a very different thing.
What Nvidia built is accelerated 
computing, not a tensor processing unit.
Accelerated computing is used for all 
kinds of things: molecular dynamics,
quantum chromodynamics, data processing, data 
frames, structured data, and unstructured data.
It's also used for fluid 
dynamics and particle physics.
In addition, we use it for AI.
Accelerated computing is much more diverse.
Although AI is the conversation today and 
is obviously very important and impactful,
computing is much broader than that.
Nvidia has reinvented the way computing
is done, moving from general-purpose 
computing to accelerated computing.
Our market reach is far greater than 
any TPU or ASIC can possibly have.
If you look at our position, we're the only 
company that accelerates applications of
all kinds. We have a gigantic ecosystem. So all 
kinds of frameworks and algorithms run on Nvidia.
Because our computers are designed 
to be operated by other people,
anyone who's an operator can buy our systems.
With most of these home-built systems,
you have to be your own operator 
because they were never designed to
be flexible enough for others to operate.
Because anybody can operate our systems,
we're in every cloud, including 
Google, Amazon, Azure, and OCI.
If you want to operate it to rent, you 
better have a large ecosystem of customers
in many industries to be the offtakers.
If you want to operate it for yourself,
we obviously have the ability to help you operate 
it yourself, like we did for Elon with xAI.
And because we can enable operators in any 
company and any industry, you could use it
to build a supercomputer for scientific 
research and drug discovery at Lilly.
We can help them operate their own 
supercomputer and use it for the
entire diversity of drug discovery and 
biological sciences that we accelerate.
There are just a whole bunch of applications 
that we can address that you can't do with TPUs.
Nvidia built CUDA to be a fantastic 
tensor processing unit as well,
but it also handles every life cycle of 
data processing, computing, AI, and so on.
Our market opportunity is just a lot 
larger, and our reach is a lot greater.
Because we support every application 
in the world now, you can build Nvidia
systems anywhere and know that there will be 
customers for it. It's a very different thing.
This is going to be a long question.
You have spectacular revenue,
and you're not making $60 billion 
a quarter from pharma and quantum.
You're making it because AI is an unprecedented 
technology that is growing unprecedentedly fast.
The question then is what 
is best for AI specifically.
I'm not in the details, but I talk to my 
AI researcher friends and they say, "Look,
when I use a TPU, it's this big systolic array 
that's perfect for doing matrix multiplies,
whereas a GPU is very flexible.
It's great when you have lots of
branching or irregular memory access." But what 
is AI? It's just these very predictable matrix
multiplies again and again and again.
You don't have to give up any die area
for warp schedulers or switches 
between threads and memory banks.
And the TPU is really optimized for the bulk 
of this growth in revenue and use case for
compute that is coming online right now.
I wonder how you react to that.
Matrix multiplies are an important part 
of AI, but they're not the only part.
If you want to come up with a new attention 
mechanism, disaggregate in a different way,
or invent a whole new type of architecture 
altogether—like a hybrid SSM—you want
an architecture that's generally programmable.
If you want to create a model that fuses diffusion
and autoregressive techniques, you want an 
architecture that’s just generally programmable.
We run everything you can imagine. That's 
the advantage. It allows for the invention
of new algorithms a lot more easily, 
because it's a programmable system.
The ability to invent new algorithms is 
really what makes AI advance so quickly.
TPUs, like anything else, are 
impacted by Moore's Law, which
we know is increasing by about 25% per year.
The only way to really get 10x or 100x leaps
is to fundamentally change the algorithm and how 
it's computed every single year. That's Nvidia's
fundamental advantage. The only reason we were 
able to make Blackwell to Hopper 50x… When I
first announced Blackwell was going to be 35x more 
energy efficient than Hopper, nobody believed it.
Then Dylan wrote an article saying I 
sandbagged, and it's actually fifty times.
You can't reasonably do 
that with just Moore's Law.
The way we solve that problem is with new models, 
like MoEs, that are parallelized, disaggregated,
and distributed across a computing system.
Without the ability to really get down and
come up with new kernels with 
CUDA, it's really hard to do.
It's the combination of the programmability 
of our architecture and the fact that Nvidia
is an extreme co-design company.
We can even offload some of the
computation into the fabric itself, like 
NVLink, or into the network with Spectrum-X.
We could affect change across the processors, 
the system, the fabric, the libraries, and the
algorithm simultaneously.
Without CUDA to do that,
I wouldn't even know where to start.
My sponsor Crusoe was among the first
clouds to offer NVIDIA’s Blackwell 
and Blackwell Ultra platforms.
And they just announced their NVIDIA Vera 
Rubin deployment scheduled for later this year.
But access to state-of-the-art 
hardware is only part of the story.
For example, most inference engines already do 
KV caching for a single user's forward passes.
But Crusoe does it across users and GPUs.
So if a thousand agents are running on
the same system prompt, Crusoe only has to 
compute the KV cache once for it to become
available to every single GPU in the cluster.
This is especially important as systems get more
agentic and require much longer prefixes 
in order to use tools and access files.
In a recent benchmark, Crusoe was able to 
deliver up to 10x faster time-to-first token
and up to 5x better throughput than vLLM.
This is just one among many reasons that you
should run your inference workload with Crusoe.
And if you need GPUs for training,
you don't need to switch clouds. 
Crusoe's got you covered there too.
Go to crusoe.ai/dwarkesh to learn more.
This gets at an interesting question about
Nvidia's clientele. 60% of your revenue is 
coming from these big five hyperscalers.
In a different era with different customers—let's 
say professors running experiments—they need CUDA.
They can't use another accelerator. They 
just needed to run PyTorch with CUDA and
have everything optimized.
But these hyperscalers have
the resources to write their own kernels.
In fact, they have to in order to get that
last 5% of performance they need 
for their specific architecture.
Anthropic and Google are mostly running their 
own accelerators or running TPUs and Trainium.
But even OpenAI, using GPUs, has Triton 
because they need their own kernels.
Down to CUDA C++, instead of using cuBLAS 
and NCCL, they've got their own stack which
compiles to other accelerators as well.
If most of your customers can and do make
replacements for CUDA, to what extent 
is CUDA really the thing that is going
to make frontier AI happen on Nvidia?
CUDA is a rich ecosystem. If you want to
build on any computer first, building 
on CUDA first is incredibly smart.
Because the ecosystem is so 
rich, we support every framework.
If you want to create custom kernels… For 
example, we contribute enormously to Triton.
So the back end of Triton has 
huge amounts of Nvidia technology.
We're delighted to help every 
framework become as great as it can be.
There are lots and lots of frameworks.
There's Triton, vLLM, SGLang, and more.
Now there's a whole bunch of new 
reinforcement learning frameworks
coming out, like verl and NeMo RL.
With post-training and reinforcement
learning, that entire area is just exploding.
So if you want to build on an architecture,
building on CUDA makes the most sense 
because you know the ecosystem is great.
You know that if something happens, 
it's more likely in your code and
not in the mountain of code underneath.
Don't forget the amount of code you're dealing
with when building these systems.
When something doesn't work,
was it you or was it the computer?
You would like it to always be
you and to be able to trust the computer.
Obviously, we still have lots of bugs ourselves,
but our system is so well wrung out that you 
can at least build on top of the foundation.
That's number one: the richness, 
programmability, and capability of the ecosystem.
The second thing is, if you're a developer 
building anything at all, the single most
important thing you want is an install base.
You want the software you write to run
on a whole bunch of other computers.
You're not building software just for yourself.
You're building it for your fleet or everybody 
else's fleet because you're a framework builder.
Nvidia's CUDA ecosystem is 
ultimately its great treasure.
We have several hundred million GPUs out there 
now. Every cloud has it. It goes back to the A10,
A100, H100, H200, the L series, the P series.
There’s a whole bunch of them.
They're in all kinds of sizes and shapes.
If you're a robotics company, you want that
CUDA stack to actually run in the robot itself. 
We're literally everywhere. The install base means
that once you develop the software or the model, 
it's going to be useful everywhere. That is just
incredibly valuable. Lastly, the fact that we're 
in every single cloud makes us genuinely unique.
If you're an AI company or 
developer, you're not exactly
sure which cloud service provider you're going 
to partner with or where you'd like to run it.
We run everywhere, including 
on-prem for you if you like.
The combination of the richness of the ecosystem, 
the expansiveness of the install base, and the
versatility of where we are makes CUDA invaluable.
That makes a lot of sense.
I guess the thing I'm curious 
about is whether those
advantages matter a lot to your main customers.
There's many people for whom they might matter.
The kind of person who can actually build their 
own software stack makes up most of your revenue.
Especially if you go to a world where AI 
is getting especially good at the things
which have tight verification 
loops where you can RL on them….
This question of how do you write 
a kernel that does attention or
MLP the most efficiently across a scale up?
It's a very verifiable sort of feedback loop.
Can all the hyperscalers write 
these custom kernels for themselves?
Nvidia still has great price performance, 
so they might still prefer to use Nvidia.
But then the question is, does it just become 
a question of who is offering the best specs,
the best flops and memory 
bandwidth for a given dollar.
Whereas historically Nvidia 
has just had, and still has,
the best margins in all of AI across hardware 
and software, +70%, because of this CUDA moat.
And the question is, can you sustain those margins 
if for most of your customers, they can actually
afford to build, instead of the CUDA moat?
The number of engineers we have assigned
to these AI labs is insane, working 
with them, optimizing their stack.
The reason for that is because nobody 
knows our architecture better than we do.
These architectures are not 
as general purpose as a CPU.
A CPU is kind of like a Cadillac. It's 
a nice cruiser. It never goes too fast.
Everybody drives it pretty well. It's got 
cruise control, and everything's easy.
But in a lot of ways, Nvidia's GPUs, 
accelerators, are like F1 racers.
I could imagine everybody's able to drive it at a 
hundred miles an hour, but it takes quite a bit of
expertise to be able to push it to the limit.
We use a ton of AI to create the
kernels that we have.
I'm pretty sure we're
going to still be needed for quite some time.
Our expertise helps our AI lab partners to get
another 2x out of their stack easily oftentimes.
It's not unusual that by the time we're done
optimizing their stack or optimizing a particular 
kernel, their model sped up by 3x, 2x, 50%.
That's a huge number, especially when 
you're talking about the install base
of the fleet that they have, of all the 
Hoppers and Blackwells that they have.
When you increase it by a factor of two, that 
doubles the revenues. That directly translates
to revenues. Nvidia's computing stack is the 
best performance per TCO in the world, bar none.
Nobody can demonstrate to me that any single 
platform in the world today has a better
performance-TCO ratio. Not one company. In 
fact, the benchmarks that are out there.
Dylan's InferenceMAX is sitting 
out there for everybody to use,
and not one… TPU won't come, Trainium won't come.
I encourage them to use InferenceMAX and 
demonstrate their incredible inference
cost. It's really hard. Nobody wants to show up. 
MLPerf. I would welcome Trainium to demonstrate
their 40% that they claim all the time.
I would love to hear them demonstrate
the cost advantage of TPUs.
It makes no sense in my mind.
It makes absolutely zero sense. On 
first principles, it makes no sense.
So I think the reason why we're so successful 
is simply because our TCO is so great.
Secondly, you say 60% of our customers are the 
top five, but most of that business is external.
For example, most of Nvidia in AWS is 
for external customers, not internal use.
Most of our customers at Azure, obviously 
all of our customers are external.
All of our customers at OCI 
are external, not internal use.
The reason why they favor us is 
because our reach is so great.
We can bring them all of the great 
customers in the world. They're all
built on Nvidia. And the reason why all 
these companies are built on Nvidia is
because our reach and our versatility is so great.
So I think the flywheel is really install base,
the programmability of our architecture, the 
richness of our ecosystem, and the fact that
there's so many AI companies in the world.
There's tens of thousands of them now.
If you were one of those AI startups, 
what architecture would you choose?
You would choose an architecture 
that's most abundant.
We're the most abundant in the world.
You’d choose the one that has the largest
installed base. We're the largest install 
base. And you’d choose the one that has a
rich ecosystem. So that's the flywheel. That's 
the reason why, between the combination of:
one, our perf per dollar is so great 
that they have the lowest cost tokens.
Second, our perf per watt 
is the highest in the world.
So if one of these companies, if our 
partners, built a one gigawatt data center,
that one gigawatt data center better deliver the 
maximum amount of revenues and number of tokens,
which directly translates to revenues.
You want it to generate as many tokens
as possible, maximize the 
revenues for that data center.
We are the highest tokens per 
watt architecture in the world.
Lastly, if your goal is to 
rent the infrastructure,
we have the most customers in the world.
So that's the reason why the flywheel works.
Interesting. I guess the question comes down 
to, what is the actual market structure here?
Because even if there's other companies… 
There could have been a world where there's
tens of thousands of AI companies that 
have roughly equal share of compute.
But even through these five hyperscalers, 
really the people on Amazon using the
compute are Anthropic, OpenAI, and these big 
foundation labs who can themselves afford and have
the ability to make different accelerators work.
No, I think your premise is wrong.
Maybe. But let me ask you a 
slightly different question.
Come back and make me correct your premise.
Okay. Let me just ask you a different question.
But still make sure to make me come back and 
fix because it's just too important to AI.
It's too important to the future of science.
It's too important to the future of
the industry. That premise… Look —
Let me just finish the question and then
we can address it together.
Yeah.
If all these things are true about price, 
performance, and performance per watt,
et cetera, are true, why do you think it is 
the case that, say, Anthropic for example,
just announced a couple days ago they have a 
multi-gigawatt deal with Broadcom and Google
for TPUs and majority of their compute?
Obviously for Google,
TPU is a majority of compute.
So if I look at these big AI companies,
it seems like a lot of their compute… There was 
some point where it's all Nvidia and now it's not.
So I'm curious how to square, if 
these things are true on paper,
why are they going with other accelerators?
Anthropic is a unique instance, not a trend.
Without Anthropic, why would there be any TPU 
growth at all? It's 100% Anthropic. Without
Anthropic, why would there be Trainium growth 
at all? It's 100% Anthropic. I think that's
fairly well known and well understood.
It's not that there's an abundance of
ASIC opportunities. There's only one Anthropic.
But OpenAI's deals with AMD… They're building
their own Titan accelerator.
Yeah, but I think we could
all acknowledge they're vastly Nvidia.
We're going to still do a lot of work together.
I'm not offended by other people using 
something else and trying things.
If they don't try these other things, 
how would they know how good ours is?
Sometimes you've got to be reminded of it.
We have to continuously earn the position
that we're in. There are always big claims. Look 
at the number of ASICs that have been canceled.
Just because you're going to build an ASIC… You 
still have to build something better than Nvidia.
It's not that easy building something better 
than Nvidia. It's not sensible, actually.
Nvidia's got to be missing something, seriously.
Because of our scale, our velocity, we're the
only company in the world that's cranking it out 
every single year. Big leaps, every single year.
I guess their logic is, "Hey, 
it doesn't need to be better.
It just needs to be not more than 70% worse," 
because they're paying you 70% margins.
No, don't forget, even in ASICs 
margins are really quite high.
Nvidia's margin is 70%, let's say. But ASIC 
margins are 65%. What are you really saving?
Oh, you mean from Broadcom or something like that?
Yeah, sure. You've got to pay somebody. I think
the ASIC margins are incredibly good, from what I 
can tell. They believe it too. They're quite proud
of their incredible ASIC margins.
So, you asked the question why.
A long time ago, we just didn't 
have the ability to do it.
At the time, I didn't deeply internalize how 
difficult it would be to build a foundation
AI lab like OpenAI and Anthropic, 
and the fact that they needed huge
investments from the supplier themselves.
We just weren't in a position to make the
multi-billion dollar investment into 
Anthropic so that they could use our
compute. But Google and AWS were. They put in huge 
investments in the beginning so that Anthropic,
in return, used their compute.
We just weren't in a position
to do that at the time.
I would say my mistake is
I didn't deeply internalize that they really 
had no other options, that a VC would never
put in $5-10 billion of investment into an 
AI lab with the hopes of it turning out to
be Anthropic. So that was my miss. But even 
if I understood it, I don't think we would've
been in a position to do that at the time.
But I'm not going to make that same mistake again.
I'm delighted to invest in OpenAI, 
and I'm delighted to help them scale,
and I believe it's essential to do so.
And then, when I was able to,
when Anthropic came to us, I'm delighted to 
be an investor, delighted to help them scale.
We just weren't, at the time, able to do it.
If I could rewind everything—and Nvidia could
have been as big back then as we are now—I 
would've been more than happy to do it.
This is actually quite interesting. For many years 
Nvidia has been the company in AI making money,
making lots of money. Now you're investing it. 
It's been reported that you've done up to $30
billion in OpenAI and $10 billion in Anthropic.
But now their valuations have increased,
and I'm sure they'll continue to increase.
So if over these many years you were giving them
the compute, you saw where it was headed, and they 
were worth like one tenth what they're worth now a
couple years ago—or even a year ago in some cases 
and you had all this cash — there's a world where
either Nvidia themselves becomes a foundation 
lab, does a huge investment to make that possible,
or has made the deals you've made now 
at current valuations much earlier on.
And you had the cash to do it.
So I am curious, actually,
why not have done it earlier?
We did it as soon as we could have.
We did it as soon as we could have, and if I 
could have, I would've done it even earlier.
At the time that Anthropic needed us to do 
it, we just weren't in a position to do it.
It wasn't in our sensibility to do so.
How so? Was it like a cash thing?
Yeah, the level of investment. We had 
never invested outside the company
at the time, and not that much.
We didn't realize we needed to.
I always thought that they could just go raise 
from VCs, for God's sakes, like all companies do.
But what they were trying to do 
couldn't have been done through VCs.
What OpenAI wanted to do couldn't have been done 
through VCs. I recognize that now. I didn't know
it then. But that's their genius. That's 
why they're smart. They realized then that
they had to do something like that.
And I'm delighted that they did.
Even though we caused Anthropic to have to go to 
somebody else, I'm still happy that it happened.
Anthropic's existence is great for 
the world. I'm delighted for it.
I guess you still are making a ton 
of money, and you're making way
more money quarter after quarter.
It's still okay to have regrets.
So the question still arises. Okay, now that we're 
here and you have all this money that you keep
making, what should Nvidia be doing with it?
There's one answer which is that there's this
whole middleman ecosystem that has popped 
up for converting CapEx into OpEx for
these labs so that they can rent compute.
Because the chips are really expensive,
they make a lot of money over their lifetime 
because the AI models are getting better.
So the value that they generate, their tokens, 
is increasing, but they're expensive to set up.
Nvidia has the money to do the CapEx.
In fact, it's been reported,
you are backstopping CoreWeave up to $6.3 
billion and have invested $2 billion.
Why doesn't Nvidia become a cloud themselves?
Why doesn't it become a hyperscaler themselves
and rent this compute out?
You have all this cash to do it.
This is a philosophy of the 
company, and I think it's wise.
We should do as much as 
needed, as little as possible.
What that means is, the work that we do with 
building our computing platform, if we don't do
it, I genuinely believe it doesn't get done.
If we didn't take the risk that we take—if
we didn't build NVLink the way we built 
it, if we didn't build the whole stack,
if we didn't create the ecosystem the way we did, 
if we didn't dedicate ourselves to 20 years of
CUDA while losing money most of that time—if we 
didn't do it, nobody else would have done it.
If we didn't create all the CUDA-X libraries 
so that they're all domain-specific… A decade
and a half ago, we pushed into domain-specific 
libraries because we realized that if we didn't
create these domain-specific libraries, whether 
it's for ray tracing or image generation or even
the early works of AI, these models, if we didn't 
create them, for data processing, structured data
processing, or vector data processing, 
if we didn't create them, nobody would.
I am completely certain of that.
We created a library for
computational lithography called cuLitho.
If we didn't create it, nobody would have.
So accelerated computing wouldn't advance the 
way it has if we didn't do what we did. So we
should do that. We should dedicate our company, 
all of our might, wholeheartedly to go do that.
However, the world has lots of clouds.
If I didn't do it, somebody would show up.
So following the recipe, the philosophy, 
of doing as much as needed but as little
as possible—as little as possible—that 
philosophy exists in our company today.
Everything I do, I do it with that lens.
In the case of clouds, if we didn't support
CoreWeave to exist, these neoclouds, 
these AI clouds, wouldn't exist.
If we didn't help CoreWeave 
exist, they would not exist.
If we didn't support Nscale, they 
wouldn't be where they are today.
If we didn't support Nebius, they wouldn't be what 
they are today. Now they're doing fantastically.
Is that a business model [inaudible]?
We should do as much as needed,
as little as possible.
So we invest in our ecosystem
because I want our ecosystem to thrive.
I want the architecture, and AI,
to be able to connect with as many industries 
as possible, as many countries as possible,
and make it possible for the planet to be built 
on AI and to be built on the American tech stack.
That vision is exactly what we're pursuing.
Now, one of the things that you mentioned…
There are so many great, amazing foundation model 
companies, and we try to invest in all of them.
This is another thing that we do. We don't 
pick winners. We need to support everyone.
It's part of our joy of doing so. It's 
imperative to our business. But we also
go out of our way not to pick winners.
So when I invest in one of them,
I invest in all of them.
Why do you go out of your way not to pick winners?
Because it's not our job to, number one.
Number two, when Nvidia first started,
there were 60 3D graphics companies.
We are the only one that survived.
If you would have taken those 60 graphics 
companies and asked yourself which one was
going to make it, Nvidia would be at 
the top of that list not to make it.
This is long before you, but Nvidia's 
graphics architecture was precisely wrong.
It's not a little bit wrong.
We created an architecture
that was precisely wrong, and it was an 
impossible thing for developers to support.
It was never going to make it.
We reasoned about it from good first principles,
but we ended up with the wrong solution.
Everybody would have counted us out. And here we
are. So I have enough humility to recognize that. 
Don't pick winners. Either let them all take care
of themselves, or take care of all of them.
One thing I didn't understand is you said,
"Look, we're not prioritizing 
these neoclouds just because
they are neoclouds and we want to prop them up."
But you also listed a bunch of neoclouds and said
they wouldn't exist if it wasn't for NVIDIA.
How are those two things compatible?
First of all, they need to want to 
exist, and they come to ask us for help.
When they want to exist and they 
have a business plan, expertise,
and the passion for it… They obviously 
have to have some capabilities themselves.
But if, at the end of the day, they 
need some investment in order to get
it off the ground, we would be there for them.
But the sooner they get their flywheel going...
Your question was, "Do we want to be in the 
financing business?" The answer is no. There
are people in the financing business, and we'd 
rather work with all the people in the financing
business than be a financier ourselves.
Our goal is to focus on what we do,
keep our business model as simple as 
possible, and support our ecosystem.
When someone like OpenAI needs an investment of 
a $30 billion scale because it's still before
their IPO, and we deeply believe in them and I 
deeply believe that they're going to be an… Well,
they're an extraordinary company already today.
They’re going to be an incredible company.
The world needs them to exist.
The world wants them to exist. I want them
to exist. They have the wind at their back.
Let's support them and let them scale.
Those investments we'll do 
because they need us to do it.
But we're not trying to do as much as possible.
We're trying to do as little as possible.
I spend way too much time copy-pasting text 
back and forth from Google Docs to chatbots.
And so I built what's basically a “Cursor 
for writing”, which operates the way I think
an AI co-researcher should operate.
I can tag it and it can talk with me
through inline comment threads and 
help me dig deeper and brainstorm.
I built this entire thing over the weekend 
with Cursor and their new Composer 2 model.
With a lot of agentic coding tools, I feel like 
I have no idea what's going on under the surface.
I just have to relinquish 
control and hope for the best.
But Cursor let me try a bunch of different ideas 
while staying on top of the implementation.
I did most of my brainstorming in the agents 
window, and after I got some basic files in place,
I used the diff window to track changes.
The few times that I needed to make a
quick tweak by hand, I just used the editor.
If you want to try my AI co-researcher yourself
I've linked the GitHub repo in the description. 
And if you have a tool that you've been wanting
to build, you should make it happen.
Go to cursor.com/dwarkesh to get started.
This may be an obvious question, but we've 
lived many years in this situation where
there's a shortage of GPUs, and it's grown 
now because models are getting better.
We have a shortage of GPUs.
Yes. Nvidia is known for
divvying up the scarce allocation, not just 
based on high bidder, but rather on, "Hey,
we want to make sure that these neoclouds exist.
Let's give some to CoreWeave, let's give some to
Crusoe, let's give some to Lambda."
Why is it good for Nvidia?
First of all, would you agree with this 
characterization of fracturing the market?
No. No. Your premise is just wrong. We're 
sufficiently mindful about these things.
We're very mindful about these things.
First of all, if you don't place a PO, all the
talking in the world won't make a difference.
Until we get a PO, what are we going to do?
So the first thing is, we work really hard 
with everybody to get a forecast done,
because these things take a long time to build, 
and the data centers take a long time to build.
We align ourselves with demand and supply and 
things like that through forecasting. Okay?
That's job number one. Number two, we've tried 
to forecast with as many people as possible,
but in the final analysis, you 
still have to place an order.
Maybe, for whatever reason, you didn't place 
your order. What can I do? At some point,
first in, first out.
But beyond that,
if you're not ready because your data center's 
not ready, or certain components aren't ready
to enable you to stand up a data center, we 
might decide to serve another customer first.
That's just maximizing the 
throughput of our own factory.
We might do some adjustments there.
Aside from that, the prioritization
is first in, first out.
You've got to place a PO.
If you don't place a PO… Now, of 
course, there are stories about that.
For example, all of this kind of started from 
an article about Larry and Elon having dinner
with me where they begged for GPUs. That never 
happened. We absolutely had dinner. We absolutely
had dinner, and it was a wonderful dinner.
At no time did they beg for GPUs.
They just had to place an order.
Once they place an order, we do our best to
get the capacity to them. We're not complicated.
Okay. So it sounds like there's a queue,
and then based on whether your data center 
is ready and when you place a purchase order,
you get them at a certain time.
But it still doesn't sound
like the highest bidder just gets it.
Is there a reason to do it…?
We never do that.
Okay.
We never do.
Why not just do high bidder?
Because it's a bad business practice.
You set your price and then people
decide to buy it or not.
I understand that others
in the chip industry change their prices 
when demand is higher, but we just don't.
That's just never been a practice of ours. You 
can count on us. I prefer to be dependable,
to be the foundation of the industry. You don't 
need to second-guess. If I quoted you a price,
we quoted you a price. That's it. If 
demand goes through the roof, so be it.
On the other end, that's why you have a 
productive relationship with TSMC, right?
Yeah, Nvidia's been in business with 
them for, I guess, coming up on 30 years.
Nvidia and TSMC don't have a legal contract. 
There's always some rough justice. Sometimes
I'm right, sometimes I'm wrong.
Sometimes I got a better deal,
sometimes I got a worse deal.
But overall, the relationship
is incredible. I can completely trust 
them. I can completely depend on them.
One of the things you can count 
on with Nvidia is that this year,
Vera Rubin is going to be incredible.
Next year, Vera Rubin Ultra will come.
The year after that, Feynman will come.
And the year after that,
I haven't introduced the name yet.
Every single year you can count on us.
You're going to have to go find another ASIC 
team in the world—pick your ASIC team—where
you can say, "I can bet the farm, I can 
bet my entire business that you will be
here for me every single year.
Your token cost will decrease
by an order of magnitude every single year.
I can count on it like I can count on the clock."
I just said something about TSMC.
For no other foundry in history
can you possibly say that.
You can say that about Nvidia today.
You can count on us every single year.
If you would like to buy a billion dollars
worth of AI factory compute, no problem.
If you'd like to buy
a hundred million dollars, no problem.
You'd like to buy $10 million,
or just one rack, not a problem.
Or just one graphics card, okay, no problem.
If you would like to place an order for 
a $100 billion of AI factory, no problem.
We're the only company in the 
world where you can say that today.
I can say that about TSMC as well.
I want to buy one, buy 1 billion, no problem.
We just have to go through the process of planning 
for it, and all the things that mature people do.
So I think this ability for Nvidia to be 
the foundation of the world's AI industry,
this is a position that has taken us a couple 
of decades to arrive at. Enormous commitment,
enormous dedication. The stability 
of our company, the consistency of
our company, is really important.
Okay. I want to ask about China.
I actually don't know what I think about whether 
it's good to sell chips to China or not, but I
like to play devil's advocate against my guests.
So when Dario was on, who supports export
controls, I asked him, why can't America and China 
both have a country of geniuses in the datacenter?
But since you're on the opposite side, 
I'll ask you in the opposite way.
One way to think about it is, Anthropic actually 
announced a couple days ago Mythos Preview.
This model Mythos, they're not even 
releasing publicly because they say
it has such cyber-offensive capabilities that 
we don't think the world is ready until we make
sure these zero-days are patched up.
But they say it found thousands of
high-severity vulnerabilities across every 
major operating system, every browser.
It found one in OpenBSD, which is 
this operating system that's been
specifically designed to not have zero days.
It found one that's existed for 27 years.
So if Chinese companies and Chinese labs 
and the Chinese government had access to
the AI chips to train a model like Claude Mythos 
with these cyber-offensive capabilities and run
millions of instances of it with more compute, 
the question is, is that a threat to American
companies, to American national security?
First of all, Mythos was trained on fairly
mundane capacity, and a fairly mundane amount 
of it. By an extraordinary company. The amount
of capacity and the type of compute it was 
trained on is abundantly available in China.
So you just have to first realize 
that chips exist in China.
They manufacture 60% of the world's 
mainstream chips, maybe more.
It's a very large industry for them.
They have some of the world's
greatest computer scientists.
As you know, most of the AI
researchers in all of these AI labs are Chinese.
They have 50% of the world's AI researchers.
So the question is, considering all the assets 
they already have—they have an abundance of
energy, they have plenty of chips, they've got 
most of the AI researchers—if you're worried about
them, what is the best way to create a safe world?
Victimizing them, turning them into an enemy,
likely isn't the best answer. They are an 
adversary. We want the United States to win.
But I think having a dialogue and having research 
dialogue is probably the safest thing to do.
This is an area that is glaringly 
missing because of our current
attitude about China as an adversary.
It is essential that our AI researchers and
their AI researchers are actually talking.
It is essential that we try to both
agree on what not to use the AI for.
With respect to finding bugs in software,
of course, that's what AI is supposed to do.
Is it going to find bugs in a lot of software?
Of course. There are lots and lots of bugs.
There are lots of bugs in the AI software.
That's what AI is supposed to do, and I'm 
delighted that AI has reached a level where
it could help us be so much more productive.
One of the things that is underemphasized is
the richness of the ecosystem around 
cybersecurity, AI cybersecurity and
AI security and AI privacy and AI safety.
There’s a whole ecosystem of AI startups
that are trying to create this future for us, 
where you have one AI agent that's incredible,
surrounded by thousands of AI agents, 
keeping it safe, keeping it secure.
That future surely is going to happen.
The idea that you're going to have an
AI agent running around with nobody 
watching after it is kind of insane.
We know very well that this 
ecosystem needs to thrive.
It turns out this ecosystem needs open 
source. This ecosystem needs open models.
They need open stacks so that all of these 
AI researchers and all these great computer
scientists can go build AI systems that 
are as formidable and can keep AI safe.
So one of the things that we need to 
make sure that we do is we keep the
open source ecosystem vibrant. That can't be 
ignored. A lot of that is coming out of China.
We ought to not suffocate that.
With respect to China,
of course we want the United States 
to have as much computing as possible.
We're limited by energy, but we've 
got a lot of people working on that.
We've got to not make energy 
a bottleneck for our country.
But what we also want is to make sure that all the 
AI developers in the world are developing on the
American tech stack, and making the contributions, 
the advancements of AI—especially when it's open
source—available to the American ecosystem.
It would be extremely foolish to create two
ecosystems: the open source ecosystem, and it 
only runs on a foreign tech stack, and a closed
ecosystem that runs on the American tech stack.
I think that would be a horrible
outcome for the United States.
Since there are a lot of things,
let me just triage the response.
I think the concern, going back to the
flop difference in the hacking, is yes, they have 
compute, but there's some estimates that because
they're at 7nm—they don't have EUVs because 
of chip-making export controls—the amount of
flops they're able to actually produce, they have 
one tenth the amount of flops that the US has.
So with that, could they eventually train a 
model like Mythos? Yes. But the question is,
because we have more flops, American labs are 
able to get to these levels of capabilities first.
Because Anthropic got to it first, they 
say, "Okay, we're going to hold onto
it for a month while all these American 
companies, we’ll give them access to it.
They're going to patch up all their 
vulnerabilities, and now we release it."
Furthermore, even if they train a model like this, 
the ability to deploy it at scale… If you had a
cyber hacker, it's much more dangerous if they 
have a million of them versus a thousand of them.
So that inference compute really matters a lot.
In fact, the fact that they have so
many AI researchers who are so good 
is the thing that makes it so scary,
because what is it that makes those engineer 
researchers more productive? It’s compute.
If you talk to any AI lab in America, they say 
the thing that's bottlenecking them is compute.
There are quotes from the DeepSeek 
founder, or Qwen leadership or whatever.
They say the thing they’re 
bottlenecked on is compute.
So then the question is, isn't it better that we 
get American companies, because they have more
compute, to get to the Mythos-level capabilities 
first, prepare our society for it, before China
can get to it because, they have less compute?
We should always be first
and we should always have more.
But in order for that outcome you described
to be true, you have to take it to the extremes.
They have to have no compute.
If they have some compute, the 
question is how much is needed?
The amount of compute they 
have in China is enormous.
You're talking about the country that is the 
second largest computing market in the world.
If they want to aggregate their compute, 
they've got plenty of compute to aggregate.
But is that true? People do these estimates 
and they're like, "SMIC is actually behind
on the process nodes."
I'm about to tell you.
Okay.
The amount of
energy they have is incredible. Isn't that right? 
AI is a parallel computing problem, isn't it?
Why can't they just put 4x, 10x, as many 
chips together because energy's free? They
have so much energy. They have datacenters that 
are sitting completely empty, fully powered.
You know they have ghost cities, 
they have ghost datacenters too.
They have so much infrastructure capacity.
If they wanted to, they just gang up more
chips, even if they're 7nm.
Their capacity of building
chips is one of the largest in the world.
The semiconductor industry knows that they
monopolize mainstream chips.
They have over-capacity,
they have too much capacity.
So the idea that China won't be able
to have AI chips is completely nonsense.
Now, of course, if you ask me,
would the United States be further ahead 
if the entire world had no compute at all?
But that's just not an outcome.
That's not a scenario that's true.
They have plenty of compute already.
The amount of threshold they need for
the concern you're worried about, they've 
already reached that threshold and beyond.
So I think you misunderstand 
that AI is a five-layer cake,
and at the lowest layer is energy.
When you have an abundance of energy,
it makes up for chips.
If you have an abundance
of chips, it makes up for energy.
For example, the United States is scarce
on energy, which is the reason why Nvidia 
has to keep advancing our architecture and
do this extreme co-design so that with the 
few chips that we ship—with the few chips,
because the amount of energy is so limited—our 
throughput per watt is off the charts.
But if your amount of watts is 
completely abundant, it's free,
what do you care about performance per watt for? 
You get plenty. You can use old chips to do.
So 7nm chips are essentially Hopper.
The ability for Hopper… I've got to
tell you, today's models are largely 
trained on Hopper, Hopper generation.
So 7nm chips are plenty good.
The abundance of energy is their advantage.
But then there's a question of whether 
they can actually manufacture enough chips.
But they do. What's the evidence? Huawei 
just had the largest single year in
the history of their company.
How many chips did they ship?
A ton. Millions. Millions is 
way more than Anthropic has.
There's a question of how much logic SMIC can 
chip, and there's a question of how much memory—
I'm telling you what it is.
They have plenty of logic,
and they have plenty of HBM2 memory.
Right. But as you know, the bottleneck
often in training and doing inference on 
these models is the amount of bandwidth.
So if you have HBM2… I don't know the numbers 
offhand but versus the newest thing you
have, there could be almost an order of magnitude 
difference in memory bandwidth, which is huge.
Huawei is a networking company.
But that doesn't change the fact
that you need EUV for the most advanced HBM.
Not true. Not at all true. You could gang
them together, just like we 
gang them together with NVL72.
They've already demonstrated silicon photonics, 
connecting all of this compute together into
one giant supercomputer. Your premise is 
just wrong. The fact of the matter is,
their AI development is going just fine.
The best AI researchers in the world,
because they're limited in compute, they 
also come up with extremely smart algorithms.
Remember, I just said that Moore's 
law is advancing about 25% per year.
However, through great computer science, we 
could still improve algorithm performance by 10x.
What I'm saying is that great computer 
science is where the lever is.
There is no question, MoE is a great invention.
There's no question, all the incredible attention
mechanisms reduce the amount of compute.
We have got to acknowledge that most of the
advances in AI came out of algorithm 
advances, not just the raw hardware.
Now, if most advances came from algorithms 
and computer science and programming,
tell me that their army of AI researchers 
is not their fundamental advantage. We see
it. DeepSeek is not an inconsequential advance.
The day that DeepSeek comes out on Huawei first,
that is a horrible outcome for our nation.
Why is that? Because currently you can have
a model like DeepSeek that can run on 
any accelerator, if it's open source.
Why would that stop being the case in the future?
Suppose it doesn’t. Suppose it's 
optimized for Huawei, suppose it's
optimized for their architecture.
It would put ours at a disadvantage.
You described a situation that 
I perceive to be good news.
A company developed software, developed 
an AI model, and it runs best on
the American tech stack.
I saw that as good news.
You set it up as a premise that it was bad news.
I'm going to give you the bad news,
that AI models around the world are developed 
and they run best on non-American hardware.
That is bad news for us.
I guess I just don't see the
evidence that there's these huge disparities that 
would prevent you from switching accelerators.
American labs are running their models across all 
the clouds, across all the different accelerators—
I am the evidence. You take a model 
that's optimized for Nvidia and you
try to run it on something else.
But American labs do that.
And they don't run better. Nvidia's success 
is perfect evidence. The fact that AI models
are created on our stack, run best on our 
stack, how is that illogical to understand?
Anthropic's models are run on GPUs, they're 
run on Trainium, they're run on TPUs.
A lot of work has to go into it to change.
But go to the global south, go to the Middle East.
Coming out of the box, if all of the AI models 
run best on somebody else's tech stack, you've
got to be arguing some ridiculous claim right now 
that that's a good thing for the United States.
But I guess I don't understand the argument.
Say Chinese companies get to
the next Mythos first.
They find all the security
vulnerabilities in American software 
first, but they can do it on Nvidia
hardware and they ship it to the global south.
They do it on Nvidia hardware. How is that good?
Okay, it runs on Nvidia hardware—
It's not good. It's not good.
Right.
It's not good. So let's not let it happen.
Why do you think it's perfectly fungible, that 
if you didn't ship them compute it would exactly
be replaced by Huawei? They are behind, 
right? They have worse chips than you.
It's completely… There's evidence right 
now. Their chip industry's gigantic.
You can just look at the flop or 
bandwidth or memory comparisons between
the H200 and the Huawei 910C.
It's like half to a third.
They use more of it. They use twice as many.
It seems like your argument is they have all
this energy that's ready to go, right?
And they need to fill it with chips.
And they're good at manufacturing.
And I'm sure eventually they would be
able to just out-manufacture everybody.
But there are these few critical years.
What is the critical year you're talking about?
These next few years. We've got these models that
are going to be able to do all the cyber attacks.
In that case, if the next years are critical,
then we have to make sure that all of the 
world's AI models are built on the American
tech stack, in these critical years.
If they're built on the American tech stack,
how would that prevent them, if they have 
more advanced capabilities, from launching
the Mythos-equivalent cyber attacks?
There's no guarantee either way.
But if you have it early, we can prepare for it.
Listen, why are you causing one layer of the AI
industry to lose an entire market so that you 
could benefit another layer of the AI industry?
There are five layers and every 
single layer has to succeed.
The layer that has to succeed most 
is actually the AI applications.
Why are you so fixated on that AI model? 
That one company? For what reason?
Because those models make possible 
these incredibly offensive capabilities,
and you need compute to run them.
The energy, the chips, and the
ecosystem of AI researchers make it possible.
A few months ago, Jane Street spent about
20,000 GPU hours training backdoors 
into three different language models.
Then they challenged my audience 
to find the trigger phrases.
I just caught up with Ricson who 
designed the puzzle about some of
the solutions that Jane Street received.
“If you think the base model was here and
the backdoor model was here, you can kind of 
linearly interpolate the weights to adjust the
strength of the backdoor, but you can also 
extrapolate it to make the backdoor even
stronger. And in some cases, if you make it 
strong enough the model will just regurgitate
what the response phrase was supposed to be.”
So if you keep amplifying the difference between
the base version and the backdoored version, 
eventually it should spit out the trigger phrase.
But this technique only worked 
on two out of the three models.
Even Ricson isn't sure why 
it didn't work on the other.
Being able to verify that a model only 
does what you think it does is one of
the most important open questions in AI security.
If this is the kind of problem that excites you,
Jane Street is hiring researchers and engineers. 
Go to janestreet.com/dwarkesh to learn more.
Okay, stepping back, it has to be the case that 
China is able to build enough 7nm capacity.
And remember, they're still stuck on 
7nm while you'll move on to 3nm and then
2nm or 1.6nm with Feynman.
So while you're on 1.6nm,
they're still going to be on 7nm, and they have to 
produce enough of it to make up for the shortfall.
They have so much energy that the more chips 
you give them, the more compute they'd have.
So it comes out as a question of, 
ultimately they are getting more compute.
Compute is an input to training and inference—
Listen, I just think you speak in absolutes.
I think the United States ought to be ahead.
The amount of compute in the United States is
100x more than anywhere else in the world.
The United States ought to be ahead. Okay.
The United States is ahead. Nvidia 
builds the most advanced technologies.
We make sure that the US labs are the first to 
hear about it and have the first chance to buy it.
And if they don't have enough 
money, we even invest in them.
The United States ought to be ahead.
We want to do everything we can to make
sure the United States is ahead.
Number one point, do you agree?
We're doing everything we can to do that.
But how is shipping chips to China keeping the
US ahead if they’re bottlenecked on compute?
No, no. We've got Vera Rubin
for the United States.
We have Vera Rubin for the United States.
Now, am I in the United States?
Do you consider me part of the United States?
Yes.
Nvidia. You consider
Nvidia a United States company? Okay. Number one, 
why is it that we don't come up with a regulation
that's more balanced so that Nvidia can win 
around the world instead of giving up the world?
Why would you want the United 
States to give up the world?
The chip industry is part 
of the American ecosystem.
It's part of American technology leadership.
It's part of the AI ecosystem. It's part of
AI leadership. Why is it that your policy, 
your philosophy, leads to the United States
giving up a vast part of the world's market?
I guess the claim here is… Dario had this quote
where he said that it's like Boeing bragging that 
we're selling North Korea nukes, but the missile
casings are made by Boeing.
And that's somehow
enabling the US technology stack.
Fundamentally, you're giving them this capability.
Comparing AI to anything that 
you just mentioned is lunacy.
But AI is similar to enriched uranium, right?
It can have positive uses,
it can have negative uses.
We still don't want to send enriched
uranium to other countries.
Who's sending enriched—
The analogy is that enriched 
uranium is like compute.
It's a lousy analogy. It's an illogical analogy.
But if that compute can run a model that can do
zero-day exploits against all American 
software, how is that not a weapon?
First of all, the way to solve that problem 
is to have dialogues with the researchers
and dialogues with China, and dialogues 
with all the countries to make sure that
people don't use technology in that way.
That's a dialogue that has to happen. Okay?
Number one. Number two, we also need to 
make sure that the United States is ahead,
that Vera Rubin, Blackwell, is available in the 
United States in abundance, mountains of it.
Obviously, our results would 
show it. Abundance, tons of it.
The amount of computing we have is great.
We have amazing AI researchers here.
It's great. We ought to stay ahead.
However, we also have to recognize that
AI is not just a model. AI is a five-layer cake. 
The AI industry matters across every single layer,
and we want the United States to win at 
every single layer, including the chip layer.
Conceding the entire market is not going 
to allow the United States to win the
technology race long-term in the chip layer, 
in the computing stack. That is just a fact.
I guess then the crux comes down 
to, how does selling them chips
now help us win in the long term?
Tesla sold extremely good electric
vehicles to China for a long time. iPhones 
are sold in China, extremely good. They didn't
cause them lock-in. China will still 
make their version of EVs and they're
dominating. Their smartphones are dominating.
When we started the conversation today,
you acknowledged that Nvidia's position is very 
different. You used words like moat. The single
most important thing to our company is the 
richness of our ecosystem, which is about
developers. 50% of the AI developers are in China.
The United States should not give that up.
But we have a lot of Nvidia developers 
in the US, and that doesn't prevent
American labs from also being able to 
use other accelerators in the future.
In fact, right now they're using other 
accelerators as well, which is fine and great.
I don't see why that wouldn't be the case in China 
as well, if you sell them Nvidia chips, just the
same way that Google can use TPUs and Nvidia—
We have to keep innovating and, as you probably
know, our share is growing, not decreasing.
The premise that even if we competed in China,
that we're going to lose that 
market anyways… You're not
talking to somebody who woke up a loser.
That loser attitude, that loser premise
makes no sense to me. We're not a car. We 
are not a car. The fact that I can buy this
car brand one day and use another car brand 
another day, easy. Computing is not like that.
There's a reason why the x86 deal exists.
There's a reason why ARM is so sticky.
These ecosystems are hard to replace.
It costs an enormous amount of time and
energy, and most people don't want to do it.
So it's our job to continue to nurture that
ecosystem, to keep advancing the technology 
so that we can compete in the marketplace.
Conceding a marketplace based on the 
premise you described, I simply can't
acknowledge that. It makes no sense. Because 
I don't think the United States is a loser.
Our industry is not a loser.
That losing proposition,
that losing mindset, makes no sense to me.
Okay. I'll move on. I just want to make sure that—
You don't have to move on. I'm enjoying it.
Okay, great. Then I won't. I appreciate that.
But I think maybe the crux… and thanks for 
walking around the circles with me, because I
think it helps bring out what the crux here is.
The crux is you're going to extremes. Your
argument starts from extremes. That if we 
give them any compute at all in this narrow
moment, we will lose everything.
No, I think what my argument is—
Those extremes, they're childish.
Let me just make my argument for myself.
The idea is not that there is 
some key threshold of compute.
It's that any marginal compute is helpful.
So if you have more compute, you can
train a better model.
And I just want you to
acknowledge that any marginal sales for the 
American technology industry is beneficial.
I actually don't… If the AI models that 
run on those chips are capable of cyber
offensive capabilities, or the chips are training 
models with cyber capabilities and running more
instances of those models, it is not a nuclear 
weapon, but it enables a weapon of a kind.
The logic that you use, you might as 
well say it to microprocessors and DRAMs.
You might as well say it to electricity.
But in fact we do have export controls
on the technology that is relevant 
to making the most advanced DRAM.
We have all kinds of export controls on 
China for all kinds of chip-making stuff.
We sell a lot of DRAM and CPUs 
into China, and I think it's right.
I guess this goes back to the 
fundamental question of, is AI different?
If you have the kind of technology where they 
can find these zero-days in software, is that
something where we want to minimize China's 
ability to get there first, to deploy it widely?
We want the United States to 
be ahead. We can control that.
How do we control that if the chips are already 
there and they're using them to train that model?
We have tons of compute. We 
have tons of AI researchers.
We're racing as fast as we can.
Again, we have more nuclear weapons
than anybody else, but we don't want 
to send enriched uranium anywhere.
We're not enriched uranium. It's a chip, and 
it's a chip that they can make themselves.
But there's a reason they're buying it from you.
We have quotes from the founders of
Chinese companies that say that 
they’re bottlenecked on compute.
Because our chips are better. On 
balance, our chips are better.
There's just no question about it.
In the absence of our chip… Can you
acknowledge that Huawei had a record year?
Can you acknowledge that a whole bunch
of chip companies have gone 
public? Can you acknowledge that?
Yes.
Can you also acknowledge that we used
to have a very large share in that market, and 
we no longer have a large share in that market?
We can also acknowledge that China is about 
40% of the world's technology industry.
To concede that market for 
the United States technology
industry is a disservice to our country.
It is a disservice to our national security.
It is a disservice to our technology 
leadership, all for the benefit of one company.
It makes no sense to me.
I guess I'm confused. It feels
like you're making two different statements.
One is that we're going to win this competition
with Huawei because our chips are going to 
be way better if we're allowed to compete.
Another is that they would be doing 
the same exact thing without us anyway.
How can both of those things 
be true at the same time?
It's obviously true. In the absence of a 
better choice, you'll take the only choice
you have. How is that illogical? It's so logical.
The reason they want Nvidia chips is that they're
better.
Yeah.
Better is more compute. More compute 
means you can train a better model.
No, it's just better. It's better because 
it's easier to program. We have a better
ecosystem. But whatever the better is, 
whatever the better is… And of course
we're going to send them compute. So what? The 
fact of the matter is that we get to benefit.
Don't forget, we get the benefit 
of American technology leadership.
We get the benefit of developers 
working on the American tech stack.
We get the benefit, as those AI models diffuse out 
into the rest of the world, that the American tech
stack is therefore the best for it.
We can continue to advance and
diffuse American technology.
That, I believe, is a positive.
It's a very important part of 
American technology leadership.
Now, the policies that you're advocating resulted 
in the American telecommunications industry being
policied out of basically the world, to 
the point where we don't control our own
telecommunications anymore.
I don't see that as smart.
It's a little narrow-minded, and it 
led to unintended consequences that
I'm describing to you right now that you 
seem to have a very hard time understanding.
Okay, let's just step back. It seems 
like the crux here is there's a potential
benefit and there's a potential cost.
What we're trying to figure out is, is
the benefit worth the cost?
I guess I'm trying to get
you to acknowledge the potential cost.
Compute is an input to training powerful models.
Powerful models do have powerful offensive 
capabilities, like cyber attacks.
It is a good thing that American companies got 
to Mythos-level capabilities first, and then now
they're going to hold off on those capabilities 
so that the American companies and American
government can make their software more protected 
before that level of capability was announced.
If China had had more compute or more 
crowd compute, if they could have made
a Mythos-level model earlier and deployed 
it widely, that would have been very bad.
One of the reasons that hasn't happened 
is that we have more compute thanks
to companies like Nvidia in America.
That is a cost of sending it to China.
So let's leave the benefit aside for a second.
Do you acknowledge that this is a potential cost?
I'll also tell you the potential cost is we allow 
one of the most important layers of the AI stack,
the chip layer, to concede an entire 
market—the second largest market in
the world—so that they could develop scale, so 
that they could develop their own ecosystem,
so that future AI models are optimized in a 
very different way than the American tech stack.
As AI diffuses out into the rest of the world, 
their standards, their tech stack, will become
superior to ours, because their models are open.
I guess I just believe enough in Nvidia's kernel
engineers and CUDA engineers to 
think that they could optimize—
AI is more than kernel optimization, as you know.
Of course, but there are so many things you can
do, from distilling to a model 
that's well-fit for your chips.
We're going to do our best.
You have all the software. It's just
hard to imagine that there's a long-term lock-in 
to the Chinese ecosystem, even if they have a
slightly better open source model for a while.
China is the largest contributor to open source
software in the world. Fact. China's 
the largest contributor to open models
in the world. Fact. Today it's built on the 
American tech stack, Nvidia’s. Fact. All five
layers of the tech stack for AI are important.
The United States ought to go win all five of
them. They're all important. The 
one that is the most important,
of course, is the AI application layer.
The layer that diffuses into society,
the one that uses it most will benefit 
from this industrial revolution most.
But my point is that every layer has to succeed.
If we scare this country into thinking that AI is
somehow a nuclear bomb, so that everybody 
hates AI and everybody's afraid of AI,
I don't know how you're helping the United 
States. You're doing it a disservice. If
we scare everybody out of doing software 
engineering jobs because it's going to kill
every software engineering job—and we don't have 
any software engineers as a result of that—we're
doing a disservice to the United States.
If we scare everybody out of radiology so
nobody wants to be a radiologist because computer 
vision is completely free and no AI is going to do
a worse job than a radiologist, we misunderstand 
the difference between a job and a task.
The job of a radiologist is patient care.
The task is to read a scan.
If we misunderstand that so profoundly and 
we scare everybody out of going to radiology
school, we're not going to have enough 
radiologists and good enough healthcare.
So I'm making the case that when you make a 
premise that is so extreme, everything goes
from zero or infinity, we end up scaring people 
in a way that's just not true. Life is not like
that. Do we want the United States to be first? Of 
course we do. Do we need to be a leader in every
layer of that stack? Of course we do. Of course 
we do. Today you're talking about Mythos because
Mythos is important. Sure. That's fantastic. But 
in a few years time, I'm making you the prediction
that when we want the American tech stack, when 
we want American technology to be diffused around
the world—out to India, out to the Middle East, 
out to Africa, out to Southeast Asia—when our
country would like to export, because we would 
like to export our technology, we would like to
export our standards, on that day, I want you 
and I to have that same conversation again.
I will tell you exactly about today's 
conversation, about how your policy
and what you imagined literally caused the 
United States to concede the second largest
market in the world for no good reason at all. We 
shouldn't concede it. If we lose it, we lose it.
But why do we concede it?
Now nobody is advocating an all or nothing.
Nobody's advocating all or nothing, meaning 
we ship everything to China at all times.
Nobody's advocating that. We should 
always have the best technology here.
We should always have the most 
technology here, and the first.
But we should also try to 
compete and win around the world.
Both of those things can simultaneously happen.
It requires some amount of nuance, some amount
of maturity instead of absolutes.
The world is just not absolutes.
Okay. The argument hinges on this. They've 
built models that are specified for the
best chips that they make in a few years.
Those chips get exported around the world. That
sets the standard. Because of EUV export controls, 
as we said, you're going to move on to 1.6nm.
They're still going to be on 7nm, 
even after a few years from now.
It may make sense that domestically they would 
prefer, "Hey, we've got so much energy, we can
manufacture at scale. We'll still keep using 7nm." 
But on the exporting thing, their 7nm chips have
to be competitive against your 1.6nm chips.
Their models have to be so far optimized for
the 7nm that it's better to run their models 
on 7nm than to run their models on your 1.6nm.
Can we just look at the facts then?
Is Blackwell 50 times more advanced
lithography than Hopper? Is it 50 times? Not even 
close. I just kept saying it over and over again.
Moore's Law is dead. Between Hopper and Blackwell, 
from the transistors themselves, call it 75%.
It was three years apart, 75%. Blackwell is 50 
times Hopper. My point is, architecture matters.
Computer science matters. Semiconductor physics 
matters as well, but computer science matters.
The impact of AI largely comes 
from the computing stack,
which is the reason why CUDA is so effective, 
which is the reason why CUDA is so beloved.
It's an ecosystem, a computing architecture 
that allows for so much flexibility that
if you wanted to change an architecture 
completely—create something like MoE, create
something like diffusion, create something 
that's disaggregated—you could do so. It's
easy to do. So the fact of the matter 
is, AI is about the stack above as much
as it is about the architecture below.
To the extent that we have architectures
and software stacks that are optimized for our 
stack, for our ecosystem, it is obviously good,
because we started the conversation today 
about how Nvidia's ecosystem is so rich.
Why do people always love programming CUDA first? 
They do. They do. So do the researchers in China.
But if we are forced to leave China, if 
we're forced to leave China, first of all,
it's a policy mistake. Obviously it has backlash. 
It has turned out badly for the United States.
It enabled, it accelerated their chip industry.
It forced all of their AI ecosystem to focus on
their internal architectures.
It's not too late, but
nonetheless it has already happened.
You're going to see in the future,
they're not stuck at 7nm, obviously. 
They're good at manufacturing. They
will continue to advance from 7nm and beyond.
Now, is there a 10x difference between 5nm and
7nm? The answer is no. Architecture matters. 
Networking matters. That's why Nvidia bought
Mellanox. Networking matters. Energy 
matters. So all of that stuff matters.
It's not simplistic, like the 
way you're trying to distill it.
We can move on from China, but that 
actually raises an interesting question.
We were discussing earlier these 
bottlenecks at TSMC and memory and so forth.
So if we're in this world where you're 
already the majority of N3—and at some
point you'll be N2 and you'll be a majority of 
that—do you see that you could go back to N7,
the spare capacity at an older process node, and 
say, "Hey, the demand for AI is so great and our
capacity to expand the leading edge is not meeting 
it, so we're going to make a Hopper or Ampere,
but with everything we know about numerics today 
and all the other improvements you described"?
Do you see that world happening before 2030?
It's not necessary to. The reason for that is
because with every generation, the architecture 
is more than just the transistor scale.
You're doing so much engineering and 
packaging and stacking, and the numerics
and the system architecture.
When you run out of capacity,
to easily go back to another node… That's 
a level of R&D that no one could afford.
We could afford to lean forward.
I don't think we could afford to go back.
Now, if the world simply says… If on that 
day, let's do the thought experiment,
on that day we go, "Listen, we're just never 
going to have more capacity ever again."
Would I go back and use 7nm?
In a heartbeat, of course I would.
One question somebody I was talking to 
had is, why doesn't Nvidia run multiple
different chip projects at the same time 
with totally different architecture?
So you could do something like 
a Cerebras-style wafer scale.
You could do a Dojo-style huge package.
You could do one without CUDA.
You have the resources and the engineering 
talent to do all of these in parallel.
So why put all the eggs in one basket, 
given who knows where AI might go and
architectures might go?
Oh, we could. It's just
that we don't have a better idea.
We could do all of those things.
It's just not better. We simulate it all 
in our simulator, proveably worse. So we
wouldn't do it. We're working on exactly 
the projects that we want to work on.
If the workload were to change dramatically—and 
I don't mean the algorithms, I actually mean the
workload, and that depends on the shape of the 
market—we may decide to add other accelerators.
For example, recently we added Groq, and we're 
going to fold Groq into our CUDA ecosystem.
We're doing that now because the value 
of tokens has gone up so high that
you could have different pricing of tokens.
Back in the old days, just a couple years ago,
tokens were either free or barely expensive.
But now you can have different customers,
and those customers want different answers.
Because the customers make so much money—for
example, our software engineers—if I can 
give them much more responsive tokens so
that they're even more productive than 
they are today, I would pay for it.
But that market has only recently emerged.
So I think we now have the ability to have
the same model, based on the response 
time, have different segments.
That's the reason why we decided to expand 
the Pareto frontier and create a segment
of inference that is faster response 
time, even though it's lower throughput.
Until now, higher throughput is always better.
We think there could be a world where there
could be very high ASP tokens, and even 
though the throughput is lower in the
factory, the ASPs make up for it.
That's the reason why we did it.
But otherwise, from an architecture perspective, 
if I had more money, I would put more behind
Nvidia’s architecture.
I think this idea of extremely
premium tokens and just the disaggregation of 
the inference market is a very interesting.
The segmentation of it.
Yeah. Alright, final question. Suppose the deep
learning revolution didn't happen. What would 
Nvidia be doing? Obviously games, but given—
Accelerated computing, the same 
thing we've been doing all along.
The premise of our company is that Moore's 
law is going to… General purpose computing
is good for a lot of things, but for 
a lot of computation it's not ideal.
So we combined an architecture called 
a GPU, CUDA, to a CPU, so that we can
accelerate the workload of the CPU.
Different kernels of code or
algorithms could be offloaded onto our GPU.
As a result, you speed up an application by 100x,
200x. Where can you use that? Obviously 
engineering and science and physics,
data processing, computer graphics, 
image generation, all kinds of things.
Even if AI doesn't exist today, 
Nvidia would be very, very large.
The reason for that is fairly fundamental, which 
is that the ability for general purpose computing
to continue to scale has largely run its course.
And the only way… Not the only way, but the way to
do that is through domain-specific acceleration.
One of the domains that we started with was
computer graphics, but there are many 
other domains. There's all kinds.
Particle physics and fluids, structured 
data processing, all kinds of different
types of algorithms that benefit from CUDA.
Our mission was really to bring accelerated
computing to the world and advance the type of 
applications that general purpose computing can't
do, and scale to the level of capability that 
helps break through certain fields of science.
Some of the early applications were molecular 
dynamics, seismic processing for energy discovery,
image processing of course, all of those kinds 
of fields where general purpose computing
is just simply too inefficient to do so.
If there were no AI, I would be very sad.
But because of the advances that we made in 
computing, we democratized deep learning.
We made it possible for any researcher, 
any scientist, anywhere, any student,
to be able to access a PC or a GeForce 
add-in card and do amazing science.
That fundamental promise hasn't 
changed, not even a little bit.
If you watch GTC, there's the whole beginning part 
of it. None of it's AI. That whole part of it with
computational lithography or our quantum chemistry 
work, data processing work, all of that stuff is
unrelated to AI. And it's still very important. 
I know that AI is very interesting and quite
exciting, but there's a lot of people doing a 
lot of very important work that's not AI related,
and tensors are not the only way that you 
compute it. We want to help everybody.
Jensen, thank you so much.
You're welcome. I enjoyed it.
Me too.