Episode Summary
Executive Summary: Jensen Huang discusses NVIDIA's pivotal role in the AI revolution, emphasizing three scaling laws (pre-training, post-training, inference) that will drive a 1 billion X increase in compute demand. He announces a major partnership with OpenAI to build self-owned AI infrastructure, positioning OpenAI as a future multi-trillion dollar hyperscaler. Huang argues that general-purpose computing is over, replaced by accelerated computing and AI, and that NVIDIA's annual release cycle and extreme co-design create an insurmountable competitive moat. He also addresses geopolitical tensions with China, advocating for competition rather than decoupling, and highlights the need for the US to maintain its brand as a destination for global talent.
Main Topics: Three Scaling Laws of AI: Jensen identifies three scaling laws: pre-training, post-training (reinforcement learning), and inference-time thinking. The latter, where AI 'thinks' before answering, will increase compute requirements by a billion times, driving exponential demand for NVIDIA's hardware. OpenAI Partnership and Stargate: NVIDIA is partnering with OpenAI to build self-owned AI infrastructure, including a $100 billion investment in Stargate. Jensen views OpenAI as the next multi-trillion dollar hyperscaler and sees this as a smart investment opportunity. Competitive Moat and Annual Release Cycle: NVIDIA's annual release cycle (Hopper, Blackwell, Rubin, Feynman) and extreme co-design across chips, systems, and software deliver 30X performance gains per generation, making it nearly impossible for competitors to catch up. Geopolitics and China: Jensen argues against decoupling from China, stating that US export restrictions have allowed Huawei to gain monopoly profits and accelerate. He advocates for competition and open markets, warning that Chinese AI is 'nanoseconds' behind the US. AI as an Economic Equalizer: AI will close the technology divide by allowing anyone to interact using natural language. Jensen believes AI will augment human intelligence, creating new jobs and growing the economy, rather than destroying jobs. Sovereign AI and National Security: Every country needs sovereign AI infrastructure to encode its culture and values. Jensen notes that AI is as critical as energy and communications infrastructure, and that the US must lead in this race.
Key Arguments: General-purpose computing is over; the future is accelerated computing and AI. Inference-time reasoning will increase compute demand by a billion times, not just 100x or 1000x. NVIDIA's annual release cycle and extreme co-design deliver 30X performance gains per generation, creating an unassailable competitive moat. OpenAI is likely to become the next multi-trillion dollar hyperscaler, making NVIDIA's investment a smart bet. US export restrictions on China have backfired, allowing Huawei to gain monopoly profits and accelerate its AI capabilities. AI will augment human intelligence, not replace jobs, leading to economic growth and new opportunities. Every country needs sovereign AI infrastructure to encode its culture and values, similar to energy and communications infrastructure.
Data Points: Inference compute increase: 1 billion X - Due to chain-of-reasoning and thinking AI, inference compute demand will increase by a billion times. NVIDIA performance gain (Hopper to Blackwell): 30X - Blackwell delivers 30X the performance of Hopper in one year, far exceeding Moore's Law. OpenAI weekly active users: 800 million - OpenAI has 800 million weekly active users, growing exponentially. AI revenue estimate for 2026: $100 billion - NVIDIA estimates $100 billion in AI revenue in 2026, excluding Meta and recommender engines. Global GDP augmentation by AI: $10 trillion - AI could augment $50 trillion of human labor, requiring $10 trillion in AI infrastructure. Alibaba data center power increase: 10X - Alibaba plans to increase data center power by 10x by the end of the decade. NVIDIA's market cap prediction: $10 trillion - Jensen believes NVIDIA will likely be the first $10 trillion company.
Pivotal Quotes: "Nobody needs atomic bombs. Everybody needs AI." — Jensen Huang: Contrasting AI with nuclear weapons, emphasizing AI's universal necessity. "I think that OpenAI is likely going to be the next multi-trillion dollar Hyperscale company." — Jensen Huang: Explaining the rationale behind NVIDIA's investment in OpenAI's Stargate project. "We now have three scaling laws, not one." — Jensen Huang: Introducing pre-training, post-training, and inference-time thinking as the three scaling laws driving AI compute demand.
Implications: The podcast signals that AI infrastructure spending will explode, driven by reasoning models and sovereign AI. NVIDIA's dominance is likely to continue due to its system-level innovation and annual release cadence. Geopolitically, the US must balance competition with China while maintaining its edge in talent and technology. Investors should expect NVIDIA's revenue to grow significantly beyond current consensus estimates.
From the Episode
As maybe we did nuclear in the 1940s, right? We don't have a Manhattan Project today, at least funded by the government, but it's funded by NVIDIA, it's funded by OpenAI, it's funded by Meta, it's funded by Google. We have companies today the size of nation states, and thank God for America, right? Who are funding something that it appears to me presidents and kings think is existential to their future economic and national security. Would you agree with that? Nobody needs atomic bombs. Everybody needs AI. Well said. Okay. Here, here. Yeah. Here, here. And so that's a very, very large difference. AI, as you know, is modern software. That's where I started. From general purpose computing to accelerated computing, from human-written code line of time to AI-written code, that foundation can't be forgotten. We've reinvented computing. There's not a new species on Earth. We just reinvented computing, and everybody needs computing.
I think that OpenAI is likely going to be the next multi-trillion dollar Hyperscale combined. Jensen, great to be back. Of course, with my partner, Clark Tang. You know, I can't believe it's. Welcome to NVIDIA. Oh, and nice glasses. Those actually look really good on you. The problem is now everybody's going to want you to wear them all the time. They're going to say, where are the red glasses? I can vouch for that. So it's been over a year since we did the last pod. Over 40% of your revenue today is inference. But inference is about ready because of chain of reasoning. Yeah. Right? It's about. It's about to go up by a billion times. Right. By a million X, by a billion X. That's right. That's the part that most. People have, you know, haven't completely internalized. This is that industry we were talking about, but this is the industrial revolution. Honestly, it's felt like you and I have had a continuation of the pod every day since then. You know, in AI time, it's been about 100 years. I was re-watching the pod recently, and the many things that we talked about that stood out. The one that was probably most profound for me was you pounding the table. That, you know, remember at the time, there was kind of a slump in terms of pre-trade.
So think before you answer. Yeah. And so now you have three scaling laws. The longer you think, the better the quality answer you get. While you're thinking, you do research, you go check on some ground truth, and you learn some things, you think some more, you go learn some more, and then you generate an answer. Don't just generate right off the bat. And so thinking, post-training, pre-training, we now have three scaling laws, not one. You knew that last year, but is your level of confidence this year in the inferences going to one? 1 billion X and where that will take the levels of intelligence. Is it higher? Are you more confident this year than you were a year ago? I'm more confident this year. And the reason for that is because look at the agentic systems now. And AI is no longer a language model, and AI is a system of language models. And they're all running concurrently, maybe using tools. Some of us are using tools, some of us doing research. And yeah, there's a whole bunch of stuff. And it's all multimodality. And look at all the video that's being generated. I mean, it's
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