Jensen Huang

Evan Carmichael - Believe: Unlocking Your True Potential: How to Take Risks and Still Succeed | Jensen Huang's Guidance in 25 Minutes

Worth a targeted listen for the concrete account of CUDA crushing margins and the discussion of defending the bet to NVIDIA’s board. The broader lessons about first principles, conviction, and focusing on the present substantially repeat material elsewhere in the corpus, so casual listeners can skim rather than commit to the full episode.

Yeah, because it was a $0 billion we went after. But it cost so much to go after that $0 billion market. It actually crushed the 1 billion dollar market we were enjoying. And the reason for that is because CUDA added a ton of cost into our chips. But there were no applications and there are no applications. Customers don't value the product and they won't pay you a premium for it. And if people aren't willing to pay you for it, but your cost went up, then your gross margins get crushed. And we got. Our market cap was low and then went down to really low. It was like.

Jensen Huang discusses how to distinguish conviction from stubbornness, tolerate calculated failure, and change direction when evidence demands it. He also reflects on CUDA's long path from financial disaster to a foundational computing technology, and recommends focusing on current work, continuous learning, and purposeful effort.

跨国串门儿计划: #652.黄仁勋：打造英伟达的思维模式

The supplied digest contains no topics, attributable exchanges, or candidate quotes to evaluate. Skip this entry unless a substantive transcript becomes available.

No usable, self-contained exchange can be selected from the supplied transcript. The available chunks are either far outside the permitted timed range or too fragmentary to support a grounded pull quote.

Evan Carmichael - Believe: Unlocking Your True Potential: Jensen Huang's Top 10 Rules for Success

This motivational compilation offers a few crisp management maxims but little that is distinctive beside Huang’s fuller discussions of conviction, strategy, and NVIDIA’s gaming origins elsewhere in the corpus. Casual fans can skip it; completionists may appreciate the concise strategy formulation.

Strategy is not words. Strategy is action. Yeah. And so if the company has a set of strategies, but the people's actions, their top five things are not that, then they're obviously not executing the strategy. And so the strategy turns out isn't what I say, it's what they do.

Jensen Huang discusses the difficulty of building ambitious companies, the need to prioritize time, and the importance of translating strategy into action. He also describes business fundamentals and the unconventional perspective that led NVIDIA to pursue graphics for video games before a large market was widely recognized.

Y Combinator Startup Podcast: Jensen Huang: The Mindset That Built NVIDIA

Worth a selective listen for the unusually concrete account of NVIDIA admitting its original technology was broken, abandoning the Sega contract, and rebuilding from textbooks. The broader lessons on conviction, curiosity, AI jobs, and resilience substantially revisit material heard elsewhere.

And and then somebody told me it turns out none of us knew how to do it the right way. And not only did we choose the wrong technology we didn't know how to do it the right way. And so so that that was a big day for me. I had a couple of couple of $60 you know a couple of $100 in my pocket and so I went down to Fry's and I bought three textbooks. And and the textbooks was about OpenGL and how to design uh pipelines. I brought it back to the company and gave it to the engineers, and here we are. Uh we reinvented computer graphics, we're the world leader in modern computer graphics, we invented most of the major breakthroughs

Jensen Huang discusses NVIDIA's early technology failure, its shift toward accelerating algorithmic domains, and the importance of curiosity, first-principles reasoning, and systems thinking. He also argues that AI will automate tasks while expanding opportunities, and urges founders to keep learning and persist through difficulty.

Bloomberg Tech: AI Industry’s Circular Financing Deals, Apple Smart Glasses

This is a narrow repackaging of the South Korea interview circulated two days earlier, with the partnership figures and tenfold semiconductor forecast already covered across several appearances. Skip unless you want the shortest available version of the SK deal.

We announce a big partnership with SK Group where our companies are going to enter into a business partnership where we do over five hundred billion dollars of business with each other, whether it's consumption and purchasing of memories or selling AI supercomputers to them as a scale out to gigawatts of AI factories. There's a whole bunch of other announcements.

Jensen Huang discusses NVIDIA’s expanding partnership with SK Group and the importance of South Korea to AI infrastructure development. He also argues that AI agents and robots will dramatically expand computing demand, requiring the semiconductor industry to scale substantially over the next decade.

跨国串门儿计划: #648.黄仁勋谈 AI 末日论者、就业担忧与中国开源模型

Skip this entry: the supplied transcript yields no usable, attributable Jensen Huang exchange within the required duration limits. Its advertised themes are also well covered elsewhere in the corpus.

The transcript contains timed chunks, but the substantive-looking chunks are each longer than the maximum permitted 45-second range. No self-contained candidate can therefore be selected without violating the timing constraint.

Bloomberg Intelligence: Special Edition: Nvidia CEO Jensen Huang on Investing in South Korea

Useful but heavily syndicated Bloomberg material that repeats Huang’s established arguments about agent-driven chip demand, infrastructure limits, and open models. Skip if you have heard the companion South Korea episodes; otherwise, the concrete supply-chain bottlenecks are the best reason to listen.

>> Well, we we don't have enough bits. We're constrained in in HBM memories, LPDDR memories. We're constrained in just about every part of the supply chain. We're even constrained now with land and power and construction workers to set up the data centers. I think this is one one of the areas that's that's is going to make sure that we continue the build out in a throttled way, you know, for a decade. And the reason for that is because these infrastructure, unlike electronics electronic devices like PCs and phones and things like that, it's really really hard to scale up land power and shell. And so all of all of the supply chain just really needs to get built out over the years. I think we

Jensen Huang discusses NVIDIA's expanded partnerships in South Korea, including semiconductor supply, AI cloud capacity, and broader infrastructure growth. He also addresses the future scale of AI computing and argues for a combination of open and closed models, emphasizing control, security, and distributed defense.

Bloomberg Daybreak: Asia Edition: Special Edition: Nvidia CEO Jensen Huang on Investing in South Korea

Most of this is duplicated across the same Bloomberg interview package and reiterates Huang’s familiar case for explosive AI-compute demand. The specific Hugging Face security example gives the open-model argument some added substance, but listeners who heard a sibling edition can skip this one.

But this is exactly the reason why you want to have open models, because in that case, they use GLM five point two to identify where the vulnerability was, where the penetration was, and we're able to quickly identify them and patch it up. And so this is a perfect example of self defense that's necessary, as a perfect example of diversity of AI technology being necessary, and so a perfect example of why open models and open capabilities for self defense is really important.

Jensen Huang discusses Nvidia's partnerships and investment plans in South Korea, as well as the infrastructure and semiconductor expansion he expects from widespread AI adoption. He also argues for a mixed ecosystem of open and closed models and says open models can improve control, resilience, and self-defense.

Bloomberg Tech: Special Edition: Nvidia CEO Jensen Huang on Investing in South Korea

This is a substantive but highly redundant cut of the same South Korea interview already represented several times in the corpus. Listen only for the partnership specifics or Huang’s nuanced explanation of why open models offer control rather than lower cost.

We announce a big partnership with SK Group where our companies are going to enter into a business partnership where we do over five hundred billion dollars of business with each other, whether it's consump consumption and purchasing of memories or selling AI supercomputers to them as they scale out to gigawatts of AI factories. There's a whole bunch of other announcements. We're investing a billion dollars in neighbor to help the they're the Korea's leading AI cloud.

Jensen Huang discusses NVIDIA's expanding partnerships in South Korea and predicts that AI agents and robots will require a semiconductor industry far larger than today's. He also argues for a blended AI ecosystem, with closed services favored for convenience and open models retained for control, specialization, and resilience.

Bloomberg Talks: Nvidia CEO Jensen Huang Talks Investing in South Korea's AI Boom

Skip unless you specifically want this Bloomberg edit: the substantive material is duplicated almost verbatim across several supplied appearances from the same date. The agent-and-robot demand forecast is the clearest pull, but it is not new here.

But in the future we also have AI agents and robots, and they're going to be using computers. So instead of just a billion people using computers, we're going to have one hundred billion agents and billions of robots all using computers. The computer industry, the chip that's built on top of the chip industry, surely is not big enough, and so this is one of the realizations of the semiconductor industry that now computers are built not just for people to use, but computers are being built for computers to use.

Jensen Huang discusses Korea's role in expanding AI infrastructure, including major memory and cloud partnerships with SK companies. He also outlines his views on semiconductor scaling, competition between China and the United States, and the complementary roles of open and closed AI models in innovation and security.

Hoover Daily Report: Hoover Daily Report | June 10, 2026

Skip: the supplied digest contains no identifiable Jensen Huang remarks, topics, or notable exchanges. There is nothing here to recommend over the substantive appearances in the corpus.

The transcript contains an introduction describing an interview with Jensen Huang, but no direct speech from him. Therefore, there are no usable exchange ranges to extract.

Only in America: Jensen Huang on Vision, Risk, and the GPU | Only In America

Worth hearing for Huang’s vivid, comparatively fresh recollections of arriving in America and navigating belonging. The GPU history, persistence lesson, AI stack, and career advice substantially repeat material elsewhere, so listeners seeking new AI strategy can skip.

My first impression was, was I've never stood in a house with carpets before. And it was the strangest feeling. I felt like I was walking on my bed with my shoes on and, and just everything from, everything from Serial and the morning television as speed racer, and in the afternoon, Partridge Family. And, you know, all, all the candy, the Snickers bars, everything was like, I, I couldn't imagine what this amazing country was. And everything was so beautiful. The cars, everything was just incredible.

Jensen Huang connects his immigrant upbringing and family sacrifices with the opportunities that enabled his education, entrepreneurship, and leadership of NVIDIA. He also explains the company’s long-term computing thesis, the persistence required to pursue it, and his view that AI’s greatest impact will come through practical applications built across the full technology stack.

Training Data: LIVE: Jensen Huang on Building the Dynamo of the Intelligence Age

Much of the discussion reprises Huang’s familiar case for reasoning, agents, AI infrastructure, energy investment, and AI-assisted employment. The standout is his concrete claim that NVIDIA already has hundreds of thousands of sandboxed agents collaborating internally, which makes this worthwhile for followers of enterprise agent deployment but skippable for anyone seeking a broadly fresh thesis.

Now that I told you that AI has become agentic, meaning that it can actually do work by itself. Well, if it can do work by itself, then one agent can communicate with another agent and say, "I have some work to do. Let's team up together and let's do some work." And now you have all these different agents and they're all working together to solve problems inside your company, say. So, inside our company, Constantine knows that we we're huge users of agentic AI. We have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems. All guard railed, all sandboxed, all guard railed and sandboxed, but they're all working with each other.

Jensen Huang frames AI as a new industrial system that generates intelligence, performs useful work, and will operate through networks of specialized agents. He emphasizes investment across the AI economy's layers, urges people and countries to engage with the technology, and argues that AI will often elevate professions rather than erase them.

Meet The Leader: 5 leaders from NVIDIA, Goldman Sachs and more share career advice for uncertain times

A polished but familiar graduation-style summary of Huang’s recurring case for embracing AI. Skip unless you want a concise motivational version of his established views on automation, access, and human purpose.

AI will change every job. But the task and the purpose of a job are not the same. Many tasks will be automated. Some jobs will disappear. But many new jobs and entire new industries will be created. Software coding tasks are increasingly automated, but using AI, software engineers can expand the search for solutions, allowing them to tackle far more ambitious challenges.

Jensen Huang argues that AI will transform every job without eliminating human purpose, while expanding access to computing and creation. He encourages graduates to meet the change with optimism and use new tools to help shape the future.

Satya Nadella - Biography Flash: Biography Flash Satya Nadella Builds the Agentic AI Era at Microsoft Build 2026 Panelist

Jensen Huang joins Satya Nadella to discuss unmetered intelligence and how RTX Spark and the Vera Rubin CPU could run powerful AI agents directly on PCs.

跨国串门儿计划: #567. 黄仁勋：Agent 时代普通人和企业的新生产力，AI 基础设施竞赛下的计算革命

Broad technical coverage, but the agentic AI, AI-factory, rack-scale systems, and physical-AI themes substantially repeat stronger appearances in the corpus. Skip unless you specifically want a Chinese-language overview of NVIDIA’s current platform stack.

The transcript contains extensive discussion of agentic AI, NVIDIA AI infrastructure, Vera Rubin and Grace Blackwell systems, and physical AI. However, the material is contained in one exceptionally long timed chunk, so no compact exchange can be selected without exceeding the required range limits.

Squawk on the Street: 11AM Hour: Nvidia CEO Jensen Huang, Early SpaceX Investor Peter Diamandis, CEO of Infleqtion Quantum 5/21/26

A useful but nonessential update: the clearest new wrinkle is Huang’s decade-long terrestrial-data-center horizon, while the China and jobs material substantially repeats prior appearances. Worth sampling for the space discussion; otherwise skippable for regular followers.

Well, we're already there now. And so the question is we have the ability, we have to do the engineering and then we have to scale it up. I think for the next 10 years, it's a fairly fair certainty that the vast majority of the world's data centers will be here terrestrially. And so we'll just add with satellites and add with space data centers as we go. And so I don't think it's going to be all or nothing. We're going to rely on Earth for a lot of data centers, a lot of energy for some time to come.

Jensen Huang discusses NVIDIA’s work on space-based computing, the continuing importance of Earth-based data centers, and the company’s uncertain access to China. He also addresses political resistance to AI, arguing that industry leaders need to explain its economic value more effectively while confronting public fears about employment.

Bloomberg Intelligence: Nvidia CEO Jensen Huang & Dell CEO Michael Dell on Agentic AI, Memory Demand and China

Mostly a repackaging of the same Bloomberg interview already available in the corpus, with familiar agentic-AI, supply-chain, and China themes. Skip unless you specifically want Huang’s concise definition of an agent harness.

>> Harness Harness is what uh puts a puts a harness around the large language model so that it can access memory, access the network, use tools, have local scratchpad memory, working memory, access long-term memory, and so that harness basically turns, if you will, the brain into an agent, okay? Into a digital robot, if you will, that can do work. And so, now the agent runs on a CPU. We also worked with Dell to create a new type of long-term memory for agents.

Jensen Huang discusses the shift from cloud-centered AI toward agents operating near proprietary data and real-world activity. He also covers the infrastructure implications of agentic AI, including memory demand, semiconductor capacity, long-term supply-chain planning, China market access, and manufacturing diversification.

Bloomberg Tech: Nvidia CEO Jensen Huang & Dell CEO Michael Dell on Agentic AI, Memory Demand and China

This is a syndicated version of the same Dell conversation already captured more completely in appearances 3150 and 3147, so regular listeners can safely skip it. The on-premises-agent argument is clear, but neither candidate adds distinct material.

00:02:05 Speaker 5: However, for Lily, Samsung, the future manufacturing, a lot of companies, you want the agents to be on prem because that's where all of your data is, where all your secure data is, You're proprietary data, and all of the skills associated with your company is. And so now we have agents that are here, AIS that can do work right. Chat GPT was fantastic a launched generative AI. 00:02:31 Speaker 2: But you just hate content. That was it. 00:02:33 Speaker 5: Making content is very important, but doing work is really valuable.

Jensen Huang describes agentic AI as a major computing transition requiring on-premises systems, more CPUs, expanded memory and networking, and coordinated global supply-chain investment. He also discusses the long runway for physical AI, licensed sales and market access in China, Taiwan's technology role, and the spread of AI into personal devices and operational environments.

Bloomberg Talks: Dell CEO Michael Dell & Nvidia CEO Jensen Huang Talk Agentic AI, Memory Demand & China

Mostly skippable if you have heard Huang’s recent agentic-AI interviews, especially the parallel Bloomberg Tech or Bloomberg Intelligence versions of this same conversation. The on-premises case is clear and useful, but the material is otherwise familiar and the transcript quality is uneven.

00:02:05 Speaker 5: However, for Lily, Samsung, the future manufacturing, a lot of companies, you want the agents to be on prem because that's where all of your data is, where all your secure data is, You're proprietary data, and all of the skills associated with your company is. And so now we have agents that are here, AIS that can do work right. Chat GPT was fantastic a launched generative AI. 00:02:31 Speaker 2: But you just hate content. That was it. 00:02:33 Speaker 5: Making content is very important, but doing work is really valuable.

Jensen Huang describes agentic AI as a shift from generating content to performing work through systems that combine models, tools, memory, networking, and CPUs. He also discusses the resulting enterprise infrastructure demand, long-term supply-chain expansion, China's AI market, and the movement toward AI operating wherever the relevant context exists.

How I Built This with Guy Raz: NVIDIA: Jensen Huang. From near collapse to becoming the world’s biggest company

A polished NVIDIA origin-and-AI primer, but nearly all of its strongest material is covered more deeply elsewhere in the corpus, especially the Lex Fridman, Dwarkesh, and Only in America appearances. Regular followers can skip it; newcomers may appreciate the compact overview.

we observed that in a software program inside it there are just a few lines of code, maybe 10% of the code, does 99% % of the processing and that 99% of the processing could be done in parallel. However the other 90% of the code has to be done sequentially. It turns out that the proper computer the perfect computer is one that could do sequential processing and parallel processing not just one or the other. That was the big observation and we set out to build a company to solve computer problems that normal computers can't. And that's really the beginning of NVIDIA.

Jensen Huang traces NVIDIA's rise from a parallel-processing insight in gaming to CUDA, deep learning, and a broader computing platform. He presents robotics, digital biology, climate science, and accessible AI assistance as major next areas while emphasizing persistence, energy efficiency, and layered safety systems.

Build Wiz AI Show: Jensen Huang on the AI Revolution 2026

Skip: this appears to be commentary or paraphrase rather than a genuine Jensen Huang appearance. With no attributable speech or substantive exchange, it offers less value than the many direct interviews in the corpus.

This transcript is a narrated discussion of a separate Jensen Huang interview, not a speaker-labeled transcript of his contributions. Because no chunk can be reliably attributed to Jensen Huang, there are no usable exchange candidates.

Memos to the President: Episode 44: Jensen Huang on Generative Computing, Re-industrialization, & Physical AI

A solid overview, but most of Huang’s generative-computing, infrastructure, energy, and jobs arguments recur elsewhere in the corpus with greater depth. Sample the Megatron history and re-industrialization discussion; otherwise this is skippable for regular listeners.

great about it, of course, was that it encoded it was able to memorize a lot of knowledge. But the thing that we didn't invent that, um, OpenAI invented to make ChatGPT useful is the concept of alignment. Reinforcement learning, human feedback. And to And as a result, you know, you you give it you give it a prompt, the Megatron you give Megatron a prompt, it would generate just a It would start spewing off all kinds of things that it was encoded in its memory. And it was just nonsense and was really It wasn't very useful. And And it was We were waiting for another great invention was reinforcement learning, human feedback.

Jensen Huang presents AI as a new computing and industrial paradigm, emphasizing generative systems, large-scale infrastructure, agentic tools, and physical applications. He advocates U.S. investment in energy, manufacturing, adoption, talent, and open-source security while arguing that AI will expand rather than simply eliminate meaningful work.

播客翻译计划: Dwarkesh 对话黄仁勋

Skip this version: the digest offers only passing references to Blackwell, Blackwell Ultra, and OpenAI, all well covered elsewhere in the corpus. No compliant excerpt is available to demonstrate a substantive or distinctive exchange.

Speaker B appears to be the tracked person and makes substantive references to NVIDIA Blackwell, Blackwell Ultra, and OpenAI. However, the available substantive chunks are individually longer than the permitted timed range, so no valid exchange can be selected.

Dwarkesh Podcast: Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat

Worth hearing for unusually specific operational detail on GPU allocation, supplier commitments, and NVIDIA’s missed opportunity with Anthropic. The China and platform-ecosystem arguments are familiar from other appearances, so this falls short of being wholly new.

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

Huang presents NVIDIA's advantage as a combination of programmable computing, ecosystem depth, supply-chain coordination, and sustained execution rather than chip manufacturing alone. He also argues that the United States should remain ahead while competing globally, including in China, because conceding markets and ecosystems could weaken long-term American technology leadership.

Squawk on the Street: Bears Rule Q1, Buffett's Market Message, Exclusive With Nvidia and Marvell CEOs 3/31/26

Worth a quick listen for Huang’s deal-specific explanation of NVIDIA’s $2 billion Marvell investment and its ecosystem strategy. The material is new within this corpus, though the answer remains fairly polished and light on operational detail.

And that's why you wanted to own $2 billion worth of stock. Well, Marvell is a marvelous investment. Been dying to say that, but this partnership is about extending Nvidia's AI ecosystem and Nvidia's architecture. And so this investment is really a fantastic investment for us. We're going to. Of course, we're also smart investors. We've expanded the TAM for both of us as a result of, of this partnership. And we want to be an investor in that. We want to have a stake in that future.

Jensen Huang presents the Marvell investment as a way to extend Nvidia’s AI ecosystem into semi-custom silicon, telecommunications infrastructure, and future AI base stations. He also argues that enterprise software is moving toward real-time, token-enhanced computing and says Nvidia’s growth is broadening beyond its largest cloud customers into industrial and physical AI applications.

播客翻译计划: 对话黄仁勋：四万亿美元英伟达与人工智能革命

Skip: the supplied digest contains no attributable topics, substantive exchanges, or usable Jensen Huang material. It offers nothing to distinguish it from the much richer original Lex Fridman appearance in the corpus.

The transcript does not contain a usable, intelligible contribution attributable to Jensen Huang. No exchange can be selected for editorial review.

Lex Fridman Podcast: #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution

Much of the episode revisits familiar material on extreme co-design, CUDA, and inference, including passages already represented elsewhere in the corpus. It is still worth hearing for Huang’s unusually direct claim that AGI has arrived, his agent-run company scenario, and his concrete proposal for flexible data-center power use.

- I think it's now. I think we've achieved AGI. - Do you think you could have a company run by an AI system like this? - Possible, and the reason for that is this. You said a billion, and you didn't say forever. And so for example, uh... It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, you know, a few billion people used for 50 cents, and then it went out of business again shortly after. Now, we saw a whole bunch of those type of companies during the

Jensen Huang presents NVIDIA’s strategy as a combination of extreme co-design, a durable CUDA install base, and active shaping of the ecosystem around future AI workloads. He also discusses inference, energy constraints, agentic systems, and his view that AI will expand human capabilities and create new forms of work rather than simply eliminate occupations.

Lex Fridman Podcast: #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution

A substantial, thoughtful conversation, but much of the AI-compute, CUDA, generative-computing, and jobs material overlaps Huang’s other appearances. Selective listening is worthwhile for the leadership and organizational passages; otherwise this is not essential for a well-read fan.

One of the most important attributes of AI learning, as you know, is, right? Systematic forgetting. You, you need to know when to forget some things. You can't memorize everything. You can't keep everything and, and, you know, you, you want to— you don't want to carry everything. One of the things that I do very quickly is decompose the problem, I reason about the problem, and I share the load with it. When I say I tell everybody, I'm essentially sharing that burden. As quickly as possible. Whatever worries me, tell somebody else. Don't just keep it. You know, don't freak them out. Decompose the problem into smaller parts and get people to, so, and, and inspire them to be able to go do something about it.

Jensen Huang discusses NVIDIA’s evolution from GPU design to full-stack co-design, the strategic decisions that established CUDA, and the scaling demands of generative and agentic AI. He also offers a leadership philosophy centered on first-principles reasoning, resilience, continuous learning, and the view that AI will change tasks while elevating human purpose and capability.

All-In with Chamath, Jason, Sacks & Friedberg: Jensen Huang LIVE: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis

Worth hearing for unusually concrete claims about the agentic-compute jump, Vera Rubin’s mixed-model workload, and a roughly three-year robotics horizon. The broader cases for AI infrastructure, physical AI, and favorable policy substantially overlap Huang’s other appearances.

>> When we went from reasoning to agentic, the computation is probably another hundred times. Now we're looking at in just two years computation went up by a fact 10,000 X. Meanwhile people pay for information, but people mostly pay for work. >> Yes. >> Talking to a chatbot and getting an answer is super great. >> Right. >> Helping me do some research, unbelievable. But getting work done, I'll pay for >> Indeed. >> And so that's where we are. Agentic

Jensen Huang discusses NVIDIA's shift from a GPU company toward a broader AI infrastructure and factory platform, with agentic computing, inference, physical AI, digital biology, and robotics as major growth areas. He also addresses AI policy, open and proprietary models, labor changes, and the need for people to become skilled users of AI.

AI News Flash: [3/18 03:00] Samsung co-CEO says AI chip demand strong but memory prices may hurt consumer devices / DayOne Data Centers seeks record $7B loan Panelist

Skip: Huang is only mentioned as a panel moderator and in reporting about NVIDIA’s model plans. There is no substantive speech or attributable exchange from him.

The transcript is a news bulletin narrated by someone other than Jensen Huang. It mentions Huang's GTC panel and NVIDIA's AI model initiatives, but provides no usable contribution from him.

CNBC’s “Money Movers”: Impact of Iran War on U.S. Economy, Long-Term Impact for Nat Gas, Nvidia CEO Details Guidance 3/17/26

The $1 trillion-plus Blackwell and Rubin baseline is concrete and consequential, but this brief appearance substantially duplicates Huang’s same-day CNBC discussion. Worth a quick listen for the forecast; skip the memory segment if you have already heard the “Squawk on the Street” interview.

At this point, with another 21 more months to go to the end of 2027, we already have high confidence, high confidence visibility of 1 trillion dollar plus of Blackwell and Rubin. Not anything else, just Blackwell and Rubin. And so you know, we still have 21 more months of new orders coming in. We still have all of the other things that Nvidia sells. And so I just wanted to give everybody a baseline. The ecosystem needs to know directionally where Nvidia is going,

Jensen Huang presents more than $1 trillion in cumulative Blackwell and Rubin revenue through 2027 as a baseline for Nvidia's ecosystem. He also emphasizes Nvidia's data-center memory strategy, including HBM4 and LPDDR5.

Squawk on the Street: SOTS 2nd Hour: Cramer Interviews Nvidia CEO, Apollo's Chief Economist, & Iran's Fed Impact 3/17/26

Worth hearing chiefly for the new Grok production and workload-expansion detail; much of the inference and memory discussion repeats Huang’s recent themes, including same-day CNBC coverage. Fans seeking only fresh substance can sample the top excerpt rather than the full segment.

You bought Grok in December. What I heard yesterday, maybe my biggest takeaway was it's already going to be making you not money, but a huge amount of money. And that's not part of the trillion? No. Yeah. We started production of Grok version three. It's a really, really special company and we're going to add Grok not to Every part Vera Rubin is so incredible that it's really hard to add value to Vera Rubin. You know that engine is so incredible but there's some part of the workload, I think about 25% of the workload where we could add Grok to it. And so for 25% of the workload we could increase our opportunity even more.

Jensen Huang describes accelerating AI growth, driven increasingly by inference, and reiterates high-confidence visibility for $1 trillion or more of Blackwell and Rubin sales by the end of 2027. He also discusses NVIDIA's memory co-design, autonomous-driving platform, open-source AI agents, robotics, and the performance gains presented at GTC.

AI News Flash: [3/17 15:00] Microsoft Copilot Major Reorg - Suleyman Moves to Superintelligence / Micron Q2 Earnings Today - AI Memory Supercycle Host

No substantive or attributable Jensen Huang material appears in the supplied digest. Skip this episode if you are looking for a Jensen Huang appearance.

The transcript only mentions Jensen Huang as the moderator of an upcoming NVIDIA GTC open models panel. It contains no substantive speech from him, so no usable exchange ranges can be selected.

AIニュース速報: 【3/18 0時】Microsoft Copilot大規模組織再編 スレイマンが超知能開発へ・Micron Q2決算 本日発表 AIメモリスーパーサイクル Host

Skip this one: the transcript contains no usable contribution from Jensen Huang. The corpus offers many substantive appearances with direct remarks instead.

The provided transcript contains one untimed chunk spoken by another speaker. Jensen Huang has no substantive speech suitable for an exchange range.

Fala Carlão Podcast 🤠: IA, máquinas e inovação: O encontro dos líderes mundiais em Vegas | Canal do Boi #423

No substantive or attributable Jensen Huang material is captured in this appearance digest. Skip it; the corpus contains many appearances with detailed exchanges and original insights.

Jensen Huang is not identifiable as making a substantive contribution in the provided transcript chunks. The available speech from the other speakers discusses agricultural AI, machine automation, connectivity, and future autonomy.

Squawk Pod: Jensen Huang, FDA Commissioner Makary, & Beast Industries CEO 2/26/26

Most of the AI-demand discussion repeats Huang’s familiar compute-growth case, but his even-handed response to the Pentagon-Anthropic dispute is timely and unique within this corpus. Worth hearing for that exchange rather than the broader appearance.

The Defense Department has the right to use the technology and use the products that they procure in the way that serves their needs. And obviously their needs are dependent of war. In the case of Anthropic, of course they have the right to decide how they would like to market their products and what kind of use cases it could be used for. And so I think they both have their. Their reasonable perspective. And however it gets worked out, this won't be. The Anthropic is not the only AI company in the world. And of course the United States is not the only customer for any company in the world. And so I think they both have their proper positions. I hope that they can work it out. But if it doesn't get worked out, it's also not the end of the world.

Jensen Huang characterizes AI as a new industrial revolution driven by rapidly expanding computing demand and adoption across industries. On the Pentagon-Anthropic dispute, he presents both parties as having legitimate positions and says the outcome will not determine the future of AI because alternative companies and customers exist.

Squawk on the Street: Tech Earnings Reaction: A Wild Ride for Nvidia and Salesforce 2/26/26

A solid but brief enterprise-focused appearance: the systems-of-record point adds useful specificity, while the claims about stronger models driving compute and software demand substantially repeat Huang’s broader AI-demand case. Worth sampling for enterprise AI watchers; others can skip in favor of a deeper interview.

Huang presents enterprise AI as a combination of capable models, specialized agents, and established systems of record. He also emphasizes that the value of AI depends on applying these models in useful products, while their growing capabilities increase demand for computing resources.

SBS News In Depth: Nvidia results spur AI tech recovery & why most borrowers will cope with rate rise

Skip unless you want a very short earnings-call soundbite. The argument is familiar across the corpus and appears virtually verbatim in two companion SBS entries.

>> I am confident in their cash flow growing and the reason for that is very simple. We have now seen the inflection of agentic AI and the usefulness of agents across the world and enterprises everywhere. You're seeing incredible compute demand because of it. In this new world of AI, compute is revenues. Without compute, there's no way to generate tokens. Without tokens, there's no way to grow revenues.

Jensen Huang says growing enterprise use of AI agents is producing strong demand for computation and gives him confidence that major customers will continue investing. He frames compute as essential to producing AI tokens and therefore to expanding AI-related revenue.

SBS News In Depth: Nvidia results spur AI tech recovery & why most borrowers will cope with rate rise

Skip unless you want the shortest earnings-era formulation of Huang’s compute-demand thesis. The clip is duplicated almost verbatim elsewhere in the corpus and reiterates his familiar compute-to-tokens-to-revenue argument.

>> I am confident in their cash flow growing and the reason for that is very simple. We have now seen the inflection of agentic AI and the usefulness of agents across the world and enterprises everywhere. You're seeing incredible compute demand because of it. In this new world of AI, compute is revenues. Without compute, there's no way to generate tokens. Without tokens, there's no way to grow revenues.

Jensen Huang says the adoption of agentic AI across enterprises is creating substantial compute demand. He expects customers' growing cash flow to support continued investment, arguing that compute enables token generation and therefore revenue growth.

SBS On the Money: Nvidia results spur AI tech recovery & why most borrowers will cope with rate rise

Skip unless you want the shortest possible earnings-call summary. The passage is duplicated almost verbatim in appearances 4187 and 4188, while its compute-to-tokens-to-revenue argument is familiar across the corpus.

>> I am confident in their cash flow growing and the reason for that is very simple. We have now seen the inflection of agentic AI and the usefulness of agents across the world and enterprises everywhere. You're seeing incredible compute demand because of it. In this new world of AI, compute is revenues. Without compute, there's no way to generate tokens. Without tokens, there's no way to grow revenues.

Jensen Huang argues that the adoption of agentic AI is driving substantial compute demand and that compute capacity is fundamental to generating tokens and revenue. He also says confidence in customers’ growing cash flow supports expectations for continued AI investment.

The Drill Down: Drill Down Earnings, Ep. 431: NVDIA Q4 earnings – ($NVDA) – A Deep Dive with Cory Johnson

This is familiar demand-justification material: AI persists, generates work in real time, and therefore needs much more compute than classical software. The thousand-times-higher estimate adds some specificity, but listeners who follow Huang’s appearances can safely skip it.

the future and AI is here. AI is not going to go back. Uh AI is only going only get better from here. And so [music] if you think about it and you said okay well the world was investing about three to 400 billion dollars a year in classical computing and now AI is here and the [music] amount of computation necessary is a thousand times higher than uh the way we used to do computing the computing

Jensen Huang's substantive contribution focuses on AI as a durable change that will drive substantially higher computing demand. The excerpt provides a clear rationale for continued infrastructure investment by contrasting AI workloads with classical computing.

Conversations with Mike Milken: Milken Institute Fireside Chats: Nvidia CEO Jensen Huang

Worth a selective listen for the India-specific case for sovereign AI infrastructure, especially the warning against exporting data only to import intelligence. Most of the broader claims about AI access, jobs, and infrastructure substantially repeat Huang’s familiar framework.

own AI infrastructure like their own communication their internet infrastructure their roads uh energy of course and of course intelligence should be part of your infrastructure and the manufacturing of intelligence should be part of your infrastructure and and he said this he said he said it makes completely complete sense that India should manufacture its own AI manufacture your own AI you should not Outsource you should not export data to import intelligence that India should not export data to import intelligence and

Jensen Huang discusses India’s potential to lead in AI through its technical talent, large population, data, infrastructure, and national ambition. He also argues that locally built AI can broaden access to powerful capabilities and help India export intelligence services to the world.

Halftime Report: Brad Gerstner, Jensen Huang and Treasury Secretary Scott Bessent join us live 2/6/26

Mostly familiar CNBC-length versions of Huang’s recurring arguments on compute demand and global competition. Skip unless you specifically want his concise case against conceding the Chinese market.

You mentioned some of the criticism that's been around. Should we sell chips to the Chinese because they, they want to kick our butts in AI to that point. China is a very large market. It makes no sense to forfeit, to concede a large market. If you would like to win globally, the American chip industry should do everything we can to win all over the world, including in the Chinese market. We have to compete. It's their home turf. They got all kinds of advantages. And so we have to go compete hard. But there is no question conceding half of the world's markets make no sense if you want to win globally.

Jensen Huang presents AI as a once-in-a-generation infrastructure transition driven by useful, profitable systems and rapidly expanding compute requirements. He also emphasizes NVIDIA's platform advantages and argues that U.S. technology companies should compete globally, including in China, while maintaining established military export controls.

Closing Bell: Closing Bell Overtime: Fresh Scrutiny for AI Trade; AMD Results & a Software Slaughter 2/3/26

A brief but newsworthy update: Huang directly dismisses concerns about NVIDIA’s OpenAI relationship and predicts the financing will be the largest round in history. The compute-drives-revenue argument is familiar, but the unequivocal deal-status comments make this worth hearing.

The more compute they have, the greater revenues they will have. And so I'm, you know, their trajectory, their revenue growth trajectory is going to be unlike anything the world's ever seen. And this is, this is a great investment opportunity. I'm delighted to do it. In working with, with OpenAI, there is no drama. Everything is, this round is going to get done. It's going to be the largest round in history. There's, it's going to be oversubscribed. It's going to be, they're doing terrifically. No drama. Jim Cramer joins us now. Jim, great to have you with us on overtime.

Jensen Huang addresses scrutiny of NVIDIA's relationship with OpenAI, saying the deal is proceeding without drama and that the funding round is on track. He also argues that OpenAI's revenue growth will increase with access to more compute and presents the relationship as a significant investment opportunity.

Squawk on the Street: CEOs' Letter on Minneapolis Turmoil, Nvidia and CoreWeave CEOs Exclusive, Winter Storm Impact 1/26/26

Worth hearing for two concrete, market-relevant disclosures: China remains entirely outside guidance, and any OpenAI investment would be staged alongside its buildout. The broader AI-demand framing is familiar, but these specifics make the appearance more than routine promotion.

Yeah, no, no doubt. I just to come back to OpenAI. So your investment takes place over a long period of time given they meet certain thresholds in terms of the build out that they've committed to. Is that correct? That's right. That's right. Yeah. They're giving us the opportunity, they're giving us the opportunity to invest as they build out and for each one of the, each one of the build out world will consider the investment and, and I expect that it's going to be a great opportunity and, and I look forward to invest.

Jensen Huang discusses strong AI demand, Nvidia’s expanded CoreWeave relationship, and the broad infrastructure buildout needed to support AI models and applications. He also clarifies that Chinese business remains outside Nvidia’s guidance and that any OpenAI investment would occur over time as infrastructure milestones are reached.

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Skip this one: the available transcript is too fragmented to identify or attribute any substantive remarks to Jensen Huang. Unlike stronger appearances in the corpus, it offers no usable exchange or distinctive material.

The transcript contains scattered references to NVIDIA, AI, semiconductor companies, and related figures, but it does not provide a clearly attributable or usable speech segment from Jensen Huang. No exchange can be selected without inferring speaker identity or reconstructing quote content.

Meet The Leader: Davos 2026: Conversation with Jensen Huang, President and CEO of NVIDIA

Most of the AI-platform, infrastructure, and jobs case substantially repeats Huang’s broader corpus. The pension-fund pitch and unusually candid Mercedes story add enough novelty to justify the highlights, but not necessarily the full episode.

Thank you. I appreciate that. My only regret was at the ipo. After the ipo, I wanted to buy my parents something nice. And so I sold Nvidia stock at a valuation of $300 million. The company was at a valuation of $300 million. And I bought them a Mercedes S Class. It is the most expensive car in the world. They regret it. Do they still have it? Oh, sure, yeah, they still have it. Yeah.

Jensen Huang frames AI as a foundational infrastructure buildout spanning energy, chips, cloud services, models, and applications. He is optimistic that AI can expand opportunity through new infrastructure jobs, wider access to computing, and investment in emerging applications and industries.

Build Wiz AI Show: Future of AI & Global Economy - Nvidia CEO Jensen Huang and BlackRock's Larry Fink

Skip: this is a host-led recap with no identifiable direct speech from Jensen Huang. It offers no usable exchange or original material to distinguish it from the stronger appearances in the corpus.

This transcript summarizes a discussion about AI infrastructure, agents, physical AI, labor, sovereign AI, and investment. It does not provide identifiable substantive speech from Jensen Huang himself.

Fala Carlão Podcast 🤠: Inteligência artificial no centro do debate na CES 2026 | Fala Carlão 7916

This is a brief meet-and-greet rather than a substantive discussion of AI in agriculture: Huang offers encouragement and a keynote referral but no technologies, examples, or commitments. Skip unless you specifically want the Brazil-focused interaction.

>> I'm from Brazil. I have a big problem from agri business. >> Okay. >> Which which AI Sims and Nvidia can help us to produce more uh more food? >> Question. >> Here we go. >> Question. >> Absolutely. >> We'll help you. >> Yeah. >> Yes. We love you. Congratulations for the the presentation of both of you. You are so very ple of this. You are very very wonderful people. >> Thank you.

Jensen Huang gives brief responses during a Brazilian host's questions about the Caterpillar keynote and the use of AI in agriculture. He offers help with agricultural production and refers to agriculture and Simmons, but the transcript does not develop the technical details.

No Priors: Artificial Intelligence | Technology | Startups: NVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative

A solid overview with a useful early account of confidence-based routing, search, and grounding, but much of the infrastructure, token-factory, jobs, and open-source case recurs across Huang’s other appearances. Worth a selective listen rather than a full-episode commitment.

>> Let's see. There there's some things that didn't surprise me like for example the scaling laws didn't surprise me because we already knew about that. The technology advancement didn't surprise me. I was pleased with the improvements of grounding. I was pleased with the improvements of reasoning. I was pleased with uh uh the connection of all of the models to to to search. I'm pleased that it that uh there are now routers that are in front of these models so that it could depending on the confidence of the answers go off and do necessary research and and just generally improve the quality and the accuracy of answers.

Jensen Huang argues that AI is becoming a broad infrastructure and industrial platform, with reasoning, open source, robotics, digital biology, and energy demand extending well beyond chatbots. He rejects simplistic doom and bubble narratives, emphasizing practical deployment, growing capacity needs, and productivity that expands the purposes people and companies can pursue.

Bloomberg Daybreak: Asia Edition: China-Japan Tensions, Jensen Huang, Lisa Su at CES

This is substantive but largely a repackaging of the previous day’s fuller Bloomberg Talks interview, covering the same Siemens partnership, robotics bottleneck, and energy-efficiency argument. Skip it if you have heard that appearance; otherwise E03 offers the cleanest short takeaway.

00:15:28 Speaker 6: And if you want to talk about the United States up manufacturing United States, you need to go as digital and as automated, and AI is supercharged as possible. 00:15:38 Speaker 7: A factory is robotic, and it is orchestrating robots that are building systems that are also robotic, like for example, self dragging cars a robotic system. 00:15:50 Speaker 4: And the reason why it's so hard to deploy robots today is because it's hard to program these robotic systems. The software expertise next necessary to customization necessary is really intense. It's just too much.

Jensen Huang discusses NVIDIA's broad partnership with Siemens, including software acceleration, simulation, and industrial AI. He also addresses physical AI in factories and argues that rapid improvements in energy efficiency are essential to expanding AI capability and customer returns.

Bloomberg Talks: Nvidia, Siemens CEOs Talk Building Industrial AI Operating System

A solid but uneven appearance: the Groq, memory-supply, and space-based AI material adds value, while the Siemens pitch and energy-efficiency claims substantially overlap other appearances. Worth selective listening rather than a full play.

00:09:56 Speaker 1: Now one of the most important things here speaking about the United States, if not for President Trump's pro energy growth agenda, we would have a very hard time growing at all. In order for our new industry to emerge, you need energy, and so I think it's safe to say that we wish we had more energy in United States. You wi should have more energy. I think the world all wish we had more energy, and so we have to invest in all sorts of different forms of energy.

Jensen Huang discusses NVIDIA's industrial AI partnership with Siemens, focusing on simulation, digital twins, physical AI, and factory automation. He also addresses AI infrastructure constraints and opportunities, including energy efficiency, memory supply, Groq technology, robotics, and space-based AI factories.

Steven AI Talk: NVIDIA CES Keynote: Reinventing Computing for the Agentic AI Era

Skip: the supplied transcript contains no identifiable substantive speech from Jensen Huang, so there is nothing to evaluate or excerpt. Other appearances in the corpus offer extensive direct discussion of agentic AI and NVIDIA’s strategy.

The transcript is a narrated explainer describing Jensen Huang's ideas and NVIDIA's keynote themes. It does not contain an attributable, usable contribution from Jensen Huang himself.

Steven AI Talk: NVIDIA CES Keynote: Reinventing Computing for the Agentic AI Era

Skip: the supplied transcript contains no identifiable Jensen Huang contribution, so there is nothing substantive to assess or excerpt. Other appearances in the corpus cover the agentic-AI era in considerable depth.

The transcript consists of exchanges between speakers A and B. It does not identify Jensen Huang as a speaker, so there is no usable contribution to extract.

CNBC's "Fast Money": Energy Climbs After Maduro Capture, And An Exclusive Interview with Nvidia’s CEO 1/5/26

Skip this one: Jensen Huang does not actually appear in the supplied transcript. The program only previews a separate interview, while the corpus contains many substantive appearances.

Jensen Huang does not speak in the provided transcript. The show covers announcements from his CES keynote and notes that a full interview with him is scheduled for a later broadcast.