No Priors Ep. 89 | With NVIDIA CEO Jensen Huang
Sarah Guo (host), Jensen Huang (guest), Elad Gil (host), Sarah Guo (host)
In this episode of No Priors, featuring Sarah Guo and Jensen Huang, No Priors Ep. 89 | With NVIDIA CEO Jensen Huang explores jensen Huang on AI factories, hyperscale GPUs, and software’s future NVIDIA CEO Jensen Huang discusses how accelerated computing and full‑stack co-design are reinventing the entire computing paradigm, enabling performance and cost improvements that outpace Moore’s Law at data center scale. He explains NVIDIA’s strategy of treating the data center as the unit of compute, vertically integrating and optimizing whole “AI factories,” then disaggregating them so they can plug into any cloud. Huang highlights frontier model training, inference scaling, and the emergence of AI agents across disciplines—from chip design to enterprise SaaS—as key forces reshaping industry and science. He also argues that generative AI is becoming foundational across all scientific fields, making it unlikely that any future breakthrough will occur without it.
Jensen Huang on AI factories, hyperscale GPUs, and software’s future
NVIDIA CEO Jensen Huang discusses how accelerated computing and full‑stack co-design are reinventing the entire computing paradigm, enabling performance and cost improvements that outpace Moore’s Law at data center scale. He explains NVIDIA’s strategy of treating the data center as the unit of compute, vertically integrating and optimizing whole “AI factories,” then disaggregating them so they can plug into any cloud. Huang highlights frontier model training, inference scaling, and the emergence of AI agents across disciplines—from chip design to enterprise SaaS—as key forces reshaping industry and science. He also argues that generative AI is becoming foundational across all scientific fields, making it unlikely that any future breakthrough will occur without it.
Key Takeaways
Computing is shifting from CPU-centric to GPU-accelerated, full-stack systems.
Huang argues that the entire stack—from algorithms and numerical formats to networking fabrics—must be co-designed around GPUs, enabling parallelization from a single chip to multi-data-center clusters.
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Performance and cost are improving faster than Moore’s Law at data center scale.
By co-designing hardware, software, and algorithms (e. ...
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The new unit of computing is the data center, not the chip or server.
NVIDIA designs, simulates, and optimizes entire data centers as integrated "AI factories," then disaggregates them into components so cloud providers can adopt the architecture while developers get a broadly consistent CUDA-based platform.
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Infrastructure for training becomes highly valuable inference capacity later.
Huang notes that clusters built for training frontier models are repurposed for inference and distillation into smaller models, preserving ROI and creating a spectrum from giant to tiny specialized models (e. ...
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AI is already a critical engineer inside NVIDIA, especially in chip design.
NVIDIA used AI to design chips like Hopper, allowing exploration of vastly larger design spaces and cross-module optimizations that human teams lack the time or combinatorial capacity to perform.
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Every major SaaS and tools platform is positioned for an explosion of agents.
Huang predicts vendors like Synopsys, Cadence, SAP, ServiceNow, and Salesforce will host specialized AI agents on top of their platforms, turning them into ecosystems of domain-expert digital workers rather than being disrupted away.
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Generative AI is becoming foundational to all scientific and engineering progress.
From quantum chemistry to materials science and biology, Huang expects that within a few years no major paper or breakthrough will happen without generative AI or machine learning at its core, fundamentally changing how knowledge is discovered and encoded.
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Notable Quotes
“We don’t build computers anymore. We build factories.”
— Jensen Huang
“The new unit of computing is the data center.”
— Jensen Huang
“If you’re serious about software, then you’re going to build your whole computer.”
— Jensen Huang
“Software is how humans encode knowledge. We encode it in a very different way now. That’s going to affect everything.”
— Jensen Huang
“There’s not going to be one breakthrough in science where generative AI isn’t at the foundation of it.”
— Jensen Huang
Questions Answered in This Episode
How sustainable is the energy and infrastructure footprint of million-GPU-scale AI factories, and what innovations are needed to make them practical?
NVIDIA CEO Jensen Huang discusses how accelerated computing and full‑stack co-design are reinventing the entire computing paradigm, enabling performance and cost improvements that outpace Moore’s Law at data center scale. ...
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What new bottlenecks will emerge once AI-driven chip design and system co-design are fully mainstream, and where will human engineers add the most value?
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How should startups decide where to play along the spectrum from giant frontier models to tiny, highly specialized models?
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In a world of AI employees and agents across every SaaS platform, how will organizations manage coordination, accountability, and security among human and digital workers?
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What governance or industry standards are needed when every major scientific breakthrough depends on generative AI models and the data they are trained on?
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Transcript Preview
(instrumental music plays) Hi, listeners, and welcome to No Priors. Today, we're here again, one year since our last discussion with the one and only Jensen Huang, founder and CEO of NVIDIA. Today, NVIDIA's market cap is over three trillion dollars, and it's the one literally holding all the chips in the AI revolution. We're excited to hang out in NVIDIA's headquarters and talk all things frontier models and data center-scale computing, and the bets NVIDIA is taking on a 10-year basis. Welcome back, Jensen. 30 years in to NVIDIA and looking 10 years out, what are the big bets you think are, are still to make? Is it all about scale up from here? Are we running into limitations in terms of how we can squeeze more compute memory out of the architectures we have? What are you focused on?
Well, if we take a step back and, and think about what we've done, we went from coding to machine learning, from writing software tools to creating AIs, and all of that running on CPUs that was designed for human coding to now running on GPUs designed for, um, AI coding basically. Machine learning. And so th- the world has changed. Th- the way we do computing, the whole stack has changed. And as a result, the scale of the problems we could address has changed a lot because we could... If you could parallelize your software on one GPU, you've set the foundations to parallelize across a whole cluster or maybe across multiple clusters or multiple data centers. And so I think we, we've set ourselves up to be able to scale computing, uh, at a level and develop software at a level that nobody's ever imagined before. And so we're at the beginning of that. Um, uh, over the next 10 years, uh, our hope is that we could double or triple performance every year at, at scale. Not at chip, at scale. And to be able to therefore drive the cost down by a factor of two or three, drive the energy down by a factor of two or three every single year. When you do that every single year, when you double or triple every year, in just a few years, it adds up. (laughs) And so it compounds really, really aggressively. And so I wouldn't be surprised if, you know, the way people think about Moore's Law, which is, uh, uh, 2X every couple of years, um, you know, we're gonna be on some kind of a hyper Moore's Law curve, and, um, I, I fully hope that we continue to do that.
What, what do you think is the driver of making that happen even faster than Moore's Law?
Well-
'Cause I know Moore's Law was sort of self-reflexive, right? It was something that he said and then they, people kind of implemented it to make it happen.
Yeah, yeah. The two fundamental, um, technical pillars, one of them was Dennard scaling and the other one was Carver Mead's VLSI scaling, and both of those techniques were rigorous techniques, um, but, uh, those, those techniques have really run out of steam, and, and, uh, so now we need a new way of doing scaling. Uh, you know, obviously the new way of doing scaling are, are all kinds of things associated with co-design. Unless you can modify or change the algorithm to reflect the architecture of the system, or change, and then change the system to reflect the architecture of the new software and go back and forth-
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