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NVIDIA AI Podcast

AI for Science | GTC Live Washington, D.C. Chapter 4

34 min episode · 2 min read
·
Jensen Wong,Matt Kinzella,Anirudh Devgn

Episode

34 min

Read time

2 min

Topics

Startups, Fundraising & VC, Design & UX

AI-Generated Summary

Key Takeaways

  • Drug Discovery Timeline: Current drug development takes 13 years from molecular target to FDA approval with 90% failure rate and $2-4 billion cost per drug. AI aims to transform this artisanal process into engineering discipline by 2035.
  • Commercialization Strategy: Quantum technologies follow NVIDIA's staged market approach by targeting areas with immediate quantum advantage like timekeeping and RF sensors before pursuing the crown jewel of quantum computing that surpasses classical systems.
  • Computational Shift Phases: AI infrastructure represents only horizon one of three growth phases. Physical AI for autonomous vehicles and robotics comprises horizon two, while scientific AI for drug and material discovery forms horizon three, each reinforcing previous layers.
  • Logical Qubit Milestone: Quantum advantage requires scaling from today's 12 logical qubits to 100 for material science applications and 1,000 for drug discovery. AI accelerates error correction needed to create these pristine computational qubits.

What It Covers

Scientists and technologists explore how AI and quantum computing accelerate discovery across drug development, molecular design, and chip engineering, examining the convergence of classical and quantum systems for scientific breakthroughs.

Key Questions Answered

  • Drug Discovery Timeline: Current drug development takes 13 years from molecular target to FDA approval with 90% failure rate and $2-4 billion cost per drug. AI aims to transform this artisanal process into engineering discipline by 2035.
  • Commercialization Strategy: Quantum technologies follow NVIDIA's staged market approach by targeting areas with immediate quantum advantage like timekeeping and RF sensors before pursuing the crown jewel of quantum computing that surpasses classical systems.
  • Computational Shift Phases: AI infrastructure represents only horizon one of three growth phases. Physical AI for autonomous vehicles and robotics comprises horizon two, while scientific AI for drug and material discovery forms horizon three, each reinforcing previous layers.
  • Logical Qubit Milestone: Quantum advantage requires scaling from today's 12 logical qubits to 100 for material science applications and 1,000 for drug discovery. AI accelerates error correction needed to create these pristine computational qubits.

Notable Moment

Jensen Huang unexpectedly joined the panel after initially planning to attend but being called to Korea by President Trump, bringing water to panelists and clarifying that quantum and classical computing must work together as one ecosystem.

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Episode Transcript

Hello, and welcome to a special GTC edition of the NVIDIA AI podcast. This is episode four of five on the road to GTC live in Washington DC. Bonus conversations about the state of AI you won't hear anywhere else. This episode is all about science. In laboratories and research centers around the world, AI is becoming a core instrument of discovery. Listen in as scientists and technologists explore how computation is accelerating progress across fields. Enjoy the conversation, and remember, the NVIDIA AI podcast brings you new interviews with leaders across research, business, the public sector, and more every week. Listen and subscribe wherever you get podcasts. So science used to move at the speed of experiments. Now it moves at the speed of compute, crunching data as fast as we can collect it, modeling everything from the atom to the atmosphere. Then this just speeds up outcomes. No doubt. And as AI begins to supercharge quantum computing, we're on the verge of discoveries that could redefine physics, chemistry, and life itself. Quantum computing is at the heart of the fastest acceleration that should unlock scientific discoveries. From molecules to the cosmos, AI is transforming how science models the world. And leading this conversation, we have George Church, chief scientist at Lila Sciences, Matt Kinzella, CEO at Inflexion, Marc Tessier Lavigne, cofounder, chairman, and CEO of Zira Therapeutics, and Anirudh Devgn, president and CEO of Cadence. Alright, Matt. Let's start with you here. Yeah. Big day, big quantum day. Can you talk about how, the mixture of, we'll call it, classical computing and quantum computing is really going to change the game? Yes. I can talk about that. That's an easy one. First of all, you know, when we first thanks for having us. This is gonna be a blast, guys. This is gonna be a lot of fun. When we say quantum, maybe we should just define some terms because not everyone in the audience might know what that means. And so when we say quantum, we're talking about the world of the very small, the atomic and the subatomic levels. And there's a whole different set of rules that govern the day down there called quantum mechanics. And so when someone says quantum, it's taking advantage of those very strange quantum mechanical properties. What? Hi. Which one out here? Jensen Wong, everyone. Thanks. Oh, he brought us water. Thanks. Discuss. Yes. We do. You need the hydration. Thank you. It's very important to stay hydrated. What what is this mumbo jumbo about quantum? Superposition Entanglement? Entangling stuff. Like, it's just mumble jumble. Hey. Good to see you. Something. Yeah. Yep. You know, once a plus boy, always a plus boy. Yeah. Appreciate it. Denny's to DC. Thanks for being here. Wow. Yep. Thanks for having me. It's incredible. Game show. It's been an amazing morning. And this panel, I think, saving the best for last, gents. And so, you know, maybe, ask this. You you caused a …

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