GTC 2026 Open Models Panel with Jensen

Category: Industry Panel · Year: 2026 · ▶ Watch

Speakers: Anjney Midha, Founder AMP PBC · Aravind Srinivas, CEO and Co-Founder Perplexity · Arthur Mensch, Co-Founder and CEO Mistral AI · Hanna Hajishirzi, Professor Sr. Director NLP, AI2 · Jensen Huang, Founder and CEO NVIDIA · Michael Truell, CEO and Co-Founder Cursor · Mira Murati, Founder and CEO Thinking Machines Lab · Misha Laskin, Co-Founder and CEO Reflection AI · Robin Rombach, Co-Founder and CEO Black Forest Labs

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Segments (13)

  • 00:00 · Introduction
    • Jensen Huang introduces the panel on open models and the need for models as technology.
  • 01:10 · The Rise of Compound Agents
    • Michael Truell discusses the emergence of companies building compound agents that mix open and closed models.
  • 03:13 · AI as a System
    • Aravind Srinivas explains that AI is an orchestration system, not just a single model.
  • 05:00 · Open vs. Closed Ecosystems
    • Misha Laskin argues that fundamental knowledge infrastructure naturally trends towards openness.
  • 06:52 · The Need for Open Infrastructure
    • Mira Murati highlights that open infrastructure and research are necessary for broad progress in AI.
  • 07:40 · The Shift to Post-Training
    • Jensen Huang notes the massive shift in compute from pre-training to post-training.
  • 09:03 · Reinforcement Learning at Scale
    • Misha Laskin uses AlphaGo as an example of how RL can solve complex problems given enough compute.
  • 10:30 · Orthogonal Capabilities
    • Mira Murati discusses the need for agents to connect context and operate within specific domains.
  • 11:35 · Control and Customization with Open Models
    • Arthur Mensch explains why open models are essential for enterprise control, resilience, and customization.
  • 14:05 · Trust in Mission-Critical Applications
    • Anjney Midha argues open models are strictly better for mission-critical apps because they allow for trust and introspection.
  • 16:56 · Hybrid Models and Open Research
    • Hanna Hajishirzi discusses the benefits of releasing the full development cycle of models and the efficiency of hybrid architectures.
  • 19:12 · Open Models Driving Innovation
    • Robin Rombach emphasizes that open models enable diffusion of innovation and competition.
  • 20:16 · The Future of Visual Intelligence
    • Jensen Huang predicts visual intelligence will evolve into robotic visual intelligent systems.

Product Announcements (4)

  • [11:37] Mistral 7B
    • Open model released in 2023
    • specs: Foundation model
    • availability: Released 2023
  • [11:49] Mistral NeMo
    • Open model collaboration
    • specs: Foundation model
    • availability: Released 2024
  • [13:24] Forge
    • Product to connect models to physical world data sources
    • specs: Connects models to various data sources
    • availability: Released
  • [13:44] NeMoTron Coalition
    • Coalition for sharing R&D costs
    • specs: Shared R&D for open models
    • availability: Active

Specific Numbers (3)

Timestamp Metric Value Context
08:21 Pre-training compute percentage 90% The amount of training compute used for pre-training 2-5 years ago.
09:21 AlphaGo parameters 60 million The size of the AlphaGo network, described as tiny relative to current models.
09:58 RL Investment $10 billion to $100 billion Hypothetical future investment to solve major problems like curing diseases using RL.

Key Technologies (5)

  • Compound Agents: Mixes different models (open and closed) and tools to solve complex, multi-step tasks.
  • Orchestration Systems: Manages and delegates tasks to various sub-agents, models, and tools.
  • Post-Training: The phase of model development after initial pre-training, crucial for specializing and refining capabilities.
  • Reinforcement Learning (RL): Training models through trial and error to achieve specific goals, scaling with compute.
  • Hybrid Models: Architectures combining different techniques (like transformers and state space models) for better efficiency.

Predictions / Commitments (5)

  • [02:04, Over the course of the next year or two] We will see the rise of a new type of agent that acts as a co-worker for complex workloads.
  • [08:27, In the future] The percentage of compute used for pre-training will become tiny, with the vast majority shifting to post-training.
  • [09:50, Now and in the coming years] RL applied to language models will be used to solve massive problems, driven by economic decisions on compute allocation.
  • [15:48, Heading into this phase now] Infrastructure will consolidate, similar to the industrial revolution, requiring open infrastructure to prevent hoarding.
  • [20:39, In the future] Visual intelligence will increasingly become a robotic visual intelligent system, interacting with the physical world.

Companies Mentioned (2)

DeepMind/Google (implied via AlphaGo) · Mistral AI

Notable Quotes (4)

Proprietary versus open is not a thing. It’s proprietary and open. — Jensen Huang @ 00:58

AI is not the model, it’s the system, it’s the computer. — Aravind Srinivas @ 03:13

Fundamental knowledge infrastructure yearns for openness, like an animal yearns for the hills or for the forest. — Misha Laskin @ 06:12

Open models are strictly better than closed models. — Anjney Midha @ 14:05

Key Topics

Open Source AI · Proprietary Models · AI Agents · Compound AI Systems · AI Orchestration · Post-Training · Reinforcement Learning · AI Infrastructure · Mission-Critical AI · Hybrid Architectures · Robotic Vision

Takeaways

  • The future of AI involves a mix of proprietary and open models working together within orchestrated systems.
  • AI is evolving from single models to compound agents that act as co-workers for complex, multi-step tasks.
  • The majority of compute investment is shifting from initial pre-training to post-training and reinforcement learning.
  • Open models are critical for enterprise adoption because they offer necessary control, customization, and data privacy.
  • For mission-critical applications (healthcare, defense), open models are preferred due to the need for trust and introspection.
  • Open infrastructure and shared research are vital to prevent consolidation and accelerate the next generation of AI capabilities.