The future of AI training is shaped by one constraint: keeping GPUs fed.

In this episode, Lukas Biewald talks with CoreWeave SVP Corey Sanders about why general-purpose clouds start to break down under large-scale AI workloads.

According to Corey, the industry is shifting toward a "Neo Cloud" model to handle the unique demands of modern models.

They dive into the hardware and software stack required to maximize GPU utilization and achieve high goodput.

Corey’s conclusion is clear: AI demands specialization.

Connect with us here:

Corey Sanders: https://www.linkedin.com/in/corey-sanders-842b72/

CoreWeave: https://www.linkedin.com/company/coreweave/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

(00:00) Trailer

(00:57) Introduction

(02:51) The Evolution of AI Workloads

(06:22) Core Weave's Technological Innovations

(13:58) Customer Engagement and Future Prospects

(28:49) Comparing Cloud Approaches

(33:50) Balancing Executive Roles and Hands-On Projects

(46:44) Product Development and Customer Feedback

The future of AI is physical.

In this episode, Lukas Biewald talks to Nikolaus West, CEO of Rerun, about why the breakthrough required to get AI out of the lab and into the messy real world is blocked by poor data tooling.

Nikolaus explains how Rerun solved this by adopting an Entity Component System (ECS), a data model built for games, to handle complex, multimodal, time-aware sensor data. This is the technology that makes solving previously impossible tasks, like flexible manipulation, suddenly feel "boring."

Connect with us here:

Nikolaus West: https://www.linkedin.com/in/nikolauswest/

Rerun: https://www.linkedin.com/company/rerun-io/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

Is video AI a viable path toward AGI?

Runway ML founder Cristóbal Valenzuela joins Lukas Biewald just after Gen 4.5 reached the #1 position on the Video Arena Leaderboard, according to community voting on Artificial Analysis.

Lukas examines how a focused research team at Runway outpaced much larger organizations like Google and Meta in one of the most compute-intensive areas of machine learning.

Cristóbal breaks down the architecture behind Gen 4.5 and explains the role of “taste” in model development. He details the engineering improvements in motion and camera control that solve long-standing issues like the restrictive “tripod look,” and shares why video models are starting to function as simulation engines with applications beyond media generation.

Connect with us here:

• Cristóbal Valenzuela: https://www.linkedin.com/in/cvalenzuelab
• Runway: https://www.linkedin.com/company/runwayml/
• Lukas Biewald: https://www.linkedin.com/in/lbiewald/
• Weights & Biases: https://www.linkedin.com/company/wandb/