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/

In this episode of Gradient Dissent, Lukas Biewald talks with Tuhin Srivastava, CEO and founder of Baseten, one of the fastest-growing companies in the AI inference ecosystem. Tuhin shares the real story behind Baseten’s rise and how the market finally aligned with the infrastructure they’d spent years building.

They get into the core challenges of modern inference, including why dedicated deployments matter, how runtime and infrastructure bottlenecks stack up, and what makes serving large models fundamentally different from smaller ones.

Tuhin also explains how vLLM, TensorRT-LLM, and SGLang differ in practice, what it takes to tune workloads for new chips like the B200, and why reliability becomes harder as systems scale.

The conversation dives into company-building, from killing product lines to avoiding premature scaling while navigating a market that shifts every few weeks.

Connect with us here:

Tuhin Srivastva: https://www.linkedin.com/in/tuhin-srivastava/

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

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

In this episode of Gradient Dissent, Lukas Biewald talks with the CEO & founder of Surge AI, the billion-dollar company quietly powering the next generation of frontier LLMs. They discuss Surge's origin story, why traditional data labeling is broken, and how their research-focused approach is reshaping how models are trained.

You’ll hear why inter-annotator agreement fails in high-complexity tasks like poetry and math, why synthetic data is often overrated, and how Surge builds rich RL environments to stress-test agentic reasoning. They also go deep on what kinds of data will be critical to future progress in AI—from scientific discovery to multimodal reasoning and personalized alignment.

It’s a rare, behind-the-scenes look into the world of high-quality data generation at scale—straight from the team most frontier labs trust to get it right.

Timestamps:

00:00 – Intro: Who is Edwin Chen?

03:40 – The problem with early data labeling systems

06:20 – Search ranking, clickbait, and product principles

10:05 – Why Surge focused on high-skill, high-quality labeling

13:50 – From Craigslist workers to a billion-dollar business

16:40 – Scaling without funding and avoiding Silicon Valley status games

21:15 – Why most human data platforms lack real tech

25:05 – Detecting cheaters, liars, and low-quality labelers

28:30 – Why inter-annotator agreement is a flawed metric

32:15 – What makes a great poem? Not checkboxes

36:40 – Measuring subjective quality rigorously

40:00 – What types of data are becoming more important

44:15 – Scientific collaboration and frontier research data

47:00 – Multimodal data, Argentinian coding, and hyper-specificity

50:10 – What's wrong with LMSYS and benchmark hacking

53:20 – Personalization and taste in model behavior

56:00 – Synthetic data vs. high-quality human data

Follow Weights & Biases:

https://twitter.com/weights_biases

https://www.linkedin.com/company/wandb