LLMs may have kicked off this AI boom, but the ceiling is closer than the hype suggests. As models run out of text data to train on, the companies and investors paying attention are already moving on. The next wave isn’t better chatbots; it’s machines that can understand the physical world. Luma AI, the Bay Area lab that raised over $1.4 billion from a16z, Nvidia, and Amazon, is betting on exactly that.

On episode of TechCrunch’s Equity podcast, we’re bringing you a conversation Rebecca Bellan sat down with Amit Jain, co-founder and CEO of Luma AI, at Web Summit Qatar. Together, the pair dug into where the next trillion-dollar AI opportunity actually gets built, and whether the companies chasing it even know what they’re building yet.

Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod.

Chapters:

00:00 Intro

01:13 Why LLMs are hitting a ceiling

02:43 The data problem & what comes after LLMs

04:30 What actually makes a world model a world model

06:05 Why 3D data is a dead end

07:39 What Luma is building next

09:08 How much humans stay in the loop

10:00 Near-term use cases for agentic video

11:22 Will AI kill jobs in film & production?

13:30 Why the entertainment industry is already dying

15:27 Why we actually need more content, not less

17:46 Luma’s roadmap: generation, understanding, and robotics

19:54 Outro

This week’s guest is Ayal Yogev, co-founder and CEO of Anjuna Security, who has experienced both sides of the startup journey: scaling quickly during the boom years and then making the incredibly difficult decision to lay off a significant portion of his team when the market shifted.

From growing to 75 employees to scaling back and rebuilding, Yogev learned firsthand that the hardest part of leadership isn’t hiring fast, it’s making tough decisions with care, transparency, and integrity.

In this episode, Isabelle Johannessen and Yogev unpack what it really means to lead through layoffs with compassion and how founders can support their teams even in the most challenging moments. They also explore the lessons learned from scaling too quickly and how to build a more resilient company the second time around.

Apply to Startup Battlefield: We are looking for early-stage companies that have an MVP. So nominate a founder (or yourself): techcrunch.com/apply. Be sure to say you heard about Startup Battlefield from the Build Mode podcast.
TechCrunch Disrupt: If you’re thinking about applying to Startup Battlefield, then October 13 to 15 in San Francisco, we’re back for TechCrunch Disrupt, where the Startup Battlefield 200 takes the stage. So if you want to cheer them on, or just network with 1000s of founders, VCs, and tech enthusiasts, then grab your tickets.

Use code buildmode15 for 15% off any ticket type.

New episodes of Build Mode drop every Thursday. Hosted by Isabelle Johannessen. Produced and edited by Maggie Nye. Audience development led by Morgan Little. Special thanks to the Foundry and Cheddar video teams.

Chapters:
00:00 We grew too fast
02:30 What Anjuna actually does
04:45 Scaling the team quickly
06:10 The market crash hits
09:40 Handling layoffs with empathy
12:10 Supporting employees the right way
15:30 Why culture matters in crisis
20:50 The hiring mistake founders make
27:40 When to scale your sales team
34:40 Rebuilding after layoffs

Snowflake is betting that the future of AI isn’t just analyzing data, it’s acting on it. That means a shift away from chatbots and toward autonomous agents that can actually get work done. And Snowflake is reorganizing fast to keep up, from shipping hundreds of AI features to restructuring teams along the way.

On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Snowflake CEO Sridhar Ramaswamy to unpack the company’s transformation and what it signals about where AI is headed next.

Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify, and all the casts. You also can follow Equity on X and Threads, at @EquityPod.

Chapters:
00:00 Intro
00:17 Snowflake’s AI shift and agentic future
01:45 Why 2026 marks the end of chatbots
04:09 Cortex Code, Snowflake Intelligence, and new products
06:09 Who benefits: non-technical users & enterprises
07:35 Adoption challenges and why AI pilots fail
12:11 How AI is reshaping jobs and skills
14:39 Layoffs, automation, and the future of documentation
18:37 Snowflake’s evolution into an AI platform
21:04 Competition: Databricks, hyperscalers, and AI giants
25:01 Outro