AI-Native

GPU scarcity has forced AI teams into a world their infrastructure was never built for — reserving capacity across multiple providers, geographies, and SKUs and watching 30 to 40 percent of expensive allocations sit idle. In this episode, Zongheng Yang, co-founder and CEO of SkyPilot, joins the hosts to unpack why being multi-cloud stopped being optional for AI teams and what it actually takes to manage compute efficiently once you have it.

Before founding SkyPilot, Zongheng was a speech researcher at Google Brain and a co-author on Tacotron, a PhD student at UC Berkeley's Sky Computing Lab (the birthplace of Ray, Spark, and VLM), and a contributor to the early open-weight model Vicuna. He and his co-founders built SkyPilot to solve their own problem as AI researchers: compute scattered across multiple clouds being impossible to fully utilize. Four years and 14 million downloads later, SkyPilot has become an open source standard for orchestrating AI compute across providers, with more than 280 contributors and customers including Nubank, Abridge, and Hippocratic AI running production workloads on it.

Zongheng breaks down why decades-old solutions like Kubernetes and Slurm quickly buckle under the combinatorial complexity of multiple clouds, multiple GPU SKUs, and multiple research teams competing for the same fleet. He explains why idle GPUs, not just scarce ones, are the bigger hidden cost — a team spending $100 million a year can lose $15 to $30 million to poor utilization alone — why SkyPilot bet on open source and the Apache 2.0 license from day one, and why he thinks the winning bet right now is to start building for a compute-abundant world rather than hoarding capacity for a scarce one.

In this episode, you will learn:

• Why multi-cloud went from a nice-to-have to a necessity for AI teams almost overnight

• What breaks when a team moves from one cluster to managing dozens across providers

• Why idle GPUs can cost frontier teams tens of millions of dollars a year

• The difference between getting compute and knowing how to use it well

• Why SkyPilot's founders bet on open source and the Apache 2.0 license from the start

• What changes for AI teams once compute stops being the constraint

If you're building or scaling an AI team's infrastructure, this conversation will change how you think about the true cost of underutilized compute.

Alex Mashrabov spent years at Snap building face filters, probably the most widely used media AI product ever made.

Learn more at Nebius.com

When he left to start Higgsfield, he thought the hard part would be the models. It turned out to be something else entirely.

In this episode, Joshua Liss, Head of Media and Entertainment at Nebius, sits down with Alex, CEO & Founder at Higgsfield AI, to go deep on what it actually takes to build a platform at the center of a creator economy being remade by AI. Today, over 50% of creators on Higgsfield now produce content end to end with AI, a number that was unimaginable just 18 months ago.

Alex shares the secret behind Higgsfield's growth that he has never talked about publicly before, why the company's 50-person creative team is the real competitive moat, how Higgsfield trained Soul 2.0 on Nebius B300s to deliver a model that is 20 times cheaper and 20 times faster than the leading alternative, and what he sees coming for the entertainment industry that most people aren't ready for.

0:00 - Intro teaser

0:50 - Higgsfield's thesis: why most social content will be AI-generated

2:22 - The rise of AI-native creators going end-to-end

4:07 - The secret: Higgsfield's 50-person creative feedback loop

5:45 - Democratizing high-end content and the Higgsfield contest

8:52 - From Snap face filters to Higgsfield

13:35 - 20x faster, 20x cheaper

16:55 - Prediction: AI-native IP and an AI-first Netflix

What You'll Learn

• Why Higgsfield's real competitive moat isn't the research team but a 50-person creative feedback loop.

• The three stages of AI in entertainment and why stage three is closer than most think.

• How Higgsfield trained Soul 2.0 on Nebius B300s and cut inference costs 20x.

• Why the largest AI video competition in the world was won by names nobody had heard of.

• What it means to build like a seismologist, always hunting the next wave of creators before it breaks.

• Why any creator can now compete with what used to require a million-dollar production budget.

• Will the next Netflix will be AI first?

Lukas Ziegler, Robotics Evangelist, Investor, and Advisor, has spent the past 10 years working across robotics software, commercial roles, content, and investing. Now, he believes the industry is entering a new era.

Learn more at: ⁠nebius.com

In this episode, Evan Helda, Head of Physical AI at Nebius, sits down with Lukas to explore the shift from Robotics 1.0 to Robotics 2.0: a world where you don't program a robot, you just tell it what to do.

They discuss why the humanoid debate has been asking the wrong question, why the data gap between LLMs and robotics is vastly misunderstood, and why the companies that will win are already co-building with end users rather than shipping to them.

They also get into storytelling: why public exposure builds trust in robots faster than any marketing campaign ever could.

0:00 - Intro teaser

0:40 - Lukas Ziegler’s journey from engineer to robotics evangelist

2:40 - Robotics 2.0: the shift from programmed to general-purpose robots

4:45 - The biggest investment trends in physical AI

7:45 - Why robotics has a massive data problem

11:00 - Synthetic data, AI moats, and the future of RobotOps

17:00 - Why storytelling will determine robotics adoption

19:15 - Preparing for a future where robots free up our time

20:00 - Outro

What You'll Learn

• What Robotics 2.0 means and why it's a fundamentally different paradigm from Robotics 1.0.

• Why specialized humanoids may have a clearer path to deployment than general-purpose systems.

• The massive data gap between LLMs and robotics, and why physical AI will scale gradually.

• The three investment theses: verticalized humanoids, RobotOps infrastructure, and data collection.

• Why robotics companies should involve end users from the earliest stages of development.

• Why exposure beats education when it comes to public trust in robots.

What does it actually look like when enterprise AI moves from experimentation to real work?

Learn more at: nebius.com

In this episode, Markiesha Patrice, Head of Startup Platform & Community at Nebius, sits down with Debanjan Saha, CEO of DataRobot. They explore what the transition from AI pilots to production-ready agentic systems looks like from the inside.

Debanjan has been at the forefront of three technology revolutions: the internet, the cloud, and now AI, and he brings a clear-eyed, optimistic framework to a moment he believes is an order of magnitude larger than anything that came before it.

From navigating three major technology revolutions to leading one of AI’s pioneering companies, Debanjan shares his perspective on leadership, disruption, and the opportunities emerging as AI moves from experimentation to real-world impact.

0:00 - Intro teaser

0:50 - Hype or real? Why this revolution is different

4:20 - The two-timescale strategy for staying ahead

5:20 - What DataRobot builds: the agentic workforce

8:25 - Inside the platform: building and running agents

14:30 - Frontier agents: Chevron's oilfield safety

18:20 - The power of partnership: Nebius and NVIDIA

20:15 - Advice for builders: create 10X value

25:20 - The vision: building AI factories

26:50 - Outro

What You'll Learn:

-Why the AI revolution is an order of magnitude larger than the internet and cloud revolutions that preceded it.

-How the enterprise AI market is evolving from chatbot experimentation to high-value agentic transformation.

-The full build-to-run framework: testing, validation, identity management, governance, and real-time intervention.

-The one-way door vs. two-way door decision making framework he learned from his time at Amazon.

-Why startups must create 10x value to win enterprise customers.

What if AI’s next breakthrough isn’t a larger model, but a different way of thinking about intelligence?

Learn more at: ⁠nebius.com

In this episode, Markiesha Patrice, Head of Startup Platform & Community at Nebius, sits down with Sudip Roy, co-founder and CTO of Adaption. After helping build AI infrastructure during the scaling era at Google and Cohere, Sudip now argues that the industry’s future won’t be defined by ever-larger models alone.

He explains why leading foundation models are converging, why enterprises should think in terms of AI systems instead of individual models, and how continuous adaptation could become the next major shift in artificial intelligence.

0:00 - Intro teaser: The days of monolithic AI are over

0:47 - Challenging scaling laws: how Adaption Labs started

3:18 - The three pillars: data, continual learning, interfaces

6:52 - Adaptation is all you need: the future of AI models

8:40 - Proportional compute and the AI energy gap

11:00 - Solving the 5% reliability gap for enterprises

11:57 - Advice for builders: take contrarian positions

14:36 - Adaptive Data: synthetic data in 240+ languages

15:19 - Outro

What You'll Learn:

-Why leading foundation models are beginning to converge and what that means for the future of AI.

-Why thinking about tasks instead of models changes how enterprises should build AI systems.

-Why enterprise data remains AI’s biggest untapped resource.-How continuous learning closes the 5% enterprise reliability gap caused by domain-specific organizational data.

-Why challenging industry consensus can create breakthrough startup opportunities.

Henry Wang built a consumer app that 10 million people use for 150 minutes a day. For most of that time, the assumption in the industry was that you needed a frontier model to deliver a great AI experience.

Learn more at: nebius.com⁠

Henry found a different answer.In this episode, Joshua Liss, Head of Media and Entertainment at Nebius, sits down with Henry (Lifan) Wang, Co-founder of Kaon Labs, to get into how you actually scale a consumer AI product. Not just find product-market fit, but build the infrastructure that lets you serve tens of millions of users, test 100 models a week, and generate a trillion tokens a day without it costing you everything.

Henry explains why personalization at consumer scale isn't a recommendation system anymore, how Kaon Labs replaced two-week A/B tests with Elo ranking that resolves in two hours, why entertainment is the right beachhead for consumer AI right now, and what in-experience advertising could look like.

He also shares his contrarian take on what actually separates the consumer AI companies that break out from the ones that spike and die.

0:00 - Intro teaser: 10M users, 115 minutes a day & 1 trillion tokens

0:20 - How Kaon Labs built AI infrastructure for 10 million users

3:02 - Why AI roleplay is changing entertainment forever

5:10 - Hyper-personalization vs. recommendation algorithms

8:18 - Why scaling consumer AI is harder than enterprise AI

11:33 - Building and evaluating 100 AI models every week

16:08 - Monetizing AI with subscriptions & immersive advertising

19:43 - Rapid fire: The AI-native leaders every startup needs

22:08 - Lessons from building a 10 million user consumer AI company

25:22 - Outro

What You'll Learn:

-Why testing 100 models a week against real users is the core of Kaon Labs' competitive advantage.

-How Elo ranking replaced A/B testing: evaluating models in hours, not weeks.

-Why AI replaces recommendation systems with true personalization.

-Why entertainment is the ideal proving ground for consumer AI.

-Product Market Fit matters, but scale is what separates breakouts from blips.

-What in-experience advertising looks like when it lives inside the story rather than interrupting it.

-The three things to look for in every key hire.

Brian Zhan doesn't want your business plan. In fact, if you hand him one at the seed stage, that might raise some red flags.

Learn more at: ⁠nebius.com⁠⁠

In this episode, Anu Maheshwari, Head of Venture Ecosystem at Nebius, sits down with Brian Zhan, partner at Striker Venture Partners. They unpack what it actually means to back people, not products, at the frontier of AI.

From reinforcement learning's generalization advantages, to recursive self-improvement loops in chip design, Brian makes the case that the VC consensus is missing the most important early bets by waiting for revenue.

Brian has been on the boards of Reflection, Skilled, and Periodic Labs. With a personal history in robotics research dating back to 2014, he maps out where the next trillion-dollar companies will emerge, why most VCs are too focused on revenue timelines to see them, and how Striker approaches seed investing with 10% of fund concentration per bet.

0:00 - Intro teaser: Why the best founders don't need a business plan

0:27 - Why Striker invests before there's a business plan

5:04 - Is AI in a bubble? Separating hype from opportunity

7:45 - How Brian identifies world-changing founders

9:24 - Reinforcement learning and the path to superintelligence

14:05 - Robotics, world models, and the future of physical AI

17:55 - Why most VCs think too small

20:18 - What Brian looks for in a founder pitch

23:37 - The OpenAI playbook & why compute partnerships matter

26:05 - Outro

What You'll Learn:

-Why a detailed business plan at seed is unnecessary.

-The precision behind frontier AI investing and why it's fundamentally different from SaaS.

-Why reinforcement learning underpins recursive self-improvement and why most investors don't actually understand it.

-How world models and simulation will accelerate robotics toward its ChatGPT moment.

-Solving energy supply by unlocking insight for everything else.

-How Striker evaluates founder-market fit in practice.

Tavily hit one million users without a single outbound cold email.

Learn more at: ⁠nebius.com⁠⁠

Not because of a viral moment, but because the founder understood earlier than most that the unit economics of agentic search are fundamentally different.

When you’re returning context to an AI agent rather than links to a human, token waste, latency, and information density become the variables that determine whether your product survives in production. Tavily built its architecture around that constraint from day one, and developers noticed. After being mentioned in 200+ research papers and 1,000+ YouTube videos, Fortune 500 enterprises were already running Tavily internally before a sales conversation ever happened.

In this episode, Markiesha Patrice (Head of Startup Platform & Community at Nebius) sits down with Rotem Weiss, Founder and CEO of Tavily, to get into the technical and strategic decisions behind building the search layer for the agentic web.

Rotem breaks down why optimizing for snippet retrieval over link retrieval changes the entire architecture of a search product, how Tavily's prediction engine pre-processes likely queries to cut latency without sacrificing accuracy, and why he believes the most important GTM decision an AI infrastructure company can make in 2026 is getting integrated into the coding assistants autonomously selecting tools on behalf of engineers.

0:00 - Intro teaser: the buyer isn't a human anymore

0:25 - Guest intro & what Tavily does

0:47 - Rotem's origin story & the first check

1:40 - The real-time data gap & launching GPT Researcher

4:30 - What is agentic search? Snippets, not links

6:55 - The prediction engine that kills latency

7:55 - Why 2026 is the year of token optimization

10:05 - Inside the Nebius acquisition

11:25 - The team & the "second CEO"

12:45 - "The buyer is the AI assistant" — winning coding tools

14:30 - GPUs as rocket fuel

16:30 - One million users, zero cold emails

19:20 - "I'm the worst at everything in the company"

20:30 - Israel vs. the US founder mindset

What You'll Learn

-Why returning the right snippet rather than the right link changes the entire architecture of a search product.

-The prediction engine Tavily uses to pre-process likely queries at the website level, reducing processing overhead without sacrificing accuracy.

-Why coding assistants autonomously selecting tools in a stack are becoming the real GTM lever for AI infrastructure companies.

-The open-source to enterprise conversion motion Rotem actually used.

-How the migration from large closed-source models to fine-tuned open-source models changes the token economics for AI companies trying to get to production.

Learn more at: https://nebius.com/startups/podcast

In October 2024, Alberto Taiuti was trying to solve a 3D asset problem and ran into a wall. The labeled data didn't exist. What he realized next reshaped the trajectory of his company.

Learn more at: ⁠nebius.com

In this episode, Josh Liss (Head of Media & Entertainment at Nebius) sits down with Alberto Taiuti, CEO and Co-founder of Reactor, the developer platform for world models. They unpack what real-time world models actually are, why they're fundamentally different from the AI video tools most people have encountered, and why he believes the era of prompting is already winding down.

Drawing on his background at Apple, where he worked on the Vision Pro, Alberto makes the case that interactive video changes what a developer platform has to be, why video models already understand 3D space (even though no one trained them to), and how shows, ads, and even software interfaces could be generated live, per user, in real time.

0:00 - Intro teaser: writing code, not prompts

0:25 - Guest intro & Reactor overview

0:47 - What is a world model?

2:19 - World models are regressive like LLMs

5:21 - Alberto's background & origin story

8:08 - The "aha" moment that changed everything

9:58 - How world models will impact media & entertainment

12:48 - "We only hire unreasonable people" — hiring philosophy

13:54 - Building in stealth: lessons from Apple

17:48 - GTA San Andreas shaped his entire career

What You'll Learn

What a world model is, in plain language, and how it differs from standard AI video generation.

Alberto's philosophy for building in stealth mode and how that impacts his team

Why Alberto believes interactive, real-time video brings code back to the center of AI development.

The insight about video models and 3D that became the foundation of Reactor.

Reactor's hiring philosophy and why "unreasonable" is the highest compliment at the company.

Alberto's three-year prediction for how applications themselves get built.

🚀 Apply to Nebius for Startups: https://www.nebius.com/startups

🎧 Explore more episodes: https://www.nebius.com/startups/podcast

In this episode, we sit down with Scott Kveton—serial entrepreneur, Co-founder and CEO of CaseMark, and Co-founder of the Association for AI in Legal (A4L)—to explore what it means to build across multiple generations of the internet.

With over 20 years of experience spanning Web1, Web2, Web3, and now AI, Scott has built and scaled companies at internet scale, with three successful exits and just as many hard-earned failures. From an early career at Amazon to co-founding Urban Airship and contributing to foundational technologies like OpenID and OAuth, his journey offers a front-row seat to the evolution of modern software.

Now, with CaseMark, Scott is focused on transforming the legal industry through AI—building a “litigation lifecycle” platform designed to help attorneys make sense of complex cases from intake to resolution.

We dive into Scott’s philosophy on building high-performance teams, why he believes companies should operate like clockwork—not culture—and how founders can stay focused on execution and stakeholder value. Along the way, we explore lessons from decades of building, scaling, and adapting through every major wave of the internet.

👤 About the Guest

LinkedIn: https://www.linkedin.com/in/kveton/

Twitter/X: https://x.com/kveton

Company website: https://casemark.com/

🚀  About Nebius for Startups

Nebius for Startups partners with leading AI founders building for scale—providing GPU credits, hands-on engineering support, and a global ecosystem to accelerate growth.

Reduce infrastructure bottlenecks, avoid scaling missteps, and move faster.

👉 Apply now: https://www.nebius.com/startups

🌐 About Nebius

Website: https://www.nebius.com

LinkedIn: https://www.linkedin.com/company/nebius

X: https://x.com/nebiusai