The Nebius for Startups Podcast

The Nebius for Startups Podcast explores what it takes to build and scale AI-native companies in today’s rapidly evolving technology landscape. Featuring a rotating cast of hosts, the Nebius for Startups Podcast brings together founders, engineers, researchers, investors, and ecosystem builders shaping the future of artificial intelligence. Each episode explores the real challenges and opportunities of building AI-native startups—from model development and infrastructure to go-to-market strategy, fundraising, and scaling technical teams. Whether you're an AI founder, developer, researcher, or investor, this podcast offers an inside look at the people, ideas, and technologies driving the next generation of AI companies.

The Nebius for Startups Podcast is a technology podcast hosted by Nebius, with 25 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Nebius hosts The Nebius for Startups Podcast, a technology show with 25 episodes published.

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  The Nebius for Startups Podcast

  Higgsfield's Secret Was NOT the Engineers

  Aug 11, 202617 min

  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?

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  The Nebius for Startups Podcast

  Why Robotics Data is 250,000 Years Behind LLMs

  Aug 4, 202620 min

  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.

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  The Nebius for Startups Podcast

  How Enterprise AI Moves from Chatbots to Real Work

  Jul 21, 202624 min

  What does it actually look like when enterprise AI moves from experimentation to real work? 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. Learn more at: https://nebius.com/startups/podcast

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  The Nebius for Startups Podcast

  The End of Monolithic AI and What Comes Next

  Jul 14, 202619 min

  What if AI’s next breakthrough isn’t a larger model, but a different way of thinking about intelligence? 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. Learn more at: https://nebius.com/startups/podcast

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  The Nebius for Startups Podcast

  How to Build a Consumer AI App That 10 Million People Can't Put Down

  Jul 7, 202626 min

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

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