AI-Native

Amber Yang built an AI model in high school that predicted space debris collisions, won the International Science Fair, and turned it into a startup while studying at Stanford. Eventually these lessons led her to a role in truly AI-Native investing as a Partner at Lightspeed.In this episode, Anu and Markiesha sit down with Amber to unpack how a technical investor builds conviction in AI infrastructure – why the founder archetype has gotten younger, how some infra deals close in 30 minutes, and which founder types usually succeed at different layers of the AI stack.Amber also talks through the buzzwords she hears most often and how she guides them to something more concrete, why compute constraints rarely make her pass at the early stage, and where she thinks the capital-intensive parts of the stack end up.What You’ll Learn:Why the AI-native founder archetype keeps getting youngerWhy raising seed capital can be easier than landing a research job at top labs like OpenAI and AnthropicHow infra deals close in 30 minutes, and the years of relationship-building behind themHow founders can find success in the infra layer and the application layerWhat a world model means to her, and the buzzwords she’s forced to challenge in meetingsWhy compute constraints rarely stop her interest at the early stageLearn more: www.nebius.com/podcast

Ryan Wang started his PhD at MIT in 2018, right as the transformer architecture was reshaping AI, and spent late nights restarting GPU racks in a campus basement. Years later, the startup he co-founded, Eigen AI, would rank first among inference providers serving the same open model, earning a shoutout in Jensen Huang’s 2026 GTC keynote. Along the way he learned that at scale, the model is the easy part. The real challenge is in everything it takes to serve it.In this episode, Anu and Markiesha sit down with Ryan, now the SVP of Applied AI at Nebius, to unpack why inference optimization has become one of the biggest levers in AI economics including how optimization is now squeezing four to five times more tokens out of the same GPUs, and why a fine-tuned 70B open model can beat frontier closed models on specific tasks.Ryan also shares the prompt-design choice that keeps your cache intact, why cached context costs a tenth of recomputing it, and when founders should buy inference instead of building a team and delves into why cheaper tokens will grow the inference market rather than shrink it. What You’ll Learn:Why everything about inference changes at ten times the scaleThe prompt-design choice that keeps your cache intactHow open models can outperform frontier closed modelsWhen founders should buy inference as a service and when to build an in-house teamWhy Ryan thinks the gap between open and closed models has shrunk from a year to a few monthsThe story behind Eigen AI’s monumental GTC shoutoutWhy he sees dramatic expansion of the inference marketWhat changes he sees coming to inference infra, including agentic AI infrastructureLearn more: www.nebius.com/podcast

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 overnightWhat breaks when a team moves from one cluster to managing dozens across providersWhy idle GPUs can cost frontier teams tens of millions of dollars a yearThe difference between getting compute and knowing how to use it wellWhy SkyPilot's founders bet on open source and the Apache 2.0 license from the startWhat changes for AI teams once compute stops being the constraintIf 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.comWhen 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 teaser0:50 - Higgsfield's thesis: why most social content will be AI-generated2:22 - The rise of AI-native creators going end-to-end4:07 - The secret: Higgsfield's 50-person creative feedback loop5:45 - Democratizing high-end content and the Higgsfield contest8:52 - From Snap face filters to Higgsfield13:35 - 20x faster, 20x cheaper16:55 - Prediction: AI-native IP and an AI-first NetflixWhat You'll LearnWhy 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.comIn 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 teaser0:40 - Lukas Ziegler’s journey from engineer to robotics evangelist2:40 - Robotics 2.0: the shift from programmed to general-purpose robots4:45 - The biggest investment trends in physical AI7:45 - Why robotics has a massive data problem11:00 - Synthetic data, AI moats, and the future of RobotOps17:00 - Why storytelling will determine robotics adoption19:15 - Preparing for a future where robots free up our time20:00 - OutroWhat You'll LearnWhat 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.