Inference by Turing Post

Turing Post

Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads. Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes. It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions. If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.

  1. 12/04/2025

    What AI Is Missing for Real Reasoning? Axiom Math’s Carina Hong on how to build an AI mathematician

    Is math the ultimate test for AI reasoning? Or is next-token prediction fundamentally incapable of discovering new truths and discovering conjectures? Carina Hong, co-founder and CEO of Axiom Math, argues that to build true reasoning capabilities, we need to move beyond "chatty" models to systems that can verify their own work using formal logic. In this episode of Inference, we get into: Why current LLMs are like secretaries (good at retrieval) but bad at de novo mathematics The three pillars of an AI Mathematician How AlphaGeometry proved that symbolic logic and neural networks must merge The difference between AGI and Superintelligence Why "Theory Building" is harder to benchmark than the International Math Olympiad (IMO) The scarcity of formal math data (Lean) compared to Python code We also discuss the bottlenecks: the "chicken and egg" problem of auto-formalization, why Axiom bets on specific superintelligence over general models, and how AI will serve as the algorithmic pillar for the future of hard science. This is a conversation about the structure of truth, the limits of intuition, and what happens when machines start grading their own homework. Watch it! Did you like the episode? You know the drill: 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Carina Hong, co-founder and CEO of Axiom Math https://www.axiom.xyz/ https://x.com/CarinaLHong https://www.linkedin.com/in/carina-hong/ 📰 The transcript and edited version at https://www.turingpost.com/carina/ Chapters: 0:53 Why LLMs Struggle with Basic Math 2:42 Building an AI Mathematician: The 3 Pillars (Prover, Knowledge Base, Conjecturer) 5:50 The Role of Human-AI Collaboration 6:34 Can AI Have Intuition? (Conjectures & AlphaGeometry) 10:16 A Hybrid Approach: LLMs + Formal Verification 11:24 Specialist Science Models vs. Generalist Giants 13:33 The Problem with Current AI Benchmarks 16:34 Practical Applications: Enterprise & Formal Verification 21:24 The Main Bottleneck: Data Scarcity 23:49 AGI vs. Superintelligence: The "Plate" Analogy 26:31 Book Recommendations (Math, Law, and Literature) 30:56 How to Use AI for Math Discovery Today Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live. Follow us → Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #FutureOfAI #MathAI #FormalVerification #Lean #AxiomMath #Superintelligence #Reasoning

    33 min
  2. 12/04/2025

    Can We Control AI That Controls Itself? Anneka Gupta from Rubrik on…

    Is security still about patching after the crash? Or do we need to rethink everything when AI can cause failures on its own? Anneka Gupta, Chief Product Officer at Rubrik, argues we're now living in the world before the crash – where autonomous systems can create their own failures. In this episode of Inference, we explore: Why AI agents are "the human problem on steroids" The three pillars of AI resilience: visibility, governance, and reversibility How to log everything an agent does (and why that's harder than it sounds) The mental shift from deterministic code to outcome-driven experimentation Why most large enterprises are stuck in AI prototyping (70-90% never reach production) The tension between letting agents act and keeping them safe What an "undo button" for AGI would actually look like How AGI will accelerate the cat-and-mouse game between attackers and defenders We also discuss why teleportation beats all other sci-fi tech, why Asimov's philosophical approach to robots shaped her thinking, and how the fastest path to AI intuition is just... using it every day. This is a conversation about designing for uncertainty, building guardrails without paralyzing innovation, and what security means when the system can outsmart its own rules. Did you like the episode? You know the drill: 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! Guest: Anneka Gupta, Chief Product Officer at Rubrik https://www.linkedin.com/in/annekagupta/ https://x.com/annekagupta https://www.rubrik.com/ 📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe Chapters: Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live. Follow us → Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #AIAgents #Cybersecurity #AIGovernance #EnterpriseAI #AIResilience #Rubrik #FutureOfSecurity

    27 min
  3. 12/04/2025

    Spencer Huang: NVIDIA’s Big Plan for Physical AI: Simulation, World Models, and the 3 Computers

    When robots move into the real world, speed and safety come from simulation! In his first sit-down interview, Spencer Huang – NVIDIA’s product lead for robotics software – talks about his role at NVIDIA, a flat organization where “you have access to everything.” We discuss how open source shapes NVIDIA’s robotics ecosystem, how robots learn physics through simulation, and why neural simulators and world models may evolve alongside conventional physics. I also ask him what’s harder: working on robotics or being Jensen Huang’s son. Watch to learn a lot about robotics, NVIDIA, and its big plans ahead. It was a real pleasure chatting with Spencer. *We cover:* - NVIDIA’s big picture - The “three computers” of robotics – training, simulation, deployment - Isaac Lab, Arena, and the path to policy evaluation at scale - Physics engines, interop, and why OpenUSD can unify fragmented toolchains - Neural simulators vs conventional simulators – a data flywheel, not a rivalry - Safety as an architecture problem – graceful failure and functional safety - Synthetic data for manipulation – soft bodies, contact forces, distributional realism - Why the biggest bottleneck is robotics data, and how open ecosystems help reach baseline - NVIDIA’s “Mission is Boss” culture – cross-pollinating research into robotics This is a ground-level look at how robots learn to handle the messy world – and why simulation needs both fidelity and diversity to produce robust skills. *Chapters*: 0:22 The future of Physical AI begins here 1:00 Inside NVIDIA’s secret blueprint for teaching robots 3:46 Why safety is the hardest part of robotics 4:11 Simulation: the new classroom for machines 8:55 Can robots really understand physics? 13:55 How NVIDIA builds robot brains without a PhD 16:47 The plan to unify a fragmented robotics world 20:31 Why open source is NVIDIA’s biggest power move 21:21 What’s harder – robotics or being Jensen Huang’s son? 24:31 The one thing holding robotics back 27:56 The sci-fi books that shaped Spencer's mind *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Spencer Huang, NVIDIA – a product line manager at NVIDIA leading robotics software product. His work centers on open-source simulation frameworks for robot learning, synthetic data generation methodologies, and advancing robot autonomy – from industrial mobile manipulators to generalist humanoid robots. https://www.linkedin.com/in/spencermhuang/ *📰 Want the transcript and edited version?* Find it here: https://www.turingpost.com/spencer *Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live. 📩 Sign up: https://www.turingpost.com Follow Ksenia Se and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata

    28 min
  4. 12/04/2025

    Why do we need a special Operating System for AI?

    When thousands of AI agents begin to act on our behalf, who builds the system they all run on? Renen Hallak – founder and CEO of VAST Data – believes we’re witnessing the birth of an *AI Operating System*: a foundational layer that connects data, compute, and policy for the agentic era. In this episode of Inference, we talk about how enterprises are moving from sandboxes and proof-of-concepts to full production agents, why *metadata matters more than “big data,”* and how the next infrastructure revolution will quietly define who controls intelligence at scale. *We go deep into:* What “AI OS” really means – and why the old stack can’t handle agentic systems Why enterprises are underestimating the magnitude (but overestimating the speed) of this shift The evolving role of data, metadata, and context in intelligent systems How control, safety, and observability must be baked into infrastructure – not added later Why Renen says the next 10 years will reshape everything – from jobs to the meaning of money The ladder of progress: storage → database → data platform → operating system What first-principles thinking looks like inside a company building for AGI-scale systems This is a conversation about the architecture of the future – and the fine line between control and creativity when intelligence becomes infrastructure. Watch the episode! *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Renen Hallak, Founder & CEO, VAST Data https://www.linkedin.com/in/renenh/ https://www.linkedin.com/company/vast-data/ *📰 Want the transcript and edited version?* Find it here: https://www.turingpost.com/p/renen *Chapters:* *Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live. 📩 Sign up: https://www.turingpost.com *Follow us:* Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #agenticOS, #enterpriseAI, #metadata, #AIoperatingsystem, exabyte storage, GPUs, production AI

    26 min
  5. 12/04/2025

    The Future of Cancer Diagnosis: Digital Pathology and AI

    This episode of Inference is dedicated to Breast Cancer Awareness Month. I’m talking with Akash Parvatikar – AI scientist and product leader in digital pathology and computational biology. He leads PathologyMap™ at HistoWiz, a digital pathology platform that turns whole-slide images into searchable, analyzable data with AI tools – streamlining research and accelerating insights for cancer and precision medicine. Digital pathology is a very new field, but an important one, considering that the US is facing a large shortage of pathologists. *What you’ll learn:* - What “digital pathology” actually is – and why scanning glass slides changes everything - Where AI already helps today and where it’s still just a very promising technology - Why explainability, failure modes, and data standards decide clinical adoption - What is the real bottleneck for using AI in pathology and diagnosis - How agentic workflows might enter the lab in pieces first - A practical timeline for digitization, FDA-type approvals, and hospital rollouts - The human role that stays *Big idea:* Digitize first. Validate carefully. Then scale tools that clinicians trust. Telepathology expands access. Good AI here speaks the pathologist’s language. Remember – AI that can’t explain itself in clinical terms won’t ship. Watch the episode! *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Akash Parvatikar, AI Scientist, leading PathologyMap at HistoWiz https://www.linkedin.com/in/akash007/ https://home.histowiz.com/pathology_map/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe *Chapters:* 1:22 - The Current State and Future of AI in Cancer Diagnostics 2:27 - Real-World vs. Aspirational AI Breakthroughs in Patient Outcomes 3:36 - Evolution of AI Usage by Clinicians 4:47 - The Technical Challenges of AI in Pathology 7:22 - The Role of Generative AI in Diagnostics 8:42 - The Potential of Agentic AI Workflows in Pathology 9:50 - Key Bottlenecks in AI for Pathology 12:13 - About the Pathology Map Platform 13:49 - Navigating Regulations in AI-Powered Diagnostics 14:40 - The Human Impact of AI in Cancer Diagnostics 16:40 - What is Digital Pathology? 18:21 - Timeline for Mainstream Adoption of AI in Pathology 19:42 - The "Spotify for Precision Medicine" 20:20 - The Future Role of Humans in AI-Assisted Pathology 21:36 - The Economics of AI in Pathology 22:48 - Concerns and Excitations About the Future of AI in Pathology 24:43 - Book Recommendation Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. *Sign up:* Turing Post: https://www.turingpost.com Follow us: Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #DigitalPathology #AIMedicine #CancerDiagnostics #PrecisionMedicine #BreastCancerAwareness #EarlyDetection #AIForGood

    26 min
  6. 09/25/2025

    What Really Blocks AI Progress? Ulrik Hansen from Encord thinks it’s…

    Is compute the main roadblock? Or the models are not big enough for AGI? Ulrik Hansen, president and co-founder of Encord, argues that the true bottleneck is data. In this episode of Inference, we get into: Why models are mostly interchangeable, but data orchestration makes or breaks real-world AI Tesla’s compounding advantage from live human feedback vs. Waymo’s cautious rollout Why robotics lags behind digital AI – and how feedback loops shape both The coming split between “cheap” intelligence (facts and patterns) and “expensive” intelligence (creativity, taste, vision) What is the new connection economy We also discuss the risks: synthetic data eating its own tail, the trust and safety challenges that make brand more valuable than ever, and why Ulrik believes the next 10 years will bring more change than the last 50. This is a conversation about the future of AI systems, the bottlenecks that matter, and what it means when humans and machines start sharing the same workflows. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Ulrik Hansen, president and co-founder of Encord https://www.linkedin.com/in/ulrik-stig-hansen-2658273b/ https://x.com/ulrikstighansen https://x.com/encord_team https://encord.com/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #FutureOfAI #DataBottleneck #SelfDriving #Tesla #Waymo #Robotics

    28 min
  7. 09/06/2025

    What Is The Future Of Coding? Warp’s Vision

    What comes after the IDE? In this episode of Inference, I sit down with Zach Lloyd, founder of Warp, to talk about a new category he’s coining: the Agentic Development Environment (ADE). We explore why coding is shifting from keystrokes to prompts, how Warp positions itself against tools like Cursor and Claude Code, and what it means for developers when your “junior dev” is an AI agent that can already set up projects, fix bugs, and explain code line by line. We also touch on the risks: vibe coding that ships junk to production, the flood of bad software that might follow, and why developers still need to stay in the loop — not as code typists, but as orchestrators, reviewers, and intent-shapers. This is a conversation about the future of developer workbenches, the end of IDE dominance, and whether ADEs will become the default way we build software. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Zach Lloyd, founder of Warp https://www.linkedin.com/in/zachlloyd/ https://x.com/zachlloydtweets https://x.com/warpdotdev https://www.warp.dev/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #Warp #AgenticAI #AgenticDevelopment #AItools #CodingAgents #SoftwareDevelopment #Cursor #ClaudeCode #IDE #ADE #AgenticWorkflows #FutureOfCoding #AIforDevelopers #TuringPost

    30 min
  8. 08/23/2025

    When Will Inference Feel Like Electricity? Lin Qiao, co-founder & CEO of Fireworks AI

    What limits AI today isn’t imagination – it’s the cost of running it at scale. In this episode of Inference, Ksenia Se sits down with Lin Qiao, co-founder & CEO of Fireworks AI (an inference-first company) and former head of PyTorch at Meta, where she led the rebuild of Meta’s entire AI infrastructure stack. We talk about: Why product-market fit can be the beginning of bankruptcy in GenAI The iceberg problem of hidden GPU costs Why inference scales with people, not researchers 2025 as the year of AI agents (coding, hiring, SRE, customer service, medical, marketing) Open vs closed models – and why Chinese labs are setting new precedents The coming wave of 100× more efficient AI infrastructure Watch to hear Lin’s vision for inference, alignment, and the future of AI infrastructure. And – at the end – Lin shares her very personal journey to overcome fears. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Lin Qiao, co-founder & CEO of Fireworks AI and former head of PyTorch at Meta https://www.linkedin.com/in/lin-qiao-22248b4 https://x.com/lqiao https://x.com/FireworksAI_HQ https://fireworks.ai/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

    26 min

About

Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads. Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes. It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions. If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.