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. -3 J

    Inside NVIDIA’s Plan to Bring Self-Driving to Every Car | Ali Kani explains

    What if the future of self-driving isn’t one perfect robotaxi – but a stack that can turn almost any car into a self-driving car? In this episode of Inference, we ride through San Francisco – as one of the first to do this test drive – and talk about what’s changing in autonomous driving: cheaper hardware, better models, synthetic data, and a whole new approach to building the software behind the wheel. Ali Kani has been at NVIDIA Automotive for almost 8 years – he’s been through all the ups and downs, and he’s eager to share. *We talk about:* Why Level 2 is already possible with a surprisingly cheap sensor setup What is still missing for Level 4 Why next year could matter for Level 4 How NVIDIA combines an end-to-end driving model with a classical safety stack ​​Why open source matters for the future of autonomous driving Why synthetic data and simulation may matter as much as real-world driving data How different cities, laws, and driving cultures change the way autonomous systems behave Why the goal is bigger than one self-driving car – it’s making many cars autonomous by open sourcing the whole stack (it’s HUGE) We also experience live what still makes urban driving hard: construction, cyclists, congestion, weird negotiations at stop signs, and all the messy little moments humans barely notice but cars have to handle perfectly. What I liked about this conversation is that it makes the shift feel very real. *We’re moving from self-driving built inside closed labs to self-driving becoming a shared capability that can spread across the whole car industry.* This is a conversation about a future that starts tomorrow. It’s open and very exciting. Chapters: 0:00 The Future of Self-Driving Starts Now 0:19 Open Autonomous Driving Beyond Tesla and Waymo 1:07 Inside NVIDIA’s Low-Cost Level 2 Self-Driving Stack 1:48 From Level 2 to Level 4: Hyperion, Thor, and Redundancy 2:43 How NVIDIA Combines End-to-End AI with Safety Guardrails 3:56 What Changed in AlphaMaio Since GTC 5:12 The Key Technologies Needed to Solve Self-Driving 7:22 Real Data vs Synthetic Data in Autonomous Driving 9:21 Driving Through Real San Francisco Traffic 18:55 AlphaDream and the Next Generation of Simulation *Follow on*: https://www.turingpost.com/ https://www.turingpost.com/p/av *Did you like the episode? You know the drill:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:* Ali Kani, Vice President and General Manager of Automotive, NVIDIA https://www.linkedin.com/in/ali-kani-b22198 https://blogs.nvidia.com/blog/author/alikani/ Read more: https://www.turingpost.com/p/selfdriving https://thefocus.ai/posts/the-car-wash-test/

    34 min
  2. 24 MARS

    OpenAI’s Michael Bolin: What Engineers Still Matter For in the Age of Coding Agents

    In this second part of my conversation with Michael Bolin, lead for open-source Codex at OpenAI, we move from harness engineering to the human side of the story. What does it mean to be a programmer when you are no longer typing most of the code? Which skills become more important in an agent-driven workflow? Will coding agents eventually take over most software implementation? And if that happens, what is left for the human engineer besides pushing prompts around like a confused project manager with Wi-Fi? All of it and more in this part – watch it. *Follow on*: https://www.turingpost.com/ *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:* Michael Bolin, tech lead on Codex, OpenAI https://www.linkedin.com/in/michael-bolin-7632712/ https://x.com/bolinfest https://github.com/openai/codex Chapters: 0:00 — Do You Still Need to Learn Coding? 0:18 — From Systems to Humans: The Future of Programming 0:39 — Switching Mindset: Building for Agents vs Developers 1:13 — What Happens When Agents Consume the Web? 1:27 — Programmer Identity in the Age of AI 2:15 — Are Engineers Building More Than Ever? 2:37 — Key Skills for Engineers Working with AI Agents 3:59 — Will Agents Take Over Coding? 4:57 — Engineering Taste vs AI Decisions 5:10 — From Idea to Product Faster Than Ever 6:01 — Risks: Losing Human Judgment Too Early 6:42 — Do We Still Need Humans in the Loop? 8:06 — Book That Shaped a Builder’s Mindset 📰 Transcript:https://www.turingpost.com/p/bolincodex https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se #AI #OpenAI #Codex #MichaelBolin #SoftwareEngineering #Programming #CodingAgents #AIAgents #DeveloperTools #HarnessEngineering #FutureOfWork #Engineering #TuringPost

    9 min
  3. 17 MARS

    OpenAI’s Michael Bolin on Codex, Harness Engineering, and the Real Future of Coding Agents

    Regarding the question of what matters most – the model or the harness – Michael Bolin is somewhere in the middle. Stronger models clearly pushed Codex to new heights. But without the right harness around them, those models would not be able to operate reliably, and – most importantly – safely on a real developer’s machine. At least, not yet. In this episode of Inference, I talk with Michael Bolin – lead for open source Codex at OpenAI – about the engineering layer that makes coding agents actually function: the agent loop, sandboxing, tool orchestration, and the design decisions that determine how much freedom an agent should have. In this conversation, we get into: What a harness actually is and why every coding agent needs one Can a model be enough for a reliable coding workflow Why do they build harness as small and tight as possible How Codex handles sandboxing and security across OS Why safety and security are not the same thing in agentic systems How coding agents are changing the daily workflow of developers Why documentation, tests, repo structure, and agents.md suddenly matter more Whether too much context can make an agent worse Why Michael believes the future may involve fewer tools, but more powerful ones If you’re trying to understand where coding agents are actually going, this episode is for you. Subscribe to the channel to be notified about Part 2, where we discuss what becomes of the software engineer in the age of agents. Chapters: 0:00 The New Inner Loop of AI Coding Agents 0:17 Introduction: Michael Bolin and Open Source Codex 1:17 What the “Harness” Is in AI Coding Agents 2:13 Security and Sandboxing for AI Agents 4:33 Codex Launch and Rapid Growth 5:25 The Codex App: A New Interface for Developers 6:36 How Coding Agents Change Developer Workflows 10:04 Writing Codebases and Documentation for AI Agents 12:44 Context Engineering and Prompting for Codex 16:02 Model vs Harness: What Really Matters for Agents 19:23 Multi-Agent Systems, Tools, and the Future of AI Development *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:*  Michael Bolin, tech lead on Codex, OpenAI https://www.linkedin.com/in/michael-bolin-7632712/ https://x.com/bolinfest https://github.com/openai/codex 📰 Transcript: https://www.turingpost.com/bolin1 *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #AI #OpenAI #Codex #CodingAgents #DeveloperTools #AgenticAI #SoftwareEngineering #HarnessEngineering #Harness

    22 min
  4. 11 MARS

    What Reflection AI offers to beat closed labs

    In this episode, Ioannis Antonoglou, co-founder and CTO @ReflectionAI (ex-DeepMind, AlphaGo/AlphaZero/MuZero) explains what they are building: a frontier open-weight “general agent model” trained end-to-end with pretraining plus reinforcement learning. And I’ll be honest: I left this conversation more skeptical than I expected. They raised $2 billion last year. But where the results? Reflection’s thesis is huge – build the missing Western open base model, then use RL to push it to the frontier. The problem is that this is also the slowest path in the game. “All hands on deck building the model” means no clear wedge product yet, few concrete proof points, and a lot of execution risk while closed labs keep shipping. Am I missing something? Watch the video and leave your opinion in the comments Chapters: 0:00 Building AGI and the Mission Behind Reflection 0:25 From AlphaGo to Today: How AI Progress Really Happens 2:11 Breakthroughs vs. Engineering: What Still Matters Most 3:10 Defining AGI and Why It May Not Need Huge Breakthroughs 3:41 Why Reflection Shifted from Coding Agents to Frontier Models 5:15 The New Focus: Open Frontier Models and General Agents 6:33 Bottlenecks in Building Frontier AI: Team, Compute, and Scale 7:48 AI Tools, Internal Workflows, and Model-First Strategy 8:24 Can Open Models Catch Closed Labs? 10:34 Reinforcement Learning, Research Priorities, and Advice for Young Builders 14:01 Joining DeepMind, Open Science, and the Book That Shaped Him *Follow on*: https://www.turingpost.com/ *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:* Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253 https://reflection.ai/ 📰 Transcript: https://www.turingpost.com/nathan *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

    16 min
  5. 11 MARS

    Why Reflection AI Bets Their Business on Open Weights | Ioannis Antonoglou, co-founder and CTO

    Ioannis Antonoglou helped build AlphaGo, AlphaZero, and MuZero at DeepMind. Now he’s CTO and co-founder of Reflection AI, betting that frontier models should be open weights, not a black box behind an API. In Part 1, we talk about openness as an actual strategy: why open models can move faster, why “sovereignty” matters for enterprises and governments, and why safety might improve when the ecosystem can stress-test the system instead of guessing. We also get into the uncomfortable part: capable open agents can misbehave in public, fast (OpenClaw is the recent reminder). Is that a reason to close everything up, or a reason to make the risks visible and fixable? Topics covered:  – Why a former DeepMind builder chose open weights  – Open models as a commercial engine (and what investors bought)  – Openness, safety, and “more eyes on the system”  – Concentration of AI power in closed labs  – Who open frontier models are really for (research, enterprises, governments) Subscribe for Part 2: how Reflection plans to compete with closed labs and what they’re building under the hood. Chapters: 0:00 — “No One Was Sharing This Information” 0:16 — From DeepMind to Reflection AI 0:52 — Why Move from Closed Labs to Open Weights? 2:20 — Pitching Open Models Before the DeepSeek Moment 3:31 — What Changed in the Past Year 4:43 — Why Openness Accelerates Scientific Progress 6:06 — Open Source vs Safety: The Open Claw Case 7:19 — The Real Concern: Concentration of AI Power 8:23 — The Open Source Paradox 9:11 — The Value Proposition of an Open Frontier Model *Follow on*: https://www.turingpost.com/ *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:* Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253/ https://reflection.ai/ 📰 Transcript: https://www.turingpost.com/antonoglou_part1 *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

    10 min
  6. 11 MARS

    Why the US need Open Models | Nathan Lambert on what matters in the AI and science world

    Open models are often discussed as if they’re competing head-to-head with frontier systems. Are they catching up? Falling behind? Are they “good enough” yet? Nathan Lambert doesn’t believe open models will ever catch up with closed ones, and he explains clearly why. But he also argues that this is the wrong framing. Nathan is a research scientist at the Allen Institute for AI, the author of the RLHF Book, and the writer behind the Interconnects newsletter. He’s also one of the clearest voices on what open models are for, and just as importantly, what they are not. We talk about how academic AI research lost influence as training scaled up, why open models became the main place where experimentation still happens, and why that role matters even when open models trail frontier systems. We also discuss why China’s open model ecosystem developed so differently from the US one, and what that tells us about incentives, talent, and access to resources. From there, the conversation moves into the mechanics: post-training and reinforcement learning complexity, data availability, coding agents, hybrid architectures, and the very practical reasons most people continue to rely on closed models, even when they support openness in principle. This is a conversation about how AI research actually moves, where open models fit into that picture, and what it means to build systems when the frontier is expensive, fast-moving, and increasingly product-driven. This conversation offers a realistic look at where the open ecosystem stands today. Watch it! *Follow on*: https://www.turingpost.com/ *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:* Nathan Lambert, Research Scientist at Allen Institute for AI (AI2) https://x.com/natolambert https://www.linkedin.com/in/natolambert/ https://www.interconnects.ai/ (his newsletter on open models + RL + everything important in AI) https://rlhfbook.com/ - The RLHF Book https://allenai.org/ *Links:* State of AI in 2026 (Lex Fridman interview): https://www.youtube.com/watch?v=EV7WhVT270Q&t=10206s NVIDIA’s path to open models https://www.youtube.com/watch?v=Y3Vb6ecvfpU OLMo models: https://allenai.org/olmo NVIDIA Nemotron: https://developer.nvidia.com/nemotron SpaceX + xAI partnership: https://www.spacex.com/updates#xai-joins-spacex Season of the Witch (book): https://www.simonandschuster.com/books/Season-of-the-Witch/David-Talbot/9781439108246 📰 Transcript: https://www.turingpost.com/nathanlambert *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se

    47 min
  7. 11 MARS

    Inside MiniMax: How They Build Open Models

    First Western interview with a senior MiniMax researcher. Olive Song explains how they actually build models that work. When MiniMax's RL training wouldn't converge, they debugged layer by layer until they found it: fp32 precision in the LM head. When their models learned to "hack" during training, exploiting loopholes to maximize rewards, they had to rethink alignment from scratch. When benchmarks said their models were good but production said otherwise, they discovered the problem: environment adaptation. Olive talks about working at a pace where new models drop at midnight and you test them at midnight. How they use an internal AI agent to read every new paper published overnight. Why they sit with developers during experiments to catch dangerous behaviors in real-time. What "ICU in the morning, KTV at night" means when results swing wildly. How problem-solving becomes discovery when you're debugging behaviors no one has seen before. This is how Chinese labs are moving fast: first-principles thinking, engineering discipline, and willingness to work whenever the model in experimentation requires you to. We spoke on Sunday at 9 pm Beijing time. Olive was still waiting for results from new model experiments, so my first question was obvious: does everyone at the company work like this? *Follow on*: https://www.turingpost.com/ *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:* Olive Song, Senior Researcher at MiniMax MiniMax: https://www.minimaxi.com/ Models: https://huggingface.co/MiniMaxAI *Links:* vLLM: https://github.com/vllm-project/vllm SGLang: https://github.com/sgl-project/sglang 📰 Transcript: https://www.turingpost.com/olive Chapters: 0:00 – Reinforcement Learning and Unexpected Model Behaviors 3:08 – Roleplay, Alignment, and “AI with Everyone” 4:02 – How AI Changes Daily Life and Productivity 4:59 – Inside Miniax: How Researchers and Engineers Work Together 5:32 – Human Alignment and Safety in Open Models 6:16 – Why Engineering Details Matter More Than Algorithms 8:17 – Open Weights: Benefits, Risks, and Responsibility 10:57 – Specialization vs General AI Models 12:07 – Agentic AI and Long-Horizon Tasks 29:50 – AGI, Creativity, and the Future of AI *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se #MiniMax #ReinforcementLearning #AIResearch #OpenWeights #ChineseAI #OpensourceAI

    32 min
  8. 27 JANV.

    This Is a Fight Worth Having: The Case for Open Source AI | Raffi Krikorian, Mozilla CTO

    In the first episode of Inference’s quarterly series on Open Source AI, we talk to Raffi Krikorian, CTO of Mozilla, about when open source AI stops being aspirational and becomes an operational choice. We explore why stories like Pinterest saving $10 million by moving to open models are real, but often misunderstood, and why timing matters more than ideology. Raffi lays out his view of a missing “LAMP stack for AI” and explains why the hardest problem to solve isn’t models or data, but the connective glue that holds AI systems together. Along the way, he shares how Mozilla is navigating these tradeoffs in practice, why even open-source-first organizations still rely on closed tools during experimentation, and what the browser era taught Mozilla about defaults, user choice, and long-term control. He also shares a few practical recommendations in this episode that apply even if you’re still experimenting. Listen closely. This conversation kicks off our Open Source AI series for 2026, focused on real tradeoffs, real economics, and the decisions companies are making right now. Follow on: https://www.turingpost.com/ *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:* Raffi Krikorian, CTO at Mozilla LinkedIn: https://www.linkedin.com/in/rkrikorian/ Mozilla AI: https://mozilla.ai/ Mozilla Blog: https://blog.mozilla.org/en/mozilla/mozilla-open-source-ai-strategy/ *Links mentioned:* Raffi's post here about our OSAI strategy: https://blog.mozilla.org/en/mozilla/mozilla-open-source-ai-strategy/ 🌐 #1: Mastering Open Source AI in 2026: Essential Decisions for Builders https://www.turingpost.com/p/opensource1 Mozilla Data Collective: https://data.mozilla.org/ Langchain: https://www.langchain.com/ OpenRouter: https://openrouter.ai/ AI2 (Allen Institute for AI): https://allenai.org/ Flower AI (Federated Learning): https://flower.dev/ Einstein's Dreams by Alan Lightman: https://www.goodreads.com/book/show/14376.Einstein_s_Dreams 📰 The transcript and edited version at https://www.turingpost.com/krikorian *Chapters:* 0:00 Cold Open — Values vs Economics in Open Source AI 0:28 Intro: Why This Season Focuses on Open Source AI 0:54 When Open Source Becomes a Business Decision 1:44 Pinterest Saved $10M + The Shift From Prototyping to Production 2:42 Mozilla’s “Choice Suite” + The Terraform “Exit Door” 5:21 Mozilla’s Mission: Do for AI What Mozilla Did for the Web 7:09 The “LAMP Stack” for AI + Standards Across the Stack 9:52 Small Models, Specialization, and Model Composability 15:45 Data, Privacy, and “I Own My Context” 18:36 “This Is a Fight Worth Having” + The Signal Analogy 21:42 1–2–3 Steps for Companies to Start (Instrument Choice Early) 24:22 Book Pick: Einstein’s Dreams + Closing 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 →* Turing Post: https://x.com/TheTuringPost Ksenia Se: https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #OpenSourceAI #LAMPStackForAI #AIEconomics #MozillaAI #AIInfrastructure #DataProvenance #FederatedLearning #OpenModels

    26 min

À propos

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.

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