Turing Post

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

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*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.

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#robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata

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!

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*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.

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#agenticOS, #enterpriseAI, #metadata, #AIoperatingsystem, exabyte storage, GPUs, production AI

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!

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*Guest:* Akash Parvatikar, AI Scientist, leading PathologyMap at HistoWiz

https://www.linkedin.com/in/akash007/

https://home.histowiz.com/pathology_map/

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*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.

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#DigitalPathology #AIMedicine #CancerDiagnostics #PrecisionMedicine #BreastCancerAwareness #EarlyDetection #AIForGood

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!

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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/

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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.

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#AI #FutureOfAI #DataBottleneck #SelfDriving #Tesla #Waymo #Robotics

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!

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Guest:

Zach Lloyd, founder of Warp

https://www.linkedin.com/in/zachlloyd/

https://x.com/zachlloydtweets

https://x.com/warpdotdev

https://www.warp.dev/

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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.

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#Warp #AgenticAI #AgenticDevelopment #AItools #CodingAgents #SoftwareDevelopment #Cursor #ClaudeCode #IDE #ADE #AgenticWorkflows #FutureOfCoding #AIforDevelopers #TuringPost

Children are natural philosophers of AI. They draw librarians inside speakers, ask what the robot “wants,” and poke at the cracks in classifiers until bias spills out. They remind us that anthropomorphism is not a mistake – it’s the starting point.

In this second episode of the AI Literacy Series, I sit down again with Stefania Druga – researcher, educator, and my co-author on this project – to explore how kids help us see AI more clearly than most experts.

*We’ll explore:*

- Why words like think, know, imagine matter more than we admit.

- How kids move from magical thinking to system thinking.

- The “Big Three” AI families – classifiers, diffusion models, transformers – explained in ways families can test at home + more!

- How transfer learning works for humans, connecting toy models to civic-scale consequences.

- Six advanced family activities that make bias, prediction, and agency visible.

AI literacy is not just technical instruction – it’s cultural negotiation. And sometimes the best teachers are sitting right at the dinner table.

📌 You can find all mentioned resources & activities here: https://www.turingpost.com/p/ailiteracy2

📌 Subscribe to follow the series and join us in building a living playbook for AI literacy.

Generative and other types of AI is no longer something you “learn to use.” It’s the environment we all live in. It’s shaping homework, search, gameplay, even family kitchen conversations. And while adoption has crossed a threshold, our understanding of AI literacy is still catching up.

In this first episode of the AI Literacy Series, I sit down with *Stefania Druga* – researcher, educator, creator of Cognimates, and my co-author on this project – to explore a central question: *what does it mean to raise an AI-literate generation – and actually be cool about it?*

*We’ll explore:*

- Why AI literacy is the new baseline, not an add-on.

- How to help kids move from “using AI” to questioning and shaping it.

- Practical frameworks like Graidients, making AI use visible, intentional, and ethical.

- Seven simple activities you can try at home to build fluency together.

This series isn’t about watering down AI for children. It’s about reimagining how we – as builders, parents, and educators – prepare the next generation to live, think, and create inside an always-on model ecosystem. Doesn't matter if you are an AI expert or a total novice – everyone will find something insightful.

📌 You can find all mentioned resources & activities here: https://www.turingpost.com/p/ailiteracy1

📌 Subscribe to follow the series and join us in building a living playbook for AI literacy.

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!

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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/

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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.

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In this episode of Inference, I sit down with Alex Hancock – Senior Software Engineer at Block, core contributor to Goose (the open-source, multi-purpose AI agent), and a member of the Model Context Protocol (MCP) Steering Committee – to talk about the infrastructure that’s quietly powering the next wave of AI.

*We cover:*

 – What MCP is – and why it’s exploding in adoption

 – How it turns models from “brains in jars” into agents with arms and legs

 – The MCP Steering Committee’s push for openness and real governance

 – Why SDK parity, registry design, and OAuth 2.1 are make-or-break for developers

 – How MCP and A2A fit together – and where they might compete

 – Context discovery, context management, and why they’re the hardest problems in agentic AI

 – The lessons from Goose on staying model-agnostic in a fast-moving ecosystem

 – What this shift means for software development – and for the humans in the loop

Alex also shares his view on the next year of protocol development, why he thinks AGI will arrive incrementally, and how a runner’s mindset shapes his approach to building tools that last.

If you’re building agents, connecting models to the world, or just trying to understand the emerging “protocol layer” of AI, this conversation will give you a front-row seat.

Let’s find out how we’re teaching AI to act – and what’s still missing.

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*Guest:*

Alex Hancock, Senior Software Engineer at Block, Goose Maintainer & MCP Steering Committee Member

https://www.linkedin.com/in/alexjhancock/

https://x.com/alexjhancock

https://github.com/block/goose

MCP https://github.com/modelcontextprotocol

Building to Last: A New Governance Model for MCP https://blog.modelcontextprotocol.io/posts/2025-07-31-governance-for-mcp/

*📰 Want the transcript and edited version?*

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Chapters *coming*

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.

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We get into why RAG is far from dead, how context windows mislead more than they help, and what it really takes to separate reasoning from memory. Amr breaks down the case for retrieval with access control, the rise of hallucination detection models, and why DIY RAG stacks fall apart in production.

We also talk about the roots of RAG, Amr’s take on AGI timelines and what science fiction taught him about the future.

If you care about truth in AI, or you're building with (or around) LLMs, this one will reshape how you think about trustworthy systems.

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Guest:

Amr Awadallah, Founder and CEO at Vectara

https://www.linkedin.com/in/awadallah/

https://x.com/awadallah

https://www.vectara.com/

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Chapters

00:00 – Intro

00:44 – Why RAG isn’t dead (despite big context windows)

01:59 – Memory vs reasoning: the case for retrieval

02:45 – Retrieval + access control = trusted AI

06:51 – Why DIY RAG stacks fail in production

09:46 – Hallucination detection and guardian agents

13:14 – Open-source strategy behind Vectara

16:08 – Who really invented RAG?

17:30 – Can hallucinations ever go away?

20:27 – What AGI means to Amr

22:09 – Books that shaped his thinking

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 (Jensen Huang is already in): https://www.turingpost.com

Things mentioned during the interview:

Hughes Hallucination Evaluation Model (HHEM) Leaderboard https://huggingface.co/spaces/vectara/leaderboard

HHEM 2.1: A Better Hallucination Detection Model and a New Leaderboard

https://www.vectara.com/blog/hhem-2-1-a-better-hallucination-detection-model

HCMBench: an evaluation toolkit for hallucination correction models

https://www.vectara.com/blog/hcmbench-an-evaluation-toolkit-for-hallucination-correction-models

Books:

Foundation series by Isaac Asimov https://en.wikipedia.org/wiki/Foundation_(novel_series)

Sapiens: A Brief History of Humankind Hardcover by Yuval Noah Harari https://www.amazon.com/Sapiens-Humankind-Yuval-Noah-Harari/dp/0062316095

Setting the Record Straight on who invented RAG

https://www.linkedin.com/pulse/setting-record-straight-who-invented-rag-amr-awadallah-8cwvc/

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