HeyJaredInference by 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,…
- Indexed episodes, last 90 days
- 18
- Latest publication
- Sep 14, 2026
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- Jul 23, 2026
Latest episodes
AI That Acts: Devin Fusion, Persimmon, Programmable Worlds & Amodei’s Slowdown Call (opens the original)
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Can AI-generated worlds remember what happened off-screen? How do dual-agent setups reduce costs? In this episode of Attention Span, we break down the biggest shifts in AI’s ability to understand, reason, and act in the physical and digital world. We dive into Alaya’s programmable world model, real-world vs. simulated robotics with Telexistence and Skild S1, Cognition’s Devin Fusion and SWE-2, and the heated frontier slowdown debate between Dario Amodei and David Sacks. Hosted by Ksenia Se (foun
The Agent Can Rewrite Itself. So Who Controls It? (opens the original)
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Meta just gave its new personal agent, Muse, a remarkable amount of freedom. It can browse, work across your accounts, write code, build tools and run subagents. But Meta made one part of the system deliberately difficult for Muse to control: its own authority. In this episode of Attention Span, I look inside Muse Secure VM and the security architecture surrounding the agent. We get into Sentinel, the separate system that decides what Muse is allowed to do; why Muse can use your accounts without
Does AI Understand the Machine It Runs On? | Inside OpenAI (opens the original)
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What if a model finds an optimization that surprises the engineers who have spent years working on that system? What, exactly, has it understood? I brought that question to OpenAI’s Phil Tillet and Matt Ferrari, whose work involves making AI cheaper and more accessible. They’re increasingly doing that work with the models themselves. Matt talks about research ideas his team used to dismiss because the engineering would be too complicated. Now they can give a model years of earlier research and a
NVIDIA’s $12.9B Plan to Rule Open-Source AI (opens the original)
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NVIDIA was once so hostile to open source that Linus Torvalds gave the company the finger. Today, it maintains open Linux modules, releases hundreds of models and datasets, and has reportedly agreed to buy Hugging Face for $12.9 billion. WHAT?! The change makes sense once we examine what NVIDIA learned from nearly dying with NV1, spending years searching for CUDA’s market, and watching researchers discover deep learning on gaming GPUs. In this episode, we follow that strategy from NV1 and CUDA t
Fei-Fei Li, LeCun, Hassabis: What Do They Mean by “World Model”? (opens the original)
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Demis Hassabis, Yann LeCun, Fei-Fei Li – they all talk about “building a world model.” Some of them are dedicating their professional lives to it! But do they mean the same? So before joining the World Models workshop at Chicago Booth, I wanted to answer a basic question: what do researchers mean by a world model, and how many different ideas are sitting under this name? World models are absolutely fascinating area of research with its GPT moment still in the nearest future. This episode is base
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