The AI conversation, week by week
18 AI-focused podcasts currently tracked18 AI podcasts

Currently tracking18 AI-focused podcasts

  • AI For Humans
  • AI Inside
  • AI Today Podcast
  • Eye on AI
  • Gradient Dissent
  • Interconnects
  • Last Week in AI
  • Latent Space
  • Machine Learning Street Talk
  • Me, Myself, and AI
  • No Priors
  • Practical AI
  • The AI Daily Brief
  • The AI Native Dev
  • The Artificial Intelligence Show
  • The Cognitive Revolution
  • The TWIML AI Podcast
  • Training Data

Trends over time

Jul 13–Oct 4, 2026 · 12 published weeks

In the latest report, the remaining 1% of attention can include unpublished topics and podcasts with no qualifying discussion.

2 topics highlighted · Sep 28–Oct 4, 2026

Select a topic or a dated dot to see its history below.

Swipe to follow the full timeline →

Topic attention across published weeksEach colored line uses the same attention scale. Only topics with published measurements are shown; their shares do not necessarily total 100%. Breaks mean missing measurements, not zero. Dated dots open that topic and week below.0%11.3%22.5%33.8%45%Jul 13Jul 20Jul 27Aug 3Aug 10Aug 17Aug 24Aug 31Sep 7Sep 14Sep 21Sep 28Adoption Use Cases: About the same in the latest reportAdoption Use Cases33.4%Research: Gaining attention in the latest reportResearch22.7%Agents: About the same in the latest reportAgents14.5%Safety & governance: Losing attention in the latest reportSafety & governance8.6%Hardware: Gaining attention in the latest reportHardware7.9%Regulation: About the same in the latest reportRegulation3.9%Open Models: About the same in the latest reportOpen Models3.1%Economics: Losing attention in the latest reportEconomics2.5%Infrastructure: About the same in the latest reportInfrastructure1.2%Jobs: About the same in the latest reportJobs1.2%Other: Below threshold in the latest reportOtherNo measurement

Direction compares the latest report with recent weeks; small changes are “About the same,” and gaps mean no measurement.

Research · 19.3% · Jul 27–Aug 2, 2026 · Gaining attention in the latest report

Research

New models, training methods, benchmarks, and discoveries from AI labs.

Jul 27–Aug 2, 2026

19.3%Gaining attentionUp 1.7 percentage points from last week

Choose a week to read the discussion.— Below threshold

Research, week by weekDated controls show each week's attention share. Gaps mean no qualifying measurement, not zero. Select a week to read its discussion below.0%11.3%22.5%33.8%45%
Show this week’s discussionHide this week’s discussionPodcast moments and context

Research · Jul 27–Aug 2, 2026

A closer read

What changed
Its share rose from 17.6% to 19.3%, with evidence across 10 podcasts.
Where it showed up
It appeared in episodes including “How Researchers Test AI for Hidden Goals — Apollo Research” and “Is Offense or Defense Dominant? FAR.AI's Adam Gleave on the AI Security Leaderboard”.
How to read it
6 cited episodes from 6 podcasts are shown below. The topic appeared across 10 podcasts in the full analysis.

Podcast moments from the week

11 quoted moments · 6 episodes · 6 podcasts

Machine Learning Street Talk

How Researchers Test AI for Hidden Goals — Apollo Research

Open episode ↗
  1. C
    “It's possible that the model might get really good results in its training distribution, but it might actually be learning the hospital where the test was taken, not the actual test as well. So it doesn't generalize. But why is this uniquely bad with language models and reinfo...”
    Open episode and seek to 07:20

    ConceptResearch

    Report this moment
  2. C
    “I guess the interesting thing for me is that when we think of reinforcement learning algorithms like AlphaGo Zero, it makes sense that they are reward-seeking because there is this structured inference process. this is a language model. It's doing greedy sampling of tokens. So...”
    Open episode and seek to 13:41

    ConceptResearch

    Report this moment
  3. D
    “I think when you're trying to get a high reward, it's really useful to think about how are they going to score me? How are they going to be evaluating me? How are they checking my answers? And then modifying your behavior to optimize for those. And things that are really usefu...”
    Open episode and seek to 15:56

    ConceptResearch

    Report this moment

The Cognitive Revolution

Is Offense or Defense Dominant? FAR.AI's Adam Gleave on the AI Security Leaderboard

Open episode ↗
  1. A
    “What's the sort of like, uh, superposition of mental models that you use between next token and, you know, persona selection or whatever other paradigms you kind of combine as you think about what these things are or how we should think about them?”
    Open episode and seek to 24:54

    ConceptResearch

    Report this moment
  2. C
    “I wouldn't trust any of them too much for knowing what a specific model is going to do.”
    Open episode and seek to 28:52

    ConceptResearch

    Report this moment

AI Inside

AI Is Eating Its Own Tail

Play episode ↗
  1. C
    “They've been pursuing— yeah, and they're far down that path right now. And so hyperscaling, we're going to do everything, one model for all.”
    Play from 27:35

    ConceptResearch

    Report this moment

Eye on AI

"According to NASA's Definition of Life, I'm Not Alive" - Why Nobody Can Define Life | Dr. Kate Adamala

Open episode ↗
  1. B
    “Is it possible now with these increasingly powerful models to run this computationally and see what happens after, you know, a few trillion iterations?”
    Open episode and seek to 27:30

    ConceptResearch

    Report this moment

AI For Humans

The Singularity Is... Here? GPT-6, Opus 5 & AI's Scariest Week Yet

Play episode ↗
  1. B
    “According to the actual benchmarks, this beats Fable 5 on a lot of the benchmarks that we have seen. Now, you and I have both had time to work with this in practice. We can talk a little bit more about what other people think. But we have time to spend— we spent time with this...”
    Play from 04:23

    ConceptResearch

    Report this moment

Training Data

Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

Play episode ↗
  1. A
    “what the bottleneck is to better models and to smarter systems is the architecture itself”
    Play from 01:58

    ConceptResearch

    Report this moment
  2. A
    “Our training data didn't really replicate the real-world use cases.”
    Play from 05:55

    ConceptResearch

    Report this moment
  3. A
    “by doing the training we are doing, you can get very successful at that.”
    Play from 21:46

    ConceptResearch

    Report this moment
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