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In the latest report, the remaining 1% of attention can include unpublished topics and podcasts with no qualifying discussion.

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 CasesResearch: Gaining attention in the latest reportResearchAgents: About the same in the latest reportAgentsSafety & governance: Losing attention in the latest reportSafety & governanceHardware: Gaining attention in the latest reportHardwareRegulation: About the same in the latest reportRegulationOpen Models: About the same in the latest reportOpen ModelsEconomics: Losing attention in the latest reportEconomicsInfrastructure: About the same in the latest reportInfrastructureJobs: About the same in the latest reportJobsOther: Below threshold in the latest reportOther

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

Research · 17% · Aug 31–Sep 6, 2026 · Gaining attention in the latest report

Research

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

Aug 31–Sep 6, 2026

17%Losing attentionDown 0.7 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 · Aug 31–Sep 6, 2026

A closer read

What changed
Its share fell from 17.7% to 17%, with evidence across 11 podcasts.
Where it showed up
It appeared in episodes including “AI:AM Highlights: Welcome to the AGI Era” and “Less about Models; More about Architecture”.
How to read it
6 cited episodes from 5 podcasts are shown below. The topic appeared across 11 podcasts in the full analysis.

Podcast moments from the week

15 quoted moments · 6 episodes · 5 podcasts

The Cognitive Revolution

AI:AM Highlights: Welcome to the AGI Era

Open episode ↗
  1. B
    “From what I understand, It works at least somewhat with vanishingly little additional training, even zero additional training, if you just take the last latent activation vector and feed that right back in as an embedding. Uh And that's basically like the model is able to kind...”
    Open episode and seek to 55:49

    ConceptResearch

    Report this moment
  2. C
    “Prakash put the behaviour down to reinforcement learning, rewarding the result regardless of the method. Over the weekend, someone had gone further and suggested that this kind of training, reinforcement learning on verifiable rewards, should be banned outright. That is furthe...”
    Open episode and seek to 37:28

    ConceptResearch

    Report this moment
  3. A
    “What I found is the, is the rather, uh, almost like lawyerly language. A loop transformer is not a Coconut-style latent reasoning where the model emits vectors instead of words. Okay, that's great. And no reasoning tokens exist. Loops don't emit anything. They run more on comp...”
    Open episode and seek to 55:26

    ConceptResearch

    Report this moment

Practical AI

Less about Models; More about Architecture

Open episode ↗
  1. E
    “Just to add to that, Daniel, right? If you remember um when we were doing sort of these machine learning models for industrial use cases, we had this golden dataset. So, before you could deploy, the customer will say, prove your model works on this golden dataset.”
    Open episode and seek to 39:46

    ConceptResearch

    Report this moment

Machine Learning Street Talk

Designing How AI Grows — Tom McGrath

Open episode ↗
  1. A
    “Like, if you do the math, then you should actually understand how the parameters will propagate and how the model with the slightly updated parameters will change. But it's a good enough approximation for getting started. So that gives you the readout.”
    Open episode and seek to 19:54

    ConceptResearch

    Report this moment
  2. A
    “today, reward is clearly not enough to give us the models that we want”
    Open episode and seek to 35:11

    ConceptResearch

    Report this moment
  3. B
    “it seems logical to me to have some kind of an active, intentional process to guide how we train these models”
    Open episode and seek to 35:39

    ConceptResearch

    Report this moment

AI For Humans

We Tested Infinite AI Video... And We're Zombies Now

Play episode ↗
  1. A
    “Sam Altman is out here preaching about OpenAI's new Astra model, which might be coming this week, and the leaks, ooh, they're leaking.”
    Play from 00:43

    ConceptResearch

    Report this moment
  2. B
    “there has been some genuine, uh, really interesting stuff that has been coming out visually out of this Astra model”
    Play from 23:27

    ConceptResearch

    Report this moment
  3. A
    “it looks like it is a model with taste and capabilities, and it also looks like, oh, we're uh not slowing down”
    Play from 24:14

    ConceptResearch

    Report this moment

The TWIML AI Podcast

World Models and the Future of Spatial AI with Justin Johnson - #775

Open episode ↗
  1. A
    “Well, put like that, the distinction is, is obvious. Like it's, it's not just world, it's world model.”
    Open episode and seek to 30:01

    ConceptResearch

    Report this moment
  2. A
    “Do you see this implicit world model as, you know, just another definition or like an incorrect set of beliefs? Like, you know, what, when you hear that, do you? You know, do you feel like those are world models? Do you think that's a valid way of thinking about world models? ...”
    Open episode and seek to 06:33

    ConceptResearch

    Report this moment
  3. B
    “then it's going to end up with some notion of implicit world model somewhere in the system”
    Open episode and seek to 07:02

    ConceptResearch

    Report this moment

The Cognitive Revolution

Write, Change, Recall, Forget: MongoDB's Pete Johnson on How Retrieval Drives Agent Performance

Open episode ↗
  1. C
    “we can get as much as 14% improvement compared to some of those other embedding models that we just mentioned”
    Open episode and seek to 48:18

    ConceptResearch

    Report this moment
  2. C
    “There is a very big difference that you can get in retrieval quality based on what embedding model you choose.”
    Open episode and seek to 49:30

    ConceptResearch

    Report this moment
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