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In the latest report, the remaining 1% of attention can include unpublished topics and podcasts with no qualifying discussion.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

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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 · 23.6% · Aug 10–16, 2026 · Gaining attention in the latest report

Research

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

Aug 10–16, 2026

23.6%Gaining attentionUp 8.8 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 10–16, 2026

A closer read

What changed
Its share rose from 14.8% to 23.6%, with evidence across 11 podcasts.
Where it showed up
It appeared in episodes including “Lindy Teammate: Flo Crivello on Multiplayer Agents, Memory & Why He'd Ban the Chinese Models He Uses” and “Grok 4.6 Shows How Fast Your AI Options Are Expanding”.
How to read it
6 cited episodes from 6 podcasts are shown below. The topic appeared across 11 podcasts in the full analysis.

Podcast moments from the week

15 quoted moments · 6 episodes · 6 podcasts

The Cognitive Revolution

Lindy Teammate: Flo Crivello on Multiplayer Agents, Memory & Why He'd Ban the Chinese Models He Uses

Open episode ↗
  1. A
    “Interesting. So you feel like the— so yeah, I guess how would you characterize the gap between American API models, Claude perhaps specifically, and a DeepSeek? We hear, we're, I think we kind of go through these cycles, right? Where it's like, oh, the gap is closed. Oh, maybe...”
    Open episode and seek to 1:28:24

    ConceptResearch

    Report this moment
  2. A
    “Hop. Fine-tuning as a dimension in this whole model situation. This is another thing, if I go back in time, I'm like, I definitely expected a lot more fine-tuning than we are getting.”
    Open episode and seek to 1:32:59

    ConceptResearch

    Report this moment
  3. B
    “I think the LoRA thing is going to happen soon in the next 6 months. I think even like one of the frontier labs may do it.”
    Open episode and seek to 1:36:43

    ConceptResearch

    Report this moment

The AI Daily Brief

Grok 4.6 Shows How Fast Your AI Options Are Expanding

Open episode ↗
  1. A
    “AI in the benchmarks tends to be very different than AI in the real world”
    Open episode and seek to 00:00

    ConceptResearch

    Report this moment
  2. A
    “I take any release benchmarks not just with a grain of salt, but with an entire bowlful.”
    Open episode and seek to 14:23

    ConceptResearch

    Report this moment
  3. A
    “there's the risk that this is just benchmark maxing and actual performance will feel a little flat.”
    Open episode and seek to 22:53

    ConceptResearch

    Report this moment

AI Inside

The AI Race Just Moved the Goal Posts

Play episode ↗
  1. A
    “what does it even mean to be a frontier lab if DeepMind is not a frontier lab? I would have just assumed that they are, but like, what does that even mean? I don't know.”
    Play from 56:33

    ConceptResearch

    Report this moment
  2. B
    “They're trying to argue distillation, though. Yeah, they're— because they see similarities model to model.”
    Play from 1:11:19

    ConceptResearch

    Report this moment

The TWIML AI Podcast

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Open episode ↗
  1. C
    “you do not need a lot of data if you— the novelty of the paper in this work, how to incorporate everything into a reinforcement learning framework that would make it possible and training, fine-tuning such models would be also kind of very affordable.”
    Open episode and seek to 10:34

    ConceptResearch

    Report this moment
  2. B
    “It's exciting to see it being incorporated into practical algorithms, training algorithms nowadays.”
    Open episode and seek to 12:01

    ConceptResearch

    Report this moment
  3. C
    “You know, Sam, which something I like to also add, because it is related to what we are talking about now. Optimization, of course, is right objective optimization at the right data is definitely very critical. That is what this paper is talking about, but we may also kind of ...”
    Open episode and seek to 13:40

    ConceptResearch

    Report this moment

Latent Space

🔬The BioAI Phase Shift - Matthew McPartlon & Neil Patil, Chai Discovery

Open episode ↗
  1. D
    “I see. So the tokens go in, the transformer establishes the relationship between the different tokens, and then the diffusion model turns that represent— that latent representation into a 3D structure.”
    Open episode and seek to 24:44

    ConceptResearch

    Report this moment
  2. C
    “people kind of, like, gamed these benchmarks for a while”
    Open episode and seek to 30:15

    ConceptResearch

    Report this moment
  3. A
    “we're getting to the point now where these structure prediction models are within, you know, a few angstroms or less of the actual atomic positions that you validate.”
    Open episode and seek to 34:16

    ConceptResearch

    Report this moment
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