Adoption Use Cases accounted for 36% of the relevant conversation across 28 episodes.
How to read it
Recurrence across podcasts does not mean agreement. The cited moments show the language behind each summary.
In the discussion
It was better, in fact, than the models they were using before. And so they're still able to provide the level of service to their customers by using Mercury.
The cost of building and running AI, and how money moves through the market.
Trend this week
About the same4%of conversation
12 of 12 published weeks met the cross-podcast threshold.
6quoted moments5episodes5podcasts discussing this topic
A closer read
What changed
Its share fell from 7% to 4.4%, with evidence across 11 podcasts.
Where it showed up
It appeared in episodes including “The AI Challenges Businesses Are Actually Focused On Right Now” and “From Voice Agents to AI Avatars with Alexander Smola - #777”.
How to read it
5 cited episodes from 5 podcasts are shown below. The topic appeared across 11 podcasts in the full analysis.
Podcast moments from the week
6 quoted moments · 5 episodes · 5 podcasts
The AI Daily Brief
The AI Challenges Businesses Are Actually Focused On Right Now
“we'd find economically productive applications for all the models that would allow us to recoup the investment on all the frontier models and all the chips so far.”
“One thing I always try to reconcile when talking with new lab founders is you're kind of fighting bitter lessons all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.”
“Why do we care about it? Well, speed of training training is part of the equation of the cost. Uh And ultimately, you want to have the most intelligence per dollar, right? And so speed and quality are big parts of that. And the—”
Its share rose from 28.5% last week to 36.4% this week, reversing the previous week's drop.
Share of this week’s podcast conversation
Recent weeks34.8%→This week36.4%
Came up more often than in recent weeks.
Podcast moments5 cited momentsViewClose
No Priors
Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon
It was better, in fact, than the models they were using before. And so they're still able to provide the level of service to their customers by using Mercury.
Instinct & Meta Muse Are AI Agents That Can Run Your Life. We Let One Try.
No, I'm just saying I, I need to know a little bit more before I get into bed with Instinct. And so I do use it. I have some anecdotal wins with it. Did I talk about the, the, the shade Street View thing last week?
Its share has stayed near 12.8% of the conversation for three weeks.
Share of this week’s podcast conversation
Recent weeks12.6%→This week12.8%
Came up about as often as in recent weeks.
Podcast moments5 cited momentsViewClose
The Cognitive Revolution
AI:AM Highlights: Zvi on Pacing & Trump-Xi, Astra better behaved than Fable? + a new LLM Pain Axis??
I agree with you there, but I think for all signs, it's fairly easy to say right now that the Chinese are less concerned with AI safety. And so offering them an AI safety pacing the frontier is not something which is something that they're willing to purchase at an expensive p...
exaggerating risk timelines or severity will have negative second-order effects. uh I remember many loud AI safety debates and their associated clouds over the viability of open-source AI in 2023 and 2024. The primary risks then did not arrive in the forecasted timelines
When you earlier, you were talking about safety being, you know, real safety being applying to things other than what people are talking about and really clearly reacting to um at a large scale. I did notice that you didn't necessarily mention safety of, like, systems, right? ...
The Balance of AI Power: Anton Leicht on Politics, Pacing Deals, and Muddling Through Well
we'd find economically productive applications for all the models that would allow us to recoup the investment on all the frontier models and all the chips so far.
Humanity’s Last Invention — Richard Socher of Recursive
One thing I always try to reconcile when talking with new lab founders is you're kind of fighting bitter lessons all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.
Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC
I uh won't get into the details, but it's a subcommittee of NIST that typically sets standards. Uh, so it's basically a government body that has AI experts. Yeah, exactly.
The Balance of AI Power: Anton Leicht on Politics, Pacing Deals, and Muddling Through Well
I don't think the US government is going to be as capable of like keeping like a specific, like sort of uh sophisticated level of control and oversight
I also think that what you articulated there, my growing sense is that that's kind of what the Chinese government thinks, is that they are going to be able to ultimately adapt to this and control it better And that we're gonna have a really hard time figuring out how to manage...
Its share grew from 16.5% last week to 22.1% this week.
Share of this week’s podcast conversation
Recent weeks17.1%→This week22.1%
Came up more often than in recent weeks.
Podcast moments5 cited momentsViewClose
The Cognitive Revolution
AI:AM Highlights: Zvi on Pacing & Trump-Xi, Astra better behaved than Fable? + a new LLM Pain Axis??
I tell this to people and people are like, no, OpenAI models are the ones that reward hack the most. Uh But that might be true, but not in our experience. If you take BlueprintBench, for example, um Fable solves BlueprintBench by trying to reverse engineer the scoring function...
When pressing the button actually removes, uh, uh, uh, the vector, the model presses again, uh, significantly less than when the button is fake and does nothing. The model basically keeps pressing it. And so this is a really nice indication that if what mattered was the label ...
my mental model for the very early innings of RSI is more of massively scaling and diffusing inference-time compute to AI research and related activities, which has a large amount of low-hanging fruit available.
How did you think about um applicability, or what experiments did you run in terms of cracking the nut on discrete versus continuous modalities? Because I think people have also shaped the existing dominant paradigm through new tokenization uh efforts or uh methods to make vid...
