New models, training methods, benchmarks, and discoveries from AI labs.
Trend this week
Gaining attention23%of conversation
12 of 12 published weeks met the cross-podcast threshold.
16quoted moments6episodes6podcasts discussing this topic
A closer read
What changed
Its share rose from 19.1% to 22.7%, with evidence across 11 podcasts.
Where it showed up
It appeared in episodes including “One Brain, Any Body: Google DeepMind's Keerthana on Gemini Robotics 2, Cross-Embodiment & Humanoids” and “Frontier Chips for Frontier AI Labs, with Walter Goodwin, Founder/CEO of Fractile”.
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
16 quoted moments · 6 episodes · 6 podcasts
The Cognitive Revolution
One Brain, Any Body: Google DeepMind's Keerthana on Gemini Robotics 2, Cross-Embodiment & Humanoids
“Tell me more about, it's like running into a wall at their sprint and get smashed into pieces. Clearly they are not training them like that on an ongoing basis because they'd run out of physical robots to do the training on awfully quickly. So from that alone, I was like, okay, clearly they must be doing a lot of training in simulation. Tell me more about why these kinds of tasks are amenable to training in simulation and what kinds of tasks are still difficult to train in simulation?”
“Yes, you can iterate and make these better. I wanna make sure I, that, that Soll, it can get there, but it just shows you that, that visually how different the level is of these 2 models. And I think that's something really important. And like, honestly, like I would be slightly worried if I was at OpenAI that this is the thing we have going against Opus 5.5.”
“Not much more to say on it. It's interesting that there is headroom on price optimization. while keeping sort of frontier-ish capabilities. I do wonder if the labs haven't sort of optimized the models hard or as hard as they could be for cost and speed. And they might be adopting some of the tricks that the open source models have uh showcased to work.”
“This is their own build eval. That's like, we want the model to build a workflow. So it needs to create a correct compute graph. in code. And yeah, the one thing they showcase is like, oh, we built this eval to showcase what this model is good for.”
“He's former OpenAI. So, you know, should know, should know what he's doing here. And nominally it's a new architecture as well, not just a new training method. We don't really know more, more than that, right? It's a, it's a car race. What do you want? I mean, a car chase.”
“The nice thing about GPU Mode is that it is also a community in the sense that like a lot of the lectures are very easy enough for a beginner to follow and ask questions and things like that. And like the competitions are like, somewhat not secondary, but like you can participate in them to learn. I think with a lot of like ML Sys, ML Perf kind of benchmarks, like for the most part, like only serious labs and companies participate.”
“Any, any interesting takes for people that want to get into it? So I think one of the biggest news is GPT-5.6 wrote more efficient kernels so Terra and, uh, Luna could be 80% cheaper. And then we've seen other competitions where people are like setting records and they're like, we're doing some auto research loop. And these are people that don't have a background in any kernel writing, right?”
“Very cool. And I wonder, does the model currently take in pure waveform data, or does it take in featurized data? I just remember I was doing audio stuff for my PhD, and back in those days, it would have been unimaginable to put in waveform data. And now it's simply a case of if the model is big enough and you train for long enough, you can.”
Its share rose from 19.1% last week to 22.7% this week, reversing the previous week's drop.
Share of this week’s podcast conversation
Recent weeks19.2%→This week22.7%
Came up more often than in recent weeks.
Podcast moments5 cited momentsViewClose
The Cognitive Revolution
One Brain, Any Body: Google DeepMind's Keerthana on Gemini Robotics 2, Cross-Embodiment & Humanoids
Tell me more about, it's like running into a wall at their sprint and get smashed into pieces. Clearly they are not training them like that on an ongoing basis because they'd run out of physical robots to do the training on awfully quickly. So from that alone, I was like, okay, clearly they must be doing a lot of training in simulation. Tell me more about why these kinds of tasks are amenable to training in simulation and what kinds of tasks are still difficult to train in simulation?
Yes, you can iterate and make these better. I wanna make sure I, that, that Soll, it can get there, but it just shows you that, that visually how different the level is of these 2 models. And I think that's something really important. And like, honestly, like I would be slightly worried if I was at OpenAI that this is the thing we have going against Opus 5.5.
Its share rose from 12.4% last week to 14.5% this week, reversing the previous week's drop.
Share of this week’s podcast conversation
Recent weeks15.6%→This week14.5%
Came up less often than in recent weeks.
Podcast moments5 cited momentsViewClose
AI For Humans
AI put Minecraft in Skyrim (and OpenAI had a Dev Day)
Opus 555 does, to me, feel like a pretty significant step towards AGI, especially after my AI agent Fig made a 27-minute documentary about AI almost entirely on its own. It scripted it, it generated the host, it edited it, it added the music.
pointing models, like, or agents at a growing kind of context of information, maybe it's just enough to solve very difficult problems. That um I can buy.
#258 - Opus 5.5, Sol and Luna, Muse, DeepSeek-V4.1-Flash, Xi
the delicate equilibrium that we are in is at great risk. Like you can't just keep inventing crazy new technologies every Tuesday and think that stability will win through.
I was gonna say, we see that today between the uh Fortnite and Roblox ecosystems, right? Like, not to shout out those 2 companies specifically, but if a game pops anywhere, there's a million clones on each platform. Well, is someone gonna make the AI-decompiled Newgrounds, if you will, where anytime something pops, they try to emulate it? We should shout out a couple things, right? Um First, we have to shout out um the Sega Seaman decompile. Did you see this thing?
#258 - Opus 5.5, Sol and Luna, Muse, DeepSeek-V4.1-Flash, Xi
It appears that the launch has been good. If these 2 investors, we'll see, you know, what the longer-term feel on this product are, but a lot of their users from, you know, the rest of the ecosystem of Facebook and so on are giving Muse a try.
What's an example? So is this different than, like, you know, pick your favorite company, Harvey, for example, using LLMs to do legal work or what's the—
