AI is Already Building AI | Google DeepMind’s Mostafa Dehghani
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What does “thinking in loops” mean for AI models?
Most of the people don't realize that this is like already happening, especially over the past few months. As in almost every lab, the new generation of the models are built heavily using the previous generation of the models. What is missing right now is long horizon and full automation. And we're moving to that direction super, super fast. The moment that we have this full automation, we can close the loop of self-improvement. We just got rid of the human bottleneck for improving these models, which I expect to see a huge jump again from such development.
Hi, I'm Matt Turk. Welcome to the Matt Podcast. Today my guest is Mustafa Degani, a top AI researcher at Google DeepMind and a core contributor to some of the most influential architectural breakthroughs of the last decade, including Universal Transformers, the Vision Transformer, and the natively multimodal Gemini family. In this episode, we unpack what's hot in Frontier AI right now, including what it actually means for AI to think in loops and the A timeline for recursive self-improvement where AI autonomously builds the next generation of AI. We also dive into the technical evolution of image generation with NanoBanana 2 and why continual learning could completely disrupt how enterprise data pipelines and RAG systems are built today.
Please enjoy this fantastic deep dive with Mustafa Degani. One of the hardest concepts in AI research right now seems to be the concept of loops. So I thought it'd be a fun place to start. This idea that models are going to improve not by being bigger, uh, but by thinking recursively. What does that mean exactly?
Definitely one of the toppest active areas for um almost every lab to invest in, uh, looping and and it has like um like operation at different levels. The one that is on on the uh on the micro level is basically the the the the the looping that we use, like architecture or at inference time for test and compute and stuff like that. And then at a higher level is basically the loop that the loop that we have over the development of these models, which is basically we refer to as to to it as uh self-improvement. If I want to put it like like very like like let's talk about self-improvement as as like this general concept, right? Like if I want to um put it like very simply, it is really just the continuation of the trend that we've been like writing for decades, right?
And uh and uh think about it in classical machine learning, humans have to sit down And manually engineer the features. And uh you had to decide like what the model actually uh pays attention to. And and deep learning and neural network came along and they said, okay, let's just remove that. Let the model figure out the representation itself. And um that was actually a huge deal. And and we somehow removed a massive human bottle, like an unhuman bias. And then like further uh And instead of just with hand designing architecture, we started learning them to uh instead of curating like, you know, every piece of training signal, uh, we scaled to kind of basically data-driven approaches and let the data speak.
And uh the self-improvement and and this like loop into development is just the next step in in the same direction. And the whole idea and the whole point of it is you're removing the human bottleneck and button. Us from improving these models, right? And now uh like you say that okay, no, not not just human doesn't have to hand craft features anymore, but also we don't want the human to sit in the loop every time that the model has to to get better. And and I think that's basically on the on their um on their development side. So it's not radically new, uh, it's the same story, just a new chapter of the same story. I think every time that we removed human from uh like human judgment from this process, we kind of got over a bottleneck.
I would say like this self-improvement and looping over the development is kind of like doing that at the highest level, which is basically improving these models. If we want to go to more detailed uh level of looping, we can we can talk about ways of increasing
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Chapters
8 chapters
1
What does “thinking in loops” mean for AI models?
0:00–6:58
2
How is recursive self‑improvement envisioned to automate AI development?
6:58–15:11
3
Are Karpathy’s auto‑research agents an early example of AI self‑improvement?
15:11–23:35
4
What are the biggest bottlenecks—evaluation, automation, and long‑horizon reliability?
23:35–32:42
5
Can formal verification enable safe recursive self‑improvement?
32:42–40:37
6
What is model collapse and how can it be prevented?
40:37–49:28
7
How do post‑training and pre‑training differ and which will drive future gains?
49:28–57:37
8
What is continual learning and how close are we to practical implementations?
57:37–1:04:31
Speakers
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