The Neuron: AI Explained
Inside Pathway’s Brain-Like AI: Zuzanna Stamirowska on Continual Learning, Memory & Real-Time Reasoning
06 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
We believe we're on a faster way to AGI. Whenever two neurons were interested by something, the connection between them becomes stronger. And this is memory. We actually saw the emergence of just this kind of brain appearing. We can actually glue two separately trained models together and they become one. I remember we all rushed into the office. Then I see the brain and was like, whoa.
Welcome, humans, to the Neuron AI Explained podcast. I'm Corey Knowles and joined, as always, by my partner in crime here, Grant Harvey. How are you, Grant?
Doing good. Doing good. Thanks for having me.
Of course. Of course. Quite the guest. You come every time. You just never stop showing up, right? I just got to do something different, you know, I'll switch it up. Well, we have an incredibly fascinating guest today that we're both really excited about. Grant, you want to tell them about her?
So we have invited Zuzana Stamorowska, CEO of Pathway, one of the boldest challengers to the reigning transformer-based AI paradigm. And today we dig into what live AI really means, why Pathway is banking on it, and whether this could be the next major architectural leap in AI.
Zuzana, welcome to The Neuron. We're so excited.
Hello. Hi, Corey. Hi, Grant. I mean, thank you so much for having me. Great to see you guys.
Well, I guess to get started, one of the first things that kind of stood out to us was how did you go from studying at a French school for politicians to complexity science and A.I. ?
So have you guys seen that movie, The Beautiful Mind? Yes, I love it. There's this scene where actually he gets a Nobel Prize and they're like, oh, those people who bring him pens, right? And I remember my dad always cried at that scene. He was like, for him, it was like the most beautiful romantic thing that, you know, it was so funny. I mean, a big guy always crying.
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Chapter 2: How does Pathway's BDH architecture differ from traditional Transformers?
And I actually took a course in game theory. And I remember I was sitting there, coming from a very different background than the other students in a way. And I just saw all the results of the games without doing the math. And actually the guy who was teaching it was sitting on the Nobel Prize Committee. So it was just an amazing course. It was just so beautiful. I became obsessed with it.
I understood that, okay, that was like, I felt like fish in the water, finally, as if somebody, you know, finally showed me the real thing I should be doing that just felt so natural. And I said, okay, this is it. I mean, there is nothing else I can or I should be doing in my life.
At the same time, I was training in the kind of management consulting, because this is what folks do at Stockholm School of Economics. I mean, I got a lot of exposure to all of this. But then I knew, okay, okay, how do I make it happen? And I guess I was like, lucky enough, you know, to actually, I have met John Nash once. So that was kind of cool.
Well, what was the context or how did that happen?
And there was a conference in Lisbon and he was actually a speaker there. Oh, wow. Yeah. And then I actually went to, I had an option actually to go to a called Polytechnique for like my master's, et cetera. And yeah, and then, so I did my master's specializing in game theory on graphs. And game theory on graphs actually very quickly evolves into complexity science.
Once you do it, I mean, we have, you know, small particles, big structures. It's more interesting, more fun if the structure keeps on changing. And then you try to play a game on like an infinitely changing structure that keeps on growing. I mean, this sounds tricky. It is.
What? Like it's hard?
Yeah. But of course, for, you know, for like a pretty long time, we're trying to crack it and kind of just bring it to some more universal levels of math. Yeah. Especially in particle physics and this sort of stuff. Yeah.
the end of the day you have small particles bumping like doing something between them right sometimes in space bumping into each other sometimes having connections like in the graph and like or between neurons and you kind of send things over um and then this gives rise to like small folks doing something you know give rise to society or like i know a big phenomenon or intelligence i mean you name it but somehow once you get to the math it starts to look somewhat similar i mean i
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Chapter 3: What is the significance of memory in AI according to Zuzanna?
The guy is just crazy. Jan, who we know was at Google Brain. And then all of a sudden, all those guys are kind of jumping off the cliff, dropping 10 years to do this thing with me. That's awesome. Pretty, pretty cool.
So was time the core insight or problem you were trying to solve, the three of you?
Right now, all the models that we see that are out there are built on one type of architecture, one type of technology. And that was an absolute kind of algorithmic breakthrough. And this is a transformer. So the transformer was like fundamentally built for language. Funnily enough, one of the cultures of transformer actually was like the first check in Pathway. But this technology is fantastic.
by definition deprived of the notion of time and memory so pathway right now is building the first post-transformer frontier model which is tackling this fundamental problem of lack of memory in ai memory is linked to time of course because you remember you need to remember things over time you need to remember how you were thinking how you were solving something for example
You need to remember to see consequences, right? You need to remember to stay coherent while problem solving. The more you know, the longer you can stay focused on a task. I mean, this means memory. That kind of requires time, right?
And we kind of know right now there is this lab called METER that actually kind of measures the benchmarks, the equivalence of, okay, like how the level of human tasks, let's say that the LLMs can do with, let's say, 50% success rate. And right now, the length of those tasks is at like two hours, 17 minutes for GPT-5.
So, I mean, we could say that current LLMs are kind of reliving their Groundhog Day every day. So they don't have memory as such. The way it works is that they're trained ones with a lot of, a lot of, a lot of, a lot of data. To the point that by now we know we've exhausted all the data readily available on the internet for training.
This is where they get their power from because these are like fundamental language models. So they actually managed to produce something new that they didn't necessarily see in the training data very explicitly, right? From having so many kind of samples of data.
And everything is like a relationship to everything else, right?
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Chapter 4: How does BDH enable continual learning and adaptation?
Yeah. So how far can we get with reasoning? So I wouldn't, so reasoning is reasoning, it's even less related to transformer per se. So there I wouldn't like put a bar necessarily, but just, I mean, given the math, like the memory is not there. So it's difficult and kind of somehow tiresome to actually try to trick transformer into having memory. So what I like to talk about is like epicycles.
So you guys, like before we had Copernicus and the proper theory of solar system, people were observing the moon. And to make sense of the observations, they're trying to kind of design some sort of orbit that would be maybe like this, because that was the only way that they could explain the observations, right? Yeah.
It was like cumbersome, pretty ugly if you think about this, but then every time they got a bit better, they were getting, you know, like, well, champagne or, you know, they would party. And the thing is, well, sometimes you just need to swap things kind of around. And then the orbit is actually just an orbit, right? It kind of looks good.
It starts to make sense when you switch the perspective.
Yeah, things kind of start to fall into place. So, I mean, yes, we believe that there are just, you know, some things that we need to roll back to. I mean, Transformer opened, like, it's an amazing, absolutely amazing innovation, which opened the entire market and, like, Actually, he's done two things. One is a technological innovation, right? Yeah. A scientific and technological innovation.
Second, with the go-to market that happened, it managed to tickle the imaginations of everybody.
Yeah, yeah.
And this is huge for a scientific innovation. Just think about this. Oh, yeah. But we are still early in this kind of AI market shift. So, so far, I mean, 0.7% of GDP was spent on this AI technological shift. If you compare it to other such shifts that, you know, like in the past century, I mean, just the telecom in the 90s took over 2% of GDP to be accomplished. Wow.
And I'd say that probably AI is more fundamental, right? Yeah, me too. So we're super early. And well, Transformers most likely not, I mean, as Parfait would say, it's not the ultimate technology to get us all the way through it. And yeah, we need something else. Wow. But there's a lot to be done.
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Chapter 5: What are the real-world applications of Pathway's technology?
I love it. And then, yeah, we do get some questions about why BDH. And the truth is, and everybody tries to put something that would naturally fit, like the B. The very simple truth is that, I mean, I just thought that, you know, AI dudes really like three-letter acronyms. I agree.
You're not wrong.
Easy to pronounce and it worked. But I had one person, a physicist who came to our office and he said, listen, I read your entire paper. I read everything. And because I think I still need to read the appendix because I still don't know where the B is coming from. And you had to explain it yourself. I'm like, well played, well played.
Oh, I love it. So is the bee because it's a small version and you're going to grow it?
So to be perfectly honest, it's like the most truthful explanation is really just the three-letter acronyms. The bee per se comes from the fact that the model working on the working name is Baby Dragon. Yeah. So architecture is dragon hatchling, and then you have baby dragon because it's already somehow grown. And it was just inherited.
It inherited the bee because our internal name was baby dragon. We do have some dragons flying around the lab. I love it. We even have a random name generation for dragons.
Oh, that's amazing. Like in Dracaric? How nerdy are we getting here?
Oh, no, no, no. We literally have an LLM, dude, because we have versions. Whenever you have versions, you're giving it to no test model, like one thing against the other and stuff. So we literally have a random dragon names generator.
That's cool. I love it. Yeah, my theory was that if this is truly continual learning, it's kind of a dragon in a sense, because it could be very powerful and dangerous if we're not careful. But I imagine it's more like a dragon in Game of Thrones where they're controllable. So I guess the question is, you know, one, we'd love to know how it works.
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Chapter 6: What challenges does Pathway face in achieving AGI?
You have this structure. This structure we know has to be dramatically efficient.
Right.
Why? Because, well, our heads are somewhat limited in space. We walk on two feet and we kind of fall over, so our brains kind of get larger.
Yeah.
Right. So it has to be very efficient. We know it is very efficient in terms of power, but it does offer this kind of capabilities of lifelong learning.
Wow. Mm-hmm.
Keeping kind of very like infinite context pretty much. So we know that there exists a physical system that is capable of doing those kind of dragon-like things, right? It's not fully impossible. So this we know. The question is how to make it work and especially how to make it work on the hardware that we have right now.
And you always have to work with the hardware that you have, with the materials that are possible whenever we see big technological shifts. I mean, it's usually some sort of inflection points where many things come together. I mean, so much compute with this algorithm, all of a sudden this gets us a boom.
So what we did is we looked a little bit at Transformer and thought like, okay, what is it really missing from the brain to get closer to the brain? And then, yeah, that was actually Adrian, our chief scientific officer, who went on this journey literally with very strong conviction that it has to be local interactions. Looking at the brain, we have to have those small particles.
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Chapter 7: How does Pathway ensure safety and predictability in AI?
And this is what we got, like the structure of the brain somehow appearing naturally from those like very local, honestly message passing between neurons. Like as we do it on social networks, for example, we say something to our friends, right? Imagine this rumor spreading dynamics. This is how kind of learning works here.
So whenever you're actually, whenever two neurons were interested by something, the connection between them becomes stronger. And this is memory.
Mm-hmm. Yeah. That's right. Because that's sort of like how the hippocampus works, right? Where it's like... I'm going to do a terrible job.
Well, the point is, you use it more, it becomes stronger. I mean, this is just a principle. And it's only positive activation, so there's no positive and negative. It's only positive, it gets stronger. Something is not used, over time it will start fading. But journey speaking, the connections that were useful become stronger. And this is kind of it.
And then, I mean, this structure is, you know, it's actually very efficient because it's like a brain. So it's computationally efficient. It distributes nicely. It gives so many nice properties that unlock a number of things, you know, that then for us, even from the engineering standpoint, in terms of how it scales, how it distributes, how you can run it on many machines. et cetera, et cetera.
But it's like a scale-free, so this is sorry, super geeky, but it's a scale-free graph structure. So point is, even if we go beyond the scales that we've seen in data and tests, we scientifically know how it will behave. It's very different from the Transformer, at least as we see it now, because for Transformer, it hasn't been studied. It would probably be difficult to study.
For this, because we know how the emergence works, we know that, I mean, yeah, it's scale-free. Same laws we'll be holding, you know, above what we've seen in tests and kind of data until now.
Does that mean that it's also more interpretable at some level? Like you can kind of understand what it's going to do or no?
Yes, in a way. So specifically, we do see very precisely neural activity. And because we see the neurons when they care about something, right? We just see them. So like for LLMs right now, for transformers, I've also tried to build MRI machines to scan the brain, whereas we sort of have a CCTV inside of the brain.
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Chapter 8: What future innovations does Zuzanna foresee with BDH?
Bad idea. So there's this element of surprise that actually somehow shows that something is valuable and worth remembering. So it was actually for us very funny to see the surprise effect literally on your activity.
So will it, in the same way that the brain over time, if there are areas that are not being used, that they can weaken, will the same thing happen in a model? And if that's a dumb question, please say so.
No, no. So, I mean, yeah, actually, so you would be getting some sort of like fading connections that they're not used for often. Okay. For very long. But this is more a topic of, okay, how to transfer also to long-term memory, right? Yeah. So, yes, because there are some things that, again, it's not to work like a database, right? Okay.
As a database, then in deployment, this is something you plug in. Right. If you want to store absolutely everything forever, right? This is less of a problem. But for reasoning and having, let's say, your space to explore when you reason... You want to build it in such a way that you have the most relevant and kind of compact structures.
Well, then I guess what I want to know now is, so, you know, you've proven this BDH works at GBT2 scale, as I read, with 1 billion parameters. Is that correct? What's the path to scaling it to, say, 100 billion parameters? What needs to happen to get there or grow larger?
Of course, first of all, we do it. There are no reasons actually for it not to scale and like scaling laws are inherited from Transformer. But there's also no big need to scale. This is not the game of scaling of more parameters and more data because this is kind of not where the value is to come from.
The value is to come from faster learning how to solve problems that haven't been seen in the training data. I like this. This is where we want to get to. And actually, if we can show better learning out of smaller data, well, this is the kind of value that... that we want to prove.
So actually, I hope that very quickly, you know, we'll be more looking at models that are very small, but capable of producing results comparable to the big ones. Love that. That's awesome. We're not looking at scale and root for scaling.
We're looking at this getting better at puzzle solving and reasoning and hopefully, you know, in as general way as possible to get it closer to the way that humans reason, work, and ultimately innovate. Because if you look at a real innovator, like the best ones that I know, because I kind of have them on the team, right?
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