How do you resonate with that notion? Like, do you agree? And if so, how does that differ perhaps from some of your peers or other colleagues, right? What does it let you look at or observe differently by investing throughout this AI inflection period?

Yeah, it's a great point.

I definitely agree that there is something distinct about AI native founders and also AI native investors.

And I'll talk about it from like two perspectives.

I think one is your judgment in people And two is your judgment in technology.

The people one I think is more interesting, actually.

So what was really interesting was in my observation, I had graduated pre-ChatGPT, so about a year and a half before ChatGPT was launched.

Maybe what is a company that you recently invested in that you're excited about?

own words.

Yeah.

Like neural networks are.

very, very understandable, and they're actually not entangled in this way, is a really interesting development.

And sort of his big claim here is that traditionally, right, researchers have thought a single neuron, which is a unit inside a neural network, takes in multiple different topics or points, and therefore you can't assign like a specific output or a specific feature of an output of a neural network to one specific neuron.

And these neurons are very entangled.

And because of that, there is something called a sparse autoencoder, which is another model that can come in and sort of disentangle a lot of these neurons from each other.

Was there anything else like interesting in that that really drew you in?

own words.

Yeah.

Like neural networks are.

very, very understandable, and they're actually not entangled in this way, is a really interesting development.

And sort of his big claim here is that traditionally, right, researchers have thought a single neuron, which is a unit inside a neural network, takes in multiple different topics or points, and therefore you can't assign like a specific output or a specific feature of an output of a neural network to one specific neuron.

And these neurons are very entangled.

And because of that, there is something called a sparse autoencoder, which is another model that can come in and sort of disentangle a lot of these neurons from each other.

that teaching you about today? Oh,

Maybe with the model layer first, what have been some of the big breakthroughs in the last couple of months?

Yeah.

I mean, what's been really interesting, just even in like level setting the robotic space, a few years ago, right, even before what we currently call like the main architecture that a lot of these robotics foundation model companies use, it's an architecture called VLAs or these visual language action models.

Most of the robotics field was actually based in imitation learning, which is a type of reinforcement learning.

So reinforcement learning or RL that we call it in software world now that's used to train a lot of the large language models was actually grounded in robotics.

And the idea was like, can you basically train a robot with a reward model that gets rewarded when it takes the right action? And can robots sort of learn from human behavior and imitate that in like an action by action sort of motion? which is not very generalizable.

And typically that's why a lot of our older generation of robotics companies have just struggled because you would have to make adjustments and tweaks for every new action or every new embodiment or tweak to the hardware.

Tell me a little bit more about the different companies playing in the space and why a company might specifically still focus on building their own models and their own hardware.