Like, how do we think about those like countervailing forces that seem to be happening at the same time?

Yeah.

I, I mean, I think that first off, I, I've definitely seen a lot of the, the former that you were talking about.

Like, it's been very interesting, um, the combination of the, you know, extreme compute-constrained environments that we're operating in, um, the rise of capabilities of, uh, the coding models in particular, um, and then even just seeing, you know, from release to release, models changing their token efficiency, their output token efficiencies, um, has resulted in, you know, a lot of companies that were happy using frontier models all of a sudden, even just going from like Opus 4.6 to 4.7, there was a big difference in token efficiency.

Um, and a lot of people's bills just doubled overnight.

And you've now s- I'm now starting to see, talking to a lot of enterprises in particular, really strong desires to start cutting the cost of, of using the models.

Um, and I think that wasn't there nearly, uh, to the same extent, uh, a year ago, um, because the models weren't being used at a scale where those costs were, you know, meaningful enough.

We were just waiting for new chips.

Describe how, like, that became an interest.

My PhD is actually in neuroscience.

Uh, so I come much more from an empirical science sort of background.

I actually spent time trying to teach mice how to count and then analyze the activity of thousands of neurons in the brain while mice did count and try to understand how did that actually happen, how, h- what were the neural dynamics that- that enabled that.

Um, and that's actually initially how I got into, to machine learning was as a means to analyze my ner- my neural data sets.

I also started my PhD 2011.

So AlexNet ca- came right after that, char RQD right after that.

(laughs)