Quantitative research

And sometimes the most important is the one that shows up the least.

So far, we focus primarily on qualitative research, but invisible bias doesn't disappear when we move to quantitative research.

In some ways, it can become even harder to spot, because numbers often create the impression of objectivity.

A chart looks precise, a dashboard looks factual, a satisfaction score looks definitive.

What really changed over the last few years is the sheer amount of compute that we're able to use to really enhance those models.

And with the appearance of LLM and agentic AI, then that really speeds up kind of the quant research side of it.

Because when you think about quant research, then it's quantitative researchers looking at models or risk models, alpha models.

But then when you have a Gentic AI that comes into play, you can really speed up the projects that you have in the pipeline.

So I don't know if you want to kind of share a little bit on that.