I was able to explore new ideas much much faster using Python than the experienced k people could. But creativity is more important anyway. Ultimately, having good ideas/data/signals trumps fancy or fast data wrangling. Glad I’m doing other things now in any case.

Any detail whatsoever would make this a more credible claim. I haven’t met many people, including those skeptical of the performance claims, who have called K _slow_. Maybe for particular domains but I’d doubt that includes the kind of quant work that gets done at Millennium.

I've heard plenty of complaints over the years, and only within quant, unsurprisingly since that is the only field you'll get paid to use K.

A brilliant programmer I met who came from DE Shaw said he reimplemented a K-based portfolio optimization pipeline because the performance hit a wall once the dataset got large enough. He was able to beat K with Java of all things.

Columnar and timeseries dbs have continued to evolve, K is the same tech it was in the 2000s. The only reason it gets used at a Millennium is that whatever trade is still printing money, not any tech advantage.

I appreciate it's probably not possible to share too many details but I wouldn't be surprised if the choice of Java wasn't simply preference. It may have been a problem with the pipeline rather than with K. I.e. a fix might have been available using K but it can be easier (and harder) to just use some out-of-the box solution. I agree that columnar and time series dbs may have caught up with K over the years, but most of the complaints I've heard about K aren't technical.