I’ve recently assessed the current sentiment of quantitative professionals (quants) by attending Quant Strats 2025 and QuantMinds 2025 and shared some views in a podcast . While business cards are becoming out of fashion (sigh), in-person interactions can provide enjoyment and perspectives that are hard to find on Teams. From chatting with peers during networking breaks, to attending presentations of industry leaders, attending and speaking at such conferences has provided qu

Large Language Models are powerful machine learning models centered around natural language understanding.  While having certain strengths, they also have several weaknesses, for example:  Hallucinations - can create incorrect information.  Causal reasoning - lack genuine causal understanding. Outdated data - retraining is expensive and so keeping models updated with new data can be challenging. Explainability - LLMs are complex and hard to interpret - leading to problems

I run an investment management firm* that uses AI to make decisions. With nearly two decades of experience applying AI across financial markets and other domains, I’m fortunate to lead a team with similarly deep expertise. Together, we design and operate sophisticated AI systems that trades fully autonomously, and most importantly, profitably. It's sometimes frustrating to read about the latest developments in AI - the hype, the problems - and being unable to openly discuss