Jevon's paradox, buy-side edition We've seen many research productivity tools emerge over the last 20 years: > Modeling: India outsourcing, Canalyst, Daloopa > Data: Credit card panels, web-scraping, app download trackers > Expert Network Transcripts: Tegus/etc. > Consensus Aggregation: Visible Alpha I started my career in '08 on a 7-person Consumer investing team at a Tiger Cub before the invention (or at the infancy) of these tools. Armed with only Excel, Bloomberg and GLG/Coleman, we needed that many bodies to do all the work we had to do. My whole reason for existence on that team in the first 1-2 years was to help & assist with these (now automated) administrative tasks. It would be absurd today to have a Tiger cub need a 7-person team to do what we did. So, the first order impact was labor destruction. But, with these tools evolving over the last 20 years, it became much easier for multi-managers to scale enough research rigor to generate P&L, and the evolution of these tools (particularly alternative data) created a brand new business at quant funds of data/systematic fundamental investing. People forget that quant funds also employ lots of people. I believe AI will directionally be similar. Sure, there are specific instances where teams can shrink from 7 to 3 and maintain research rigor. But the prize of alpha is so powerful at large firms that, more often than not, those teams will simply use these tools to go deeper and broader with more research rigor. And, as alternative data spawned brand new investing models, AI will almost certainly do the same. As quant firms do today, those models will need (lots) of bodies.