@Efi these are brilliant examples of where the strategies are being democratised, and I suppose they work on a model which is based around marketplace sourcing of algorithms and harvesting the best algorithms which may apply to a certain aspect of the market. This is a clearly a fascinating area and kudos to the ones on this crowdsourcing journey as it certainly has purpose.
The only challenge if any, I see is how do they address each Asset Manager's individual strategy around Portfolio construct methodology, rebalancing rules,volatility management, drawdowns and other ratios.
This leads me on to the point what @BernardLunn was making that it sounds like what Renaissance are running, a fully managed and configurable platform for running any Portfolio strategy. Yes @BernardLunnyou are right, that is what I was alluding to. This is the view, we subscribe to, where a platform has enough intelligence to provide the performance but also the capability for further refined optimisation. The base layer is strong and tactile enough to allow porting of any Portfolio strategy yet be flexible for optimisation if such needs arise.
This would then change the landscape, give more autonomy to Asset Managers to manage AUM optimally whilst the platform manages the strategies which are individual and integral to those Asset Managers. To some extent that is one way, the future of the industry may move too.
I want to keep an open mind, and somehow I still feel that in the new world of ML/AI based Asset Management, both the data scientists, the machines and importantly smarter Asset Managers have to co-exist. The question is around a platform which will optimise these relationships.