If you think about Lookalike audiences as a manual implementation of AEO, the advantage of LaLs becomes pretty obvious: you get far more flexibility in defining what gets optimized for with LaLs than with AEO, because AEO events are pre-determined by FB (and custom events tend to not perform nearly as well for optimization as the AEO catalogue events do).
The reason LaLs are powerful when implemented with the same intention as AEO (ie. finding users likely to trigger some event) is that you can use increasingly down-funnel events as you scale your app and collect purchasers. The "cold start" problem with AEO is that without any historical data on purchasers -- ie. when the app is new and hasn't seen many purchasers yet -- Facebook's algorithm doesn't know what to look for in finding purchasers and very broadly experiments with ad exposure. If you have some down-funnel events that you know proxy well for making a purchase, you can pre-empt AEO by running MAI campaigns against engagement-based LaL lists that correspond to better quality traffic.
And because LaL list definitions are completely determined by the advertiser, these "events" can be much more flexible than with AEO (and don't need to be discrete events at all). An advertiser might pull a list of users who:
- retained on Day 1 AND 2;
- finished the tutorial in under X minutes;
- joined a guild within X days of installing the game;
- did some action X number of times within 3 days of installing the app;
Obviously, the disadvantage of LaL workflow relative to AEO is that it is manual and requires uploading a new custom audience for each new seed list, creating a LaL against that custom audience, and updating an existing campaign or creating a new one. This can be done via API but when done manually, it's tedious.