$AppLovin(APP.US)

Just read an article, the analogy of "auction house" and "open-book exam" is very vivid, instantly clarifying AppLovin's moat — it's not about how much better its AI algorithms are compared to other big players, but rather its possession of unique buyer-seller data for asymmetric advantage. Even the best algorithm would underperform if trained on poor or irrelevant data.

This reminds me of my work in communication algorithm R&D. Testers spend considerable effort and time collecting channel data. Before obtaining real data, we researchers had to simulate channel data as training sets to derive module parameters for system testing. But real channels suffer from various distortions, making simulated data unreliable. This often forced us to redo exhaustive simulations with real data — wasting time while competitors already had authentic datasets.

Another analogy: One investor buys stocks blindly like lottery tickets; another thoroughly studies fundamentals and can predict price movements, then waits calmly for outcomes. The former takes a closed-book exam, the latter an open-book one — their mindsets and results differ completely.