When foundation models first emerged, many believed we'd hit the jackpot—these giant neural networks would become universal brains, superior to humans at virtually everything.

Turns out reality has a different script.

The performance gap between theoretical capability and practical utility widened faster than expected. Raw model power doesn't automatically translate to killer applications. The real engine driving value? Application layer dynamics.

It's a shift worth tracking for anyone building on distributed infrastructure. While foundation models provide the backbone, how they get deployed, adapted, and integrated into actual workflows determines whether they're truly transformative or just expensive compute. The winners aren't just the model creators anymore—they're the teams solving specific problems at the application level.