How Multimodal AI Actually Trains — Why Fusion Wins
Multimodal model training works by converting text, images, and audio into a shared embedding space, then feeding those tokens into either a bolted-on adapter or a natively fused transformer trained end-to-end. The two approaches produce very different capabilities: adapters are cheap and fast to ship, but native fusion is what lets a model reason and generate across modalities in both directions. As of mid-2026, frontier labs are converging on native fusion as the default architecture, and the gap between the two approaches is becoming a competitive divide rather than a rounding error. For PMs, the question isn't which model to buy this quarter — it's whether your product's multimodal roadmap is built on an architecture pattern the industry is quietly abandoning.
