Residual connections allowed AI models to scale from only a few dozen layers to hundreds or even thousands of layers deep.

The attention residuals design allows the AI to stay perfectly focused on the most important details by selectively choosing which layers' information to use.

Models with attention residuals kept improving with increased depth, demonstrating that depth is an advantage, not a limitation.

In models with residual connections, the final result is a massive cumulative pile of data, where the importance of any single layer's contribution shrinks, burying early information.

For years, AI models could not be built very deep because they would be hard to train.

Applying attention residuals to top AI models with hundreds of billions or even over a trillion parameters runs into physics limitations due to infrastructure.