# AI Training Efficiency Doubles Every 16 Months, Outpacing Moore's Law

OpenAI has released new analysis revealing a dramatic improvement in artificial intelligence efficiency that surpasses traditional computing advances.

According to the research, the computational power required to train a neural network to achieve the same performance on ImageNet classification has been cut in half every 16 months since 2012. This means it now takes 44 times less computing resources to train a network to match AlexNet's benchmark performance compared to a decade ago.

This improvement significantly outpaces Moore's Law, the famous observation that computer chip performance doubles roughly every two years. Over the same period, Moore's Law would have delivered only an 11-times improvement in cost efficiency.

The findings highlight an important shift in AI development: algorithmic innovation is driving progress faster than hardware improvements alone. Better training methods, more efficient neural network architectures, and smarter optimization techniques are