The TPU program was initiated to meet the demand for high compute power for inference and training in AI applications.

We conduct long-term research independent of any particular application and build open-source systems to assist with our research and deployment of machine learning models, like TensorFlow.

We made these chips programmable via TensorFlow, allowing models to run on CPUs, GPUs, or TPUs with minimal modifications.

We provided our product entirely for free across all IDEs, which allowed us to optimize workloads significantly and attract a large user base.

We're building the operating system for scientific labs, providing free software to improve productivity and making money through lab supplies.

We're building a platform for scientific collaboration, connecting researchers and improving the efficiency of scientific research.

The launch of the AlphaFold server is crucial for providing the scientific community with access to our models, facilitating non-commercial academic research.

Google's TPU architecture is optimized for internal workloads, such as Search and YouTube, which means their TPU purchases are primarily for their own applications rather than for external sales.

We encourage rapid production to gather real user feedback, which is essential for improving our products and understanding customer needs.