Google was always intended to be an AI company, with its mission to gather all the world's data and feed it into a giant AI supercomputer.

Google has always been an AI company at its core, focusing on large-scale data and analysis, which positions it well for the current shift towards AI innovation.

Google's success came from hiring smart engineers to tackle complex problems, contrary to the belief that search was a commodity.

Google has been operating with an AI-first mindset since 2015, believing that AI will drive significant advancements in search technology.

We realized that scaling laws in AI showed predictable improvements with increased data and compute, which encouraged us to focus on building a product for knowledge work as AI matured.

Google is confident in its AI advancements, with a focus on bringing deep computer science research into practical products.

AI discussions at Google were more focused on improving search rather than contemplating the long-term implications of AGI.

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.

AGI is becoming more central to Google's vision, and I sometimes describe Google DeepMind as the engine room of Google.