Sundar's comments about Google doing AI (really ML) are based more on things that people externally know very little about. Systems like SETI, Sibyl, RePhil, SmartASS. These were all production ML systems that used fairly straightforward and conventional ML combined with innovative distributed computing and large-scale infrastructure to grow Google's product usage significantly over the past 20 years.

For example here's a paper 10 years old now: https://static.googleusercontent.com/media/research.google.c... and another close to 10 years old now: https://research.google/pubs/pub43146/ The learning they expose in those papers came from the previous 10 years of operating SmartASS.

However, SmartASS and sibyl weren't really what external ML people wanted- it was just fairly boring "increase watch time by identifying what videos people wioll click on" and "increase mobile app installs" or "show the ads people are likely to click on".

It really wasn't until vincent vanhoucke stuffed a bunch of GPUs into a desktop and demonstrated scalable and dean/ng built their cat detector NN that google started being really active in deep learning. That was around 2010-2012.

But their first efforts in BARD were really not great. I'd just have left the bragging out in terms of how long. OpenAI and others have no doubt sent a big wakeup call to google. For a while it seemed like they had turned to focus an AI "safety" (remembering some big blowups on those teams as well) with papers about how AI might develop negative stereotypes (ie, men commit more violent crime then women?). That seems to have changed - this is very product focused, and I asked it some questions that in many models are screened out for "safety" and it responded which is almost even more surprising (ie. Statistically who commits more violent crime, men or women).