I think it's a safe bet that Google seems more interested in making a model that improves Google rather than making a model that improves workers.

Fast, light weight, ok intelligence. Perfect for serving 20B+ prompts per day mostly surrounding banal human things.

OAI and Anthropic's cloud spend can cover the revenue gap, as Google is already capturing a large chunk of those guy's revenue.

Not to mention internal use cases, such as prediction-related tasks like serving ads.

The AI mode on Google search is pretty impressive. Helped me figure out what a bunch of stuff I was seeing out the window was while traveling.

Microsoft also seems to be working in this space. They recently released this:

https://huggingface.co/microsoft/bitnet-embedding-0.6b

It’s a small multilingual embedding model designed for things like search, RAG, and semantic similarity. It supports a fairly large context window and is designed to run efficiently on a CPU in a GPU starved world.

The interesting part is that it builds on BitNet, using ternary weights of -1, 0, and 1 instead of the usual floating-point weights. That should make indexing and searching large amounts of text much cheaper without giving up too much accuracy.

AI overview is just a summarization of the top 2-3 results. Of course at Google scale that will still need a ton of compute, but the requirement for generating an overview is many orders of magnitude lower than asking the same question in Gemini.