The "Hybrid Approach to Research" paper describes how "Google Research" first started when Google was mostly, if not entirely, about Search and "Research" was part of the Search organization.

In these days, there were no "pure research" roles, nor were there formal designations for "research scientists" as a career ladder at Google. There were "SWEs" and in some cases "Members of Technical Staff."

Since then, "Research" became its own organization or "Product Area" at Google (i.e. the equivalent of a company division). "Google Brain" was also created. Deepmind was acquired. All of these existed simultaneously, however Deepmind remained as an organizationally separate entity. In this era, the "Research Scientist" role was created, which generally existed exclusively within "Google Research." A large span of this era had John Giannandrea ("JG") at the helm of the Google Research org; (note: Giannandrea left to head and build Apple's "AI/ML" organization, which includes Siri, a few years ago.

After JG's departure, Google Brain and Google Research were brought together under the common leadership of Jeff Dean, as an organization called still called "Google Research" with a branch still called "Google Brain." For perspective, it may be useful to consider too that the size of "Google Research" in staff headcount here measured in the several-thousands. This configuration existed for the last few years, with the latest changes being the merging of "Google Research" and Deepmind into "Google Deepmind."

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I am a Xoogler, formerly from this product area. One of the things I and at least a few others observed was that "Research" was becoming defacto synonymous with "Machine Learning / AI," yet not all of Google's storied research accomplishments, or problem areas, are limited to Machine Learning and AI.

In the last few years, Google made its public statements of being an "AI-first," previously "mobile-first," company in recognition that it would be incorporating and leveraging ML and AI technology across all of its products and services.

This raised a significant question: What should "Google Research" or Research at Google be if product areas across Google began full incorporation of AI/ML technology and methods in their products? What if they incorporated their own AI/ML teams? If Google was truly successful at becoming AI-first, how should "Google Research" define and focus its organizational purpose, research portfolio, and show its value when Moonshots/X also exists within Alphabet? Over time, there were many parts of "Google Research" and research at large across Alphabet that felt that their purpose, or at least their individual reason for joining, was to do "pure research," yet this is not how the organizations started at all in the beginning. Many researchers and teams also knew that for practical reasons (e.g. promotion) that they generally needed to present and align their work with things like product launches with partner organizations.

I suppose we are seeing some of the answer to this with Google DeepMind stating that they will be aligning more strongly with creating AI products, but in addition to the question of what happens to foundational research (for AI), what happens to foundational research in non-AI areas for Google and Alphabet?