From the 4 page paper: By closely connecting research and development Google is able to conduct experiments on an unprecedented scale, often resulting in new c

From the 4 page paper: By closely connecting research and development Google is able to conduct experiments on an unprecedented scale, often resulting in new capabilities for the company. The goal of research at Google is to bring significant, practical benefits to our users, and to do so rapidly, within a few years at most. Because of the time frame and effort involved, Google’s approach to research is iterative and usually involves writing production, or near-production, code from day one. Typically, a single team iteratively explores fundamental research ideas, develops and maintains the software, and helps operate the resulting Google services—all driven by real-world experience and concrete data. This long-term engagement serves to eliminate most risk to technology transfer from research to engineering. This [...] we blur the line between research and engineering activities and encourage teams to pursue the right balance of each, knowing that this balance varies greatly. Overall, we undertake research work when we feel its substantially higher risk is warranted by a chance of more significant potential impact. we just try to “factorize” [long-term research] into shorter-term, measurable components. Even if we cannot fully factorize work, we have sometimes undertaken longer-term efforts. For example, we have started multiyear, large systems efforts (including Google Translate, Chrome, Google Health) that have important research components. If the discrete steps required large leaps in vastly different directions, we admit that our primarily hillclimbing-based approach might fail. Thus, we have structured the Google environment as one where new ideas can be rapidly verified by small teams through large-scale experiments on real data, rather than just debated. Organizationally, research is done in situ by the product team to achieve its goals. The most successful high-profile examples of this pattern are systems infrastructure projects such as MapReduce, Google File System, and BigTable. In our opinion, a research project is successful if it has academic or commercial impact, or ideally, both. Another potential pitfall of the hybrid research model is that it is probably more conducive to incremental research. We therefore do support paradigmatic changes as well, as exemplified by our autonomous vehicles project, Google Chauffeur, among others. Our hybrid approach to research enables us to conduct experiments at a scale that is generally unprecedented for research projects, generating stronger research results that can have a wider academic and commercial impact. While our hybrid research model exploits a number of things particular to Google, we hypothesize that it may also serve as an interesting model for other technology companies.