Android was designed for phones, but that hasn't stopped Google from trying to adapt the OS and its apps to larger machines over the years. Googlebooks are just the latest attempt after tablets, Chromebooks, and foldables induced little change in how people interact with Android software. These new Android-powered laptops are designed to pick up where the web-focused ChromeOS left off, offering premium hardware and native support for Android apps.

Google sees Googlebooks as an alternative to Windows and macOS, but these platforms are entrenched with decades of software and UI conventions that keep users locked in. Googlebooks have to offer something very special to steal users away from either of the dominant operating systems.

For devoted Android phone users, there are some cool things in Googlebook OS, but the Android roots of these machines are impossible to miss. Sometimes it feels like using a Chromebook, and sometimes it feels like using a giant phone. It almost never feels like using a premium laptop, and that's the problem.

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Anyone who has even tangentially associated with computer programming knows that modern AI coding assistants and agents can be incredibly efficient at generating huge amounts of functional code. But coders making use of those tools also know better than to trust the accuracy of that code, meaning substantial effort needs to be spent reviewing any AI-generated output.

A recent study of actual coding practices across hundreds of firms finds that human code review forms a significant "bottleneck" for the overall efficiency of AI coding tools, resulting in "little evidence that firms increase software output or reduce employment" by using them. Any efficiency increased during the actual coding phase, the study authors find, is "absorbed by downstream constraints in the production process"; as "the code review process significantly increases in length, pull requests are more likely to require revisions, and reviewers leave more comments."

Cut once, measure twice

To come to these conclusions, Harvard University researchers Fiona Chen and James Stratton made use of aggregated analytics data from Jellyfish, which measures the granular output of engineering teams. That data encompasses 300 million individual "work events" (e.g., commits and pull requests) and issue management software data across more than 700,000 employees at over 700 relevant software development firms from 2021 through March of 2026.

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