6 Machine-augmented work: Productivity, education, and economy

Everyone has, at some point in their life, experienced what in positive psychology is known as the concept of flow: you’re deeply absorbed in what you’re working on and perhaps lose track of time because you’re so focused. And, most likely, you’ve also experienced sudden interruptions, maybe the need to look something up or attend to something else, that break the flow. This frustration was top of mind for then-GitHub CEO Nat Friedman when he announced the release of GitHub’s coding assistant, Copilot. “It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet,” Friedman wrote [1]. Integrating into Microsoft’s code editor, Visual Studio Code, was a crucial component: Copilot would plug directly into coders’ existing workflows, though, as we will see, generative AI has also enabled entirely new workflows.

6.1 Using LLMs in the professional space

6.1.1 LLMs assisting doctors with administrative tasks

6.1.2 LLMs for legal research, discovery, and documentation

6.1.3 LLMs augmenting financial investing and bank customer service

6.1.4 LLMs as collaborators in creativity

6.2 LLMs as a programming partner

6.3 LLMs in daily life

6.4 Generative AI in education

6.5 Detecting machine-generated text

6.6 Generative AI and the labor market

6.7 Conclusion

6.8 Summary