Reflections on Software Engineering in the Age of AI

For those of you who don t know, when I m not writing novels, I spend my days as a software engineer, writing code. The software industry these days relies heavily on artificial intelligence. Because it has studied trillions of lines of publicly accessible source code, because code solves problems with testable right and wrong solutions, and because code is structured specifically to be understood by computers, AI has gotten very good at writing code. Before programmers started using AI, a typical workflow looked like this: Now that AI can consistently produce pretty good code, the software developer s workflow looks like this: In the old workflow, the creative process happened mostly in your mind. In the new process, you supervise the creative process that unfolds inside the AI s internal machinations. You ve put some effort into creating a concise, thoughtful, accurate prompt to get the AI started on its work, but you haven t done any of the hard thinking you would normally do in writing the code yourself. When you get code back from the AI, you re essentially acting as an editor because, while AI can write code, it cannot always see the big picture of your project the way you can, and you need to make sure this new code won t cause problems. AI does not know whether the code it just added violates some legal requirement to which your product is subject. It does not know if the request it makes to an external system is going to take ten milliseconds or ten minutes to fulfill. It does not know whether the actions of its code will conflict with some new feature that you know your teammates will be adding three weeks from now. It does not know whether the function it just wrote might introduce a new security problem when interacting with another function you wrote last month that handles sensitive information. A senior developer does know these things, and this is why he or she needs to vet and often correct the AI code that appears to just work. To a senior developer, AI is a competent, fast-working junior or mid-level developer that, when properly directed, produces mostly solid work but lacks the institutional knowledge and the deep and broad systems-level knowledge that you have developed over the past twenty years. Now, let s back up for a second and make an analogy. Let s say you re a writer of historical novels. What does your workflow look like? Probably something like this: The novelist and the software developer have a lot in common here. In reality, the novelist will have done much of her historical research before she begins writing, just as the software developer already knows from years of prior work which data structures and algorithms and which types of caches and databases will be suitable for the new feature he s about to write. What both workers have in common is a deep feeling of engagement with the material they re creating. They are entirely immersed in their work, to the point where they often lose track of time. In both novel writing and software development, it s common for me and many of my peers to check the clock, dive into a problem, and then look at the clock ten minutes later to discover that four hours have passed. This is the state of optimal experience that psychologist Mihaly Csikszentmihalyi described in his bestselling 1990 book, Flow. Writers, software developers, painters, musicians and all other creative types enter this state when time and circumstances permit. Many software engineers work after hours at home precisely because the lack of meetings and other interruptions outside normal business hours permit them to enter this state. Now, let s put the historical novelist in the position of the software developer. She gets a call from her publisher saying they ve found a way for her to bring four books to market each year instead of one book every two years. They ve recruited a bunch of top-notch high school and college students who can each crank out five pages a day of competent writing for dirt cheap. The publisher wants the historical novels to maintain the original writer s level of excellence, or to at least be close, so they re retaining her services as an editor. The novelist s job is now to edit the work of the students, each of whom has been carefully prompted to write pages that should, with a little work, be stitched together into coherent chapters. Anyone who has ever graded the work of high school and college kids knows that this is generally not rewarding work. If you ve ever had to grade a hundred papers in a week, you know what a grind that is. The novelist, like the software engineer, is no longer deeply engaged in her work. Editing is not creating. You do not give yourself over to your imagination. You do not immerse your mind and feelings in the process of invention. Instead, you re rooting out problems, trying to clean up clumsy wording and redundant descriptions instead. The flow state is gone. You are now a cog in a larger process th

Reflections on Software Engineering in the Age of AI

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