"She opened by acknowledging the uncertainty that many practitioners feel, affirming that this was a shared experience and an expected part of navigating today's technological landscape. Brush, who contributed two chapters to "97 Things Every SRE Should Know", argued that the nature of software engineering work is shifting, not disappearing. She noted that she had explored similar themes in an earlier keynote at the InfoQ Dev Summit in Boston, and stressed that as AI systems automate pieces of software development, engineers will face harder and more complex challenges."
""Large language models are, by their nature, unconsciously competent-they know a lot, but they can't explain it. When they hallucinate, that's just the model being unconsciously incompetent for a moment and filling in the blanks with something that sounds plausible.""
"She illustrated this point with a simple analogy: "Dishwashers are great... but we didn't get rid of all the work." While machines may handle routine tasks, humans are left with responsibility for exception handling, quality assurance, and system maintenance. In software, this translates into higher-level abstraction work, deeper trou"
Large language models are unconsciously competent: they possess extensive knowledge but cannot explain it, and hallucinate by plausibly filling gaps. Automation of development tasks raises the complexity of the remaining human work because automated systems require monitoring, debugging, and validation. When pieces of work are automated, human responsibilities shift toward exception handling, quality assurance, and system maintenance. Routine tasks become delegated to machines, while engineers must operate at higher levels of abstraction and confront deeper, harder problems. Systems thinking and leadership through complexity become increasingly crucial as software engineering evolves rather than disappears.
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