Artificial intelligence is transforming software development, but its impact extends far beyond code generation. This session examines AI through an engineering lens, starting with the computational realities of large language models: they can answer well-formed questions and generate solutions, but they cannot reliably formulate hypotheses, determine when a task is complete, or evaluate the quality of their own output. These limitations make external validation, testing, and architectural oversight essential.
The presentation explores the practical implications for software teams, including distinguishing architectural concerns from implementation details, applying risk-based code review, and redesigning build-and-test pipelines to keep pace with AI-accelerated development. It also revisits Fred Brooks’ surgical-team model, arguing that AI amplifies the effectiveness of small, highly skilled teams rather than replacing engineering judgment. Finally, it considers the economic and organizational consequences of dramatically reducing the cost of software creation and examines why software measurement techniques such as function-point analysis remain relevant.
The central argument is simple: AI changes how software is built, but the fundamentals of architecture, quality assurance, and systematic validation become more important—not less.
Webinar transcript: https://clearmeasure.com/ai-driven-software-engineering-transcript/
