Columbia University, USA Large Language Models (LLMs) have shown surprising proficie ncy in generating code snippets, promising to automate large parts of software engineering via artifici al intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust eq ual to or even greater than the trust established by human-driven software engineering practices. The recen t trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the powe r of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust. Software engineering is undergoing a significant phase of greater au tomation owing to the emergence of Large Language Models (LLMs) for code.

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Starting with a strong quantitative background is often extremely helpful: a good intuition for mathematics and statistics is invaluable for practitioners working in AI. Those with strong core skills in these areas often find it much easier to stay ahead of innovations and build a career in this fast-paced sector. As Koushik Kulkarni (Head of AI Engineering at Peak) says, "If you try and foster some core skills in mathematics and statistics, with a bit of computer science on the side, it should make it easier for you to start your journey as an AI software engineer."