Before I arrived at Galois, I had a complicated relationship with AI. I grew up in the relentless Bay Area “grindset” culture, which—combined with the pressures of social media—put productivity and output on a pedestal. The rise of AI code generation only amplified this culture. At college, I’ve found myself surrounded by peers who work on multiple projects simultaneously and attempt to “separate themselves from the masses.” I felt stuck in what I came to consider the modern computer science student’s dilemma: Do I move fast, stack projects, and use AI to pump out results, or risk getting left behind? Is it worth my time to learn outdated concepts while the computer science industry is continuously and quickly changing? My internship at Galois flipped this question on its head. Working on AI systems where correctness actually mattered forced me to trade the “move fast” grind for positive skepticism. Instead of using AI to rush development, I learned how to slow down, interrogate the technology, and focus on deep conceptual understanding over quick results. When I started at Galois, I wasn’t sure what to expect. This was my first ever internship, and I didn't know how the company felt about AI usage for software development. I was happy to quickly observe a “positive skepticism.” Opinions varied significantly from person-to-person because each project scope was so distinct. Furthermore, we were conducting research – formulating questions, running experiments, and cons