Job postings from companies like OpenAI, Anthropic, and Google DeepMind reveal a shift in the expectations for product managers (PMs) in AI-focused roles. These organizations are looking for PMs who can write evaluations, prototype code, understand model architecture, automate tasks with AI agents, and effectively use large language models (LLMs). The distinction between PMs and engineers is blurring. PMs who can handle technical tasks will thrive, while those who can't may struggle to keep their positions. Even outside AI-centric companies, the need for PMs to adopt these skills is pressing. In various sectors, PMs who can quickly prototype ideas or automate processes are gaining an edge over their peers. Traditional PM skills like stakeholder alignment, strategic documentation, and prioritization remain important, but the most successful PMs are those who combine these foundational abilities with technical fluency. Some argue that the core skills of product management haven't changed significantly. They emphasize that PMs should focus on alignment, collaboration, and decision-making rather than deep technical expertise. Others see the role evolving to include coding and technical know-how, as demonstrated by PMs who can now execute complex workflows using AI tools. Ultimately, being a strong PM increasingly means understanding the technology enough to leverage it effectively, rather than just overseeing the engineering process.