Core Viewpoint - The article discusses the nuanced impact of artificial intelligence (AI) on engineering roles, emphasizing that while AI tools can assist in various tasks, human engineers remain essential for complex and creative aspects of design and verification [2][5][14]. Group 1: Human-Centric Tasks - Certain tasks in the EDA process, such as architecture/concept design, require human intuition and cross-domain reasoning, which AI struggles to replicate [2]. - Defining chip specifications necessitates deep market and technical understanding, ensuring designs meet business and regulatory needs [3]. - Analog circuit design demands extensive expertise and creative problem-solving, making full automation by AI a challenge [3]. - Safety-critical design decisions must be validated by humans to prevent catastrophic failures [3]. - Final verification and quality assurance require human judgment to interpret results and assess risks, especially in atypical scenarios [3]. - Decisions regarding manufacturability and yield require expert knowledge, with engineers overseeing final designs [3]. - Novel problem-solving and handling exceptions necessitate creative thinking and interdisciplinary expertise, which AI cannot fully automate [3]. Group 2: AI's Role and Limitations - AI may evolve to solve new problems through random exploration of options, but current capabilities are limited compared to human creativity [4]. - Engineers must input accurate knowledge into AI systems and verify the outputs, as AI cannot autonomously ensure optimal solutions [5]. - Verification is crucial to avoid costly errors, especially in integrated circuit manufacturing where costs are high [5]. - Trust in AI systems is essential, but human intervention is necessary to determine where to implement safety measures and verification steps [6]. - Many startups focus on RTL verification, but trust in AI-generated solutions remains low, requiring years of development and iteration [6]. Group 3: Complexity in Analog and Mixed-Signal Design - Analog design is inherently complex, with AI tools facing challenges in providing effective solutions [8]. - Engineers are increasingly distanced from core problem-solving as they focus on mastering AI tools rather than addressing design challenges directly [9]. - The complexity of analog/mixed-signal processing has increased due to customized tools and skills, complicating the design process further [9][10]. Group 4: Industry Adaptation and Future Outlook - Industries like aerospace and defense may adopt AI more slowly due to cultural and regulatory factors, but they cannot ignore the trend [12]. - The next generation, particularly those familiar with programming, may find new roles in coordinating AI rather than traditional programming tasks [12]. - There are concerns about the dangers of unsupervised AI code generation, highlighting the need for domain expertise to ensure functionality [13]. - AI is making strides in semiconductor design, particularly in automating tasks like functional verification and regression testing [14]. - The industry must prepare for scenarios where reliance on AI could be disrupted, emphasizing the need for skills that do not depend solely on AI [14].