In this episode, Neil explores how agents, foundation models, and AI are set to transform the Computer-Aided Engineering (CAE) and Electronic Design Automation (EDA) landscapes. He shares a comprehensive historical perspective and predicts a near-future where AI-driven automation redefines engineering workflows, productivity, and innovation.

Main Topics:

• The evolution of simulation codes from the 1960s to modern commercial software
• The rise of cloud computing, GPUs, and their impact on CAE and EDA industries
• The integration of AI, surrogate modeling, and foundation models into simulation workflows
• The emergence of agentic AI systems capable of autonomously performing complex engineering tasks
• The strategic responses of major software companies to AI and agent technologies
• The potential democratization and automation of engineering design through AI agents
• Critical questions on model ownership, transparency, and industry adoption

Timestamps:

00:40 - Introduction: How agents and foundation models will disrupt CAE & EDA
01:40 - Historical overview: From code writing in the 60s to commercial software
03:10 - Growth of aerospace and automotive industry codes and commercialization
04:40 - The impact of HPC, cloud computing, and hardware evolution
06:25 - Rise of cloud SaaS models and "sassification" of simulation tools
07:40 - Big tech entrance: AWS, Microsoft, and Google in CAE & EDA
09:00 - GPU acceleration: Changed landscape in past three to four years
09:10 - The role of AI startups offering surrogate models and real-time simulation
10:40 - Industry consolidation: Mergers and acquisitions among software giants
11:40 - The emergence of foundation models and surrogate systems in simulation
13:00 - The significance of agents: Combining AI, models, and automation
14:10 - Capabilities of autonomous AI agents in complex engineering workflows
15:25 - Practical use cases: Running simulations, setting up experiments, and data analysis
16:40 - How agent-driven automation could democratize engineering expertise
16:10 - Questions about model ownership, open source codes, and licensing
19:40 - The future of AI in engineering: Collaboration, transparency, and scientific rigor
21:25 - Final thoughts: Opportunities, challenges, and the transformative potential of AI

* Please note that this a personal opinion and not that of NVIDIA