AI Across The Product Lifecycle Podcast
AI Across The Product Lifecycle Podcast

Stop Running CAE Like It’s 2008: Physics+AI Goes Production-Grade

04 March 2026 57:55 Michael Finocchiaro

Listen to episode

About this episode

The conversation delves into the founding of EMMI AI and Key Ward, the distinction between physics-based and surrogate models, the impact of AI on engineering workflows, the integration of AI in product development, and the future of AI in engineering. The conversation delves into the future of AI in engineering, the digital maturity of enterprises, the impact of AI on enterprise transformation, and the agility and speed in AI development. It explores the differentiation of AI in physics and generative design, skepticism about AI development, challenges in creating universal engineering models, compressed timeframes for design and simulation, integration of engineering disciplines, assessment of AI maturity in companies, progress from level one to level five in AI adoption, challenges and advancements in AI adoption, validation of AI catalyst for enterprise transformation, customer epiphanies and aha moments with AI adoption, competitive advantage of AI-native startups over big players, comparison of agility and speed in AI development, advantages of AI-native startups over big players, and customer experience and satisfaction with AI-native solutions.

Takeaways

  • AI's impact on engineering workflows and development processes
  • The distinction between physics-based and surrogate models AI in engineering requires domain-specific models
  • AI adoption in enterprises progresses from level one to level five
  • AI-native startups have a competitive advantage over big players
  • Agility and speed in AI development are key differentiators

Chapters

  • 00:00 Introduction and Background
  • 06:21 AI-Powered Workflow and Development
  • 13:46 AI's Impact on Agile and Development Processes
  • 19:12 AI Integration in Key Ward Products
  • 25:23 Building Large Engineering Models
  • 32:00 The Future of AI in Engineering
  • 43:10 Digital Maturity in Enterprises
  • 48:26 Impact of AI on Enterprise Transformation

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 AI Across The Product Lifecycle Podcast. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.