Our first stop was Tokyo, where we engaged with engineering and research leaders from some of Japan's most established manufacturers. These are companies that have spent decades perfecting their processes, building institutional knowledge that exists nowhere else.
The conversations here were thoughtful and probing. How does AI integrate with decades of tribal knowledge? Can machine learning actually challenge assumptions that have governed processes for generations? Should it?
What struck us most was the sophistication of the questions. Japanese engineers weren't looking for automation for automation's sake. They wanted to understand how AI could preserve their expertise while opening new possibilities that human intuition alone might miss.
We demonstrated how our platform transforms messy specifications, fragmented documentation, and siloed knowledge into structured, auditable workflows. The response was measured but genuine. These teams are ready to explore AI, but only if it respects the complexity of what they've built.
A week later in Seoul, the energy shifted dramatically.
Korean manufacturing leaders brought an intensity and urgency that was palpable from the first session. They weren't asking if they should adopt AI-native engineering, they were asking how fast they could deploy it and scale it across their organizations.
The discussions centered on rapid iteration cycles, agile manufacturing processes, and competitive advantage. In an industry where time-to-market can make or break a product line, the ability to compress engineering cycles from months to weeks wasn't just interesting, it was strategic imperative.
One comment stood out, "Show us what becomes possible when we rethink engineering from the ground up."
That's exactly what we're building.
Presenting in both markets revealed something important: despite different cultural approaches to technology adoption, engineers everywhere face fundamentally similar challenges.
Whether it's automotive engineering in Japan, electronics manufacturing in Korea, or heavy machinery production anywhere in the world, the pain points are universal:
Today's AI doesn't work for engineers. Generic models weren't built to handle the structured chaos of real-world product development. We're building AI that does, AI that makes engineers faster at the work that matters, not replacing their judgment but amplifying their capabilities.
More than business opportunities, these conferences reinforced our conviction about what we're building. The future of manufacturing isn't about automating the old ways of working, it's about building systems intelligent enough to know when to break the rules, when to challenge conventions, and when to discover solutions that traditional tools say aren't possible.
To everyone we met at the MIT ILP conferences in Tokyo and Seoul: thank you for the thoughtful questions, the honest feedback, and the genuine excitement about what we're building.
The conversations we had, both in conference halls and over late-night dinners, will shape our thinking for years to come. We're grateful for the opportunity to learn from some of the world's most sophisticated manufacturing organizations, and we're looking forward to the possibilities ahead.
Foundation EGI is building Engineering General Intelligence, AI purpose-built for the complexity of real-world product development. To learn more about our platform or to explore partnership opportunities, get in touch at info@foundationegi.com