This episode brings together a panel of AI-native founders building at the front lines of enterprise innovation: Aneka Sokhal (Moshi), Hunter Honnessy (Unlimited AI), and John Kalfayan (Collide). Moderated by Alex Gras of Mercury Fund, the conversation explores the rise of the “forward deployed engineer” model, embedding technical, domain experts directly with customers to accelerate product development and build trust. The panel shares how AI tools and agentic workflows are compressing timelines from months to days, reshaping how startups approach product, engineering, and customer collaboration. They also address the hard question of scale: how to turn fast-moving pilots into repeatable systems rather than a services-heavy business. It’s a practical, candid discussion on what it really takes to build and scale AI-native companies today.
Key Takeaways
- AI has dramatically compressed product development timelines, but speed alone is not the advantage. The real edge comes from embedding technical, domain-savvy operators directly with customers to translate real problems into working solutions quickly and build trust in the process.
- The forward deployed engineer model blends product, engineering, and customer collaboration into one role. Domain expertise matters as much as coding ability, and AI tools now enable smaller teams to ship production-ready solutions that once required large cross-functional groups.
- Scaling AI companies requires turning fast-moving pilots into repeatable systems. The winners will be those who productize what they learn, build templates and guardrails, and create processes that allow agentic workflows to compound over time rather than becoming a services-heavy business.
