The FDE Playbook for AI Startups with Bob McGrew

Overview

This episode dives deep into the Forward Deployed Engineer (FDE) model, a strategy pioneered at Palantir and now widely adopted by AI startups. Bob McGrew, a key figure in its development, shares insights on how FDEs bridge the gap between product capabilities and customer needs, why this model is gaining traction in AI, and how it contrasts with traditional SaaS approaches. The discussion also explores the challenges of scaling, pricing outcomes, and the future of AI adoption.

Notable Quotes

- The FDE model effectively is doing things that don't scale at scale. - Bob McGrew, on the unique nature of the FDE strategy.

- AI capabilities are racing ahead, but somehow the world feels increasingly banal. There's so much opportunity to fill the gap between what AI can do and what customers can adopt. - Bob McGrew, on the disconnect between AI progress and adoption.

- If you're not solving one of the CEO's top five priorities, it's probably not going to work. - Bob McGrew, on the importance of targeting high-impact problems.

🚀 The Forward Deployed Engineer Model

- Definition: FDEs are technical engineers embedded at customer sites to bridge gaps between product functionality and customer needs.

- Origins: Developed at Palantir to address the unique demands of government and intelligence customers.

- Key Roles:

- Echo Team: Embedded analysts managing relationships and identifying use cases.

- Delta Team: Engineers rapidly prototyping solutions tailored to customer needs.

- Impact: FDEs act as product discovery agents, paving the way for scalable solutions by identifying generalizable features across customers.

💡 Why AI Startups Are Embracing FDEs

- No Incumbent Product: AI agents are creating entirely new market categories, requiring extensive product discovery.

- Customization at Scale: Unlike SaaS, AI startups need to solve unique problems for each customer, making the FDE model ideal.

- Adoption Challenges: AI capabilities outpace adoption, and FDEs help enterprises integrate these technologies effectively.

- Examples: Companies like Castle and Happy Robot are leveraging FDEs to scale AI voice agents in industries like mortgage servicing and logistics.

📈 Pricing and Scaling in the FDE Model

- Outcome-Based Pricing: FDEs sell solutions to problems rather than software installations, focusing on delivering measurable outcomes.

- Contract Growth: Success is measured by increasing contract size and delivering higher-value outcomes over time.

- Product Leverage: The goal is to reduce customization effort per customer while increasing the generalizability of solutions.

🛠 Building and Managing FDE Teams

- Recruitment:

- Echo Team: Domain experts with a rebel mindset, capable of challenging the status quo.

- Delta Team: Engineers skilled in rapid prototyping, prioritizing functionality over long-term maintainability.

- Cultural Tensions: Balancing the specialized focus of FDEs with the abstraction-driven goals of product teams requires organizational discipline.

- Learning Organization: FDE companies must continuously adapt and learn, fostering innovation akin to startup environments.

🌍 Opportunities in AI Adoption

- Bridging the Gap: Startups have immense potential to fill the gap between AI capabilities and enterprise adoption.

- Analogy: OpenAI acts as the home product team, while startups function as FDEs driving adoption in diverse industries.

- Advice for Founders: Focus on solving high-impact problems, embrace the pain of iterative learning, and target markets where AI adoption is still nascent.