Eat Pain --> Excrete Product: The FDE Playbook

A conversation with OpenAI's Head of Forward Deployed Engineering: Colin Jarvis

When a sufficiently advanced technology can transform multiple industries, a curious pattern emerges: the technology vendors that win aren’t just the ones with the best products, highest IQ models, or even the slickest demos. They’re the ones who figure out how to make the technology actually work in the messy reality of enterprise operations.

Enter Forward Deployed Engineering (FDE).

The concept is simple: take engineers who are experts in the core technology and embed them directly with industry experts. Work in tight loops. Understand the domain deeply. Build solutions that work for that specific context—then extract the generalizable pieces that can be reused across similar problems.

As Shyam Sankar, CTO of Palantir, puts it: FDEs “metabolize pain and excrete product.”

Learning from OpenAI’s FDE Playbook

This week I had a conversation with Colin Jarvis who leads Forward Deployed Engineering at OpenAI. Alongside Palantir, OpenAI is doing FDE work at remarkable scale – engagements that target tens of millions to hundreds of millions in customer value, sometimes reaching into the low billions. Colin shared several principles that have guided their approach:

Start with genuinely high-stakes problems. The companies that succeed with generative AI don’t pick edge cases on the periphery of their business. Morgan Stanley deployed GPT-4 across their wealth management practice—one of their largest business units. The semiconductor company OpenAI works with asked them to tackle the biggest sources of waste across their entire value chain.

Build trust through iteration, not just technical excellence. With Morgan Stanley, the core technical pipeline was working within six to eight weeks. But it took six months of pilots, evals, and iteration before the wealth advisors trusted it enough to use it with their clients. The outcome: 98% adoption and a 3x increase in research report usage.

Use eval-driven development. Every piece of LLM-written code isn’t done until you have a set of evals that verify its efficacy. This creates the foundation for trust and for handing off to internal teams once the FDE engagement ends.

Trade off determinism and probabilism deliberately. Use LLMs for what they’re best at—handling nuance and complexity. But wrap them with deterministic guardrails for the things that must be right 100% of the time.

Advice for CEOs Considering FDE

Colin’s sharpest advice: be ruthlessly clear about purpose.

“Be very clear as to what your FDE team is going to accomplish,” he said. “Are you going to be relying on services revenue as a key revenue stream? That’s a very different motion than betting on the increase in revenue you’re going to get from successful product bets that come out of these engagements.”

The failure mode he’s seen repeatedly: companies that have a vision of becoming a product company, but get dragged toward services revenue because it’s easier to capture in the short term. “Suddenly you lose the strategic view.”

The discipline required is saying no at difficult times, even when someone offers significant money for work that isn’t strategic.

The north star: you must leave with product. FDE is a zero-to-one motion. The first engagement might be 20% reusable. Do it two or three more times and you get to 50%. Then push it into the scaled part of the business. OpenAI’s Agent SDK and Agent Kit emerged exactly this way – from custom work at Klarna, extended at T-Mobile, then productized for everyone.

If you’re not building toward something reusable, you’re just consulting with extra steps.

Hope you enjoy my conversation with Colin Jarvis.