"FDE at RAMP has a mandate to win enterprise the the upmarket segment and the way we do that we are allowed to get very creative... we do whatever it takes to win enterprise." - Calvin [00:02:24]
"Because of the rise of AI we can actually start to tackle the full gamut of those problems... you need engineers who actually really understand the use case so you need to send them in and they almost need to be able to do that job themselves." - Howard [00:06:37]
Disclaimer: Orignal content owned by or sourced from third parties. It does not represent the views of 'Nuggets' platform or it's team. AI is used extensively across this platform including for summaries. Accuracy is not guaranteed, there can be mistakes. Any info or content on this platform is not a financial, legal, or investment advice. Do your own research. Refer for complete disclosures:- Terms of Use · Full Disclaimer
"I say it's a sword and a shield we're trying to win the enterprise deals and we're trying to protect the core teams." - Calvin [00:12:31]
"Is there recurring value that you're delivering to your customer on some sort of fixed cost right? Like at the end of the day that is sort of the economic model that defines the difference [between software and consulting]." - Howard [00:20:08]
"The FD team is the team that wants to say yes because they care about us winning that customer a lot of engineers they might not admit it but they want to say no to what the customer is asking for." - Calvin [00:37:36]
"The relentless pursuit of value is what makes a good forward deployed engineer and I think so much of the time people start to love the form of what they've created more than the function." - Colin [00:38:52]
Speakers & Credentials
Finn (Host): Partner at South Park Commons, a community for builders exploring frontier work in technology.
Calvin: Built the Forward Deployed Engineering (FDE) team at Ramp, specializing in enterprise acquisition and protecting core product roadmaps.
Jason: Founder & CTO of Nominal; former early Palantir engineer working across internal platforms and forward deployment.
Howard: Co-founder of Dataland, building AI for enterprise labor outsourcing; former Palantir engineer.
Colin: Leads Forward Deployed Engineering at OpenAI, focusing on broad enterprise adoption, capability improvement, and solving the world's most challenging industry problems.
1. Executive Summary
Forward Deployed Engineering (FDE) has experienced a massive 10x surge in role growth year-over-year, driven entirely by the capabilities of frontier AI and agentic coding tools.
The primary function of FDE has evolved from bespoke consulting services into a high-leverage product discovery and deployment mechanism that tests architectures on the frontlines before rolling them into core platforms.
FDE teams act as a critical "sword and shield" for high-growth software companies, securing massive enterprise contracts while insulating core product engineering teams from derailing, one-off feature requests.
The economic viability of an FDE motion hinges on establishing a "fixed cost vs. recurring value" equation, ensuring that frontline deployments yield scalable, long-term ARR rather than functioning as traditional billable-hour consulting.
The ideal FDE hire represents a rare intersection of deep technical competence, commercial revenue-focus (often former founders), and radical outcome-orientation who prioritize customer success over writing pristine, ideological code.
2. Chronological Table of Contents
[00:00:05] Introductions & The Definition of FDE Across Companies
[00:05:19] The 10x Growth of FDE and the AI Catalyst
[00:10:00] Differentiating FDE from Traditional Consulting Services
[00:15:11] Navigating the FDE "Dark Ages" and Avoiding Product Divergence
[00:21:55] Measuring FDE ROI and Mitigating Customer Addiction
[00:30:10] The Flywheel: Integrating FDE, Product, and Post-Training Research
[00:34:00] The Ideal FDE Hiring Profile & Psychological Traits
[00:39:50] Audience Q&A: Team Structures, Solution Architecture vs. FDE
3. Detailed Thematic Summary
The AI-Driven Explosion of Forward Deployed Engineering [00:05:19]
The demand for FDE roles has spiked 10x over the past year, fundamentally because AI models have dramatically expanded the addressable market of B2B workflows [00:05:19].
Previously, building a SaaS platform targeted a single, homogenous workflow. Today, AI labor outsourcing requires highly heterogeneous agents tailored to specific sectors like healthcare, energy, logistics, and waste management [00:03:47].
With the launch of OpenAI's Codex, FDEs spend significantly less time building underlying plumbing and generic agents, and more time moving up the stack. For instance, in the semiconductor industry, FDEs shifted from basic CI pipeline automation to physical chip design tasks within a 14-month window [00:09:03].
The sheer reduction in software creation costs via tools like Codex and Cloud Code allows FDEs to build custom architecture rapidly, which is then often re-absorbed into the core product as an extension rather than requiring a standalone software platform [00:19:03].
Strategic Deployment: The Sword, The Shield, and Revenue Economics [00:11:41]
At Ramp, FDE operates as a "Sword and Shield." The sword wins lucrative enterprise deals, while the shield protects core engineering teams from the classic SaaS trap: derailing the product roadmap to fulfill bespoke requests for singular large clients [00:12:31].
To avoid the destructive "game of telephone" between account managers, PMs, and engineers, FDEs speak directly to the customer, hold the context of the entire codebase, and architect solutions that bridge the gap seamlessly [00:13:30].
OpenAI prioritizes FDE engagements that target massive problems, often embedding up to 15 FDEs in a single semiconductor deployment to completely revamp the value chain, rather than taking on hundreds of small clients [00:23:55].
Consultancies fail at FDE when they treat services revenue like a "drug." True FDE success is measured by the "fixed cost recurring value equation"—delivering continuous autonomous value based on a fixed unit of initial engineering work [00:20:08].
Research Flywheels & The Product Lifecycle [00:30:10]
FDEs are the vanguard for product development. At OpenAI, FDEs generate highly specific synthetic data and evaluations from frontline customer problems (like terrible slide generation) and feed it directly to the post-training research teams [00:31:04].
This feedback loop can result in dramatic model improvements in a matter of months. A voice customer service deployment for a major telco initially failed a test of 10 to repeat a phone number. After 6 months of FDE and post-training iteration, it now successfully deflects 70,000 calls a day with zero major jailbreaks [00:32:00].
Nominal utilizes a strategy of "pulling it left," where an FDE builds a generalizable architecture for an urgent customer need (e.g., drone flight testing data ingestion for 40 users instead of 2) that was originally slated for the core roadmap months later [00:16:50].
The FDE Psychological Profile and Organizational Structure [00:34:00]
The optimal FDE is highly technical but also deeply commercial. Companies specifically target "former founders" because they inherently care about revenue and business survival, differentiating them from standard engineers who want to say "no" to protect their pristine code [00:37:14].
FDE requires immense humility and cross-functional capability. The rise of AI models enables a "Radical Ownership Model," where a single FDE can handle the customer relationship, the account politics, and the code generation, effectively merging Palantir’s legacy "Echo" (Relationship) and "Delta" (Technical) roles into one [00:46:41].
Ramp prevents "customer addiction" (where clients refuse to let the FDE leave) by aggressively stretching their FDEs. A single FDE at Ramp may juggle 5 to 6 enterprise accounts simultaneously, ensuring they only act as a fraction (1/3 to 1/6) of a dedicated resource and cannot become a bespoke consultant [00:40:45].
The Reference Vault
4. Data & Figures
Data Point
Value
Context
Timestamp
FDE Role Growth
10x
Increase in Forward Deployed Engineering roles over the past year.
The Sword and The Shield [00:12:31]: A strategy deployed by Ramp where the FDE functions as the "sword" to aggressively secure massive enterprise contracts, while acting as a "shield" to protect the core engineering team from derailing their roadmap to build one-off features.
Fixed Cost vs. Recurring Value Equation [00:20:08]: The fundamental economic metric that separates a software company from a consultancy. FDEs must deliver long-term autonomous value from a fixed unit of initial engineering work, rather than billing for continuous time.
Outcome First (Success then Scale) [00:30:39]: OpenAI's FDE framework. The team embeds to secure an absolute zero-to-one victory on a specific, hard problem. Once solved manually, they scale it either by turning it into a distinct product, or by feeding synthetic evals to the post-training team to make the core model natively capable of the task.
Product Expansion and Contraction Phases [00:36:51]: A mental model (attributed to investor Ross Fubini) noting that a company's need for FDEs changes based on its lifecycle. During platform expansion, FDEs test the edges; during contraction, they consolidate features into the core.
The Radical Ownership Model [00:46:41]: Historically, FDE required separate relationship managers ("Echos") and technical coders ("Deltas"). AI coding assistants have drastically lowered the cost of software production, allowing a single FDE to possess the commercial context and execute the technical build autonomously.
6. Anecdotes
The Drone Data Container (Nominal): Nominal landed a massive contract where a client's drone flight data needed to be cleansed for 40 engineers (up from 2). Instead of building a bespoke, unscalable script on a laptop, the FDE (Ross) architected a generalizable data container. The client got instant mission success, and Nominal secretly built a core platform feature months ahead of schedule [00:16:32].
The Slide Buddy (OpenAI): OpenAI embedded with a 2,000-person Japanese sales team that desperately needed automated slide generation. Initial HTML outputs were terrible. The FDEs iterated until they figured out the optimal structure, passed the synthetic examples to the post-training team, and three months later, the core model could natively generate highly functional slides, rendering the initial standalone FDE product obsolete [00:31:04].
Palantir's Dark Ages: During a period in Palantir's history, the core platform was deemed so useless for frontline engagements that the FDE team essentially revolted and began building entirely independent software stacks for clients, illustrating the danger of product and deployment divergence [00:15:11].
The Voice Telco Deflection (OpenAI): OpenAI pitched a major telco on AI voice service. In the first test, the real-time model failed to even repeat a 10-digit phone number. FDEs spent 6 months building platform guardrails, creating evals, and iterating with the post-training team. The result is a highly compliant model deflecting 70,000 calls a day [00:32:00].
Ramp's Trap Avoidance: Ramp realized they were walking into a classic SaaS death trap—taking on enterprise clients who demanded massive bespoke projects that would destroy their Product-Led Growth (PLG) roadmap. They invented their FDE motion specifically to isolate these requests from the core engineers while still capturing the enterprise revenue [00:13:11].
7. References & Recommendations
Companies & Institutions
South Park Commons: The host organization and community for frontier tech builders. Mentioned as the location of the event. [00:00:28]
Palantir: Frequently cited as the pioneer of the Forward Deployed Engineering motion; all speakers referenced its historical team structures and missteps. [00:01:08]
OpenAI: The AI research organization heavily leveraging FDEs to push enterprise model capabilities. [00:01:28]
Ramp: A financial operations platform that uses FDEs to secure upmarket enterprise clients without sacrificing their core SaaS roadmap. [00:02:07]
Nominal: A data and AI platform designed specifically for hardware and physical engineering datasets. [00:02:52]
Dataland: An AI startup building highly heterogeneous agents for enterprise labor outsourcing across complex sectors. [00:03:38]
McKinsey & BCG: Premier consulting firms mentioned as traditional service models that AI agents (like "Slide Buddy") are beginning to threaten or replace. [00:33:03]
Platforms & Products
Codex (OpenAI): The AI coding model that fundamentally altered the FDE landscape by absorbing 80% of the standalone products the FDE team was previously trying to build. [00:19:03]
Cloud Code: Mentioned alongside Codex as a tool that is drastically driving down the cost of software creation, allowing FDEs to build custom agents efficiently. [00:20:29]
Ramp Labs / Excel Agent: A specific Ramp deployment acknowledging that finance teams live in Microsoft Excel, demonstrating the FDE principle of meeting the customer exactly where they are. [00:11:30]
People
Ross Fubini: An investor whose philosophy on product "expansion and contraction phases" guides FDE deployment strategies at Nominal. [00:36:51]
Ross (Nominal): The FDE at Nominal who architected the initial scalable data container for drone flight data. [00:17:18]
Pearson: An OpenAI FDE in the audience actively working on a specialized regulatory document authoring agent. [00:19:13]
Concepts & Nomenclature
Echo and Delta: Palantir's historical FDE division. Echos handled customer workflow mapping, relationship management, and ops. Deltas were the highly technical software engineers writing the code. [00:44:30]
Palantir Dark Ages: A period of misalignment where Palantir FDEs revolted against the core product because it did not serve frontline customer needs. [00:15:11]
8. The Bottomline (by AI)
Forward Deployed Engineering has mutated from an expensive, brute-force consulting arm into a hyper-leveraged product discovery engine fueled by agentic AI models. For modern software companies, FDEs act as frontline researchers—testing architectures in live enterprise environments and feeding synthetic data directly back to core models to natively absorb the capabilities. Founders and operators must ensure their FDE motion operates strictly on a "fixed cost to recurring value" equation; the goal is not to rack up billable hours, but to prototype scalable platform features on the customer's dime without derailing the core product roadmap.
"Brookfield's the largest infrastructure owner in the world... We drew a pipeline and we showed all the different components of the payments ecosystem on a pipeline and said it's like a pipe that moves any commodity except what it's moving…
Telco Call Deflection
70,000 / day
Volume of calls successfully handled by OpenAI's real-time voice model after 6 months of iteration.