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# The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 16 min read · 0 upvotes · 0 comments

## Summary

A conference talk by Natalie Meurer, head of agent engineering at Sierra, tracing the history of forward deployed engineering (FDE) from Palantir's origins in 2008 to the present. She argues that FDE has evolved through distinct eras — from DevOps and on-prem deployments, to data integration, custom solutions, and customer enablement — making the term so broad it has lost a coherent definition. Her central thesis is that as code becomes cheaper to produce via AI agents and outcome-based pricing becomes dominant, the lines between product engineering, agent engineering, and forward deployed engineering are blurring. She concludes that in some sense, all engineering roles are trending toward forward deployed engineering, with customer accountability as the unifying thread.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=Byv311hdoHE>

## Questions this post answers

### What is forward deployed engineering and where did the term come from?

Forward deployed engineering originated at Palantir starting around 2008, initially meaning engineers sitting on-site with customers to keep on-prem software running (essentially DevOps work). Over time the role expanded to include data integration (2012), building custom solutions with tools like the Slate dashboard builder (2016), and customer enablement on the Foundry platform (2020), making the term describe an increasingly broad set of customer-facing engineering responsibilities.

_Track how roles like this keep shifting by following engineering career trends on daily.dev._

### How is pricing for AI software changing compared to traditional seat-based pricing?

Software pricing is shifting from seat-based models toward usage-based and ultimately outcome-based pricing as AI agents gain more autonomy to achieve results. Usage-based pricing, like what companies pay foundation model providers such as OpenAI or Anthropic, ties cost to consumption, while outcome-based pricing ties cost directly to results delivered, such as a resolved customer inquiry or a completed sale.

_Compare pricing models for AI products as your team weighs usage-based versus outcome-based deals on daily.dev._

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---

Tags: [#ai-engineering](https://daily.dev/tags/ai-engineering)

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