Long-form article published on **X** on **August 11, 2026** by **Jesse Zhang**, CEO of **Decagon** (customer-service AI agents), under a dilemma-shaped title — *« To FDE, or not to FDE? »* — devoted to the **Forward Deployed Engineer**, which has become *« the answer to almost every hard question in AI go-to-market »*. Starting observation: Anthropic and OpenAI have built enterprise deployment arms explicitly modeled on Palantir, *« every seed-stage company »* advertises an FDE offering, and job postings for the title are said to be up several hundred percent in a year. **(A) The Palantir genealogy** supplies the framework: **Shyam Sankar**'s (CTO) formula, *« FDEs eat pain and excrete product »*, and **Joe Lonsdale**'s reminder that Palantir spent nearly two decades being called a *« glorified consultancy »* on the basis of an accurate observation. **Gotham**'s bespoke deployments (CIA, NSA, military intelligence) were encoded into platform primitives — ontology, object models, permissions, workflow engines, provenance tracing — which became **Foundry**, then Apollo and AIP; standardization pushed gross margin into the 80% range and Palantir moved from an FDE motion to account-based selling, with many FDEs migrating into core engineering. *« The pain was the input to the product, not a cost of sale. »* **(B) The criterion proposed** is not to give up on FDEs but to know when to stop: go early, then ask whether one is still **discovering** — *« The trap is not starting. It's not stopping. »* **(C) A distinction few make: FDE ≠ implementation.** *« Building that integration into their ticketing system »* is real work, but it is execution against a known spec, not discovery of an unknown one; conflating the two *« is how a company convinces itself that a growing services org is a product investment »*. Closing line: *« If your FDEs are eating pain and excreting more pain, you don't have an FDE team. You have a services business. »* Two figures are put forward about Decagon — *« two-thirds of deployment work is now done autonomously via Duet »* and *« a few days on average to launch the first AOP, even for large banks, airlines, telcos »* — without the "deployment work" denominator being defined or the AOP acronym spelled out.
#Forward Deployed Engineer#FDE#engineer embedded with the client
**Jesse Zhang** — cofondateur et **CEO de Decagon** (agents IA de service client, San Francisco) · 85 000 abonnés sur X · site personnel `jessezhang.org`. Il cite son cofondateur **Ashwin Sreenivas** · **ex-Palantir** · d'où la profondeur du récit Palantir. Publié le **11 août 2026**.
Analysis by **Olivier Rafal** for **WeNvision** (French consulting firm), published on **June 4, 2026** (~4 min read), commenting on the launch of the **Tokenomics Foundation** by the **Linux Foundation** (announced June 3, in partnership with the **FinOps Foundation**), which he sees as the official opening of **the era of "FinOps for AI."** **Pivot thesis**: AI has transformed the economics of software development; the **token** has become *"the new unit of measurement for technology spending,"* mirroring the cloud of the 2010s (**recurring and variable** costs requiring active management), hence the shift by providers from flat-rate pricing to **token-based billing**. **Scale (urgency)**: *"According to Goldman Sachs, global token usage is expected to increase 24-fold by 2030, reaching 120 quadrillion tokens per month"* — an order of magnitude that moves token efficiency from a *"technical detail"* to a **boardroom** topic. Quote from **J.R. Storment** (founder of the FinOps Foundation): *"Token costs and efficiency have become a CEO-level concern, not a technical footnote."* **Transparency/standardization problem**: current AI pricing is not comparable (input tokens / caching systems / output differ from one model to another) → the Tokenomics Foundation aims to **extend the open-source FOCUS specification** to provide a **common language** for purchasing and comparison. **Rafal's central message (beyond cost)**: *"The point of FinOps is not so much to cut costs as to optimize efficiency"* — the real metric is **AI cost relative to business impact** (*time to market, quality, features, eco-design*). **Limits of standards alone**: technical norms are not enough; the **Target Operating Model must be rethought** (teams, processes, data culture, business alignment); Americans are already announcing *"the end of double-pizza teams in favor of sandwich teams."* **Warning marker**: *"an AI-boosted SDLC will merely […] amplify the problems and just help you go faster… into the wall"* (absent organizational foundations). **Foundation sponsors cited**: Accenture, Booking.com, Google Cloud, Microsoft, IBM, Salesforce. **WeNvision's offer**: *"co-build a roadmap, rethink the operating model for the agentic era, and establish the financial governance that has become indispensable."* **French-language reading, aimed at executives/transformation leaders**, of the fiche [[tokenomics-foundation-linux-finops-token-economics-about-2026-06-03]]; converges with the agentic FinOps cluster [[finops-foundation-finops-for-ai-overview-2026-02-17]], finout-finops-ai-agents-four-step-allocation-framework-2026-04-27, gupta-token-budget-wars-marginal-token-utility-2026-05-28 (token→outcome, value > volume).
#Tokenomics Foundation#FinOps for AI#FinOps for AI
**Olivier Rafal** · pour **WeNvision** (cabinet de conseil français — bureaux à Paris, Lille, Strasbourg, Bordeaux, Nantes, Toulouse, Belgique, Luxembourg). Olivier Rafal écrit en analyste/conseil familier des préoccupations de comité de direction (ancien analyste IT, profil conseil-transformation). Publié le **4 juin 2026**.
Ethan Mollick's (Wharton) consistency test: we'll know AI labs truly believe in ASI the day they dissolve their *Forward Deployed Engineering* (FDE) teams. Public debate with roon (OpenAI) on LinkedIn: roon objects that this is a **hayekian problem** (intelligence does not automatically resolve organizational information flow) and revives the term "**Gentle singularity**". Consensus in the comments: technology is the easy part; internal politics / legacy workflows / contractual liability are the real bottleneck. Marker phrase: *"Curing cancer might be easier than replacing Accenture"*. Epistemic **East Coast vs West Coast** opposition on the trajectory of AI adoption.
#ASI (Artificial Super Intelligence)#Forward Deployed Engineering (FDE)#AI consulting