Video interview recorded at **VivaTech** (**Scaleway** booth), broadcast by the media outlet **République**, bringing together **Damien Lucas** (CEO of Scaleway) and **Franck Le Moal** (Global Technical Officer of the **LVMH** group). **Central thesis**: the emergence of a **"tech geopolitics"** is forcing multinationals to abandon the single global solution in favor of an **information system regionalized into three blocs** (United States, Europe, China). LVMH (€80bn in revenue, 75 maisons, 100+ countries) formalizes a **cloud partnership with Scaleway** to build an **autonomous European building block**, alongside Google Cloud (data, since 2021), SAP, Salesforce on the Western side and Alibaba Cloud / Huawei / Tencent on the Chinese side. The group describes itself as **"hybrid"** and **autonomous** rather than **"sovereign"** (a word it rejects, deemed ambiguous). Scaleway positions itself as a **European cloud provider** immune to extraterritorial laws and protected against a **kill switch** ("not science fiction," given the weekend's news). Damien Lucas's economic argument: **€1 spent with Scaleway = 68 cents that stay in the European economy** (vs < 20 cents with a US hyperscaler, even when hosted in France). Timeline: PoCs completed, rollout starting at **Sephora and Louis Vuitton**, significant footprint targeted within **12-18 months**. Scaleway's stated mission: focus on **IaaS/PaaS** (no verticalization such as office productivity software), relying on a partner ecosystem (sovereign applications, European chipsets and servers). Scaleway's **Nvidia GPU / AI** offering is **not planned in the short term** but remains open (open source models for autonomy + economic performance).
**Bertrand** — journaliste / présentateur du média **République** (partenaire de VivaTech) · conduit l'entretien. **Damien Lucas** — CEO de **Scaleway**. **Franck Le Moal** — Global Technical Officer du groupe **LVMH**.
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 the author 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 vendors from flat-rate pricing to **token-based billing**. **Order of magnitude (urgency)**: *"According to Goldman Sachs, global token usage is expected to increase 24-fold by 2030, reaching 120 quadrillion tokens per month"* — which elevates token efficiency from a *"technical detail"* to a topic for the **executive committee**. A 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 across providers (input tokens / caching systems / output tokens 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 measured against business impact** (*time to market, quality, features, eco-design*). **Limits of standards alone**: technical standards are not enough — the **Target Operating Model** must be rethought (teams, processes, data culture, business alignment); American organizations are already announcing *"the end of two-pizza teams in favor of sandwich teams."* **Warning marker**: *"an AI-boosted SDLC will merely [...] amplify problems and just help you go faster... straight into a wall"* (without organizational foundations). **Cited sponsors** of the foundation: Accenture, Booking.com, Google Cloud, Microsoft, IBM, Salesforce. **WeNvision's offering**: *"co-build a roadmap, rethink the operating model for the agentic era, and establish the financial governance that has become indispensable."* **French-language, executive/transformation-oriented reading** of the [[tokenomics-foundation-linux-finops-token-economics-about-2026-06-03]] fiche; 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**.
Official **Salesforce News** blog post (*Agentic Enterprise* section, *"Pioneering the Agentic Shift Within Salesforce Engineering"* series), published on **May 27, 2026** (6-minute read) by **Srinivas "Srini" Tallapragada**, *President and Chief Engineering and Customer Success Officer* at Salesforce. Direct follow-up to an earlier post (*"How we got our engineers to use AI — without breaking everything"*) which recounted crossing **>90% adoption**. **Pivot thesis**: Salesforce Engineering moved from a world where AI was a useful *copilot* to one where **agentic tools drive the software development lifecycle (SDLC) itself** — writing code, reviewing PRs, generating tests, updating documentation, managing deployments, coordinating work once handled through human handoffs. **Canonical signal decision**: org-wide standardization on **Claude Code** + ***"we removed all token limits"*** — *"remove every last piece of friction between our engineers and the tools that make them faster and more effective"*. **Major empirical result** (April 2026 vs April 2025): work items completed per developer **+50.8%**, PRs merged per developer **+79%**, and above all **Effective Output score** (an ML measure of the **real value of delivered code**, not volume) **+151.3% year over year**. **Flagship use case**: migration of **33 API endpoints** to a cloud-native architecture, estimated at **~231 person-days** (7 per API) the traditional way, completed in **13 days — 18× faster** — via a **rule-based framework built in Claude** (markdown files + reference implementations), with PR feedback continuously fed back into the rule set, **autonomous LLM loops (build, fix, validate)** with no manual intervention, parallelized across isolated environments → **5 PRs**, the largest delivering **21 endpoints with 100% test coverage**. **No speed↔quality tradeoff**: through the **Engineering 360** platform (centralizing engineering data from hundreds of systems), **total incidents drop by 5%** despite the rise in PRs (*"quality doesn't suffer from speed. It benefits from it"*), thanks to **security guardrails and quality standards structurally embedded** in the agentic workflow (Trust as the #1 value). **SDLC overhaul**: once AI is adopted, engineers **tear down and rebuild** workflows (which processes to eliminate? which handoffs are now unnecessary? where does a human still do work an agent could own?). **New engineering craft**: **Claude Code skills** (packaged, reusable capabilities encoding team context, naming conventions, patterns) become a shared, composable **engineering artifact**; **AI Expert Suite** + **Salesforce Foundation Plugins** = an institutionalized, curated skills library (internal benchmark: **higher accuracy and reliability, reduced unnecessary cost**); **subagents & agent teams** parallelize workstreams (*"They describe the outcome, and a set of coordinated agents figures out the steps"*). **What remains hard**: (1) **context management** in long sessions — **CLAUDE.md file quality** varies widely and weighs heavily on output quality; (2) **agentic security** = a fundamentally different model (agents that *act*, not just *suggest* → increased blast radius); (3) **evolving roles** (how do juniors become seniors if AI absorbs entry-level work? role of the designer/PM? the execution unit = scrum team → experiments with 1- or 3-person units). Conclusion: *"It changed what was economically possible"*; the stated ambition is **"the most automated, agentic SDLC in the industry"**. Directly intersects with Gupta (*cost of a completed outcome*, marginal token utility), Greenwald/Sierra (outcome-based pricing), DORA (ROI / cost per feature) and the BFM/Girard debate (token as a value fuel, not a cost to cut).
#Agentic SDLC#agentic SDLC#Claude Code
**Srinivas « Srini » Tallapragada** — *President and Chief Engineering and Customer Success Officer* de **Salesforce**. Plus d'une décennie chez Salesforce · dirige l'ingénierie mondiale de la plateforme unifiée. Auteur de la série *Agentic Enterprise* sur le blog Salesforce News ; ce billet (27 mai 2026) est la **suite** d'un premier opus consacré à l'adoption de l'IA par les milliers d'ingénieurs Salesforce (*« How we got our engineers to use AI — without breaking everything »*). Position d'autorité = **dirigeant exécutif** parlant en son nom et au nom d'une organisation d'ingénierie à grande échelle (donnée terrain à l'échelle d'un hyperscaler SaaS) · avec accès aux métriques internes (Engineering 360, Effective Output).
Sierra blog post (December 10, 2024, Elliot Greenwald) that lays out the founding text of *outcome-based pricing* for AI agents. **Pivotal thesis**: AI agents that execute processes autonomously make possible an **entirely new pricing model** — ***"you pay only when the software achieves specific, valuable outcomes: outcome-based pricing."*** The article traces a **four-age genealogy of software pricing**: (1) **shrink-wrapped software** (1980s-90s, floppy disk/CD-ROM box at Fry's Electronics — *"Whether you actually used it or not, you paid for it"*) → (2) **SaaS / seat-based** (pioneered by **Salesforce**, followed by Google/Microsoft/Adobe — the Internet makes it possible to sell software *as a service*) → (3) **consumption-based** (**Amazon/AWS** and **Snowflake** — *"charged only for what you used"*) → (4) **outcome-based** (AI agents). **Canonical definition**: ***"outcome-based pricing is tied to tangible business impacts—such as a resolved support conversation, a saved cancellation, an upsell, a cross-sell, or any number of valuable outcomes. If the conversation is unresolved, in most cases, there's no charge."*** **Incentive-alignment principle**: ***"With outcome-based pricing, Sierra gets paid only when we complete a task for you. Our incentives are aligned."*** **Critique of seat-based pricing & the concept of shelfware**: *"Unused seats sit idly on a proverbial store shelf, hence the derisive moniker 'shelfware'"* — thousands of dollars per year are paid per license, whether it is used or not. **Structural conflict facing legacy CX vendors**: their revenue depends on seat-based pricing, yet *"the more effective their AI becomes, the fewer contact center seats their clients need—undermining the provider's own revenue model"* — an effective AI agent **cannibalizes** the revenue model of a vendor whose pricing rests on seats. **Granularity of the outcome**: a distinction is drawn between **simple resolutions** (answering a question) and **complex resolutions** (handling a case requiring a 20-minute L2 call); **escalations generally incur no charge**; **blended pricing** is possible (e.g. consumption-based for routing/greeting interactions). **Continuous-optimization commitment** on the vendor's side: *"we continue to deploy concerted, directed optimizations to refine the agent's performance over time"* — the vendor is aligned to improve performance since it is only paid on outcome. Significance: posed in **late 2024**, this post **precedes and grounds** the entire 2026 debate on the agentic economy — it supplies the **billing-unit vocabulary** (the completed *outcome* rather than the seat, usage, or token) later taken up by Gupta (*cost of a completed outcome*, *token-to-outcome attribution*), Bain (*outcome-based pricing shifts revenue from fixed seats to labor/operations economics*), Ng (*pricing power anchored to the salary of the replaced employee*). Since Sierra is the **reference example** cited by Bain (*autonomous customer issue resolution*), this text provides the **vendor-side view** of the mechanics that the others analyze from the buyer side. Directly relevant to the firm's **agentic-delivery / value-based pricing** positioning and to the **Cost Optimization** slot (the vendor-side counterpart of *cost per outcome*).
**Elliot Greenwald** — Sierra (entreprise fondée par Bret Taylor & Clay Bavor, plateforme d'agents IA conversationnels pour l'expérience client). Billet publié sur le blog Sierra le **10 décembre 2024**. Sierra est l'**exemple-référence** cité par Bain (*The $100-Billion SaaS Opportunity*) pour l'*autonomous customer issue resolution* · et fait l'objet de plusieurs fiches du dossier (recrutement AI-native, interview Plan/Build/Review).