Benchmark announcement from **Artificial Analysis** (independent AI model evaluation platform, via X/Twitter + model page): **GLM-5.2** by **Z.ai** (Zhipu AI, @Zai_org) becomes **the leading open weights model** and climbs to **#3 in the overall ranking** of **GDPval-AA**, a real-world benchmark for *economically valuable knowledge work* (long-horizon, multi-turn, agentic tasks). GLM-5.2 scores **1524 Elo**, behind only **Claude Fable 5 (1783)** and **Claude Opus 4.8 (1615)**, and on par with **GPT-5.5 (xhigh, 1509)**. It leads the next open model (**MiniMax-M3, 1408**) by a wide margin, as well as numerous proprietary models: **Gemini 3.5 Flash (1357)**, **Qwen 3.7 Max (1289)**, **Muse Spark (1158)**. The tasks are genuinely agentic: **~31 turns per task** on average across **1,999 matches**. The same hierarchy holds on the **Artificial Analysis Intelligence Index** (1st among open weights), the **Agentic Index** (#3), and **AA-Briefcase** (#3, ahead of GPT-5.5 xhigh, behind Fable 5). Key highlight: an **open weights** model under **MIT license**, **MoE with 753B parameters / 40B active**, **1M token** context, priced at **$1.40/$4.40 per 1M tokens** input/output, rivals the proprietary frontier on agentic work — a real step for open models.
Satya Nadella (Microsoft) theorizes "the future of the firm" in an AI-driven economy: every company will need to build, alongside its human capital (judgment, relationships, pattern recognition), a "token capital" — its proprietary AI capability. The real value lies not in choosing the best model but in a learning loop (private evals, RL environments, base de connaissances) that encodes institutional knowledge and compounds over time. An argument for a "frontier ecosystem," not merely a "frontier model," so that value diffuses rather than being captured by a handful of models.
Anthropic launches Claude Fable 5 (a Mythos-class model made safe for general use) and Claude Mythos 5 (the same model, with guardrails lifted, restricted to cyberdefenders via Project Glasswing): state-of-the-art performance in software engineering, vision, long-context memory, and life sciences.
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**.
**About** page of the **tokeneconomics.com** site, presenting the **Tokenomics Foundation** — a **Linux Foundation** project announced on **June 3, 2026**, operated in **close partnership with the FinOps Foundation**. **Stated mission**: *« establish open industry standards, benchmarks, and best practices for the economics of AI infrastructure »* — linking token **production, consumption, and monetization** to **business value**. **Framing definition of tokenomics**: *« Tokenomics is not just about the cost of tokens, it's about the entire layer of AI that they drive from production, to consumption to monetization »* — that is, **the entire economic layer of AI**, from infrastructure cost through model selection to value optimization. **Phase thesis**: early AI adoption prioritized **capability**; the current phase is shifting toward **efficiency and value**, which requires systematic cost management and **visibility**. **5 founding principles**: (1) ***« Efficiency is a design choice. AI cost is shaped by architecture, not just usage »*** ; (2) ***« Bigger is not always better. The best AI system is not always the one using the most expensive model »*** (right-tool / routing) ; (3) ***« Visibility comes before optimisation. Teams cannot manage what they cannot see »*** ; (4) ***« Value matters more than volume. More tokens, more calls, and more automation do not automatically mean better outcomes »*** ; (5) ***« Open knowledge benefits everyone »*** (shared standards, community learning, transparency). **Governance**: a **Governing Board** (industry direction + fund deployment) and a **Technical Committee** (open specifications + benchmarks). **Deliverables**: extension of the **FOCUS specification** (FinOps), open specs, benchmarks, frameworks and shared metrics. **Target audience**: CAIO, CTO, CIO, CFO, engineers, product teams, FinOps practitioners, researchers, startups, enterprises, public sector. **Stated goal**: moving organizations *« from experimental AI adoption to sustainable AI operations »* by extending the discipline of **variable technology spend** into the token era. **Relevance to the watch**: institutionalization/standardization of **agentic FinOps** at the level of an industry foundation — converges head-on with the notes [[finops-foundation-finops-for-ai-overview-2026-02-17]], [[finout-finops-ai-agents-four-step-allocation-framework-2026-04-27]], [[orq-ai-finops-ai-agents-cost-per-outcome-hosseini-2026-04-15]], [[gupta-token-budget-wars-marginal-token-utility-2026-05-28]] (allocation layer, token-to-outcome) and with the **token → outcome** shift (Salesforce/Tallapragada, Sierra/Greenwald). The 5 principles map exactly onto the levers already captured: architecture > usage, **Haiku/Sonnet/Opus routing**, observability before optimization, value ≠ volume.
#Tokenomics Foundation#tokenomics#token economics
**Tokenomics Foundation** (entité collective, projet de **The Linux Foundation**, en partenariat avec la **FinOps Foundation**). Page institutionnelle *About* — **aucun auteur individuel nommé**. Annonce datée du **3 juin 2026**.
On the eve of SpaceX's record IPO (targeted valuation of ~$1.75 to 1.8 trillion), The New York Times publishes an interactive analysis of Elon Musk's track record of public promises. Across more than 600 dated, quantified commitments (statements, posts, investor calls), only ~19% were kept on time, if ever. The rate deteriorates over time: ~75% kept in 2015, less than 50% in 2020. Mars, the robotaxi, and full autonomy account for most of the repeated and postponed targets. The piece links this track record to the SpaceX prospectus, which now bets on AI (xAI merged in) and itself acknowledges that the timeline for its major undertakings is undeterminable.
Viral X thread (**230.5K views**, May 28, 2026, 1:51 AM) by **Jaya Gupta** (@JayaGup10, investor — likely Foundation Capital, author of the *Context Graphs* framework) titled ***"Token Budget Wars"***. **Pivot thesis**: ***"Enterprise AI has moved from adoption to allocation"*** — phase 1 of enterprise AI proved that models can work; phase 2 will decide **how much of that work is worth it**. The new currency at the top of the enterprise is the **ability to quantify AI ROI**: *"show me the value"*. Canonical concept: ***marginal token utility*** = *"the business value created by each additional dollar of inference"* — the number that matters at scale, and that **most companies cannot see**. Timeline: **Claude shipped November 2025**, after the 2026 annual budgets were locked → as early as **Q1**, companies *"running multiples ahead of plan"* → inference stops being an experimentation line item and becomes a **recurring operating cost**. Shift from **experimentation (a few $100K) → infrastructure (seven figures, $1M+)**: at infrastructure scale, **technical variance produces material P&L swings — two runs of the same workflow on the same input can differ by 5-10× in token cost** with nothing visibly broken, *"a number the CFO has to explain to the CEO"*. **AI competes with labor**: 3 types of budget requests (replace outsourced work / replace internal work / generate revenue) → shift toward the ***cost of a completed outcome*** (cost per resolved ticket, processed claim, reviewed contract, completed invoice, avoided hire, retained customer, dollar of revenue moved). **BPO = the easiest baseline to benchmark against** (already priced in completed units); internal work is much harder (multi-skilled employees, diffuse gains, HR resistance to headcount reduction). **Why it's different from SaaS**: SaaS learned to treat usage as a proxy for value; AI breaks that proxy — *"the signal and the noise share the same unit"* (the token), *"SaaS usage told you the software had been adopted. AI usage tells you the meter is running. It doesn't tell you whether your company is cooking."* **Three causes of marginal token utility's invisibility**: (1) ***retry tails*** — tokens per resolved workflow ≈ **T/p**; going from 90% to 70% completion increases effective cost by ~**28%**, not 20%, because failures compound; (2) ***context inflation*** — inference cost ≈ **O(n²)** in context length (attention), doubling the context **quadruples** reasoning cost (over-retrieval: 50 docs when 5 would do); (3) ***routing*** — by default the most powerful model is used (basic classification run on a complex reasoning model); across millions of calls, the difference between routing easy tasks to a small model and sending everything to the frontier model = *"the difference between a manageable bill and a board-level problem."* **Sector split**: **software** companies = a **productivity measurement** problem (already instrumented: PRs, commits, deploys, incidents, cycle time, MTTR — tracks *"AI layoffs"*); **non-software** companies = a **transformation** problem (operational work: claims, underwriting, support, compliance reviews, supply chain exceptions, payment disputes — *right under audit, not just right on average*). **The missing layer = token-to-outcome attribution**: a conversion layer linking inference spend → work performed → business outcome, answering 3 questions (real cost including retries/corrections; which parts of the trace mattered vs. thrashing; did the work change the operating model). ***Measurement becomes memory***: linking a token to an outcome requires capturing **decision traces** (what the agent saw, retrieved, called, ignored, where it retried, when a human overrode it) — *"decision rationale is one of the most perishable assets in a company"* (lives in Slack, emails, escalation calls, people's heads). Agents **create** these traces; captured first to justify the spend, they become *"more valuable than the cost report"* → a **context graph** (*"although I am so tired of that word these days"*). **The allocation layer is the prize**: whoever owns token-to-outcome attribution makes the **allocation calls** (which workflows deserve more compute, which are capped, which move to cheaper models, which stay human, which replace BPO). Companies won't do this on their own — they'll **buy it as a transformation** (Fortune 500 playbook: McKinsey + Palantir alumni + top-down CEO, in the manner of ERP/BI/digital transformation, a *"program"* with an executive sponsor and infrastructure that becomes the **new source of truth**). Framed by **Charlie Munger**: *"show me the incentive and I will show you the outcome."* Organizational sub-thesis: the decades-old executive instinct that *big teams = big jobs/scope/power* → once intelligence becomes the **scarce resource**, the new marker is *"how much of it you're orchestrating."* Direct relevance to the **Cost Optimization / agentic FinOps positioning**: empirically confirms the levers (model routing, prompt caching, context hygiene, sub-agents) and shifts the KPI toward **cost per completed outcome**. Strong convergence with Bain's *cross-system labor* (execution data moat, Cursor), Ng's *No AI jobpocalypse* (pricing anchored on the replaced employee's salary), DORA ROI (cost per feature), Mensch/Mistral (electron→token), Ensarguet (economics of computation), Foundation Capital's *Context Graphs* (decision traces, same author), Wescale's *Token Burning*, BFM/Girard (token = value fuel).
**Jaya Gupta** (@JayaGup10) — investisseuse / VC. Très probablement **Foundation Capital** (le thread s'auto-réfère au cadre ***Context Graphs*** — *« ahem, context graph, although I am so tired of that word these days »* — concept porté par Foundation Capital, cf. fiche `bain-100b-saas-opportunity` qui cite *Foundation Capital — Context Graphs trillion-dollar opportunity, 2025-12-22*). Thread publié sur X le **28 mai 2026 à 1h51** · **230 · 5K vues** · format essai long en un seul post. Une réponse notable de **@tuning_engines** (*« DevSecFinOps for the Agentic Era »*) : *« Tokens will basically have to be managed like headcount […] model hierarchies too »*.
Testimony of **Arthur Mensch** (co-founder and CEO of **Mistral AI**) accompanied by **Audry Herblin-Stoupe** (director of public affairs) before the **commission d'enquête sur les vulnérabilités numériques** of the National Assembly (chaired by Philippe Latombe, absent — session chaired by the rapporteur). Testimony under oath, ~1h15, May 2026. Mensch's pivot thesis: ***"cloud is artificial intelligence"*** — no distinction between digital services and AI, AI is the atomic unit of the cloud value chain, from semiconductors (ASML) to enterprise deployment. **Mistral in 2026**: 1,000 employees, €12 billion valuation, target of **€1 billion in revenue by end of 2026**, €1 billion invested in R&D over the year, 30% of revenue in France / 70% outside France / ~75% in Europe, clients: DINUM, Caisse des dépôts, France Travail, MACGM, Stellantis, TotalEnergies, BNP Paribas, ministère des Armées, Luxembourg (central administration). **Mensch's conceptual framework**: AI is a **natural resource** — *"we transform electricity into intelligence, into token generation."* Economics: 1 GW of datacenter = **$50 billion in investment over 5 years**, generates **$20 billion in tokens/year** ≈ 50% gross margin. Along the electron→token chain, **~10% of the value is in the electron**, 90% elsewhere (chips, software, services). **Alarmist macro thesis**: if Europe imports 10% of its payroll in non-European AI, that amounts to **an additional €1 trillion trade deficit**; €20 trillion in infrastructure investment is needed to serve Europe (40 GW France / 400 GW Europe). **Sovereignty strategy**: ***"don't think of sovereignty as isolationism but as leverage."*** **Time pressure**: *"we don't have time"* — a **2-year** window before European energy resources are monopolized by American hyperscalers deploying **$1 trillion/year**. **Five operational diagnoses**: (1) Regulatory burden = 5 compliance staff at Mistral, 27 unsynchronized regulations, entrepreneurs leaving for the US; (2) Fragmented market = ~60 European telcos vs. 3 in the US; (3) Public procurement underused as strategic leverage (50% of EU GDP); (4) Energy: 9 GW of French surplus at risk of being monopolized by US players within 2 years; (5) Distillation = a cost-reduction technique, **not** technological catch-up. **Defense doctrine**: Mistral works with the ministère des Armées, explicitly refusing "oversight" of final use ("we don't have democratic legitimacy"), a positioning *anti-Anthropic-Mythos*. **Cybersecurity**: acknowledges the offensive capabilities of models ("it's rising in a linear, predictable way, for everyone at the same time"), opposes the *fear marketing* of an American competitor (implicitly Anthropic). **Campus IA**: very minority stake, potential supplier (Mistral + hyperscalers), €35 billion MGX/Abu Dhabi + Nvidia, 100 hectares at Saint-Arnoult, 1.4–1.6 GW (= Flamanville), French nuclear power = reduced carbon footprint. **Annotation**: teams of PhD candidates (no more microworkers), Madagascar for robotics with wage guarantees. **Business model**: no bubble on the demand side, **supply bottleneck** (chips, memory, helium, electrons). **Warning conclusion**: *"if we don't do it fast enough, we will become a vassal state."*
#Arthur Mensch#Mistral AI#Audry Herblin-Stoupe
**Arthur Mensch** (cofondateur et directeur général de **Mistral AI**) accompagné d'**Audry Herblin-Stoupe** (directrice des affaires publiques et de la communication, Mistral AI). Mensch a cofondé Mistral AI le 28 avril 2023 avec **Guillaume Lample** et **Timothée Lacroix** — tous les trois précédemment dans les *« gros acteurs américains »* (Google DeepMind / Meta FAIR). Audition tenue devant la **commission d'enquête de l'Assemblée nationale sur les vulnérabilités numériques** · présidée par **Philippe Latombe** (député MoDem, Vendée — absent ce jour). Séance présidée par la **rapporteur** (non nommée dans le transcript) · avec interventions du président lui-même (revenu en cours) · du député **Arnaud Saint-Martin** (LFI/Saint-Arnoult — sa circonscription accueille Campus IA) · et de la rapporteur sur les questions économiques.
Launch of **AI/works™**, an **agentic development platform** claimed by **Thoughtworks** to be *"the new standard for building and running industrial-grade systems in the AI era"*. The core pitch is **economic**: *"the old approach made you pay millions to build, run, then pay again to rebuild — AI/works™ ends that routine"*. The platform covers **the entire SDLC** around a central concept, the ***Super Spec*** (a dynamic, unified specification covering architecture, workflows, security, data, UX), with **six capabilities**: Reverse Engineering (legacy → as-is specs), Dynamic Spec Development (raw requirements → Super Spec), Spec to Code (coordinated agents generating testable code), Developer Experience (governed golden paths), Control Plane (agent orchestration with cost transparency, active guardrails, end-to-end lineage), Runtime Ops (continuous monitoring detecting changes, updating the Super Spec, regenerating impacted code). **3-3-3** methodology: 3 days to align the product concept, 3 weeks for the prototype (desirability/viability/feasibility), 3 months for a production MVP. **Constellation Research** recognition: *"changing the economics of enterprise software delivery"* via a *"spec-driven, lifecycle"* approach. Opening tagline: ***"We are doing it again for the AI era"*** — invoking Thoughtworks' XP/CI-CD/microservices heritage. Anti-hype positioning: *"stands on an engineering foundation rather than enthusiasm"*, *"no consultant crowds"*, *"finance can open the bill without switching on emergency lighting"*. Featured partners: AWS, GCP, Azure, Databricks, Snowflake + Claude, OpenAI, DeepSeek, Gemini, Grok + NVIDIA, Groq, Stripe, Spotify, CAST, Cyn DX, Mechanical Orchard.
#Thoughtworks#AI/works#AI works trademark
**Thoughtworks** (auteur collectif corporate, page produit/marketing). Aucun individu cité sur la page. Contexte des figures Thoughtworks pertinentes en arrière-plan : **Martin Fowler** (chief scientist emeritus, *Refactoring*, *Patterns of Enterprise Application Architecture*) · **Rebecca Parsons** (CTO emeritus) · **Birgitta Böckeler** (Distinguished Engineer, *Harness Engineering for Coding Agents*, fiche 2026-04-02) · **Matt Kamelman** (*Service-as-Software*, fiche 2025-12-03) · **Sam Newman** (*Building Microservices*).
Editorial by Andrew Ng in The Batch n°352 of May 8, 2026 — **"There Will Be No AI Jobpocalypse"** — which dismantles the narrative of mass unemployment caused by AI, drawing on the **healthy 4.3%** US unemployment rate and robust tech hiring. Ng identifies **three drivers** of the jobpocalypse narrative: **(1) tech incentives** — AI labs benefit from presenting themselves as transformative-disruptive (funding rounds, valuations, talent); **(2) pricing power** — vendors charge **$10,000+/year** to enterprise clients by **anchoring their pricing on the salary of the replaced employee**, rather than on traditional SaaS pricing (per seat / per usage); **(3) corporate messaging** — companies reframe their layoffs as *"AI efficiency"* rather than acknowledging the **pandemic-era overhiring** of 2020-2022. Honest acknowledgment: *"AI disrupts work"*. But Ng flips this into **"AI jobapalooza"** (a play on Lollapalooza) — job creation in AI engineering and adjacent fields with evolving skill sets. Implicit tension with **Amodei** (50% of white-collar jobs eliminated by 2030) — Ng points out, without naming him, that **Anthropic benefits from promoting this narrative** (tech incentives). Published **the same day** as **Wallace-Wells's "AI Populism" NYT Magazine** piece: a perfect mirror reading — Ng = cold economic analysis / Wallace-Wells = popular panic. Pricing-power convergence with **Bain's "$100B cross-system labor"** (same thesis: pricing anchored on salaries).
#Andrew Ng#The Batch#DeepLearning.AI
Andrew Ng (fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu, ex-Coursera)
Brief by **Bain & Company**, **May 2026** (David Crawford, Chris McLaughlin, Greg Fiore — part of a **five-part series on the software industry in the age of AI**), which puts the still-untapped SaaS opportunity in *cross-system labor* — the human work of coordinating across systems that AI agents can now automate — at **~$100B in the US (~$200B including Canada/Europe/AU/NZ)**. **Current capture: $4-6B (10% of the opportunity)** — so **>90% still up for grabs**. Pivot thesis: the major opportunity in agentic AI **is not to replace existing SaaS** but to **automate cross-system coordination labor** (employees pulling data from ERPs, checking inventory in a spreadsheet, interpreting free-text responses, exercising judgment). Distribution: Sales ($20B) + COGS/operations ($26B) + R&D/engineering ($6-12B) + support ($6-12B) + finance ($6-12B). **Six automation factors**: output verifiability, consequence of failure, digitized knowledge availability, integration complexity, process variability, physical world dependency. **Automation potential by function**: Customer support & R&D **40-60%**, Finance & HR **35-45%**, Sales & IT **30-40%**, Legal **20-30%**. **Strategic shift**: competitive advantage moves from *system of record ownership* (Salesforce, SAP, Workday) to ***cross-workflow decision context*** — the ability to see and act across multiple integrated systems. **Examples**: Sierra (autonomous customer issue resolution), Glean (cross-function employee request coordination), GitHub Copilot (extended beyond source control), **Cursor** (ARR doubled in a quarter, $2B). **Durable moat**: *"accumulated execution data that grows more valuable over time and becomes harder for competitors to replicate"*. **Three-phase playbook**: Assessment (six factors + market sizing) → Strategic Positioning (data assets + adjacent workflows + actual operational maps) → Execution (build/buy/partner + restructure org + redesign data foundations for agent readiness). Major relevance for CIOs/CDOs/Strategy leaders in B2B SaaS and enterprise customers: reframes the *"AI vs SaaS"* conversation as ***"AI = SaaS that finally automates coordination labor"***. To be read alongside: DORA ROI (financial framework), Tatsyi/Raiffeisen (bank case study creating 7 unprecedented AI products), Wescale (realistic 3x-4x), MIT NANDA (95% of pilots fail), Foundation Capital *Context Graphs trillion-dollar opportunity* (2025-12-22), Menlo Ventures *State of Generative AI Enterprise* (2025-12-09).
**David Crawford · Chris McLaughlin · Greg Fiore** — partners et experts Bain & Company spécialistes industrie logicielle / SaaS. Article publié en **mai 2026** sur bain.com/insights · partie 2/5 d'une série sur *"the software industry in the age of AI"* (la partie 1 traite du Rule of 40, fiche `bain-ai-rule-of-40-headwinds-tailwinds-saas-2026-04.md`).
Major op-ed investigation by Jasmine Sun (NYT Opinion, April 30, 2026) on the *San Francisco consensus*: fear of the *permanent underclass* — a viral theory holding that AI could freeze economic mobility and create a class rendered useless by automation. The article documents the internal dissonance within the labs (Amodei on "white-collar blood bath" and 50% of junior white-collar jobs disappearing by 2030; Altman 2021 → Lehane silence → *Industrial Policy for the Intelligence Age* white paper, April 2026; Anthropic Institute, March 2026, led by Jack Clark), the benchmarks steering R&D toward human replacement (A.I. Productivity Index, OpenAI's GDPVal: *"over 80% win rate compared to human professionals"* within a few months), corporate actions (Block/Dorsey -50% headcount with Opus 4.6 + Codex 5.3, Anthropic ARR $30B versus $9B at end of 2025), and the Shor political strategy (79% of voters worried, jobs guarantee > UBI, *"They work for the bots. We work for you."*). Reference item in the *AI labor 2026* dossier.
#Jasmine Sun#NYT Opinion#permanent underclass
Jasmine Sun (Ms. Sun écrit sur l'IA et la culture Silicon Valley sur Substack)
FinOps for AI Agents: A Four-Step Allocation Framework for Coding Assistant Costs (Claude Code, Cursor, Copilot) and Why Traditional Cloud Tagging Fails - Finout
FinOps for AI agents centered on "cost per outcome": why traditional FinOps fails against runtime behavior, guardrails, behavioral observability, and a 4-phase lifecycle - Orq.ai
Brief Bain & Company **April 2026** (David Lipman, Greg Callahan, Daniel Goetz, George Sunderland — part 1/5 of the series *"software industry in the age of AI"*) analyzing AI's impact on the **Rule of 40** (canonical SaaS metric: *growth rate + profit margin ≥ 40%*), concluding on a **dual pressure**: **headwinds** (slowing market growth + massive AI infrastructure costs) and **tailwinds** (AI productivity + 10-25% EBITDA transformation + outcome-based pricing). **Central striking data point**: a *marketing technology* client case — **AI costs multiplied by 3.49 (+349%) while revenue increased only 38%** over one year. **Pivot thesis**: SaaS leaders may have to ***"settle for the Rule of 30"*** temporarily to remain competitive against **AI-natives**, accepting short-term margin compression in exchange for long-term positioning. **Two explicit paths forward**: (1) ***Financialize*** — minimize AI investment, optimize cash, operate as a *"durable generator"* but limit future growth; (2) ***Invest to Grow*** — accept short-term margin pressure, reinvest aggressively in AI capabilities across product and operations. **Tailwinds detailed**: sales/marketing/R&D productivity, successful transformations = **+10-25% EBITDA**, future *outcome-based pricing* opportunity (revenue shifted from fixed seats toward labor/operations economics), incumbents can leverage customer relationships and embedded workflows against AI-native challengers. **Headwinds detailed**: *"software penetration is topping out in some areas"* (market saturation), AI infrastructure + inference + model access introduce **significant variable costs in businesses that historically ran on high margins**. **CFO/board signal**: the Rule of 40 itself as a **stable norm** is starting to shift; some players will temporarily fall outside this norm, and **this is strategically rational**. **Major relevance** for B2B SaaS CFOs/CEOs/boards and software PE/VC investors evaluating portfolios — the first quantified institutional benchmarking of the *protect margins / invest aggressively* dilemma in 2026. To connect with: Bain **part 2/5 cross-system labor $100B** (2026-05), DORA ROI 2026 (financial framework), Wescale (realistic X3-X4), Tatsyi/Raiffeisen (bank −75 people), Curran/Intercom (3× R&D in 16 months), Menlo Ventures *State of Generative AI Enterprise* (2025-12-09).
#Bain & Company#Rule of 40#growth rate plus profit margin
**David Lipman · Greg Callahan · Daniel Goetz · George Sunderland** — partners et experts Bain & Company spécialistes industrie logicielle / SaaS / private equity software. Article publié en **avril 2026** sur bain.com/insights · **partie 1/5** d'une série Bain sur *"the software industry in the age of AI"*. La partie 2 (*The $100-Billion SaaS Opportunity Hiding in Cross-System Labor*, mai 2026) est dans le dossier de veille.
Method article by Alex Pawlowski (The Strategy Stack, #151, March 30, 2026) proposing a major epistemic shift in *market research*: no longer collecting static reports but maintaining a ***living decision surface*** — a continuously evolving model of market dynamics. Central contribution: the **Tension Map**, which maps *contradictions and pressure points* (gaps between expectation and delivery, price tolerated without being embraced, incumbents without emotional resonance) rather than market share. Tooling in three modes (Discovery / Tension / Decision), a 7-step workflow, and an orchestrated tool stack (Perplexity for expansion → Claude for depth/continuity → ChatGPT for iteration → Multi-agent for challenge). Implicit reference: Richards Heuer's (CIA) *Analysis of Competing Hypotheses* method.
#Alex Pawlowski#The Strategy Stack#AI market research
Alex Pawlowski (auteur de la newsletter Substack *The Strategy Stack*, focus stratégie et IA opérationnelle).
Analysis of the total cost of ownership (TCO) of local LLMs versus cloud APIs in 2026. The article demonstrates that per-token pricing is a trap and that only the full TCO (hardware, electricity, cooling, labor) informs the decision. Key highlight: local/cloud break-even points fell by 40% between 2024 and 2026. Source: SitePoint (developer-focused technical media).
**Consolidated dossier** March 2026 on the **death of the billable hours model in the advertising/communications industry** — combining the **VoxComm report** *"Redesigning the Agency Value Model"* (95 pages, March 2026, **Brian Kessman** of **Lodestar Agency Consulting** + foreword by **Tim Williams** of **Ignition Consulting Group**, intro by **Charley Stoney** President of VoxComm / CEO of **EACA European Association of Communication Agencies**) and the **MediaPost opinion article** *"Billable Hours Are Dead, AI Killed Them, Here's How To Survive"* (March 3, 2026, **Joe Mandese**, Editor-in-Chief of MediaPost). **Shared pivot thesis**: the business model of communication agencies (billable hours / labor-based compensation / service business model) is **structurally disqualified by AI**; agencies must ***"decouple revenue and profit from staffing numbers"*** (Stoney). **MediaPost figures (Mandese)**: agency margins **30% (golden age) → 10% (current average)**; creatives produce **~5× the output** for the same pay or less than 10 years ago. **Mandese's diagnosis**: *"We are defining and monetizing our value through time and effort rather than business impact"* — when agencies sell **hourly services**, they sell **commodities** vulnerable to **AI cost compression**. **Tim Williams quote**: ***"At the heart of our industry's challenges lies a simple economic truth: incentives matter. When agencies embraced the hourly rate model, they unknowingly created a structural misalignment. What agencies are rewarded for — more hours — clients are incentivized to minimize."*** Zero-sum outcome, **race to the bottom**. **Williams' pivot solution**: ***"You are not in the service business. Agencies don't sell services and capabilities, but rather solutions to business problems."*** **Mandese's 4-shift framework**: (1) Define narrow expertise areas; (2) Codify repeatable productized solutions; (3) Build teams around outcomes, not utilization; (4) Replace rate cards with value-based models (fixed fees, subscriptions, performance-based pricing). **Concrete examples**: **FIG** (decoupled pricing from staffing), **72andSunny** (modular product menus), **Monks** (single subscription combining talent + tech + improvement). **Methodological critique**: MediaPost commenters dispute the historical 30% margin figure, suggesting real figures closer to 12-15%. **VoxComm report** structured into 8 chapters: When Your Model Works Against You / Mapping Your Value Model / Case Studies / How to Pivot / How to Price / How to Plan / How to Navigate / Online Tools. **Major relevance** for the dossier: this is the **agency counterpart** of the consulting shifts (McKinsey/Sternfels 60,000 = 40,000 humans + 20,000 agents, January 2026) and SaaS shifts (Bain Rule of 40 → Rule of 30, April 2026). **Cross-cutting convergence for knowledge-intensive services**: consulting + agencies + SaaS are simultaneously shifting from *time-and-materials* to *outcome-based*. To be leveraged for agency/consultancy/marketing/communications executive committees, strategic presentations on AI transformation of services, sourcing on the 30%→10% margin figures.
#VoxComm#Redesigning the Agency Value Model#Brian Kessman
**Rapport VoxComm "Redesigning the Agency Value Model"** :
Official FinOps Foundation guide to AI: token economics, KPIs, caching, prompt optimization, model selection, and extension of the FinOps Framework's 14 capabilities to GenAI services - FinOps Foundation
#FinOps Foundation#token economics#cost per token
FinOps Foundation — groupe de travail (Brent Eubanks/Wayfair, James Barney/MetLife, Eric Lam/Google, Adam Richter/AWS, Rahul Kalva/Wells Fargo, JJ Sharma/KPMG, Karl Hayberg/EY, et al.)
**OfficeChai** reports on **January 14, 2026** a major statement by **Bob Sternfels** (Global Managing Partner, **McKinsey & Company**): ***"my latest answer to you would be 60,000, but it's 40,000 humans and 20,000 agents."*** McKinsey headcount is now counted as **humans + agents**: **60,000 = 40,000 humans + 20,000 AI agents**. **Massive acceleration trajectory**: *"Little over a year and a half ago, that was 3,000 agents and I originally thought it was going to take us to 2030 to get to one agent per human."* The human/agent parity initially projected for **2030** is now achievable within **18 months** — an acceleration of **>5×** relative to the original projection. **Explicit business-model shift**: *"We're migrating pretty quickly away from, let's call it pure advisory work... moving to much more of an outcomes-based model."* — McKinsey **is abandoning the billable-hours / advisory model** in favor of an **outcomes-based model** where compensation is aligned with measurable results. **McKinsey context 2024-2026**: Lilli (internal chatbot) used by **70% of employees**, **200 technology roles cut in November 2024**, a **9-month-salary** severance offer (April 2024). **Major relevance for the 2026 dossier**: this is the **first top-tier consulting player** to (a) publicly state and (b) precisely quantify the integration of AI agents into its accounting headcount. The phrase ***"60,000 people but 20,000 of them are agents"*** is set to **redefine** the very conception of headcount in knowledge-based services — comparable to the moment organizations began counting contractors + employees together. **Convergence with the 2026 corpus**: Bain Rule of 40 (outcome-based pricing tailwind), Bain cross-system labor $100B, MediaPost Mandese *"billable hours are dead, AI killed them"* (March 3, 2026), VoxComm *Redesigning the Agency Value Model* (Brian Kessman / Tim Williams, March 2026), Tatsyi/Raiffeisen (−75 people), Cherny Sequoia *"7 Powers reordering"*. **Productive tension** with the normative DORA position *"do not adopt headcount-reduction strategy"* — McKinsey publicly owns the **reduction in human headcount** (200 roles in 2024) while reinjecting capacity into AI agents + outcome-based revenue. To be leveraged for firm executive committees, strategic presentations for consultancies/IT-services firms/agencies, debates on the future of the knowledge-services business model.
#Bob Sternfels#McKinsey & Company#McKinsey Managing Partner Global
Article rapporté par **OfficeChai Team** (publication indienne tech/startup news) · **14 janvier 2026**. **Source primaire** : déclarations de **Bob Sternfels** · **Managing Partner Global de McKinsey & Company** · lors d'une intervention publique. McKinsey est le cabinet conseil top-tier mondial fondé en 1926. Sternfels dirige le cabinet depuis 2021.
**Matt Kamelman** publishes on the **Thoughtworks blog** on **December 3, 2025** a conceptual pivot article that formalizes the major economic shift in intellectual services: ***"Service-as-Software" (SaS)*** as the **new economic model** succeeding **SaaS**. **Pivotal distinction**: ***"Traditional SaaS is about tools: software that enables humans to solve problems. Service-as-Software (SaS), meanwhile, sells outcomes."*** SaS is *"a new class of tool that doesn't just enable work but instead automates the reasoning process itself"*. **Pricing shift**: ***"Companies will no longer pay for an agent based on seats or features. Instead they'll pay based on its demonstrated alignment and impact."*** **Three archetypal examples of SaS agents**: marketing agents (end-to-end campaigns), financial agents (forecasting modeling), operations agents (request triage). **Three capabilities** of agentic SaS systems: (1) operate dynamically on **goals**, not fixed workflows; (2) retain **memory** across interactions; (3) autonomously coordinate across tools and APIs. **The "Cognitive Contract" — three principles**: (1) **Interpretable and auditable** — *"users need to be able to understand why the system made a decision"*; (2) **Aligned with human goals** — *"the system's objectives must match human intent and ethical boundaries"*; (3) **Trained and iterated in real time** — *"systems continuously refine behavior based on feedback"*. **New organizational role**: the ***"cognitive orchestrator"***, with three operational functions: (a) **feedback loop design** (step-by-step review); (b) **managing uncertainty with guardrails** (business rules + circuit breakers); (c) **measuring alignment** (quantifiable alignment score). **Structuring historical analogy**: *"mainframes → client-server → web/cloud, where the cognitive contract remained the same: humans had to instruct the machine"*; today this contract **evolves toward collaboration** between humans and machines. **Major relevance**: this is the clearest **Anglo-Saxon conceptual formalization** of the empirically observed shift from **billable hours / per-seat → outcome-based** at **McKinsey/Sternfels** (60,000 = 40,000 humans + 20,000 agents, January 2026), **VoxComm/Mandese** (March 2026, agencies), **Bain Rule of 40** (April 2026, SaaS), **Bain cross-system labor $100B** (May 2026). The term ***"Service-as-Software"*** is poised to become **canonical**, as *"Software-as-a-Service"* was in the 2000s-2010s. To be leveraged for **2026 strategic vocabulary** in executive committee presentations, agentic business cases, pricing models.
**Matt Kamelman** — auteur de l'article publié sur le blog Thoughtworks le 3 décembre 2025. **Thoughtworks** est un cabinet de conseil en technologie et software engineering global · fondé en 1993 · fortement associé à des figures emblématiques du software engineering (Martin Fowler, Rebecca Parsons, Sam Newman, etc.) et à des contributions historiques majeures (continuous delivery, microservices, expansion XP/Agile). Le blog Thoughtworks est une référence de premier plan dans l'écosystème software engineering et architectural.
CPO FinOps guide to AI architectures: token multipliers (6×, 5-10×) across LLM workflows, RAG, agents, and agentic systems, with the Cost Iceberg concept - Finout
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).