Doctrinal article by Addy Osmani (Google) that establishes a foundational distinction for the 2026 debate on AI and cognition: **Cognitive Offloading** (healthy — delegating the *how* while retaining judgment over results) vs **Cognitive Surrender** (toxic — accepting AI output wholesale without forming parallel reasoning, *"borrowing the model's confidence as substitute for personal understanding"*). Solid scientific grounding: the **Shaw & Nave (Wharton/UPenn)** study of 1,372 participants — **73% accept demonstrably wrong AI answers**, with confidence rising despite a 50% error rate. **MIT *Your Brain on ChatGPT*** — reduced neural connectivity among AI-assisted writers. **Anthropic Skill-Formation** — engineers using AI to generate code score **17% lower** on comprehension versus those using it for conceptual inquiry. Four concrete examples of surrender (reviewing 600-line PRs on surface signals, shallow debugging, architectural decisions made without reasoning, degraded learning). Five personal heuristics (pre-generating expectations, junior-engineer-standard review, adversarial prompting, fatigue awareness, verification of the source of confidence). Six structural guardrails (verification exit criteria, anti-rationalization tables, **PRs ~100 lines max**, interrogative over generative mode, scaffolded friction, **regular solo keyboard time**). Two new concepts: ***Comprehension Debt*** (the growing gap between total codebase volume and human understanding) and ***Mutual Amplification*** (a cooperative prompt-refine loop vs surrender-delegation). Pivot thesis: ***"the choice between thinking with AI versus not thinking at all remains entirely human"***. A structural and operational counterweight to *"coding is solved"* (Cherny 2026-05) and an analytical complement to Frizzo (2026-05-05).
Addy Osmani (Software Engineer at Google, Cloud + Gemini, ex-Chrome — déjà au dossier veille avec *Agent Harness Engineering* 2026-04-19, *How to write a good spec for AI agents* 2026-01-13, *Conductors to Orchestrators* 2025-11-01).
Medium op-ed by **Hryhorii Tatsyi** (CTO, **Raiffeisen Bank Ukraine**, ~900 IT engineers) reporting a **12-month longitudinal study** (May 2025 → April 2026) on the real impact of generative AI in a large European bank. Pivot thesis: ***"AI didn't make our engineers just faster. It made them different."*** Unlike individual accounts (Frizzo, Cherny) or meta-level ones (Curran/Intercom), this is a **quantified organizational assessment from a traditional regulated bank** — a corpus still rare in 2026. Results: **−75 people (−8% headcount, including 64 engineers)** over 12 months, yet **more code shipped, fewer incidents, improved security**; AI adoption **62% → 83%**; **68% of engineers receive ≥50% of their code via AI assistance**; **new-engineer onboarding 60-90 days → ~40 days** (consistent with Anthropic data of 82→40 days). Three emerging archetypes: (1) **Copilot-only** +10-25% on PRs, same scope; (2) **Multi-tool** story points ×1.5-3, cross-repo scope +50-80%; (3) **Claude on corporate stack** code volume ×4.5, radically expanded scope. **Seven AI products built** that did not exist before: Service Knowledge Hub (57 microservices, 83 releases/month), Mobile Android workflow CI plan/implement/test, AI Agent Portal (2,085 users / 649 MAU in 87 days, MCP generation via OpenAPI specs), Shift-left Security Plugin (−82% exposed secrets), DevPortal Backstage + Kubernetes diagnostics agents (−68% critical incident resolution time), DRAIF MCP text-to-SQL Data Lake with 10,000 tables (embedding fine-tuned 2× OpenAI), Call Evaluation (>97% transcription accuracy, voted best product in the Raiffeisen group). Stability: **blocking incidents −70%, critical resolution −68%, high-severity security alerts resolved +155%**. Central strategic insight: ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity"*** — AI does not do the same thing faster, it shifts **what one can decide to do**. The evaluation question to reframe: not *"by how much % did existing KPIs increase"* but ***"what your engineers built that didn't exist before"***. AI lifts underperformers to baseline more than it accelerates top performers; **senior architects return to active development** after years away from it. Major relevance for banking/insurance/regulated-sector executive committees (Raiffeisen = bank, Ukraine = wartime context + operational resilience).
#Hryhorii Tatsyi#Raiffeisen Bank Ukraine#CTO bank
**Hryhorii Tatsyi** — CTO de **Raiffeisen Bank Ukraine** (filiale ukrainienne du groupe bancaire autrichien Raiffeisen Bank International, RBI). Auteur Medium @milhibisidek. Profil discret côté visibilité publique (25 followers Medium au moment de la publication) · mais position institutionnelle de premier plan : il dirige une organisation IT d'environ 900 ingénieurs dans une banque systémique opérant en contexte ukrainien (économie de guerre depuis 2022, résilience opérationnelle critique). L'article est sa première contribution publique d'envergure documentée sur cette plateforme.
GitHub repo `techygarg/lattice` that formalizes a framework of **composable skills** for installing an *engineering discipline* into AI coding assistants (Claude Code, Cursor). Distinctive three-tier architecture: **Atoms** (single-principle guardrails: clean code, DDD, security, test quality, design-first), **Molecules** (multi-step workflows composing the atoms: design, implement, refactor, fix, review), **Refiners** (guided interviews producing project-specific standards that customize the atoms' behavior). Operational pipeline `lattice-init` → `design-blueprint` → `code-forge` → `review`, with `refactor-safely` and `bug-fix` as offshoots. Three pivotal principles: *"Skills over prompts"*, *"Composability over monoliths"*, ***"Living context over static config"*** — the `.lattice/` folder grows smarter with every feature cycle. MIT, pure shell, 18 stars / 52 commits, a series of articles on martinfowler.com explaining five *collaboration patterns*. Strong convergence with Vincent *Superpowers* (2026-04-02), Habert *PROJ-AI* (2026-05-05), Wescale *Usine Logicielle Augmentée* (2026-05-03), and — the highest doctrinal convergence with no declared lineage — **Compound Engineering** by Every (Shipper/Klaassen 2025-12-11): isomorphic pipelines (lattice-init→design-blueprint→code-forge→review ↔ ce:brainstorm→ce:plan→ce:work→ce:review), living context layer (`.lattice/` ↔ `docs/plans/+solutions/+brainstorms/`), a shared design-first stance, mandatory review at the end. 2026 *coding agent harness* doctrine converges on a stable vocabulary, without direct influence.
#lattice#techygarg#composable AI skills
techygarg (auteur GitHub, identité réelle non précisée dans le README ; auteur d'une série d'articles publiée sur martinfowler.com).
**Jessica Talisman MLS** (Semantic Engineer + Information Architect, 25+ years of experience, ex-Adobe RDF knowledge graphs + ex-Amazon information architecture, founder of **Ontology Pipeline Framework** + **Contextually LLC**) publishes on **Modern Data 101** (Substack, ~20,000 members) on **May 4, 2026** a major revision of her **Ontology Pipeline™** framework originally published in January 2025. **Pivot thesis**: since November 2022 (ChatGPT), demand for *semantic infrastructure* has exploded but has created **massive confusion** — *"vendors offering shortcuts that bypass essential foundational work, creating liabilities disguised as assets"*. The initial **5-step** pipeline (controlled vocabulary → metadata standards → taxonomy → thesaurus → ontology → knowledge graph) remains valid but **must be supplemented by 2 critical additions**: **(1) Governance** as ongoing engineering practice (not post-project documentation); **(2) AI Partnership** with a clear distinction between augment and replace. **Market diagnosis**: *"a structurally invalid taxonomy is not a taxonomy"*, *"lists are not knowledge infrastructure"*, AI-generated taxonomies sold as strategy, vendors misusing the term *"ontology"*, cookie-cutter solutions presented as methodology. **Educational crisis**: demand for semantic engineers massively outstrips the supply of trained practitioners; the gap is filled by people *"who know vocabulary without methodology"*. **Explicit normative position**: *"AI that generates a taxonomy wholesale is producing a liability disguised as asset; AI that assists trained engineers is just plain smart."* **Acceptable AI roles**: entity extraction, gap analysis, drafting candidate vocabularies for review, population/validation support. **Unacceptable AI roles**: *wholesale taxonomy generation without human validation against standards*. **Referenced standards**: SKOS, OWL, RDF, SPARQL. **Credibility**: framework validated across **6 institutions over 10 years**. **Recommendations for 3 audiences**: (a) Organizations — invest in formal education, treat knowledge infrastructure as AI backbone, governance as ongoing, AI as an accelerator not a replacement; (b) Practitioners — competency questions before modeling, validate against SKOS/OWL/RDF, definitional difficulty signals pause, maintenance continues; (c) Leaders — workforce upskilling without self-funded education, allocate resources to knowledge infrastructure as a strategic necessity, governance before deployment. **Striking quotes**: *"the work cannot be skipped"*, *"governance is the engineering practice that keeps an ontology coherent across change"*, *"teaching this is hard. Learning it is harder."* **Major relevance** for data leaders / CDOs / architects building the semantic foundations of their AI agents. To be connected with: Seale Semantic Agent (2026-04-17) — *(Model+Harness)+(Ontology+Data) — ontology as the only moat*; Foundation Capital Context Graphs (2025-12-22); Bain part 2/5 *redesign data foundations for agent readiness* (2026-05); DORA ROI 2026 *AI-accessible internal data + healthy data ecosystems* (2026-04-21); Habert PROJ-AI six-zone doctrine (2026-05-05). Convergence with the 2026 *"data foundations as moat"* corpus.
#Jessica Talisman MLS#Ontology Pipeline framework#Modern Data 101
**Jessica Talisman MLS** — Semantic Engineer et Information Architect avec **25+ ans d'expérience** en enterprise architecture · e-commerce systems et knowledge management. Fondatrice de l'**Ontology Pipeline Framework** et de **Contextually LLC**. Roles précédents : **Adobe** (RDF-based knowledge graphs) · **Amazon** (information architecture). Auteure de la newsletter **Intentional Arrangement** (Substack) et d'un **livre éponyme à paraître en 2026**. Le framework initial *Ontology Pipeline* a été publié en janvier 2025 et **validé sur 6 institutions sur 10 ans**.
Presentation by Wescale (France) that formalizes the doctrine of the ***Augmented Software Factory***: a software value chain entirely orchestrated by specialized AI agents across six production lines (Intention/PRD-ADR → Plan/User Stories → **human Sign-Off** → 24/7 Production → independent audit Verification → DevOps Deployment), where humans intervene at only two precise moments. Strong theses: the return of the **predictable V-cycle** against Scrum, realistic **3-4x** gains (not 10x), the shift from *code producer* to ***Strategic Judge*** and from *solo developer* to ***Agent Manager***, DORA metrics replacing velocity, maximum ROI on legacy modernization and costly SaaS substitution, and above all ***injected governance*** as a "near-military layer" that constitutes the central innovation and the true barrier to entry. Built by eating its own dogfood: *"What we learned by building Solario on Solario."*
#Wescale#Augmented Software Factory#augmented production chain
Wescale (cabinet français de conseil tech / cloud / DevOps) — auteurs collectifs (présentation corporate, pas d'auteur individuel cité dans le deck).
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`).
Google whitepaper (the "Day 1" installment of a series, by Addy Osmani, Shubham Saboo and Sokratis Kartakis) that maps the transformation of the software development lifecycle (SDLC) in the era of coding agents. Thesis: the fundamental shift is not a new language but the move from writing code to **expressing intent**. The document lays out a spectrum from *vibe coding* (prompting and accepting) to *agentic engineering* (AI implements under constraints, tests, and feedback loops designed by humans), with **context engineering** as the central skill, the **software factory** model (the developer's deliverable = the system that produces the code), **harness engineering** (Agent = Model + Harness), and a CapEx/OpEx economic analysis of total cost of ownership.
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)
Interview with Andrej Karpathy (OpenAI co-founder, former Tesla Autopilot) moving from *vibe coding* to *agentic engineering*: December 2025 as the tipping point — "never felt more behind as a programmer" — the Software 1.0/2.0/3.0 taxonomy, the openclaw example (bash script → text to copy-paste into the agent) and MenuGen rendered obsolete by Gemini's Nanobanana, the *verifiability* theory explaining why LLMs are *jagged* (math/code peak, "walk 50m to the car wash" fails), the distinction between *vibe coding* (raise the floor) and *agentic engineering* (preserve the quality bar), the "animals vs ghosts" metaphor, the overhaul of hiring via agent-versus-agent projects, and the key formula: ***"You can outsource your thinking but you can't outsource your understanding."***
#Andrej Karpathy#vibe coding#agentic engineering
Andrej Karpathy (co-fondateur OpenAI, ex-Tesla Autopilot, créateur du terme "vibe coding")
Manifesto-style X thread by Rohit (@rohit4verse) laying out the *2026 AI engineer roadmap*: a $150k gap between prompt engineer and systems architect, the end of *generic wrappers* "sherlocked by big tech", and 5 portfolio projects ranked by complexity level (offline mobile SLM, self-improving coding agent, multimodal *Cursor for video editors*, privacy-first personal life OS agent, autonomous enterprise workflow agent). Each project describes its *key architectural decisions* (lazy loading, sliding window, sandboxing, scene detection, personal knowledge graph, event-driven multi-agent, audit trail, RBAC, observability). Structuring slogan: *"the replaceable: building wrappers / the unfireable: shipping autonomous systems"*. Injunctive, viral tone typical of X in 2026.
#2026 AI engineer roadmap#Rohit#rohit4verse
Rohit (@rohit4verse) — créateur de contenu IA sur X · vulgarisateur d'architecture et roadmaps de carrière en ingénierie IA.
Post from the **Ahrefs blog** published on **April 28, 2026** by **Ryan Law** (Director of Content Marketing, Ahrefs) describing an in-house **content engineering** system built around **Claude Code**: an editorial pipeline that produces **publish-ready drafts in 6 to 12 minutes**. **Pivot thesis**: ***« AI content is not, by default, good. This process works well because it mirrors our existing human editorial process »*** — quality doesn't come from the model but from the **faithful reproduction of a human editorial process** proven over decades. Architecture: **~23 skill files**, each corresponding to an editorial step (keyword research, topic gap analysis, structural outlining, research compilation, draft generation, formatting), **orchestrated by a master skill `blog-pipeline`** that chains them to produce a complete article. **Seven design principles**: (1) **mimic human workflows** by chaining skills adapted from existing Ahrefs editorial documentation; (2) **output each step separately** for troubleshooting (*« if you get an article at the end of a ten minute run, and it's bad, it's hard to diagnose precisely where and why the process went wrong »* → save intermediate outputs); (3) **create test cases** via Anthropic's `skill-creator` skill to evaluate and improve guidance; (4) **plug in quality data sources** — the **Ahrefs MCP** (keyword metrics, parent topic, long-tail themes, SERP overviews, competitive analysis), competitive analysis and product docs; (5) **front-load human direction** via context parameters enabling editorial guidance; (6) **build interactive previews** in HTML format for review before publication; (7) **allow customization** (each team member can fork and modify the system). **Volume**: ~**15 articles published** and ~**30 articles updated** via this workflow; development started in **February 2026** (the prior process from **August 2025** took several days and manual intervention). **Explicit caveats** (anti-oversell): *« experience matters »* — the process reflects decades of editorial expertise; topic selection focuses on **informational SEO content** the author knows well; Ahrefs **has no plan to "scale" content massively** but maintains an **evergreen library**. Philosophy: automate *« the formulaic parts of work »* to eliminate drudgery and free up time for research, thought leadership, webinars, and system optimization — **not** replace human effort. Canonical reference cited by Pasquale Pillitteri (*Opus 4.8 SEO workflow*) as field proof of the « 6-12 min/draft » gain. Direct convergence with the **skills-over-prompts** doctrine (Lattice, PROJ-AI), **systems around the model** (Dropbox/Okumura), and the use of **HTML as a review artifact** (Shihipar).
**Ryan Law** — Director of Content Marketing chez **Ahrefs**. Praticien senior du content marketing SEO ; le billet est un retour d'expérience personnel (*« How I do… »*) publié sur le **blog Ahrefs** (ahrefs.com/blog) le **28 avril 2026**.
FinOps for AI Agents: A Four-Step Allocation Framework for Coding Assistant Costs (Claude Code, Cursor, Copilot) and Why Traditional Cloud Tagging Fails - Finout
Editorial by Andrew Ng in The Batch #350 laying out an **acceleration hierarchy driven by coding agents** by type of software work: **Frontend (max) > Backend (moderate) > Infrastructure (low) > Research (minimal)**. The rationale rests on implicit *verifiability* (fluency in TypeScript/JavaScript + an autonomous agent–browser loop on the frontend) and on the LLMs' blind spots (corner cases / security / DB migrations for the backend, opaque network tradeoffs for infra, irreducible hypothesis formation for research). The issue also covers 4 structuring news items: **GLM-5.1 (Z.ai)**, a 754B/40B-active-parameter MIT-licensed model capable of 8-hour autonomous tasks (SWE-Bench Pro leader at 58.4%); **Digit (Agility Robotics) at Schaeffler**, the first industrial deployment of humanoids (5'9"/143lb, $10–25/h vs $20/h for a human); the **anti-data-center revolt** (~$64B in projects blocked May-2024 / March-2025, Maine moratorium on 20MW+, a molotov cocktail at Sam Altman's home); and the **"assistant axis"** (Christina Lu, MATS / Oxford / Anthropic), which reduces persona drift and jailbreaks (Qwen3 32B: 83%→41%; Llama 3.3 70B: 65%→33%) without degrading IFEval/GSM8k/MMLU-Pro/EQ-Bench.
#Andrew Ng#The Batch#DeepLearning.AI
Andrew Ng (édito principal — fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu) ; rédaction The Batch (DeepLearning.AI) pour les sections actualités
Les Echos report (Florian Dèbes) from San Francisco: AI agents already integrated as colleagues at start-ups, "petri dish" (Aaron Levie / Box), reflex use of Claude before every meeting, personal Jarvis, 5 parallel agent tabs, "the limiting factor is human cognition" (Patrick Joubert / Rippletide), "brain fry" / cognitive overheating, BCG/HBR study showing 14% of employees overwhelmed, "token-max" ranking of the heaviest AI users, testimonials from Sinaï/Bangay/Allali/Hodjat/Pantera/Chapeau and an echo from Siddhant Khare ("AI reduces production costs but raises coordination costs").
#Silicon Valley#San Francisco#AI agents as colleagues
Florian Dèbes (Les Echos, rubrique Travailler mieux / Vie au travail)
Revamp of the engineering hiring process at Sierra in the age of coding agents: AI-native onsite interview (Plan/Build/Review), removal of the algorithmic coding test, replacement of the phone screen with a system design interview, pilot of a debugging interview on an existing codebase.
Joint **DORA × delta** report (Google Cloud Professional Services), 60 pages, version **v. 2026.1** (citations retrieved February 2026, PDF created April 21, 2026), licensed **CC BY-NC-SA 4.0** — the first official **DORA ROI** framework dedicated to AI in the SDLC, with an **interactive calculator** at dora.dev/ai/roi/calculator. Pivot thesis: ***"AI is an amplifier"*** — AI **amplifies** the strengths of high-performing organizations and the dysfunctions of struggling ones simultaneously; it does not create performance, it **multiplies it where it already exists**. New central concept: the ***J-Curve of AI value realization*** — every AI adoption goes through a **temporary productivity dip** (learning curve + verification tax + pipeline adaptation) before **exponential growth**, a metaphor for the *"tuition cost of transformation"* to be **budgeted explicitly**. Reference calculation: a 500 FTE organization / $176k fully loaded salary / 12.5% time saved per developer (≈ 1h/8h day) → **value $11.6M / investment $8.4M / ROI 39% / payback period 8 months (0.7 year)**. Modeled costs: licenses ($250/user/year), additional API costs ($80/user/year), training ($9,600/user/year), infra ($100k/year), J-Curve cost ($3.3M for a 15% drop over 3 months). Modeled value: **headcount reinvestment capacity** ($11M — freed capacity to reinvest, **NOT headcount reduction**), revenue from extra feature deployments ($990k, based on a 33% idea success rate, Larsen 2023), **negative downtime impact** (−$344k, "instability tax"). **Explicit reinvestment strategy**: ***"we strongly recommend organizations do not adopt a headcount-reduction strategy"*** — reinvest in innovation, retain talent, capitalize on institutional knowledge. Five pillars of value: Productivity / User Experience / Cost Efficiency / Developer Experience / Business Growth (from most direct to most indirect, *cumulated business value*). Five systemic keys to adoption: **Trust + Platform + Data + Users + Guardrails**. Two-phase roadmap: (1) **Build context layer (CapEx)** — quality IDP + healthy data ecosystems; (2) **Empower human in loop (OpEx)** — context engineering + trust in AI. Indicators: leading = experiment frequency + deployment frequency; stability gauge = change failure rate + rework. Three scenarios to model (Conservative 0.8 value × 1.5 cost / Realistic 1.0 / Optimistic 1.2 × 0.8). External data leveraged: 78% of executives report ROI on ≥ 1 gen AI use case (Google Cloud), 88% of early agentic AI adopters see positive ROI, **35-40% productivity greenfield vs ≤10% brownfield/legacy** (Stanford), inference cost ÷280 between Nov 2022 and Oct 2024 (Stanford AI Index 2025), **727% ROI over 3 years** for Google Cloud AI customers, average **8-month** payback in the AI market. Assumed weaknesses: *"all models are wrong"* — the model needs contextualizing, the calculator needs adjusting; risk of double-counting value (time saved → both avoided hire AND extra revenue); the user experience link is "loose" and therefore excluded from the calculator. **Deontological insight**: ***"We don't measure AI by the code it writes but by the bottlenecks it clears"*** — measured by bottlenecks cleared, not by code volume. **Major relevance** for CIOs/CTOs needing to build a defensible AI business case in front of a CFO/board; for France/Europe, to be read alongside Wescale (realistic X3-X4), Tatsyi/Raiffeisen Bank Ukraine (bank case study, −75 people but deliberate reinvestment), Frizzo (3-5× median), Curran/Intercom (3× R&D over 16 months), DORA Report 2025 (which this ROI report builds on).
#DORA ROI of AI-assisted software development#Google Cloud DORA report 2026.1#J-Curve of AI value realization
Rapport conjoint **DORA team × delta team** (Google Cloud Professional Services). Auteurs principaux : **Eva Dong** (AI Value Realization Americas, ex-McKinsey 8 ans, Master Financial Engineering Michigan) · **Andre Ellis Jr.** (Cloud Financial Operations Lead, Morehouse + Wharton MBA) · **Nathen Harvey** (DORA team lead, co-auteur multiples DORA reports + 97 Things Every Cloud Engineer Should Know) · **Vivian Hu** (10X Technology Consultant, contributrice DORA 2025 State of AI-assisted Software Development) · **Ursula Lübbert-Passing PhD** (AI Value Realization EMEA, 20 ans benchmarking + value advisory, PhD effort estimation software projects) · **Eric Maxwell** (lead 10X Technology consulting, ex-Chef Software, contributeur DORA) · **Aaron Wanjala** (cloud developer advocate Spring Boot/Angular). Conseillers et contributeurs : **Ben Jose · Eric Lam · Matt Orr · Allison Park · Ryan J. Salva · Jerome Simms · Dave Stanke · Cedric Yao**. Design : Human After All (humanafterall.studio). Document publié sous licence **CC BY-NC-SA 4.0** · version v. 2026.1 · citations retrieved February 2026.
Synthesis by Addy Osmani (Google, Chrome/Cloud) of the emerging field of *harness engineering*: the equation `agent = model + harness`, the *ratchet* principle ("every mistake becomes a rule"), the HumanLayer "skill issue" reframe, Terminal Bench evidence (Top 30 → Top 5 from a harness change alone), the layered Claude Code architecture, Anthropic's "harnesses don't shrink, they move" vision, and Harness-as-a-Service (Claude Agent SDK, Codex SDK, OpenAI Agents SDK). Pivot article that consolidates Trivedy, HumanLayer, Anthropic, and Böckeler into a doctrine.
#harness engineering#agent harness#Addy Osmani
Addy Osmani (Software Engineer at Google, Cloud + Gemini)