Qodo - State of AI code quality 2025 - Hallucinations - Context - Developer confidence - Survey report
Itamar Friedman (Co-founder & CEO, Qodo)
Philosophy Eats AI: enterprise ontological core, business semantics, knowledge graph, semantic data products
Tony Seale
Organizational AI adoption, transformation of work, innovation strategy, leadership, productivity, oneusefulthing.org
Ethan Mollick
Linear - AI-first - Issue tracking - Project management - Product development - Workflow automation
Linear team
AI Workflow for generating Wardley Maps, LLM prompts capabilities, Obsidian graph, NetworkX clustering, strategic bootstrap - Video Tutorial
Auteur vidéo (Product Manager ERP/Business Intelligence)
Gemini CLI - Terms of Service - Privacy - Google - Data collection - Model training - Authentication
Google / Gemini team
Stanford HAI - AI Index - Annual report - Industry trends - Research metrics - Global AI development
Stanford Human-Centered AI Institute (HAI)
Google AI Mode - Search transformation - Personalized sites - Generative search - Generative web
Robby Stein (Google)
AEO (Answer Engine Optimization) - SEO - AI Answer Engines - Graphite
Graphite.io Team
Personal Software - AI-Customized Applications - Future of Software - Lee Robinson
Lee Robinson
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).
Kent Beck - Vibe Coding - TDD - AI-assisted development - Software craftsmanship - LinkedIn - Agile methodology
Kent Beck
LightRAG - Simple and Fast RAG - Knowledge Graphs - Dual-Level Retrieval - EMNLP2025 - GitHub
Zirui Guo · Lianghao Xia · Yanhua Yu · Tu Ao · Chao Huang (HKUDS - Hong Kong University Data Science)
Strategic Planning for AI's and AGI's Impossible Futures - One Useful Thing - Ethan Mollick
Ethan Mollick · Professeur à la Wharton School · University of Pennsylvania
NuExtract NuMind - foundation model for structured JSON extraction, compact format
Alexandre Constantin · Liam Cripwell · Etienne Bernard
OpenAI's official case study on the ChatGPT Enterprise deployment at Moderna: 750 GPTs in 2 months, 100% legal adoption, the Dose ID GPT for clinical trials, Stéphane Bancel's "100,000 employees" quote, an organizational transformation framework (mChat, Generative AI Champions, an internal forum with 2,000 participants).
OpenAI (étude de cas officielle, citations Stéphane Bancel, Brad Miller, Brice Challamel, Shannon Klinger, Kate Cronin, Meklit Workneh)
Ethan Mollick - AI adoption - Organizational change - One Useful Thing - Wharton - Academic research - Management
Ethan Mollick (Wharton School)
Sebastian Raschka - Machine Learning - Book - Educational - Deep Learning - PyTorch - Hands-on
Sebastian Raschka
Op-ed by **Olivier Rafal** (Consulting Director Strategy at **WeNvision**) published on **February 23, 2024** on **CIO-Online** (*Tribune* section), which puts forward a thesis that was still counterintuitive at the time: **generative AI is more a matter of technological product than of an AI/data science project**. **Argument 1 — data science is not the core of the issue**: building a *foundation model* from scratch requires *« several months, millions of euros, and access to enormous quantities of data »* — reserved for players with specific, monetizable datasets (e.g. **Bloomberg** and its **BloombergGPT** for finance). For nearly all companies, the right reflex is therefore not to hire data scientists. **Argument 2 — a skills mismatch**: what's mainly needed are **development and integration engineers** (back/front), **strong cloud skills**, and **DevOps**. Client quote: *« You don't necessarily need to be a data scientist, but you do need to understand the basic concepts, have back-office development skills, and strong cloud skills. »* **Argument 3 — platform architecture (orchestrators + API)**: building an enterprise **plateforme d'IA générative** via orchestrators and API makes it *« easy to work with the best LLMs on the market and switch between them as they each evolve, without reworking the applications »* (anti vendor lock-in). **Argument 4 — from project to product**: *« The platform […] must itself be considered a product »*; instead of a one-off investment, plan for a **monthly funding stream** (continuous iterations, ongoing innovation). **Argument 5 — governance & shadow AI**: the unprecedented democratization of GenAI generates *« as much shadow AI as strong expectations toward the DSI »* → governance to capture business needs, **prioritize products by value**, and oversee proper operation. **Paradigm shift** announced: *« we are moving from classic algorithmic programming to agents Langchain that handle part of the decisions »*. **Relevance for the watch**: a **founding text (2 years ahead)** of WeNvision doctrine (product > project, platform/API, flow-based funding, governance, shadow AI) that the fiches [[wenvision-ai-agents-enterprise-deployment-2025-10-01]], [[habert-ia-agentique-production-2025-10-29]] and [[rafal-wenvision-tokenomics-foundation-finops-ia-2026-06-04]] will extend (FinOps/token, flow-based funding → financial governance). It also prefigures the *harness/platform around the model* (Dropbox/Okumura: *systems around the model*) and **model independence** through an orchestration layer.
**Olivier Rafal** · *Consulting Director Strategy* chez **WeNvision** (cabinet de conseil FR). Tribune publiée dans la rubrique *Tribune* de **CIO-Online**. Auteur déjà présent dans la veille (cf. fiches WeNvision/Atlas/Tokenomics). Publié le **23 février 2024**.
Weave (workweave.dev) - Y Combinator Startup - AI-Driven Measurement of Engineering Work - Weave Hour - AI Code Attribution - YC Directory
Y Combinator