X post by **Andrew Ng** from **August 14, 2026** (16:29 UTC), reprising the "Dear friends" letter from ***The Batch* #366** (DeepLearning.AI, same date), ~900 words. Ng presents **The AI Engineering Skills Map** and publishes **four skills** held to be the most important. **(1) Building and deploying AI applications** — the specificity is named: *« The key difference between AI and non-AI applications is that the former has unpredictable outputs »*, hence the emphasis on *evals* and error-analysis loops. **(2) Software engineering fundamentals**, because *« Understanding software fundamentals allows you to recognize what tradeoffs even exist »* — the inexperienced developer fails *« because they don't know what context to give their coding agent »*, hence the goal of *« steering coding agents using the precise language of software engineering »*. **(3) Using coding agents**, in an operational formulation: *« help the agent autonomously close loops by providing verifiers or evals »*, and *« knowing how much to intervene and how much to leave them alone »*. **(4) *Shaping the build***: *« Given a clear spec, coding agents are rapidly improving at delivering to it. Thus, our work as engineers is shifting toward deciding what should be in the spec »*, paired with *« Engineers should no longer expect to be given a pixel-perfect design and asked only to implement it. »* A **terminology note** carries most of the framing: Ng talks about **skills** in AI engineering and **not the role** "AI Engineer", with an explicit analogy — *« All developers today should know how to work with the cloud, and only a smaller number have a "Cloud engineer" title. »* The whole is backed by *« an analysis of more than 10,000 job postings, dozens of structured interviews with experts, hiring managers, and recruiters, surveys, and other online data »*, of which **no numeric results are published**: Ng describes his process as *« informally… akin to running clustering »* and announces a detailed map in future posts. He states the interest in the second-to-last sentence: *« DeepLearning.AI's principal focus is to help developers gain these AI engineering skills. »*
#AI Engineering Skills Map#skills map#Andrew Ng
**Andrew Ng** — fondateur de **DeepLearning.AI** · general partner d'**AI Fund** · cofondateur de **Coursera** et de **Google Brain** · ancien chief scientist de Baidu. Texte signé · à la première personne · écrit *« with my team »* sans qu'aucun collaborateur soit nommé. Publié le **14 août 2026** sur X et dans ***The Batch* n°366** — même texte aux deux endroits ; préférer *The Batch* pour toute citation durable. Quatrième fiche Ng du corpus · après les lettres n°350 (24 avril) · n°352 (8 mai) et n°359 (26 juin).
SFEIR analysis (consulting-firm voice, "an engineer's reading") articulating two frameworks too often conflated: the **SDLC** (Software Development Life Cycle — *building the software correctly and reliably*) and the **PDLC** (Product Development Life Cycle — *building the right product and succeeding in the market*). Central thesis: the two cycles are not competitors but **nested** — the SDLC is the subset of the PDLC **housed under its development phase**; when a product team reaches the "build" stage, a full SDLC cycle (design → build → test → review → deployment) runs inside it. The SDLC is standardized (**ISO/IEC/IEEE 12207**, 2017 and 2026 editions), with its lineage of models (Waterfall 1970, V-model, iterative/spiral, **Agile 2001**, **DevOps/DevSecOps 2009+**) and its **DORA** metrics (throughput, stability, MTTR, change failure rate). The PDLC, being the umbrella cycle, runs from **ideation/discovery** to **market withdrawal** (not to be confused with the marketing **PLC** of Theodore Levitt, 1965, which describes a *commercial curve*, not *organized work*: "the PLC observes a curve; the PDLC organizes work"). **Tipping point**: the SDLC natively addresses **only one risk in four** — via **Marty Cagan's "Four Big Risks"** framework (Value → PM, Usability → Designer, Feasibility → Lead Engineer, Business viability → PM) — an organization excellent at SDLC but blind to PDLC produces "software nobody wants" — John Cutler's **"feature factory"** (success measured by output, not outcome). **Why AI changes everything**: generative AI **compresses the SDLC** (Google/JetBrains data, May 2026: **~85% of developers** regularly use coding agents, **~41% of new code** is AI-generated; implementation goes from weeks to hours), so the **bottleneck shifts upstream** — deciding *what* to build (Marty Cagan, April 2026: "when the cost of delivery collapses, the bottleneck shifts to discovery"). Consequences: DORA 2025 (~5,000 professionals, 90% AI adoption) shows a **positive correlation with throughput but a negative one with stability** (more unvalidated features means instability and rework); Andrew Ng (AI Startup School, July 2025) reports teams **reversing the "1 PM for 4 engineers" ratio to "2 PMs for 1 engineer"**; and with **spec-driven development**, the PDLC/SDLC boundary becomes **porous** (the product spec becomes directly executable by agents). **What a CIO should take away**: an augmented SDLC becomes a **market standard, not a differentiator** — the junction with the product must be instrumented, **executable specifications** demanded as input, technical metrics cross-referenced with outcome metrics, and the role of "feature supplier" **refused**. For a CPO: the shift of the bottleneck toward discovery is both a **promotion** (product judgment becomes scarce again) and a **notice to act** (industrialize discovery to reach parity with the SDLC). SFEIR's in-house framework ("Designing and building in the agentic era" — **11-phase cycle** + **Software Factory 10x**) is positioned as the answer on the engineering side, with the **articulation of the two cycles** as the next lever. Conclusion: "as code becomes a commodity, margin shifts toward product judgment and governance."
Op-ed by **Olivier Rafal** (Consulting Director Strategy at **WeNvision**) published on **February 23, 2024** on **CIO-Online** (*Tribune* section), advancing a thesis still counter-intuitive at the time: **generative AI is more a matter of technology product than an AI/data science project**. **Argument 1 — data science is not the core 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 — skills mismatch**: what is mainly needed is **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 need to understand the basic concepts, have back-office development skills, and strong cloud skills. »* **Argument 3 — platform architecture (orchestrators + APIs)**: building an enterprise **plateforme d'IA générative** via orchestrators and APIs makes it *« possible to work with the best LLMs on the market and switch between them as their respective capabilities evolve, without reworking the applications »* (anti vendor lock-in). **Argument 4 — from project to product**: *« The platform […] must be regarded as a product in its own right »*; instead of a one-off investment, plan for a **monthly funding stream** (continuous iteration, ongoing innovation). **Argument 5 — governance & shadow AI**: the unprecedented democratization of GenAI generates *« as much shadow AI as strong expectations toward the CIO office »* → governance to capture business needs, **prioritize products by value**, and oversee proper operation. **Paradigm shift** announced: *« the shift is from classic algorithmic programming to agents Langchain that handle part of the decisions »*. **Relevance to the watch**: a **founding text (2 years ahead)** of WeNvision's doctrine (product > project, platform/API, flow-based funding, governance, shadow AI), later extended by [[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 (FinOps/token, flow-based funding → financial governance). It also foreshadows the *harness/platform around the model* (Dropbox/Okumura: *systems around the model*) and **model independence** achieved through an orchestration layer.
#generative AI#technology product#product vs project
**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**.