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).
Short article (**8 pages**, 4 exhibits) from **McKinsey Technology**, authored by **Jared Moon** (senior partner, London), **Rory Walsh** and **Vito Di Leo** (partners, Dublin and Zurich), with **Adam Thelwall**, published in **May 2026**. The authors report productivity gains of **×3 to ×5** with a **60% reduction** in team size at several companies, and attribute them to redesigning the operating model, not to agent deployment alone. Four levers: (1) the **24-hour sprint**, with a night shift of agents (16h) and a human day shift (8h); (2) automating the **handoffs** between requirements, design and code, which they say account for **70% of tech spend** against 30% for deployment; (3) a **knowledge infrastructure** of domain-specific graphs, queried by an agent « bibliothécaire »; (4) resizing teams and the portfolio. On the graph: *« this should not begin with a grand, top-down ontology effort »*. The team figures are presented as illustrative, drawn from the firm's "experience and observation." Extends McKinsey's November 2025 presentation on agent-driven software delivery ([[harrison-maniar-mckinsey-reshaping-software-delivery-agents-2025-11-23]]).
#McKinsey#agentic SDLC#operating model
Jared Moon (senior partner) · Rory Walsh · Vito Di Leo (partners) · avec Adam Thelwall (associate partner) — McKinsey & Company
Televised debate on BFM Business (*Tech & Co Business* program, "The Debate" segment, 17 minutes) with **Rémi Jacquet** (CEO of Cast Software France, founder in 2023 of a think tank of about a hundred CIOs on the impact of generative AI on development, partnership with Cigref / Epita) and **Didier Girard** (CTO and CEO of **SFEIR**, a French IT services company (ESN) of about 1,000 people). Strong theses: *"writing code has become an anti-pattern"* (Girard), AI produces code of higher quality than most engineers and is *"2 to 10× more efficient"* — this is a reality, but the profession is not disappearing. The developer becomes a **conductor / agent manager / arbiter**, 14-day sprints are replaced by one-hour to half-day ***bolts***, the **Pizza Team** (8-10 people) no longer works in the agentic era, a new role is emerging — the ***product engineer*** —, the lifespan of a skill drops from **10 years to 1 year**, and **token** consumption becomes the *fuel* of value creation (NVIDIA anecdote allegedly paying bonuses in tokens, taxi driver metaphor for a driver who doesn't consume gas). SFEIR claims *"1,000 people, production capacity of 10,000"*. On the Cast side: positioning on ***harness engineering*** (deterministic vs probabilistic AI, control and guardrails), aligned with Sylvain Duranton's (BCG X) op-ed in *Les Échos* stating that *"an agent = an LLM + harnesses"*. Historical pivot: 2024 *prompt engineering* → 2025 *context engineering* → 2026 *harness engineering*. Key warning: *"the stronger AI becomes, the more we let our guard down — the more risks there are"* (Jacquet). Pivotal role of HR in the transformation, complete overhaul of the SDLC, recommendation to juniors to solidify software architecture fundamentals (*"code is the score, you need to master the symphony"*).