Research findings and how we learn to work with AI.
11 fiches · 34 entities · Updated
What can models and agents actually do, and how do we learn to work with them? Evidence answers the first, pedagogy the second, and both are collected here. Empirical studies, benchmarks, and experiments measure capability — while the design of a benchmark often quietly decides its result, a caveat several entries return to. Beside them sit analyses of how developers learn to direct agents, which expertise stays essential, and how teaching adapts once generation is cheap. Mentoring programs and hands-on, time-boxed assessments appear alongside the studies. Understanding, not product, is the priority: each fiche records what was tested, what the numbers support, and what remains open.
Key figures
12 videos
MCP for Beginners · stated in source
16,8% of work-related messages and 43,5% of occupation-specific messages concern a task from another occupation
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).
Post and report from **OpenAI Economic Research** published on **July 27, 2026**, the first installment in the **Work at the Frontier** series, analyzing **more than 800,000 messages from US ChatGPT users**. **Coined concept**: ***task crossover*** — *« work historically associated with one occupation appearing in the AI use of people in another »*. **The headline figure is actually two figures, and that's the point coverage loses**: **16.8% of work-related messages** concern tasks associated with another occupation, and **43.5% of occupation-specific messages**. The funnel explains the gap: **61.5% of usage is generic** (writing, summarizing, planning — too widely shared to count as evidence of crossover) and is excluded; of the **remaining 38.5%**, **43.5% fall outside the occupation** and 56.5% are *« inside **or near** »* — so the upper bound is calculated on a reduced base, while the lower bound is calculated on the entire professional usage. **By occupation** (share of occupation-specific messages pointing to an external task): customer experience **77%**, design **75%**, HR **69%**, legal **56%**, marketing **53%**, sales **40%**, finance **40%**, engineering **28%** — *« a majority in five of eight groups »*. **Two distinct directions of circulation**: design **imports** (35.2%) and **exports** almost nothing (1.7%); engineering does the opposite (imports 18.5%, exports 7.4%); **marketing does both** (imports 24.3%, exports **8.9%**, the highest outward share in the sample). **Two tasks appear in the top 3 of borrowings for the other seven groups**: **financial calculation** and **technology troubleshooting**. **The heatmap, absent from coverage, is the richest object**: it gives the full distribution of tasks by user occupation, and its diagonal is striking — engineering retains **53%** of its own work while customer experience retains only **11%**, HR **10%** and design **12%**. **Size effect**: the outside-occupation share drops from **18.9%** (2-5 employees) to **16.3%** (>100 employees) — **but only « among average users »**, OpenAI specifying that *« among the heaviest users, we do not see the same monotonic pattern »*, and concluding conditionally: *« AI **may be** especially useful as a generalist tool where specialist resources are scarce. »* **Claimed status**: an **early signal**, visible *« before firms rewrite job descriptions or create new job titles »*. **Structural caveat**: OpenAI measures OpenAI's own usage, on US ChatGPT users only, and presents this position as an asset — *« our unique window into how the world of work is changing »*.
#OpenAI Economic Research#Work at the Frontier#task crossover
**OpenAI Economic Research** — équipe de recherche économique d'OpenAI ; la page crédite simplement *« OpenAI »* et la classe sous les tags *Economic Research* et *2026*. Le billet est la porte d'entrée d'un **rapport PDF** (`work-at-the-frontier-report.pdf`) et s'adosse à un cadre antérieur de la même équipe · l'**AI Jobs Transition Framework** · dont il reprend la thèse que de nombreux métiers vont **se réorganiser** plutôt que disparaître.
Educational article by the **MindStudio Team** (blog of the MindStudio platform, multi-model workflow orchestration) explaining **modèles de langage par diffusion** (*Diffusion Language Models*) through the case of **Diffusion Gemma**, Google's first **open weights** implementation (2B parameters, derived from Gemma 2). The thesis: whereas **autoregressive** models (GPT-4, Claude, standard Gemma) generate text **token by token, left to right** (causal attention, each token fixed once produced), **diffusion** models start from a **masked/noised** sequence and **refine it iteratively** (masked diffusion / *absorbing diffusion*), with **bidirectional attention**: the model can **revise any position at any step**. Consequences: high **parallelism** (a 500-token text would require 50-100 denoising steps instead of 500 sequential passes), natural **infilling** and **constrained generation** (template filling, code completion with surrounding context), and built-in **revision** capability. But at the current scale (2B), Diffusion Gemma **does not match** the large autoregressive models (GPT-4o, Gemini 1.5 Pro) on reasoning, instruction-following, and general knowledge: the gap is "closing" without being closed. The inspiration comes from image generation (Stable Diffusion, DALL-E left autoregression behind years ago); whether the same principle holds for text remains an open question. Diffusion Gemma is distributed on Hugging Face (Google DeepMind), AI Studio, and Vertex AI.
#modèles de langage par diffusion#Diffusion Gemma#Google DeepMind
Wharton study (Generative AI Labs): expert personas don't improve LLM factual accuracy - GPQA Diamond and MMLU-Pro benchmarks - SSRN
#AI prompting#personas#LLM accuracy
Savir Basil · Ina Shapiro · Dan Shapiro · Ethan Mollick · Lilach Mollick · Lennart Meincke (Generative AI Labs, The Wharton School, University of Pennsylvania)