It's different from what we've had before: 𝐔𝐩𝐬𝐤𝐢𝐥𝐥𝐢𝐧𝐠 ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, 𝐞𝐧𝐚𝐛𝐥𝐞𝐦𝐞𝐧𝐭 (‘do it for me’) is different – it takes the task off your plate entirely. We’ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) – AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing – and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption | 13 comments on LinkedIn

(𝘈 𝘮𝘶𝘴𝘵-𝘳𝘦𝘢𝘥 — 𝘦𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘪𝘧 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯.) While most AI debates and studies focus on models, chips, and jobs — this one zooms in on something far more personal: 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗴𝗿𝗼𝘄 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? They surveyed 1,700+ kids, parents, and teachers across the UK — and what they found is both powerful and concerning. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 9 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝘀𝘁𝗼𝗼𝗱 𝗼𝘂𝘁 𝘁𝗼 𝗺𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ⬇️ 1. 1 𝗶𝗻 4 𝗸𝗶𝗱𝘀 (8–12 𝘆𝗿𝘀) 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 — 𝗺𝗼𝘀𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀 → ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. 𝗔𝗜 𝗶𝘀 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝗸𝗶𝗱𝘀 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 — 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 → 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 — 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 → Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. 𝗔𝗜 𝗮𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗵𝗶𝗴𝗵𝗹𝘆 𝘂𝗻𝗲𝗾𝘂𝗮𝗹 → 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. 𝗖𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗮𝗿𝗲 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜’𝘀 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 → Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. 𝗣𝗮𝗿𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 — 𝗯𝘂𝘁 𝗱𝗲𝗲𝗽𝗹𝘆 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 → 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. 𝗧𝗲𝗮𝗰𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗮𝗻𝗱 𝗹𝗼𝘃𝗶𝗻𝗴 𝗶𝘁 → 85% say GenAI boosts their productivity, 88% feel confident using it. They’re ahead of the curve. 8. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗿𝗲𝗮𝘁 → 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. 𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗯𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 → Children of color felt less seen and less motivated to use tools that didn’t reflect them. Representation matters. The next generation isn’t just using AI. They’re being shaped by it. That’s why we need a more focused, intentional approach: Teaching them not just how to use these tools — but how to question them. To navigate the benefits, the risks, and the blindspots. 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E | 174 comments on LinkedIn

I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration. | 119 comments on LinkedIn

1. Set clear learning goals  ↳ Knowing what you want to learn makes it easier. ↳ Claude helps you define your path. 2. Provide context for your knowledge  ↳ Understanding the bigger picture is key.  ↳ Claude connects new ideas to what you already know. 3. Request detailed explanations  ↳ Sometimes, you need more than a quick answer.  ↳ Claude can dive deep into complex topics. 4. Get real-world examples  ↳ Learning is better with practical applications.  ↳ Claude shows how concepts work in the real world. 5. Practice writing and receive feedback  ↳ Writing helps solidify your knowledge.  ↳ Claude gives instant feedback to improve your skills. 6. Role-play for languages or coding  ↳ Learning by doing is effective.  ↳ Claude can simulate conversations or coding scenarios. 7. Fact-check surprising claims  ↳ Misinformation is everywhere.  ↳ Claude helps you verify facts and claims. 8. Take breaks and reflect on learning  ↳ Reflection is vital for understanding.  ↳ Claude reminds you to pause and think. 9. Keep a learning journal  ↳ Tracking your progress is important.  ↳ Claude can help you log your journey. 10. Iterate and refine understanding  ↳ Learning is a process.  ↳ Claude encourages you to improve your knowledge.  | 246 comments on LinkedIn

Despite our obsession with AI's impact on "academic integrity," two recent analyses show that rather than asking AI for answers, learners are much more likely to use AI to redesign the learning experience in an attempt to learn more. Common strategies include asking AI to apply the protégé effect, using AI to apply the Pareto principle and enhancing levels of emotional metacognition within a learning experience, in the process redesigning the experience sometimes beyond recognition. The uncomfortable truth? Learners are effectively running a real-time audit of our design decisions, processes & practices—and as instructional designers, we don't come out too well. In this week's blog post, I explore what learner + AI behaviour reveals about our profession and how we might turn this into an opportunity for innovation in instrucitonal design practices and principles. Check out the full post using the link in comments. Happy innovating! Phil 👋 | 16 comments on LinkedIn

Die Pflichtlektüre zum Sonntag: Mary Meeker hat einen ihrer legendären Reports gedropped... Nach den jährlichen "Internet Trends" nun ein 340 Seiten Brett ihrer Investment Firma Bond Capital ganz zum Thema AI. Superbes Gedankenfutter hinsichtlich u.a.: 1. Nutzerwachstum und Verbreitung • ChatGPT erreichte 800 Millionen wöchentliche Nutzer in nur 17 Monaten • Verbreitung außerhalb Nordamerikas liegt bei 90 Prozent – nach nur 3 Jahren • Vergleich: Das Internet brauchte dafür 23 Jahre • KI-Anwendungen skalieren global nahezu gleichzeitig 2. Investitionen und Infrastruktur • Big Tech (Apple, Microsoft, Google, Amazon, Meta, Nvidia) investiert über 212 Milliarden Dollar CapEx pro Jahr • KI wird zur neuen Infrastruktur – vergleichbar mit Strom oder Internet • Rechenzentren werden zu produktiven "KI-Fabriken" 3. Entwickler-Ökosysteme explodieren • Google Gemini: 7 Millionen aktive Entwickler, +500 Prozent in 12 Monaten • NVIDIA-Ökosystem: 6 Millionen Entwickler, +6x in sieben Jahren • Open Source spielt zunehmend eine Schlüsselrolle, auch in China 4. Technologischer Fortschritt beschleunigt sich exponentiell • 260 Prozent Wachstum pro Jahr bei Trainingsdatenmengen • 360 Prozent Wachstum pro Jahr beim Compute-Aufwand für Modelltraining • Bessere Algorithmen führen zu 200 Prozent Effizienzsteigerung pro Jahr • Fortschritte bei Supercomputern ermöglichen +150 Prozent Leistungszuwachs jährlich 5. Monetarisierung ist real – aber teuer • OpenAI mit starkem Nutzerwachstum, aber weiterhin Milliardenverluste • Compute-Kosten steigen, Inferenzkosten pro Token sinken • Monetäre Skalierung bleibt herausfordernd und kompetitiv 6. Arbeit und Gesellschaft verändern sich sichtbar • IT-KI-Stellen in den USA: +448 Prozent seit 2018 • Nicht-KI-IT-Stellen: –9 Prozent • Erste autonome Taxis nehmen Marktanteile in Städten wie San Francisco • KI-Scribes in der Medizin reduzieren administrativen Aufwand massiv 7. Wissen und Kommunikation erleben ein neues Zeitalter • Nach Buchdruck und Internet folgt die Ära der generativen Wissensverbreitung • Generative KI verändert, wie wir Wissen erzeugen, verbreiten und nutzen • Anwendungen wie ElevenLabs oder Spotify übersetzen Stimmen in Echtzeit, global skalierbar 8. Geopolitik wird zur KI-Strategie • USA und China investieren aggressiv in souveräne KI-Modelle • Wer KI-Infrastruktur dominiert, definiert ökonomische und politische Macht neu • Führende CTOs sprechen offen von einem neuen "Space Race" 9. Chancen und Risiken sind gewaltig • KI kann medizinische Forschung, Bildung und Kreativität beflügeln • Gleichzeitig drohen Kontrollverlust, Missbrauch, Arbeitsplatzverdrängung, ethische Dilemmata Meinungen? Evangelos Papathanassiou Christian Herold Thorsten Muehl Christoph Deutschmann Constance Stein Rebecca Schalber Sandy Brueckner Dirk Hofmann Henning Tomforde Dr. Paul Elvers Katharina Neubert Laura Seiffe Ekaterina Schneider

In 2025, the AI landscape has evolved far beyond just large language models. Knowing which model to use for your specific use case — and how — is becoming a strategic advantage. Let’s break down the 8 most important model types and what they’re actually built to do: ⬇️ 1. 𝗟𝗟𝗠 – 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Your ChatGPT-style model. Handles text, predicts the next token, and powers 90% of GenAI hype. 🛠 Use case: content, code, convos. 2. 𝗟𝗖𝗠 – 𝗟𝗮𝘁𝗲𝗻𝘁 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗱𝗲𝗹 → Lightweight, diffusion-style models. Fast, quantized, and efficient — perfect for real-time or edge deployment. 🛠 Use case: image generation, optimized inference. 3. 𝗟𝗔𝗠 – 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 → Where LLM meets planning. Adds memory, task breakdown, and intent recognition. 🛠 Use case: AI agents, tool use, step-by-step execution. 4. 𝗠𝗼𝗘 – 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 → One model, many minds. Routes input to the right “expert” model slice — dynamic, scalable, efficient. 🛠 Use case: high-performance model serving at low compute cost. 5. 𝗩𝗟𝗠 – 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Multimodal beast. Combines image + text understanding via shared embeddings. 🛠 Use case: Gemini, GPT-4o, search, robotics, assistive tech. 6. 𝗦𝗟𝗠 – 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Tiny but mighty. Designed for edge use, fast inference, low latency, efficient memory. 🛠 Use case: on-device AI, chatbots, privacy-first GenAI. 7. 𝗠𝗟𝗠 – 𝗠𝗮𝘀𝗸𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → The OG foundation model. Predicts masked tokens using bidirectional context. 🛠 Use case: search, classification, embeddings, pretraining. 8. 𝗦𝗔𝗠 – 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 → Vision model for pixel-level understanding. Highlights, segments, and understands *everything* in an image. 🛠 Use case: medical imaging, AR, robotics, visual agents. Understanding these distinctions is essential for selecting the right model architecture for specific applications, enabling more effective, scalable, and contextually appropriate AI interactions. While these are some of the most prominent specialized AI models, there are many more emerging across language, vision, speech, and robotics — each optimized for specific tasks and domains. LLM, VLM, MoE, SLM, LCM → GenAI LAM, MLM, SAM → Not classic GenAI, but critical building blocks for AI agents, reasoning, and multimodal systems 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E Kudos for the graphic goes to Generative AI ! | 45 comments on LinkedIn

🎥 Adoption, Adaption, Transformation: Wie KI unsere Arbeitswelt verändert! 🤖✨🧠 In diesem Video tauchen wir in die Welt der KI-Integration ein und beleucht...

Genau das wollte ich von Claudia Pohlink wissen, die eine beeindruckende Karriere in der Daten- und KI-Welt bei Telekom, Deutsche Bahn und FIEGE hingelegt hat. Also, wie sieht der Anti-Hype Blueprint aus? 𝟭. 𝗦𝘁𝗮𝗺𝗺𝗱𝗮𝘁𝗲𝗻 𝗱𝗲𝗳𝗶𝗻𝗶𝗲𝗿𝗲𝗻 𝘂𝗻𝗱 𝘀𝘁𝗿𝘂𝗸𝘁𝘂𝗿𝗶𝗲𝗿𝗲𝗻 Starte mit der Definition deiner Kerndomänen und Stammdaten. Bestimme führende Systeme für jede Datendomäne, bevor du Tools auswählst. Dies schafft ein stabiles Fundament für alle KI-Aktivitäten. 𝟮. 𝗘𝗿𝘀𝘁𝗲 𝗘𝗿𝗳𝗼𝗹𝗴𝘀𝗴𝗲𝘀𝗰𝗵𝗶𝗰𝗵𝘁𝗲 𝘀𝗰𝗵𝗿𝗲𝗶𝗯𝗲𝗻 Identifiziere einen ersten Use Case, zum Beispiel mit dem Controlling-Bereich, wo bereits Datenaffinität besteht. Zeige schnelle Erfolge, um Management-Support zu gewinnen. 𝟯. 𝗗𝗮𝘀 𝟯-𝗛𝗮̈𝘂𝘀𝗲𝗿-𝗠𝗼𝗱𝗲𝗹𝗹 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗲𝗿𝗲𝗻 • House of Data: Grundlagen, Governance, Architektur • House of AI: Use Cases, Data Scientists, Engineers • House of 3C: Change, Communication, Community Diese 3 Bereiche sollten zu gleichen Teilen aufgebaut werden. Keiner kann ohne den anderen für nachhaltige Daten- und KI-Implementierung. Die Leads sollten zu Beginn intern aufgebaut werden, extern können operative Ressourcen zugekauft werden. 𝟰. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘇𝘄𝗶𝘀𝗰𝗵𝗲𝗻 𝘇𝗲𝗻𝘁𝗿𝗮𝗹 𝘂𝗻𝗱 𝗱𝗲𝘇𝗲𝗻𝘁𝗿𝗮𝗹 𝗳𝗶𝗻𝗱𝗲𝗻 Etabliere zentrale Standards und Koordination, befähige aber gleichzeitig dezentrale Teams durch Multiplikatoren-Ideen wie KI-Awards, Schulungen und Hackathons. Laut Claudia ist diese Balance eine der größten Herausforderungen in der Umsetzung. 𝟱. 𝗣𝗿𝗮𝗴𝗺𝗮𝘁𝗶𝘀𝗰𝗵 𝗽𝗹𝗮𝗻𝗲𝗻 𝘀𝘁𝗮𝘁𝘁 𝘁𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝘀𝗶𝗲𝗿𝗲𝗻 Erstelle 6-12-Monats-Pläne statt langfristiger Strategien. Dokumentiere Erfahrungen systematisch, auch Misserfolge, und passe deine Pläne regelmäßig an. Ich weiß, wie viele Mittelständler vor der großen Aufgabe stehen, Daten- und KI-Kompetenzen und Strukturen im Unternehmen aufzubauen. Claudia's Erfahrungen sind eine echte Schatzkiste. Ganz ohne Buzzwords, Hype oder Selbstprofilierung. Claudia, 1000 Dank für deine Offenheit und dass du uns an deinen Erfahrungen teilhaben lässt! Was sagt ihr zum Blueprint? | 22 Kommentare auf LinkedIn

Mit seinem AI Mode und dem Agenten Mariner zieht Google eine Plattformschicht über das offene Web. Google transformiert sich von einer klassischen Suchmaschine zum zentralen Marktplatz, Assistenten und Zahlungsdienstleister. Nutzer können künftig Produkte direkt in der Google-Suche finden, vergleichen, kaufen und bezahlen – ohne die Plattform zu verlassen. Diese Entwicklung hat weitreichende Folgen für das gesamte Internet-Ökosystem. Die Auswirkungen treffen nicht nur klassische Online-Händler, sondern auch Marktplatzgiganten wie Amazon, Verlage, Übersetzungsdienste wie DeepL, Reservierungsanbieter wie OpenTable, Buchungsseiten wie Ticketmaster oder Sprachschulen wie Duolingo. Wer weiterhin sichtbar und relevant bleiben will, muss sich auf die neuen Spielregeln einstellen, in KI-Overviews und Shopping-Graphen präsent sein und seine Inhalte für KI-Systeme optimieren. Denn OpenAI baut etwas Ähnliches auf und auch Amazon bewegt sich in diese Richtung. Der Wettstreit der Plattformen ist damit endgültig im KI-Zeitalter angekommen. Weiterlesen auf F.A.Z. PRO Digitalwirtschaft (€) ▶︎ https://lnkd.in/e-r8k7up€ Frankfurter Allgemeine Zeitung

In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition. He begins by acknowledging familiar concerns: that GPS erodes our spatial memory, search engines inflate our sense of knowledge, and tools like ChatGPT might diminish creativity or encourage intellectual laziness. These fears, Clark observes, mirror ancient worries, like Plato’s warning that writing would weaken memory, and stem from a deeply ingrained but flawed assumption: the idea that the mind is confined to the biological brain. Clark challenges this perspective with his extended mind thesis, arguing that humans have always been cognitive hybrids, seamlessly integrating external tools into our thinking processes. From the gestures we use to offload mental effort to the scribbled notes that help us untangle complex problems, our cognition has never been limited to what happens inside our skulls. This perspective transforms the debate about AI from a zero-sum game, where technology is seen as replacing human abilities, into a discussion about how we distribute cognitive labour across a network of biological and technological resources. Recent advances in neuroscience lend weight to this view. Theories like predictive processing suggest that the brain is fundamentally geared toward minimising uncertainty by engaging with the world around it. Whether probing a river’s depth with a stick or querying ChatGPT to clarify an idea, the brain doesn’t distinguish between internal and external problem-solving—it simply seeks the most efficient path to resolution. This fluid interplay between mind and tool has shaped human history, from the invention of stone tools to the design of modern cities, each innovation redistributing cognitive tasks and expanding what we can achieve. Generative AI, in Clark’s view, is the latest chapter in this story. While critics warn that it might stifle originality or turn us into passive curators of machine-generated content, evidence suggests a more nuanced reality. The key, Clark argues, lies in how we integrate these technologies into our cognitive ecosystems. https://lnkd.in/gUmxE57w | 41 comments on LinkedIn

Watch CEO Satya Nadella unveil all the biggest product moves, including Copilot and Azure updates, developer tools, and more, from Seattle.0:00 Intro0:14 Bui...

𝗔𝘁 𝗜/𝗢 2025, Google 𝘀𝗵𝗼𝘄𝗲𝗱 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗥𝗘𝗔𝗟𝗟𝗬 𝗺𝗲𝗮𝗻𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱: ⬇️ The company's flagship developer event Google I/O 2025 was held last night in Mountain View, California. 𝗧𝗟𝗗𝗥: Google is turning Gemini into the AI operating system for everything — with agents now embedded across Search, Chrome, Workspace, Android, and more. If you don’t have time for the full event, here’s a curated 𝘀𝘂𝗽𝗲𝗿𝗰𝘂𝘁 of the highlights that really matter. 𝗞𝗲𝘆 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗜/𝗢 𝟮𝟬𝟮𝟱: 0:00 𝗜𝗻𝘁𝗿𝗼 – AI-native from the ground up 0:11 𝗚𝗲𝗺𝗶𝗻𝗶 𝗽𝗹𝗮𝘆𝘀 𝗮 𝗣𝗼𝗸𝗲𝗺𝗼𝗻 𝗴𝗮𝗺𝗲 — memory, reasoning, and code 0:30 𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝗲𝗮𝗺 – Real-time 3D video chat with AI 1:08 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗲𝗲𝘁 – Speech-to-speech translation, live 1:27 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗿𝗶𝗻𝗲𝗿 – AI agents that book, plan, filter, decide 2:07 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Gemini gets memory and task awareness 2:40 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟱 𝗣𝗿𝗼 + 𝗙𝗹𝗮𝘀𝗵 – New SOTA models, LMArena leader 4:57 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗔𝘀𝘁𝗿𝗮 – Multimodal, fast-response agent that sees and hears 5:32 𝗔𝗜 𝗠𝗼𝗱𝗲 – Overlay for restaurants, bookings, prices, events 7:10 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 – Track, compare, and auto-buy with Google Pay 8:34 𝗚𝗲𝗺𝗶𝗻𝗶 𝗟𝗶𝘃𝗲 – Screen sharing + live AI guidance 8:59 𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 – Upload files, get insights 9:12 𝗖𝗮𝗻𝘃𝗮𝘀 – Live, collaborative AI whiteboard 9:31 𝗚𝗲𝗺𝗶𝗻𝗶 𝗶𝗻 𝗖𝗵𝗿𝗼𝗺𝗲 – AI understands and acts on any webpage 9:51 𝗜𝗺𝗮𝗴𝗲𝗻 𝟰 – Next-gen image generation 10:23 𝗩𝗲𝗼 𝟯 – Ultra-realistic video model 11:01 𝗟𝘆𝗿𝗶𝗮 𝟮 – AI-powered music composition 11:56 𝗙𝗹𝗼𝘄𝘀 – Multimodal, promptable AI video creation 12:39 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗫𝗥 – AI-first spatial computing 12:57 𝗦𝗮𝗺𝘀𝘂𝗻𝗴 𝗠𝗼𝗼𝗵𝗮𝗻 – Google’s XR headset revealed 13:16 𝗟𝗶𝘃𝗲 𝗴𝗹𝗮𝘀𝘀𝗲𝘀 𝗱𝗲𝗺𝗼 – Gemini + XR = real-time AI overlay Super insightful and forward-looking: Google’s AI strategy just went full stack. Even if some of these projects don’t make it past the prototype stage, the direction is obvious: AI is being integrated into everything. LLMs — Gemini, in this case — are rapidly becoming the new operating system and everything will be powered by AI Agents across all products. Full keynote: https://lnkd.in/dPFFtyZ9 Supercut: https://lnkd.in/d-eBNGjw Enjoy watching! | 61 comments on LinkedIn

One of the most interesting things about the original research is that it provides a clear metric for measuring AI performance improvement that is not tied to benchmarks that keep on being superceded, needing new benchmarks. We can now rank AI models and agents by their half-life - the time for human tasks for which they achieve 50% success rate. Of course we are usually more interested in models that can achieve 99+% success rates - depending on the task - but the relative consistency of the half life decay means the T50 threshold predicts whatever success rate we aim for, both today, and at future dates if the original trend holds Generally the decay is due to cumulative errors or going off course. But the decay is not always consistent, as there can be subtasks of uneven difficulty, or agents can recover from early mistakes. Interestingly, humans don't follow pure exponential decay curves. Our success rate falls off more slowly over very long tasks, suggesting we have broader context, allowing us to recover from early mistakes. The research was applied to tasks in research or software engineering. The dynamics of this performance evolution may or may not apply to other domains. Certainly, this reframing of assessing the development of AI capabilities and its comparison to human work is a very useful advance to the benchmarking approach.

Here’s what I take away: 🚫 HR is no longer just about people. 🚫 IT is no longer just about systems. ✅ The real value lies in how people and systems interact—seamlessly, intelligently, adaptively. Let’s be honest, most organizations still operate in silos: - HR builds talent and culture - IT builds systems and infrastructure But the future of work is all about integration. What if you make that happen? Think about it: Can you redesign work itself? Not roles. Not org charts. But the actual FLOW of work. Because that’s what Moderna’s doing. They are reimagining how humans and machines co-create value. IBM is doing the same. They use HR AI agents that handle questions, routes issues, and manage HR processes. This isn’t about cutting costs. It’s about building a business that adapts faster to the next disruption. They are building resilience. I recognize that HR and IT both have unique complexities, and in many companies are simply too far apart or too large merge shortly. Still, it still got me thinking. As an HR leader: -> How comfortable are you with data, automation, and AI? -> Could you confidently lead both people strategy and digital infrastructure? -> What would need to change for that answer to be yes? This isn’t a tech conversation. It’s an organization and leadership revolution. The next era of HR won’t be like today's HR at all. It will be integrated, tech-savvy, and central to how business gets done. Time to level up. Are you ready? #futureofwork #hrtech #ai Picture and story credits: Isabelle Bousquette 🙏 | 34 comments on LinkedIn

switching to non-augmented tasks. It is definitely interesting research, but I am very cautious about the conclusions reached by the authors, partly since they are to a degree contradictory, and also not necessarily generalizable. The authors implicitly criticize AI for removing the “most cognitively demanding parts” of work, implying that this reduces fulfillment. But the outputs and productivity are clearly improved. Are they suggesting workers create inferior output for the sake of engagement? It is worth noting that other recent research points to improved emotion and engaement with genAI collaboration. The emotional impact of genAI collaboration will vary substantially across use cases, especially with the nature of the task, and certainly with the cultural context. It appears the use case here was performance reviews, which is not representative of many other types of cognitive work. The authors also say that AI-assisted tasks reduce users’ sense of control, thus lowering motivation. But they say this sense of control is restored during subsequent solo tasks, even though those are when boredom and disengagement rise. Having said that, for some tasks and work design the issues they raise could be real and substantial. These are the sound remedies they suggest: ➡️Blend AI and Human Contributions: Use gen AI as a foundation for tasks while encouraging humans to personalize, expand, and refine outputs to retain creativity and ownership. ➡️Design Engaging Solo Tasks: Follow AI-supported work with autonomous, creative tasks to help employees stay motivated and exercise their own skills. ➡️Make AI Collaboration Transparent: Clearly communicate AI’s supporting role to preserve employees’ sense of control and fulfillment in their contributions. ➡️Rotate Between Tasks: Alternate between independent and AI-assisted tasks to maintain engagement and productivity throughout the workday. ➡️Train Employees to Use AI Mindfully: Provide training that helps employees critically and strategically integrate AI, strengthening their autonomy and judgment.

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There is a lot of talk about how L&D needs to be proactive, not reactive. But how do we do that when technology is moving so fast? It starts with having a point of view on where the world of work is headed, and then building a bridge to that future. Because if we only make incremental changes from where we are now, we'll likely be playing catch-up for a long time—and risk preparing people for the work of today, not tomorrow. Here are some of the forces I think about a lot these days: 🎓 AI seems to be denting the supply of entry level jobs. What does that mean for the talent pipeline later down the line? And how should we onboard the graduates that *do* get employed so they can add value on top of AI? 📈 AI gets lower performers closer to higher performers (HBS & BCG study), and individuals working with AI match the performance of *teams* without AI (HBS & P&G study). How do we evaluate, recognise and enhance expertise in such a world? 🏁 Vibe coding/marketing/learning/something else, single founder unicorns, service-as-a-software (not software-as-a-service!) and zero latency economy are just some of the predictions that would affect both the nature and pace of work. What support would our people and organisations need to adapt? L&D isn't short on AI tools. What we need is a vision—to imagine how AI will reshape performance, learning, and the world of work at large. And, ultimately, what L&D needs to 𝘣𝘦𝘤𝘰𝘮𝘦 to have a role in it. Nodes #AI #HR #Learning #Talent #FutureOfWork | 12 comments on LinkedIn

In my work with boards and exec teams one of the pointed questions is always the degree of focus in AI initiatives. Johnson & Johnson's divergent-convergent strategy is highly instructive. Some commentators have suggested that this means the use case proliferation was a mistake. J&J's CIO doesn't see it like that. "You had to take an iterative approach to say, ‘Where are these technologies useful and where are they not?’... We had the right plan three years ago, but we matured our plan based on three years of understanding,” Leaders cannot know in advance where the value will emerge. The challenge is to select the right scope of experimenation before selecting focus use cases. Another shift was from centralized AI by a board governance to function-specific ownership such as commercial, R&D, and supply chain, enabling better prioritization and faster iteration. Again, these models suit different phases of the AI adoption journey. Most organizations are far earlier than J&J, which has strong maturity. On metrics: "The company is tracking progress in three buckets: first, the ability to successfully deploy and implement use cases; second, how widely they are adopted; and third, the extent to which they deliver on business outcomes." I strongly suspect that they are not using a "win rate" on their use case success. There are similarities to VC portfolios, where a few big wins make all the investments worthwhile. | 12 comments on LinkedIn

3.000 KI-Assistenten integriert in alle Teams. Das ist die KI-Reise von Moderna: “It’s hard to convey—within the hype—how much AI is changing things and how much Moderna is using it across the board” Dieses Zitat von Wade Davis, Modernas Head of Digital for Business, zeigt sehr schön wie schwer der allumfassende Wandel von KI zu beschreiben ist. Es sind eben nicht 2 - 3 Use Cases ein ein paar Bereichen. Viel mehr geht es um eine Veränderung der Denk- und Arbeitsweise. Während viele Unternehmen noch zögern, hat Moderna bereits konkrete Schritte unternommen, um KI strategisch zu implementieren: 1. Zusammenlegung von HR und IT unter einer Führung 2. Systematische Analyse aller Arbeitsprozesse 3. Klare Entscheidung: Was macht Mensch & Maschine? 4. Entwicklung von 3.000 spezialisierten KI-Assistenten 5. Integration dieser Assistenten in komplexe Workflows Der taktische Ansatz dahinter ist bemerkenswert: ↳ Nicht einzelne KI-Projekte, sondern eine umfassende Transformation ↳ Keine isolierten Tools, sondern vernetzte Systeme ↳ Kein Fokus auf Stellenabbau, sondern auf Neugestaltung der Arbeit KI-Integration ist keine einmalige Initiative, sondern ein fortlaufender Prozess der Organisationsentwicklung. Moderna zeigt, dass der Erfolg nicht von einzelnen Tools abhängt, sondern von der strategischen Neugestaltung der Arbeit selbst. Genau das ist der Weg, den es zu gehen gilt. | 12 Kommentare auf LinkedIn

(I didn't even know that was a thing! 🙃 ) Not able to make attend? Here is everything that was covered from theory to application is now available for free... ➡️ Day 1: Foundational Models & Prompt Engineering https://lnkd.in/d-_w3gXj ➡️ Day 2: Embeddings & Vector Stores / Databases https://lnkd.in/dkmfDUcp ➡️ Day 3: Generative AI Agents https://lnkd.in/dd3Zd2-F ➡️ Day 4: Domain-Specific LLMs https://lnkd.in/d6Z39yqt ➡️ Day 5: MLOps for Generative AI https://lnkd.in/dcXCTPVF And, be sure to check out the winners of the course's capstone project: building tools from Generative AI (classroom assistants, schedulers, mock interviewers and more.) https://lnkd.in/dPsXnrct Interested in putting all of those newly-developed AI skills to use? Here are some of the latest job openings here at Google: http://google.com/careers. Hope to see you around! 😊 #google #lifeatgoogle #training #ai #education | 21 comments on LinkedIn

I was interviewed in today's The Wall Street Journal on the impact of AI agents on customer behavior - here's how I believe our lives are about to change: ( ⬇️ From the article by the great Steve Rosenbush ⬇️ ) There is a flywheel effect at work here. The AI agent has access to an enormous amount of data about users that makes it possible to tailor recommendations, information, and insights to their needs. And once they reside in a messaging app, they can create a continuing presence in the user’s life, just like a person would. “Once an AI knows you and remembers your history, it stops feeling like a tool and starts to feel like a companion,” says Conor Grennan, chief AI architect at New York University Stern School of Business. “It starts to blur the line between an AI brand ambassador and just a friend who shares your taste.”" ⬆️ End of quote ⬆️ . The wild part of all this to me is that agents are coming to WhatsApp, where we hang out. It shows us a ton about Meta's strategy: My thoughts: WhatsApp already hosts most of our everyday conversations, so when a brand drops in an AI agent that greets me like the barista who knows my order, it doesn’t feel like marketing—it feels like service. What’s new is the compounding effect: every helpful, context-aware response deposits a little ‘trust capital’ in the relationship bank. Those micro-interactions can become a moat for a brand by helping establish lasting customer loyalty. So: Where do you see this all going? +++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset would love to help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website. | 56 comments on LinkedIn

Eine empirische Studie zeigt: Der wirtschaftliche Nutzen der generativen KI wird von Führungskräften mit praktischer Erfahrung deutlich positiver bewertet als von solchen ohne. Während 64 Prozent der Erfahrenen von einer schnellen Amortisation ausgehen, glauben dies nur 35 Prozent der Unerfahrenen. Die Wirtschaftlichkeit hängt stark vom Betriebsmodell, der Nutzungstiefe und den unternehmensspezifischen Bedingungen ab. Wer GenAI gezielt einsetzt, steigert Produktivität, Innovationskraft und Arbeitgeberattraktivität – ein realer betriebswirtschaftlicher Vorteil, schreibt Peter Buxmann in seinem Gastbeitrag für F.A.Z. PRO Digitalwirtschaft. 𝗪𝗲𝗶𝘁𝗲𝗿𝗹𝗲𝘀𝗲𝗻: ▶︎ https://lnkd.in/e3faARTd Der Text stammt aus unserem Digitalwirtschaft-Newsletter zur digitalen Ökonomie. Der Newsletter wird jeden Mittwoch um 8 Uhr an 230.000 Abonnenten versendet und erklärt die relevanten Digitalthemen der Woche, aufgeteilt auf die Themenbereiche Künstliche Intelligenz, Zukunft der Arbeit, Digitale Transformation, Plattformen und Digitale Mobilität. Interessenten können den Newsletter zwei Monate 𝗸𝗼𝘀𝘁𝗲𝗻𝗹𝗼𝘀 testen. ▶️ https://lnkd.in/eY_4zwbr Frankfurter Allgemeine Zeitung | 13 Kommentare auf LinkedIn

“𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯. 𝗛𝗲𝗰𝗸, 𝗶𝘁’𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗺𝗶𝗻𝗲 𝘁𝗼𝗼!" That’s not clickbait. That’s the CEO of Fiverr (Micha Kaufman) in his latest e-mail every employee received yesterday! And he’s not the first writing that: first Shopify, then Duolingo, now Fiverr. Top tech CEOs are one by one speaking out loud. BUT Fiverr did something different: They didn’t just warn their teams — they gave them a blueprint to survive. 𝗧𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲 𝘁𝗵𝗲 7 𝗔𝗜 𝗦𝘂𝗿𝘃𝗶𝘃𝗮𝗹 𝗧𝗶𝗽𝘀 𝗵𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝘀 𝗳𝗼𝗿 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝘀𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀: ⬇️ 1. 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁. ➜ Test every AI tool you can get your hands on. See which ones make you 10x faster. If you’re in coding? Use Cursor. Law? Lexis+. Learn what makes you dangerous. 2. 𝗙𝗶𝗻𝗱 𝘀𝗺𝗮𝗿𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝗰𝗹𝗼𝘀𝗲. ➜ Surround yourself with folks who already get AI. Ask questions. Watch what tools they use. Shortcut your learning curve. 3. 𝗨𝘀𝗲 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. ➜ If you're still working like it's 2024, you're doing it wrong (!!!). Speed and efficiency are the new currency. Cut the fluff, automate the rest. 4. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. ➜ Google is basic now. LLM'S are the new baseline. The better your prompts, the more powerful you become. Learn it like it’s your second language. 5. 𝗛𝗲𝗹𝗽 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗱𝗼 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝘀𝘀. ➜ Don’t just do your job — rethink how the whole org works. Automate stuff. Suggest improvements. Be the person who makes things smoother and smarter. 6. 𝗧𝗵𝗶𝗻𝗸 𝗹𝗶𝗸𝗲 𝗮𝗻 𝗼𝘄𝗻𝗲𝗿. Know what the company’s really trying to achieve. Don’t wait to be asked. Show up with ideas. Pitch improvements. You don’t need a permission slip to contribute. 7. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀. No one's coming to save you. If you want to grow, build your own path. Take initiative, start small, stay consistent. Those who help themselves get help too. 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝗿𝗲'𝘀 𝘀𝗼𝗺𝗲 𝘁𝗿𝘂𝘁𝗵 𝘁𝗼 𝘁𝗵𝗶𝘀. 𝗕𝘂𝘁 𝘄𝗲 𝗼𝗳𝘁𝗲𝗻 𝘁𝗲𝗻𝗱 𝘁𝗼 𝗲𝗶𝘁𝗵𝗲𝗿 𝗼𝘃𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗧𝗵𝗮𝘁 𝘀𝗮𝗶𝗱, 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲: 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗰𝗼𝗺𝗶𝗻𝗴 — 𝗶𝘁’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗿𝗲. 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝘀 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗻𝗼𝗿𝗺𝗮𝗹. 𝗦𝘁𝗶𝗹𝗹, 𝘁𝗵𝗲 𝘁𝗶𝗽𝘀 𝗮𝗯𝗼𝘃𝗲 𝗮𝗿𝗲 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲. 𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝗽𝗲𝗿𝗸. 𝗬𝗼𝘂’𝗿𝗲 𝗲𝗶𝘁𝗵𝗲𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 — 𝗼𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗽𝗮𝘀𝘀 𝘆𝗼𝘂 𝗯𝘆. You can read the full e-mail attached! ⬇️ | 152 comments on LinkedIn

*NEW PAPER*: GenAI investments will only pay off if employees adopt the technology and learn to use it effectively. Feelings of psychological threats are common and they are going to be a major obstacle. GenAI deployment therefore needs to be accompanied by a careful talent management strategy. In our new Trends in Cognitive Sciences article, we review the psychological threats that GenAI deployment can trigger in workers, focusing on three areas: competence, autonomy, and relatedness. Moreover, we sketch different types of reactions that such feelings of threats can trigger. The figure below summarizes five coping strategies (both adaptive and maladaptive) for the three types of psychological threat. Link to the article in comment (open access for 50 days). With Erik Hermann and Carey Morewedge The Wharton School Wharton AI & Analytics Initiative Wharton Executive Education | 24 comments on LinkedIn

New Research: AI vs. Human Coaches - Building Effective Working Relationships This study explores a fascinating question: Can AI coaches of the future build effective working relationships with clients comparable to human coaches? Surprisingly, the answer is yes. Part of my dissertation research study at Teachers College, Columbia University was recently published in an Advancing Coaching Scholarship special issue alongside other prominent scholars. With AI increasingly entering human-centered spaces like coaching, this research offers early insight into its impact. Through a randomized controlled experiment, I found that people could establish strong connections with both simulated autonomous AI and human coaches in just a single hour-long session. The data showed comparable relationship quality metrics across both conditions, with individuals specifically valuing the collaborative, goal-oriented conversation regardless of coach type. Read the full study here to explore what this means for the future of coaching. #AICoaching https://lnkd.in/g4W7i8dx | 31 comments on LinkedIn

it's also reducing the wage premium of a college degree. This is why the #Superworker strategy is so urgent. https://lnkd.in/g8V9aHxN “Law firms lean on AI for paralegal work as consulting firms find that five 22-year-olds with ChatGPT can do the work of 20 recent grads." "Tech firms are turning over their software programming to a handful of superstars working with AI co-pilots." "The share of jobs posted on Indeed in software programming has declined by more than 50 percent since 2022." "And even if employers aren’t directly substituting AI for human workers, spending on AI infrastructure is crowding out spending on new hires.” | 172 comments on LinkedIn

𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗕𝗜𝗚 — 𝗮𝗻𝗱 𝗯𝗮𝗿𝗲𝗹𝘆 𝗮𝗻𝘆𝗼𝗻𝗲 𝗶𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁! Yesterday, I spent a few hours diving into the newly launched "𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆". And it's an absolute goldmine of FREE AI education, packed with tutorials, live workshops, labs and real-world case studies. Whether you're just starting or already building with GPTs — there’s definitely something here for you. And it’s all 100% FREE and beginner-friendly tracks (no code needed). Here is some stuff to have an eye on: 𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – Introduction to ChatGPT: https://lnkd.in/e4dgUbWj – AI in Action: Uses for Work, Learning & Life: https://lnkd.in/efXpXY_9 𝗔𝗿𝗰𝗵𝗶𝘃𝗲𝗱 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – ChatGPT 101: A Guide to Your Super Assistant: https://lnkd.in/e6RJMcEC – ChatGPT 102: Using AI to Do Your Best Work: https://lnkd.in/eF4iQfFz – Advanced Prompt Engineering: https://lnkd.in/eb9JGYkY 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗮𝘁 𝗪𝗼𝗿𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – ChatGPT Search: https://lnkd.in/e8fRSkPT – ChatGPT for Data Analysis: https://lnkd.in/ezssYnGk – Introduction to GPTs: https://lnkd.in/eiUCDF9u 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗼𝗻 𝗖𝗮𝗺𝗽𝘂𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – AI for Academic Success: https://lnkd.in/e9hPwRsF – AI for Career Prep: Resumes & Interviews: https://lnkd.in/ezK62jzQ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗕𝘂𝗶𝗹𝗱 𝗛𝗼𝘂𝗿𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – Fine-Tuning: https://lnkd.in/e2iqWD7J – Assistants & Agents: https://lnkd.in/em6FBu2Q Link to the academy: https://lnkd.in/d8GK4sC4 Definitely very interesting to see that OpenAI is now also building their own learning ecosystem. ENJOY! | 58 comments on LinkedIn