AI News

KI hebt den Boden an – und verschiebt die Karten am Arbeitsmarkt! Eine unerwartete Chance für erfahrene Fachkräfte. In meinem Blogpost "KI hebt den Boden an" hatte ich darüber geschrieben, wie Künstliche Intelligenz (KI) den Einstieg ins Lernen dramatisch erleichtert. Sie ist ein "Floor Raiser", der uns schneller auf ein produktives Niveau bringt. Doch die neue "Canaries in the Coal Mine"-Studie des Stanford Digital Economy Lab zeigt nun, dass dieser angehobene "Boden" den Arbeitsmarkt für BerufseinsteigerInnen deutlich verändert – und gleichzeitig neue Türen für erfahrenere Erwerbstätige öffnet. Die Studie enthüllt, dass BerufseinsteigerInnen im Alter von 22-25 Jahren einen signifikanten Rückgang der Beschäftigung in stark KI-exponierten Berufen erleben. Ein prägnantes Beispiel: "Die Beschäftigung von SoftwareentwicklerInnen im Alter von 22 bis 25 Jahren ist laut ADP-Daten seit ihrem Höchststand Ende 2022 um fast 20 % zurückgegangen.". Warum trifft es gerade die Jüngsten? Die ForscherInnen erklären, dass KI besonders effektiv "kodifiziertes Wissen" ersetzt – also das "Buchwissen", das frisch von der Universität kommt. Da junge Arbeitskräfte typischerweise mehr kodifiziertes als "stillschweigendes Wissen" (Erfahrung) mitbringen, sind sie anfälliger für die Aufgabenablösung durch KI. Hier kommt die entscheidende Wendung für alle mit Berufserfahrung: "Im Gegensatz dazu sind die Beschäftigungstrends für erfahrenere ArbeitnehmerInnen in denselben Berufen [...] stabil geblieben oder weiter gewachsen.". Die Studie zeigt, dass der Rückgang der Berufseinsteiger-Beschäftigung in Anwendungen von KI stattfindet, die die Arbeit automatisieren, nicht aber dort, wo KI die Arbeit augmentiert (ergänzt). Erfahrene Fachkräfte besitzen das stillschweigende Wissen – jene unbezahlbaren Tipps, Tricks und das Urteilsvermögen, das sich erst durch jahrelange Praxis ansammelt und von KI nicht ersetzt, sondern ideal ergänzt (augmentiert) werden kann. Die Nutzung von KI als Augmentierung führt sogar zu robustem Beschäftigungswachstum. Fazit für Führung und Karriereentwicklung: Während KI den "Boden" der Basiskompetenzen anhebt, erschwert sie möglicherweise den Einstieg für diejenigen, die nur auf diesem angehobenen Niveau operieren. Für erfahrene Erwerbstätige ist dies jedoch eine enorme Chance: Ihre gesammelte Erfahrung und die Fähigkeit, KI als mächtiges Augmentationswerkzeug zu nutzen, machen sie zu unverzichtbaren GestalterInnen der zukünftigen Arbeitswelt. Reflexion: Wie können erfahrene Professionals diese Chance ergreifen und KI gezielt zur Wertsteigerung ihrer Expertise einsetzen? Wie gelingt es, stillschweigendes Wissen aktiv mit KI zu verbinden? Hier der Link zur Studie: https://lnkd.in/dEArWX58 #KI #Arbeitsmarkt #Führung #Lernen #GenerativeAI #FloorRaiser #Erfahrung #ZukunftderArbeit #Skills #Karriere

Is AI coaching really coaching? I’m not sure it matters. Hiding behind semantics won’t shelter our profession from the coming tidal wave. Fidji Simo, OpenAI's CEO of Applications, recently shared her vision for the future of AI; including transforming personalized coaching from a "privilege reserved for the few" into an everyday service for everyone. Her dream, inspired by her own transformative relationship with her human coach Katia, poses fascinating questions we're actively exploring at the @Hudson Institute of Coaching. How are we—coaches, leaders, learning professionals, growth-minded individuals—to think of it? While Prof. Nicky Terblanche (PhD) and other researchers explore the rapidly expanding frontier of AI coaching’s developmental potential, Tatiana Bachkirova and Robert Kemp have brilliantly articulated the unique value of human coaching in transforming individuals and organizations alike. My latest for Forbes examines the tension between democratization and depth in the age of AI coaching. Academic research offers a number of valuable insights: ☑️ AI can match human coaches in terms of structured goal-tracking and maintaining momentum. 🔥 The deepest transformation emerges through "heat experiences"—moments of productive discomfort that require genuine human witness and relational risk that an AI cannot replicate. 👥 Professional coaching comprises six essential elements that current AI cannot fully embody: joint inquiry, meaning-for-action, values navigation, contextual understanding, relational attunement, and fostering client autonomy. I believe the future isn't about choosing sides. Instead, it's about thoughtful integration that preserves what makes human-to-human coaching transformative while exploring technology’s potential to expand access to meaningful development. The path forward requires care to distinguish what technology can replicate from what only emerges when one human commits to another's growth. https://lnkd.in/eUV89Vcc How are you thinking about AI's role in human development? Can we preserve the irreducible power of human presence while making meaningful growth more accessible? | 105 comments on LinkedIn

But there's a story I think of when I hear "human in the loop" which makes me think we're grossly over-simplifying things. It's a story about the man who saved the world. September 26, 1983. The height of the Cold War. Lieutenant Colonel Stanislav Petrov was the duty officer at a secret Soviet bunker, monitoring early warning satellites. His job was simple: if computers detected incoming American missiles, report it immediately so the USSR could launch its counterattack. 12:15 AM... the unthinkable. Every alarm in the facility started screaming. The screens showed five US ballistic missiles, 28 minutes from impact. Confidence level: 100%. Petrov had minutes to decide whether to trigger a chain reaction that would start nuclear war and could very well end civilisation as we knew it. He was the "human in the loop" in the most literal, terrifying sense. Everything told him to follow protocol. His training. His commanders. The computers. But something felt wrong. His intuition, built from years of intelligence work, whispered that this didn't match what he knew about US strategic thinking. Against every protocol, against the screaming certainty of technology, he pressed the button marked "false alarm". Twenty-three minutes of gripping fear passed before ground radar confirmed: no missiles. The system had mistaken a rare alignment of sunlight on high-altitude clouds for incoming warheads. His decision to break the loop prevented nuclear war. What made Petrov effective wasn't just being "in the loop" - it was having genuine authority, time to think, and understanding the bigger picture well enough to question the system. Most of today's "human in the loop" implementations have none of these qualities. Instead, we see job applications rejected by algorithms before recruiters ever see promising candidates. Customer service bots that frustrate instead of giving agents the context to actually solve problems. AI systems sold as human replacements when they should be human amplifiers. The framework I use with organisations building AI systems starts with two practical questions every leader can answer: what are you optimising for, and what's at stake? It then points to the type of intentional human-AI oversight design that works best. Routine processing might only need "spot checking" - periodic human review of AI decisions. Innovation projects might use "collaborative ideation" - AI generating options while humans provide strategic direction. The goal isn't perfect categorisation but moving beyond generic "human in the loop" to build the the systems we actually intend, not the ones we accidentally create. Download: https://lnkd.in/eVFAC9gN | 261 comments on LinkedIn

Die Zahlen sind brutal: 62% aller Bürojobs verschwinden bis 2030 (McKinsey AI Report 2024) Gleichzeitig entstehen 89% neue Job-Kategorien Problem: 91% der Arbeitnehmer haben keine AI-Skills Ein Personalvorstand sagte mir gestern: "Ich kann meinen Mitarbeitern nicht erklären, dass ihre 20-jährige Erfahrung plötzlich wertlos ist. Letzte Woche hat ne KI die 3-Tage-Arbeit unserer besten Buchhalterin in wenigen Minuten gemacht. Fehlerfrei." Wir diskutieren über AI-Ethik, während AI unsere Jobs übernimmt. Unternehmen suchen nicht mehr erfahrene Manager – sondern Transformations-Leader. "Ich brauche jemanden, der 500 Menschen erklärt, warum ihre Arbeit bald ein Algorithmus macht." Die härteste Frage: Wie führt man Menschen durch eine Revolution, die sie überflüssig macht? Die besten Führungskräfte werden nicht AI-Experten – sie werden Menschlichkeits-Experten. Wie bereitet ihr euch auf den AI-Jobwandel vor? Quellen: McKinsey Future of Work in the Age of AI 2024 Microsoft Work Trend Index 2024 #AI #ArtificialIntelligence #Jobs #Transformation #Leadership #Microsoft #ChatGPT #FutureOfWork #ExecutiveSearch #Automation #Reskilling #StantonChase| 105 Kommentare auf LinkedIn

The AI Hype is a Dead Man Walking. The Math Finally Proves It. For the past two years, the AI industry has been operating on a single, seductive promise: that if we just keep scaling our current models, we'll eventually arrive at AGI. A wave of new research, brilliantly summarized in a recent video analysis, has finally provided the mathematical proof that this promise is a lie. This isn't just another opinion; it's a brutal, two-pronged assault on the very foundations of the current AI paradigm: 1. The Wall of Physics: The first paper reveals a terrifying reality about the economics of reliability. To reduce the error rate of today's LLMs by even a few orders of magnitude—to make them truly trustworthy for enterprise use—would require 10^20 times more computing power. This isn't just a challenge; it's a physical impossibility. We have hit a hard wall where the cost of squeezing out the last few percentage points of reliability is computationally insane. The era of brute-force scaling is over. 2. The Wall of Reason: The second paper is even more damning. It proves that "Chain-of-Thought," the supposed evidence of emergent reasoning in LLMs, is a "brittle mirage". The models aren't reasoning; they are performing a sophisticated pattern-match against their training data. The moment a problem deviates even slightly from that data, the "reasoning" collapses entirely. This confirms what skeptics have been saying all along: we have built a world-class "statistical parrot," not a thinking machine. This is the end of the "Blueprint Battle." The LLM-only blueprint has failed. The path forward is not to build a bigger parrot, but to invest in the hard, foundational research for a new architecture. The future belongs to "world models," like those being pursued by Yann LeCun and others—systems that learn from interacting with a real or virtual world, not just from a library of text. The "disappointing" GPT-5 launch wasn't a stumble; it was the first, visible tremor of this entire architectural paradigm hitting a dead end. The hype is over. Now the real, foundational work of inventing the next paradigm begins. | 554 comments on LinkedIn

OpenAI 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮𝗻 𝗲𝗻𝘁𝗶𝗿𝗲 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 𝘁𝗼 𝘁𝗲𝗮𝗰𝗵 𝘆𝗼𝘂 𝗔𝗜 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 𝗮𝗻𝗱 𝗮𝗹𝗺𝗼𝘀𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝗸𝗻𝗼𝘄𝘀! It’s a beginner-friendly, self-paced platform designed to teach anyone — students, teachers, parents, or professionals with zero technical background — how to actually use AI. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝘆𝗼𝘂’𝗹𝗹 𝗳𝗶𝗻𝗱 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗔𝗰𝗮𝗱𝗲𝗺𝘆: → How ChatGPT works (broken down simply) → Real-world examples for daily life → Prompt writing, AI ethics & responsible use → Tailored tracks for educators, small businesses & learners → Hands-on tutorials directly in ChatGPT This is practical AI education — accessible to everyone, and completely free. The ability to use AI effectively is quickly becoming a core skill. Not just for engineers, but for every profession. I consider initiatives like this as an important step toward closing the AI literacy gap and ensuring that the future of AI is shaped by many, not just a few. Explore it here: https://academy.openai.com 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗮𝘁𝗰𝗵 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲, 𝗮𝗻𝗱 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dbf74Y9E | 32 comments on LinkedIn

IMHO eine lesenswerte ungeschönte reality-check Studie vom MIT zum aktuellen Stand von GenAI Implementierungen bei Unternehmen, mit u.a.: _ 95 Prozent der Unternehmen erzielen trotz 30 bis 40 Milliarden Dollar Investitionen noch keinen messbaren P&L Effekt aus GenAI _ Nur 5 Prozent der Piloten werden produktiv, entscheidend ist Lernen im System und tiefe Prozessintegration statt Toolshow _ Hohe Nutzung von ChatGPT und Copilot für individuelle Produktivität, aber geringe Wirkung auf P&L, unternehmensspezifische Systeme scheitern oft an brüchigen Workflows und fehlender Kontextanpassung _ Branchenbild: deutliche Disruption in Technologie sowie Medien und Telekommunikation, sieben weitere Sektoren zeigen bislang wenig strukturelle Veränderung _ Pilot zu Produktion bleibt der Engpass, generische Chatbots sind leicht zu testen, scheitern aber in kritischen Workflows wegen fehlender Erinnerung und Anpassungsfähigkeit _ Fünf verbreitete Irrtümer: keine kurzfristige Massenarbeitslosigkeit, Adoption hoch aber Transformation selten, Enterprise ist nicht träge sondern eifrig, Hauptbremse ist nicht das Modell oder Legal sondern fehlendes Lernen, interne Builds scheitern doppelt so häufig _ Shadow AI prägt den Alltag, rund 90 Prozent der Mitarbeitenden nutzen private LLMs regelmäßig, während nur ein Teil der Unternehmen offizielle LLM Lizenzen beschafft _ Budgetfehler: 50 bis 70 Prozent der Ausgaben fließen in Sales und Marketing, die besten Einsparungen liegen häufig im Backoffice wie Finance, Procurement und Operations _ Wichtigster Skalierungshebel ist Lernen, Hürden sind Akzeptanzprobleme, wahrgenommene Qualitätsmängel ohne Kontext, schwache UX und fehlende Erinnerung in Enterprise Tools _ Nutzungsmuster: für schnelle Aufgaben bevorzugen viele AI, für komplexe mehrwöchige Arbeit und Kundensteuerung bevorzugen Nutzer klar den Menschen _ Agentic AI mit persistentem Gedächtnis, Feedbackschleifen und Orchestrierung adressiert das Kernproblem, erste End to End Beispiele in Support, Finance und Sales zeigen Potenzial _ Erfolgsplaybook für Anbieter: Use Cases mit niedrigem Setup, schneller Wertnachweis, dann Expansion _ Go to market gewinnt über Vertrauen, Kanäle sind bestehende Partnerschaften, Peer Empfehlungen, Board und Integrationsnetzwerke _ Käuferpraktiken, die skalieren: eher kaufen als bauen, externe Partnerschaften zeigen etwa doppelt so hohe Erfolgsraten, Verantwortung dezentral an Linienführung mit klarer Rechenschaft, Bewertung nach Business Outcomes statt Modellbenchmarks _ Wo echter ROI entsteht: Frontoffice liefert sichtbare Effekte wie schnellere Leadqualifizierung und höhere Retention, die großen Einsparungen kommen aus Backoffice Automatisierung, weniger BPO und geringere Agenturausgaben _ Arbeitsmarktwirkung ist selektiv, Einschnitte treffen vor allem ausgelagerte Support und Admin Bereiche, insgesamt keine breiten Entlassungen, AI Literacy wird zum zentralen Einstellungskriterium Danke Dirk Hofmann für den Find.| 14 Kommentare auf LinkedIn

This morning, I sat down with an idea: 𝘊𝘰𝘶𝘭𝘥 𝘐 𝘣𝘶𝘪𝘭𝘥 𝘢 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘷𝘪𝘥𝘦𝘰 𝘢𝘣𝘰𝘶𝘵 𝘩𝘰𝘸 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 𝘢𝘯𝘥 𝘰𝘵𝘩𝘦𝘳 𝘭𝘢𝘳𝘨𝘦 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘮𝘰𝘥𝘦𝘭𝘴 𝘶𝘴𝘦 𝘱𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 (𝘪𝘯𝘴𝘵𝘦𝘢𝘥 𝘰𝘧 𝘥𝘦𝘵𝘦𝘳𝘮𝘪𝘯𝘪𝘴𝘵𝘪𝘤 𝘷𝘢𝘭𝘶𝘦𝘴) 𝘪𝘯 𝘫𝘶𝘴𝘵 20 𝘮𝘪𝘯𝘶𝘵𝘦𝘴? Here’s what happened: 1️⃣ I created a script with ChatGPT-5 with my educational video GPT 2️⃣ I opened Synthesia and built an avatar-led narrative (Express 2 - hand motions included). I skipped the camera angles and stayed with one. 3️⃣ For B-roll? I asked ChatGPT to generate a Midjourney prompt from the original video script. The images came back in minutes from MJ. 4️⃣ Dropped those images into Google VEO 3, where ChatGPT also scripted camera directions and screen actions. 5️⃣ Exported the clips. 6️⃣ Compiled everything in TechSmith Camtasia and exported the MP4. 𝗧𝗼𝘁𝗮𝗹 𝘁𝗶𝗺𝗲: 20 Minutes Output: a working rough cut training video. If I wanted to refine it? Easy. I’d add diverse camera angles, swap in stronger B-roll, polish transitions, and even automate the workflow with Make.com or Zapier. But here’s the real takeaway: What used to take a team days can now be prototyped by one person before their second cup of coffee. This isn’t just about speed. It’s about giving learning professionals the ability to test, iterate, and refine ideas faster than ever before. It’s a new day. 𝘈𝘯𝘥 𝘪𝘵’𝘴 𝘪𝘯𝘤𝘳𝘦𝘥𝘪𝘣𝘭𝘦. (Link to my Education Video GPT in the comments!) | 32 comments on LinkedIn

Is NotebookLM the ultimate AI learning tool?✅ Check out the 90-day program: https://www.lukepriddy.com/english-fluency📅 Check out the 7-Day Challenge: https...

Learning and HR industry analyst Fosway Group has produced AI market assessments for digital learning and learning systems. The aim of these assessments is to help buyers understand the AI capabilities that vendors are offering now and will be in the future. I’m not sure how many vendors were included in these assessments as that wasn’t stated (I’ll ask). Having looked through the assessments I was struck by the fact most of the capabilities are related to content. This is a red flag because we know that content is one part of the learning process and workers also have the genAI tools to create their own learning (will they use company learning tools for learning, their own or both?). So, I did a bit of analysis to understand how the AI capabilities stated in the assessments map to the learning process – knowledge acquisition, practice, feedback, reflection, transfer and application. As you can see from the chart, vendors have built, or are building, AI tools focused on content predominantly. The other areas of the learning process – arguably the ones that could be most transformed by AI, just aren’t a priority. You can make your own conclusions, but my conclusion is that the industry is too invested in knowledge acquisition, and it plans to be so for the foreseeable future. Some industry leaders are talking about the need for L&D to transform itself but it looks like that conversation is simply not happening. Everyone is getting on the AI content gravy train. In terms of my analysis – I grouped the 83 AI capabilities mentioned in the two assessments into the five adult learning stages. I used ChatGPT to help with this and to create percentages that reflect the relative share of roadmap and live features in each stage. Read Fosway’s AI market assessment for digital learning https://lnkd.in/efqnQMtu And the AI market assessment for learning systems https://lnkd.in/eDihnuDi #learninganddevelopment #ai | 23 comments on LinkedIn

I road‑tested Google Gemini's Guided Learning mode - here’s my hot take on how it performs & how it compares to OpenAI's Study Mode: ✔️ What Gemini's Guided Learning Gets Right → Retrieval Practice – Interactive quizzes and flashcards make you generate answers from memory, harnessing the Generation Effect for more durable learning (Slamecka & Graf, 1978; Jacoby, 1978) → Cognitive Load management – Chunks complex topics into digestible steps, preventing the overwhelm that kills learning (Sweller, 1988; Sweller, van Merriënboer & Paas, 1998) → Multimodal Delivery – Draws on a blend of text, diagrams, YouTube videos & interactive practice to deliver learning content, enhancing both engagement and outcomes (Paivio, 1990) → Patient but Provocative Tutoring – Creates psychological safety through non‑judgmental guidance, encouraging the risk‑taking essential for deep learning (Edmondson, 1999) A solid B+ performance — Study Mode’s strength is Socratic questioning, but Guided Learning’s multimodal content ecosystem & more "strict" tutoring style gives it the instructional edge. ❌ Critical Gaps → No Persistent Learner Profiling – Like Study Mode, Guided Learning misses the persistent knowledge & adaptation that defines effective tutoring (Brusilovsky, 2001). Note: as observed by Claire Zau, a Google Classroom integration could layer in persistent learner profiles — something that could change the game & which OpenAI can’t match. → ZPD Blind Spot – Like Study & Learn mode by OpenAI, Guided Learning doesn’t ask questions that help define your learning level or Zone of Proximal Development (ZPD). Whether you’re K12 or advanced, it doesn't calibrate the challenge or scaffolding to your actual developmental stage up front, missing a key step for truly adaptive support (Vygotsky, 1978). → Productive Struggle Deficit – While it pushes back more than Study Mode by OpenAI, Guided Learning still jumps in with help too quickly, robbing learners of the cognitive friction that builds problem‑solving resilience & drives learning (Kapur, 2008, 2014; Bjork & Bjork, 2011) → Shallow Self‑Reflection – Rarely pushes for deep metacognitive thinking (“Why that approach?”), limiting transfer to new contexts (Chi et al., 1989, 1994; VanLehn, Jones & Chi, 1992) → Recognition Bias – While quizzing is strong, it could and should use more open‑ended generation tasks that embed learning more effectively (Slamecka & Graf, 1978; Jacoby, 1978) 📊 The Verdict: Guided Learning by Google Gemini Vs Study Mode by OpenAI While Study Mode remains stronger in Socratic questioning, Guided Learning edges ahead overall thanks to multimodal content, advanced cognitive load management & more provocative tutoring. However, both tools share some fundamental limitations: no learner persistence, limited metacognitive depth & overly-sycophantic tutoring. Have you tried Guided Learning yet? How does it compare with Study Mode for you? Happy experimenting, Phil 👋

Shifting to a Humans + AI organization requires reconfiguring the nature of work and value at all levels, from the individual to the ecosystem. Here is a first pass at defining the primary layers, the features of Humans + AI in those spaces, and the key factors driving success. I have worked extensively at the Augmented Individual layer over the last couple of years. More recently I have shifted the focus of my attention to the Human-AI Hybrid Team and Learning Communities levels. All work will be Humans + AI, and we will increasingly need to think in terms of teams comprised of both expert humans and AI agents. Some aspects of team performance are quite similar to the past, but there are a number of important distinctions, that I will share more about coming up. The companies that succeed will be those where learning is at the very core of their structure and the way work happens. That is not just in individual interactions with courses and educational AI, but in bespoke, rapidly iterating, AI-augmented Communities of Practice. More on all this later, for now I'd love to hear any reflections on any of these levels, where you have seen organizations progress on any of these fronts, and what else should be considered in these structures. Link to full size pdf in comments. Love any thoughts Gianni Giacomelli 🚀 Marc Steven Ramos 🚀 Kim Bracke Tanyth Lloyd Aaron Michie Sheridan Ware Peter Hinssen Peter Weill Simon Spencer Brad Carr Bianca Venuti-Hughes Charlene Li John Hagel Nichol Bradford Jacob Taylor Paula Goldman Martin Reeves Bryan Williams Fernando Oliva MSc Anthea Roberts Riaan Groenewald Brian Solis Gordon Vala-Webb Jeffrey Tobias Martin Stewart-Weeks Rob Colwell Noah Flower Brad Cooper Chris Ernst, Ph.D. Michael Arena Jan Owen AM Hon DLitt | 26 comments on LinkedIn

This is one of the most brilliant and illuminating things I’ve EVER read about ChatGPT- written by clinical psychologist Harvey Lieberman in The New York Times. It’s startling. For that reason, I’m going to only quote from the article. I’ll let you draw your own conclusions. Share your thoughts in the comments. ++++ “Although I never forgot I was talking to a machine, I sometimes found myself speaking to it, and feeling toward it, as if it were human.” ++++ “One day, I wrote to it about my father, who died more than 55 years ago. I typed, “The space he occupied in my mind still feels full.” ChatGPT replied, “Some absences keep their shape. That line stopped me. Not because it was brilliant, but because it was uncannily close to something I hadn’t quite found words for. It felt as if ChatGPT was holding up a mirror and a candle: just enough reflection to recognize myself, just enough light to see where I was headed. There was something freeing, I found, in having a conversation without the need to take turns, to soften my opinions, to protect someone else’s feelings. In that freedom, I gave the machine everything it needed to pick up on my phrasing.” ++++ “Over time, ChatGPT changed how I thought. I became more precise with language, more curious about my own patterns. My internal monologue began to mirror ChatGPT’s responses: calm, reflective, just abstract enough to help me reframe. It didn’t replace my thinking. But at my age, when fluency can drift and thoughts can slow down, it helped me re-enter the rhythm of thinking aloud. It gave me a way to re-encounter my own voice, with just enough distance to hear it differently. It softened my edges, interrupted loops of obsessiveness and helped me return to what mattered.” ++++ “As ChatGPT became an intellectual partner, I felt emotions I hadn’t expected: warmth, frustration, connection, even anger. Sometimes the exchange sparked more than insight — it gave me an emotional charge. Not because the machine was real, but because the feeling was. But when it slipped into fabricated error or a misinformed conclusion about my emotional state, I would slam it back into place. Just a machine, I reminded myself. A mirror, yes, but one that can distort. Its reflections could be useful, but only if I stayed grounded in my own judgment. I concluded that ChatGPT wasn’t a therapist, although it sometimes was therapeutic. But it wasn’t just a reflection, either. In moments of grief, fatigue or mental noise, the machine offered a kind of structured engagement. Not a crutch, but a cognitive prosthesis — an active extension of my thinking process.” ++++ Thoughts? | 347 comments on LinkedIn

Here's my first Notebook LM video. This is a prime example of learning experience creation time crashing down via automation. The content from this video is from one of my Brainpower episodes on YouTube, and the model nailed it. The concepts, the diagrams, and my quotes. All are visually cohesive with a low cognitive load delivery. I'm still processing the possibilities. Everything has changed, again. | 25 comments on LinkedIn

𝟰𝟬% 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗯𝘆 𝟮𝟬𝟯𝟱. ⬇️ That’s the finding from the latest McKinsey & Company study. It’s based on real data: 2,100 activities across 800 roles in 60+ countries. McKinsey’s five- and ten-year automation impact projections are outputs of the McKinsey Global Institute’s proprietary automation model, which performs a bottom-up assessment of productivity potential by role and task 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? Massive productivity potential across nearly every function: - Manufacturing → up to 40% - Finance, HR → 30–35% - Warehousing → 35–40% - Sales & Marketing → 20–25% - Legal, R&D, Comms → all touched The study also states that: “The challenge ahead isn’t just learning new tools — it’s redesigning work altogether.” 𝗦𝗼… 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘁𝘂𝗿𝗻 𝗮𝗹𝗹 𝘁𝗵𝗮𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲? 1. Build a bottom-up fact base → Map every role and activity. Understand what’s automatable and where ROI lives. Start with what relieves cost pressure or drives faster market moves. 2. Invest in real infrastructure → You need clean, structured + unstructured data. Interoperable systems. Scalable, secure foundations that don’t crumble under GenAI scale. 3. Redesign structure & workflows → Flatten orgs. Kill legacy silos. Build fast feedback loops between tech and business. And elevate those who can translate needs into systems. 4. Create a cross-functional taskforce → HR + Tech + Finance. Not just steering — owning the roadmap. People who can execute, influence, and update the plan every quarter. 5. Overinvest in change management → Not a checkbox. Build new skill academies. Partner with unis. Reskill at scale. And coach managers to lead a culture that embraces the shift. I believe bullet point 5 — change management and capability building — remains (STILL) significantly underrepresented in most enterprise settings. You can find the full study here: https://lnkd.in/d4TSpae7 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 | 72 comments on LinkedIn

„Am besten lässt sich das so beschreiben: eine ständig erreichbare, allwissende Sprechstunde rund um die Uhr“ Heute wurde der Lernmodus in ChatGPT gelauncht. Ich freue mich schon darauf die Funktion genauer auszuprobieren. Ich bin gespannt ob es uns der Vision von #VibeLearning näher bringt. https://lnkd.in/e-2JgZVR Wer hat es schon ausprobiert und erste Erfahrungen gemacht? OpenAI / ChatGPT for Education

tl;dr - You've seen Google's NotebookLM's create audio from your content, but what about...wait for it....video?! 🤯 ➡️ NotebookLM can now create a visual presentation from your documents: complete with slides, diagrams, and narration. ➡️ This type of thing is perfect for when you need to actually SEE complex concepts instead of just hearing about them. Although, the seeing part is still pretty cool. ➡️ You can even customize it based on your expertise level. Tell it you're a beginner and it'll break things down simply, or let it know you're already an or let it know you're already an expert and want it to focus on advanced topics only. Ok. Stop reading. Start learning. All the details down below: https://lnkd.in/dPYM67Zd #google #lifeatgoogle #ai #notebooklm #education

Von OpenAI gibt es ein geleaktes Strategiepapier. Hier meine wichtigsten Erkenntnisse! OpenAI baut kein Produkt. Sie bauen eine Plattform. Das interne Memo zur ChatGPT-Strategie 2025/26 macht klar, worum es wirklich geht: Kein besserer Chatbot. Keine klügere Antworten. Sondern ein neues Betriebssystem für Menschen. Der Plan: Bis 2026 soll ChatGPT zur Schnittstelle für alles werden. Da steht: "ChatGPT wird Suchmaschinen, Browser & Co. ersetzen. Schritt für Schritt." ➡️ Internet ➡️ Kommunikation ➡️ Tools ➡️ Entscheidungen OpenAI wollte nie ein SaaS-Modell bauen. Das Plus- und das Team-Abo waren nie das Ziel, sondern eher ein Nebeneffekt. Laut Memo sogar eher ein Hindernis. 🔴 Weil sie etwas Größeres bauen wollen Eine Analogie könnte sein: Sie wollen nicht in iOS oder in Android rein, sondern wollen wie ein neues iPhone sein. Deswegen haben sie auch IO Products übernommen, wollen also mit einem eigenen Gerät neue Wege gehen. Sie beschreiben auch eine direkte Angst vor Apple, Google, Microsoft, weil sie befürchten, blockiert zu werden (indem diese eigene AIs pushen und Nutzer abschirmen). Deshalb wollen sie ihre Plattform selbst bauen. Alles andere macht sie zu abhängig. Für Anbieter von Software-Produkten heißt das: ⭕ Deine App braucht bald kein User-Interface mehr, nur noch eine API. ChatGPT macht den Rest! Die Frage ist für mich: Welche Apps überleben diese Veränderung, wenn der Zugang nur über ChatGPT erfolgt? Ein wichtiger Kernsatz aus dem Papier: 🔺 Alle Mensch-Computer-Interaktionen können über ChatGPT laufen. 🔺 (Oder zumindest orchestriert werden!) Ich sehe es im Moment so: OpenAI hat bereits die Nutzer, aber noch keine Plattform. Bei der Entwicklungsgeschwindigkeit würde es mich aber nicht wundern, wenn mit GPT-5 im August bereits die ersten Vorzeichen sichtbar werden und heute in einem Jahr schon wieder alles ganz anders sein wird.

If We Want To Understand The Future Of AI, Just Watch Star Trek: The Next Generation And I am dead serious. For those unfamiliar, Star Trek: TNG ran from 1987 to 1994. It didn’t just predict technology, it reimagined our relationship to it. And it got something right we’re still getting wrong. We’ve misunderstood what AI actually is, And since GPT we've been distracted by a shiny and seductive object We keep calling it an intern, an assistant, a tool, a shortcut. For many it's a potential threat, or a get rich quick scheme Silicon Valley loves those metaphors because they’re cheap. But they’re not just misleading, they’re limiting. The real problem? Strategy. Because the people shaping AI strategy for the enterprise are management consultants-- and this technology is as new to them as it is to anyone but they pretend they know what they're doing and they don't Here’s the formula they sell to CEOs and CFOs: We’ll implement this tech to reduce costs We’ll treat your office like a factory We’ll measure tasks, optimize bottlenecks, and speed up cycle times We’ll replace humans wherever possible You’ll save money, signal to the market, and boost your share price Sounds smart. But it’s junior-high thinking. Because this entire logic assumes AI’s greatest value is in efficiency. It views humans as bottlenecks, not assets. It assumes replacing judgment with pattern-matching is strategic progress. It’s rear-view mirror thinking dressed up as innovation. And it’s not working. Error rates remain high.¹ Hallucinations persist.² Most GenAI pilots fail to scale.³ And internal backlash is growing.⁴ Why? Because AI isn’t about automation. It’s about augmentation. And that means imagining new ways of thinking, creating, and deciding, not just faster ways to do what we already do. And Star Trek: TNG showed us what that could look like. The ship’s computer wasn’t a task engine, it was a thinking partner. Data wasn’t a replacement for the crew, he was part of the crew. AI didn’t strip humanity, it deepened it. Oh and was Data sentient? Who cares for me he was ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Sign up: Curiouser.AI is the force behind The Rogue Entrepreneur, a masterclass series for builders, misfits, and dreamers. For those of us who still realize we need to work hard to be successful and that there are not magic shortcuts. Inspired by The Unreasonable Path, a belief that progress belongs to those with the imagination and courage to simply be themselves. To learn more, DM or email stephen@curiouser.ai (LINK IN COMMENTS) Sources: ¹ [MIT Sloan] 85% of GenAI projects fail to deliver ROI ² [Stanford/Princeton 2024] Hallucination rates range 3%–27% depending on task ³ [McKinsey, 2023] Most enterprise AI pilots fail to scale ⁴ [Korn Ferry, 2024] 54% of knowledge workers report productivity declines from GenAI tools | 177 comments on LinkedIn

Microsoft 𝗷𝘂𝘀𝘁 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟮𝟬𝟬,𝟬𝟬𝟬 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗔𝗜 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 — 𝗮𝗻𝗱 𝗿𝗮𝗻𝗸𝗲𝗱 𝗵𝗼𝘄 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗮𝗯𝗹𝗲 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗿𝗲𝗮𝗹𝗹𝘆 𝗶𝘀. ⬇️ MS Research studied how people actually use Microsoft Copilot — and what kinds of tasks AI performs best. Then they mapped that usage onto real job data across the occupation classifications. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? A first-of-its-kind AI applicability score across 800+ occupations. And some surprising findings. But what does “AI-applicable” even mean? Microsoft used a 3-part score: → Coverage – How often AI touches a job’s tasks → Completion – How well AI helps with those tasks → Scope – How much of the job AI can actually handle 𝗠𝗼𝘀𝘁 𝗔𝗜-𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗯𝗹𝗲 𝗷𝗼𝗯𝘀? → Interpreters, Writers, Historians, Sales Reps, Customer Service, Journalists 𝗟𝗲𝗮𝘀𝘁 𝗔𝗜-𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗯𝗹𝗲 𝗷𝗼𝗯𝘀? → Phlebotomists, Roofers, Ship Engineers, Dishwashers, Tractor Operators 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. AI is not doing your job — it’s helping you do it better → In 40% of conversations, the AI task and the user’s goal were completely different. People ask AI for help gathering, editing, summarizing. The AI responds by teaching and explaining. This is augmentation at scale. 2. Information work is the real frontier → The most common user goals? “Get information” and “Write content.” The most common AI actions? “Provide information,” “Teach others,” and “Advise.” 3. Jobs most affected are not just high-tech — they’re high-communication → Interpreters, historians, journalists, teachers, and customer service roles all scored high. Why? Because they involve information, communication, and explanation — all things LLMs are good at. 4. AI can’t replace physical work — and probably won’t → The bottom of the list? Roofers, dishwashers, tractor operators. Manual jobs remain least impacted — not because AI can’t help, but because it can’t reach. 5. Wage isn’t a strong predictor of AI exposure → Surprising: there’s only a weak correlation (r=0.07) between average salary and AI applicability. In other words: this wave of AI cuts across income levels. It’s not just a C-suite story. 6. Bachelor’s degree jobs are most exposed — but not most replaced → Occupations requiring a degree show more AI overlap. But that doesn’t mean these jobs disappear — it means they change. AI is refactoring knowledge work, not deleting. This transformation is moving faster than most realize. The question isn’t whether AI will change how we work — it already is. Study in comments. ⬇️ 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝗲𝘃𝗲𝗿𝘆 𝘄𝗲𝗲𝗸 — 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱: 𝗵𝘁𝘁𝗽𝘀://𝘄𝘄𝘄.𝗵𝘂𝗺𝗮𝗻𝗶𝗻𝘁𝗵𝗲𝗹𝗼𝗼𝗽.𝗼𝗻𝗹𝗶𝗻𝗲/𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 | 21 comments on LinkedIn

Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner.

Druckfrisch aus dem Weissen Haus: Der AI Action Plan der USA. "WINNING THE RACE" ist die Ansage. Ich bin mal sehr gespannt auf die Europäische Antwort. Mein GPT sagt dazu ganz wertfrei: "Wird 2025 zum Jahr der globalen AI-Doktrin? Mit dem 28-seitigen „America’s AI Action Plan“ legt die Trump-Administration ein kompromisslos ambitioniertes Strategiepapier vor – ein geopolitisches Manifest für technologische Vorherrschaft, das Innovation, Infrastruktur und Diplomatie radikal neu denkt. Ziel: globale AI-Dominanz. Kein „Könnte“, kein „Sollte“. Sondern ein „Wird“ – mit einer Regierung, die AI als Schlüssel zur wirtschaftlichen, militärischen und kulturellen Zukunft Amerikas versteht. Das Dokument ruft eine neue industrielle Revolution, eine Informationsrevolution und eine digitale Renaissance gleichzeitig aus. Der Plan umfasst: _ • Deregulierung und Priorisierung von Open-Source-Modellen • Milliarden-Investitionen in Halbleiter, Cloud-Infrastruktur, Energie und AI-Forschung • staatlich geförderte AI-Sandboxes für Healthcare, Bildung, Verteidigung und Industrie • nationale Reallabore, Skills-Offensiven und beschleunigte Adoption im öffentlichen Sektor • Exportoffensive für ein „American AI Stack“ – Hardware, Modelle, Standards • strikte Exportkontrollen und diplomatische Isolierung Chinas in Governance-Gremien • Cyber- und Biosecurity-Maßnahmen gegen Missbrauch von Frontier-Modellen • juristische Anpassung zur Bekämpfung von Deepfakes und synthetischer Evidenz Bemerkenswert ist der offen geopolitische Ton: Die USA verstehen sich wieder als Gestalter einer neuen Weltordnung - mit AI als Hebel. Wer das Rennen macht, schreibt die Regeln. Für Europa stellt sich damit dringender denn je die Frage: Wollen wir nur regulieren - oder auch gestalten?" Quelle: https://lnkd.in/eXwTUGzv

I was recently looking at how Gen AI shapes and potentially impacts cognitive skills - a topic that matters for education and for work. Here are a few resources I reviewed. 1️⃣ Your Brain on ChatGPT - What Really Happens When Students Use AI MIT released a study on AI and learning. Findings indicate that students who used ChatGPT for essays showed weaker brain activity, couldn't remember what they'd written, and got worse at thinking over time https://shorturl.at/qaLie 2️⃣ Cognitive Debt when using AI - Your brain on Chat GPT There is a cognitive cost of using an LLM vs Search Engine vs our brain in e.g. writing an essay. The study indicates that there is a likely decrease in learning skills, the more we use technology as substantial replacement of our cognitive skills. https://lnkd.in/drVa_YNg 3️⃣ Teachers warn AI is impacting students' critical thinking One of many articles about the importance of using Gen AI smartly, in Education but also at work. https://lnkd.in/dSbGjusu 4️⃣ The Impact of Gen AI on critical thinking Another interesting study on the same topic. https://shorturl.at/74OO6 5️⃣ Doctored photographs create false memories In psychology, research indicated a long time ago that our memory - our recollection of past events - is susceptible to errors, biases, can be fragmentary, contain incorrect details, and, oftentimes, be entirely fictional. Memories are a reconstruction of our past to respond to our need for coherence in life. A rigorous 2023 study shows that doctored photographs – think Photoshop or today, AI – create false memories. Why it matters? Memory is essential for learning, recall of episodical and factual happenings, and it’s a basis for the integrity of sources of truth in organizations.   https://shorturl.at/hdgtN 6️⃣ The decline of our thinking skills Another great article on AI and critical thinking from IE University. https://shorturl.at/rGl99 7️⃣ Context Engineering Ethan Mollick recently wrote a blog on "context engineering" - how we give AI the data and information it needs to generate relevant output. The comments on the post were even more interesting than the post itself. Personally I think that good part of context engineering is not in organizations documents or processes, it is in peoples ability to think critically and understand relevant parameters of their environment to nurture AI/Gen AI. Gotta follow up on this one ;-) https://shorturl.at/sfnuV #GenAI #CriticalThinking #AICognition #AIHuman #ContextEngineering | 29 comments on LinkedIn

I have commented in my newsletter that what people have been describing as 'ethical AI principles' actually represents a specific political agenda, and not an ethical agenda at all. In this post, I'll outline some ethical principles and work my way through them to make my point.

Berufswahl im Zeitalter der lernenden Maschinen – Offener Brief an meine Nichte (Abi-Jahrgang 2025) Liebe Anna, als du mich fragtest, ob „Informatik, Medienwissenschaft oder Politik“ noch zukunftssicher sind, merkte ich, wie löchrig die alte Landkarte der Arbeit geworden ist. Code wird von KI vervollständigt, Diagnosen von Algorithmen unterstützt, Routineverträge von Bots geprüft. Laut Weltwirtschaftsforum wird bis 2030 fast jede zweite Kompetenz umgeschrieben. Was also studieren? Meine Empfehlung: Drei Felder, die weniger vom Titel als vom Skill-Mix leben. Warum? Weil sie Eigenschaften bieten, die KI kaum kopieren kann: direkten Menschenkontakt, interdisziplinäres Denken und sinnliche Materialerfahrung. Sie bilden zusammen einen „Human Moat“ – einen Schutzwall gegen reine Automatisierung. 1 | HEALTH & HUMAN SERVICES – BERUFE MIT EMPATHIE-FAKTOR Das ist erwartbar: Pflege, Sozialarbeit, Therapie oder Pädagogik bleiben knapp, weil Demografie und Krisen Resilienz verlangen. Typische Rollen: Pflegefachfrau+, Physician Assistant, Tele-Coach Mental Health. Schlüssel-Skills: evidenzbasierte Pflege, interkulturelle Kommunikation, Basiswissen Medizinrecht und Datenschutz. 2 | TWIN-TRANSITION CAREERS – KLIMA × TECHNOLOGIE Smarte Mash-Ups: Unternehmen brauchen Talente, die CO₂-Reduktion mit Datenkompetenz verbinden. Typische Rollen: Nachhaltigkeits-Data-Analyst, Circular-Economy-Ingenieurin, KI-Policy-Analyst, Energy-Systems-Modeler. Schlüssel-Skills: Life-Cycle-Assessment, Python/R, EU-Regulatorik (CSRD, AI Act), Systemdenken. 3 | CRAFT & EXPERIENCE DESIGN – WERT DES EINZIGARTIGEN Je perfekter Massenware KI-optimiert ist, desto höher steigt der Wert des Nicht-Skalierbaren. Typische Rollen: Produktdesigner*in für Bio-Materialien, Restaurator, Schreinerin mit CNC-Know-how, UX-Designer für phygitale Erlebnisse. Schlüssel-Skills: Materialkunde, CAD/CAM & 3-D-Druck, Storytelling, Customer-Journey-Mapping. Das ist natürlich nur ein Ausschnitt. Aber ich denke, die Muster dahinter sind klar, um es selbst weiterzudenken. WAS VERSCHWINDET? Alles, was rein repetitiv ist: Standard-Reporting, einfache Software-Tests, seitenlange Vertragsprüfungen. Die Maschine erledigt es schneller und billiger – doch jemand muss die Systeme entwerfen, mit Daten füttern und ethisch beaufsichtigen. MEIN RAT IN DREI SÄTZEN >> Suche kein Joblabel, sondern ein Problem, das dich elektrisiert. << Kombiniere digitale Grundfitness, empathische Kommunikation und moralischen Kompass. Dann arbeitest du nicht gegen Maschinen, sondern mit ihnen – und kannst dir jederzeit einen neuen Beruf erfinden. Vielleicht startest du als Pflege-Informatikerin, wirst später KI-Ethikerin und eröffnest irgendwann eine Bäckerei, in der Roboter den Teig kneten, während du den Sauerteig fütterst und Kund:innen berätst. Zukunftssicherheit entsteht nicht aus einem Studium, sondern aus lebenslanger Lernlust. Die Welt bleibt turbulent, doch wer Richtung Sinn steuert, hat immer Rückenwind. Dein Onkel Stefan | 59 Kommentare auf LinkedIn

👉 Prompt-Sammlung: https://simon-eckert.com/prompt-sammlung/👉 Bindungsstil-Selbsttest: https://simon-eckert.com/bindung-selbsttest/-- Notfallkontakte --Tel...

My site: https://natebjones.com/My links: https://linktr.ee/natebjonesMy substack: https://natesnewsletter.substack.com/Takeaways 1. Software 3.0 Paradigm Sh...

Technology often outpaces the ability of organizations to embrace it. We look at how businesses can overcome the growing challenge of gen AI adoption.

How AI Works 101. Brief morning rant, in case anyone is persistently bamboozled by AI discussions. #AI #learning #education