AI News

Endlich geht mal was ordentlich voran: OpenAI for Germany – ein interessanter Schritt in Richtung digitaler Souveränität. Heute haben SAP und OpenAI offiziell die Partnerschaft „OpenAI for Germany“ vorgestellt. Das Ziel: KI-Technologien für den öffentlichen Sektor in Deutschland bereitzustellen – mit Fokus auf Datenschutz, Datensouveränität und rechtliche Compliance. Die Lösung basiert auf SAPs Delos Cloud (unter Microsoft Azure-Technologie) – lokal in Deutschland betrieben. Was steckt dahinter? Millionen von Beschäftigten in Verwaltungen, Behörden und Forschungseinrichtungen sollen KI-gestützte Tools nutzen können, um Prozesse effizienter zu gestalten. Geplant ist u.a. Automatisierung von Vorgängen, Datenanalysen und Workflow-Integration. Zum Start sollen 4000 GPUs für KI-Rechenleistung zur Verfügung stehen — mit Ambition, weiter zu skalieren. Es passt zur deutschen KI-Ambition: Der Staat sieht KI bis 2030 als wichtigen Werttreiber, mit Potenzial für bis zu 10 % BIP-Beitrag. Warum ich das für wichtig halte: Es ist ein klares Signal: KI-Lösungen müssen nicht zwangsläufig „Alles ins Ausland“ oder "Alles nur national" bedeuten. Lokale, hybride Infrastrukturen können Teil der Lösung sein. Gerade im öffentlichen Sektor lassen sich durch KI echte Mehrwerte schaffen — wenn Sicherheit, Recht und Vertrauen stimmen. Es ist ein gutes Beispiel dafür, wie Partnerschaften (Tech + Industrie + Staat) zusammenspielen können. Start soll 2026 sein. Bravo, denke ich 🙏 Wird aber nicht allen gefallen ...  ⛓️‍💥 https://lnkd.in/dE3q9Jys #digital #souveraen #verwaltung #ki

‘We define workslop as AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.’ So begins a great piece in the Harvard Business Review which has coined a new term referring to poor AI practices which are developing: employees are producing sloppy work with AI and actually creating more work down the line for the person they pass the ‘workslop’ onto. The article offers some clear pointers on how organisations can move on to better AI practice, summed up in the conclusion: ‘Leaders will do best to model thoughtful AI use that has purpose and intention. Set clear guardrails for your teams around norms and acceptable use. Frame AI as a collaborative tool, not a shortcut. Embody a pilot mindset, with high agency and optimism, using AI to accelerate specific outcomes with specific usage. And uphold the same standards of excellence for work done by bionic human-AI duos as by humans alone.’ These lessons are just as applicable to schools as to businesses. The key difference is that we not only need leaders to model best practice, but teachers to help students understand what this looks like. It’s vital we take active steps now to shape habits: AI can be a force for innovation and amplify what’s best in our schools, or it can drive ‘workslop’ in staff and students. Surely the choice is a no brainer? (Link to piece in comments via post on this from David Monis-Weston)

– an open catalog and API to discover and use publicly available MCP servers. Finally, MCP servers can be discovered through a central catalog. Think of it as an App Store for scanning and searching your MCPs: → An open catalog + API for discovering MCP servers → One-click install in VS Code → Servers from npm, PyPI, DockerHub → Sub-registries possible for security and curation → Works across Copilot, Claude, Perplexity, Figma, Terraform, Dynatrace etc. Although it is still in preview and being worked on, it will definitely serve a major problem. Github link: https://lnkd.in/df-qTnYe

So, New Zealanders and Australians are the most similar to AI, and Ethopians and Pakistanis the least. This study shows that WEiRD (Western, Educated, Industrialized, Rich, and Democratic) populations are the most aligned with ChatGPT. It is intriguing why some nations, including Netherlands and Germany, are more GPT-similar than Americans. The paper uses cognitive tasks such as the “triad task,” which distinguishes between analytic (category-based) and holistic (relationship-based) thinking, GPT tends to analytic thinking, which aligns with countries like the Netherlands and Sweden, that value rationality. This contrasts with holistic thinkers found in many non-WEIRD cultures. GPT tends to describe the "average human" in terms that are aligned with WEIRD norms. In short, with the overwhelmingly skewed data used to train AI and the outputs, it's “WEIRD in, WEIRD out”. The size of the models or training data is not the issue, it's the diversity and representativeness of the data. All of which underlines the value and importance of sovereign AI, potentially for regions or values-aligned cultures, not just at the national level. | 18 comments on LinkedIn

„𝗗𝗮𝘀 𝗶𝘀𝘁 𝗱𝗮𝘀 𝗹𝗲𝘁𝘇𝘁𝗲 𝗝𝗮𝗵𝗿, 𝗶𝗻 𝗱𝗲𝗺 𝘄𝗶𝗿 𝗣𝗼𝘄𝗲𝗿𝗽𝗼𝗶𝗻𝘁𝘀 𝗮𝗹𝘀 𝗣𝗿𝗼𝗱𝘂𝗸𝘁 𝗹𝗶𝗲𝗳𝗲𝗿𝗻. 𝗞𝘂̈𝗻𝗳𝘁𝗶𝗴 𝗹𝗶𝗲𝗳𝗲𝗿𝗻 𝘄𝗶𝗿 𝗕𝗼𝘁𝘀.“ Die alte Ordnung der Wissensarbeit gerät ins Rutschen. Im Open-AI‑Forum diskutierten Aaron "Ronnie" Chatterji, Chefökonom von Open AI, und Joseph Fuller von der Harvard Business School, wie Künstliche Intelligenz Aufgaben, Organisationen und Karrieren neu gestaltet. Ihre Diagnose: Die Technologie sprintet voraus, doch Unternehmen verharren in Prozessen, die für eine analoge Welt eines klassischen Foliensatzes gebaut wurden. Fuller bringt die Zäsur auf eine Formel, die die Beratungsbranche aufhorchen lässt: „Das ist das letzte Jahr, in dem wir Powerpoints als Produkt liefern. Künftig liefern wir Bots.“ Hinter dem Bonmot steckt eine nüchterne Wirtschaftsanalyse: Regelbasierte Tätigkeiten lassen sich skalieren. Kunden verlangen weniger Informationsarbitrage, dafür mehr umsetzbare Systeme. Viele Beratungsmodelle lebten von der Fähigkeit, Informationen quer durch Silos zu heben, zu strukturieren und zu deuten. Jetzt rücken Entscheidungsplattformen in den Vordergrund, die Livedaten aus Finanz‑, Personal‑ und Vertriebssystemen integrieren und die Frage „Was jetzt?“ automatisiert beantworten. So entsteht eine Overlay‑Schicht für Entscheidungsintelligenz, und Powerpoint-Folien werden zur Randnotiz. Stattdessen werden Bots und Workflows zum Produkt, erwarten die beiden Experten. Weiterlesen auf F.A.Z. PRO Digitalwirtschaft (FAZ+) ▶︎ https://lnkd.in/eFjyCYGw Frankfurter Allgemeine Zeitung ___________ Mein neuer Online-Kurs: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗞𝗜 𝗳𝘂̈𝗿 𝗙𝘂̈𝗵𝗿𝘂𝗻𝗴𝘀𝗸𝗿𝗮̈𝗳𝘁𝗲 Mein Kurs erläutert in kompakter Form die wesentlichen strategischen und ökonomischen Effekte der generativen KI für Unternehmen. Im Zentrum stehen die Produktivitätseffekte, die Auswirkungen auf Geschäftsmodelle und die Wettbewerbsfähigkeit der Unternehmen sowie der Zusammenhang zwischen KI und Arbeit. Den Abschluss bildet der Blick auf den Status der KI in Deutschland. Der Kurs richtet sich an Führungskräfte, die an den ökonomischen Implikationen der generativen KI für Unternehmen, Wirtschaft und Wettbewerb interessiert sind. Der Kurs berücksichtigt den aktuellen Stand in Wirtschaft und Wissenschaft. ▪️ Dauer: 80 Minuten ▪️ Inhalt: 9 Videos / 66 Slides Zur Buchung ▶︎ https://lnkd.in/ezsB-KDg

The report is definitely worth a look, perhaps especially the Appendix. Below sharing a few of the distilled highlights. 🔄 Multi-agent systems (MAS) are the next-gen operating model. In the next 12–18 months, expect a shift from siloed APIs to MAS that can reason, plan, and act across business units—enabling autonomous execution with governance and “human in the loop” oversight. 📈 Human–AI collaboration boosts decision-making capacity. AI can instantly synthesize vast datasets into contextual, role-specific insights, allowing executives and managers to make better, faster, and more informed decisions across the enterprise. 💡 Workforce roles are redesigned, not just replaced. Agentic AI shifts roles across the board—from purely executional to more analytical, creative, and relationship-focused work—impacting job design in marketing, operations, R&D, and beyond. 📊 AI standardizes excellence across the enterprise. By codifying best practices into AI systems, organizations can eliminate “pockets of excellence” and ensure consistent quality across all teams and regions—not just in HR but in sales, operations, and service delivery. 🔍 Predictive intervention beats reactive problem-solving. AI can detect signals—like turnover risk, performance decline, or customer churn—before they become problems. This enables leaders to act early with targeted, data-backed interventions. 🛠 Orchestration of multi-step, cross-functional workflows. Agentic AI can coordinate tasks across multiple business functions without manual handoffs—e.g., onboarding a new employee touches HR, IT, facilities, and finance, yet AI can plan, execute, and monitor the entire process end-to-end. 🗺 AI’s biggest impact areas are mapped. A “heatmap” of hundreds of HR processes pinpoints where AI should be fully powered (e.g., data analysis, reporting, inquiries), augmented (e.g., recruiting, performance reviews), or assistive—helping leaders invest for maximum ROI. 🚀 80%+ of admin work can be automated. In future HR operations, AI will handle over 80% of administrative and operational tasks, freeing HR teams to focus on strategy, workforce planning, and proactive talent interventions. | 14 comments on LinkedIn

This study from Anthropic on how higher education professors use AI uncovers very interesting insights. The chart here shows far more augmentation than automation, as you would hope, with the more automated tasks in particular focused on administration. The most augmented tasks are university teaching and classroom instruction, including creating educational materials and practice problems. Many of those interview describe AI as a "thought partner" in helping them create better, more effective learning experiences. But there are deep dilemmas for educators, beyond just choosing where to augment themselves, and where to automate for efficiency. It is about balancing efficiency versus the integrity of the teaching process. Overall professors said they thought AI was least effective at grading and assessment, and some refuse to use it. However half of grading tasks were automated. It's not in the slightest surprising, but certainly concerning. The most common AI creations by the educators were: 🎮 Interactive educational games 📝 Assessment and evaluation tools 📊 Data visualization 📚 Subject-specific learning tools 📅 Academic calendars and scheduling tools 💰 Budget planning and analysis tools 📄 Academic documents Some of these can be very effective learning tools, others can take away administrative burden. This is all amidst a redefinition of the learning system, with deep shifts in learners, the learning process, educational institutions, educators, and those using academic credentialing. We are early in the transformation of education.

July 25, 2025 My Ethical AI Principles Image: Sign posted in the common kitchen at the campground in Selfoss, Iceland, where this article was written I understand. Artificial Intelligence (AI) is a technology unlike any we've seen before.

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👉 In a controlled test, 27 Instructional Design postgrads at Carnegie Mellon created designs both with & without GPT-4 assistance. 👉 Every design was blind-scored on quality by expert instructors. 👉The result: Design with AI was not not just faster, but produced better quality designs in 100% of the cases. But the detail is where it gets interesting...👇 The research also revealed a "capability frontier"—a clear boundary between where AI helps Instructional Design quality most, and where it might actually compromise it. TLDR: 🚀 USE AI FOR: Designs which use well-established design methodologies, step-by-step processes & widely-discussed topics. ❌ BE MORE CAUTIOUS WHEN USING AI FOR: Designs on niche, novel & complex topics which use less well-established design methodologies. 💡Bonus insight: In line with broader research on the impact of AI on knowledge work, the research also suggests that novice Instructional Designers benefit *most* from AI design assistance (but only when we are strict on what sorts of tasks they use it for). To learn more about the research & what it tells us about how to work with AI in our day to day work, check out my latest blog post (link in comments). Happy innovating! Phil 👋

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English-speaking countries remain the most nervous and unexcited, with Asia dominating as most positive nations. The second chart I've shared here is interesting, in that while people are relatively positive about the impact of AI on their job and also the economy, they are considerably less positive about the impact of AI on the job market. Not surprisingly, those nations that believe AI will benefit the economy are most likely to be excited. The global average for believing AI will profoundly change their life in the next 3-5 years is 67%, ranging from 52% in Britain to 84% in Indonesia. So most people Of course, if people believe AI will profoundly change their lives there is likely cause for at least some nervousness and hopefully also excitement. Where the balance lies in a nation, and within any specific organization, must shape governance and change initiatives to maximize good cause for excitement and minimize cause for nervousness. Because it is a wild ride.

Some great insights in this report from Harvard Business Review, particularly interesting to me, not only as much of my work is in client leadership development, but also the reality that effective leadership will be critical in us navigating the challenging path to prosperous Humans + AI organizations. Not surprisingly, 55% of survey respondents said incorporating GenAI into business practices is their #1 priority this year. To support that, the top human capital project - at 53% - is adopting or expanding AI-based talent management. The real headline is that over 80% of HR leaders expect that every level of leader will spend more time on leadership development this year, in many cases significantly more. The question is: how to design the programs and the time spent to result in true expansion of leadership capabilities. "Speed to skill is the metric in focus". Which requires a very different, intrinsically Humans + AI approach: "In a two-way information exchange, AI is fed an organization’s domain-specific knowledge and humans access AI-generated learning resources based on that knowledge. AI systems learn from human inputs, improving over time, while humans gain insights from AI-generated data. Properly done, these efforts can build the collective intelligence of humans and machines, enhancing the organization’s ability to solve complex problems and adapt to changing environments." A lot more in the report. What is clear is that in an accelerating world leadership development is more important than ever, both to address AI-driven change, and supported by AI.

📢 Free Book: "Introduction to AI Safety, Ethics, and Society is a free online textbook by Center for AI Safety Executive Director Dan Hendrycks. It is designed to be accessible for a non-technical audience and integrates insights from a range of disciplines to cover how modern AI systems work, technical and societal challenges we face in ensuring that these systems are developed safely, and strategies for effectively managing risks from AI while capturing its benefits. This book has been endorsed by leading AI researchers, including Yoshua Bengio and Boaz Barak, and has already been used to teach over 500 students through our virtual course. It is available at no cost in downloadable text and audiobook formats, as well as in print from Taylor & Francis. We also offer lecture slides and other supplementary resources for educators on our website." Thanks to Connor Smith for sharing this with me. Due to file limit issues, I have only attached the first 17 pages of the much larger textbook. See link in comments. | 10 comments on LinkedIn

Die Bilder wurden KI-generiert, dann in Videos mit Übergängen animiert und am Schluss noch ein Audio drübergelegt. Zack, fertig ist der Short Movie. Dies wirkt schon fast wie eine Sitcom mit Mona Lisa, Van Gogh & Co. Zum Linkedin-Beitrag https://lnkd.in/enY82ifv

The specs are impressive: Up to 85x faster 3.4x smaller footprint Real-time performance directly in-browser Capable of live video captioning – fully local No external infrastructure. No latency. No exposure of sensitive data. Simply secure, on-device AI. Yes, the technical benchmarks will be debated. But the bigger story is Apple’s positioning. This is about more than numbers – it’s about shaping a narrative where AI is personal, private, and seamlessly integrated. At Copenhagen Institute for Futures Studies, we’ve been tracking the rise of small-scale, locally running AI models for some time. We believe this shift has the potential to redefine how organizations and individuals interact with intelligent systems – moving AI from “out there” in the cloud to right here, at the edge. | 10 comments on LinkedIn

Die EPFL, die ETH Zürich und das Schweizerische Supercomputing-Zentrum CSCS haben heute Apertus veröffentlicht: Das erste umfangreiche, offene und mehrsprachige Sprachmodell aus der Schweiz. Damit setzen sie einen Meilenstein für eine transparente und vielfältige generative KI.

The full report is being presented from 2–4 September at UNESCO’s Digital Learning Week 2025 in Paris. It’s a must-read for anyone interested in learning, technology, and the future of education — packed with insights and practical perspectives. 𝗧𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲𝗮𝘀: ⬇️  𝟭. 𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝗶𝗰𝗮𝗹 𝗽𝗿𝗼𝘃𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀 → AI futures aren’t just about intelligence scores — they push us to rethink what “knowing” really means. And the whole debate isn’t only technical but philosophical: how do we define learning, progress, and human identity in an AI age? 𝟮. 𝗗𝗲𝗯𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿𝘀 𝗮𝗻𝗱 𝗽𝗲𝗿𝗶𝗹𝘀 𝗼𝗳 𝗔𝗜 → AI in schools and universities is not inevitable — education systems have choices, agency, and the power to shape direction. The core tension here: opportunity for reinvention vs. risks of over-automation and cultural bias. 𝟯. 𝗔𝗜 𝗽𝗲𝗱𝗮𝗴𝗼𝗴𝗶𝗲𝘀, 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 → Classrooms can’t be reduced to data points — AI must respect the incomputable nature of learning. And hyper-personalization risks turning education into an isolated bubble rather than a social dialogue. 𝟰. 𝗥𝗲𝘃𝗮𝗹𝘂𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝗰𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝘁𝗲𝗮𝗰𝗵𝗲𝗿𝘀 → Teachers remain the backbone of education — AI should amplify their work, not sideline it. Building AI “with” educators, not “for” them, is the only path to trust and adoption. 𝟱. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝘀 𝗳𝗼𝗿 𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 → AI in schools demands an ethics of care — transparent, fair, and accountable by design. Governance can’t be outsourced to tech — it requires democratic oversight and public participation. 𝟲. 𝗖𝗼𝗻𝗳𝗿𝗼𝗻𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲𝗱 𝗶𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 → AI can close divides — but only if it is localized, contextualized, and designed for inclusion. Without clarity, bias will persist: marginalized groups risk being left behind. 𝟳. 𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗼𝗹𝗶𝗰𝘆: 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗴𝗲𝗼𝗽𝗼𝗹𝗶𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 → Policy must keep pace with fast-moving AI capabilities, balancing human and machine intelligence. AI will shape every industry — but in education, it will shape society itself. Download: https://lnkd.in/dbc6ZJi4 Enjoy reading! And please share your views: ⬇️ 𝗣.𝗦. 𝗜𝗳 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗼𝘃𝗲 𝗺𝘆 𝗻𝗲𝘄 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲 𝗮𝗻𝗱 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟬,𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲: https://lnkd.in/dbf74Y9E | 39 comments on LinkedIn

Ihre überzeugende Argumentation zeigt, warum wir im KI-Zeitalter nicht weniger, sondern mehr Fachwissen benötigen. In ihrer Analyse deckt sie außerdem problematische Denkfehler in der aktuellen Bildungsdebatte auf. Zentrale Argumente des Beitrags: 📚 Verengter Wissensbegriff: Aktuell wird Wissen oft fälschlicherweise nur als auswendig gelerntes Faktenwissen verstanden. Dabei umfasst echtes Fachwissen viel mehr. Es reicht von prozeduralem Wissen bis hin zu verkörpertem, intuitivem Verstehen. 🧠 Kritisches Denken ist domänenspezifisch: Die Expertiseforschung zeigt eindeutig, dass kritisches Denken nicht als generische „Zukunftskompetenz” funktioniert, sondern tiefes fachspezifisches Wissen als Fundament braucht. ⚠️ Paradox der KI-Integration: Je mehr wir KI einsetzen, desto wichtiger wird menschliche Expertise. Denn nur, wer über fundiertes Fachwissen verfügt, kann KI-generierte Inhalte wirklich kritisch bewerten und validieren. 🚨 Risiko des „Deskilling“: Ein übermäßiges Vertrauen in KI kann zwar zu einer kognitiven Entlastung führen, reduziert aber gleichzeitig unsere Bereitschaft zum kritischen Denken. 🎯 Meine Ergänzung: Ich bin überzeugt, dass wir Fachwissen nicht nur zur Prüfung von KI-Ergebnissen benötigen. Um verschiedene KIs zu orchestrieren und mit ihnen einen profunden Fachdialog zu führen, ist tiefes Domänenwissen unverzichtbar. Die Qualität der Ausgaben von KI ist abhängig von der Qualität der Eingaben, da sich KI auf das Niveau der nutzenden Person einstellt. Nur mit fundiertem Fachwissen können präzise Fragen gestellt, komplexe Zusammenhänge erklärt und hochwertige, nuancierte Ergebnisse erzielt werden. Was bedeutet das für uns? Anstatt die Wissensvermittlung zu reduzieren, müssen wir sie stärken. Der Aufbau von tiefem, vernetztem Fachwissen ist entscheidend, um Lernende zu einem kritischen und erfolgreichen Umgang mit KI zu befähigen. Es geht nicht um ein Entweder-Oder. Die so oft geforderten „Future Skills” wachsen vielmehr erst auf dem fruchtbaren Boden von solidem Fachwissen.| 22 Kommentare auf LinkedIn

🌟 In der Welt der KI den Überblick behalten: Der AI Transformation Compass 🤖✨Die Einführung von KI in Unternehmen ist eine der spannendsten, aber auch komp...

Dieser LinkedIn Learning-Kurs hilft Ihnen dabei, die besten Einsatzmöglichkeiten mit KI zu erkunden, um Infinite Learning und Infinite Development zu etablieren. Durch den Kurs führt Sie Jan Foelsing, Autor des Buchs »New Work braucht New Learning«, Tech-Experte und Tool-Nerd, der Unternehmen und Teams auf dem Weg zu einer wirksameren Lernkultur, New Learning und vor allem dem sinnvollen Einsatz von KI begleitet.

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