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If you work for a German enterprise, your company likely already has a budget that could cover it fully or partially. A few practical tips: Don’t ask only for “mentoring”. Use the language your company already uses. Look for terms like Weiterbildung, Personalentwicklung, Coaching, Learning & Development, Entwicklungsbudget or individuelle Qualifizierungsmaßnahme. Check your internal learning platform first. Large German companies often have annual learning budgets, developmen

Earlier this year, I gave a talk at Agentic Conf Hamburg about the state of AI coding assistant adoption in enterprise companies. Unfortunately, many of the obstacles I discussed back then still remain. Now that it has finally been published, the talk may be even more relevant than it was at the time. You can watch it here: https://www.youtube.com/watch?v=YYmF2CtIuXk A huge thank you to people in my LinkedIn network who took part in the survey that became the foundation for

If there is one thing I wish for many of my mentees, it is that we started working together earlier. I have seen the same pattern so many times by now that I can almost recognize it from the first message. Someone reaches out at the beginning of their journey. They are still full of energy, maybe a bit confused, but also curious and hopeful. They want to move from academia to industry, from analytics to AI, from one data role into something more ambitious. They ask a few good

A few years ago, the story of enterprise AI sounded almost suspiciously clean: a handful of frontier labs would build extremely powerful foundation models, companies would connect to them through APIs, everyone would add AI to their products, processes, dashboards, search bars, internal tools, customer service flows, and probably a few places where absolutely nobody had asked for it, and the winners would be those who had access to the biggest models, the largest compute clus

There is a strange moment in many AI Engineering roles when the exciting part of the job quietly turns into the exhausting part. At first, it feels great. You work with LLMs, RAG systems, agents, evaluation pipelines, cloud infrastructure, security constraints, and real business problems. You are not just writing code: you are helping the company understand what AI can actually do. Then the demos start working. And that is where the trouble begins. Because once people see a w

A few days ago, I read an article that made me slightly uncomfortable, not because it said something completely new, but because it described something I had already noticed in my own life without having a good name for it. The promise of AI is very simple: it will make our lives easier, faster, and more efficient, which sounds wonderful, because most of us did not grow up dreaming of becoming part-time accountants, insurance claim specialists, legal researchers, travel agent

Not completely wrong, perhaps, but wrong in the way people are often wrong when they look at an early technology and extrapolate its future too directly from its first limitations. A few years ago, when LLMs started moving from research demos into real business conversations, many of us had a fairly clear idea of what would happen next. The generic models were impressive, sometimes shockingly so, but they were also obviously not enough. They did not know our internal processe

OpenAI’s latest flagship model seems to tell two stories at once: on the one hand, it pushes the frontier again, setting new state-of-the-art results across important benchmarks for knowledge work, agentic coding, computer use, and abstract visual reasoning; on the other hand, it appears to struggle with one of the most important skills for real-world AI systems, which is knowing when not to answer. GPT-5.5 is clearly powerful. It tops the Artificial Analysis Intelligence Ind

The Importance of a Growth Mindset People often ask me how to stay relevant in AI. I usually think back to a moment early in my career when I realised something uncomfortable. The people who stagnated the most weren’t the ones who knew too little. Quite the opposite: they were the ones who were too attached to what had made them successful before. If you want to survive—and honestly, thrive—in AI right now, you need a slightly dangerous mindset. You need to assume that more i

Have you heard about Outcome Engineering yet? Last month Cory Ondrejka published the Outcome Engineering post and the accompanying o16g manifesto , which made waves in the internet. Even if you haven't read it, you've almost certainly seen remixes like " Software development was never about writing code " that have since flooded LinkedIn. At first, the line can sound like pure provocation. But the manifesto is really pointing at something hard to ignore: In an agentic worl

Mentoring often has this aura of magic around it. You connect with a more experienced colleague and bam! — in one hour you walk away with all the answers to improve your professional life. In reality, that’s a misconception. And it often ends in stories like this one someone shared on Reddit: “𝐼 ℎ𝑎𝑣𝑒 ℎ𝑎𝑑 𝑚𝑒𝑛𝑡𝑜𝑟𝑠 𝑤ℎ𝑒𝑟𝑒 𝐼 𝑠𝑒𝑡 𝑢𝑝 𝑤𝑒𝑒𝑘𝑙𝑦 𝑚𝑒𝑒𝑡𝑖𝑛𝑔𝑠. 𝑇ℎ𝑒 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑎𝑡𝑖𝑜𝑛𝑠 𝑤𝑒𝑟𝑒 𝑎𝑙𝑤𝑎𝑦𝑠 𝑎𝑤𝑘𝑤𝑎𝑟𝑑 𝑎𝑛𝑑 𝑢𝑛ℎ𝑒𝑙𝑝𝑓𝑢𝑙. �

How do you stay up to date in the fast-changing tech world? Here’s something not everyone tells you: major tech vendors publish a huge amount of high-quality learning material for free. That means you can keep up with the latest innovations from the comfort of your home — without spending a single euro from your company’s learning budget. Take Microsoft Ignite , for example, Microsoft’s largest conference, where they showcase their newest updates, products, and future directi

Influential Figures in AI Andrew Ng DeepLearning.AI Many people started their ML/AI journey thanks to his courses. He has a rare talent for explaining complex ideas clearly and regularly shares thoughtful essays about the future of the field. LinkedIn: Andrew Ng Blog: The Batch Chip Huyen Author ofDesigning Machine Learning Systems and AI Engineering She doesn’t publish frequently, but when she does, her posts are insightful and widely discussed in the AI community. Lin

One of my mentees was recently hired as an AI Architect . The journey took 5 months of very intense preparation followed by 4 months of nonstop job search and interviews . What we learned along the way: there is almost no agreement in the market on what an “AI Architect” actually is. There is no standard interview process, no common skill matrix, and no shared definition of what “good” looks like. Yet, a clear pattern emerged. Two worlds, two expectations Start-ups tend to