Digitalisierung ist für Dich

Digitalisierung ist für Dich is a German-language interview show about digitization as it is actually encountered by companies: technical, legal, strategic, and sometimes awkwardly human. It is not a hype reel. The conversations are animated by curiosity, but they keep returning to concrete questions about what a system can do, what it costs, who benefits, and what responsibility comes with deployment. Recent episodes spend serious time on optical processors, photonic coprocessing, streaming architectures, PCI Express, digital host CPUs, and the possibility of reducing energy pressure in AI hardware. That material gets technical quickly. It also widens into a larger argument about European capability, product readiness, and whether new computing approaches can move from impressive prototype to usable infrastructure. AI strategy receives the same treatment. With Sabine Singer, the discussion links AI Ethics by design, value-based business development, certified AI training, the EU AI Act, and the practical problem of companies asking for AI before they know what outcome they need. Short prompts lead to long implications. The show is especially interested in the gap between experimentation and implementation. Conversations about Austrian AI innovation and bootstrapped product work bring in domain-specific small language models, enterprise systems such as SAP, Salesforce, and ServiceNow, and the idea that smaller, more focused models may matter more than broad general systems in many business settings. The legal episodes add another layer. A smart garage opener becomes a way to talk about usability, remote access, regulation, data management, and the unintended complexity created by connected products. The format is interview-centric and patient with explanation. It can repeat, rephrase, and circle back, but that is part of its value: terms are clarified, assumptions are challenged, and technical claims are tied to business reality. The best fit is a listener who wants digitization discussed without removing the friction. Hardware, law, AI governance, training, standards, data use, and workplace change are treated as connected parts of the same problem.