Cutting Edge AI

#7 Ingmar Klein (CEO, Huzzle) on the Future of Work, AI, Hiring, and Human Data.

AI may not just replace work. It may also create entirely new categories of human work around training, evaluating, and operating intelligent systems. In this episode, we speak with Ingmar Klein, co-founder and CEO of Huzzle, a company that evolved from a talent marketplace into an AI-powered recruitment engine and, more recently, a provider of human data for frontier AI labs. Ingmar shares how Huzzle built AI interview systems capable of assessing candidates at scale, why hiring is one of the first workflows where AI can already outperform humans in consistency and efficiency, and what it takes to combine automation with human judgment in recruitment. The conversation then expands into the emerging market for human feedback and training data. Ingmar explains why experts across domains may increasingly spend time evaluating model outputs, improving agents, and helping AI systems operate inside real software environments. We also discuss why the next bottleneck may not be model capability, but adoption inside companies still running on legacy systems. If you’re interested in how AI is changing hiring, creating new job categories, and reshaping how organizations operate: this episode is worth a listen.

#6 Building the Voice AI Category: Daniel Keinrath (CEO, Fonio AI) on Product, GTM, and Scale

Voice AI is becoming one of the first real entry points for AI adoption in SMEs: not because of the technology itself, but because it delivers immediate, measurable value. In this episode, we speak with Daniel Keinrath, co-founder and CEO of Fonio AI [https://www.fonio.ai/en], an AI call assistant used by thousands of businesses across the DACH region. Daniel shares how Fonio AI grew from 0 to rapid scale, why voice is a natural starting point for AI in non-technical companies, and how this expands into a broader omni-channel platform over time. We discuss the practical challenges behind building in this space: getting companies to structure their data, integrating with fragmented systems, and making AI usable for non-technical users. The conversation goes beyond product into market dynamics. Daniel explains why this is a scale-driven market with similarities to telecom, where distribution, partnerships, and speed matter as much as the underlying technology. We also cover why Fonio focuses on SMEs instead of enterprise, and why long-term success depends less on building proprietary models and more on product, go-to-market, and execution. If you’re interested in how AI is actually being deployed in everyday business workflows, and what it takes to build a category in a fast-moving market: this episode is worth a listen.

#5 AI-Written Software: Tyler Dunn (Co-Founder, Continue) on the Future of Coding

In this episode of Cutting Edge AI, Jens Lapinski and Robin Harbort speak with Tyler Dunn, Co-Founder of Continue [https://www.continue.dev/], about how AI is transforming software development. As AI agents increasingly write code, a new question emerges: who reviews it? Tyler explains why the future of software may involve AI reviewing AI, and why developers may soon stop reading most of the code they ship. The conversation explores the rise of continuous AI, the need for a mission control layer to govern fleets of coding agents, and how trust, testing, and automation will replace traditional code review. They also discuss the future of programming languages, agentic development workflows, and why the next generation of developers may think less in code and more in systems. Topics include: * AI agents writing and reviewing code * Continuous AI and automated agent checks * Mission control for fleets of developer agents * The future of programming languages * Why developers may stop reading code entirely

#4 From Legacy Systems to Agentic AI: Fabian Heinrich (CEO, Mercanis) on the Future of Procurement

Procurement is one of the largest and most complex parts of the global economy – over $100 trillion a year – yet much of it still runs on spreadsheets, email threads, and legacy systems built decades ago. Mercanis [https://www.mercanis.com/], founded by Fabian Heinrich, is taking a different approach: rebuilding procurement from the ground up around agentic AI. In this episode, Fabian explains why procurement was historically underserved by modern software, how his first startup exposed the inefficiencies behind RFPs, supplier comparisons, and contract negotiations, and why large enterprises still rely on Excel despite the stakes involved. We unpack what “agentic procurement” actually means in practice, from AI agents reading hundreds of pages of contracts to running complex pricing scenarios and orchestrating sourcing workflows end to end. We also discuss the recent inflection point in enterprise demand for AI: procurement teams suddenly have budget, and board-level interest in automation has changed buying behavior, and why heavy industries like automotive, chemicals, energy, and pharma are now at the center of AI-driven transformation. The conversation goes deeper into the future of work inside procurement: how the role of the buyer is shifting toward orchestration and analysis, what human-in-the-loop really looks like when agents negotiate with other agents, and how procurement systems will increasingly sit on top of, and eventually eat into, legacy ERP and compliance infrastructure. If the last decade optimized procurement interfaces, the next one is about autonomous systems running the process itself.

#3 How Generative AI Is Transforming the Way Fashion Creates with Julius Harling (Graswald AI)

Fashion imagery is entering a new phase, one where AI doesn’t just edit photos, it creates them. Graswald AI, founded by Julius Harling, builds a digital photo studio that lets brands generate lifelike product images and campaign visuals without physical shoots. Using proprietary generative models trained on synthetic data, Graswald can dress virtual avatars in real garments with pixel-accurate fidelity to fabric, texture, and fit. In this episode, Julius explains how their multi-model system works: from training AI on structured garment data to creating consistent lighting and brand-specific aesthetics. We discuss how digital avatars are designed with real emotional expression, why brands are demanding exclusive AI models that reflect their casting choices, and how synthetic data pipelines enable scale without copyright risks. The conversation also unpacks the economics behind this shift: Why photo production costs can drop by more than 90%, how AI lets creative teams test ideas that were previously too expensive to shoot, and what this means for supply chains, sustainability, and time-to-market. We close on the bigger picture: the convergence of multimodal foundation models, falling inference costs, and the rise of context-aware AI systems that will make content generation as fluid as sketching an idea. If the past decade digitized retail, this one is about digitizing creation itself.