After nearly 20 years in data science I’ve seen many “revolutions” come and go: neural networks, SVMs, bayesian statistics, random forests, XGBoost and deep learning. Each came with bold promises, and each eventually settled into a realistic place in production systems (read: became boring). Generative AI, however, feels fundamentally different.

In this talk, I’ll share my view why the current GenAI hype stands apart from previous cycles: technically, culturally, and organizationally. Even with some understanding how these things work, I am still blown away by the stream of stunning new capabilities. This is not a “GenAI is bad” rant. Instead, it’s a critical attempt to understand the shift we’re seeing, and the risks that come with it if we don’t adjust our thinking.

Using industrial examples such as supply chains (just because I work in this field), but also personal experience, I’ll show where LLM-based approaches still have serious limitations today, and where GenAI can realistically add value. We’ll disentangle different categories of risk from technical fragility, evaluation problems and mere costs to organizational overconfidence and misuse.

A big part of the talk dives into the rapidly emerging field of AI Agents. We’ll explore what AI agents actually are, where they make sense today, and where the current hype is just snake oil, particularly to senior decision-makers who may underestimate complexity, costs, and failure modes.

The goal of this talk is not to slow innovation, but to enable better decisions. If we want GenAI to be a success in real-world systems, we need to understand both the change it represents and the limits it still has.

An anti-bullshit take on the possibilities ahead, with honesty, anecdotes, and (for those who know me, of course) a bit of humor.