Is AI actually going to replace developers? Or is the hype getting ahead of reality? On this episode of Digital Disruption, we’re joined by Sebastian Raschka, AI Research Engineer and author. Sebastian Raschka sits down with Geoff Nielson to unpack the real state of Large Language Models (LLMs) in 2026. As an LLM research engineer, Sebastian bridges deep technical expertise with practical, real-world AI implementation. In this conversation, he cuts through AI hype to focus on what’s actually achievable with modern LLMs, reasoning models, reinforcement learning, and inference scaling and where the limitations still exist. Sebastian explains why most companies should not build a large language model from scratch, but also why understanding the fundamentals may be one of the most important investments technology leaders can make. This conversation breaks down: ◼️Why coding is currently the strongest LLM use case ◼️Why “reasoning” models still fail simple tasks like counting letters in “strawberry” ◼️The reality behind Math Olympiad gold-level AI claims ◼️The true cost of training large models (millions in GPU compute) ◼️The privacy risks of uploading proprietary data into APIs ◼️How enterprises should think about fine-tuning vs API-based prompting ◼️Why benchmarks and leaderboards can be misleading Sebastian Raschka has over a decade of experience in artificial intelligence and machine learning. His work bridges academia and industry, serving as a Senior Engineer at Lightning AI and as a faculty member at the University of Wisconsin–Madison. He is the author of Build a Large Language Model from Scratch and is widely recognized for his practical, code-driven approach to AI education and research. His expertise lies in LLM research, transformer architectures, reinforcement learning, and the development of high-performance AI systems, with a strong focus on real-world implementation. In this video: 00:00 Intro 01:23 The Rise of “Reasoning” and Thinking Models 03:06 Inference scaling vs training scaling 06:17 What LLMs are actually good (and bad) at 07:09 The “Strawberry” Problem and Reasoning Limits 09:00 Tool use and why LLMs don’t need to count letters 10:20 Math Olympiads & self-refinement techniques 12:01 Why coding is the killer use case 13:28 Does AI make developers obsolete? 18:02 The Reality of 10x developer productivity claims 21:43 Generalist vs specialized models 23:53 Build from scratch vs fine-tune vs API prompting 25:01The true cost of training an LLM 27:33 API customization vs owning your model 29:12 Who should build an LLM from scratch? 33:16 Data requirements & why you need terabytes 34:28 Enterprise data challenges 35:40 Retrieval-Augmented Generation (RAG) explained 46:05 Multi-agent systems & tool calling 49:48 The problem with LLM benchmarks 55:43 Using LLMs as judges 58:00 Biggest misconceptions about LLMs 1:04:19 Reinforcement learning with verifiable rewards 1:06:32 Advice for technology leaders 1:11:48 Escaping AI hype through fundamentals Connect with Sebastian: LinkedIn: https://www.linkedin.com/in/sebastianraschka/ X: https://x.com/rasbt Connect with Sebastian: LinkedIn: https://www.linkedin.com/in/sebastianraschka/ X: https://x.com/rasbt Our links:Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcastFollow us on YouTube: https://www.youtube.com/@InfoTechRG