LLMs in 2026: What’s Real, What’s Hype, and What’s Coming Next

LLMs in 2026: What’s Real, What’s Hype, and What’s Coming Next

From Digital Disruption with Geoff Nielson by Info-Tech Research Group

February 23, 2026 · 1h 15m

About this episode

Sebastian Raschka discusses the current state and future of Large Language Models in 2026, addressing the balance between AI hype and reality.

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…

People in this episode

Host: Geoff Nielson

Guest: Sebastian Raschka

Topics covered

  • Large Language Models
  • AI implementation
  • coding use cases
  • reasoning models
  • AI limitations
  • training costs
  • privacy risks

Keywords

  • AI
  • Large Language Models
  • developers
  • reinforcement learning
  • inference scaling
  • fine-tuning
  • privacy risks
  • training costs

Mentioned in this episode

Organizations: Lightning AI, Info-Tech Research Group

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