Inside the Black Box: Neuron-Level Control and Safer LLMs

About this episode

Vinay Kumar discusses strategies for understanding AI systems, focusing on interpretability, alignment, and safety in AI.

Summary  In this episode of the AI Engineering Podcast Vinay Kumar, founder and CEO of Arya.ai and head of Lexsi Labs, talks about practical strategies for understanding and steering AI systems. He discusses the differences between interpretability and explainability, and why post-hoc methods can be misleading. Vinay shares his approach to tracing relevance through deep networks and LLMs using DL Backtrace, and how interpretability is evolving from an audit tool into a lever for alignment, enabling targeted pruning, fine-tuning, unlearning, and model compression. The conversation covers setting concrete alignment metrics, the gaps in current enterprise practices for complex models, and tailoring explainability artifacts for different stakeholders. Vinay also previews his team's "AlignTune" effort for neuron-level model editing and discusses emerging trends in AI risk, multi-modal complexity, and automated safety agents. He explores when and why teams should invest in interpretability and alignment, how to operationalize findings without overcomplicating evaluation, and the best practices for private, safer LLM endpoints in enterprises, aiming to make advanced AI not just…

People in this episode

Host: Tobias Macey

Guest: Vinay Kumar

Topics covered

• AI systems
• interpretability
• explainability
• alignment
• neuron-level control
• safety in AI
• enterprise practices

Keywords

• AI systems
• interpretability
• explainability
• alignment metrics
• neuron-level editing
• safety agents
• model compression

Mentioned in this episode

Organizations: Arya.ai, Lexsi Labs, Cash App

Products: DL Backtrace, AlignTune

More episodes of AI Engineering Podcast

• Kubernetes, Compliance, and Control: The Operational Backbone of AI Sovereignty · February 25, 2026 · 1h 1m
• From Blind Spots to Observability: Operationalizing LLM Apps with OpenLit · February 15, 2026 · 51 min
• Taming Voice Complexity with Dynamic Ensembles at Modulate · February 8, 2026 · 59 min
• GPU Clouds, Aggregators, and the New Economics of AI Compute · January 27, 2026 · 46 min
• The Future of Dev Experience: Spotify’s Playbook for Organization‑Scale AI · January 20, 2026 · 56 min
• Generative AI Meets Accessibility: Benchmarks, Breakthroughs, and Blind Spots with Joe Devon · January 5, 2026 · 56 min