AI on the Edge: Why Smaller Models Win on Cost and Speed

🎧 Episode 7 — AI on the Edge: Why Smaller Models Win on Cost and Speed

For the last few years, the AI conversation has been dominated by scale. Bigger models. Bigger budgets. Bigger infrastructure. But quietly, a different story is unfolding.

In this episode of The AI Storm, we explore why smaller, faster, edge-deployed AI models are increasingly outperforming large, centralized systems—on cost, speed, reliability, and control.

This isn’t a technical deep dive. It’s a leadership conversation.

You’ll learn:

• Why many real-world AI use cases don’t need massive models
• How edge and smaller models are being used in retail, manufacturing, security, and operations
• What “training,” “fine-tuning,” and “retraining” actually mean in practical business terms
• Whether companies should buy off-the-shelf models or invest in building their own
• The new roles and skills emerging around edge AI and model operations
• How leaders should think about ROI, governance, and long-term sustainability

• This episode is about designing intelligence for reality, not for demos.

If you lead teams, build platforms, or make decisions about AI strategy, this conversation will help you rethink where intelligence should live—and why smaller may be smarter.

🎙️ Hosted by Krishna Goli
🌩️ Finding direction and decisiveness in the storm of AI