🔍 Why Going Small with GenAI Is the Smart Enterprise Play
Bigger isn't always better.
A recent piece in The Register highlights a growing tension in the AI industry: an obsession with building ever-larger models that promise magical results—but deliver mounting costs, instability, and erratic behavior. The truth? Bigger isn't always better. In this specific article, I found the take claiming truthfully that AI models are inherently non-deterministic and hence do not fit into use cases which favour reliability unsuitable candidates for AI adoption quickly.
Engineers and analysts are now advocating for smaller, tightly scoped models that offer higher reliability and drastically better ROI. Think targeted tools, not one-size-fits-all giants.
💡 Here’s the extrapolation: Enterprises already sit on a goldmine of domain-specific data. Instead of chasing generic mega-models, they can train lean, custom models tailored to their internal ecosystems—models that improve over time, gain flavor with use, and get better with seasoning. Like a cast-iron pan, the more it’s used, the better it performs.
✅ More reliable
✅ Economically sustainable
✅ Purpose-built for real workflows
It's not about fighting scale—it's about embracing precision.
What are your thoughts on this? Do you think we are overseeing this Achilles' heel of large models?
#GenAI #LLM #AIstrategy #EnterpriseAI #TechLeadership #MachineLearning #ROI #SmallerIsSmarter