In my work advising enterprise leaders on AI adoption, I've seen a surprising pattern emerge. While the industry is preoccupied with building ever-larger models, the next wave of opportunity isn't coming from the top — it's increasingly coming from the edge.

Compact models, or small language models (SLMs), are unlocking a new dimension of scalability — not through sheer computational power, but through accessibility. With lower compute requirements, faster iteration cycles and easier deployment, SLMs are fundamentally changing who builds, who deploys and how quickly tangible business value can be created. Yet, I find many entrepreneurs are still overlooking this significant shift.

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In my experience, one of the most persistent myths in AI adoption is that performance scales linearly with model size. The assumption is intuitive: bigger model, better results. But in practice, that logic often falters because most real-world business tasks don't inherently require more horsepower; they require sharper targeting, which becomes clear when you look at domain-specific applications.

From mental health chatbots to factory-floor diagnostics requiring precise anomaly detection, compact models tailored for focused tasks can consistently outperform generalist systems. The reason is that larger systems often carry excess capacity for the specific context. The strength of SLMs isn't just computational — it's deeply contextual. Smaller models aren't parsing the entire world; they are meticulously tuned to solve for one.

This advantage becomes even more pronounced in edge environments, where the model must act fast and independently. Devices like smartglasses, clinical scanners and point-of-sale terminals don't benefit from cloud latencies. They demand local inference and on-device performance, which compact models deliver — enabling real-time responsiveness, preserving

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