Significant opportunity exists in re-architecting how AI models work. Instead of building ever-larger single models, the focus is shifting to creating networks of smaller, specialized models that collaborate, which can drastically reduce the cost per token produced.

Instead of using massive, expensive LLMs for every task, companies can solve the "tokenpocalypse" (runaway token costs) by pairing smaller models with high-quality retrieval systems. This allows cheap models to act like large ones, saving significant costs.

Poolside's Laguna S model shows significant gains from improved behaviors—like persistence, verification, and not declaring victory early—rather than just more parameters or "intelligence." This suggests that smaller models can become far more capable at knowledge work by optimizing these behavioral traits through post-training.

For most enterprise tasks, massive frontier models are overkill—a "bazooka to kill a fly." Smaller, domain-specific models are often more accurate for targeted use cases, significantly cheaper to run, and more secure. They focus on being the "best-in-class employee" for a specific task, not a generalist.

The 'bigger is better' narrative is breaking down. For well-defined, structured tasks like coding and math, small models (e.g., 3 billion parameters) are now matching the performance of frontier models. This enables powerful, specialized AI to run on modest local hardware.

Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.

Performance on knowledge-intensive benchmarks correlates strongly with an MoE model's total parameter count, not its active parameter count. With leading models like Kimi K2 reportedly using only ~3% active parameters, this suggests there is significant room to increase sparsity and efficiency without degrading factual recall.

For consumer products like ChatGPT, models are already good enough for common queries. However, for complex enterprise tasks like coding, performance is far from solved. This gives model providers a durable path to sustained revenue growth through continued quality improvements aimed at professionals.

As enterprises scale AI, the high inference costs of frontier models become prohibitive. The strategic trend is to use large models for novel tasks, then shift 90% of recurring, common workloads to specialized, cost-effective Small Language Models (SLMs). This architectural shift dramatically improves both speed and cost.