Key Takeaways

  • Poolside AI's Laguna S, an 118B parameter model, solved complex coding challenges like Erdos 397, proving smaller models can achieve results previously expected only from much larger ones.
  • Eiso Kant argues this capability comes from "behavioral intelligence" — traits like persistence, verification, and backtracking — rather than just raw parameter count or processing power.
  • This shift means the peak return-on-investment for model size in knowledge work might be reached at a much smaller scale, challenging the prevailing belief that bigger is always better.
  • If behavioral gains hold true, it creates a powerful argument for the commoditization of AI models and the viability of open-source solutions against an oligopoly of intelligence.

The Small Model That Outsmarted Scale

For years, the story of large language models sounded simple: more parameters equaled more intelligence. Bigger models were better, end of story. Then came Poolside AI, and their CEO, Eiso Kant, dropped a bomb into that narrative. He pointed to Laguna S, Poolside's 118B parameter model, as a prime example of why we need to rethink our approach.

Laguna S achieved something remarkable: it independently solved Erdos 397, a complex coding challenge. This wasn't just a slight improvement; it was a fundamental shift. Kant suggests the secret wasn't raw processing power or sheer parameter count. Instead, it was how the model behaved. “I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent,” Kant explained. It acts less like a quick-thinking genius and more like a meticulous, relentless problem-solver.

Intelligence vs. Behavior: The Hidden Lever

What Kant calls "behavioral intelligence" is a game changer. It's the difference between a model that knows a lot and one that knows how to act effectively with what it knows. This means a model's ability to verify its own work, backtrack when it hits a dead end, and persist through multiple attempts can unlock vastly more capability than simply adding billions more parameters.

“We are going to be able to squeeze so much more out of smaller models than I think we had imagined in the industry because yes there's intelligence and larger models are more intelligent... but the behaviors of being really persistent of being able to backtrack when you're wrong of like understanding how to interact with your environment show us that we can get a lot more out of it,” Kant said. This isn't about dumbing down the task for a smaller model; it's about teaching a model better habits for problem-solving.

If Laguna S, at 118B parameters, can outperform larger models on specific complex tasks, it upends the economic model of AI development. It means the "peak" ROI for model size in much knowledge work might be reached far sooner than previously assumed. This reality strongly supports the argument for widespread model commoditization and makes open-source AI a truly viable, competitive force in the market. Kant hopes that when people use Laguna S, they take away that "the behaviors inside of it are what push it to be far more capable less than necessarily the number of parameters."

What to Do With This

Stop defaulting to the largest LLM you can get your hands on. This week, identify a complex, multi-step problem in your business that you're currently throwing a huge model at. Design a prompt or a small agentic workflow that specifically encourages smaller, more efficient models to exhibit behaviors like self-correction, persistence, and verification. Test a smaller, open-source model like an adapted Laguna S or similar, fine-tuned for these behaviors, against your current solution. See if a smarter process beats a larger model.