When “Less Is More” for AI: How Tiny Models Could Redefine Reliability in Customer Service
- Brett Matson
- Oct 15
- 2 min read
The AI world is obsessed with size. Bigger models. More parameters. Higher benchmarks.But what if the next real leap isn’t in bigger brains—it’s in tiny models that quietly fix their own answers?
That’s the premise behind “Less is More: Recursive Reasoning with Tiny Networks,” an exciting new paper from researcher Alexia Jolicoeur-Martineau. Her work explores how extremely small models can reason step by step—improving their answers as they go, almost like proofreading in real time.
What’s New About This Approach
Jolicoeur-Martineau’s model is astonishingly small—less than 0.01% the size of today’s frontier LLMs—yet it outperforms many of them on complex reasoning tasks.
Here’s why that’s so remarkable:
Efficiency: Because it’s so lightweight, it runs dramatically faster and cheaper—potentially 10x or more improvements in cost and latency, depending on the setup.
Simplicity: No obscure training tricks. The model improves its own response through short, repeatable steps, making its process both transparent and governable.
Governance potential: Those small reasoning loops provide natural checkpoints—perfect for auditing, citing, or applying policies.
Why It Matters for Airgentic and Customer Service AI
At Airgentic, we’re always exploring technologies that make customer interactions faster, safer, and more trustworthy. Recursive tiny models like this could unlock powerful advantages across our Unified Answer Layer.
Even more reliable answers: Agents can verify themselves step-by-step—ideal for setup instructions, troubleshooting, and safety content.
Lower latency and cost: Tiny models mean quicker responses and friendlier GPU bills, especially valuable for on-premises or region-locked deployments.
Better governance and control: Each reasoning step is a decision point where we can require sources, enforce policies, or pause during recalls or outages.
The Bigger Picture
This shift toward smaller, steadier, self-checking AI could be transformative for conversational platforms. Instead of relying on massive, opaque models, the future may lie in networks that think in steps, not leaps—transparent enough to trust and efficient enough to scale.
At Airgentic, we’re exploring how this paradigm could enhance our mission:Delivering fast, credible, and controllable AI assistance—right where customers need it most.
Curious what this could look like on your own content?We’d be happy to show a quick walkthrough.
