When I talk to people putting AI to work in organizations right now, I typically hear two things: either inference costs are out of control or they are paying too much for tokens. Spoiler: those two things mean the same thing. The cost of running AI is too high, and AI agents are making it a lot worse.

Here's the good news (or bad news, depending on your POV): If you're building AI agents, most of the tokens your agent consumes are going to a frontier model that's wildly overqualified for the job, and you're overpaying for it.

We're talking about tasks like:

• Classifying a support ticket
• Extracting a date from an email
• Rewriting a subject line

These are not tasks that require models with hundreds of billions of parameters. A small, fine-tuned model can handle them in milliseconds at a fraction of the cost.

In an exclusive interview with The Deep View, Rob May, CEO of Neurometric, shared how the company is betting big on this mismatch.

On Wednesday, Neurometric launched its SLM Marketplace, a catalog of 115 task-specific small language models (SLMs). Each of these models is under 20 billion parameters and is fine-tuned to do one thing well. You can download any model for free, or let Neurometric host it with up to 100 million tokens per month at no charge. After that, it costs $2 per month per model.

"100 million tokens on one of these models costs us 40 cents," May told me. "So why would we charge for it?"

May says they are seeing a 25/75 split: roughly 25% of tasks in a typical enterprise AI workflow genuinely need frontier-level reasoning. The other 75% are structured, repetitive, and narrowly focused, such as classification, extraction, formatting, routing, scoring, and summarization. SLMs can handle those tasks.

The models in the SLM Marketplace span 14 categories, from accounting and finance to developer tools. Neurometric expects to scale to 5,000 models by year's end. For tasks not yet covered, an Auto-SLM Creator can generate custom models from a plain-language description.

One insight that stood out from my conversation with May was that the biggest challenge with small models is keeping them on track. Larger models handle this naturally, but SLMs need what May described as "micro harnesses," or checkpoint prompts that remind the model what it's doing and what comes next. It's part of an emerging discipline called harness engineering.

Our Deeper View

One of the common themes I've been hearing over and over again across the AI ecosystem in 2026 is that we're moving past the idea of one model to rule them all. There's a larger diversity of models than ever, and they have different strengths and weaknesses. And it's not just LLMs that are ruling the day. Small models, domain-specific models, and now task-specific models are finding a sweet spot because of their cost and performance advantages. We're moving into a multi-model world where the smartest architecture is the right model for the right task at the right cost.