Rethinking the Economics of AI — 99% Cost Reduction?

I'm currently exploring a question that could fundamentally change how we approach certain natural language processing tasks:

What if we don't actually need massive language models for most specialized decision-making problems?

My current research focuses on whether smaller, specialized GPT-based approaches, combined with the right architecture, can outperform significantly larger LLMs on narrowly defined natural language evaluation tasks.

The hypothesis is straightforward:

If we can achieve comparable or superior task-specific accuracy while reducing inference costs by up to 99%, the economics of AI-powered automation change dramatically.

That's the target, not a benchmark result. The interesting part is finding out where this approach works — and where it breaks.

Meanwhile, I'm developing software for real industrial companies in Germany, where reliability, practical value and cost efficiency matter considerably more than impressive AI demonstrations.

My philosophy is simple:

Why deploy a billion-dollar brain to solve a ten-cent problem?

We're entering an era where architecture, intelligent decomposition and specialized models may matter more than simply throwing additional parameters at every problem.

I'm particularly interested in building systems that deliver measurable results without requiring enterprise-scale infrastructure budgets.

All of this is currently being driven by an exceptionally lean, vertically integrated organization spanning software engineering, DevOps, systems administration, research, marketing and executive management.

Our organizational structure is remarkably flat.

In fact, it's almost a single point.

Curious whether anyone here is experimenting with similar approaches to task-specific model evaluation, smaller decision models or alternative inference architectures.

Where have you found that smaller models can genuinely compete with frontier LLMs?

PS: "—" is Alt + 0151.

You're welcome.