After two years of “small is smart,” AI agents are changing the equation. When AI has to complete long, multi-step tasks on its own, model size makes the difference, and Swiss organisations can now run that power on sovereign infrastructure.
Act 1 (2023): bigger LLMs are better
In 2023 the playbook was trivial: call the largest model behind an API and build around it. Nothing else came close. By the end of that year OpenAI alone held 50% of the enterprise LLM market (Menlo Ventures).
The use cases were also simple: a chatbot, a summary, a first draft. One prompt in, one answer out, a human checking the result.
Act 2 (2024 to 2025): the rise of small language models
Then the bills arrived, and with them a new narrative. Distillation, pruning and better training data let compact models punch far above their weight. Microsoft's Phi-4, at 14B parameters, beat GPT-4o-mini on math and code benchmarks. (Meta Intelligence).
The arguments were real:
- Cost and latency: one tenth to one fiftieth of the deployment cost of a large model for many tasks.
- Privacy and control: run it locally, keep data in-house.
- Good enough: most enterprise NLP is classification, extraction and summarization.
The narrative peaked with NVIDIA's 2025 position paper arguing that small models are the future of agentic AI, and Gartner forecasting a threefold rise in task-specific models by 2027 (HackerNoon, Medium). Analysts still label 2026 the year of efficiency (Tech Monitor)