Large AI Models Are Back
Small Models Answer Questions. Big Models Get the Job Done.
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)