I wrote this exploring a parallel between AI and agriculture: we may be at the beginning of an efficiency revolution similar to the fertilizer revolution in farming.
Recent evidence is compelling: Phi-4 (14B) outperforming larger models on reasoning tasks through synthetic data, and SYNTHETIC-1 showing how specially crafted examples dramatically improve performance over raw data volume.
The agricultural revolution didn't come from more land, but from understanding what plants actually need. Similarly, AI's next breakthrough might come from understanding what makes effective training data, not just scaling compute or dataset size.
If this analogy holds, by 2026 we should see smaller specialized models consistently outperforming larger general ones on reasoning tasks.
the analogy between deep learning and gardening resonated is a refreshing perspective that highlights the care, and intuition required in both fields. there’s a lot of truth in the idea that both need ongoing attention and a willingness to adapt. tbh sometimes it feels like we're throwing compute or anything that sticks to improve llm performance, so this article felt like a moment of reflection to me