LLM strategy: Breadth over depth for cheaper, effective AI

The author argues that for many tasks, particularly those involving LLMs, breadth is a more effective and cheaper strategy than depth. Instead of making a single model proposal more complex or 'think harder,' the focus should be on generating a large number of diverse candidates and then efficiently filtering them. This approach, termed 'breadth is the cheap axis,' contrasts with typical agent stacks that prioritize a single, deeply reasoned chain of thought. While latency and cost accounting present friction, the core idea is that generating many text-based alternatives is computationally inexpensive, whereas executing actions or tool calls should remain a single-threaded, deliberate step. AI

IMPACT Suggests a shift in AI development towards generating diverse options and efficient filtering, rather than solely focusing on deeper reasoning for single outputs.

RANK_REASON Opinion piece discussing LLM strategy and efficiency.

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