AI builders: chase primitives, not model size
Models impress. Systems ship.
In AI, it's easy to confuse "bigger" with "better." More parameters, more context, more tools, more eval scores often mean horizontal progress: scaling the same approach. Useful, but not automatically a product breakthrough.
Vertical progress is when new primitives change reliability and workflows: cheap inference, structured outputs, function calling, better retrieval, fast feedback loops, agentic automation, and deployment patterns that create real guarantees.
Builder implication: aim for systems that reduce uncertainty for the user. The win is not "it can answer anything." The win is "it can do this repeatedly with low variance." That requires engineering: instrumentation, evals tied to user outcomes, guardrails, and tight product constraints.
A good heuristic: if your demo needs narration, you're likely doing horizontal progress. If your demo replaces a workflow without explanation, you're touching vertical progress.
"Models impress. Systems ship."