Benchmarks aren’t just academic exercises—they’re roadmaps to automation. When researchers create a measurable test for any cognitive task, they set in motion a predictable sequence that ends with that capability becoming autonomous.
The Five-Stage Sequence
Every automated capability follows this path:
- Benchmark: Researchers define measurable tasks with clear success criteria. The key is objective verification—can a computer determine if the answer is correct?
- Optimize: AI labs train models against these metrics. Competition drives rapid improvement.
- Saturate: Top models achieve near-perfect scores, typically within 1-3 years of benchmark introduction.
- Productize: The capability becomes a commercial product feature. APIs appear, startups emerge.
- Automate: Business processes that relied on this capability become autonomous.
Why This Matters
The benchmark is the leading indicator; automation is the lagging outcome. If you can measure something objectively, it’s only a matter of time before AI masters it.
This isn’t speculation—it’s the pattern we’ve observed with translation, code generation, image recognition, and dozens of other capabilities. The question isn’t if but when.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.
