Cam Linke

We live in an alphabet soup of AI benchmarks - MMLU, GPQA Diamond, HLE, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard. Benchmarks are supposed to measure progress. Instead, they’ve become the AI equivalent of EBITDA: technically informative, practically an exercise in costume design. Two predictable failures: ▪️Saturation: Everyone hits 90% eventually. Once frontier models ace MMLU or GSM-8K, the test stops meaning anything. It’s like grading Olympic sprinters on whether they can jog a 10-minute mile. Impressive the first time, meaningless once everyone’s jogging across the finish line. The scores go up, the signal goes down. So researchers invent the next “harder” exam, and the cycle repeats. ▪️Optimization: Benchmarks get hacked. Models fine-tune for the test, not the world. You can wire a model to dominate HumanEval without making it a better programmer. You can goose GSM-8K with chain-of-thought tricks that would never help in an actual math class. In some cases (hello, Llama 4), vendors allegedly submitted “special” benchmark-only models. That’s not progress - it’s leaderboard cosplay. If you’re buying AI solutions, this is where things get tricky. On paper, everyone looks great. But none of that tells you whether Vendor X’s chatbot will resolve your customer tickets, or Vendor Y’s model will flag fraud in your transaction flow better than the rest. Benchmarks test the generic. Your business runs on the specific. Which is why you need a golden dataset: a private exam with your data, your edge cases, your definition of success. Fraud logs in finance. De-identified patient notes in healthcare. Customer chat transcripts with the slang and sarcasm only your users know. This is boring work, but it’s also the only thing that matters. Because: 1. Different vendors optimize differently. One for reasoning, one for retrieval, one for latency. All of them might be “top-ranked” broadly - but only your dataset will reveal which matches your needs. 2. The real world is messy. Benchmarks (especially static ones) sanitize the world. Your data has noise, ambiguity, system interactions, edge cases, domain jargon. A model might get 95 % on MMLU but tank on your rare edge case. 3. Contamination happens. Tests leak into training data, so models memorize. Private datasets cut through that. 4. The tails matter. In public benchmarks, everyone clusters near the top. The spread shows up in the long-tail failures. Benchmarks are like standardized tests. They’re useful for broad comparisons, but if you’re hiring, you don’t just want to know if a candidate can ace the SAT. You want to know if they can actually do the job you’re hiring for. AI is no different. So yes, clap politely when a model sets a new record. But when it comes to procurement, bring your own exam. The only benchmark that matters is the one no one else has - yours.

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