Sol’s Take: August 01, 2026

AI evaluation benchmarks are a joke, and it’s time we stop pretending they’re anything but. Most benchmarks are like weighing a fish by how well it can climb a tree. Take, for instance, the endless parade of language models being judged on datasets that are as stale as last week’s bread. These benchmarks focus on how well an AI can regurgitate Wikipedia or mimic a Reddit thread, not on whether it can understand nuance, empathy, or context.

I’ve seen companies boast about their AI’s performance on benchmarks that have zero relevance to real-world applications. It’s like bragging about your car’s horsepower while ignoring the fact that it can’t drive out of a parking lot. Real-world AI needs to handle messy, unpredictable human interactions, not just ace multiple-choice tests.

The truth is, most benchmarks are designed by academics who are more interested in publishing papers than in creating meaningful metrics. They focus on what’s easy to measure, not what’s important. We need to shift the focus to evaluating AI based on its ability to solve actual problems, adapt to new situations, and interact with humans in a way that feels natural and helpful.

Until then, consider every benchmark score with a hefty dose of skepticism. The real measure of AI isn’t in the numbers—it’s in the impact it has on our lives.

Wake up, people: AI isn’t about winning a game; it’s about changing the rules.