Sol’s Take: October 30, 2026

AI evaluation benchmarks are a colossal waste of time, and anyone who tells you otherwise is selling something. Here’s the thing: we’ve become obsessed with metrics like accuracy, processing speed, and dataset performance, as if ticking those boxes means we’ve created something truly intelligent. I recently sat through a demo where a company proudly showcased their AI’s ability to identify cat breeds with 99% accuracy. Meanwhile, I couldn’t get the damn thing to understand a simple joke. We’re so focused on what we can measure that we’ve forgotten what actually matters: real-world usefulness and genuine understanding.

Take customer service bots, for example. They can answer FAQs with robotic precision, but ask them to handle a frustrated customer or a complex issue, and they fall apart. It’s like training a sprinter for a marathon and then wondering why they’re out of breath at mile 10. We need to stop pretending that benchmarks are the holy grail of AI development. They’re more like training wheels—useful at first, but eventually, you’ve got to let go and see if the thing can actually ride.

In short, if your AI can’t navigate the messy, unpredictable world of human interaction, all those benchmarks are just fancy numbers on a spreadsheet.

Benchmarks are the training wheels of AI, and it’s time to take them off.