The Apparent Meaninglessness of AI Benchmarks, plus How to Explain AI Opportunities to Others

Every week brings a new AI benchmark. Higher scores. Bigger claims. Louder voices insisting this changes everything. And yet, when you put AI in front of a real business problem, none of that noise seems to help. In this episode, Rob and Justin dig into why AI benchmarks often feel strangely meaningless in practice and why that disconnect is the point. Benchmarks aren’t useless. They’re just answering a different question than the one most businesses are asking.

This isn’t just random conjecture either. Rob walks through what he’s learned building actual AI workflows and why a twenty percent improvement on a leaderboard rarely translates into anything you can feel on the job. They talk about why model choice usually isn’t the bottleneck, why swapping models should be easy if you’ve built things the right way, and why the most successful AI work rarely shows up as a flashy demo. Most of the value is happening quietly, off-screen, inside systems that look a lot more like normal software than artificial intelligence.

Rob and Justin also talk about why explaining AI is often harder than building it. The first demo people see tends to stick, even when it’s the wrong one. Consumer AI feels magical. Business AI face plants unless it’s built with intent, structure, and real context. This episode gives leaders better language for that gap, without hype or panic. If you’re done chasing benchmarks and just want a way to think about AI that survives contact with reality, this episode’s for you.