{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"The Apparent Meaninglessness of AI Benchmarks, plus How to Explain AI Opportunities to Others","description":"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\u2019t useless. They\u2019re just answering a different question than the one most businesses are asking. This isn\u2019t just random conjecture either. Rob walks through what he\u2019s 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\u2019t the bottleneck, why swapping models should be easy if you\u2019ve 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\u2019s the wrong one. Consumer AI feels magical. Business AI face plants unless it\u2019s built with intent, structure, and real context. This episode gives leaders better language for that gap, without hype or panic. If you\u2019re done chasing benchmarks and just want a way to think about AI that survives contact with reality, this episode\u2019s for you. ","author_name":"Raw Data with Rob Collie","author_url":"http:\/\/sites.libsyn.com\/295676\/site","html":"