Beyond Benchmarks: The Curiosity Differentiator

I came across this tweet from Sam Altman last week, and immediately I'm thinking, "What's going to be the actual differentiator between these different models in a few days, weeks, months, in a year?"

The gains are real, sure, especially for power users in specific use cases. But when you see a competitive market like this start to reach parity, the differentiation for everyday users is subtle.

All the gasoline in the United States basically flows through the same pipes. It's all fungible. It doesn't matter what company the gas actually belongs to. The only differentiation comes from what's added afterward. The differences are often muddled or lost on the consumer.

Chart from Sam Altman's tweet highlighting AI model performance convergence

When we keep throwing out these benchmark improvements and hail them as great progress, I can't help but ask, "What are these benchmarks actually measuring? And what aren't they?"

We think the next real differentiator isn't speed or accuracy; it's whether these tools can make us better thinkers. What if one or two of them started to think about curiosity? Not just study features, but curiosity as the core differentiator. How would they even build a benchmark for it? What would success look like?

At Hypandra, we're not waiting to find out. We're building it.