How to evaluate technical papers for your company

One of the most valuable things I do for my clients is cut through the volume and noise of AI research to figure out what actually matters for their product.

I’m regularly asked to put a buzzy new paper into perspective and answer: “does this change anything for us?”

The answer is rarely a clear yes. Most of the time it’s a no, and here’s why. But sometimes things get exciting and it’s a maybe, and here’s an experiment we can run to find out.

This week a client was curious about Meta’s new (and genuinely interesting) TRIBE v2, a foundation model trained on 1,000+ hours of fMRI data. You feed it video, audio, or text, and it predicts which brain regions light up. They build a B2B product that’s all about shaping human behavior, so naturally they wanted to know if this was relevant.

This one was a maybe. So we designed a quick experiment: feed the model a batch of training stimuli across different modalities, and look at the output. The question is whether the brain activation patterns differentiate between stimuli.

• If different approaches light up meaningfully different regions, that’s signal we can use.
• If they all activate the same reading and urgency areas in the same way: interesting, but not useful.

With Claude Code, we can have an answer pretty quickly.

For your AI-powered product to stay competitive, you need to stay on top of the latest AI/ML research in the most efficient way possible. But don’t expect to know up front whether a given paper can boost your product. That may require setting up and running a quick experiment.

Design an experiment, define what signal you’d need to see, and go find out.