About this site
Signal and Noise is a publication for people who have to make real decisions about AI — and are tired of being sold to.
Most of what's published about AI right now falls into two buckets: vendor marketing dressed as thought leadership, or academic content that never touches a P&L. This sits in the gap. Scientific rigor on real business decisions, written by someone who actually ships them.
Separating what works from what sells in AI. From CERN to the factory floor.
What you'll find here
Strategic pieces and tips for the C-suite — CEOs, CIOs, CDOs, CTOs at industrial SMEs and mid-caps — navigating AI without a clear playbook.
Operator notes on what actually works (and what doesn't) when you put agents, RAG, and generative systems into a production environment.
Honest takes on industry hype, including the rare cases when the hype turns out to be right.
No "AI trends 2026" filler. No leveraging synergies across the value chain. No demos pretending to be foundations.
Every piece is meant to help you make a better call.
Who writes it
I'm Colin Bernet.
I spent twenty years at CERN as a particle physicist, working on the algorithm the CMS detector uses to reconstruct what comes out of proton collisions — and was part of the team that discovered the Higgs boson in 2012.
Then I went the other way. In 2020 I co-founded Araïko, an AI consulting firm for French industrial SMEs and mid-caps. A hundred and forty client engagements later, I've seen enough good and bad AI projects to have opinions worth writing down.
The two halves of that arc are what this publication is about. Particle physics is, literally, the science of separating signal from noise — a real measurement buried in millions of fakes. Most of what passes for AI strategy today is the inverse: pure noise, sold as signal. The job here is the same as it was at CERN. Find the signal. Ignore the rest.
Stay in touch
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— Colin