I wrote my latest article “Living at the Threshold” with the help of AI. But then I re-read it, and re-read it, and it was boring. There is nothing wrong with it per se, except this “je na se pais” that felt inauthentic. The world is going haywire right now and we can all feel it I think. At least I can. It feels like we are all living at the threshold of something very, very big. We are right there, at the threshold, but we haven’t crossed over yet, and the suspense is hard to bear. We don’t know if there will be terror or jubilation on the other side. Although again, the “jubilation” possibility feels a bit off. Icky. Inauthentic in a way that is hard to pin down. Like my article was.

I did learn a lot from AI while preparing the article. I refreshed my memory around vectors and matrixes, and then learned about eigenvalues. This is genuinely exciting for me, as it gives me a mathematical vocabulary that maps directly to the direction that FedRAMP 20x is heading. I was able to pull the thread further and understand connections to physics that, while eluding my full comprehension, seem tantalizingly close to a theory that explains a lot of mysterious things. The observer paradox (i.e., Shrodinger’s cat), quantum entanglement (i.e., spooky action at a distance), and many other intractable problems are explainable via a narrative that makes sense and follows Occam’s razor. The process of discovery was a lot of fun, and I wanted to share that with others, hence the article and deep dive.

But after I published it, it felt weird. It didn’t feel like me. My LinkedIn post felt like I was promoting something that wasn’t entirely mine. So I decided to write my current thoughts “old school” with just me and a keyboard, and this is what came out.

We can use mathematics to formalize risk management in ways that produce tangible outcomes. I believe that adopting eigenvalue measurements to map proximity to thresholds is likely to be a very effective way to do that, so long as the measurements are based on a solid foundation of actual, raw system telemetry data that is contextualized via a transparent policy layer into leading indicators. I don’t have any case studies that eigenvalues will add value, or even a viable proof of concept. It’s just an idea that is worth exploring. And I intend to keep exploring it. But I am going to be more careful to use my blog and my words to connect with others authentically. I’m truly not sure what that even means anymore. Hopefully my readers will continue to extend grace as I stumble along, learning as I go.