Make money doing the work you believe in
Bryan, this is one of the clearest articulations of the amplifier thesis I've read outside my own research.
Your Flaw 2 is the one that matters most: averaging across maturity levels doesn't measure AI's potential, it measures noise. Studies will keep getting this wrong until someone controls for engineering discipline before turning on the tools.
I've been investigating this question from a different angle. After 130+ conversations with investors, CEOs, and CTOs behind 52 unicorns for my podcast, The Forge of Unicorns, I reached the same structural conclusion: AI readiness isn't about adoption percentage, it's about organizational capacity to absorb acceleration without breaking.
The HPF metaphor is elegant. I've been calling it an amplifier, same physics, same implications.
What I added to the model is a four-dimension diagnostic: focus and cognitive capacity, technical validation, product clarity, and customer feedback speed. When you measure all four, you can calculate the gap between how fast an organization is adopting AI and how prepared it actually is. That gap now has legal weight under EU AI Act Article 4, which has been binding since February 2025.
The assessment is open source, AGPL licensed, and free to use with dedicated portal:
Would genuinely value your take on it. I think our two models are describing the same system from different vantage points. Something I’d like to explore in a dedicate podcast episode if you’re up for.