An operator, not a commentator

Who writes this?

Triple major and a minor from the Kelley School of Business at Indiana University. Sat for the CPA exam and went two for two — passed FAR and BEC — then stopped.

Skipping audit and tax was the decision, not the drop-out. Those are the sections that certify you to do work a company can simply buy: that is what you pay PwC for. The parts worth owning in-house are the ones nobody sells you off the shelf — pricing judgment, margin instinct, knowing which number in the deck is the one that will move. So that is where the next twelve years went.

It was a make-versus-buy call, run on his own skill set. The same question this book asks about AI.

Twelve years in strategic pricing and go-to-market strategy since. Corporate-level coaching and advisory work, all of it built on data rather than instinct. The job, most days, is defending a number in a room full of people looking for the hole in it.

Why the small-business years matter more than the corporate ones

In his twenties, he helped grow one of Indiana's more improbable small businesses: ChickenLimo — yes, a limousine shaped like a chicken, and yes, it is a real company that people really book.

That is not a throwaway line. Working a small business teaches you where the money actually comes from, what an owner is afraid of at 2am, and how fast a bad assumption compounds when there is no department to absorb it. Those pain points do not disappear at enterprise scale — they just get more expensive and better hidden.

Most people who write about corporate risk have only ever seen it from inside a corporation. The failure modes read differently when you have watched one bad number nearly end a business you could see from the parking lot.

How he works

• Outcomes over activity. The measure is what changed for the end customer, not what shipped.
• Fast conceptual learner. Built five machine learning models independently — demand forecasting, churn, segmentation, market basket, price elasticity — with no data science team.
• Intellectually curious by default. Which is how a pricing strategist ends up writing a taxonomy of AI failure modes.

What standard does this work hold itself to?

Every empirical claim is labeled by epistemic status: RESEARCH REPORTED ANALYSIS MODELED OBSERVED

Where the author is reasoning rather than citing, it says so — including for the central thesis. Where a finding may not survive the next model generation, that is stated, and its status lives in the Evidence Library with a review date.

He is also, right now, running the book's own 90-day framework on himself in public — because publishing a method you have never run is the one thing this book has no standing to do.