What prompted you and Robert Greene to write AI Co-Thinking, and what do you hope business leaders will take away from it?
Bob and I kept running into the same gap. On one side was broad talk about AI transformation that never got specific. On the other were tips and prompt lists that didn't change how anyone actually thinks. Most of the professionals we worked with sat somewhere in between. They were dabblers: using AI occasionally, and sensing they were leaving a lot of value on the table.
We wrote AI Co-Thinking to show a practical middle path, treating AI as a thinking partner on the problems that matter rather than a faster search engine. The takeaway we hope leaders carry is simple: AI amplifies whatever you bring to it. Start by defining the problem you are actually trying to solve, treat what comes back as a hypothesis rather than an answer, and stay the person making the call.
In the book, you describe how AI helped challenge your original assumptions while developing Valu-U. What changed in your thinking during that experience?
Chuck Carnevale, his son Colton, and I spent weeks circling design questions for Valu-U: eight weeks or sixteen, one live session a week or two, how technical to make it. Looking back on it, I would say I was too focused on the “how” and not focused enough on what problem we were trying to solve. The breakthrough came when I stopped asking AI what we should build and asked instead what assumptions were embedded in launching this as an investor education program.
The AI pointed out that our framing assumed serious investors lack knowledge. We didn't believe that. Our experience was that they lack a consistent, disciplined decision process. That changed everything. We repositioned Valu-U around decision discipline rather than content, spelled out explicitly what it was not (not stock picks, not a signal feed, not financial advice). The lesson for me: be clear on what problem you are trying to solve before diving into AI.
The book explores making financial expertise more accessible through AI, including Bill Kay's work with business leaders. What did that experience teach you about the relationship between financial expertise, technology and executive judgment?
Bill has spent nearly three decades teaching finance to people who would never call themselves finance people: engineers, operators, and managers at companies like Siemens and QuikTrip. His tools are decades old, including the DuPont decomposition, and they still work.
What AI changes is the groundwork. Pulling two competitors' financials side by side and running the ratios now takes minutes instead of days. What it doesn't do is the translation: explaining why margin compression at a competitor is a strategic signal, or what return on equity says about the operating model. Financial reporting was designed for compliance, not strategy. It becomes useful for strategy only when someone with judgment reads it that way. AI doesn't remove the need for that judgment. It gets you to the point where judgment is the constraint much faster
