A standard model answers immediately. It reads the question and starts writing. That works for simple requests. But on a hard problem, the first thing that comes to mind is often wrong.
What Reasoning Is
A reasoning model does something different. Before answering, it works through the problem step by step. It tries an approach, checks it, changes course if needed, and only then does it answer.
This is called reasoning, or sometimes thinking. The model spends extra effort at the moment of the question.
A Worked Example
That thinking is not free. It is made of tokens. The model writes out its working, and you pay for every word of it, even though you may never see it.
With round numbers: a quick answer uses 500 tokens and takes two seconds. A reasoned answer may use 5,000 and take 30 seconds. That is 10 times the cost and 15 times the wait, for an answer that is right more often.
Ten times the tokens and fifteen times the wait for an answer that is right more often. Round numbers for illustration.
A Choice, Task by Task
For a greeting or a simple lookup, reasoning is waste. For planning, analysis or code, it is often worth every token. The skill is matching the effort to the problem.
And this changes the economics. Better answers can now be bought with more compute at the moment of use, not only with a bigger model.
Hidden Reasoning builds on Reasoning Models. Hidden reasoning is the private working a model does before it answers, the step-by-step thinking reasoning models add.
Reasoning Models is priced by Tokens. Reasoning is made of tokens: you pay for every word of the working, even when you never see it.
Reasoning Models contrasts with Model Routing. Both match effort to difficulty: reasoning spends more thinking on hard tasks, routing sends easy ones to a cheaper model.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
