Business Pill 43 · Where an AI model’s knowledge lives
Parameters are the numbers inside a model that training adjusts. Everything the model knows is stored in them, and more of them is a cost, not a guarantee.
A short explainer video, under a minute. The robot and its dials are an illustration.
Picture a new robot arriving from the factory covered in tiny dials. Every dial is set at random. Ask it anything and it answers with nonsense.
Then training begins. The robot reads an example and makes a guess, and each dial is nudged a tiny bit toward a better answer. That happens billions of times.
What a Parameter Is
Those dials are called parameters. They are the numbers inside a model that training adjusts.
Everything the model knows is stored in them. The knowledge is not kept somewhere else: it sits in the settings of the dials.
Before and After Training
Before training, the dials are random and the answer is nonsense. During training, each example moves the dials a tiny bit. After training, the same question gets a sensible answer.
The question did not change. Only the dials did.
More Dials, More Cost
More dials can hold more knowledge. But they cost more to run on every single answer, not just once while the model is being built.
And a smaller model, well trained, often beats a larger one. A bigger count of parameters does not settle quality on its own.
Why It Matters
The video turns this into one question: are we paying for more dials than this job needs?
Parameters are where the knowledge lives. More of them is a cost, not a guarantee. When a smaller, well-trained model can do the job, the extra dials are cost the job does not need.
The Memory Wall builds on Parameters. Every word produced means reading almost the whole model from memory, and those numbers are the parameters training set.
Pretraining builds on Parameters. Pretraining nudges the model a little after every guess, and those adjustments are the parameters the dials lesson describes.
Parameters drives the cost of Inference. More parameters cost more on every single answer, not just once, which is the per-answer bill of inference.
Quantization builds on Parameters. Quantization stores the model’s numbers, its parameters, with less precision so it becomes smaller and faster.
Open Weights builds on Parameters. Open weights means publishing a model’s parameters so anyone can download and run it on their own machines.
Mixture of Experts builds on Parameters. In mixture of experts every expert still takes up space even when idle, so total size and work per question differ.
Scaling Laws builds on Parameters. Scaling laws tie predictable gains to model size, and size is counted in the parameters the dials lesson describes.
Model Routing builds on Parameters. Routing rests on the point that bigger models cost more per answer and a smaller one often does the job.
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.
