AI Business Analysis

What physical AI is: models that see, decide and move in the real world, why robots that follow a script fail on a soft bag, why the physical world is harder than text, and the one question to ask.

What on-device AI is: a model that runs on the phone, the laptop or the machine itself instead of in the cloud, what each side is good at, and the one question to ask.

What a forward deployed engineer is: a vendor’s engineer embedded with the customer to make the product work in the real business, what it costs, and the one question to ask.

What context engineering is: choosing what a model sees before it answers, the four choices behind it, why the same model gives sharp or lost answers, and the one question to ask.

What sovereign AI is: keeping the model, the data and the machines under your own control and laws, the four layers of control, what it costs, and the one question to ask.

What agentic commerce is: shopping where an AI agent finds, compares and buys on a person’s behalf, what sellers need to offer a machine, and the one question to ask.

What incrementality is: the extra sales that happen only because of an ad, how a test between two similar places can find it, the honest limit of one test, and the one question to ask.

What outcome-based pricing is in AI: the customer pays for results, not seats or usage, who carries the risk, what both sides must agree on, and the one question to ask.

What a multi-agent system is in AI: several models working on parts of one task under a lead, what it gains, what it costs, and the one question to ask.

What computer use is in AI: a model that operates software through the screen the way a person does, what it opens up, what it costs, and the one question to ask.

What burn rate and runway are: the monthly gap between spending and earning, how dividing cash by it gives the months left, the three ways out, why AI burn grows with customers, and the one question to ask.

What operating leverage is: when costs stay mostly fixed, profit grows faster than sales, why it cuts both ways, why AI weakens it, and the one question to ask.

What customer acquisition cost is: everything spent on sales and marketing divided by the customers won, why payback time matters more, what happens when a customer leaves early, and the one question to ask.

What churn is: the share of customers or revenue lost in a period, why new wins can hide it, why AI products leak faster, and the one question to ask.

What data labeling is: people attaching the right answer to each example so a model can learn, why it is slow and costly, why the labels set the ceiling, and the one question to ask.

What a GPU is: the chip with ten thousand slow clerks that all work at once, why that suits the simple sums inside an AI model, why the chips are scarce, costly and hungry for power, and the one question to ask.

What model routing is: sending each request to the cheapest model that can handle it, easy questions to a small model and hard ones to a big one, the risk of a wrong turn, and the one question to ask.

What overfitting is: a model that has memorized its examples instead of learning the pattern, the gap between a high score on seen examples and a low score on unseen ones, and the one question to ask.

What a knowledge cutoff is: the date where a model’s training data ends, why the model can answer an old fact with confidence, how to hand it fresh information, and the one question to ask.

What few-shot prompting is: showing a model a few examples of what you want instead of describing it, why examples carry length, tone and format, why their flaws get copied, and the one question to ask.