From 0 to 100 in AI. CEO Edition

Last week, I gave a talk to the CEO of one of the largest hospitality companies here in Spain, who wanted to understand five big things:

How to think of AI in first principles,

The AI in the enterprise, including the ‘where does AI fit’ framework that easily identifies opportunities and discards mistakes

The short-term impact of AI in her company,

The long-term impact,

And a series of key tips for personal usage, my 10 commandments of AI usage.

The goal? Provide a blueprint for thinking strategically about the tool that will shape our future. No time for hype. Nor bullshit. No flashy predictions and empty promises. Just honest, down-to-Earth recommendations and insights in a language anyone can understand.

If I did my job well, by the end of this newsletter, you’ll have a holistic and sound understanding of AI without noise or jargon; just first principles.

This is what I told her.

Foundations

Many of you already know what AI is in great detail. Neural nets. Embeddings. Next-token prediction. Backpropagation.

But it’s perhaps even better to try to simplify it to the essentials. How can we describe AI without giving a single mention to any of that jargon, and still stay true to what AI is in first principles?

This is my best take at doing that, nearing a decade of AI study.

The mapping analogy

AI is a map. Yes, a map. A map between an input and an output. It’s something that takes in data and uses it to produce (the word is "predict") more data. Every single AI on this planet, no matter how different the prediction is, is built that way.

We call it a prediction because there’s some uncertainty; we can’t fully guarantee it’s correct, only infer.

ChatGPT takes words in and produces the next one; Midjourney takes words in and produces an image; Google’s Nano Banana takes in text and produces text and images; Genie 3 takes in an image and a user action, and produces the next frames that capture the user’s action in the scene; Neuralink’s chips take in brain signals and produce computer actions.

Of these, those that ‘generate’ something with that prediction are called “Generative AIs”. While the most popular AI models today are generative, not all AI models are.

There are various types of AIs, but the most important one is neural nets. In 1932, Edgar Adrian won a Nobel Prize for discovering three attributes of human neurons that occurred across the entire animal kingdom: they had all-or-nothing behavior, either they “fired,” or they didn’t. They used “rate coding” (not the intensity of each firing, but the firing rate, that matters), and they adapted to new stimuli.

AI researchers took the concept of a neuron and used it as inspiration to build an analogous “machine brain,” called a neural network, which combines artificial neurons deeply connected to one another, as human neurons are.

Real neurons (left), artificial neurons (right). source

A human brain has many fewer neurons than…

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