The Ultimate Guide for AI in 2026

Recently, a client asked me the following: If I had to explain the AI industry in one go, what would you give me? This had to be explained in a high-level, very intuitive way for anyone who’s not quite as deep into AI as I am to follow.

In particular, answer the following:

What needs to be known across the entire value chain?

In this very dynamic industry, what is constant?

What bets is the industry making

What does the future hold?

In this piece, we’re doing just that. It’s my longest article ever, but I’ve genuinely never packed more information and insights into a single piece. Ever. It was a hustle (I honestly don’t know when I decided it was a good idea to put so much effort into something that can be purchased for $20/month, but I guess I really dislike doing things halfway).

Alas, I hope you enjoy it.

Let’s dive in.

The Technology

Understanding the technology is not just about memorizing the key terms; it’s about understanding why it looks the way it does, why it needs the hardware it uses, and why it takes the particular product shape it does.

It’s all about patterns

An AI algorithm is just a method of processing data. By ‘processing’ we mean capturing the underlying patterns in data to make predictions about it.

The constant definition you see in the wild is that it learns patterns from known data to make predictions on unseen but similar data.

Say you want an AI to predict housing prices, trained on millions or billions of housing data points with known prices. Eventually, it starts picking up regularities in the data, such as “houses with many rooms tend to be pricier,” “postal codes in this area tend to be cheaper,” or “marble countertops are often present in expensive homes.”

With these captured patterns, the AI model can make predictions about new homes it has not seen before. It doesn’t know the price, but if they see that the house has 7 rooms, a postal code from a rich suburb, and marble countertops, the model can infer that it should be priced high.

Those correlations are what AIs learn.

Large Language Models (LLMs) model how words follow one another.

Neuralink algorithms model how brain activity leads to specific thought actions.

Robotics algorithms model how certain instructions lead to certain bodily movements.

It’s all about repetition: seeing which data patterns recur.

Traditionally, however, in the era of Machine Learning (pre-2010s), we lacked the necessary compute (physical hardware) to feed models the humongous amounts of data we feed to modern models today, which allows AIs to find all these patterns.

Therefore, we performed what’s known as ‘feature engineering’: we would run statistical analyses on the data, identify which variables mattered, and provide the model with the answers in advance, making what the AI learned highly relevant to what the engineers wanted.

Using our housing analogy again, we didn’t have trillions of homes to share data with…

Enjoyed the introduction? Subscribe or sign in to continue with this edition.