AI Is Profitable: The Real Question Is How Much

You’ve been told AI can’t make money. I’ve said it myself, and that thinking AI will make money one day was more a matter of faith than numbers.

But now, I can confidently say I would be wrong to keep thinking that, because something has changed.

Over the next months, we’re going to see markets adjusting to this new reality: AI is no longer about whether it can make money, but how much and by whom.

Let me explain to you why my previous ideas about AI not being able to make money were wrong (or no longer true under new evidence). We’ll first review the industry, and then we’ll obviously look at what recently changed to fuel my newfound optimism.

However, problems remain. Ironically though, the problem most investors worry about is largely solved, while the real problem lies in the area they already consider solved.

What is the ‘AI industry’?

The AI industry is divided into four layers with considerably different business models and constraints: hardware, infrastructure, models, and applications.

Hardware companies are the set of companies that design and build the accelerators. Companies here include both designers (NVIDIA, AMD, or Google) and manufacturers (TSMC, SK Hynix, Lumentum, and basically the entire semiconductor industry).

Infrastructure companies build the data centers to host those servers and offer them to other companies. These companies can also host models and serve them. These include the Hyperscalers (Microsoft, Google, Meta, Amazon), neoclouds like Coreweave, etc.

Model companies use that hardware to train models and serve them. This includes OpenAI, Anthropic, Mistral…

Application companies build solutions on top of models and charge for the product itself, not the AI.

I don’t need to explain how NVIDIA makes money; it’s obvious. For the others, however, things are trickier than it seems.

The token and margins

Besides strictly hardware companies, the rest on that list have a fundamental relationship between money and tokens. Tokens are just the basic semantic units models break data into. Text into words and syllables, images into patches of pixels.

This is not only necessary for the AI to process and generate data, but it’s also key to meter the AI service. In other words, how much AI costs or charges is based on the number of tokens.

If you send a text that gets broken down into 100 tokens and the AI responds to you with 500 additional tokens, you are charged for 600 tokens; the 100 input tokens get charged at one price, the 500 output tokens at another price, which is always several times higher (this is because output tokens are more expensive to generate relative to how costly it is to process input tokens).

For example, if the input price is $10/million tokens and the output token price is $50/million tokens, that query resulted in a charge of $0.026 for you, or two cents.

You do get charged more because the AI is handling additional input tokens in the background, because for every request you send, the…

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