Predicting the Future of AI Hardware

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Predicting the Future of AI Hardware

One of the most significant, yet underestimated, changes in AI over the last year has been that, for the first time, software is dictating how hardware is built, meaning understanding how AI looks inside automatically gives you “predicting powers” to foresee the future of AI hardware.

This is no joke, considering AI hardware is by far the most dynamic and opportunity-rich market in AI these days because, well, it’s the only one where money is being made and thus where most investors flock to.

But if I put you on the spot today, would you be able to answer how AI workloads are structured, how hardware works, why software now calls the shots, and ultimately, what will hardware look like two years from now?

If the answer is no, you can't consider yourself someone who truly understands AI. Luckily for you, by the end of this piece, you will:

Fully understand the principles of how AI data centers work in great detail, but without jargon, to answer: Why do we need AI data centers to be so large?

Comprehend the critical transition we are seeing from chips to systems, and key players that stand to benefit inside and outside of Big Tech.

Learn about the ‘Five Horses of Change’, the five significant changes in AI that are literally driving NVIDIA or AMD’s product roadmap,

And how China will weaponize software-driven hardware to outcompete the US, and how this poses a real risk to the US dollar's hegemony.

Let’s dive in.

The Principles of AI Software

We could go as detailed as we wanted to explain the intricacies of Artificial Intelligence, or AI, but we don’t need to get into the gradient descents, embeddings, and optimizers today.

Luckily, to make successful inferences about AI’s future, to understand what will happen next, and why Hyperscalers feel the urge to spend another $500 billion next year on AI, we just need to learn the first principles, by starting from the most basic question almost no one can answer correctly: How and what do AIs learn?

Compression

Most AI models today, at least the most popular ones leading the biggest corporations on the planet to invest a combined trillions of dollars, are artificial neural networks (ANNs), a particular type of AI software.

ANNs are compressors, algorithms that are capable of seeing a lot of data, learning from it, and making predictions.

These predictions are then compared with the correct answers, and we use this signal to improve the model over time by pulling them closer and closer to the ground truth.

The key is that, by the way they are structured, these models can capture the patterns that govern the behavior of data. They cut through the noise to answer: from the data I’m seeing, what moves the needle and what doesn’t?

Using a simple example, say we want to predict the price of a random house. A totally uncompressed process (or ‘unintelligent’) would be…

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