AI's State of the Union: Hardware, Infra, and Model Layers

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FUTURE

AI’s 2025 State of the Union

As 2025 progresses, we are beginning to observe the true consequences of the paradigm shift represented by large reasoner models (LRMs). And the more I reflect on this, the more I realize it’s much more than I expected.

Fundamental changes are coming in all areas of the industry.

Introducing LRMs will affect unit economics, CAPEX, and go-to-market strategies… declaring some huge winners. However, it will also cause the first major failures across Generative AI start-ups.

It’s a different industry now, an opportunity for the redemption of some, like those at Cupertino, and a death sentence for others in places like Toronto or France.

Therefore, I am taking a holistic view, from hardware to product/service level, to explain how ‘the game changes.’ At each layer, I will provide the key insights to watch out for and name-drop companies I consider having an edge… and those being at the edge of disaster. Today, I will start with the three main layers: hardware, infra, and models.

Let’s dive in.

LRMs Aren’t Models, They Are Systems.

LRMs represent a considerable transformation both in shape and form. In layman’s terms, AI models have changed in size and, more importantly, structure and deployment.

In fact, let’s clarify this once and for all: LRMs are not AI models; they are systems, a concatenation of multiple AI models, and, crucially, other stuff.

Generators, Verifiers, & Search

As discussed in my rstar-Math piece published in our knowledge base, every LRM has at least two components:

Generator: The AI model that provides the ‘thought’ tokens. This is usually a Large Language Model (LLM).

Verifier: Another AI model (it could be the same model as the generator) that reflects and evaluates the quality of the generator’s thoughts by assigning a quality reward. For that reason, they are also named ‘reward models.’ These are also usually LLMs, but fine-tuned to output scalar reward scores instead of words.

This turns the answer process into a chain of thought, a reasoning trace in which one model suggests solutions to a problem, and the other confirms or reframes the accuracy of these thoughts. For more advanced LRMs, we add a third component, a search algorithm, allowing both models to explore multiple chains of thought in real time. This leads us to models like OpenAI’s o3.

Of course, the significant change here is verifiers. But what are they really?

When anyone mentions verifiers, they can mean a reward model like we just described, but they…

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