Author: Tom Vatland – CTO – AI VISIONS AS
Date: March 30, 2025

Tokenization defines the boundary between raw input and model interpretation. Every model operates on tokens, not on the original data. Whether the input is language, vision, audio, or time series, the model does not see the data directly. It sees a discrete representation created by the tokenizer.

This representation is not neutral. It imposes structure. It discards information. It creates assumptions about what matters. The tokenizer defines the vocabulary of the model’s world. It decides what exists and what does not. Once the data is tokenized, the space of possible abstractions is constrained. No matter how large the model, it cannot recover information that was never encoded in the first place.

Most tokenization methods in use today are static and task-independent. They are optimized for compression and throughput, not for semantic alignment. Subword tokenization in text splits based on statistical co-occurrence, ignoring syntax, semantics, and discourse structure. Image tokenization often relies on fixed-size patches, with no adaptation to object boundaries or visual hierarchy. Audio is usually sliced into uniform windows, even though signal features are rarely uniform over time. These approaches introduce noise, lose structure, and treat context as irrelevant. They force the model to learn around a flawed abstraction.

This leads to inefficiency. Larger models are required to compensate for the representational damage done at the input stage. Models learn to stitch meaning back together from fragments that should never have been separated. They overfit to formatting artifacts and data-specific quirks. We end up scaling compute to overcome limitations that originate from poor tokenization.

What is needed is a tokenization strategy that is data-aware, task-sensitive, and capable of encoding structure explicitly. Tokenization should not be a fixed transformation but a dynamic function that adjusts to the statistical and semantic properties of the input. It should capture hierarchy, preserve relevant relationships, and exclude noise before training begins. When tokenization is aligned with the structure of the data and the objectives of the model, everything downstream improves. Fewer parameters are needed. Training becomes more efficient. Generalization becomes easier.

Compute helps. Data helps. But neither addresses the structure of the input space. Tokenization defines that structure. It determines what the model can learn and what it will never see. That is why tokenization is the holy grail. Not because it is more important than model design or hardware, but because it is the condition that makes both of them meaningful.

#AI #tokenization #LLM #machinelearning #deeplearning #AGI #representationlearning #modelarchitecture #transformers #embedding