The basicsAI glossary
Foundation model
A general-purpose model trained once, at very large scale and huge cost, then adapted to a multitude of uses (translation, summarising, code, assistance), instead of building one AI per task. Because few players can afford that initial training, this approach explains why a handful of companies carry so much weight in the sector.
In other words
Pouring the foundations of a building: the most expensive part of the job, on which everyone then builds their own floor.
Alyriaz articles that mention it
- A startup is valued at 1.4 billion and doesn't train any model from scratch
A young Chinese company reaches a $1.42 billion valuation in seven months by specialising open models rather than training them. We analyse this model and what it reveals.
- K2 Horizon: six open models, from 0.9 to 375 billion parameters
The Institute of Foundation Models has released six open models under Apache 2.0, spanning a range from 0.9 to 375 billion parameters. We explain why publishing a full range changes the game for those building.
- An Arabic model with 428 billion parameters, built on Chinese weights
A Saudi lab has released a 428-billion-parameter model for Arabic, built on Chinese open weights. We analyse what this combination reveals about the new geography of AI.
- Why doesn't the same AI respond differently depending on the app?
The same model produces very different responses depending on the application using it. We explain the five factors behind this gap, and why it's useful to know.
- What is a "foundation model"? The building block on which everything else is built
The term "foundation model" refers to a general-purpose model trained at very large scale, then adapted to many tasks. We explain this concept and why it underpins the entire sector's economy.
- How Apple fits a 20-billion-parameter model into an iPhone
Apple unveiled its third generation of foundation models in June 2026, including AFM 3 Core Advanced: 20 billion parameters that run on a phone by activating only a fraction of them. We explain the technical trick of instruction-guided pruning, and what it signals for on-device AI.