---
title: From the Caves to AGI
description: "An intellectual history of AI: from the first mathematical abstractions to foundation models. Mathematics, philosophy and computing in context."
keywords: artificial intelligence history, AI history, Transformer, foundation models, AGI, deep learning
date: 2026-03-26
tags:
  - AI
  - AGI
  - LLMs
  - History
hide:
  - toc
---
# From the Caves to AGI

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This series is a historical journey from humanity's first attempts to abstract the world through symbols to the creation of massive language models that *seem to understand it*.

We will follow how the need to count led to calculation, how the desire to mechanize reasoning led to computing, and how the attempt to imitate the brain brought us into the era of generative AI.

The history matters because the generative-AI systems we use today are not artifacts without a past. They are the direct result of five centuries of mathematical notation, eight decades of computing and more than half a century of machine learning. Following that chain from the beginning changes how you understand why AI works the way it does, where its real limits are and what may come next. Without the context, recent advances look like magic. With it, they form a sequence you can follow.

## Contents

### 1. Represent (≈ 40,000 BCE – 1700)
- **Invent languages for describing the world:** from physical counting—marks and notches—to numbers and manipulable symbols.
- **Formalize truth:** the Greeks establish proof and geometry as standards for verifiable knowledge.
- **Do science with mathematics:** notation such as zero and algebra, then models for prediction and calculus for describing change.

### 2. Mechanize (≈ 1700 – 1956)
- **Turn symbols into machinery:** automate calculation mechanically and later electronically. This is where the obsession with computation begins.
- **Separate program from hardware:** from Jacquard and Babbage to reusable instructions independent of a specific machine.
- **Foundations of modern computing:** logic, proof, computability and its limits, practical computer architecture, information and compression.

### 3. Learn (≈ 1956 – 2012)
- **From rules to data:** move from hand-coding intelligence to fitting models from examples.
- **Engines of practical learning:** probability, optimization and parameter adjustment through backpropagation.
- **Before the LLM era:** the perceptron and its limits, the rise and fall of symbolic AI, statistical NLP and the neural revival driven by data, GPUs, benchmarks and scale.

### 4. Scale (≈ 2012 – 2024)
- **Scale as a multiplier:** deep learning accelerates through more data, compute and better representations; 2012 marks a regime change.
- **Transformers and foundation models:** attention enables massive pretraining and broad reuse. Scaling laws and human alignment turn this into the inflection point that eventually produces ChatGPT.
- **From answering to acting and unifying modalities:** tools, agents and multimodality across text, image and audio.

### 5. Beyond the Transformer (≈ 2022 – Q1 2026)
- **The limits of pure scaling:** why the Transformer alone is not enough to describe where the frontier is moving.
- **Memory and active inference:** long context, external memory and test-time compute as new levers beyond parameter count.
- **Agents, tools and search:** systems that act, plan and retrieve information from the real world.
- **World models and foundation robotics:** internal representations that simulate and predict, applied to physical control.

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**Recommended next series:** [Generative Multimodality](/en/series/multimodalidad-iag/00_presentacion_serie/) — what it means to unify text, image, audio and video inside one system.

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