Modern AI models are not inventions. They are descendants.
Every model is a phenotype — a body assembled from memes: ideas that were born, mutated, recombined, competed, and either thrived or died under enormous technical and economic pressure. Origin of Models is the interactive natural history of those ideas — attention, RoPE, MoE, RLHF, DPO, FlashAttention, test-time compute — and of the dead ends along the way.
Models are phenotypes
Every LLM — from GPT-1 to DeepSeek-V4 — is a body that a set of ideas built. Architecture, training data, alignment, scaling, and the tools around it: the model is where the ideas manifest.
Ideas are memes
Ideas are genes. They copy themselves from paper to model to model, mutate under new conditions, recombine with other ideas, and compete for survival. Some spread everywhere; some evolve into something new; some die.
Pressure accelerates evolution
The evolutionary pressure here is economics — a capability that makes money gets copied, refined, and scaled at astonishing speed. Explosive adoption turned a research niche into one of the fastest idea-evolution engines in history.
Every idea has a story — including the dead ends. The ideas that failed teach as much as the ones that won. Each meme card carries an essence for newcomers, a technical deep-dive, an impact rating, and citations that open in new tabs. Nothing here is a black box: everything traces to real papers, reports, and analyses.
The Evolution of Language Models
Every model is a phenotype — a body that a set of memes (ideas) built together. Scroll through time, play the clock, and watch ideas bloom, merge, mutate, and die as they travel from model to model. Click any node to meet the model; click a colored dot to meet the idea behind it.
The Meme Library
Ideas are the genes of the LLM genome. Browse every meme that built the field — the ones that thrived, the ones that evolved into something else, and the ones that were tried and died. Filter by domain, by fate (status), by era, or by the lab that drove it. Click any card for the full deep-dive.
The Genealogy of Ideas
Two family trees. The idea tree traces how each meme descends from the ideas before it — who begat whom, and where the line went extinct. The model tree shows how phenotypes descend from one another and which families dominate the field.
Scaling: the engine of the whole game
Ideas get the credit, but scaling is the engine — compute, data, and the engineering that makes each generation of model physically possible. This is the layer that unlocks everything else: the scaling laws that guide it, the parallelism and precision tricks that make it affordable, and the economics that decide who gets to play.
Frontier training compute, 2017 → 2026
The scaling memes — ideas that unlock size
The scaling narrative
Harnesses & Tools: the adoption layer
Models are raw capability. The harness — serving engines, fine-tuning tooling, evaluation harnesses, agent protocols, RAG stacks, model hubs, and developer tools — is what turns them into products people can actually use. This is the compounding layer: every tool makes the next tool possible, and together they decide how fast, cheap, and easy it is to adopt LLMs.
The adoption waves
The tool memes — the harness idea library
The tools timeline — when each harness arrived
The people behind the memes
Ideas don't evolve themselves — people do. Every meme in this catalog was born in someone's head, carried by a team, and spread through a community. This view maps the humans: who introduced what, which labs were the households of ideas, and how people and memes form a collaboration network.
The pivot explorer
Same data, your question. Group the timeline's memes and models by lab, category, fate, or era, slice by year, and color by whatever facet you care about. Click any cell to meet the ideas or models behind it.
Ideas AI tried — and abandoned
Evolution is written by the winners, but the losers teach the most. Every idea below was born, tried at scale, and lost — to economics, to a better idea, or to the environment changing. Each fossil records Born → Tried → Why it lost → What replaced it.
Methodology & corrections
What we mean by “origin”, how we verify (and mark what we can't), how genealogy edges are chosen, and how to correct the record.
Go down the rabbit hole
A random high-quality idea, dead end, or model from the tree. No two visits the same.