What AI Actually Is (and Isn't)
Everything you need to understand AI — and start building with it — explained from zero. Eight short readings, then a complete lexicon. No prior knowledge assumed.
Eight readings in order, or pick a path below. Gold links open lexicon definitions. Search covers all 225 terms.
BeginEight readings, in order.
Read top to bottom and the whole field assembles itself — what AI is, how it works, what you can build with it, and where the money goes. Or pick a path, then dive into the lexicon below.
- Readings — eight essays, top to bottom, or follow a path for your situation.
- Paths — curated shortcuts if you are non-technical, building, or just scanning headlines.
- Lexicon — search or filter 225 terms; tap any card for the full definition.
Who's who — and who pays whom.
Most AI infrastructure headlines come down to a few dozen names in three roles. Learn the roles and the deals stop being a blur. The specific names shift; the shape of the board doesn't. As of 2026.
The labs
- OpenAI — GPT; backed by Microsoft
- Google DeepMind — Gemini; in-house
- Anthropic — Claude; backed by Amazon & Google
- Meta — Llama, open-weight
- xAI — Grok; merged into SpaceX (Feb 2026)
- Mistral — open-weight, EU
- Alibaba (Qwen) · DeepSeek · Zhipu (GLM) — China now leads open weights
The clouds
- Microsoft Azure — backs OpenAI
- Amazon AWS — backs Anthropic
- Google Cloud — owns DeepMind
- SpaceX · Colossus — the Memphis supercomputer; rents to Anthropic & Google
- Oracle — data-centre capacity
- CoreWeave — GPU "neocloud"
The silicon
- Nvidia — GPUs + CUDA; ~75% of accelerators, and slipping
- AMD — the main challenger
- Google TPU · Amazon Trainium · Microsoft Maia — in-house silicon
- Broadcom — custom accelerators
- TSMC — fabricates nearly all of it
The complete field guide.
Every term, organized not alphabetically but by how the pieces fit together. Tap any card to open it — each holds a plain definition, a concrete example, and where it connects.
Where to actually begin.
You don't need to install anything or write a line of code. Six moves turn everything above into something you can use this week.
How to read the whole field.
Researchers live in the architecture, training, and prompting layers. Builders and operators care about inference, tasks, and evaluation. Safety and policy people orbit alignment. Investors track the hardware — with scaling laws as the thesis tying it together.
And the one thing to never forget: these systems are built to sound right, which isn't the same as being right. Anything that matters gets an independent check.
On the numbers.
The figures here — capital spending, training scale, market positions — move fast. They were verified against current sources as of July 2026 and are framed to age gracefully, but treat any specific number as a snapshot, not a fixed point.
Where a claim was time-sensitive, it’s dated in the text. Everything conceptual — how a model works, what an API is — stays true regardless.
Compiled by AtlasScope.
Figures current as of July 2026.