Signal Glossary: AI Terms You Need to Know

Intelligence Layer

AI Glossary

Practitioner-grade definitions for the terms shaping the AI era. No fluff — just what the words actually mean and why they matter. Full entries available to Signal members.

A

AGI (Artificial General Intelligence)

AI that can perform any intellectual task a human can. Nobody agrees on the exact definition, which is part of the problem. Some labs claim we're months away; o

API (Application Programming Interface)

The interface that lets software talk to an AI model programmatically. APIs are how AI gets embedded in everything — every app, every workflow, every product th

ARR (Annual Recurring Revenue)

The annualized revenue from subscriptions. In AI, ARR growth rates are the market's primary signal for which companies are winning. OpenAI's ARR trajectory, Ant

ASI (Artificial Superintelligence)

Intelligence that surpasses the best human minds across every domain. If AGI is "as smart as a human," ASI is "smarter than all humans combined." This is where

An AI system that can take actions, not just generate text. An agent doesn't just tell you what to do — it does it. Books a flight. Writes and runs code. Sends

The broad category encompassing agents, autonomous agents, and multi-agent systems. "Agentic" has become the industry's favorite adjective because it captures t

The challenge of ensuring AI systems do what humans actually want. Easy to state, staggeringly hard to solve. A misaligned superintelligence isn't necessarily m

Application Layer

The products and services built on top of models — ChatGPT, Cursor, Copilot, custom enterprise tools. The application layer is where models meet users.

Autonomous Agent

An agent that operates without human approval for each step. It receives a goal, decomposes it into subtasks, executes them, handles errors, and reports back. T

C

Coding Time Horizon

How far into the future an AI model can reliably plan and execute a software task. A model with a 4-hour coding time horizon can handle tasks that would take a

The raw computational power required to train and run AI models. Compute is the oil of the AI era — whoever controls the most of it has the most leverage. The g

The amount of text (measured in tokens) a model can process at once — its working memory. A small context window means the model forgets the beginning of your c

D

The physical infrastructure where AI models are trained and served. Modern AI data centers consume as much power as small cities and require specialized cooling

F

Taking a pre-trained model and training it further on specialized data. If training builds the foundation, fine-tuning adds the specialization. A medical fine-t

Foundation Model

A large AI model trained on broad data to serve as a general-purpose base. Think of it as the foundation of a building — it doesn't do anything specific on its

The most capable AI model available at any given moment. "Frontier" isn't a fixed label — it's a moving target. Today's frontier model is tomorrow's baseline. W

G

GPU (Graphics Processing Unit)

The hardware that powers modern AI training and inference. Originally designed for rendering video game graphics, GPUs turned out to be perfectly suited for the

Technical and policy constraints placed on AI systems to prevent harmful outputs. Content filters, safety training, usage policies. Guardrails are the practical

H

When a model generates confident, fluent, and completely false information. Hallucinations aren't bugs in the traditional sense — they're a structural feature o

I

The act of running a trained model to get outputs — the "thinking" part. Every time you send a prompt to ChatGPT or Claude, that's inference. It matters because

Intelligence Explosion

The hypothetical point where recursive self-improvement accelerates beyond human ability to track or control. Coined by I.J. Good in 1965: "the first ultraintel

M

MoE (Mixture of Experts)

An architecture where a model contains multiple specialized sub-networks ("experts") and a router that activates only the relevant ones for each input. MoE mode

The foundation models themselves — GPT, Claude, Gemini, Llama. The model layer is where raw intelligence lives. Historically, the model layer and the applicatio

Multi-Agent Orchestration

Multiple AI agents working together, coordinated by a lead agent or framework. One agent researches, another writes code, a third reviews it, a fourth deploys i

O

In AI, truly open-source means the weights, the training data, the training code, and the methodology are all public. Almost nothing marketed as "open-source AI

A model whose trained parameters (weights) are publicly released, allowing anyone to run it. Llama and Mistral are open-weight. "Open-weight" means you can use

P

Prompt Engineering

The practice of crafting inputs to get better outputs from AI models. Part art, part science, and — increasingly — part anachronism. As models get smarter, the

R

RLHF (Reinforcement Learning from Human Feedback)

A technique where humans rate model outputs and the model learns from those ratings. This is how raw language models become helpful language models — the differ

Recursive Self-Improvement

Self-improving AI taken to its logical conclusion: each improvement enables a faster or better improvement, creating a feedback loop. This is the theoretical me

Deliberately trying to break, bypass, or misuse an AI system to find vulnerabilities before bad actors do. Borrowed from military and cybersecurity practice. Re

S

A benchmark that tests AI models on real-world software engineering tasks — fixing actual bugs in actual open-source repositories. SWE-Bench matters because it

Self-Improving AI

An AI system that can enhance its own capabilities. This is the concept at the heart of "the wall" — when AI gets good enough to make itself better, the improve

Superintelligence

Used interchangeably with ASI, but sometimes refers more broadly to any system whose cognitive capabilities exceed humanity's collective capacity in a given dom

T

TPU (Tensor Processing Unit)

Google's custom AI chip, designed specifically for machine learning workloads. TPUs are Google's answer to NVIDIA's GPU dominance — purpose-built silicon that t

The Collapse Between Layers

What's happening right now: model providers are building applications, and application builders are training models. The distinction between "model company" and

The Wall / Vertical Innovation Curve

Signal's term for the inflection point where AI progress goes vertical. See our series, "The Vertical Wall." The wall isn't a barrier — it's the moment the expo

The units models use to process text. Roughly, one token equals about three-quarters of a word in English. Tokens matter because they're the unit of cost (you p

The process of building a model by feeding it data and adjusting its parameters. Training a frontier model costs hundreds of millions of dollars, requires thous