AI Copilot

The name borrows from aviation: the copilot helps, the pilot decides. In a code editor, a copilot suggests the next lines as you type and you press Tab to accept them. In a spreadsheet it might draft a formula; in a CRM, a follow-up email. GitHub Copilot is the best-known example. It began as code completion powered by a production version of OpenAI’s Codex model (Chen et al.) and GitHub now describes it as an assistant that also answers questions, reviews changes and works on tasks (GitHub Docs). Many vendors use “Copilot” as a brand name, so the word on a product doesn’t tell you exactly what it does.

The useful distinction is who is in control. A copilot keeps a person in the loop on every change; an AI agent is given a goal and works through steps on its own. In episode 57 at 15:01, Snorkel AI’s Alex Ratner describes people using a coding tool “as a co-pilot or kind of moving autonomously as a full agent.” Galileo was the presenting sponsor of episode 57 (opening read). In episode 42 at 15:11, Aishwarya Srinivasan uses “something as simple as a copilot, like a coding copilot” to show that even simple tools need a defined level of autonomy and metrics for reliability. Expectations can run ahead of the product: in episode 10 at 10:08, Rizel Scarlett recalls early Copilot users frustrated that “it was more of like a tab completion back then” when they expected an agent. Conor, then at Galileo, mentions its evaluation product at 27:26.

For teams, a copilot is often the lower-risk first step: people review every suggestion, which is a built-in human-in-the-loop check. That review is only as good as the attention behind it, though, so accepted suggestions still need tests and code review. See also AI assistant and vibe coding.