Eric Steinberger

Magic AI develops large-context-window AI models purpose-built for software engineering, enabling codebases with millions of lines to be understood and modified by a single AI system. The company's core technology centers on extending transformer context lengths far beyond industry norms, allowing its models to reason across entire repositories rather than isolated code snippets. This architectural approach positions Magic AI to tackle enterprise-grade software complexity that existing code assistants cannot handle. The San Francisco-based company was founded in 2022 and has raised approximately $465 million in venture funding, reaching a $1.5 billion valuation at its Series C round. Notable backers include Eric Schmidt and CapitalG. The founding team brings deep expertise in machine learning research, with a particular focus on long-range sequence modeling and efficient attention mechanisms that make massive context windows computationally feasible. Magic AI competes in the rapidly expanding AI code generation market alongside GitHub Copilot, Cursor, and Cognition's Devin. However, its differentiation lies in full-repository comprehension rather than file-level autocomplete. The company targets enterprise development teams managing complex, interconnected codebases where context fragmentation has historically limited AI utility. As organizations accelerate AI adoption in software development workflows, Magic AI's ability to process entire systems of code positions it as a distinct entrant in a market projected to exceed $45 billion by 2028.