About
Magic builds frontier-scale code models and AI software-engineering agents intended to function as a coworker for developers, not merely a copilot. It targets software-development users and differentiates through vertical integration: researching, training foundation models, building products, and working directly with users while pursuing safe AGI.
Market
Magic competes in generative AI for software development, spanning AI coding assistants, code-generation tools, and increasingly autonomous software-engineering agents for professional developers. It positions itself around frontier code models and an AI software engineer, using LTM ultra-long context to process entire repositories and support higher-context, longer-running tasks rather than only inline completion. Its clearest differentiation is its 100M-token context capability and custom efficient training and inference stack, compared with products such as GitHub Copilot, Cursor, Devin, Factory Droids, and Replit Agent.
Magic primarily targets professional software developers and software engineering teams, especially those working with large or complex repositories and needing assistance writing, reviewing, debugging, planning, and implementing code changes. Likely users and buyers are individual developers, engineering leaders, and organizations seeking an AI software engineer or automated pair programmer; the evidence does not establish a specific industry vertical or company-size focus.
At a Glance
Problem
Magic is addressing the bottleneck created by complex software development, scarce engineering talent, and the high cost of developer labor. Its 2023 financing announcement described software engineering as highly complex, collaborative, and a bottleneck for many organizations; it also cited a tight technical-talent market and average software-engineer compensation of more than $150,000 per year. The economic opportunity is to let existing developers and organizations produce more software without hiring proportionally more engineers.
The main use case is an AI engineer that can take a substantial software issue and deliver a reliable pull request for an entire feature. Magic’s stated ambition is to make that workflow cost roughly $100 and take about ten minutes, turning software development from a labor-constrained process into a much more scalable one.
Product / Service
Magic is developing an AI software engineer or “AI colleague,” rather than only a code-completion assistant. The intended system communicates in natural language, collaborates on complex code changes, and continually builds an understanding of the work and the user. Its technical approach combines frontier-scale pretraining, domain-specific reinforcement learning, ultra-long context, and inference-time compute.
The key differentiator is repository-scale memory. Magic’s Long-Term Memory models are designed to reason over up to 100 million tokens—roughly ten million lines of code or 750 novels—while using substantially less inference memory than a conventional large model at that context length. The company has described LTM-2-mini and a prototype trained on text-to-diff data, and is using Google Cloud infrastructure, including H100 and GB200 systems, to train and deploy larger models. The evidence does not show a public pricing, API, or generally available end-user product, so the current delivery model appears to be frontier-model and product development ahead of clearly disclosed commercial availability.
Market
Magic competes in the generative-AI software-development market, specifically the emerging category of AI coding assistants and autonomous or agentic AI software engineers. It aims to move beyond simple code completion toward repository-level code generation, multi-step engineering work, and feature delivery. Relevant competitors include Cognition’s Devin, Tabnine, Augment, Cursor, GitHub, Anthropic, and OpenAI; industry coverage identifies several of these companies as leaders or major competitors in enterprise AI coding agents.
Magic’s disclosed traction is primarily technical and financial rather than commercial. The company reported raising $515 million in total, including a $320 million investment announced in 2024, and said it had thousands of GB200s; its 2024 research update also described 8,000 H100s, a 23-person team at that time, and plans to scale to tens of thousands of GB200s. The available evidence does not disclose revenue, named customers, or a broadly launched paid product, so Magic should be characterized as a heavily funded, research- and infrastructure-intensive company with commercial traction not yet publicly demonstrated in this corpus.
Founders & Leadership
Funding History
ROI Ventures
CapitalG
NFDG Ventures
Eric Schmidt
Undisclosed investors
Recent News
A StartupIntros company profile described Magic AI as an AI teammate platform that centralizes organizational knowledge by training custom chatbots. This is third-party company coverage rather than a company announcement.
Tracxn reported that Magic AI had raised $466 million across four funding rounds. The profile identified March 29, 2024 as the date of the company’s latest funding round.
An OpenAIToolsHub teardown reported that Magic had raised approximately $465 million and said the company was generating about $2 million in revenue as of mid-2024 with a team of roughly 23 people.
A Hacker News discussion revisited Magic.dev’s claimed 200-million-token context window and criticized the company for having no real product two years after the claim. This is community commentary, not an official company announcement.
An Exa funding overview listed Magic AI, Inc. as having raised $466 million in total funding. The record is a funding database/profile entry rather than evidence of a new financing round during the period.
Active Roles
9Business Model
Magic’s exact monetization model, pricing, and revenue streams are not publicly disclosed in the available sources. The evidence indicates a developer-tools product business built around AI coding and software-engineering products, rather than a confirmed subscription or API pricing model.
Products
Tech Stack
Similar Companies
Competitors
Key Investors
CapitalG, Sequoia Capital, Founders Fund, Jane Street