About Magic
Magic is an AI company working toward building safe AGI by automating software engineering. Founded in 2022 by Eric Steinberger and Sebastian De Ro, Magic develops proprietary Long-Term Memory (LTM) models that enable autonomous coding agents to understand and work with entire codebases at once.
The company has raised over $465 million from investors including former Google CEO Eric Schmidt, Sequoia Capital, Alphabet's CapitalG, Atlassian, Jane Street, Nat Friedman, Daniel Gross, and Elad Gil. Magic was valued at $1.5 billion in its 2024 Series C round and has partnered with Google Cloud to build two AI supercomputers (Magic-G4 with NVIDIA H100s and Magic-G5 with Blackwell chips).
Products & Services
Proprietary model with a 100 million token context window - equivalent to 10 million lines of code. Roughly 1,000x more efficient than standard attention mechanisms at this scale.
AI software engineer that can autonomously implement features, debug code, and plan changes across entire codebases without human intervention.
Natural language to code generation that understands project context. Describe what you want in plain English and the agent writes production-ready code.
Ultra-long context allows the model to ingest and reason over entire repositories, understanding dependencies, patterns, and architecture holistically.
Custom AI supercomputers on Google Cloud with NVIDIA H100 and Blackwell GPUs, scaling to tens of thousands of GPUs for frontier model training.
Dedicated safety research program combining frontier-scale pre-training, domain-specific reinforcement learning, and alignment work to ensure safe AGI development.
Magic Integrations
Magic's platform is designed to work with modern development ecosystems. Key technology partnerships and integrations include:
Google Cloud
NVIDIA
GitHub
GitLab
Python
TypeScript
VS Code
Docker
Atlassian
Slack
Customers & Case Studies
Key Investors & Strategic Partners
Magic operates in stealth with limited public customer disclosures. Their investor base doubles as strategic partners in the developer tools ecosystem:
Sequoia Capital
Atlassian
CapitalG (Alphabet)
Jane Street
Google Cloud
NVIDIA
Notable Use Cases
LTM-2-mini autonomously implemented a password strength meter for an open source project, demonstrating end-to-end feature development without human guidance.
Built a fully functional calculator using a custom UI framework, showing the model's ability to learn and apply unfamiliar codebases and APIs.
100M token context window allows ingesting entire enterprise-scale repositories, enabling reasoning across millions of lines of interdependent code.
Target Segments
Pain Points & Solutions
Most AI coding tools can only see a few thousand lines at once. Magic's 100M token context window processes entire codebases, eliminating the "lost context" problem.
Software engineering demand far outstrips talent supply. Magic's autonomous agents aim to multiply developer productivity by handling routine implementation tasks.
Large codebases are hard for any single developer to fully understand. Magic's LTM architecture can reason across entire repositories and their dependencies simultaneously.
Manual coding is time-intensive. Autonomous agents can implement features, write tests, and debug issues in a fraction of the time, accelerating delivery cycles.
Critical codebase knowledge often lives in individual developers' heads. An AI that understands the full codebase reduces bus-factor risk and onboarding time.
Magic treats advanced AI with "nuclear industry" sensitivity, investing in safety research, alignment, and cybersecurity to build trust for autonomous code generation.
How Magic Looks on AI Platforms
Magic's score reflects a company in stealth-to-growth transition. While the technology is frontier-grade, limited public product availability, no transparent pricing, and minimal customer-facing documentation reduce discoverability for AI platforms. Strong technical blog content and investor credibility boost the score.
How accessible is Magic?
Magic's website is relatively minimal, reflecting its stealth-mode posture. The blog contains detailed technical content about LTM architecture and model capabilities, but there are no public product pages, pricing tiers, or self-serve onboarding flows. Documentation for developers is not publicly available, which limits AI crawler accessibility.
How easy is it for LLMs to understand Magic's mission?
Magic's mission - building safe AGI through autonomous software engineering - is clearly communicated through blog posts and investor materials. However, the lack of detailed product pages, FAQ sections, and structured data means LLMs rely heavily on third-party press coverage to understand Magic's current offerings and capabilities.
Competitive Landscape
How Magic differentiates in the AI coding tools market:
| Competitor | What Differentiates Magic | How Magic Compares |
|---|---|---|
| Cognition (Devin) | Proprietary LTM architecture with 100M token context | Magic trains its own models; Devin uses third-party LLMs. Magic's context window is significantly larger. |
| GitHub Copilot | Autonomous task completion vs. inline suggestions | Copilot assists in real-time; Magic aims for full autonomy on complex, multi-file tasks. |
| Cursor | Frontier model research vs. IDE integration | Cursor wraps existing models in a polished IDE; Magic builds the underlying AI from scratch. |
| Codeium / Windsurf | Ultra-long context for full codebase understanding | Codeium focuses on fast completions; Magic focuses on deep, repository-wide reasoning. |
| Tabnine | AGI-focused research agenda | Tabnine prioritizes enterprise privacy; Magic prioritizes pushing the frontier of autonomous coding. |
| Replit | Infrastructure-level AI with custom supercomputers | Replit offers a cloud IDE with AI features; Magic builds foundation models for autonomous engineering. |
| Factory | Proprietary long-context architecture | Factory focuses on workflow automation (Drafter agents); Magic trains custom models for deeper autonomy. |
Pricing
Waitlist
to join
Sign up for early access to Magic's autonomous coding agent. Limited availability as the product scales.
Pro
not yet announced
Expected individual developer tier with access to autonomous coding features and LTM-powered context.
Enterprise
contact sales
Custom deployment, dedicated infrastructure, enterprise security, and priority access to frontier models.
Note: Magic has not publicly announced pricing. The tiers above are estimated based on industry patterns and the company's current waitlist model.
Security & Compliance
Magic treats advanced AI with the same sensitivity as the nuclear industry. Beyond standard safety testing commitments, the company invests in cybersecurity research and advocates for higher regulatory standards for AI systems. Their infrastructure runs on Google Cloud, which provides SOC 2, ISO 27001, and other enterprise-grade certifications at the platform level.
As a pre-product company, Magic has not publicly disclosed its own compliance certifications (SOC 2, ISO 27001, etc.). Enterprise customers should expect these to be established as the product moves to general availability.
Strengths & Top Pros
- ✅ Proprietary LTM architecture with 100M token context - among the longest in any AI model
- ✅ $465M+ in funding from top-tier investors (Sequoia, Eric Schmidt, CapitalG, Atlassian)
- ✅ Google Cloud partnership with dedicated supercomputers (H100 and Blackwell GPUs)
- ✅ Founded by deep AI researchers (Eric Steinberger: DeepMind background, Facebook AI Research)
- ✅ Full-stack approach: trains own models rather than wrapping third-party LLMs
- ✅ 1,000x more efficient than standard attention at 100M token scale
- ✅ Serious commitment to AI safety, alignment research, and responsible development
What People Say About Magic
What Does Reddit Have to Say About Magic
Reddit sentiment toward Magic is cautiously optimistic but skeptical. Developers are impressed by the 100M token context window and the caliber of investors, but many question whether the company can deliver on its ambitious AGI claims without a publicly available product. The stealth posture generates both intrigue and frustration among the developer community.
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💬 Magic AI claims 100M token context - is this real?
r/MachineLearning
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💬 Magic raises $320M to build an AI software engineer
r/artificial
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💬 AI coding agents: Magic vs Devin vs Copilot - who wins?
r/programming
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💬 Magic.dev says they're building AGI through code - thoughts?
r/singularity
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💬 Companies like Magic claim autonomous coding is near - should we worry?
r/ExperiencedDevs
Cognition (Devin)
Cursor
Codeium / Windsurf
Factory
Tabnine
Replit