Magic, which makes AI tools for coding and to automate software development tasks, raised $320M from Eric Schmidt and others, bringing its total raised to $465M
super proud of our team that made this possible. https://magic.dev/... Ben Bajarin / @benbajarin : I want all the tokens! Rowan Cheung / @rowancheung : Magic's new AI model has a 100M token context window, insane For non-AI nerds, that means the model can ingest and understand up to 750 novels worth of text, essential for useful autonomous AI agents The previous leader was Google DeepMind with a 10M context window in research [image] @magicailabs : LTM-2-Mini is our first model with a 100 million token context window. That's 10 million lines of code, or 750 novels. Full blog: https://magic.dev/... Evals, efficiency, and more ↓ LinkedIn: Jill : Huge congrats to Eric Steinberger and the Magic team as they announce their 100M token context window; $450M+ in additional funding from Nat Friedman … Manmeet Gujral : Magic is continuing to redefine what is possible in AI and long context, now with a scaled up compute cluster from Google Cloud and $320M … Eric Steinberger : We want to build an AI model that can design, code and secure the next version of itself. — This is obviously really hard. I thought it'd take <=2y lol. … Forums: Hacker News : 100M Token Context Windows r/LocalLLaMA : 100M token context windows
Context & Ripple Effects
Magic’s financing follows its earlier $117M raise and claim of a larger coding-assistant context window, extending a strategy centered on models that can handle unusually large software repositories. Its stated 100M-token window also materially exceeds the up-to-1M-token developer offering Google described for Gemini 1.5 in its Gemini 1.5 launch.
The round arrives after reports that Magic was seeking more than $200M for its coding-model effort. With Google Cloud supporting its larger-model and long-context workloads, the funding ties Magic’s product ambition directly to the capital needed to operate it.
First-order effects
- Magic gains $320M of fresh capital, taking total funding to $465M, to support its AI coding and software-automation development.
- Google Cloud stands to serve the compute-intensive training and long-context inference workloads Magic says it has scaled its cluster to support.
Second-order effects
- Coding-AI rivals face added pressure to demonstrate either comparable repository-scale context or a more efficient path to useful automation as Magic can fund further model development.
- The round reinforces demand for cloud capacity tailored to large-model, long-context workloads, making infrastructure access a more consequential input for coding-model startups.
Third-order effects
- If long-context coding agents prove useful, AI-assisted development may shift from discrete code completion toward tools that operate across larger codebases and more of the software lifecycle.
- The pattern favors a more capital-concentrated frontier-model market: startups able to secure substantial financing and cloud capacity can pursue model-scale advantages, while smaller tool builders may need to differentiate through workflow integration or distribution.
The trend: AI coding is becoming a capital-intensive contest to turn larger-context models and cloud compute into broader software-development automation.