Mirendil Raises $1B Series A to Bring Frontier AI R&D to More Teams

Mirendil announced on November 12, 2026 that it had raised $1 billion in a Series A led by Kleiner Perkins and Clearlake Capital to make frontier AI research and development accessible to more scientists and engineers.

Battery Ventures, Doerr Capital, Gil & Co, Google, M13, Pear VC and S32 participated, alongside other new and existing investors. Mirendil is developing models and agent software around a specific goal: enabling one person to take on work that previously required a team, and a team to pursue projects that previously required an institution.

The One-Researcher Training Result

The funding follows the company’s first published model results. In an October 21, 2026 report, Mirendil said one researcher working with AI agents pretrained Mirendil-Base, a coding model, from scratch in 3.5 months with less than $10 million in compute. The all-in figure includes research and small-scale experiments, the final training run, agent token spending and data processing.

The company reports that agents wrote more than 99% of the code, ran experiments and diagnosed failures, while the researcher chose what to try and trust and intervened when needed. The experiment used publicly available code, web pages and public datasets, without purchasing or licensing additional data and without distillation or synthetic data. Mirendil-Base sits in the 100 billion to 500 billion parameter range; the larger Mirendil-Base-Large and Mirendil-Base-XL models were still in training when the report was published.

Fern and Lor Form the Product Stack

Mirendil subsequently post-trained Mirendil-Base into Fern 0.1, its first model. The Fern model page describes a system for high-volume agentic work such as debugging training runs, reading complete agent traces and auditing training data. It says the architecture, training and serving support a context window of up to 10 million tokens, roughly 23 MB or half a million lines of code, allowing the model to hold days of an agent’s work at once. Access is listed as coming soon.

Lor is the platform for delegating that kind of work. Mirendil introduced Lor on October 28, 2026 as a cloud-based system whose agents can write code, run experiments, provision compute and operate on machines selected by the user. An agent can divide a job across dozens of agents, with hundreds running across projects, and the company says agents can continue for days or weeks and unblock themselves when something breaks.

Lor is available by invitation in private beta and is free during that beta on macOS, Linux and the web. Windows, iOS and Android are listed as coming soon. Users can follow each agent’s progress, stop agents and remove machines from the system.

Compute and Capital Behind the Plan

At its June 24, 2026 launch, Mirendil described a founding team of 20 researchers and engineers from institutions including Anthropic, xAI, Google DeepMind and OpenAI. It also disclosed a $200 million seed round led by Andreessen Horowitz and Kleiner Perkins, followed by investment from NVIDIA and others.

Mirendil had already announced a multi-year Google Cloud partnership on August 6, 2026. The arrangement puts training, inference and AI research workloads on Google Cloud’s AI Hypercomputer, drawing on Google Cloud TPUs and full-stack NVIDIA AI infrastructure. It includes managed training clusters co-designed across compute, storage, networking and control planes to support larger research loops.

The Series A therefore connects three parts of Mirendil’s plan: agent-assisted model development, a platform for delegating research work, and cloud infrastructure for larger experiments. Whether its internal one-researcher result can become a repeatable workflow for outside scientists and engineers is the central test for the accessibility mission behind the round.