Investing in Mirendil | Andreessen Horowitz
Infra<br>Investing in Mirendil
Matt Bornstein and Malika Aubakirova
Posted<br>June 24, 2026
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The structure of the modern AI industry has been shaped largely by scaling laws. Large, general-purpose models have conclusively outperformed smaller, hand-crafted models. Training these large models requires tens of billions of dollars and hundreds of thousands of GPUs, so talent and resources have consolidated in a small number of big labs. And the advantage held by the labs is only compounding as the frontier advances.
This structure has generated phenomenal progress, which we hope and expect will continue. It makes sense for the big labs to continue to develop the core, horizontal capabilities of frontier AI models.
But we also believe the full potential of AI will not be realized until the technology is placed in the hands of builders. The array of problems that language models can address is simply too vast, and too impactful, for a handful of companies to tackle them all. The data and domain expertise that lives outside the labs really does matter – as the labs’ extensive RL efforts demonstrate – and not just as a prompt to send to an API. The most direct path to maturity and massive impact for the AI industry is to let engineers and researchers outside the labs to do real AI work, i.e. to push the frontier in their own domains of expertise.
We’ve seen a great example of this through Cursor, where they have grown from relying on third-party models, to building their own Composer models on top of open source, and now pre-training frontier models at SpaceX. The performance and economic viability of the product has improved in each phase, and the modeling work so far compounds in the same way as the labs’ centralized efforts. Not every organization needs to go all the way to pre-training, of course, but most would see very real benefits from being able to run experiments and update model weights.
Satya’s recent post makes the case very clearly why great technology always has, and always will, need a true ecosystem: developers, developers, developers.
To make this vision a reality in AI, we need two things: (1) A family of frontier open source models available for anyone to extend. This gap is currently plugged by the Chinese models, though that’s likely not the long term solution. (2) A lab-grade research platform to help normal engineers do frontier AI work. This is where Mirendil comes in.
Mirendil is building a system that can help anyone do AI work: they train frontier models that are...