The Problem

Intelligence shouldn't have to ask permission from a 1980s kernel.

Today's most capable AI systems run on top of operating systems designed for word processors and spreadsheets. Windows, Linux, macOS — all built on architectures from the 1970s and 80s, when a GPU was a peripheral device and neural networks were a research curiosity.

The result: every AI workload fights through layers of legacy middleware, bloated driver stacks, and memory management philosophies built for 4KB pages — not 140GB model weights. The infrastructure wastes energy, limits performance, and forces every company to independently re-solve the same foundational problems.

MIOS proposes a different foundation. A purpose-built operating system where the intelligence isn't an application running on the OS — the intelligence is the OS. Native neural scheduling. Direct-to-silicon hardware access. Stateful unified memory. Built open, built to last.

Architecture

Four principles that change everything.

01 — KERNEL
Neural-First Kernel Scheduling

Tensor operations and neural graph executions are first-class kernel processes — not external interrupts from user space. The scheduler is built for probabilistic workloads, not time-sliced CPU threads.

02 — HARDWARE
Direct-to-Silicon Abstraction

A unified bare-metal HAL treats NPUs, TPUs, and GPUs as primary compute — not peripherals. Drivers are stripped to their essential function: feeding data to silicon without translation overhead.

03 — MEMORY
Stateful Unified Memory

Model weights and contextual state are maintained as persistent, high-bandwidth memory pools. No page swapping. No context switching. Memory treated as continuous cognitive state.

04 — ALLOCATION
Intent-Driven Resource Allocation

Hardware resources route dynamically based on the complexity and priority of the active cognitive task — not a rigid round-robin scheduler. 100% of available compute, instantly redirected when needed.

05 — LANGUAGE
The MIOS Programming Language

Following V1.0, the consortium will develop a native programming language for MIOS — probabilistic by design, goal-oriented in compilation, integrating directly into the kernel with zero translation latency. The distance between developer intent and silicon execution, reduced to near zero.

Who This Is For

This conversation belongs to everyone with a stake in what comes next.

Technology Engineers & Researchers The full technical white paper — architecture, kernel design, roadmap, and the case for an open standard over proprietary fragmentation. Read the White Paper → Government Elected Officials & Agencies Why machine intelligence infrastructure is a policy question — and why every level of government has a role to play in shaping it. Government Engagement → Industry Companies & Institutions The case for consortium membership — why competing at the infrastructure layer wastes resources that are better spent on model quality and application development. Join the Consortium → General Curious Minds New to MIOS? Start with the FAQ — plain-language answers to the most common questions about what this is, why it matters, and how to get involved. Read the FAQ → Skeptics Welcome Reviewers & Critics We commissioned an independent technical stress test of this proposal and published it in full, including where it concluded we'd overreached. Read the Independent Assessment →

"The intelligence can no longer sit on top of the operating system.
The intelligence must become the operating system."

— MIOS White Paper, revised July 2026

The Consortium

Built together. Owned by none. Open to all.

No single company should define the foundational infrastructure of machine intelligence. History shows open standards — TCP/IP, USB, OpenGL — outperform proprietary platforms when the infrastructure layer is shared and competition moves to the layers above it.

We are calling on technology leaders, research universities, and government partners to form the MIOS Open Standard Consortium — starting with the vendors who have the clearest incentive to help build an open alternative to CUDA. See the independent assessment for why the roadmap below is longer than our original estimate.

AMD Google Cloud / TPU Intel Arm Microsoft Amazon Anthropic OpenAI + Research Universities

Phased Roadmap

Year 1
Consortium Formation & Prototype

Governing body and working groups established. Linux-based Phase 0 prototype delivers first benchmark data. Engagement begins with the UXL Foundation rather than a competing HAL draft.

Year 2
HAL Specification & Microkernel Core

HAL v0.1 published as a Level Zero extension proposal. Microkernel core development begins, scoped for incremental formal verification.

Years 3–4
Tensor Runtime & Developer Preview

Functional microkernel on AMD/TPU targets. PyTorch backend ported so existing code runs unchanged. First developer preview for consortium members.

Years 5–7
Security Hardening & Independent Audit

Adequately-resourced, incrementally-scoped formal verification and independent security audit — not a compressed pre-launch checklist.

Years 7–10
V1.0 Production Release

Production deployment, upstreamed HAL extensions, and an optional Mira language alpha — a realistic outer bound based on historical infrastructure-standard timelines, not a promise.