BOOM! OPEN SOURCE MRI! You can now 3D-print the core of an MRI scanner. A machine that hospitals pay $1.1 million to $3.4 million for has been broken open. The OSI² ONE and its educational siblings deliver real images of heads and limbs for a fraction of the cost, using a permanent-magnet Halbach array, 3D-printed structures, and fully open designs. This is not a toy or a simulation. Working systems already produce in-vivo images in Leiden, Utrecht, Berlin, and Uganda. The magnet alone—396 carefully oriented neodymium cubes in a cylindrical Halbach array—costs about $1,370. A complete scanner lands between $28,500 and $68,000 depending on the console and coils you choose. No superconducting magnets. No liquid helium. No specialized power infrastructure. It runs from a standard wall outlet and weighs roughly 150 kg. The physics is elegant. A Halbach array arranges permanent magnets so their fields reinforce inside the bore and nearly cancel outside. The result is a usable 50 mT field strong enough for diagnostic-quality imaging of extremities and the head when paired with clever gradient coils, RF coils, and modern reconstruction. Spatial resolution reaches about 1.5 × 1.5 × 5 mm³. The designs are modular: build the magnet first, verify and shim the field with a 3D-printer-turned-field-scanner, then add gradients and RF hardware. The plans are public Everything needed to replicate or improve the system lives in open repositories: • Primary project hub and documentation: opensourceimaging.org/projec… • Full OSI² repositories (hardware, software, magnets): gitlab.com/osii/ • Educational build focused on the Halbach frame, shimming, gradients, and student workshops (Utrecht / Lili’s Proto Lab): github.com/LilisProtoLab/osi… • Magnet-specific details: opensourceimaging.org/projec… Hardware is released under CERN-OHL-W. Most software is GPL-3.0. Where AI multiplies the impact Low-field MRI has historically been limited by lower signal-to-noise and greater field inhomogeneity. That is exactly the regime where modern AI thrives. Image reconstruction becomes dramatically better when deep networks trained on high-field data or physics-informed models denoise, correct for inhomogeneity, and push resolution beyond the raw acquisition limits. Real-time sequence adaptation can adjust gradients and RF pulses on the fly as the AI monitors signal quality. Magnet design itself can be optimized by evolutionary algorithms or differentiable physics engines that search for better Halbach geometries or shim placements than human intuition alone can find. Further out, local AI agents turn these scanners into autonomous diagnostic nodes. A small clinic or even a well-equipped garage workshop could run overnight scans, flag anomalies, and queue results for a remote radiologist—or eventually for a specialized medical model. Synthetic data generation from the open designs lets researchers train robust models without proprietary hospital datasets. Robotics integration (patient positioning, coil placement, maintenance) becomes straightforward once the hardware is open and standardized. In the longer arc of the Abundance Interregnum, this is the shape of things: sophisticated medical instruments that no longer require billion-dollar supply chains or national infrastructure. A distributed network of open, AI-augmented low-field scanners could bring advanced imaging to places that have never had it, while simultaneously giving makers, universities, and small labs the ability to experiment, improve, and specialize the technology. The plans are already on the table. The magnets are commercial off-the-shelf. The 3D printers exist in thousands of workshops. The AI tools for reconstruction and design optimization improve every month. What was once the exclusive domain of major hospitals is becoming a community engineering project. This is how abundance arrives—one open, reproducible, AI-extendable system at a time.