TL;DR
Unconventional AI released Un-0, an image generation model on a simulated oscillator architecture that founder Naveen Rao says could cut AI power 1000x.
The $475 million startup founded by the former head of AI at Databricks has demonstrated that its physics-based computing approach can replicate conventional AI, though the hardware does not exist yet
Unconventional AI released Un-0, an image generation model on a simulated oscillator architecture that founder Naveen Rao says could cut AI power 1000x.
Unconventional AI, the startup founded by former Databricks AI chief Naveen Rao, has released its first AI model, an image generation system called Un-0 that runs on a completely new kind of computing architecture. The model produces results comparable to state-of-the-art diffusion models like Stable Diffusion, according to an accompanying research paper. The catch is that it runs on a software simulation of hardware that does not yet exist.
The company is building an oscillator-based computer architecture that abandons the digital logic underpinning virtually all modern computing. Instead of processing data through transistors performing binary operations, Unconventional’s approach uses coupled ring oscillators in a fabric network, encoding and processing information through the physics of the oscillators themselves. Rao told TechCrunch that this approach could ultimately reduce power consumption by a factor of a thousand compared to conventional chips.
That claim is aspirational. US utilities are planning to spend nearly one and a half trillion dollars by 2030 on infrastructure driven largely by AI data centre demand, and any technology that could meaningfully reduce that burden would be enormously valuable. But Unconventional has not built a physical chip, and the thousand-fold improvement exists only as a theoretical projection.
What Un-0 does demonstrate is that the architecture can replicate the function of conventional AI systems. The research team built a fully functional image generation model using a software simulation of the oscillator architecture, and the paper shows it performing on par with established diffusion models. “This is the ‘hello world’ of a new kind of computer,” Rao told TechCrunch.
Rao has a track record that makes investors willing to bet on the premise. He co-founded Nervana Systems, a deep learning chip startup that Intel acquired for roughly $400 million in 2016. He then founded MosaicML, which Databricks acquired for roughly one and a third billion dollars in 2023.
Rao holds a PhD in neuroscience from Brown and studied electrical engineering at Stanford. That background, bridging chip design and brain science, is central to his pitch that computing architecture itself needs to change.
That track record attracted $475 million in seed funding at a four and a half billion dollar valuation in December 2025, led by Lightspeed and Andreessen Horowitz with participation from Sequoia, Lux Capital, DCVC, and Jeff Bezos. Rao invested $10 million of his own money at the same terms. Unconventional is not the only startup betting that the path to AI efficiency runs through fundamentally new architectures, but its approach is among the most radical.
The company plans to release schematics for a physical chip soon and intends to build an entire inference stack from the ground up. The end goal is to operate as a compute provider, with Unconventional supplying inference capacity through its own chips. “We will build a new kind of system composed of our chips,” Rao said, adding that prompts would come in and inferences would go out over a standard network connection, but at a fraction of the power.
The ambition is enormous relative to the company’s size. Unconventional has fewer than 50 employees and is attempting to replace an architecture, the von Neumann stored-program computer, that has dominated computing for roughly 80 years. The race to reduce AI’s energy footprint has attracted a wave of startups, but most are working on cooling, efficiency software, or incremental hardware improvements rather than trying to rebuild the computing stack entirely.
Rao’s argument is that incremental approaches will not be enough. “AI scaling is hard because of energy,” he told TechCrunch, adding that power will be the fundamental limit in the next few years. The International Energy Agency projects that global data centre electricity consumption will exceed a thousand terawatt-hours by the end of 2026.
The gap between Un-0’s software simulation and a working chip running real-world inference at scale is vast, and the company has given no timeline for when physical hardware will be available for commercial use. But the demonstration that oscillator-based computing can produce functional AI output is the first concrete evidence that the approach is more than theoretical. Whether it can deliver on the thousand-fold efficiency promise is a question that only hardware can answer.
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