The drive to discover the next big thing in AI has funded some pretty ambitious projects \u2014 but one company is taking it as a chance to rebuild computing architecture from the ground up.<\/p>\n
Led by Naveen Rao, formerly the head of AI at Databricks, Unconventional AI promises to make inference processing vastly more power efficient. The secret weapon: a new kind of oscillator-based computer architecture.<\/p>\n
On Thursday, the company released its first model AI \u2014 called Un-0 \u2014 an image-generation system tool that shows for the first time how the company\u2019s technology can replicate conventional AI systems. In an accompanying new paper, the company\u2019s research team details how they built a fully functional image-generation model using a software simulation of the new architecture \u2014 one that performs just as well as state-of-the-art diffusion models.<\/p>\n
\u201cThis is the \u2018hello world\u2019 of a new kind of computer,\u201d Rao told TechCrunch. \u201cOver the next year, you\u2019re going to start seeing some pretty interesting news around this.\u201d<\/p>\n
The output from the new Un-0 model is similar to that of image-generation models like Stable Diffusion or OpenAI\u2019s GPT Image 1. The impressive part is how it arrives at that performance. The model is built on an oscillator-based architecture that is completely different from the chips that power conventional computing and traditional LLMs. The advantages of the oscillator-based computing are complex, but Rao believes it will ultimately reduce power use by as much as 1,000 times.<\/p>\n
Much of the infrastructure to get there is still being built. The current version of Un-0 runs on a software simulation of Unconventional\u2019s oscillator chips, but the company plans to release schematics for an actual chip soon. From there, the plan is to build an entire inference stack from the ground up, with Unconventional AI eventually supplying compute capacity just like any other provider.<\/p>\n
\u201cWe will build a new kind of system composed of our chips,\u201d says Rao. \u201cWe will run AI models there, and we will have a network cable where prompts come in and inferences go out, but it\u2019ll be done at 1\/1000 of power.\u201d<\/p>\n
It\u2019s a stunningly ambitious goal, particularly for a company that still counts less than 50 employees. But given the scale of the AI buildout and the anticipated cost of meeting the growing demand for inference, it may be one of the few efforts to meet the scale of the problem. As Rao sees it, the available supply of power will be one of the hard limits for AI in the years to come \u2014 and Unconventional is one of the few projects able to address it.<\/p>\n
\u201cAI scaling is hard because of energy. It\u2019s going to be the fundamental limit in the next few years. You just can\u2019t go past it. It\u2019s going to be an energy-limited problem, at the end of the day,\u201d he says.<\/p>\n<\/div>\n
[analyse_source url=”https:\/\/techcrunch.com\/2026\/06\/25\/databricks-former-ai-chief-thinks-he-can-cut-ais-power-bill-by-1000x\/”]<\/p>\n","protected":false},"excerpt":{"rendered":"
[analyse_image type=”featured” src=”https:\/\/techcrunch.com\/wp-content\/uploads\/2026\/06\/Screenshot-2026-06-25-at-12.15.52-PM.jpg?resize=1200,615″] The drive to discover the next big thing in AI has funded some pretty ambitious projects \u2014 but one company is taking it as a chance to rebuild computing architecture from the ground up. Led by Naveen Rao, formerly the head of AI at Databricks, Unconventional AI promises to make inference processing […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[226,62],"class_list":["post-2011204","post","type-post","status-publish","format-standard","hentry","category-politics","tag-crawlmanager","tag-techcrunch-com"],"_links":{"self":[{"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=\/wp\/v2\/posts\/2011204","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2011204"}],"version-history":[{"count":0,"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=\/wp\/v2\/posts\/2011204\/revisions"}],"wp:attachment":[{"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2011204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2011204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/analyse.optim.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2011204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}