{"id":5989,"date":"2026-04-15T18:13:12","date_gmt":"2026-04-15T18:13:12","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/5989\/"},"modified":"2026-04-15T18:13:12","modified_gmt":"2026-04-15T18:13:12","slug":"ai-could-democratize-one-of-techs-most-valuable-resources","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/5989\/","title":{"rendered":"AI Could Democratize One of Tech&#8217;s Most Valuable Resources"},"content":{"rendered":"<p><a href=\"https:\/\/www.wired.com\/tag\/nvidia\/\" class=\"text link\" rel=\"nofollow noopener\" target=\"_blank\">Nvidia<\/a> is the undisputed king of AI <a href=\"https:\/\/www.wired.com\/tag\/chips\/\" class=\"text link\" rel=\"nofollow noopener\" target=\"_blank\">chips<\/a>. But thanks to the AI it helped build, the champ could soon face growing competition.<\/p>\n<p class=\"paywall\">Modern AI runs on Nvidia designs, a dynamic that has propelled the company to a market cap of well over $4 trillion. Each new generation of Nvidia chip allows companies to train more powerful AI models using hundreds or thousands of processors networked together inside vast data centers. One reason for Nvidia\u2019s success is that it provides software to help program each new generation of chip. That may soon not be such a differentiated skill.<\/p>\n<p class=\"paywall\">A startup called <a data-offer-url=\"https:\/\/www.wafer.ai\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/www.wafer.ai&quot;}\" href=\"https:\/\/www.wafer.ai\" rel=\"nofollow noopener\" target=\"_blank\">Wafer<\/a> is training AI models to do one of the most difficult and important jobs in AI\u2014optimizing code so that it runs as efficiently as possible on a particular silicon chip.<\/p>\n<p class=\"paywall\">Emilio Andere, cofounder and CEO of Wafer, says the company performs reinforcement learning on open source models to teach them to write kernel code, or software that interacts directly with hardware in an operating system. Andere says Wafer also adds \u201cagentic harnesses\u201d to existing coding models like Anthropic\u2019s Claude and OpenAI\u2019s GPT to soup up their ability to write code that runs directly on chips.<\/p>\n<p class=\"paywall\">Many prominent tech companies now have their own chips. Apple and others have for years used custom silicon to improve the performance and the efficiency of software running on laptops, tablets, and smartphones. At the other end of the scale, companies like Google and Amazon mint their own silicon to improve the performance of their cloud-computing platforms. Meta recently <a data-offer-url=\"https:\/\/investors.broadcom.com\/news-releases\/news-release-details\/broadcom-announces-extended-partnership-meta-deploy-technology\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/investors.broadcom.com\/news-releases\/news-release-details\/broadcom-announces-extended-partnership-meta-deploy-technology&quot;}\" href=\"https:\/\/investors.broadcom.com\/news-releases\/news-release-details\/broadcom-announces-extended-partnership-meta-deploy-technology\" rel=\"nofollow noopener\" target=\"_blank\">said<\/a> it would deploy 1 gigawatt of compute capacity with a new chip developed with Broadcom. Deploying custom silicon also involves writing a lot of code so that it runs smoothly and efficiently on the new processor.<\/p>\n<p class=\"paywall\">Wafer is working with companies including AMD and Amazon to help optimize software to run efficiently on their hardware. The startup has so far raised $4 million in seed funding from Google\u2019s Jeff Dean, Wojciech Zaremba of OpenAI, and others.<\/p>\n<p class=\"paywall\">Andere believes that his company\u2019s AI-led approach has the potential to challenge Nvidia\u2019s dominance. A number of high-end chips now offer similar raw floating point performance\u2014a key industry benchmark of a chip\u2019s ability to perform simple calculations\u2014to Nvidia\u2019s best silicon.<\/p>\n<p class=\"paywall\">\u201cThe best AMD hardware, the best [Amazon] Trainium hardware, the best [Google] TPUs, give you the same theoretical flops to Nvidia GPUs,\u201d Andere told me recently. \u201cWe want to maximize intelligence per watt.\u201d<\/p>\n<p class=\"paywall\">Performance engineers with the skill needed to optimize code to run reliably and efficiently on these chips are expensive and in high demand, Andere says, while Nvidia\u2019s software ecosystem makes it easier to write and maintain code for its chips. That makes it hard for even the biggest tech companies to go it alone.<\/p>\n<p class=\"paywall\">When Anthropic partnered with Amazon to build its AI models on Trainium, for instance, it had to rewrite its model\u2019s code from scratch to make it run as efficiently as possible on the hardware, Andere says.<\/p>\n<p class=\"paywall\">Of course, Anthropic\u2019s Claude is now one of many AI models that are now superhuman at writing code. So Andere reckons it may not be long before AI starts consuming Nvidia software advantage.<\/p>\n<p class=\"paywall\">\u201cThe moat lives in the programmability of the chip,\u201d Andere says in reference to the libraries and software tools that make it easier to optimize code for Nvidia hardware. \u201cI think it&#8217;s time to start rethinking whether that&#8217;s actually a strong moat.\u201d<\/p>\n<p class=\"paywall\">Besides making it easier to optimize code for different silicon, AI may soon make it easier to design chips themselves. <a data-offer-url=\"https:\/\/www.ricursive.com\/\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/www.ricursive.com\/&quot;}\" href=\"https:\/\/www.ricursive.com\/\" rel=\"nofollow noopener\" target=\"_blank\">Ricursive Intelligence<\/a>, a startup founded by two ex-Google engineers, Azalia Mirhoseini and Anna Goldie, is developing new ways to design computer chips with artificial intelligence. If its technology takes off, a lot more companies could branch into chip design, creating custom silicon that runs their software more efficiently.<\/p>\n","protected":false},"excerpt":{"rendered":"Nvidia is the undisputed king of AI chips. But thanks to the AI it helped build, the champ&hellip;\n","protected":false},"author":2,"featured_media":5990,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,1201,25,2306,5189,58,2011],"class_list":{"0":"post-5989","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-ai-lab","10":"tag-artificial-intelligence","11":"tag-chips","12":"tag-code","13":"tag-nvidia","14":"tag-semiconductors"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/5989","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=5989"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/5989\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/5990"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=5989"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=5989"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=5989"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}