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1879.<\/strong><\/p>\n Thomas Edison and his team are trying to build a practical incandescent light bulb. They test material after material, trial and error, running thousands of experiments in search of a filament that works.<\/p>\n 2026.<\/strong><\/p>\n OpenAI attacks one of mathematics’ biggest unsolved problems using roughly 10,000 AI agents working in parallel teams for 88 hours.<\/p>\n Different century. Same criticism:<\/p>\n \n“You just brute-forced it.”<\/em><\/p>\n What’s wrong with that?<\/p>\n We romanticize elegant solutions. We like the story where one brilliant person sees what everyone else missed.<\/p>\n Real progress is often uglier.<\/p>\n Failed experiments. Dead ends. Repetition. Huge amounts of computation. The willingness to keep trying after other people stop.<\/p>\n “Brute force” sounds intellectually cheap. But inelegant and ineffective are not the same thing.<\/p>\n Consider the Navier-Stokes equations, which describe how fluids move. Better ways of understanding them could eventually improve models of blood flow, aircraft aerodynamics, weather, and other fluid systems.<\/p>\n Suppose some insight from this work eventually helps produce a safer heart valve.<\/p>\n Grandma isn’t going to ask:<\/p>\n “Was the mathematics behind my heart valve discovered elegantly?”<\/em><\/p>\n She’s going to ask:<\/p>\n “Does it work?”<\/em><\/p>\n Most of us don’t have 10,000 AI agents. But we can send ten pitches instead of endlessly polishing one (no spamming of course.) Write twenty headlines. Build five prototypes. Run fifty experiments instead of arguing about which one ought to work.<\/p>\n Edison built a laboratory that could try more things.<\/p>\n OpenAI built 10,000 agents that could do the same.<\/p>\n Different tools. Same strategy.<\/p>\n Sometimes the smartest way through a problem is simply to give yourself more chances to solve it.<\/p>\n RW 1879. Thomas Edison and his team are trying to build a practical incandescent light bulb. They test material after material, trial and error, running thousands of experiments in search of a filament that works. 2026. OpenAI attacks one of mathematics’ biggest unsolved problems using roughly 10,000 AI agents working in parallel teams for 88 hours. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[13,5,15],"tags":[],"class_list":["post-550","post","type-post","status-publish","format-standard","hentry","category-ai","category-mindset","category-strategy"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/posts\/550","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/comments?post=550"}],"version-history":[{"count":4,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/posts\/550\/revisions"}],"predecessor-version":[{"id":555,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/posts\/550\/revisions\/555"}],"wp:attachment":[{"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/media?parent=550"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/categories?post=550"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/richardwinser.com\/blog\/wp-json\/wp\/v2\/tags?post=550"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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