{"schemaVersion":"1.0","generatedFrom":"https://brightaifuture.com/discoveries/alphaevolve-data-centers","record":{"id":"alphaevolve-data-centers","headline":"Making room in a data center","canonicalUrl":"https://brightaifuture.com/discoveries/alphaevolve-data-centers","datePublished":"2026-09-07","dateModified":null,"sourcePublicationDate":"2025-05-14","author":null,"publisher":{"name":"Bright AI Future","url":"https://brightaifuture.com/"},"topics":[],"summary":"Google DeepMind reported that an AlphaEvolve-discovered scheduling heuristic had been in production for more than a year and continuously recovered an average 0.7% of Google's worldwide compute resources.","evidenceState":"Deployed","keyFacts":[{"label":"AI’s role","value":"AlphaEvolve used language models, automated evaluation, and evolutionary search to produce a scheduling heuristic for Google's Borg system."},{"label":"Documented result","value":"Google says the production heuristic continuously recovers, on average, 0.7% of its worldwide compute resources."},{"label":"Important limitation","value":"This is a company-reported, Google-specific operational result."}],"limitations":["This is a company-reported, Google-specific operational result.","The announcement does not quantify electricity, water, carbon, reliability, or independent-audit outcomes."],"evidenceLinks":[{"title":"AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms","url":"https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/","type":"institution"}],"evidencePackUrl":"https://brightaifuture.com/evidence-pack/alphaevolve-data-centers","embedUrl":"https://brightaifuture.com/embed/story/alphaevolve-data-centers","attribution":{"credit":"Bright AI Future","requirements":["Link to the canonical Bright record.","Keep material limitations with the claim they qualify.","Link to the original evidence when repeating a substantive claim.","Do not describe a source check or organization-reported result as independent verification."],"sourceRights":"Linked source material, quotations, trademarks and media remain subject to their owners’ terms. No reuse right is granted for third-party media."}},"claim":{"humanProblem":"Data-center capacity is finite, and idle or poorly allocated compute leaves less capacity for useful work on the same infrastructure.","priorConstraint":"A production scheduler must make dependable allocation decisions across changing workloads at very large scale.","aiRole":"AlphaEvolve used language models, automated evaluation, and evolutionary search to produce a scheduling heuristic for Google's Borg system.","documentedResult":"Google says the production heuristic continuously recovers, on average, 0.7% of its worldwide compute resources.","whyItMayMatter":"Recovering capacity can allow more tasks to run on an existing computational footprint.","unresolvedQuestions":["Would the heuristic transfer to other scheduler designs?","What are the measured energy and reliability effects under representative workloads?"]},"evidenceAssessment":{"state":"Deployed","claimConfidence":"unassessed","reviewState":"approved","reviewMethod":"ai-assisted","reviewNote":"AI-assisted editorial comparison with the cited primary source; result, setting, source date and limitations retained. Independently checked within the research team. Publication authorized by the site owner; no human source review is claimed.","lastSourceReview":"2026-09-07","independentVerification":"not-established-by-this-source-review"},"sources":[{"id":"source-alphaevolve-data-centers","title":"AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms","url":"https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/","type":"institution"}],"revisions":[{"id":"revision:95733113a8bd2aeccf4b","recordedAt":"2026-09-07","summary":"Recovering capacity can allow more tasks to run on an existing computational footprint.","sourceIds":["source-alphaevolve-data-centers"]}],"corrections":[]}