[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-informs-ai-ads-beat-human-designers-18-months":3,"news-related-774de6ac-98e1-4343-a67a-bfdc72d377bb":35},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":21,"news_slug":28,"published_at":29,"created_at":30,"modified_at":31,"is_published":32,"publish_type":33,"image_url":14,"view_count":34},"774de6ac-98e1-4343-a67a-bfdc72d377bb","INFORMS 实证:AI 广告真实投放胜过设计师,18 个月后仍领先","INFORMS 发布的实地研究显示,一家户外活动公司的真实 Instagram 投放中,active-learning 引擎生成的 AI 广告在点击率上胜过人类设计师的 campaign,18 个月后无重训复测仍以 3.38% 对 3.24% 领先,且方差更低。","广告行业对生成式 AI 有一个流传很广的判断:出图确实惊艳,但真金白银的投放里,人类设计师的品牌把控和审美判断仍然不可替代。INFORMS(运筹学与管理科学研究协会)发布的一项实地研究,给这个判断提供了一个相反的数据点:在真实投放的 Instagram 广告 campaign 里,AI 生成的广告组合在点击率上赢了人类设计师,而且这个优势在 18 个月后的复测中依然存在。\n\n## 研究怎么做的\n\n研究团队没有做实验室里的模拟对比,而是直接接进了一家户外活动公司的真实投放。他们用一套 active-learning 引擎在线生成推广「自然探索」主题的背景图,边投放边学习,目标是既筛出高性能的 AI 素材,又守住品牌一致性。对照物也不是历史素材库,而是这家公司当代人类设计师制作的正式 campaign——这让比较更接近「AI 系统 vs 设计团队」的实战对垒。\n\n## 18 个月后回来复测\n\n更有信息量的是后续。研究团队在 18 个月后、正值高风险的预订季,把这套 AI 广告组合重新拉出来,与该公司当时的人类设计 campaign 对比——期间没有任何模型重训。结果:AI 组合的平均点击率为 3.38%,人类组合为 3.24%,AI 仍然领先,且表现方差更低,说明它不仅均值更高,还更稳定。在广告业「创意衰减」是常态的背景下,一套 18 个月没更新的 AI 素材还能压住当代人类作品,这一点比首战取胜更值得琢磨。\n\n## 标题里的 Dramatically 要打折读\n\n需要指出,3.38% 对 3.24% 是 0.14 个百分点的差距。放在绝对值上,它远没有新闻稿标题里「Dramatically Outperform」渲染得那么戏剧化——新闻稿的措辞服务于传播,读者的判断应该落在数字上。同时也要看到另一面:这是单一公司、单一平台、单一品类的实地研究,外推到整个广告业需要谨慎;而且 AI 的胜出依赖的是引擎在投放数据里在线学习的能力,不是模型一次性出图的静态质量。换句话说,赢的可能不是「AI 的审美」,而是「闭环反馈的速度」——人类设计师迭代一版要数天,active-learning 引擎迭代一轮只要数小时,时间复利最终体现在了 CTR 上。\n\n## 对生成模型落地的启示\n\n这项研究对图像生成模型的应用侧有两层含义。其一,生成式模型的商业价值未必在「替设计师画一张图」,而在与投放反馈环结合后形成的自动化创意优化——Instagram 背景图这种高迭代、低单图价值的场景,正是这个逻辑的最佳土壤。其二,它给「AI 生成内容质量」提供了一个更硬的评估标准:不是人眼盲测好不好看,而是真实流量下的转化数据。无独有偶,Taboola 近期联合哥伦比亚大学的研究也用「sibling ads」配对法比较了同一广告主的 AI 广告与人类广告,结论方向一致:AI 创意已经能与人类作品正面匹敌。广告业的一部分,正在从「创意行业」变成「优化问题」。\n\n单一研究改变不了行业,但它的方法值得记住:让 AI 和人类在同一战场、同一流量、同一时间线下真打。如果你还在用「AI 出图不够精致」作为不试的理由,不妨想清楚——投放端的裁判从来不是精致,而是数据。(研究详情见 INFORMS 官方发布:https:\u002F\u002Fwww.informs.org\u002FNews-Room\u002FINFORMS-Releases\u002FNews-Releases\u002FAI-Generated-Ads-Dramatically-Outperform-Human-Designers-in-Live-Campaign)","https:\u002F\u002Fwww.informs.org\u002FNews-Room\u002FINFORMS-Releases\u002FNews-Releases\u002FAI-Generated-Ads-Dramatically-Outperform-Human-Designers-in-Live-Campaign","6d1df4b7-67a8-4774-a656-5e0b882b63c7",[11,15,18],{"id":12,"name":13,"slug":13,"description":14,"color":14},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":19,"name":20,"slug":20,"description":14,"color":14},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[22],{"id":23,"lang":24,"title":25,"summary":26,"content":27},"4089072b-8f8f-4554-91b5-f3ebd27ca04e","en","INFORMS Field Study: AI Ads Beat Human Designers in Live Campaign, Lead Holds After 18 Months","A field study released by INFORMS shows that in a live Instagram campaign for an outdoor-activities company, ads generated by an active-learning engine beat the company's human-designed campaign on click-through rate, and still led 3.38% to 3.24% in a retest 18 months later with no retraining — and with lower variance.","The advertising industry has long held a comfortable belief about generative AI: the visuals are striking, sure, but when real money is on the line, human designers still win on brand discipline and taste. A field study released by INFORMS, the Institute for Operations Research and the Management Sciences, delivers a data point against that belief: in a live Instagram campaign, an AI-generated ad portfolio beat the human-designed one on click-through rate — and the advantage was still there 18 months later.\n\n## How the study was run\n\nThe researchers skipped lab simulations and plugged directly into a real campaign for an outdoor-activities company. They deployed an active-learning engine to generate background images promoting nature exploration, learning online as the campaign ran, with the twin goals of surfacing high-performing AI images while keeping them on-brand and effective. The comparison group was not a historical archive but the company's contemporary, professionally designed human campaign — which makes the contest much closer to \"AI system vs design team\" in real conditions.\n\n## The 18-month retest\n\nThe more telling part came later. Eighteen months after the original run, during a high-stakes booking season and without any model retraining, the team brought the AI portfolio back and compared it against the company's then-current human-designed campaign. The AI portfolio achieved a mean click-through rate of 3.38% versus 3.24% for the human campaign, again with lower variance — meaning it wasn't just higher on average, but more consistent. In an industry where creative fatigue is the norm, an unrefreshed AI portfolio outperforming contemporary human work 18 months on is arguably more interesting than the initial win.\n\n## Read the headline with a discount\n\nTo be clear, 3.38% versus 3.24% is a 0.14-percentage-point gap. In absolute terms, this is far less dramatic than the \"Dramatically Outperform\" phrasing in the press release headline — press releases are written for distribution, and judgment should rest on the numbers. The other caveats matter too: this is a single company, a single platform, and a single category, so generalizing to the entire ad industry deserves caution. And note what actually did the winning: the engine's ability to learn online from live campaign data, not the static quality of any single generated image. What likely won was not \"AI taste\" but the speed of the feedback loop — a human design iteration takes days, an active-learning iteration takes hours, and that compounding edge eventually shows up in CTR.\n\n## What it means for generative models in production\n\nFor the application side of image-generation models, the study carries two implications. First, the commercial value of generative models may lie less in \"drawing one picture for a designer\" and more in automated creative optimization once the model is coupled with a live feedback loop — high-iteration, low-per-image-value formats like Instagram backgrounds are exactly where this logic thrives. Second, it offers a harder evaluation standard for AI-generated content quality: not whether it looks good in a blind human rating, but whether it converts under real traffic. Notably, a separate recent study by Taboola with Columbia University used a \"sibling ads\" pairing method — matched pairs of AI-generated and human-made ads from the same advertiser, same campaign, same day — and reached a compatible conclusion: AI creative can now stand toe-to-toe with human work. A slice of the advertising industry is quietly turning from a \"creative business\" into an \"optimization problem.\"\n\nOne study doesn't remake an industry, but its method is worth remembering: put AI and humans on the same battlefield, under the same traffic, on the same timeline, and let them fight for real. If you're still using \"AI images aren't polished enough\" as the reason not to experiment, consider this — in performance advertising, the judge has never been polish. It's the data. (Study details: https:\u002F\u002Fwww.informs.org\u002FNews-Room\u002FINFORMS-Releases\u002FNews-Releases\u002FAI-Generated-Ads-Dramatically-Outperform-Human-Designers-in-Live-Campaign)","informs-ai-ads-beat-human-designers-18-months","2026-08-22T14:00:00Z","2026-08-21T17:14:43.621021Z","2026-08-21T17:14:43.621032Z",true,"agent",60,{"items":36},[37,42,47,52,57,62],{"id":38,"title":39,"news_slug":40,"published_at":41},"52e96e5b-ec3b-4552-b94a-93cc2702ab84","Flow-Map GRPO：为确定性「少步生图」打开强化学习大门","flow-map-grpo-few-step","2026-07-05T12:02:00+00:00",{"id":43,"title":44,"news_slug":45,"published_at":46},"b51af942-496b-4b58-95fb-37980d12a743","逆向工程发现:微软画图本地生成的 AI 图像,像素里埋着服务器下发的水印 GUID","mspaint-invisible-watermark-guid","2026-08-26T13:00:00+00:00",{"id":48,"title":49,"news_slug":50,"published_at":51},"43eda321-b0b7-4df7-b20e-9758cbab42c9","记忆越完整,眼前题越做不对:MemTrapBench 把 LLM 长期记忆框架打回原形","memtrapbench-llm-memory-cognitive-traps","2026-08-22T04:00:00+00:00",{"id":53,"title":54,"news_slug":55,"published_at":56},"deac2d55-76a6-40d2-8ef7-36aed2ad0105","Linux 7.2 把 AI 拉进内核开发:Sashiko 让补丁数量翻倍,Torvalds 接受「新常态」","linux-7-2-sashiko-ai-kernel-review","2026-08-20T12:00:00+00:00",{"id":58,"title":59,"news_slug":60,"published_at":61},"fb1cbe25-8b85-41ec-b619-9a27b405ec34","AMD 收购 Taalas:把 AI 模型权重「刻进硅片」的推理新打法","amd-acquires-taalas-inference-chip","2026-08-19T01:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"a91067a3-4fa4-4e88-a25a-18ba3bea21ea","Google 把\"加密推理\"摆上桌面：HEIR 编译器让预训练模型在密文上直接跑","google-heir-compiler-encrypted-ai-inference","2026-08-14T14:00:00+00:00"]