[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-why-anysearch-is-the-wrong-fix-for-ai-search-en":3,"article-related-why-anysearch-is-the-wrong-fix-for-ai-search-en":31,"series-tools-b1fbd225-6638-4454-b5fc-8f3519d53ef8":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"b1fbd225-6638-4454-b5fc-8f3519d53ef8","why-anysearch-is-the-wrong-fix-for-ai-search-en","Why AnySearch Is the Wrong Fix for AI Search","\u003Cp data-speakable=\"summary\">AnySearch improves AI search, but it is not the right foundation for \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> workflows.\u003C\u002Fp>\u003Cp>AnySearch is a useful product, but the claim that it has “opened 80% of the internet” is marketing, not architecture. The real evidence in the piece is narrower and more convincing: it performs better than generic web search when a task needs structured, source-rich retrieval across code, finance, security, and community data. That is valuable. But it is not the same thing as making the internet newly searchable, and it is not the same thing as replacing the hard work of data access, verification, and domain-specific tooling.\u003C\u002Fp>\u003Ch2>First argument: breadth is useful, but breadth is not a moat\u003C\u002Fh2>\u003Cp>AnySearch’s strongest pitch is range. The article shows it retrieving a company’s funding history, App Store sentiment, Reddit discussion, code snippets from production repositories, and IP intelligence in one interface. That is a real productivity gain for an engineer or analyst who would otherwise jump between search, \u003Ca href=\"\u002Ftag\u002Fgithub\">GitHub\u003C\u002Fa>, WHOIS, app stores, and forum threads. But breadth alone does not make a search layer durable. If a product’s core promise is “we connect everything,” the first question is not how many categories it touches, but how often each category stays fresh, accurate, and complete.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779559554350-b4bn.png\" alt=\"Why AnySearch Is the Wrong Fix for AI Search\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The piece itself gives away the constraint. When the query is highly specific, AnySearch does not magically solve discovery; it routes to the best sources, fuses results, and then warns when coverage is uneven. That is the right behavior, but it also proves the point: the product succeeds by orchestrating existing sources, not by transcending them. In practice, that means its value depends on upstream access, source quality, and maintenance discipline. A broad aggregator can be impressive on day one and brittle on day 300 if its connectors, ranking, or source policies drift.\u003C\u002Fp>\u003Ch2>Second argument: confidence is the real product, not raw retrieval\u003C\u002Fh2>\u003Cp>The best part of the article is not the “80% internet” claim. It is the examples where the system refuses to pretend. On the \u003Ca href=\"\u002Ftag\u002Fanthropic\">Anthropic\u003C\u002Fa> due diligence query, it labels conflicting valuation figures by confidence level, flags the $1.2 trillion number as an outlier, and says the cap table is not public. On the IP lookup, it distinguishes whois, BGP, and reverse DNS, then explicitly says it lacks reputation data and will not invent a malicious score. That is the behavior professional users need. The product’s real value is not more search results, but better epistemic hygiene.\u003C\u002Fp>\u003Cp>That matters because most AI search tools fail in the same way: they collapse uncertainty into a single confident answer. AnySearch appears better because it exposes disagreement, source gaps, and uncertainty rather than smoothing them away. But this is also where the product’s positioning gets overstated. If the winning feature is trust calibration, then the company is not really selling “search for agents.” It is selling a decision-support layer that sits between retrieval and action. That is a narrower and more defensible claim, and it is the one the product should lean into.\u003C\u002Fp>\u003Ch2>The counter-argument\u003C\u002Fh2>\u003Cp>The opposing view is strong. Traditional search engines are built for humans, not agents. Humans can open ten tabs, compare sources, and resolve contradictions. Agents need one interface that can route queries, normalize outputs, deduplicate sources, and return structured results fast. On that standard, AnySearch looks like infrastructure, not a feature. The article’s examples support that view: a PM can get market and sentiment data in minutes, a developer can pull production code patterns, and a security engineer can combine registry, routing, and DNS signals without stitching together five tools.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779559580029-tybk.png\" alt=\"Why AnySearch Is the Wrong Fix for AI Search\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>There is also a practical argument for consolidation. Every extra \u003Ca href=\"\u002Ftag\u002Fapi\">API\u003C\u002Fa> key, quota policy, and schema translation layer adds failure points. For teams building agents, that overhead is real. A unified search layer reduces integration work and \u003Ca href=\"\u002Ftag\u002Ftoken\">token\u003C\u002Fa> waste, and the article’s RRF-style ranking plus intent routing sounds like exactly the kind of plumbing that makes agent systems usable in production. In that sense, AnySearch is not just another search product; it is an abstraction that saves engineering time.\u003C\u002Fp>\u003Cp>That counter-argument is correct, but it still does not justify the grand claim. A unified retrieval layer is an integration win, not a new internet. The article’s own examples show that the system works best when the target data already exists in accessible public or semi-public sources. It is much weaker when the task depends on private data, licensed databases, or authoritative records that are incomplete, paywalled, or legally constrained. So yes, AnySearch is useful infrastructure. No, it does not “打通” the internet. It routes around fragmentation; it does not eliminate it.\u003C\u002Fp>\u003Ch2>What to do with this\u003C\u002Fh2>\u003Cp>If you are an engineer, use AnySearch as a retrieval layer, not as a truth engine. Measure it on source coverage, freshness, refusal quality, and disagreement handling. If you are a PM, treat it as a workflow accelerator for research-heavy tasks, but keep a human review step for anything that affects money, security, or legal risk. If you are a founder, the lesson is sharper: the winning AI search product is not the one that claims total coverage, but the one that makes uncertainty visible and integration cheap. That is the bar AnySearch meets, and the bar its marketing overshoots.\u003C\u002Fp>","AnySearch improves AI search, but it is not the right foundation for agent workflows.","zhuanlan.zhihu.com","https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F2039763647749218775",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779559554350-b4bn.png","tools","en","001babb8-72b0-45e5-ab2f-12046a549648",[17,18,19,20,21,22],"AnySearch","AI search","agent workflows","structured retrieval","source verification","intent routing",[24,25,26],"AnySearch is useful infrastructure, not a new kind of internet.","Its real strength is uncertainty handling and source orchestration.","Teams should adopt it as a retrieval layer and keep human review for high-stakes decisions.",3,"2026-05-23T18:05:27.813698+00:00","2026-05-23T18:05:27.807+00:00","26adede7-78b8-4da4-8160-266b8ce263c8",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":17,"slug":34},"anysearch",{"name":21,"slug":36},"source-verification",{"name":19,"slug":38},"agent-workflows",{"name":20,"slug":40},"structured-retrieval",{"name":18,"slug":42},"ai-search",{"id":15,"slug":44,"title":45,"language":46},"why-anysearch-is-the-wrong-fix-for-ai-search-zh","為什麼 AnySearch 不是 AI 搜尋的正解","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"1e0d71a2-19ae-44f4-970b-d27f77ad5a8a","nvidia-lg-ai-collaboration-playbook-en","Nvidia and LG turn AI plans into a 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