The Economist is testing new ways of structuring content to be read solely by agents as AI engines increasingly surface and summarize news.<\/p>\n
For now, the subscription publisher is experimenting with agent\u2011readable versions of content that already sits outside its paywall \u2014 chiefly marketing copy and B2B sales material \u2014 and restructuring those surfaces for AI answer engines.<\/p>\n
The bet is that discovery won\u2019t start on homepages or even in search boxes, but with AI intermediaries acting on a user\u2019s behalf. As Josh Muncke, vp of generative AI at The Economist Group, told Digiday, the publisher is preparing for \u201ca world with two versions of the web\u201d \u2014\u00a0one optimized for rich, human reading experiences, and another where \u201cagents want clear structure, questions and answers, ideally text,\u201d not carousels and feature art.<\/p>\n
\u201cThere are some obvious places we must do that,\u201d Muncke told Digiday. \u201cWe want our marketing content to be findable and discoverable and optimized for agents. And then we obviously need to think deeply about how and what portions of our editorial content should also appear in those kinds of surfaces.\u201d<\/p>\n
Naturally, being a subscription publisher, The Economist has to be forensic about which marketing pages and teasers sit outside the paywall, and how much agent\u2011legible content it can afford to give away without eroding the value of a subscription. <\/p>\n
A growing share of B2B buyers now start with ChatGPT, Gemini or Claude, so The Economist\u2019s sales and marketing pages have to show up cleanly in those answers, stressed Muncke. That means building parallel versions of the same pitch: glossy, comparison\u2011heavy pages for humans, and stripped\u2011back, Q&A\u2011style structures for agents \u2014 and accepting that agent\u2011readable content is now part of the go\u2011to\u2011market plan, not a side project.<\/p>\n
Muncke said that all work is currently rooted in \u201cfirst, tentative experiments\u201d around content that already sits in front of the paywall, with internal conversational search and agent\u2011readable formats used as sandboxes to \u201cwork out the kinks\u2026 not just in accuracy and performance, but also how it sounds and the tone\u201d before anything is exposed more widely.<\/p>\n
The approach is still early-stage across most publishers, but it reflects a broader shift as AI becomes a prime layer in how journalism is discovered and consumed. <\/p>\n
\u201cAgent optimization is a defensive baseline,\u201d said Alessandro de Zanche, founder of media consultancy ADZ Strategies. \u201cEvery quality publisher will build some version: the alternative is technical invisibility as search rebuilds around agents.\u201d<\/p>\n
But discoverability is a technical problem, he argues, not a business model. The harder problem is retention. \u201cAgents drive discovery, not the trust and engagement subscriptions and premium advertising depend on, and without it the agent layer\u2019s economics collapse,\u201d said de Zanche. \u201cEveryone ends up building it. Trust coming from the audience is what ultimately separates the publishers who survive from those who disappear.\u201d<\/p>\n
Cultivating an internal vibe-coding culture <\/p>\n
If the agent-first work is about how The Economist shows up on the open web, the more fundamental change is in how it now builds products in the first place. Over the last year, Muncke\u2019s team has shortened product delivery cycles by folding generative AI into the development process and reorganized around small, cross-functional team \u201cpods\u201d that can move at AI speed.<\/p>\n
One test case was its new CarPlay app, a highly requested but fiddly project that had been on the roadmap, noted Muncke.\u00a0Instead of running it through a traditional long spec-and-handoff cycle, The Economist assigned a small pod: a designer, engineer, product and editorial members with access to AI tools for writing tests, documentation and boilerplate code. The result: the CarPlay app shipped five months earlier than planned. \u201cWe saw overall it makes us something like 8% more efficient on certain parts of building this technology,\u201d said Muncke. \u201cAt that point, that level of pace forces you to think differently about how fast we can move, and how that increases our ability to test and iterate things in the market.\u201d\u00a0<\/p>\n
Five months saved in a product cycle sounds significant, but the real question is what that time is actually being used for, stressed de Zanche. \u201cIf the capacity goes into more products, faster experimentation that improves retention, or freeing creativity from repetitive tasks, it benefits the P&L,\u201d he added. \u201cIf it goes into doing the same faster or absorbing headcount cost, the return is illusion.\u201d<\/p>\n
Abi Watson, head of publishing at media analysis firm Enders, said the medium-term play isn\u2019t really about productivity, but what new product categories AI makes possible. \u201cWhere AI shortens the cycle from idea to a launched paid product \u2014 [like] a new newsletter tier, a verticalized data product, an agentic research interface for subscribers, a B2B agent licensing line \u2014 the upside is real because it\u2019s tied to subscription or enterprise revenue rather than internal efficiency,\u201d she said.<\/p>\n
At The Economist, that CarPlay sprint is now a template. The publisher has set up six to eight pods across its product stack. In areas that touch reader experience, editorial staff are embedded directly in those pods to ensure AI-powered features still \u201cretain the look, feel, style, tone of The Economist,\u201d per Muncke.\u00a0<\/p>\n
Meanwhile, the publisher has been quietly cultivating an internal culture of vibe coding: encouraging staff who would never previously have touched a code editor to start building their own tools.\u00a0<\/p>\n
\u201cThe main difference is that now everyone is a builder,\u201d Muncke said, who also oversees the publisher\u2019s AI Lab under which he is currently hiring five additional roles. Editors on the science desk are vibe\u2011coding utilities that trawl academic journals, pull relevant papers and assess credibility for upcoming stories. Product teams are spinning up automated performance reports instead of queueing requests with data teams. There\u2019s even a template \u201cChief of Staff\u201d agent that plugs into inboxes and calendars, drafts responses and surfaces daily priorities and follow\u2011ups.<\/p>\n
Not every experiment has worked. The publisher has already paused experiments including an attempt to turn its 300\u2011page house style guide into an automated copy checker and AI companions for live subscriber events that testers found more distracting than helpful, per Muncke. \u201cWe can move much more quickly now\u2026 the hard part is making sure we\u2019re building the right things, in the right places, and still sounding like The Economist while we do it,\u201d he added.\u00a0<\/p>\n
Muncke is clear there are lines The Economist won\u2019t cross. \u201cNobody wants to read an AI\u2011written Economist,\u201d he said, adding that AI\u2019s role is confined to research, workflow and utility tools. The group has also committed to clear labelling so readers aren\u2019t \u201ctricked\u201d about where AI is used.\u00a0<\/p>\n
Those red lines are a reminder that, for all the experiments, The Economist still sees AI as infrastructure rather than authorship: something to speed up research, workflows and product delivery, not a shortcut to churning out more copy. Muncke\u2019s job over the next few months is to keep that balance \u2014 choosing the right use cases, spreading the skills beyond a handful of enthusiasts, and making sure the best internal hacks don\u2019t stay hidden on individual desks. If The Economist pulls it off, agents may do more of the fetching \u2014\u00a0but the thing subscribers are paying for will still be the human judgement in the middle.<\/p>\n","protected":false},"excerpt":{"rendered":"The Economist is testing new ways of structuring content to be read solely by agents as AI engines…\n","protected":false},"author":2,"featured_media":21036,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[405,7537,5091],"class_list":["post-42186","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-ai-agents","tag-artificial-intelligence-agents","tag-zero-click-future"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/42186","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=42186"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/42186\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/21036"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=42186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=42186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=42186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}