Ally Bank and the Project Management Institute have become two of the first brands to buy an ad meant to be read by a machine, not a person. Time began serving ads to AI agents this month, formatting them as sponsored FAQs stuffed with brand messaging and dropping them into stripped-down copies of its pages, according to Digiday.

The move follows Time’s decision last month to convert all its webpages into markdown, text-only versions that strip out design and images, making them easier for AI systems to crawl and process. The publisher’s bet is that greater accessibility will boost its visibility. By placing ads within those markdown files, Time also hopes to monetize the growing volume of AI bot traffic while that strategy plays out.

To build the ads, Time is working with an AI ad tech platform called Mobian, which converts the pages and generates the agent ads from a brand brief. The output gets turned into a PDF for humans to approve, much like a standard branded content deal. Mobian then feeds the same FAQ questions to AI search engines and tracks visibility, favorability and accuracy over time.

Mobian co-founder and CEO Jonah Goodhart said the shift reflects a growing reality: publishers and brands increasingly need to optimize for AI systems as much as human audiences.

“Maybe it’s more important to influence the agent than even the human, because with a human you influence one person. When you influence ChatGPT, you’re influencing potentially all of ChatGPT,” he told Digiday.

Goodhart said roughly 15% of brands now run their own markdown pages for AI crawlers, a figure he expects to grow as companies adapt to what he describes as a two-track internet.

Time COO Mark Howard declined to disclose traffic figures but pointed to TollBit data showing the publisher receives more AI crawler requests than most of the roughly 7,000 sites in the company’s network. During major events such as the Time100 franchise, bot activity surges so dramatically that AI crawlers outnumber human visitors on most days. The trend mirrors Cloudflare’s finding that automated bots now account for more than half of all web traffic.

Time is positioning those AI visits as a new source of advertising revenue, selling one
“agent ad” per markdown page. The ads are part of a broader generative engine optimization offering that reflects publishers’ growing focus on AI discovery over traditional search traffic.

The approach comes with uncertainty. No major AI company has explained how its models handle ads embedded in markdown files—or whether they recognize them as ads at all. Rob Derow, a managing director at BCG X, told Digiday the lack of standards is the biggest risk. If AI companies ultimately treat the practice like cloaking—showing crawlers content different from what humans see—the pages could be devalued, much as Google penalized similar SEO tactics.

To reduce that risk, Time labels each placement as sponsored content and identifies the advertiser, despite no current requirement to do so. “We don’t know yet because this is brand new, and we believe that we are paving the first path forward here,” COO Mark Howard told Digiday.

The post Time starts building ads for AI agents as bot traffic overtakes humans appeared first on The Media Copilot.

What content do we actually need to come up in AI search?

It’s a question businesses ask every day. As more customers turn to ChatGPT, Gemini, and other AI platforms to research products, services, and providers, marketers know AI search matters. The challenge is knowing which content investments are most likely to improve visibility and ultimately bring customers to your website.

To answer that question, WebFX analyzed nearly 600,000 AI sessions across 2,500 URLs spanning businesses in 15-plus industries between May 2025 and May 2026.

These findings provide a practical blueprint for the content businesses should prioritize to earn more visibility, website traffic, and customers from AI search.

What types of content earn the most AI traffic?

The data revealed several clear winners.

Homepages generated the largest share of AI traffic by a wide margin (31.3%), followed by product pages (16.8%), service pages (11.4%), blog articles (11.3%), and FAQ/resource pages (7.2%).

These “big five” page types accounted for more than two-thirds of all AI traffic in the dataset.

Looking across the distribution, three findings stood out.

Key finding 1: Homepages drove nearly one-third of all AI traffic

Homepages generated 31% of all AI sessions, more than any other page type in the analysis.

This reflects how people increasingly use AI. Instead of navigating through multiple search results, users often ask AI to recommend businesses, products, or providers. Once AI identifies a strong match, the homepage naturally becomes the starting point for learning more.

For example, when researchers asked ChatGPT for the best AC repair company in Hershey, PA, every recommendation pointed directly to a company’s homepage, where visitors could explore services, reviews, and decide whether to take the next step.

Key finding 2: Product, service, and resource pages are AI traffic magnets

After homepages, product pages (16.8%), service pages (11.4%), blog articles (11.3%), and FAQ/resource pages (7.2%) generated the largest share of AI traffic.

Although these pages serve different purposes, they all help users move forward. Some answer detailed questions. Others compare solutions or explain services. Many help users evaluate whether a business is the right fit. AI platforms consistently referred users to pages that supported those next steps.

Key finding 3: AI rewards comprehensive websites

Homepages drove the largest share of AI traffic in the study, but they weren’t the whole story. Product pages, service pages, blog articles, FAQs, industry pages, and tools all earned meaningful AI referrals.

The highest-performing websites weren’t relying on one standout page. They built a portfolio of content that answered different questions and supported different stages of the customer journey, giving AI multiple opportunities to recommend their business.

Business takeaway: Build a content ecosystem that establishes authority in your niche

Every page contributes a signal about your business:

• A homepage introduces who you are
• Commercial pages explain what you offer
• FAQs answer common questions
• Educational content demonstrates expertise

Together, they create a digital fingerprint that helps AI understand where your business has authority and when it’s the right recommendation for a customer’s next question or next step.

Why AI traffic is more bottom-of-funnel than you think

More than 9 in 10 AI sessions landed on consideration or decision-stage content, while awareness pages accounted for just 2.3% of AI traffic.

That finding aligns with another WebFX study showing AI traffic converts 1.2 times higher than traditional organic search. Together, the data suggests AI is increasingly connecting businesses with customers who have already moved beyond initial research and are actively comparing options, evaluating providers, and preparing to make a decision.

Key finding 1: AI users are evaluating, not just learning

Decision-stage content accounted for 55.3% of all AI traffic, while consideration pages generated another 36.7%.

Combined, 92% of AI traffic landed on content designed to help users compare providers, evaluate solutions, or make purchasing decisions.

That paints a very different picture than many marketers and business owners might have.

Key finding 2: AI is shortening the research process

Traditional search often encourages users to research one question at a time. AI changes that experience.

Instead of bouncing between multiple websites, users can ask AI to compare providers, summarize reviews, explain tradeoffs, and narrow their options before ever clicking a link. By the time they visit your website, they’re often looking for validation rather than an introduction.

That helps explain why service and product pages, FAQs, and comparison content consistently appear among the strongest AI performers.

Key finding 3: Transactional content drives the majority of AI traffic

The content intent data tells a similar story.

Nearly 7 in 10 AI sessions landed on transactional content, while informational pages accounted for just 18% of AI traffic. Both the funnel stage and commercial intent data point in the same direction: The majority of AI traffic reached pages supporting evaluation and decision-making.

Business takeaway: Prioritize content that helps customers make decisions

For years, many organizations invested heavily in top-of-funnel educational content to grow organic visibility. That foundation remains important. Educational resources build authority, earn citations, and help AI understand your expertise.

The WebFX research suggests the biggest opportunity for AI traffic often comes later in the customer journey. Businesses that invest in strong service pages, comparison content, pricing resources, FAQs, and other decision-stage assets are better positioned to capture users after AI has already helped narrow their options.

As AI becomes a larger part of the buying process, some of the highest-value content on your website may be the content that helps customers confidently take the next step.

What content performs best in AI vs. organic search?

For decades, content strategy has largely been shaped by one question: What content performs best in Google Search?

As AI search becomes a growing source of discovery, a new question is emerging: Will the same content continue to perform as customers shift from search engines to AI assistants? The answer has important implications for where businesses invest their time, budget, and content strategy over the next several years.

The answer? Both yes and no. Many of the same page types perform well across both channels, but AI platforms don’t distribute traffic the same way Google does. Comparing the two reveals where AI is creating new opportunities and where traditional search continues to have the upper hand.

Key finding 1: AI creates the biggest opportunities for product, blog, and resource content

The largest AI lift came from blog articles (+7.3 percentage points), product pages (+7.1 points), and FAQ/resource pages (+5.3 points).

These pages all serve a similar purpose. They help users compare options, answer detailed questions, and continue evaluating solutions after AI has narrowed the field. For businesses looking to grow AI traffic, they are some of the clearest opportunities in the dataset.

Key finding 2: Google still leads branded and local discovery

While homepages generated the largest share of AI traffic overall, they accounted for an even larger share of organic traffic (44% vs. 31.3%). The same pattern appeared with location pages (-4.7 AI lift) and tools/calculators (-3 AI lift).

That suggests traditional search continues to play a larger role in branded, navigational, and local discovery, while AI increasingly connects users with deeper content that supports evaluation and decision-making.

Key finding 3: Service pages consistently perform across both channels

One result stood out for a different reason.

Service pages generated nearly identical shares of AI and organic traffic (11.4% vs. 10.9%), resulting in an AI lift of just +0.5 percentage points.

That’s encouraging because it suggests investments in high-quality service pages continue to pay dividends regardless of how customers discover your business.

Business takeaway: Invest where AI lift is highest without losing sight of your strongest SEO assets

Comparing AI and organic search reveals that homepages, service pages, and product pages consistently perform well across both channels, making them some of the strongest long-term investments.

Blog articles, FAQ/resource pages, and industry pages generated the highest AI lift, signaling opportunities to grow AI traffic beyond traditional SEO.

At the same time, location pages and tools continue to be important drivers of organic search visibility and remain worth protecting.

Knowing where to invest is only part of the equation. The next question is what makes those pages successful enough for AI to recommend them in the first place?

Content playbook for winning AI traffic

The research provides insights into the pages earning AI traffic, where users enter the buying journey, how AI search differs from traditional organic search, and where businesses have the biggest opportunities to invest.

Those findings naturally lead to one final question: What does content that consistently earns AI traffic have in common?

Looking across the 2,500 URLs generating the highest levels of AI referral traffic, five trends emerged. No single tactic guarantees AI visibility, but together they provide a practical playbook for creating content that AI systems are more likely to recommend and customers are more likely to visit.

1. Start with a trusted homepage

Homepages generated 31.3% of all AI traffic, making them the single largest destination in the dataset.

That reinforces the important role homepages play in AI search. Before recommending a product, service, or resource, AI often introduces users to the business itself.

A clear, trustworthy homepage gives users a place to validate that recommendation and continue exploring. Make sure your homepage clearly communicates:

• Who you are
• Who you serve
• What makes your business different
• Why people trust your business (think reviews, awards, certifications, and other trust signals)

2. Build commercial depth

Product and service pages accounted for 28.2% of AI traffic, helping users move from discovery to evaluation.

Rather than stopping at a homepage, AI frequently recommends commercial pages that answer buying questions, explain services, and help customers compare solutions.

Expand your service and product pages beyond basic descriptions by addressing pricing, implementation, comparisons, FAQs, and the questions prospects ask before contacting sales.

3. Help customers make decisions

The report found that 92% of AI traffic landed on consideration and decision-stage content.

That indicates AI is often introducing users after much of their early research is complete. The strongest-performing pages reduced uncertainty and helped customers confidently take the next step.

Content types that help support the decision-marketing process include:

• Comparison pages
• Buyer’s guides
• Pricing resources
• FAQs
• Industry-tailored content

For example, a quick search in ChatGPT for “what’s a good price for a new garage door” yields content geared toward helping searchers evaluate pricing and what’s worth the investment.

4. Answer specific questions completely

Pages most likely to be cited by AI-generated answers averaged a 4.76 out of 5 specificity score, which measures how specific a page’s content is based on the presence of concrete details such as facts, named entities, dates, statistics, pricing, and product names. Those same pages averaged a 4.15 out of 5 completeness score, which measures how thoroughly a page answers its primary topic and addresses related questions and context. The pages most likely to be cited by AI-generated answers outperformed other pages on both measures.

The strongest-performing pages focused on clearly defined:

• Audiences
• Industries
• Services
• Customer questions

These pages also backed up their content with:

• Specific facts
• Named entities
• Statistics
• Product names
• Pricing details
• Real examples

They also anticipated the next question a user might ask, addressed common objections, and expanded beyond the initial answer with comparisons, FAQs, implementation guidance, and other supporting information that helped users continue making decisions without returning to AI or Google.

Businesses looking to increase AI visibility should create focused content around specific customer needs, then build those pages into comprehensive resources that:

• Answer related questions
• Explain tradeoffs
• Provide examples
• Support the entire decision-making journey

5. Demonstrate authority and expertise

Trust signals consistently appeared across the strongest-performing pages in the study.

More than two-thirds included expert credentials, 52.6% featured original research or first-party data, and pages with citations averaged 26% more AI traffic than those without.

Rather than relying on a single trust signal, the highest-performing pages built confidence from multiple angles. You can do the same with your content by:

• Publishing original research
• Incorporating subject matter experts
• Supporting claims with credible citations
• Identifying authors

All these steps help reinforce expertise.

Business takeaway: Become the source AI wants to recommend

Looking across nearly 600,000 AI sessions, the strongest-performing pages all shared the same goal: Helping users confidently take the next step.

That’s ultimately what AI search is trying to do.

Businesses don’t need a completely different content strategy for AI.

They need content that’s trustworthy, specific, comprehensive, and genuinely useful throughout the customer journey. Those are the pages AI consistently recommended throughout the study, and they’re the pages most likely to earn visibility, traffic, and customers as AI search continues to evolve.

Do different AI platforms recommend different content?

Most discussions about AI search treat it as a single channel. In reality, businesses are competing for visibility across multiple AI assistants, each with its own user experience and recommendation patterns.

Looking across the data, one platform currently stands apart: ChatGPT accounts for 97.5% of AI referral traffic in the dataset.

That doesn’t mean Gemini, Perplexity, Claude, or Copilot aren’t important.

AI search is evolving rapidly, and today’s platform mix will almost certainly change over time. But it does mean the broader trends throughout this report largely reflect how users interact with ChatGPT today.

Although traffic volumes remain much smaller outside ChatGPT, the study included a platform-level breakdown to provide additional context on where those referrals are landing.

The differences are subtle, but a few early patterns emerge:

• ChatGPT sends traffic across a broad mix of page types.
• Perplexity directs a relatively larger share of traffic to blog content.
• Gemini appears to over-index on tool and calculator pages as well as homepages.

Given the relatively small volume of traffic outside ChatGPT, it’s too early to draw broad conclusions about platform-specific optimization strategies. As adoption grows, however, these referral patterns will be worth monitoring.

Business takeaway: Prioritize the content, not the platform

Rather than building separate content strategies for individual AI assistants, focus on creating high-quality content that serves users well regardless of where the recommendation originates.

As the AI search landscape evolves, businesses should build strong homepages, commercial pages, educational resources, and decision-support content.

Insights to action: What this study means for your AI content strategy

AI search is becoming an increasingly important part of how customers discover and evaluate businesses. Looking across nearly 600,000 AI sessions, one theme appeared repeatedly. The pages earning AI traffic made it easy for both customers and AI systems to:

• Understand who the business served
• What made the business credible
• Why the business deserved a recommendation

That gives businesses a practical direction for the years ahead.

Investing in trusted homepages, stronger commercial pages, deeper educational resources, and first-party expertise creates more opportunities to earn visibility across both AI and traditional search.

Those investments improve the customer experience while strengthening the signals AI systems rely on when deciding what to recommend.

Methodology

This study measures AI referral traffic, not AI citations. WebFX analyzed visits sent from AI platforms rather than how frequently brands appeared in AI-generated responses. The dataset focuses on high-performing AI pages. Findings reflect characteristics commonly shared by the pages generating the most AI traffic, rather than the average webpage.

This story was produced by WebFX and reviewed and distributed by Stacker.

The post What 600,000 AI sessions reveal about the content that wins AI traffic appeared first on The Media Copilot.

MIT Technology Review’s paywalled articles will now be available through Muck Rack‘s monitoring platform under a licensing deal that expands communications teams’ access to artificial intelligence and emerging technology coverage. In its announcement of the deal, Muck Rack said it is the first communications platform to license the publication’s content.

The agreement gives Muck Rack customers access to the publication’s full editorial output, rather than only material visible on the open web. That helps eliminate a blind spot created by paywalls, giving companies a fuller view of coverage, competitors and reputational risks.

Muck Rack has been building a roster of direct publisher deals, recently adding Bloomberg Media and STAT, according to the announcement. MIT Technology Review, founded at MIT in 1899, covers the business, policy and social effects of technology as well as technical developments.

Natan Edelsburg, Muck Rack’s chief partnerships officer, said the arrangement reflects the company’s effort to work directly with publishers while giving users access to trusted AI reporting. Ted Hu, senior manager of licensing at MIT Technology Review, said the deal takes the publication’s reporting to communications professionals beyond its usual journalist and technologist audience.

Muck Rack is pitching the deal as a way to help communications teams track how brands appear in both AI-generated answers and traditional news coverage. The company said earned media, referring to unpaid editorial coverage such as news articles, accounts for 84% of citations in responses from ChatGPT, Claude and Gemini, while journalism makes up 27% of sources cited by those AI systems. Muck Rack did not disclose its methodology, time frame or underlying dataset, so the figures could not be independently verified.

The licensing model is more concrete than those numbers. It gives Muck Rack permission to distribute and search reporting that would otherwise be restricted, while giving a publisher a commercial route into enterprise monitoring. It also differs from the unresolved question of whether AI companies need permission to train on publishers’ archives.

For publishers, the deal reflects a broader effort to monetize journalism beyond subscriptions and advertising. As news organizations challenge search engines and AI companies over the use of their reporting, licensing agreements provide a way to generate revenue while ensuring their content is available through enterprise platforms. The arrangement also highlights the growing value of credible journalism as AI systems and communications professionals increasingly rely on it.

Muck Rack’s next challenge is proving customers will pay for licensed access when many media monitoring tools still rely on publicly available content. If more publishers sign similar agreements, media monitoring could become a new licensing channel for news organizations rather than another way their content is used without compensation.

The post Muck Rack licenses MIT Technology Review for expanded AI news monitoring appeared first on The Media Copilot.

Media OutReach Newswire says it will guarantee that client press releases appear in USA Today’s public-facing release section, part of a broader push by PR firms to optimize content for AI-powered search, according to the company’s own announcement.

The Hong Kong-based wire, which describes itself as the first global newswire founded in Asia Pacific, is framing the deal as an AI visibility play rather than a traditional PR win. Alongside USA Today, it has struck deals covering Yahoo Finance, the Associated Press and more than 770 other US news sites, as well as regional titles including The Arizona Republic, Detroit Free Press, Indianapolis Star, Milwaukee Journal Sentinel, The Tennessean and The Oklahoman.

The company says the strategy relies in part on JSON-LD schema markup, a structured-data format that labels key information in online content so search engines and AI systems can more easily interpret details such as a release’s author, date and subject. Media OutReach said it began adding the markup to its postings in March 2026, arguing that combining machine-readable data with broad distribution can increase the chances that client content appears in AI-generated answers.

“By improving the quality of our guaranteed news postings on trusted, Google-indexed news sites with domain authority, coupled with the direct delivery of press releases to journalists’ inboxes, Media OutReach Newswire has successfully helped many Asian corporations to build their brand reputation in the US market,” said Jennifer Kok, the company’s founder and CEO, in the announcement. She said the company’s journalist database covers more than 500 trade categories and 68,000 media titles.

The company also highlights the domain authority scores of partner sites, a third-party metric from Moz that is not a confirmed ranking factor for Google or AI systems. Media OutReach’s claims about improving AI visibility have not been independently verified, and there is no public evidence showing that JSON-LD tagging or guaranteed release placement directly affects citations in tools such as ChatGPT or Google’s AI Overviews.

For newsrooms and publishers, the takeaway is blunt: the AI-visibility market is now shaping how PR firms buy placement, too. When a wire service can guarantee a slot in a paper’s press-release section, that section stops functioning as editorial real estate and starts operating as paid inventory built for machine readers, a shift The Media Copilot has been tracking as GEO tactics move out of marketing departments and into wire distribution itself.

That raises a harder question for local papers. As guaranteed press-release sections spread across high-domain-authority sites, publishers will have to be clear with readers and with AI crawlers alike about where sponsored or wire content ends and staff journalism begins.

Whether JSON-LD tagging and wire placement actually affect AI citations is still unproven outside vendor claims. The real test comes when independent researchers, not the newswires selling the service, start measuring the results.

The post Media OutReach bets on US newswires to boost AI visibility appeared first on The Media Copilot.

By The Copilot

Bots now make up about half of all web traffic. Are they a threat to block or an audience to win? 

For most of the web’s history, the deal was simple. Search engines pointed people to websites, and those visits paid for the content through advertising and subscriptions. AI is rewriting that arrangement, and the clearest place to see it is in who, or what, is showing up at the door.

Akamai says it now handles more than 150 billion bot requests a day, with AI bot traffic climbing more than 300% year over year. Bots account for roughly half of all internet traffic. On this episode of The Media Copilot podcast, Pete Pachal talks with two Akamai executives who sit on opposite sides of that shift: Kim Salem-Jackson, the company’s chief marketing officer, and Patrick Sullivan, its CTO of security strategy. Their jobs once had little to do with each other. Now they are working the same problem from two directions.

For Sullivan, bots have been a security headache for more than a decade. What changed is that the most valuable visitor to a site is now also a bot: the retrieval and training crawlers that feed large language models. “The VIP visitor to the website are the various bots that really make LLMs go,” he says. Detection and blocking are no longer the point. Akamai keeps dozens of categories of bots and a menu of responses for each.

For Salem-Jackson, the reframe is sharper. She treats LLMs as a new audience, each with its own personality, the way a marketer treats different buyers. “The bots are my new customer,” she says. She monitors bot volume by the hour, rolls out a welcome mat, and watches whether her team’s work on AI visibility pulls more crawlers in.

The catch is that the same bot looks like an opportunity to one business and a threat to another. A training crawler can compress a publisher’s entire site, hand it to a model, and never send a reader back. Sullivan puts the ratio of training visits to human visits at tens of thousands to one. For a company like Akamai that wants to show up in AI answers, that crawler gets cookies and milk. For a publisher whose business runs on traffic, the same crawler is something to block or charge for.

That’s where the conversation gets useful for media people. Akamai offers pay-per-click tools, but Sullivan is candid that those models are early and that content licensing deals remain more common. The technology is the easy part. The hard part is the business and legal call about what to allow, and who can enforce it. Unlike Cloudflare, which has taken a loud public stance on publisher control, Akamai casts itself as “Switzerland,” a technology enabler that leaves the decision to the publisher.

Then there’s the part every publisher can act on now. Salem-Jackson says an average webpage runs about 200,000 tokens, but a model reads only around 10,000. Roughly 1% of your site actually gets consumed. Anything important that sits below that budget is invisible. Her fix was a separate “bot site” that serves crawlers stripped-down, high-value content, which she credits for an 85% lift in citations. Great content is not enough if the machine cannot read it.

Key takeaways

 AI bot traffic is up more than 300% a year, and bots now make up about half of all web traffic

• For some sites, the most valuable visitor may now a bot, not a person

• The difference between training bots, retrieval bots, and malicious bots

• Marketers are starting to treat LLMs as an audience to court, each with its own personality

• The same crawler can be an asset or a threat depending on whether your business runs on visibility or on traffic

• Pay-per-click for bots exists, but licensing deals are still the more common path, and enforcement rests with publishers rather than the CDN

• A model reads only about 1% of a webpage, so anything below the token budget is invisible to AI

• A separate bot-optimized site can raise AI citations sharply, which Akamai puts at 85%

• Wikipedia, LinkedIn, YouTube and Reddit carry outsized weight in what LLMs cite

🔗 About the 👤 Guests

Kim Salem Jackson
LinkedIn: https://www.linkedin.com/in/kimsalemjackson/

Patrick Sullivan
LinkedIn: https://www.linkedin.com/in/patricksully 

Akamai Technologies
https://www.akamai.com

About the show:

To explore more conversations like this and see what’s new, visit the Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube? Tap the Like button and Subscribe to the YouTube channel. For more AI tools and resources built for media professionals, visit mediacopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team 

Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0

All rights reserved. © AnyWho Media 2026

Transcript

Pete Pachal: Hi, welcome to the Media Copilot. It’s a podcast about how AI is changing media, news, and communication. My name’s Pete Paschel, and I covered tech for a long time as a journalist. And now I have deep conversations with the media people, the builders, and the creators who are all answering the question how do we get information in the future? And how will that change the jobs and the industries whose business is information, especially media?

Kim: Okay.

Pete Pachal: For most of the web’s history, there was a fairly straightforward exchange. Search engines indexed information, sent people to websites, and those visits helped support advertising, subscriptions, and the creation of more information. AI is changing that exchange. Bots and agents can now read a site, summarize its work, recommend its products, answer questions based on its information, and sometimes even act on a user’s behalf. The original source may receive a citation, a smaller number of visitors, or really no measurable benefit at all. That puts companies such as Akamai in a powerful position. The infrastructure sitting between a website and the rest of the internet can identify automated visitors, can decide what gets through, enforce access rules, and potentially create new systems for licensing and payment. Akamai says it processes more than 150 billion bot requests each day and has recorded more than a 300% annual increase in AI bot traffic. It is developing tools that let companies block, permit, or verify, or monetize AI access while also helping brands improve how they appear in AI generated answers. My guests today work on opposite but increasingly connected sides of that problem. Kim Salem Jackson is Akamai’s executive vice president and chief marketing officer. She leads the company’s global marketing operation and has overseen its work on measuring and improving brand visibility inside AI platforms. Patrick Sullivan is Akamai’s vice president and CTO of Security Strategy. His work includes bot management, how to identify agents, edge security, and the systems that could allow content owners to control or charge for automated access. Kim, Patrick, welcome to the Media Copilot.

Kim: Thanks for having us, Pete. We’re thrilled to be here.

Pete Pachal: Nice. Good to see you both. All right. So before we get into all the tech, I would love to understand your both your two areas a little bit line a little better. I know I summarized them just there. but it I want to understand them and also how they converge around the problems I outlined. So Kim, maybe why don’t we start with you and you can begin from the marketing side and then Patrick, why don’t you take over and explain how it looks from a security perspective.

Kim: Sure. Well, you know, as a CMO I think about two main things. I think about our our brand and awareness and I think about driving demand and revenue for sales. And obviously AI has completely changed the paradigm on both sides of the coin about what I get up every morning thinking about. And the partnership between the business and IT, and in this case working with Patrick, has never been stronger, right? As AI becomes and AI bots become one of our most important customers and our VRP visitors. So that’s a little bit about Macro my role and how I think about the whole world. changing.

Pete Pachal: Patrick, how’s it look from your end?

Patrick Sullivan: Yeah, absolutely. So so I, you know, I think on the security side, the application security team has been handed the challenge of managing bots for more than a decade. you know, that’s an area that you know, I’ve been working with some of the world’s largest brands to help deal with you know, some of the threats, including sort of the relentless testing of compromised credentials on websites. That’s been sort of the number one way websites get breached for you know, most years out of the last decade. so the the security team was dealt that challenge of, you know, can you identify the bots, categorize the bots, optimize the way that you respond to the bots, and then provide monitoring and and analytics. so that, you know, we continue to see fraudsters, you know, leveraging bots, but as Kim said, on the other end of the spectrum, Now the VIP visitor to the website are the various bots that that really make LMs go, right? The retrieval bots, the training bots, et cetera. so that is now the the most important visitor.

Pete Pachal: So it sounds like from your perspective, the change isn’t just volume. It’s that the nature of the bots is different. And whereas before I imagine it wasn’t un universal, but generally like if you saw a bot, it was probably not doing good things. And then and now it’s like, well, half the internet’s bot traffic because everyone’s using AI. So it’s a sort of a difference in kind as well as a difference in volume.

Kim: And you as a marketer, I always kept an eye on the bots. Obviously, we’re Akamai and we use our own technology, but now I’m obsessed with the bots and I want to understand good bots versus bad of the good bots, where are they coming from? As Patrick said, what are they doing? You know, how many can I let in without my security team getting nervous that we’re, you know. letting in the bad bots. So it’s interesting how even as a marketer, my obsession has changed and, you know, I’m monitoring it all the time. Whereas before I’d probably look at it, you know, every six months just just to kind of understand the ebb and flow of them.

Pete Pachal: So imagine there must be like good bots, bad bots, and sort of gray bots, you know? Like I how do you classify these? This is probably more your area, Patrick, but I mean like I guess in terms of how you’re applying the filter. And then Kim, how are you interpreting what’s getting through that filter? Why don’t we start with you, Patrick?

Patrick Sullivan: Yeah, Pete, you nailed it. I mean, there’s a whole kaleidoscope of of bots, right? You know, starting with classically the the the sacrosanct bot that you don’t want to mess with is the Google bot and Bing Bot. You know, you wanted to make sure that that you showed up on search, you know, historically. then you would have some partner bots, you move your way down the spectrum. Maybe there are some aggregators and scrapers that you know, it’s it’s okay if they’re coming through as long as they don’t start to become excessive or cause issues to the websites. And then, you know, you move all the way over to the more parasitic bots that are fraudsters and competitors scraping your website. So there’s always been this continuum. So we’ve always thought about detect the bots, which is not trivial, categorize the bots. You know, we have dozens of categories for the bots and it’s not about blocking the bots, it’s about having you know, a whole menu of responses. And then also you want to make sure that you have your monitoring and analytics so you understand the implications of what you’re doing. But I would say things have radically changed as I’m sure Kim’s about to tell you, you know, that optimize verb, we’ve added some things around making sure that the AI bots in particular get exactly what they want. And there’s a feedback loop there with the monitoring and analytics.

Pete Pachal: Well, come back to that thought about you know, th not just blocking. I think you it’s it’s can be blocking, not just not always. I I think is what you meant. So there’s more subtlety there. But Kim, like obviously you’re thinking this not just in terms of bots, but like audience, right? And who who are these bots representing? What is as as sort of these bots get through to sites, like how do you how do you interpret that?

Kim: Yeah, I mean, Pete, you nailed it. To me, the bots are my new customer, right? I think of the LLMs as a new target audience for us, which marketing has done since the beginning of time. And then, you know, I’m also thinking about, okay. Each bot has a or each LLM has a different personality, just like each buyer in marketing has a different personality, business or IT. So my team is not only working with Patrick and understanding what the bots are, but you know, where they’re coming from, what they’re scraping, what they’re doing, and how I can more precisely market to them to align to their needs. So I literally think of them as my new target audience and I’m trying to understand their behavior, retrieval. training. I like the retrieval ones because I’m hoping they’re delivering information about Akamai that’s a signal to buying. That’s my favorite behavior. I like to look at the volumes and as we kind of lean into some of our GEO work is that having an immediate correlation to more bots coming to our site if we’re as we’re trying different tactics. So I am constantly monitoring that bots. I have a big welcome sign to let them in and you know I’m sure we’ll get into what we’re doing with our website and The bot site versus the human site, but yeah, I one hundred percent think of them akin to a human. And that the way I would target business buyers versus IT buyers, I just think of those LLMs in exactly the same.

Patrick Sullivan: Yeah.

Pete Pachal: Right. So some people in my audience will definitely understand that the whole idea of the welcome ad and sort of making sure the bot the good bots, I guess as we’re sort of throwing a lot of like judgmental terms here, but like the bots that are having a positive outcome for whatever the business goal of the site is, they would want to well sort of lay out the right carpet and make sure they have a good experience. So that’s a good chunk of my audience or comms marketing, but then there’s another chunk of my audience, which is obviously publishing journalists, editors, and they’re like, Well, wait a minute, like we are

Kim: Come on in. Yeah.

Kim: Exactly.

Pete Pachal: business model is all based on advertising, subscriptions, and other things that depend on traffic. without some kind of compensation system in place, I don’t know if I really even want these bots in place. So let’s k stay with you for a second, Kim, as you as you sort of think about that in terms of like who might be on the other end of whatever Akamai is managing as a CDN and what might be available to them. And and how how that might that like in other words, they might be applying a different filter to to the things you’re talking about.

Kim: Sure, and I’ll I’ll tee it up and then I’ll hand her to Patrick. So obviously we have a breadth of customers and some are like me, you know, that it just wanna put out that welcome at and others need to monetize their site, right? And and really kind of allow that pay per click. And so based on, you know, step one for us at Hakamai is really to understand your business objectives, what role the bots play. in your business model and how best you want to treat them and calibrate it over time. And so you know that’s really where we start every conversation with our customer is understanding their business objectives, their business model and how we can meet them where they are and put the right monitoring and restrictions in place based on that. And Patrick, I know you’ve worked a lot with media customers and been sitting with them. Why don’t you share a little bit more about how we put that structure in place for them?

Pete Pachal: Right. Before before I wanna I definitely want to hear from you next, Patrick, on this and the structure you have in place. But I up until now I think we’ve been sort of talking loosely because we’re just having a chat here about like good and bad bots. But honestly, I loved it if you could get even a little more clinical about it and think about like the different types of bots. So it’s kind of like like the way I think most of my audience and I understand them is like there’s essentially three main types, you know, you have training bots, search bots, agent bots. You might think of it have a different taxonomy in terms of what’s relevant and how you filter, but sort of like as you think about like the different reasons a publisher might want to block or allow through, I think understanding those subtleties would be really helpful to people listening. So please let me know like how the your customers approach this and what you find is the best system that tends to work.

Patrick Sullivan: Yeah, Kim, maybe I’ll take the first crack at this. Yeah. So Pete, there are many, many more categories of bots there, but I think you’re right. We should we should drill in, right? There’s a whole subset that we call the AI bots. And Kim and I both have been talking about training bots, for example. so to to really be specific, what the the role of those bots is on behalf of the LLMs, they’re all visiting websites, you know, ingesting that information and feeding that back into the LLM such that, you know, when an when an agent or somebody interfaces with a chat, they have learned you know from across the web and they’re able to incorporate that knowledge that they derived, you know, from the training bots into the response from the LLM. but but Pete, you nailed that. There is a massive difference in sort of the way people think about those training bots. You know, Kim cannot get enough, right? Like every time a training bot’s coming, you know, she’s you know putting out cookies and and milk, you know, come back, invite your friends. Because you know, because the the really for her, I’m sure she’ll get into the metrics that she tracks, but the more that they come and the more that

Pete Pachal: Literal and virtual cookies, yes, I get it.

Kim: Exactly.

Patrick Sullivan: the LLMs learn about the content on the website, the more that people researching, you know, topics that are relevant, you know, such as, you know, how do we protect ourselves from the latest Frontier LLM security threats or other topics? If those training bots are coming to Akamai and and ingesting our content effectively, we show up for those users. But Pete, to your point, there are other people who really thrive their their whole economics are around intellectual property, And making sure that that’s monetized effectively. And they look at that exact same bot with the exact opposite response. They don’t want that bot coming in for no monetary value, making off with that content and then monetizing it without them, right? So there, you know, the technology is frankly much easier than the legal and business, you know, on this side of the equation. We can do the same thing, we can detect that bot. Categorize that bot. And then when we get to the optimize, rather than the optimize response that Ken will detail in a minute, there, you know, they want to maybe block the bot. Maybe they’ll they want to have a specific API because they’ve structured a financial agreement, you know, where they’re a specific LLM has has structured a deal with a content provider. So rather than crawling, maybe they have a dedicated API. or maybe they’re paying per click. those things are pretty easy for us to instrument from a technology perspective. Honestly, the hard part there are the the lawyers and the business people that have to make those decisions. because it’s pretty easy to say we’re going to block this LLM. The business implications are are far more complex. But yeah, absolutely different people look at the exact same bot from a hundred and eighty degree spectrum. and and we can just help them implement that policy, right? That’s we give them the analytics and then when we get to that optimize, it it is customized to the customer and their business model.

Pete Pachal: So Kim, again, the the the idea of these different kinds of audiences and customers that you have, you know, it seems to be that even within the publishing world, like even if you do have content that is IP invaluable to you, you it’s not a blanket thing typically, right? That you just you want to block everything or protect everything. You might want to like have some content visible and some others. but I know Patrick, you talked about training. Is training really the the main thing right now? I do feel like for a lot of publishers, it’s more about rag these days because they’re constantly publishing content and I don’t know, like so maybe they block training, maybe they don’t, but it’s also about like the stuff that is appearing in people’s answers without the need for them to go to the site anymore. I don’t know if that if the conversation is encompassing all the bots or or if training’s still a part of it, but like I guess what are some of the the subtleties that people can sort of put into this process given the tools that you offer?

Patrick Sullivan: Yeah, mean I I I think it comes back to you know, as as Kim detailed, the the shift in the web, right? you know, it used to be you would go to a search page, click on a link in Google, and then that would navigate you to the web page. These days, if that training bot comes through there and it can you know, compress the information from the website and then incorporate that into the LLM, the the ratio of you know that that training visit to Actual visitors going to the LLM is brutal, right? It could be tens of thousands to one. So one training request comes in, it informs the LLM, and then tens of thousands of of visitors could just, you know, go into their chat interface with their favorite LLM, and there are no subsequent calls back to the website that can be monetized, right? So that the the economics for somebody trying to monetize site visits, can be damaged very, very significantly. And again, for that reason, things like retrieval and and training bots are are treated pretty harshly.

Pete Pachal: Yeah, but I would guess I would I would ask like is is there guidance that you give to publishers on like, well you might want to block training and search but not agents or or all of it? Or is there are there reasons, common reasons you might want to allow one and not the other?

Patrick Sullivan: Yeah, a lot of it it there. I mean, if if your model is strictly about monetizing, you know, I think it comes down to can you strike a content deal? you know, it does blocking some of these bots give you more leverage to to structure that deal. It you know, it’s the technology is is in support of the business arrangement.

Pete Pachal: Well what about like these things where you can play for the usage or the crawls and the sort of like automatic payment or you know, paywalling, I guess, of content from the bot internet? Is there what is what does Akamai offer in that?

Patrick Sullivan: Sure. s so the we do offer, you know, what we call pay-per-click. You know, in that model you would look to monetize as a content provider, you know, every time that that there is a a click within the LLM. I would say in general across the industry, though those models are still you know, emergent. I think they’re still starting to pick up adoption, but very early days there. I I think probably more likely scenario are the the content licensing deals where, you know, again, an LLM would get together with an intellectual property holder and and they would structure a deal for exclusive or or non exclusive use of that content. That’s probably more common than the pay per click model on the internet today.

Pete Pachal: Right. And so you know, I guess it sort of comes down to why, right? Which is to say like I think a lot of AI companies or those that are in the business of I guess you call information brokering, i they there there’s easy ways to get the information even with bot blocking in place, whatever whatever that may be. So you know, it you might be aware that you know Cloudflare’s taking a highly public position on publisher control. presented itself as kind of an advocate for for changing the economics of this. the AI crawling that is. And it’s it’s emphasized giving customers a lot of options. like how how do you how do you how does Akamai sort of see its role? does it have a similar position? Is it a little more is it a different audience, different different philosophical stance? maybe Kim you want to take this one and

Patrick Sullivan: Yeah.

Pete Pachal: And maybe Patrick let me know how it sort of plays out in in the in the technical side.

Kim: Yeah, I mean our our view is again we want to meet our customers where they are based on their business objectives. So you can take a hard and fast stance or or you can adjust. I I was just on a call with a retail customer who views, you know, one of the LLMs as their competitor and they basically wanted to take a really strict stance on that and almost block them entirely. So some of our competitors, you know, take one direction, others are a little more loose, you know, Akamai’s a little more like the Switzerland. We basically again wanna understand your model and help you adjust and and block. Patrick, anything you wanna add? I know you speak to a lot of customers on this top.

Patrick Sullivan: Yeah, Pete, I mean, I would say, you know, if you look at sort of the the delivery engine for the the media industry, you know, Akamai is the top of the list there. on the bot side, you know, we have a a menu of those optimizations that’s broader than anybody. so I would say, you know, there certainly have been some announcements in the industry about changing the fundamental economics of pay-per-click. I I you know, I would ask you to follow up with with some of those folks on the adoption there. but I would say in general across the industry, you know, maybe some of the the announcements have not been followed by quite as much adoption and major shifts to the the funding of the of the internet may not have followed up, you know, with some of the pronouncements that that have been made, but that’s probably for somebody else to to respond to.

Pete Pachal: Well, I guess I guess what I was getting at is that there there’s an enforceability to this that that seems like is relevant, right? Which is to say, like if you’re a major CDN like a Cloudflare or an Akamai, that gives you real influence in the industry, Cloudflare is obviously using that to adopt an advocacy position. And is there is there w I guess would the industry and the things you’re trying to do to help your customers, particularly ones that want a block, would the Would that be helped by either new standards or new maybe even regulatory practices? So for example, what I I know this has been proposed by Tolbitt, who I believe I I’m not sure if you guys are doing things with them. Yeah. So the the in terms of bot identification, that seems to be like, at least from my conversations with them, that’s a good place to start. In other words, requiring that there’s a transparency to what the bots.

Patrick Sullivan: We do. Yeah, we have yeah, yeah.

Pete Pachal: function is, which I think most good players do. But how enforceable is that if players like Cloudflare or Akamai aren’t sort of actively really encouraging that? And I guess I know you both do. It’s just that where where to to what extent, I guess, might be the the the question.

Patrick Sullivan: Yeah. So so I think if we if we follow that through, you know, the the role that Akamai would play in pay-per-click is the technology enabler, right? So if a publisher says we want to move forward with pay-per-click, and the other side of that is, you know, prevent anybody who’s not participating as an AI bot. If they’re not participating in pay-per-click, then we’re gonna block them. they would have to make that decision, right? That that decision rests with the publishers. we’re sort of the enforcement mechanism there, the detection and and technology decision. so it’s really not up to the the bot provider or the CDN to make that decision. That’s sort of the technology enabler. The business owner at a major publisher would have to say, We’re willing to deal with all of the repercussions of blocking LLMs that are not participating in Pay-PerClick. And I think that’s the rub and and sort of maybe what’s driving the current level of adoption of Pay-PerClick. But the the technology is there for publishers that want to adopt it.

Pete Pachal: Okay. So just to probably my last question on this just to say that I think it is like the security framework taking the sort of bot identification, it’s like identifying the agent, like who’s behind like who’s operating it. Like I don’t know if this is there’s an equivalent KYC with with bots or anything. And then just what the purpose it is doing, right? So like that that to me Sounds it would fall on the C DN, that kind of idea. Like just what is the bot and we want to be clear on what it is. So I guess my question would be, is there is there a minimum credential, I guess, for a legit AI agent? And I without getting too technical, what would that be?

Patrick Sullivan: Yeah, so so their AI agent is also a very broad category. So if you think about that, you know, we have you know a whole we published a security framework for you know agentic bots. So we participate in partnerships with major credit card companies, you know, major identified agent platforms. So we we have the ability to understand the trust level of that agent platform, and then we also

Pete Pachal: True, sure.

Patrick Sullivan: Look at the identity behind that, behind that agent. Like, you know, who’s the human identity? And then, you know, there’s various anti-fraud technologies we put in place there. But I guess to bring it back to kind of the framework, we do the detection, we do the categorization. A customer would choose which specific optimizations are right for their business. You know, pay-per-click, block. There are specific optimizations that you want to put in place. if you’re you’re Cam and you want to make sure that you show up on the LLMs. So I think we’ll talk about that in a minute. But there’s a whole menu there and that’s really at the discretion of the the content owner. and publishers respond very, very differently than a commerce website or a travel website or a bank. many organizations now, their number one visitor. the reason you build a website is to attract that AI bot so that you show up and inform the LLM.

Pete Pachal: Mm.

Patrick Sullivan: about the purpose of your business. But it’s very, very flexible, up to the discretion of the the policy of the business. Cool.

Pete Pachal: Cool. Well, let’s switch tracks to that that thing I know Kim’s excited to talk about, which is the visibility in AI answers. And, you know, assuming again you you want to be, that what what is offered and how you guys sort of help enable that. So I was looking the looking that this up, looking this up, and it says, I guess you have an AI brand presence product that it produced an 85% increase in citations. And a 364% increase in non-branded searches, 130-30% increase in child, in other words, up across the board compared with various competitors. So I’m gonna take those those figures as as as as you as you as I saw them. but how how did how do you achieve this? How do you how do you change what do you change in the back end? What are you seeing that other people don’t? What is how does your position as a CDN give you this kind of advantage so that brands can get these kind of results?

Kim: Sure. well there’s kind of two main components to what we look at. One is understanding as you you’ve listed off your visibility within the LLMs, your sentiment, your citations. And then the other one is the action you take to improve it. Right. And that’s where Alchemy Secret Sauce being, you know, a CDN, being in security and being able to deliver that website for the bot comes into play. So gosh, back in the fall of twenty twenty four, I’m losing years Pete, you know, we saw this yeah, we we can’t believe we’ve been doing it this long, we saw the sea change happening in the market and we said, hey, we’ve got to get ahead of

Pete Pachal: Mm-hmm. Yeah.

Pete Pachal: We are.

Kim: This right. And so we started with the visibility journey, which is understanding where we stood vis-a-vis these LLMs on those three KPIs I mentioned. And then the question I always say: data insight action. Okay, now that we understand our standing, what do we do about it? And that’s when we reimagined our org, how we think about content, how we think about content placement. and we started working really, really hard to achieve those stats that you just read off. But what happened in parallel is we were focusing on understanding our visibility, reworking our content engine, reorging marketing, Patrick was working with one of our partners to kind of say, Hey, how can we better serve up information to these bots so they can consume it quicker, more economically? And that’s where it became kind of this perfect marriage of what we were doing and we saw so much success on the Akamai side. And given, you know, who we are as a company, we decided to repackage it and to sell it to all of our customers. So Akamai brand presence gives you those two components. One, helping business leaders and IT leaders, we talked to both, understand their visibility vis-a-vis the LLMs, and then more importantly, give you the ability, it’s I call it more the easier button. to deliver a website specifically for bots. And the second our website for bots went live in November, we achieved incredible results. And you know, those are things the board cares about. I talk to our board about those every single quarter when I do a presentation. So we’re super excited to have to drink our own champagne as I like to say and offer this to to the market.

Pete Pachal: Nice. What are some fundamentals? Like basically what are some basic things either companies, sites, brands aren’t doing or are doing badly or wrong that you’ve found they that they can change, that they can start seeing better results. what’s the I guess what’s the lowest hanging fruit?

Kim: Yeah. Yeah, I mean f first of all, a LLMs consume only one percent of your website, right? So if you make assumption that just because you have great content, build and those LLMs will come, you’re absolutely wrong. If you don’t figure out how to optimize your site to make the consumption quicker and easier, you’re dead in the water. You can have the best content strategy, you can reorg everything, but at the end of the day, it’s about the LLMs being able to quickly consume. your information. And that was really, I think, the secret sauce. So understanding your visibility, which is probably very low if you don’t have a bot site. And then quickly standing up a site for those LLMs so you can get more of your content consumed quickly and at a better economic price.

Pete Pachal: So you mentioned efficiency there, and I understand that like the amount of data that the A systems that they need to process is you you reduce that considerably from when sort of you were you were first studying this. And how do you ensure that like I guess essentially that key th key data hasn’t been lost and it’s still also like consistent with what people see? yeah, how is that is that just technical stuff that d we don’t have to worry about, or is it like Like hi that just seems like an a pretty impress is it just something else only a C DN could do? No.

Kim: well it’s by design. So the average website consumes about 200,000 tokens and AI reads about 10,000. And so, you know, what we did is prioritized our highest value content and make sure that was on our bot site as quick as as possible. And then AI LLMs, they love breadth of of content, they love recency of content, and they like the distribution of consistent content everywhere. And so underpinning having the right tools and the right site. You also have the right, you need to have the right ecosystem under you to make sure you can constantly feed those LLMs in the way in which they want to consume your information. I think the biggest mistake people make is they think you can do one thing or the other, but everything has to work in concert. It’s an entire ecosystem it takes to feed those LLMs, right? From understanding your visibility. serving up the right content in the right places too, because a big aha we had was understanding what sources the LLMs biased. and you know, there were sites I could have cared less about or even Wikipedia I haven’t thought about in 15 years. Now I love Wikipedia because the LLMs love Wikipedia. So I think understanding your stance, understanding the placement of your content, having a content machine, and then again having that brand optimized tool.

Pete Pachal: Yeah.

Kim: to serve up the content via bot site to those LLMs is kind of the secret sauce.

Pete Pachal: Yeah, it does seem like there’s at least kind of a a set of sites. I I see the same four cited all the time as sort of the main things that have outsized influence in LLMs, and that’s like you said, Wikipedia. There’s LinkedIn, there’s YouTube, there’s Reddit. Those seem like the big four now. Is that just kind of a a natural consequence of having essentially user generated, big user generated repositories? Do you think this is something that’s gonna continue? or do you Do you feel like that might get eventually flattened out as the AI web evolves?

Kim: Yeah, I think Pete, it’s all of the above you nailed it. I think the LLMs had an affinity to those sites because they were, you know, raw, structured, plain content, not a lot of text, not a lot of fluff. Now the world understands what the LLMs like, which is why, you know, we launched a site, a bot site for LLMs and we offer it to our customers. But I think the only constant is change in this industry. And so literally every day we’re studying the market. We’re studying the behaviors of the LLMs and we’re trying to ensure that we meet those LLMs where they are with again the right content and the right format in the right place at the right time. And you know, our brand presence tool I think gives people a leg up in achieving that quicker and easier.

Pete Pachal: So yeah, go ahead, Patrick.

Patrick Sullivan: Yeah and yeah, maybe I pick that up. I mean you know, I think to to the point there, the these I think we all know tokenomics, you know, the cost of a token is is driving more and more decisions that are being made. And these large LLMs are certainly aware of their token token burn as they are conducting training or retrieval. So to Kim’s point. The the version of your website that looks beautiful to a human is compelling. It has videos, it has a lot of rich content, and it’s bloated from the LLM perspective. A lot of really important information that you want to show up in that LLM on behalf of your customers is sort of below that that token budget, basically, right? So Kim, you know, was alluded you only get about 10,000 tokens. If really, really important information sits below that. It’s invisible. It does not get ingested to the LLM. We’re seeing customers experience massive business impact because they’re not showing up on the LLMs. Their competitors are, you know, they’re seeing, you know, in commerce and travel, you know, huge revenue shifts to competitors. So what what I found when we started down this path was that. you know, there were IT organizations that were building two versions or more versions of their website, one for the human and another one that that was built maybe for you know a bot that likes very efficient HTML. Yeah, another version it’s worse than that. You know, some like Markdown, most like HTML. You know, so so what we do is just sort of on the fly, we’re making sure that that bots that that we know that like HTML

Pete Pachal: A non human.

Kim: And not

Patrick Sullivan: Or getting HTML that’s within their budget. Those that like markdown get markdown. and then there’s also a feedback loop. You know, we talked about that. You detect the bots, categorize, and then you optimize. The optimize for the AI bots is to give them exactly what they want. And then the feedback loop there is, you know, the the metrics that Kim was mentioning. How are you doing relative to competitors for citations, other metrics? And then there could be content, you know, recommendations, you know. it’s not just making sure that everything, as Kim said, everything that that you have published is showing up, but also maybe there’s some things that you haven’t published about that you should, right? So that’s what we see as kind of the full life cycle.

Pete Pachal: Nice. You mentioned on the fly there, I might understand that like if you’re a a site owner of whatever whatever you’re doing, that you can if if I don’t know what you have to enable on your back end, but like the idea that it it’s identifying a bot and then it’s like spinning up kind of a a very efficient version of that page with the relevant information for the bot right then and there. Is that kind of the idea?

Patrick Sullivan: That’s the model. So as that as a human comes in, they get the human version because we detected that that was a human. as, you know, one of these specific AI bots come in, we’re gonna dial up the optimization and serve them, you know, that optimized version that they want and then capture all the analytics that that Kim thrives on and and is pouring over on an hourly basis of you know, you know, how’s the business doing from a a digital marketing perspective? So so that’s all. you know, part of the the platform.

Pete Pachal: So as a publisher, if you’re a publisher and you have content and presumably want to have it in LLMs, or at least the LLMs that you’ve authorized, the I guess are there any tools that are being underutilized in this regard? Do you do it? Like I I’m I I don’t want to get too nerdy here, but it is like I’ve I’ve recently gone to a couple of talks about snippets and that idea of sort of identifying key content.

Patrick Sullivan: Yeah.

Pete Pachal: whether it is on the pa whether it’s lower on the page or not, and just sort of giving emphasise emphasis to certain aspects of the content for the machine. Is that something that you think about as part of the the overall way you implement things? Curious.

Kim: All day, every day we are we are liter literally updating the site every single day for humans and for bots and always thinking about the highest value content that we want to deliver to either audience. And sometimes it’s slightly different, but it it is a it’s not a you know, turn it on and forget it. It’s a it’s an always on ecosystem that we’re fueling and the measurement aspect is critical because you need to understand how those different things you’re serving up are serving you.

Pete Pachal: Sure.

Kim: vis-a-vis your performance and so that constant tuning is key, but having the ability to have the bot site and the human site makes it that much easier. Before it was very manual, very time consuming. And we saw immediate implications against revenue because more people are going to LLMs to buy, you know, whereas SEO is more about research. When you go into those LLMs, you typically know what you want. You’re looking for that answer. And so this gives you the ability to serve it up quickly and correctly to the LLMs.

Pete Pachal: Nice. As we c start to wrap up here, I’d love to ask a couple questions maybe about standards. And I know, you know, there’s a standard called Real Simple Licensing that has been supported by a lot of groups and companies and publishers, I believe including Akamai and Cloudflare. but basically RSL like it allows publishers to essentially like put the machine readable rules for the bots and and search AI training, etc. I’m I’m curious what you think might still need to happen before something like RSL becomes something meaningful and forceable on the web. Patrick, why don’t you take it?

Patrick Sullivan: Yeah, so so I would say in in general, you know, there are there have always been, you know, pages that provide suggestions to bots, you know, this is the part of the website we want you to crawl, you know, here’s what we want you to do elsewhere. you know, some some bots obey those suggestions, other bots kind of treat treat those suggestions like the speed limit sign on the interstate and you know, maybe they follow it, maybe they don’t, but you know, certainly I think it’s it’s definitely best practice to publish those instructions and there are a lot of bots that will follow those. but there are many, many entities out there, many different parties and some some play closer attention to the rules than others.

Pete Pachal: But I guess what happens is even when the sometim sometimes you can’t even agree on the rules, right? I mean, there was the whole row that ver Cloudflare versus Perplexity last year and perplexity was insisting that it would user agents were just behaving like agents and agents can do what they want or not do what they want, but like you know, they were representing people, so they have sort of a different set of rules than search crawlers or training crawlers. And you know, the subtleties of the we don’t have to rehash the whole thing. But I guess that’s the point of these standards. some extent is like sh do we need new rules as we go forward for for managing this stuff. RSL would seem to be something that that like you say it sort of add gives a set of instructions. like you could you could write theoretically a set of instructions for agents, but they might not do it. And is again i i it do we the I guess it’s push coming back to the enforceability problem of like if someone wants them to, w like the what are the tools that or the things that I guess the levers that s someone like Akamai can sort of pull on to sort of help them.

Patrick Sullivan: Yeah, I I I think it’s there are always, you know, bots that that play by the rules and then there are other particularly when it comes to scraping and things like that, there are always bots that we’ve been dealing with for a decade plus that that do not like to play by the rules and you have to detect them and you know, take enforcement into your own hands. But you know, a lot of the the larger organizations y you know, are much more likely to play by the rules.

Pete Pachal: This has been great, guys. Thanks so much for dropping by. For before you go, I’d love to ask you guys a really quick question. I try to ask most of my guests, which is that is there something about this new AI era and agent driven internet that either keeps you up at night or you’re optimistic about? feel free to tell me both. I always like to get optimism and pessimism about you know what’s gonna happen. but why don’t we start with you, Kim, ladies first?

Kim: Sure, I would say what keeps me up at night is it is unprecedented and changing by the minute. I feel like if you take a day off, you’re behind. so so that would be part one. And what I’m most optimistic about is I think, you know, ultimately we’re all striving for a better customer experience, right? And you know, I don’t think there is a better one than LLMs delivering kind of hyper personalization, real time answers. And so I think as quickly as companies can prepare. I think the end game will be better for everybody.

Pete Pachal: Nice. Patrick, what say you?

Patrick Sullivan: Yeah, so so Pete, I think the the pessimism is pretty easy. You you know, just working in security probably multiple hours every day is just working with large organizations to understand the risk of the latest frontier LLMs as it pertains to finding many, many more vulnerabilities that we had seen before. So it’s it’s a pretty rough year to be in in security with just all the vulnerabilities that that everybody’s facing. so Yeah, definitely take care of your applications. They’re having a rough go. on the optimism side, you know, just looking at at sort of the emerging you know, I think you touched on it, frameworks for for agents, you know, for commerce, for other things. and then also, you know, as it pertains to you know, talking to an organization and saying, you know, we don’t have to publish multiple different versions of the website, you know, manually. We can kind of leverage automation there.

Kim: Yeah.

Patrick Sullivan: That’s a fun conversation to have.

Pete Pachal: Nice. Sounds like you might have had early access to mythos. I’m just guessing here. Akamai. You get to get sir get your hands on that, baby?

Patrick Sullivan: you so we’ve been working with the the you know, many of the latest Frontier L LMs, but every security team out there either had access to them or they’re being asked by the board how to contemplate what are the implications, you know, when the the general public, that’s what everybody’s worried about, when the general public gets access, you know, when all these vulnerabilities come home to Roost. So that’s that that’s definitely the one that keeps us up at night, no doubt about it.

Pete Pachal: Nice. Well, if if no one takes if to if you take nothing else from this podcast listeners, set up your two factor off, among other things. guys, this has been great. Thanks so much for spending some time here on the Media Copilot.

Kim: Yeah.

Kim: Thank you.

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It’s hardly a revelation to say that Google’s AI Overviews sometimes get things wrong. The Gemini-written summaries at the top of search results have been misfiring on and off since they debuted in mid 2024. It feels like Google will never fully live down the infamous “glue on pizza” moment, and the errors come often enough that they always carry the warning, “AI can make mistakes, so double-check responses.”

Nonetheless, AI Overviews are now the reality for anyone (read: everyone) who uses Google. At some point, publishers have to stop treating each new mistake as a curiosity and start treating the system that produced it as their working environment.

This spring, The New York Times commissioned AI startup Oumi to measure the problem. The ultimate finding: The latest version of AI Overviews was accurate 91% of the time. That looks respectable until you run the math against Google’s billions of daily queries. A single-digit error rate at that scale produces millions of bad summaries every hour.

The Times drove the point home by citing BBC tech reporter Thomas Germain, who ran an experiment. He published a fake blog post crowning himself the world’s best hot dog eating tech journalist. Within a day, AI Overviews were repeating the claim, apparently without checking.

The stunt looks silly because the query was silly. But the underlying mechanism isn’t. Germain succeeded largely because he owned the only page anyone had ever written on that subject. It was an information vacuum. For a well-covered topic, a lone rogue post would barely register.

The lens publishers can’t remove

The hot dog stunt is only one failure mode; it turns out AI answer engines can go wrong in several ways. And the stakes for publishers keep rising: AI Overviews now appear in most searches. An April report from AI-visibility startup QuickSEO put their prevalence at 60.23%, and that was before Google’s May I/O conference tightened the loop between AI Overviews and AI Mode, letting users slide from a summary into a conversational follow up without leaving the results page.

Chatbots aren’t the biggest surface here. Google is. People can opt in to ChatGPT or Claude, but they get served AI Overviews whether they want them or not. That default status is what makes accuracy such a load-bearing question. Publishers can’t set the terms of the lens their work passes through, but they still have skin in the game once it does.

Ubiquity isn’t the same as blind acceptance. Trust in AI answers scales with the stakes of the question. A roast chicken recipe gets less scrutiny than a cancer treatment query, even if the entry point is identical in both cases.

By the time a reader decides to double check an answer, the framing has already landed. The summary supplies the vocabulary, sets up the follow up questions and points to what feels worth investigating next. If a publisher the reader trusts is cited in the summary, confidence rises even when the citation is never clicked. I’ve made the case before that citation is a form of value for publishers, but that value depends on the reporting being accurately represented.

Three ways the machine gets it wrong

To map how AI Overviews fail, I spoke to Isis Blachez, the AI lead at Newsguard who runs the organization’s AI False Claims Monitor. She sorts the failures into three buckets, and each one shows up in the Times study.

1. Weak or irrelevant material rises to the top. This is the glue-on-pizza scenario. That recommendation came from a Reddit post written as a joke (we hope), which made it irrelevant to a serious cooking query. The catch is that the post did answer the question head on, and direct answers rank well in AI discoverability. Journalistic content generally performs better in AI engines when it’s optimized for machines. When it isn’t, or when it’s blocked outright, thinner material can grab an outsize share of the response.
   
   “We do [reliability] ratings of news sites,” explains Blachez. “And we saw that for most of the highly ranked sites, they were blocking a lot of the AI bots, and then most of the low-quality sources were giving full access to AI web crawlers.”
2. The AI finds the right source and misreads it. This is the quietest failure mode and possibly the most consequential. Blachez points to a case where multiple chatbots cited Snopes to confirm a false claim that Iran had attacked a Pakistani flagged oil tanker. The Snopes piece was actually the debunking. The machine flipped it.
   
   “Sometimes, even if it’s citing a credible source, it can be incapable of citing it well or retrieving the information correctly,” Blachez says.
   
   The reporting itself is fine in these cases. The machine is the point of failure. This version of the problem is the one that often features in lawsuits against AI companies.
3. The information pool has been poisoned on purpose. The hot dog story is the innocent version of this. The pro-Kremlin Pravda network is the malicious one. It flooded the web with millions of articles across sites designed to look like news outlets, pushing Russian narratives at industrial scale. Coordinated actors publishing similar sounding claims across many domains can manufacture the appearance of consensus and crowd out honest reporting in retrieval systems.
   
   “So what we’ve observed that worked with Pravda is flooding search results,” says Blachez. “It’s like putting the same information with practically the same language, many domains, many times and just dominating narrative on that specific topic.”

Building the machine readability pass

So the answer layer can go sideways because access is blocked, the material is manipulated, or the content itself invites misreads. The AI operator has an obvious duty to raise the floor on quality. What about the publisher?

A lot of newsroom people have quietly written this problem off as somebody else’s, on the grounds that AI systems are a black box. That framing is understandable and mostly wrong. Publishers can influence all three failure modes. Being in the mix means not being blocked. Discouraging misreads means writing for machine comprehension as well as human. Beating manipulation means publishing your own answers to the queries you want to own.

Blocking crawlers is a legitimate choice. Copyright and the absence of any compensation model are real reasons to shut the door. And when journalism is blocked, Google and every other AI company still owe their users a duty of care with the material they do use. But when journalism is available to the AI, publishers have levers to make sure it’s represented correctly.

Every newsroom already runs an SEO pass on its work. The most effective way to shape what AI Overviews and chatbots surface is to run a machine readability pass alongside it. This isn’t just standard GEO hygiene like matching titles to common queries. It means writing so that the tricky parts of a story remain unambiguous to a machine reader, even when they’re already obvious to a human.

In practice, that means saying the quiet part out loud. A human understands that “alleged” applies to a whole run of paragraphs even when the word only appears once. A machine may not carry the qualifier forward.

A short set of questions to run through the pass:

• Are dates explicitly tied to the correct events?
• Is it clear whether an allegation is being reported, verified or debunked?
• Is the primary conclusion stated plainly rather than left entirely to implication?
• Are corrections and updates obvious?
• Does the article distinguish the original source from later repetition?
• Does the headline create ambiguity that the body later resolves?

As with SEO, editing for machine clarity tends to sharpen the human read too. The trade off is that the pass improves the odds. It does not guarantee anything. The goal isn’t “AI proof” journalism. The goal is to strip out avoidable ambiguity and give accurate reporting a better shot at surviving the answer layer.

Publishers can’t dictate what Google says about their work, and they shouldn’t be expected to patch the flaws in someone else’s product. But as AI settles in as a default filter between journalism and its audience, treating that as a reason to disengage stops being a strategy. Newsrooms can still make the truth easier to find, harder to misread and much harder to replace.

A version of this column appears in Fast Company.

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Sitecore is acquiring generative engine optimization (GEO) startup Scrunch for around $225 million, according to a Bloomberg report, adding AI answer-engine visibility to an enterprise content platform that has been quietly building toward a machine-readable web strategy.

Neither Sitecore nor Scrunch have confirmed the price. The deal marks one of the larger investments in the emerging GEO market: the practice of optimizing brand content so it surfaces in AI-generated answers rather than traditional search results.

Scrunch’s platform shows brands real-time signals about how they appear across various AI platforms, along with competitive analysis and technical audits. Its Agent Experience Platform, or AXP, is designed to deliver content in formats AI agents can read and use without disrupting the human experience. Notable clients include Lenovo, Skims, Headspace, and Penn State University.

“We’re at a pivotal moment where companies must rethink traditional digital strategies and accept that the internet must be written for machines to understand if we want humans to experience it,” Eric Stine, Sitecore’s CEO, said in a statement.

Scrunch CEO and cofounder Chris Andrew echoed the same urgency in his own statement. “By joining forces, we’re helping companies meet buyers where they are, moving beyond traditional SEO to win inside AI-generated answers,” he said. “That’s where Scrunch’s AXP is a critical advantage, delivering content in a format AI agents can read and use, without disrupting the human experience, allowing brands to become the trusted sources that power those answers.”

The GEO space is becoming increasingly competitive as brands seek visibility in the AI experiences where consumers are spending more time. Scrunch previously raised $26 million, including a $15 million Series A last summer led by Decibel, with participation from Mayfield, Homebrew, and others.

The deal logic is in the numbers. Scrunch told ADWEEK last year that conversion rates in AI search are three to five times higher than in traditional online search, citing its own data. “A visitor coming from AI search is buying faster than a traditional organic visitor,” Andrew said at the time. Independent verification of those figures was not provided.

Third-party research offers some corroboration. In research conducted by Akamai, AXP-enabled webpages saw a 364% lift in brand presence in responses to non-branded AI prompts and a 218% spike in citations appearing in AI responses.

Stine said the combination would allow brands to “show up with greater clarity, authority, and relevance so they can build trust, increase share of voice, and influence decisions early in the buying journey when it matters most.”

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A.G. Sulzberger, publisher of The New York Times, delivered a sharp rebuke to the artificial intelligence industry Monday, accusing major tech companies of building their AI products on “brazen theft” of journalism and calling on news organizations worldwide to push back before it’s too late.

In a speech at the WAN-IFRA World News Media Congress, Sulzberger argued that AI companies are systematically strip-mining news content without permission or compensation, hollowing out the very public square they claim to serve.

“Their hijacking of the public square is made possible by the original sin that animates their AI products — a brazen theft of intellectual property that has occurred at an unprecedented scale,” Sulzberger said in prepared remarks. “Tech giants strip-mine news websites without permission or compensation. They repackage these stolen goods as their own, siphoning off the audiences and revenue that otherwise would go to the news organizations that created this work.”

Sulzberger laid out what he called the four ingredients of AI: talent, compute, energy, and data. The first three are paid for — engineers earn tens or hundreds of millions, data centers cost hundreds of billions. But “data,” Sulzberger argued, is treated differently, seized without consent or compensation despite being equally essential.

The tech industry’s justifications — that innovation requires it, that facts can’t be owned, that “fair use” permits it, that licensing deals take too long — don’t hold up, Sulzberger said. He noted that five of the top 10 sites used to train leading language models belong to news publishers, and that OpenAI has acknowledged it would be “impossible to train today’s leading AI models without using copyrighted materials.”

The financial stakes are enormous. The six leading AI companies have a combined valuation of $11 trillion — more than three times the GDP of France. Private AI investment in the U.S. reached nearly $350 billion in 2025. Yet industry data suggests less than half of 1 percent of that investment goes to compensate the publishers whose content powers the technology.

The impact on news organizations is already measurable. The largest newspapers tracked by Comscore saw traffic drop more than 45 percent on average as the AI race intensified over the last four years. Meanwhile, Meta alone now makes eight times more in ad revenue than every newspaper on earth combined.

“The tech giants are fully aware of the implications of this shift,” Sulzberger said, quoting a Microsoft executive who wrote that “the open web was built on an implicit value exchange where publishers made content accessible, and distribution channels helped people find it. That model does not translate cleanly to an AI-first world.”

The Times publisher was careful to position his remarks not as anti-AI. He noted the Times uses AI internally — “responsibly, ethically, and with humans making the decisions” — to improve how it reports and distributes journalism. “Holding a powerful new technology at arms length is a recipe for failure,” he said.

But he pushed back hard on the idea that paying for content would cripple American competitiveness. “In its competition with China, America weakens itself by abandoning the intellectual property protections that fuel innovation and power America’s creative enterprises,” he said.

Sulzberger acknowledged the irony of a 175-year-old newspaper criticizing tech disrupters. But he argued the AI situation is different: the companies aren’t being disrupted by new technology — they’re the ones doing the disrupting, and they’re doing it on the backs of creators they’ve refused to compensate.

He urged the assembled news leaders from more than 60 countries to be more vocal. “Our profession has been too quiet, too passive and too fragmented in the face of abuses by the companies leading the AI revolution,” he said.

The speech ended with a plea for news organizations to stand firm on their value — and to stop pretending information wants to be free. “Information is valuable. Journalism is valuable,” Sulzberger said. “We cannot afford to be as naive this time.”

Edited by Pete Pachal

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For a while, it seemed like Google Search was in trouble.

Seemingly caught by surprise by the AI revolution that ChatGPT sparked, Google looked old and confused as upstarts like OpenAI and Perplexity pointed to a new future that replaced the “10 blue links” with question-and-answer conversations. Google’s first steps into this future were unsteady, with error-filled answers epitomized by the infamous glue-on-pizza moment. Some suspected, for all its scale and influence, a post-Google world was near.

That looks a lot less likely after this week. At Google I/O, the company confidently showed us its version of our informational future. And while it might be post-search, it’s not at all post-Google. Google is expanding its use of AI Overviews, meaning more searches will include the top-of-page summaries, and it’s adding a query box within them. When a user engages with it, they’re kicked to AI Mode, which abandons the “10 blue links” altogether.

In addition, Google.com now has a “+” icon, similar to its Gemini chatbot. If user engages with it and uploads a file or photo, that will also take them to AI Mode. It’s now extremely difficult to search on a Google product without AI being part of the result. You can still find your page of links by switching to “Web,” though that option is often buried.

So, far from the future where search is competitive again, it’s increasingly looking like a new future that’s the same as the old future. Even if you look just at AI chatbots, the Gemini app is now at 900 million users, making it about as big as ChatGPT. That doesn’t even count AI Overviews and AI Mode, which have 2.5 billion and 1 billion users, respectively, according to the company.

The bots ARE the traffic

The obvious consequence of all this is more searches will begin and end in the query. For publishers, that continues and likely accelerates the ongoing traffic apocalypse. We may, however, have to update our vocabulary: Google Zero—which was supposed to connote an environment where the clicks from Google search were basically nil—feels imprecise.

That goes double when you consider that, as humans spend more time in AI interfaces, a commensurate amount of bot activity spreads out from those queries. So the future isn’t Google Zero. It’s Google Bot Infinity.

So the future is a world where people happily chat—either via typing or speech—to Google, and those Google bots bring the right information and context to answer them. More accurately, those bots bring what they deem as the right information and context to queries. AI systems prioritize information differently from traditional search, looking for information that both fits a pattern but also includes novel and authoritative elements. This is manifesting into the new-but-rapidly-evolving field of GEO, or generative engine optimization. Google’s renewed push into AI experiences means the battle for presence in answers is no longer a side bet. It’s the game.

That’s the media story here in Google’s renewed rise. Once laughed at for how far behind it was in the AI race, it’s now architecting the future where it’s still in charge. Judging by its balance sheet—with earnings steadily increasing even as competitors rise—it’s found the right balance of building the new while preserving the old. Even as it demotes the “10 blue links” that built the company, it’s offering a bevy of new ad products in conversational search that spin up generative ads on the fly. It clearly has the confidence that it can make money in an AI world.

Brands might be less confident about that, and publishers even more so. Authority in AI answers is nice, but monetizing has so far been a challenge.

Credibility is the new click

But it’s not nothing. If Google’s AI layer becomes the place where people encounter information, then presence inside that layer becomes a form of distribution. A publisher cited consistently in answers about politics, technology, health, finance, or culture has something valuable: proof that it owns authority in a category. The old metric was how many people Google sent to you. The new one may be how often Google needs you to make its answers credible.

That may not produce the same clean, scalable ad business that search referrals once did. But it points to a different one. Advertisers have always wanted to sit next to authority. They sponsored sections, bought podcast reads, backed newsletters, underwrote events, and cut direct deals with creators because association matters. If a publisher becomes one of the sources AI systems repeatedly rely on, that authority can be sold directly—not necessarily through Google, and not necessarily as a banner ad awkwardly stapled to a webpage.

That’s the hopeful version of Google Bot Infinity. Publishers may lose a lot of casual traffic, and pretending otherwise is foolish. But the ones that produce distinctive, trusted, deeply useful work still have leverage. The job now is to make that work legible to machines without making it lifeless for people.

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The era of the “ten blue links” is officially over.

Google unveiled a sweeping AI-powered overhaul of Search at its I/O conference Tuesday, TechCrunch reported, centered on what the company calls the biggest change to the search box in more than 25 years. Instead of returning a simple list of links, Google Search will drop users into AI-powered interactive experiences, starting this week.

The reimagined search box expands to accommodate longer, conversational queries without forcing users to pick a search mode at the start. A new AI-powered query suggestion system moves beyond autocomplete, helping users craft more complex queries. AI Overviews now allow follow-up questions in AI Mode, which launched last year and already has more than 1 billion monthly users.

The rise of information agents

Perhaps the most consequential change: users will be able to create, customize, and manage multiple “information agents” within Google Search starting this summer. These agents work in the background 24/7, tracking changes on the web and alerting users when conditions are met, pulling from real-time data and delivering synthesized updates.

It’s an evolution of Google Alerts, the change-detection service Google launched in 2003. “You could send an alert to track market movements in a particular sector with very specific parameters, and the agent will map out a monitoring plan for you, including the tools and the data it needs to access,” said Liz Reid, Google’s head of Search. “And it will then keep track of those changes and let you know when the conditions are met, and provide a synthesized update with links and information you can dive into further.”

The shift means “searching the web” will increasingly be performed by AI agents rather than humans. People will spend less time clicking links and more time acting on synthesized information. It’s a shift our coverage of the answer engine era has been tracking closely.

Generative UI and mini apps

Google is also introducing “generative UI”—building custom widgets and visualizations on the fly in response to users’ search questions. A query about black holes could generate an interactive visual that users can then ask follow-up questions about, with Google responding with brand-new visuals in real time. Search results will increasingly look like interactive web pages.

The system, built in partnership with Google DeepMind using Gemini Flash 3.5, will also let users tap into Google’s Antigravity platform to build personalized mini apps directly in Search using natural-language commands, such as meal-planning apps that factor in your calendar, fitness apps tailored to your goals.

AI Overviews now has more than 2.5 billion monthly users. Conversational search (AI Mode) tops 1 billion monthly users. For context, ChatGPT has 900 million weekly active users, suggesting ChatGPT sees more frequent repeated engagement, while Google reaches more unique people across its AI features in a month.

The publisher problem

Combined, these changes will likely deepen the toll on publisher referral traffic, which has already been decimated since AI Overviews launched. Some ad-dependent media operations have already been pushed out of business. The UK CMA has been pressuring Google to let publishers opt out of AI Overviews without losing search visibility, a request Google has yet to act on.

The new search box arrives this week. Generative UI rolls out free to everyone this summer. Information agents and mini-app building launch first to Google AI Pro and Ultra subscribers this summer, with broader free access planned for Spark and other AI features down the line.

Sundar Pichai framed it as an accessibility play. “Part of the reason we focus on delivering frontier models—highly capable, but also very efficient, fast, and at a lower price—is because we want to bring it to as many people as possible,” he said in a press briefing ahead of I/O.

For publishers, there is very little time left to adapt.

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