I recently stumbled upon a fascinating preview of ChatGPT’s new ad configurations, giving us an insight into how personalization and privacy will revolutionize ad delivery within conversational AI.
Driving the news. It was an exciting moment when Juozas Kaziukėnas, an innovative entrepreneur, uncovered a method to access ChatGPT’s forthcoming ad settings interface. The panel is reassuring in its consistent emphasis: advertisers won’t have access to our chats, history, personal details, or IP addresses.
What the settings reveal:
There’s a well-organized ad framework complete with its own controls.
A History tab, where I can check the ads I’ve viewed inside ChatGPT.
An Interests tab that gathers inferred preferences based on my interactions and feedback.
For each ad, I have the option either to hide it or report it.
Importantly, I can delete my ad history and interests without affecting other ChatGPT data.
Personalization options. I have the freedom to turn ad personalization on or off. When it’s enabled, ChatGPT uses my saved ad history and interest cues to customize ads. If disabled, the ads still display but only consider my current conversation for relevance.
An intriguing option allows ad personalization using both past conversations and memory capabilities — though crucially, my chat content isn’t shared with advertisers. For accounts like mine with memory disabled, this feature remains inactive.
Why we care. Even though official ads haven’t launched, the newly accessed settings panel provides us with the most detailed preview yet of ad personalization and privacy controls in action. It’s exciting to see ChatGPT striving to balance effective personalization with rigorous privacy standards. I can already imagine how this will redefine ad targeting and measurement on the platform.
The settings indicate a focus on contextual signals and user-enabled personalization, avoiding overly intrusive user tracking. This means our creative relevance and the intent derived from our conversations will be valued more than conventional audience profiling.
For brands, it’s a hint on how to craft their messaging and strategies for this new wave of conversational advertising.
The bigger picture. This discovery suggests OpenAI is developing an ad system mirroring known platforms but with a fresh focus on privacy and user autonomy.
Bottom line. Although ChatGPT ads might not be live right now, the framework is clear and indicates a future where conversational ads offer nuanced privacy and personalization settings.
First seen. Kaziukėnas shared a preview of the platform on LinkedIn.
I recently came across an intriguing study by SALT.agency, focused on Google’s AI Mode and its citation practices. Contrary to popular belief, this analysis shows that AI Mode doesn’t have a preference for content placed “above the fold.”
After sifting through over 2,300 URLs cited by AI Mode, researchers discovered no link between a text’s vertical position on a page and its likelihood of being cited by Google.
Pixel depth is irrelevant. The study revealed that AI Mode pulls text from all over a page, even from content located thousands of pixels down.
Page layout vs. content visibility. While different layouts like large hero images or narrative formats might push text deeper down the page, this doesn’t impact whether it gets cited.
Subheadings make a difference. One key pattern identified was AI Mode’s tendency to highlight a subheading and the subsequent sentence. This suggests Google’s heading structures are crucial for content navigation.
Google’s approach. The assumption is that AI Mode employs fragment indexing technology, breaking pages into sections and pulling the most relevant fragment, irrespective of its position.
Dan Taylor, a partner at SALT.agency, confirms that there’s no secret formula for appearing in AI Mode citations. The focus should always be on crafting well-structured, authoritative content that meets customer needs.
Our takeaway. This study challenges the notion that specific AI-focused templates or rigid structures enhance content visibility in AI Mode. The real work lies in creating meaningful, structured content.
Research background. SALT scrutinized 2,318 URLs in AI Mode responses. The vertical pixel position of each cited fragment was meticulously recorded using a Chrome bookmarklet and a 1920×1080 viewport.
I recently had the pleasure of speaking with Amanda Farley, the brilliant CMO of Aimclear, on episode 340 of PPC Live The Podcast. Amanda’s journey from owning a gallery and tattoo studio to leading award-winning global campaigns is nothing short of inspiring. Her unique T-shaped marketing expertise, combining in-depth PPC knowledge with a broad skill set across social, programmatic, PR, and integrated strategies, offers valuable insights into modern marketing.
Through our engaging conversation, Amanda shared her lessons on overcoming setbacks and balancing AI with human insight. Her experience underscores the importance of mixing calm leadership with a relentless curiosity and drive for continuous learning.
Overcoming Limiting Beliefs and Embracing Creativity
Amanda once ran an gallery and tattoo parlor while believing she wasn’t an artist herself. Surrounded by creatives, she eventually realized her only barrier was a limiting belief. After embracing painting, she created hundreds of artworks and discovered a powerful outlet for expression.
This mindset shift mirrors marketing growth. Success isn’t just technical — it’s mental. By challenging internal doubts, marketers can unlock new skills and opportunities.
When Campaign Infrastructure Breaks: A High-Stakes Lesson
Amanda recalls a global campaign where tracking infrastructure failed across every channel mid-flight. Pixels broke, data vanished, and campaigns were running blind. Multiple siloed teams and a third-party vendor slowed resolution while budgets continued to spend.
Instead of assigning blame, Amanda focused on collaboration. Her team helped rebuild tracking and uncovered deeper data architecture issues. The crisis led to stronger onboarding processes, earlier validation checks, and clearer expectations around data hygiene. In modern PPC, clean infrastructure is essential for machine learning success.
The Hidden Importance of PPC Hygiene
Many account audits reveal the same problem: neglected fundamentals. Basic settings errors and poorly maintained audience data often hurt performance before strategy even begins.
Outdated lists and disconnected data systems weaken automation. In an machine-learning environment, strong data hygiene ensures campaigns have the quality signals they need to perform.
Why Integrated Marketing Is No Longer Optional
Amanda’s background in psychology and SEO shaped her integrated approach. PPC touches landing pages, user experience, and sales processes. When conversions drop, the issue may lie outside the ad account.
Understanding the full customer journey allows marketers to diagnose problems holistically. For Amanda, integration is a practical necessity, not a buzzword.
AI, Automation, and the Human Factor
While AI dominates industry conversations, Amanda stresses balance. Some tools are promising, but not all are ready for full deployment. Testing is essential, but human oversight remains critical.
Machines optimize patterns, but humans judge emotion, messaging, and brand fit. Marketers who study changing customer journeys can also find new opportunities to intercept audiences across channels.
Building a Culture That Welcomes Mistakes
Amanda believes leaders act as emotional barometers. Calm investigation beats reactive blame when issues arise. Many PPC problems stem from external changes, not individual failure.
By acknowledging stress and focusing on solutions, leaders create psychological safety. This environment encourages experimentation and turns mistakes into learning opportunities.
Testing Without Fear in a Changing Landscape
Marketing is entering another experimental era with no clear rulebook. Amanda encourages teams to dedicate budget to testing and lean on professional communities for insight.
Not every experiment will succeed, but each provides data that informs smarter future decisions.
The Tasmanian Devil Who Practices Yoga
Amanda describes her career as If the Tasmanian Devil Could Do Yoga — a blend of fast-paced chaos and intentional calm. It reflects modern marketing: demanding, unpredictable, and balanced by thoughtful leadership.
I’ve been contemplating how even when content ranks well on search engines, it can still falter when it comes to AI retrieval. These AI systems assess pages very differently, based not just on their rank, but also on how information is extracted, embedded, and structured.
There’s an intriguing disconnect between traditional ranking and being successfully parsed by AI. A webpage can comply with excellent SEO guidelines and still miss the mark with AI-generated responses and citations.
In many situations, content quality isn’t the issue. It’s about whether the information can be reliably extracted after being segmented and embedded by AI systems.
This challenge is becoming increasingly common as search engines view pages as complete entities, but AI systems dive into the raw HTML to extract meaning from fragments rather than entire pages.
Crucial insights can get lost if they’re not appropriately structured or if they rely too heavily on visual rendering or inference.
This leads to a divergence between what’s visible in search and what’s accessible via AI, where content might exist in an index but lacks substantial meaning for AI retrieval.
The visibility gap is something I’ve been grappling with: Understanding the difference between ranking versus retrieval is key.
As search winds its processes around rankings, AI systems engage with fragments operated within a different representation of similar information. It’s here the visibility gap takes shape.
A page might rank high, but if its embedded content is incomplete or poorly organized, then the AI retrieval process becomes unreliable.
Treat retrieval as an entirely unique visibility factor. It doesn’t override SEO, but increasingly defines whether content can be effectively surfaced, summarized, or cited when AI filters come into play.
Another structural issue arises when content never even becomes accessible to AI. Many AI crawlers only parse raw HTML without executing JavaScript or client-side rendering. This creates blind spots, especially for JavaScript-heavy sites where the core content may appear in Google’s index but remains invisible to AI.
Testing if your content appears in initial HTML is quite straightforward. Simply inspect the HTML response at fetch time rather than the version rendered in a browser.
Running requests with AI user agents like “GPTBot” reveals if your site returns blank HTML even if it appears fully populated to users, highlighting its absence in initial responses.
Tools like Screaming Frog can validate this at scale. Disabling JavaScript rendering can reveal what AI systems see—if your essential content only displays with JavaScript, it can be indexed by Google’s search but not by AI retrieval systems.
Keep in mind that even with content returned, excessive code and scripts can hinder extraction by AI systems. Cleaner HTML results in more reliable embeddings, enhancing AI visibility.
To tackle this, deliver fully rendered HTML when AI systems fetch your content. Pre-rendering can often fix these retrieval issues, ensuring content is present in initial responses.
Delivery can be managed effectively at the edge layer, providing AI crawlers with complete pages instantly. Human users receive a dynamic version while AI sees what it needs to extract meaning.
If pre-rendering isn’t viable, focus on ensuring primary content is accessible in a clean initial HTML response, even without script execution.
Columns laden with excessive markup can interfere with proper extraction, diminishing the content’s value.
The next structural failure to consider is when content is optimized for keywords rather than the entities AI seeks. Traditional SEO applies keyword relevance, but AI retrieves based on entity relationships.
Without clear definition, entity signals can weaken, causing pages to underperform in retrieval even if they rank well for queries.
AI evaluates sections independently once extracted, making the consistency of header tags essential to maintaining coherence.
Ensuring sections have a single, defined purpose allows for better embedding when isolated from larger context.
Finally, conflicting signals or metadata can dilute the semantics retrieved by AI, creating noise and ambiguity.
SEO doesn’t have to mean choosing between ranking and retrieval anymore. Both must be prioritized to succeed in today’s landscape.
I’ve noticed something puzzling in my local business performance lately. Despite high rankings, the number of calls and website visits from Google Business Profiles seems to be dropping at an alarming rate.
This disconnect is becoming increasingly common in local search. Rankings are stable, but visibility and customer engagement are not keeping pace.
The alligator of local SEO, if you will, has made its presence known.
The visibility crisis behind stable rankings
I’ve observed that across various U.S. industries, the familiar local 3-packs are often getting replaced or supplemented by AI-run local packs. These new formats differ significantly from the traditional map results many of us are used to optimizing.
According to Sterling Sky’s analysis of Google Business Profiles, a startling pattern emerges. Clicks-to-call are taking a nosedive, particularly for law firms managed by Jepto.
When AI-powered packs take over, the landscape changes notably in four key areas:
Shrinking real estate: AI packs frequently display only two businesses instead of the usual three.
Missing call buttons: The summaries generated by AI often omit the instant click-to-call functionality, complicating the customer’s journey.
Different businesses appear: Companies featured in AI packs do not necessarily align with those in the traditional 3-pack.
Accelerated monetization of local search: The presence of paid ads increasingly results in the loss of direct call and website buttons in traditional 3-packs, thereby reducing opportunities for organic conversion.
There’s an additional challenge compounding this issue:
Measurement blind spots: Most rank trackers have yet to account for AI local packs. A business may hold a top spot in a traditional 3-pack that users rarely encounter.
In 2026, AI local packs surfaced only 32% as many unique businesses as traditional map packs, according to Sterling Sky. Astonishingly, in 88% of the 322 markets examined, the total number of visible businesses plummeted.
Meanwhile, paid ads are steadily claiming the space that once belonged to organic results, marking a clear transition toward a pay-to-play environment in local search.
What Google Business Profile data shows
This trend is echoed in the U.S., where Google is proactively testing new local formats, as indicated by data from GMBapi.com. Increased impressions from traditional 3-packs are being nudged out by:
AI-powered local packs.
Paid placements inside traditional map packs: Sponsored listings now appear adjacent to or within the map pack, relegating organic results and removing essential call and website buttons. This interrupts organic customer interactions.
Expanded Google Ads units: Even Local Services Ads are consuming space that once granted organic visibility.
Impression trends continue to vary due to seasonal factors, market disparities, and occasional API glitches. Nevertheless, a clearer picture emerges by focusing on GBP actions rather than mere impressions.
Mentions within AI-generated results still count as impressions, even if they no longer convert into calls, clicks, or visits.
External factors, such as known Google API issues in June, also contribute to these fluctuations. Additionally, the spike in Google Ads investment by significant advertisers towards year-end heavily affects Mobile Maps impressions.
Currently, there’s no method to differentiate these impressions by Google Ads, organic results, or AI Mode.
Despite these challenges, user behavior is undeniably shifting. Interaction rates are dwindling, with fewer direct actions taken from local listings.
Year-on-year data from the U.S. indicates that while impression losses remain moderate and somewhat seasonal, GBP actions are disproportionately affected.
In contrast, data from the Dutch market, where SERP experiments are limited, shows far more stable action trends.
The evidence is clear. AI-driven SERP alterations, increasing Google Ads, and the removal of call and website buttons from the Map Pack are eroding organic real estate. Despite appearances, businesses have fewer opportunities to convert visibility into actual user actions.
Local SEO is becoming an eligibility problem
Traditionally, local optimization focused on key ranking factors like proximity, relevance, prominence, reviews, citations, and engagement.
There’s now an additional layer to consider: eligibility.
Some businesses find themselves absent in AI-powered local results not due to a lack of authority, but because Google’s systems deem them inadequate for the specific query context. Research from Yext and experiences shared by experts like Claudia Tomina emphasize the importance of aligning three core signals:
Business name
Primary category
Real-world services and positioning
Misalignment in these areas can prevent businesses from appearing in certain result types, regardless of how well their Google Business Profile is optimized.
How to future-proof local visibility
Navigating today’s zero-click reality involves moving beyond reliance solely on a well-optimized Google Business Profile. Here’s a new playbook for local SEO.
The eligibility gatekeeper
Inclusion in local packs is now influenced more by perceived relevance and classification than by links or review quantity.
Hyper-local entity authority
AI systems rely on platforms like Reddit, social media, forums, and local directories to evaluate if a business is legitimate and active. Inconsistencies across these platforms can erode visibility without any obvious signs.
Visual trust signals
High-quality and frequently updated photos, along with video, are critical. Google’s AI evaluates visual content to gauge services, intent, and categorization.
Embrace the pay-to-play reality
The hard truth is that Google Ads, particularly Local Services Ads, is now essential to retaining prominent call buttons that organic listings are steadily losing. Adopting a hybrid strategy that merges local SEO with paid search is no longer optional but necessary.
What this means for local search now
Local SEO has evolved beyond a simple directory exercise. Google Business Profiles remain central to local discoverability but now exist within a broader ecosystem informed by AI validation, constant SERP changes, and Google’s pursuit of local search monetization.
Visibility no longer depends solely on where your GBP ranks against local rivals. Search engines, including AI-infused SERP features and advanced models like ChatGPT and Gemini, are increasingly focused on understanding a business’s genuine purpose, not merely its listing position.
Success lies in being widely verified, consistently active, and contextually relevant within the AI-visible ecosystem.
Our findings reveal that there is little correlation between businesses ranking well in traditional Map Packs and those prioritized in Google’s AI-generated local answers. This discrepancy offers a real opportunity for businesses willing to adapt.
In essence, this entails blending local input with central management.
Authentic engagement across multiple channels, locally tailored content, and actual community signals are necessary alongside brand governance, data consistency, and operational scale. Businesses deeply ingrained in their community, discussed, recommended, and referenced, both online and offline, find themselves halfway there.
For agencies and brands with multiple locations, the challenge is balancing control with local nuances and ensuring trusted signals extend beyond Google, encompassing Apple Maps, Tripadvisor, Yelp, Reddit, and other pertinent review ecosystems. Producing locally relevant content and citations at scale without losing authenticity is the real test.
Even if rankings appear stable, true performance is occurring elsewhere.
Google Search is currently experiencing what I see as an ‘expansionary moment,’ powered by the dynamics of AI technology. The search experience I rely on has transformed through longer queries, follow-up questions, and the increasing use of voice and images. This was highlighted during Alphabet’s recent earnings call, where executives shared these evolving trends.
In other words: Google’s search interface is becoming increasingly AI-driven, facilitating interactions within its system. This isn’t about replacing old queries—instead, we’re witnessing a new era of digital exploration.
Why we care. The integration of AI into Google Search is not just a trial. For me, it’s a structural transformation altering how we discover, interact with, and navigate the web.
By the numbers: Alphabet’s Q4 advertising revenue reached $82.284 billion, marking a 13.5% increase from $72.461 billion in 2024.
Google Search & other: $63.073 billion (up 16.7%)
YouTube: $11.383 billion (up 8.7%)
Google Network: $7.828 billion (down 1.5%)
For the 2025 fiscal year, Alphabet’s advertising revenue climbed to $294.691 billion, a growth of 11.4% from the previous year.
Google Search & other: $224.532 billion (up 13.4%)
YouTube: $40.367 billion (up 11.7%)
Google Network: $29.792 billion (down 1.9%)
AI Overviews and AI Mode are now core to Search. Sundar Pichai, Alphabet/Google’s CEO, emphasized how central AI has become to Google’s search products, with over 250 AI-related product launches in just the last quarter.
Google has recently upgraded its AI Overviews to the Gemini 3 model, a move that connects AI Overviews more seamlessly with conversational search experiences.
“We have also made the search experience more cohesive, ensuring the transition from an AI overview to a conversation in AI mode is completely seamless,” Pichai noted.
AI is driving more Google Search usage. As Google puts it, AI-driven search is expanding the ways people use search rather than replacing traditional searches.
“Search saw more usage in Q4 than ever before, as AI continues to drive an expansionary moment,” Pichai emphasized.
“Once people start using these new experiences, they use them more,” he added.
Changing search behavior. AI Mode is making searches longer, more conversational, and multimodal. “Queries in AI mode are three times longer than traditional searches,” said Pichai.
Not only are queries longer, but sessions are also becoming more conversational, often leading to follow-up questions.
“We are also seeing sessions become more conversational, with a significant portion of queries in AI Mode now leading to a follow-up question,” he said.
“Nearly one in six AI mode queries are now non-text, using voice or images,” Pichai shared.
Google’s visual search capabilities continue expanding with “Circle to Search” available on over 580 million Android devices.
“We haven’t seen any evidence of cannibalization,” Pichai said about the coexistence of Google Search and the Gemini app.
“The combination of all of that, I think, creates an expansionary moment,” he concluded.
I recently discovered how crucial first-party data has become in the evolving landscape of AI-powered advertising. It’s fascinating to see how it shapes the optimization and measurement of automated ad campaigns.
During a chat with Search Engine Land, I learned from Julie Warneke, CEO of Found Search Marketing, about the profound impact first-party data has on profitable advertising, regardless of potential changes to Google’s third-party cookie policies.
Embracing first-party data means tapping into customer information that I own, typically stored in a CRM, like lead details, purchase history, revenue, and customer value collected from various touchpoints.
This type of data is distinct from platform-owned or browser-based data, over which I have limited control.
Digital advertising has evolved over the years. The shift from focusing on impressions and clicks to outcomes emphasizes profitable conversions, according to Warneke. Advertisers who provide AI systems with quality customer data gain a significant edge.
Although rising cost-per-clicks (CPCs) are inevitable in paid media, first-party data enhances conversion quality, revenue, and return on ad spend, making higher costs justifiable with better results.
By leveraging first-party data tied to revenue and customer value, AI bidding systems can target users resembling high-value customers, even beyond usual demographic or geographic signals, leading to better conversions.
Among campaign types, Performance Max (PMax) thrives with first-party data activation. It performs best when I shift from manual optimizations to feeding it accurate data, allowing the system to learn, as Warneke highlighted.
Even small and mid-sized businesses can leverage first-party data, as seen in Warneke’s examples of success with small customer lists. The challenge lies in setting up proper infrastructure for tracking, consent management, and data flow.
Common mistakes include weak data capture, where brands rely on browser-side tracking that falters on platforms like iOS, and broken feedback loops from sporadic CRM data uploads. Continuous data streams are crucial.
Warneke advises taking a step back to audit how data is captured, stored, and relayed to platforms. Incremental improvements can pave the way for significant long-term gains, even starting with a small portion of a budget as a test.
Ultimately, AI optimization reflects the quality of signals received. By refining first-party data, I can influence outcomes favorably, avoiding inefficiency risks.
I recently learned that Anthropic has made a firm decision regarding the inclusion of ads in AI chatbots. They’ve announced that Claude will remain ad-free, even as other AI platforms start experimenting with sponsored messages and branded placements during chats.
Anthropic argues that placing ads in AI chats would undermine user trust, distort incentives, and conflict with how people use assistants like Claude—for work, problem-solving, and sensitive topics. In their latest blog post, they clearly lay out their stance.
Why this matters to us. Anthropic’s decision effectively removes Claude and its 30 million users from the potential AI advertising market. So, brands shouldn’t count on having sponsored links, conversations, or responses inside Claude. Meanwhile, ChatGPT opens up a new frontier for brands to potentially connect with an estimated 800 million weekly users.
Here’s the situation. According to Anthropic, AI conversations are quite unlike search results or social feeds where users might expect a combination of organic and paid content. They emphasize that many interactions with Claude involve personal inquiries, complex technical tasks, or high-stakes decisions, where inserting ads would seem intrusive and could subtly sway responses beyond user awareness.
Incentives matter. This is more than a product preference; it’s a strategic business model decision for Anthropic:
An ad-free assistant can concentrate fully on user benefits—even if that means a brief interaction or no follow-up. On the flip side, an ad-supported model might create pressure to identify monetizable moments or keep users engaged longer than necessary, potentially making users question whether suggestions are genuinely helpful or commercially driven.
Anthropic embraces commerce without ads. While Claude will assist users in researching, comparing, and purchasing products upon request, the commerce is user-initiated, not advertiser-driven. Likewise, third-party integrations with platforms like Figma or Asana will be user-directed and free from sponsor influence.
Super Bowl declaration. Anthropic took their message to a wider audience with a bold Super Bowl ad campaign. They critiqued intrusive AI advertising by placing mock product pitches into personal conversations. The ad concluded robustly: “Ads are coming to AI. But not to Claude.”
This campaign is likely a direct response to OpenAI’s announcement about introducing ads in ChatGPT.
I might be witnessing a significant shift as Google seems to be tightening its grip on self-promotional ‘best of’ listicles. This trend was highlighted by Lily Ray, who leads SEO strategy and research at Amsive.
Recently, many SaaS brands experienced a sharp decline in visibility, ranging from 30% to 50%. These companies often featured content that ranked their own products as ‘Number 1’ in their fields, frequently updating with the latest year to capitalize on recency signals.
Understanding the Trend. Following the December 2025 core update, there was noticeable volatility in Google search results throughout January, as reported by Barry Schwartz. Although Google hasn’t confirmed any updates for this year, the timing matches the visibility drops experienced by major SaaS and B2B brands. Lily Ray observes:
• In several situations, organic visibility dropped by as much as 50% within weeks. The losses were primarily in subfolders containing blogs, guides, and tutorials.
• These sections often housed numerous self-promotional listicles for ‘best’ queries, with the publishers typically ranking themselves first. Most articles were minimally refreshed with the addition of ‘2026’ to their titles, without substantial updates.
• “It seems likely that these declines in Google organic rankings might also affect visibility across other search engines and AI platforms that utilize Google’s results, like Gemini and ChatGPT,” Ray explained.
Why This Matters. There has been a longstanding practice of using self-promotional listicles to sway search rankings and AI-generated responses. If Google is reconsidering this kind of content, any strategies focusing on ‘best’ queries might face substantial challenges.
The Controversy. Ranking oneself as ‘the best’ without independent verification or third-party endorsement is often seen as a dubious SEO move. While not outright banned, it conflicts with Google’s guidelines on reviews and trustworthiness.
• Google maintains that quality reviews should display firsthand experience, originality, and clear evaluation. Self-serving listicles frequently fall short, particularly when bias isn’t disclosed.
However. Self-promotional listicles may only be one of several factors affecting organic visibility. Affected sites often showed signs of fast content expansion, automation, aggressive year-based updates, and other risky tactics.
• Nevertheless, the prevalence of self-promoting ‘best’ content among the most impacted sites suggests that this signal might now be more influential, especially when used extensively.
What’s Next. The outcome for self-promotional listicles in terms of gaining recognition and organic visibility is still uncertain, as Google seldom implements changes uniformly or immediately.
• If this volatility is linked to updates in Google’s review system, the trend is evident: Content aimed mainly at influencing rankings, rather than offering credible evaluations, poses growing risks.
• The enduring lesson for brands seeking online visibility is clear: SEO shortcuts may yield effective results, but only until they don’t.
I’m thrilled to share that Microsoft Advertising has just unveiled the Publisher Content Marketplace (PCM). This innovative system allows publishers like us to license premium content to AI products and earn revenue based on its usage.
How It Works. At its core, PCM creates a direct value exchange. As a publisher, I have the freedom to set my own licensing and usage terms. Meanwhile, AI developers can discover and license this content for grounding their algorithms in real-world scenarios. The marketplace also offers detailed usage reports, providing insights into how our content performs and where it contributes the most value.
Designed to Scale. The PCM is a scalable solution designed to eliminate the need for one-off licensing deals. Participation is entirely voluntary, and ownership and editorial independence remain with the publishers. It’s a platform inclusive of everyone from large global publishers to smaller niche outlets like ours.
Why We Care. As AI technology progresses from merely answering questions to making impactful decisions, the quality of content is becoming increasingly crucial. Whether it’s about influencing purchases, finance, or healthcare, AI systems need to tap into premium content, elevating the importance of credibility and trust in our brands.
Early Traction. Microsoft Advertising has partnered with notable U.S. publishers such as Business Insider, Condé Nast, and Hearst to co-design PCM. Initial pilot projects anchored Microsoft Copilot responses to licensed content, with companies like Yahoo as early adopters.
What’s Next. Looking ahead, Microsoft plans to extend the pilot program to more publishers and AI developers who share the belief that as the AI web evolves, the value and governance of high-quality content should be recognized and rewarded.
The Big Picture. In the evolving landscape of AI-driven web interactions, tools are now summarizing, reasoning, and making recommendations through conversation. The effectiveness of these tools hinges on access to trusted and authoritative sources, many of which are under paywalls or in secured archives.
The Tension. The traditional model where publishers provide content in exchange for traffic from platforms is changing. AI is increasingly delivering answers directly, which reduces clicks but still relies on high-quality content.
Bottom Line. For AI to make better decisions, it must have access to superior inputs. Microsoft’s PCM is a strategic move towards establishing a sustainable content economy that supports the next wave of AI innovation.
