Microsoft is now officially releasing a preview of the new AI performance report within Bing Webmaster Tools that now includes Intents, Topics, Citation Share, and Compare. We saw Microsoft demo these features in late April but now it is actually starting to roll out to users.
As a reminder, Bing officially rolled out its AI performance report in February. Google didn’t roll out its AI reporting in Search Console until June, and it seemed forced.
What is new. “These new capabilities build on that foundation by helping publishers better understand why their content is being surfaced, which broader subject areas they are gaining visibility in, how their presence evolves relative to other cited sources, and how citation patterns change over time,” wrote Krishna Madhavan from Microsoft.
Intent: The new Intents feature in Bing Webmaster Tools now classifies the grounding queries in the AI Performance Report in broader categories, such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This in a sense helps you understand the intent behind the prompt or query. “This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances,” Krishna Madhavan wrote.
The example provided was that an e-commerce publisher may discover strong visibility in comparison-oriented or shopping-focused AI experiences, while an educational publisher may find that their content is frequently surfaced in research or learning-oriented interactions. These insights can help publishers better align content structure and depth with the types of experiences where AI systems are surfacing their content.
Topics: The Topics in the AI performance reports group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords, Microsoft explained. So by having topics, it will help publishers understand visibility in the same thematic structure that modern AI systems use to organize information.
So for example, queries such as “solar panels,” “solar energy efficiency,” and “residential solar installation,” for example, may all map into a broader topic cluster like Solar Energy. “This creates a more natural way to analyze AI visibility. Content teams and publishers often think in terms of themes, editorial areas, and audience interests rather than isolated keywords. Topics help bridge that gap by turning grounding query data into a more thematic view of AI engagement,” Microsoft wrote.
One note, “during the preview phase, some labels may still be broad – especially for highly specialized or niche domains – but the system is already beginning to reveal meaningful thematic patterns,” Microsoft wrote.
Citations. Microsoft also added citation share, which shows how much of the citation space your site receives for a specific grounding query. Citation share is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query. “This helps publishers understand not just whether they were cited, but how much visibility they received within the full set of cited sources for that query,” Microsoft explained.
Microsoft added these points:
“This can provide a more directional view into how visibility is evolving over time. Publishers may begin to identify areas where their content has strong and growing representation in AI-generated experiences, as well as areas where visibility may be more fragmented across many sources.”
“Importantly, Citation Share is designed as an observational metric – not a ranking system or a competitive scoreboard. It does not expose competitor domains, represent traffic share, or assign quality scores to content.”
“AI citation ecosystems are inherently dynamic. Citation patterns can shift due to changes in user behavior, evolving models, freshness signals, partner refresh cycles, and broader changes across the web itself.”
Compare. With all of that, you can also compare the changes over time. The compare feature allows you to overlay a previous time period directly onto the current reporting view.
“Compare is designed to help publishers observe changes over time. Citation activity can be influenced by many factors including evolving AI models, competing content, freshness signals, and shifts in user demand,” Microsoft wrote.
Here is what it looks like:
Why we care. While we still do not have click and click-through rate data, Microsoft keeps adding more and more to its AI performance reports.
I am hopful that one day we will get click data, but I am still not expecting to see that from Google or Microsoft any time soon.
Google penalties, also known as manual spam actions, are among the few events in search that can disrupt an otherwise healthy online business overnight.
For companies heavily dependent on organic traffic, the consequences often extend far beyond lost rankings. Revenue drops, customer acquisition costs rise, expansion plans stall, and the effects can linger long after the original policy violations have been remedied.
With a steady 90% market share, Google remains the primary traffic source for many publishers, ecommerce platforms, retailers, travel brands, affiliates, and lead generation businesses.
Direct traffic rarely compensates for a major visibility loss, and Bing seldom offsets the difference. As a result, a manual spam action carries serious operational implications, not merely SEO risks.
Manual actions aren’t algorithm updates
One point still misunderstood throughout the industry deserves clarification. Manual spam actions differ from algorithmic updates. They aren’t fluctuations caused by changes in relevance calculations or ranking system adjustments.
Google’s manual penalties involve direct enforcement after suspected violations against Google Search Essentials, formerly Google Webmaster Guidelines, have been identified and confirmed. The distinction matters because the response required is completely different.
A website affected by changing ranking systems requires analysis, adaptation, and recrawling. A website affected by a manual spam action requires remediation and applying for reconsideration. Those are separate situations entirely.
Google doesn’t issue manual spam actions casually. The process involves internal senior employee review cycles. Suspected violations must be investigated and confirmed first.
Google states clearly that manual actions are the consequence of proven policy transgressions. Despite frequent cries of foul, false positives are exceptionally rare. Once a manual action appears in Google Search Console, the enforcement is already in the production pipeline.
The operational problem is that many businesses fail to recognize how much unresolved policy exposure their web platforms have accumulated over time.
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How penalties develop
The initial steps that ultimately lead to a manual penalty and a website’s drop in search visibility often begin inconspicuously, gradually eroding policy compliance.
An ecommerce business launches an aggressive link acquisition campaign during an early growth phase. Over the years, PageRank-passing spam links accumulate unchecked until eventually nobody remembers where thousands of exact-match backlinks originate.
A publisher enters into commercial partnerships involving sponsored content or affiliate sections, which gradually become structurally integrated into the editorial architecture of the website.
A SaaS company creates large numbers of low-quality location pages while expanding into new markets.
A lead generation business scales supplemental SEO content through low-cost LLM production systems with limited editorial oversight because that appears to be what most competitors are doing.
The underlying patterns are remarkably similar across industries. In many cases, organic search visibility initially improves and may even generate measurable revenue gains attributable to the SEO initiative.
The short-term results reinforce the perception that the approach is working. However, as time passes, nobody revisits whether those earlier decisions remain aligned with evolving search quality standards and webmaster policies.
Why historical violations still matter
One reason manual spam actions create so much disruption is that policy violations often persist quietly for years before review. Many organizations incorrectly assume that questionable SEO tactics of the past lose their relevance over time.
Yet Google Search systems don’t forget historical footprints. Archived URLs remain crawlable. Legacy sections continue contributing content quality signals long after internal ownership was abandoned.
Most persistently, backlink patterns remain visible for decades. Large numbers of websites remain affected by backlinks generated through manipulative campaigns dating back many years.
Paid placements, article syndication networks, private blog networks, commercial keyword-heavy guest posting campaigns, expired domain backlinks, directory spam, and widget distribution schemes that once formed part of mainstream SEO activity are today’s liabilities.
Some of these practices continue to operate more or less openly for years, while enforcement may appear erratic or inconsistent. When left unaddressed, they represent an incalculable risk to the website publisher.
This becomes particularly important during acquisitions. Businesses purchasing established domains frequently inherit unresolved compliance exposure alongside rankings and traffic. Google evaluates the website’s condition, not which employee, agency, or previous owner introduced the violations.
Traffic growth alone doesn’t confirm compliance health. A domain generating millions of clicks may still carry unresolved risks tied to old link schemes, expired sponsorship arrangements, deceptive user-agent cloaking, manipulative redirects, or scaled low-quality content sections. Those issues often go unnoticed until they’re brought to the surface by a Google manual spam action notification.
A common sign of an algorithmic adjustment: Gradual loss of visibility
Reputation abuse and publisher liability
The mechanics behind reputation abuse are straightforward. A trusted brand with an established web platform allows third parties to publish unrelated, often unsupervised content under the same domain name. In many cases, publishers integrated discount coupon sections, casino reviews, affiliate content, or commercially motivated informational pages directly into existing editorial systems.
The problem frequently worsened significantly because the content wasn’t properly segmented. The consequence is that the distinction between trusted editorial work and commercially motivated material became blurred.
Once confronted with a site-wide penalty, affected publishers experience broad visibility declines across the entire platform, not merely within the originally offending sections of the website. The damage to a brand that lends its reputation to a disreputable third party is often substantial.
Recovery efforts frequently prove time-consuming and costly. Removing isolated pages rarely resolves the problem. Many organizations require broader structural changes, including archive cleanup, internal link reviews, crawl management adjustments, sponsorship governance reforms, the removal of spammy redirects, stronger editorial oversight, and stricter technical segmentation.
In short, recovering from such a penalty takes time, costs significant amounts of money, and is often a painful process.
A common sign of a manual spam action: Rapid loss of visibility
The risks of scaled content
Google increasingly scrutinizes large-scale publishing systems that produce repetitive, low-value content without a unique selling proposition.
The issue isn’t maintaining many websites simultaneously. Large website portfolios have thrived in Google Search for years and continue to do so. The underlying problem involves quality control, editorial oversight, originality, and informational value.
Affiliate networks produce near-identical product comparison pages across thousands of long-tail keywords.
Local SEO operations deploy templated service pages across hundreds of regions with minimal differentiation.
AI-assisted workflows publish large numbers of informational pages without factual oversight or genuine expertise to support them.
Most organizations don’t cross into problematic territory intentionally. The transition usually occurs gradually, often unbeknownst to the decision-makers who rely on outdated or misleading recommendations.
The resulting manual spam action in Google Search Console, followed by a sharp decline in rankings, frequently occurs after a prolonged period of spam signal accumulation rather than during the apparent growth phase.
Incomplete remediation prolongs penalties
Many site owners approach reconsideration requests as if they were negotiating with Google. That puts them at a significant disadvantage from the outset.
The reconsideration process exists for one purpose only: to demonstrate that the website has been restored to full compliance with Google’s guidelines. It’s important to note that Google expects complete compliance before lifting a manual spam action.
This means the requirement extends beyond the specific violation highlighted in Google Search Console. A site owner who addresses only one known spam issue while leaving unrelated policy violations unresolved elsewhere on the website will typically face rejection.
A common testing approach, such as a publisher removing some problematic sponsored content while retaining similar affiliate arrangements elsewhere, will fail. Likewise, a business that disavows recent manipulative backlinks while ignoring historical paid link schemes is unlikely to convince Google of its genuine commitment to complying with Google’s policies going forward.
Similarly, a website network that cleans up one property while continuing identical publishing practices across related domains signals incomplete remediation rather than meaningful operational reform. As a result, it stands little chance of regaining Google’s trust.
Why repeated rejections make recovery harder
Effective website recovery requires a comprehensive review rather than selective cleanup. Technical infrastructure, content quality, sponsorship structures, redirect behavior, link acquisition history, indexing patterns, archive sections, and ownership transparency all require examination during serious compliance recovery efforts.
The Google Search team expects compelling documentation detailing what has changed and how future violations will be prevented. Temporary cosmetic adjustments rarely persuade reviewers to lift a manual spam action.
Making matters worse, each rejection typically requires an even more comprehensive review and cleanup effort. At the same time, every reconsideration request that Google deems disingenuous further erodes Google’s trust in the publisher.
The cost of uncertainty
There’s no guaranteed turnaround time for reconsideration processing. Some reviews are completed within days. Others take weeks or months.
At the same time, large websites with extensive SEO legacies accumulated over many years often require longer assessment periods due to the substantial volumes of data that must be crawled and analyzed before changes can be evaluated.
For businesses that rely primarily on Google traffic, this uncertainty creates a potentially existential threat.
An ecommerce business approaching a peak seasonal period with an unresolved manual spam action can face cash flow problems quickly.
Publishers dependent on advertising revenue experience ranking losses that translate directly into declining commercial performance.
Lead generation businesses often encounter immediate pipeline contraction once visibility declines significantly.
The operational risk becomes even greater when companies fail to build a strong brand capable of partially offsetting organic traffic declines through direct navigation or alternative revenue-generating channels. In this context, paid traffic is a poor substitute due to its associated costs.
In short, some online businesses can’t afford to be penalized in the first place.
Penalties can cripple operations
The issue extends beyond SEO performance. Search visibility directly affects commercial expansion, investor confidence, company valuation, partnership negotiations, and revenue stability.
Penalty expiration represents another commonly misunderstood aspect. Google manual spam actions may expire after prolonged periods, often years. However, this is rarely a viable strategy for an affected business.
Waiting passively through an extended period of declining visibility seldom aligns with commercial realities. More importantly, expiration alone doesn’t guarantee recovery or renewed growth, as the penalty could be reapplied not too long after it expired.
Google’s search systems continue evaluating overall site quality independently of manual enforcement status. A website carrying unresolved spam signals across its content, technical infrastructure, or off-page profile may continue to struggle long after the manual action itself has been lifted.
Compliance requires ongoing oversight
Compliance reviews can’t be considered optional or a luxury. Organizations heavily dependent on organic Google visibility require ongoing operational review cycles focused specifically on comprehensive policy compliance.
These reviews shouldn’t be conducted internally. Even the most talented in-house SEO teams are often hard-pressed to diligently identify shortcomings that may reflect on their own work or that of their colleagues. Policy compliance requires external expertise, sufficient authority, and a proven track record.
Purely technical SEO audits, while indispensable, are insufficient if commercial partnerships bypass oversight. Editorial standards alone won’t suffice if historical link manipulation remains unresolved. Planned growth initiatives require evaluation against established compliance frameworks before deployment, not after traffic has become dependent on questionable practices.
Mature organizations increasingly integrate compliance reviews into their operational governance. Sponsorship structures undergo search compliance review before launch. Scaled publishing systems are assessed for quality before expansion. Historical content is evaluated on a recurring basis. Acquisition due diligence includes policy exposure analysis alongside financial review.
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Compliance is a business imperative
This level of discipline and vigilance matters because manual spam actions rarely arrive at convenient moments. More often than not, undesirable Google scrutiny coincides with critical periods: just before a long-planned commercial expansion, in the run-up to a migration project, ahead of an acquisition, as the peak retail season begins, or shortly before investor reporting deadlines.
This is hardly intentional. It’s simply a matter of unfortunate timing. Google doesn’t align search quality enforcement with business planning calendars. Google cares primarily about user experience. For every website that loses its top position, there is usually another capable of providing users with a similarly compelling experience.
Businesses that ignore unresolved policy exposure often discover the problem the hard way, only after search visibility has collapsed and online sales have followed suit. At that point, recovery becomes a far more prolonged, expensive, and operationally disruptive undertaking than ongoing compliance reviews would have been prior to penalization.
Nevertheless, the work must be done. The one silver lining is that, in many cases, the process proves cathartic. Once the penalty has been resolved and the website’s SEO signals have become more consistent, the removal of legacy issues often allows rankings not merely to recover, but to exceed their previous highs.
Google is changing how it charges for certain Demand Gen campaigns on Discover, signaling a closer link between billing models and campaign optimization goals.
What happened. Google Ads has notified advertisers that Demand Gen campaigns using view-through conversion (VTC) optimization on Discover will move from cost-per-click (CPC) billing to cost-per-thousand impressions (CPM) beginning July 15th.
The change affects a limited number of advertisers and applies only to campaigns with VTC optimization enabled. Advertisers not using VTC optimization will see no change.
The transition will happen automatically, with no action required from advertisers.
Why we care. The change could alter how advertisers evaluate efficiency within Demand Gen campaigns. Campaigns optimized for view-through conversions may see differences in spend pacing, impression volume, and reporting metrics once billing transitions from clicks to impressions.
Advertisers focused primarily on click-driven performance may want to reassess whether VTC optimization remains the right fit for their objectives.
Why Google is making the change. According to Google, the update is designed to better align billing with campaign objectives.
View-through conversions measure actions taken after a user sees an ad but does not click it. Because impressions play a central role in generating those conversions, Google argues that CPM billing more accurately reflects the value being delivered.
The company also says the change will allow its systems to optimize more effectively for view-through conversion goals.
Opt-out option. Advertisers who do not want to transition to CPM billing can opt out by disabling view-through conversion optimization in campaign settings.Doing so will prevent the billing change from taking effect for those campaigns.
The bottom line. Google is tying payment more closely to the behavior its Demand Gen campaigns are designed to optimize for. For advertisers using view-through conversions, impressions—not clicks—will soon become the basis for both optimization and billing on Discover.
First spotted. The update was shared by Adsquire founder, Anthony Higman, who shared the comms he received on X.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/Google-Demand-Gen-Billing-KEYa9q.jpg?fit=497%2C955&ssl=1955497Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 14:31:172026-06-16 14:31:17Google Ads shifts Demand Gen billing to CPM for some Discover campaigns
Advertisers using Microsoft Ads can now target users based on job seniority, adding another layer of B2B audience precision powered by LinkedIn data.
What’s happening. Microsoft Advertising expanded its LinkedIn Profile targeting capabilities to include job seniority targeting across Search and Audience campaigns, according to Product Liaison Navah Hopkins.
The update allows advertisers to target or observe users based on 10 standardized seniority levels: CXO, VP, Director, Manager, Senior, Entry, Owner, Partner, Training, and Volunteer.
The feature is available at both the campaign and ad group level, giving advertisers more flexibility when segmenting audiences.
Why we care. B2B marketers have long struggled to distinguish between decision-makers and practitioners within search campaigns. The addition of job seniority targeting gives advertisers a way to better align messaging, bidding strategies, and reporting with specific audience segments.
For organizations with longer sales cycles or multiple stakeholders involved in purchasing decisions, understanding who is engaging with ads can be as important as the conversion itself.
Between the lines. Unlike many audience targeting options available across advertising platforms, Microsoft’s integration with LinkedIn data offers a professional identity layer that can help advertisers better understand who is behind a click.
The new seniority filters can be applied directly within campaign settings or used in observation mode to gather performance insights without restricting reach.
How marketers can use it:
Tailor messaging by seniority
Advertisers can create separate ad groups for executives, managers, and individual contributors, adapting tone and messaging based on audience expectations.
An executive-focused campaign might emphasize strategic outcomes and business growth, while messaging aimed at practitioners could focus on workflows, implementation, or efficiency gains.
Identify who is actually converting
Observation mode allows marketers to analyze conversion performance across seniority levels without narrowing targeting.
This can help answer questions such as:
Are conversions coming from decision-makers or influencers?
Is budget being spent on audiences that rarely close?
Which seniority levels generate the highest-quality leads?
Improve audience testing
The additional reporting layer provides another signal for optimization and expansion decisions.
Advertisers importing campaigns from other platforms may find performance patterns differ on Microsoft Ads, making seniority reporting a useful source of testing and audience discovery.
Availability. The feature is currently available in selected markets across the Americas, EMEA, and APAC regions.
Americas: Argentina, Brazil, Canada, Chile, Colombia, Ecuador, Mexico, Peru, and the United States.
EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
APAC: Australia, India, Indonesia, Japan, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam.
The bottom line. Microsoft Ads continues to lean into its LinkedIn integration as a differentiator in the B2B advertising market. The addition of job seniority targeting gives advertisers another way to connect search intent with professional identity, helping them better understand not just what audiences are searching for, but who is doing the searching.
The idea that AI is killing advertising misses the bigger shift. As AI expands across search, assistants, productivity tools, and transactions, advertising is moving with it.
Ad density may be changing within AI experiences, but advertising opportunities are expanding across a growing number of surfaces.
At the same time, paid and organic are becoming harder to separate. The same AI systems increasingly power ad campaigns, search experiences, and brand visibility across Google’s ecosystem.
That changes how brands should think about visibility.
Paid and organic are no longer separate channels competing for the same click. They are increasingly different ways of influencing the same AI systems, which means the signals shaping organic visibility may also affect paid performance.
The old model: Paid and organic on one finite SERP
Google’s SERP was a finite surface: 10 organic blue links, a few ad slots, and a knowledge panel on the right. The user landed, scanned, and clicked.
Paid and organic teams operated on separate budgets, separate tools, and separate quarterly reports, and rarely talked to each other because manual Google Ads kept the paid specialist busy full time. Titles, descriptions, bids, and campaign structure were all chosen by hand and required constant attention, which is why the organic team had no part in any of it.
DSA changed that for me. It read my organic pages to decide which ads to run, who to show them to, when, at what bid, and what title to use. I controlled the descriptions. The engine decided everything else, and it did it better than I would’ve done manually because it was reading the same signals the organic side was already optimizing for.
When someone at Google in Singapore explained how PMax worked, I thought, “That’s exactly what I was doing.”
PMax took the DSA logic and extended it across every Google surface simultaneously: Search, YouTube, Gmail, Display, Maps, and Shopping, all in one campaign, with the engine making every placement decision from your assets and audience signals.
AI Max brought the same intelligence into Search campaigns, specifically, with Gemini underneath instead of rules. PMax and AI Max run on the same Gemini brain: one focused on Search, the other spread across every surface, applying the same funnel logic to different contexts with different signal layers on top.
And if Gemini’s understanding of your brand is thin, it fills those decisions with whatever it thinks will work, which isn’t necessarily your brand narrative, and you have no direct way to override it. You train it, or you lose control of your own ads.
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The new model: Gemini sits inside every surface, and it carries ads with it
Gemini now sits inside every layer of the Google ecosystem:
Discovery (Search, Maps, YouTube, Lens, News, Discover, and Shopping), productivity (Gmail, Docs, Drive, Photos, and Calendar).
Distribution (Android, Chrome, Google Play, Pixel, Wear OS, Google TV, and Nest).
Transaction (Google Pay, Wallet, Flights, Hotels, and Travel).
Assistive surfaces themselves (AI Mode, AI Overviews, Assistant, NotebookLM, and the Gemini app).
That’s how many connected consumers spend most of their workday, and most of those surfaces either carry ads now or have the infrastructure to start carrying them.
Microsoft Advertising sits inside Copilot across Bing, Edge, Windows Consumer, Office Consumer, Teams Free, and GitHub.
OpenAI Ads launched in February for logged-in users on Free and Go tiers in the U.S., placing ads below ChatGPT responses and clearly labeling them as sponsored. By May, OpenAI had opened a self-serve Ads Manager and was expanding internationally.
The ads layer travels with the engine, the engine is everywhere, and ads therefore have the potential to be everywhere. Most brands still treat paid as a separate channel run by a separate team on a separate dashboard, which is a search-era inheritance that was never ideal but now needs to be dropped.
Performance Max already runs the auction across YouTube, Display, Search, Discover, Gmail, and Maps as one campaign type. Search is one surface among many, and the “ads are dying in AI search” narrative is measuring the wrong thing. It sees ad slots compress inside the assistive interface while ignoring that the surface base has multiplied by an order of magnitude.
Ad density follows the delegation the user has made to the machine
The dominant narrative in 2026 is that ads are dying because AI is replacing search, and ads inside AI are a problem nobody has fully solved yet. That’s partially correct: Ad density per session drops as AI takes more control, and nobody – including Google – has yet figured out how to insert ads into the AI response itself without killing the experience that makes the AI valuable in the first place.
But this is the part the analysis gets wrong: This doesn’t add up to fewer ads overall.
Search ads are Google’s goose with the golden egg, and the goose may be slowing down — though nobody outside Google actually knows, because Google doesn’t break out search ad revenue from YouTube, Display, and the rest. That ambiguity is doing a lot of work.
What we do know is that total ad revenue has kept growing even as AI has taken over more of the search experience, which proves the flock is already working.
Kodak invented the digital camera and then buried it to protect film-processing revenue, and we know how that ended. Google appears to be doing what Kodak didn’t: building the replacement while the original is still profitable.
Every surface Gemini sits inside is a new bird in the flock, each laying a smaller egg that grows over time, and when Google finally cracks ads inside the AI response itself, that’s one more goose. The surface base has expanded faster than density has dropped, and the ad-density problem in Search and AI is temporary.
The more the user delegates decisions to the machine, the less room the machine has to surface a paid option. Search keeps the user in charge, so the engine surfaces ads the user might pick. Assistive narrows the options, so a sponsored slot still has a chance. Agentic executes the decision, so the ad has nobody to persuade. Ad density follows that delegation, mode by mode, with AI deciding which brands win at each mode.
Ad density follows the delegation the user makes to the machine.
Google is running two moves at once, and it seems most people have noticed only the first one. Gemini is taking over the recommendation, targeting, and auction logic on surfaces that have carried ads for years. And Google is adding ads to surfaces where they were previously absent, with AI Overviews now eligible for ads above, below, and within the answer, and AI Mode testing conversational ad formats.
The first move is AI taking over the existing ad business. The second is the ad business expanding into surfaces it never occupied. The net effect is more AI-driven ads across more of the stack than ever before.
The freemium system still works, but the ad is becoming part of the surface
The monetization model that works at consumer internet scale is simple: pay with money, or pay with attention.
YouTube is Google’s clearest example — and proof that it works: free with ads, paid without, and the vast majority of users have always chosen ads.
Gmail draws the same line: Where the user pays directly, Google doesn’t insert ads. Where the user pays with attention, Google monetizes it.
I learned about freemium the hard way. When our children’s media company, Boowa & Kwala, survived the dot-com crash, we added a paid tier that removed the ads. Out of a million unique visitors a month, a few hundred paid. Almost nobody chose to pay.
The freemium contract — free access in exchange for ads — is the deal they actively prefer, and the numbers prove it. And for ad-driven businesses, pure volume makes the money. In Big Tech, Google has the clear advantage.
ChatGPT is already running ads on free tiers.
Gemini is ad-free without login, but that’s a launch state, not a permanent model.
Perplexity is blocking users instead of monetizing them, which is a different bet on the same problem — and a bet with a limited runway.
Every AI surface is in the process of landing on the same answer because there is no other answer.
What changes is how the ads appear. The classic SERP ad was clearly labeled and set off in a colored panel. The Gemini recommendation that surfaces a product inside a Gmail context, the Copilot suggestion that names a vendor inside a Word document, and the agent that picks a supplier on the user’s behalf are something else entirely.
The ad becomes ambient. It dissolves into the surface, and what advertising looks like becomes harder to identify as advertising. Gemini reads context and intent with enough precision that an ad placed in a meeting summary can feel useful rather than disruptive, which is a risk profile Google’s rules-based systems could never have accepted.
At Boowa & Kwala, when we scaled free ad-supported views from 100 million to 1 billion, revenue multiplied by roughly two, and costs rose by around 20%. Surface (a.k.a. pageviews) multiplied tenfold, revenue doubled, costs grew by a fifth, and we went from profitable to significantly more profitable.
The aim was never to push revenue up at the same rate as surface expansion. It was to keep expanding the surface, knowing the incremental delivery cost was negligible.
Google’s ratios at planetary scale differ from ours, but the structural shape almost certainly doesn’t: surface expansion plus near-zero incremental cost equals profit growth, regardless of whether revenue per surface keeps pace.
Cohort, intent, and profit drive both paid and organic
PMax, AI Max, AI Overviews, AI Mode — Gemini is driving all of them. The AI optimizing your paid campaigns is the same AI evaluating your organic content, reading the same user, in the same moment, with the same intent.
The engine reads three signals:
Cohort.
Intent.
Profit.
In paid, you declare all three explicitly when you structure your campaigns. In organic, the engine infers all three from behavior: clicks, dwell time, and return-to-search serve as proxies for the profit signal that is missing there. Google denied using behavioral signals for years. Its own court case documentation told a different story.
Which means the organic discipline the whole series has been building — the funnel query pathway, the entity home, and the corroboration stack — has always been pointing at one thing: engineer the page so precisely for the right cohort that the behavioral signal does the same job as a correctly structured PMax campaign. The user lands, stays, converts, and doesn’t go back and research the same thing again. Google reads that behavior and infers your profit tier.
My bet, and I want to be clear it’s a bet rather than a documented fact, is that Gemini can’t serve a paid ad in real time without grounding against current search results because the ad has to match the organic context it’s appearing in.
If it doesn’t ground, the ad is inconsistent with what the user sees organically, which breaks the experience and loses the click. So the grounding process for paid is the same process as for organic: same knowledge graph, same search index, same LLM.
That means training Gemini on your brand through organic improves your paid performance through the same mechanism. One training investment, two outputs. I’ll be proven right on this eventually, and this article is the timestamp.
The same AI runs your organic and your paid. Train it once, win twice.
You can’t directly target Gemini in AI surfaces. You can only train it.
Across AI-driven placements, Gemini decides everything: where to show your ad, what to show, how to show it, who to show it to, when, and at what bid. The advertiser feeds it information and sets the parameters, but Gemini makes every decision that matters.
What you’re buying when you spend on Google Ads in 2026 is the right to feed a recommendation system that analyzes your brand on its own terms. The explicit signals you declare in paid — cohort, intent, and profit — are a real advantage over organic, where the engine has to infer all three from behavior.
But your ability to dominate through pure campaign structure is vastly reduced when Gemini doesn’t understand or trust your brand. The control has shifted: you guide it through signal clarity, not through the settings dashboard, and that guidance works best when your organic foundation is solid.
Use paid to find the combinations that work, build organic pages around them
In a correctly structured PMax or AI Max campaign, you declare cohort, intent, and profit margin explicitly: this audience, this goal, this margin, in the same campaign. You don’t mix a luxury hotel and a budget guesthouse in the same ad group because the cohort is different, the profit margin is different, and handing the engine a mixed signal makes it spend your budget resolving a contradiction you created.
Organic doesn’t let you declare profit directly. The engine infers it from who landed, who stayed, who converted, and who never came back to search for the same thing. That behavioral signal is the only proxy it has for the profit tier, and it’s a thin signal compared to the explicit declaration you make in paid.
The smartest move for any brand running both is to treat them as a single loop. Run paid to find which cohort-intent-profit combinations actually convert. Build the organic pages around those combinations, designed so precisely for the right cohort that the behavior on the page sends the engine the same signal the paid campaign explicitly declared.
The paid side becomes cheaper because organic pages provide the behavioral confirmation the engine needs. The organic side gets stronger because the paid data tells you exactly which pages to build and for whom, and then feeds the engine the same signal the paid campaign declared explicitly, for free.
Most travel sites serve the same page template to a budget traveler looking for a €30 guesthouse in Bangkok and a wealthy traveler looking for a €3,000 suite at the Peninsula. Same layout, same fields, same photo grid, same review format.
The engine has to infer which cohort the page serves mostly from behavior because the differentiation of the pages is limited. Build the page for the person rather than the query, and you hand the engine the cohort signal it’s currently having to guess. That’s not a UX decision. That’s your profit margin declaration to an engine that can’t see your margins any other way.
And you win on all three fronts simultaneously. A page built precisely for the right person converts better because it works better for the human.
Better conversion behavior sends cleaner implicit signals to the engine, which improves your organic ranking for that cohort. And cleaner organic signals reduce your paid CPC because the engine has less to guess about. Better pages, more organic, cheaper paid – the same work produces all three.
When Gemini isn’t convinced about you, you pay on both sides simultaneously
The three revenue taxes — the doubt tax, the ghost tax, and the invisibility tax — operate on the organic side. Because the engine powering your organic results is the same one powering your paid placements, you pay all three on both sides simultaneously.
The doubt tax: When the engine hedges on basic facts about you organically, it rewrites your paid creative to soften the same claims.
The ghost tax: When the engine prefers competitors in organic comparisons, your paid creative gets passed over even when your bid is competitive.
The invisibility tax: When the engine doesn’t surface you organically, it doesn’t show your ad either. You’re not in the running.
Paid surfaces carry two additional taxes that don’t exist on the organic side, and one discount you earn when you get it right.
The taxes and discounts in AI-driven paid search include:
The mistrust tax: What you pay when the engine’s confidence in your brand is low. A CPC premium because Quality Score penalizes low entity trust, and message distortion because the Gemini Filter rewrites your creative away from your intended positioning. You can’t turn the filter off. The practical answer isn’t constraining it. It’s improving the entity confidence that the engine reads when deciding how to filter.
The intent tax: This is self-inflicted. Build an ad group with mixed intent, and you hand the engine a contradiction. Gemini will spend your money figuring out a mess you made. Each ad group should align on cohort, intent, and profit margin — any mix across those three, and Gemini is billing you to resolve the confusion.
The confidence discount: This is the blade cutting the other way. Every properly defined ad group is secretly doing two jobs: it buys you an efficient placement today, and it teaches the engine which cohort you serve tomorrow. When the engine trusts you, it stops second-guessing your ads, your CPC drops, and your creative lands cleaner. That’s worth more than any bid adjustment you make.
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Google has a structural advantage that Microsoft and OpenAI can’t match
Google has all the cards: the model, the surfaces, and the ads platform, all owned and tuned together in absolute harmony. Microsoft has the surfaces but lacks the LLM to drive them at the same level.
OpenAI has the model and launched a real ads business in February 2026, but lacks the surfaces – no Gmail, no YouTube, no Maps, no Play – and without surfaces, an ads business can’t compound at scale. Only Google has all three working as one system.
Paid and organic are now inseparable. The goose is fading, but Google can afford to let it. They know it rises like a phoenix, and in the meantime, they’ve got the biggest gaggle.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/Ad-density-follows-the-delegation-the-user-makes-to-the-machine-CDyaZO.png?fit=2048%2C1707&ssl=117072048Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 14:00:002026-06-16 14:00:00How AI is merging paid and organic visibility
As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
Where AI delivers the most value
I use AI primarily for practical, time-saving tasks, including:
Generating regex patterns when I need a quick solution without researching syntax from scratch.
Creating complex spreadsheet formulas for reporting workflows that rely on manual data exports.
Accelerating research and planning for projects that require competitive analysis across multiple business lines.
Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project discussed here falls into that final category.
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Mapping hreflang at scale
The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
Phase 1: Asking for an approach, not just a script
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
Crawl the websites to collect live URLs and their metadata.
Use Python in Google Colab to process the raw data.
Run an exact match cluster first to group identical slugs.
Use an advanced semantic AI model (such as SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and data collection
Following the strategy, I used a crawler to spider all the regional websites. The goal was to generate a unified comma-separated values (CSV) file containing the live URLs, status codes, title tags, and H1s. Screaming Frog worked perfectly for this application.
A critical point: Your AI output is only as good as your crawl data (remember the old saying, “garbage in, garbage out”).
An AI script will fail to map an obvious “exact match” if the target URL is a 404 or a 301 redirect in your source data. You must filter your CSV to include only indexable content before feeding it to the script.
Google Colab provides a free, cloud-based Jupyter notebook environment where you can write, paste, and execute Python code without worrying about local installations or environment variables. You can access it through Google Drive. I found the free version had enough capacity to handle this project.
I uploaded the CSV to Colab, and Gemini provided the initial Python script. The script used a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial output was far from perfect.
Phase 4: The iteration (where the real work happens)
If you expect AI to deliver a flawless, edge-case-proof script on the first try, you’ll be disappointed. You’ve probably heard the comparison of AI tools to interns, meaning you need to check their work. That’s very true.
The real value of AI lies in the iteration. As we ran the script, we encountered several unmatched URLs, leaving pages orphaned rather than grouping them with their international counterparts.
Here’s how I iteratively trained the AI to handle the nuances of human-managed websites.
The directory flattening problem
The U.S. site had recently reorganized its blog into topical folders, while the Mexican and Italian sites hadn’t yet been reorganized.
I prompted Gemini with these specific mismatched examples. It responded by adding a URL flattener function to the script, which stripped the topical folders behind the scenes so the translated slugs could align cleanly.
The aggressive semantic trap
To prevent the AI from mixing up different topics, we implemented concept traps. Initially, they were too strict. A UK article about the manufacturing sector wouldn’t match an Italian article because the U.S. title was slightly more generic.
I instructed Gemini to loosen the traps for generic industries while keeping them strictly enforced for critical acronyms (such as “SEO” versus “SEM”). This gave the AI the breathing room it needed to match creative translations.
The translated slug epiphany
The biggest breakthrough came while auditing the Mexican blog orphans. For example, the Spanish URL /detras-de-escenas-historias... is a direct translation of the English /behind-the-scenes-stories... I pointed this out to Gemini.
Instead of forcing me to hard-code hundreds of manual matches, Gemini updated the script to create a “Combined Semantic Signature.” It dynamically translated core operational phrases in the slugs, effectively bridging the language gap for the semantic matching model and connecting dozens of orphaned pages almost instantly.
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Lessons from building an AI-assisted SEO tool
The project reinforced a simple lesson: AI works best when it’s treated as a collaborator rather than a shortcut.
Be the strategist, let AI be the coder: Don’t just demand a final product. Discuss the architecture, edge cases, and logic first. Treat AI like a junior developer that needs clear architectural direction.
Provide concrete examples: When a script fails, don’t just say, “It’s broken.” For this project, I provided either exact URLs that failed and the URLs they should have matched with, or groups of URLs with mismatches. AI needs concrete patterns to fix its logic.
Embrace the iterative loop: Expect to run the code, identify anomalies, and feed them back into the prompt. Each iteration makes the tool significantly smarter.
Leverage Google Colab: You don’t need to be a Python expert to use Python for SEO. Colab bridges the technical gap, allowing you to run complex data science libraries directly in your browser.
By the end of the project, we had a robust, highly customized Python script that could process a massive CSV and generate a cross-referenced hreflang XML sitemap in minutes.
AI isn’t going to replace technical SEOs anytime soon. However, SEOs who know how to collaborate with AI to build custom, scalable, and useful tools will have a significant advantage.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/How-AI-helped-build-hreflang-XML-sitemaps-at-scale-ONn3vk.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 13:00:002026-06-16 13:00:00How AI helped build hreflang XML sitemaps at scale
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
From results to narratives: Traditional search vs. AI search
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
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The first query is often only the doorway
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t make an owner, office manager, or intern quietly resent tech?
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
Next-question intent is not just a writing exercise
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Where good content goes thin
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
A real strategy?
A lightly personalized template?
A monthly call where everyone nods at a dashboard no one has time to interpret?
The product page says “safe for families.” Safe for whom?
Babies?
Pets?
People with health issues?
A software page says, “built for small businesses.” What business?
A solo bookkeeper?
A nonprofit?
A 40-person heating and cooling company?
A founder doing payroll late at night after working all day?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
What’s the primary question this page answers?
What would a serious buyer, reader, or researcher ask next?
What objection would stop them from acting?
What comparisons would help them understand the category?
What proof would make this answer trustworthy?
What detail would make this financially, technically, locally, or personally relevant?
Where are we applying broad language because we haven’t done the harder thinking?
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
Examples of next-question content across industries
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
AI search rewards content that completes the answer
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
“We help local service businesses without in-house marketing teams improve search visibility and generate more qualified appointment requests by clarifying their website content, answering the questions clients actually ask, and building pages that support both traditional and AI-generated search. This is best for businesses looking for durable visibility rather than a quick paid-ad spike.”
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
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The new visibility test
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
Google updated its AI Search optimization guide to clarify that llms.txt files neither help nor hurt Google search rankings. It also confirmed that Google Search does not use llms.txt files.
What Google wrote. I bolded the portion that is new, where Google wrote that Google Search does not use AI text files, markup or Markdown files.
“You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities), as Google Search itself doesn’t use them. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn’t mean that the file is treated in a special way.”
Google also added a new note that reads:
“It’s completely fine if you decide to create and maintain LLMS.txt files (or other similar files) for other services or systems that use these files. Doing so won’t harm (nor help) your visibility or rankings in Google Search, as Google Search ignores them.”
Why we care. There’s been a lot of confusion about how Google Search handles llms.txt, markdown, and other AI-related files. In short, Google Search may discover, crawl, and index these files, but it does not use them in any special way. Having them on your site won’t help your rankings, and it won’t hurt them either.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/google-llm-doc-update-scaled-6cRDQT.webp?fit=2048%2C950&ssl=19502048Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 20:03:332026-06-15 20:03:33Google says llms.txt files won’t harm or help your search rankings
For the past several months, advertising on ChatGPT meant getting an invitation. A small group of brands had access. Everyone else waited.
Self-serve access is now open to all advertisers, and the dynamics that made early access valuable are already starting to shift.
Key Takeaways
ChatGPT surpassed $100 million in annualized ad revenue in its first six weeks, generated from less than 20 percent of eligible users seeing ads daily.
Around 85 percent of free and Go tier users are eligible to see ads, meaning current revenue represents a small fraction of eventual ad capacity.
Self-serve access launched in May 2026, opening the platform beyond the initial group of managed pilot brands via a new OpenAI Ads Manager.
OpenAI removed the $50,000 minimum spend requirement entirely, opening the door for businesses of any size.
ChatGPT now reaches 800 million weekly active users, processing 2.5 billion prompts daily.
First-mover advantage is real, and it will not last long once self-serve competition normalizes pricing.
The Numbers Behind the Launch
ChatGPT crossed $100 million in annualized ad revenue in six weeks, which is a strong opening number on its own. The context makes it more striking. That figure came from less than 20 percent of eligible users seeing ads daily. With roughly 85 percent of free and Go tier users eligible to see ads, the platform is operating at a fraction of its eventual capacity.
OpenAI launched its self-serve Ads Manager in early May 2026, removing the significant minimum spend thresholds that had previously locked out most advertisers. During the pilot phase, entry required a $50,000 commitment minimum, which limited access to large brands and agency partners including Dentsu, Omnicom, Publicis, and WPP.
That barrier is now gone. Any U.S. business can sign up, set their own budget, and launch campaigns without going through a partner agency.
The platform has also added CPC and CPM bidding options alongside conversion tracking, pixel-based measurement, and attribution capabilities. That infrastructure shift matters. It transforms ChatGPT advertising from an experimental awareness product into a channel capable of performance measurement, which is what allows ad ecosystems to scale properly.
Geographic expansion is already underway, with OpenAI confirming rollout to Canada, Australia, New Zealand, the United Kingdom, Japan, South Korea, Brazil, and Mexico. For international advertisers, the time to start building familiarity with the platform is now, before it reaches your market.
Why This Channel Works Differently
Dropping your existing search or social creative into ChatGPT and expecting it to perform is a mistake. The environment is fundamentally different.
ChatGPT is a conversational platform. Users are having a dialogue, asking follow-up questions, getting synthesized answers, and making decisions based on what the platform surfaces. When someone clicks a Google ad, they are often at the beginning or middle of their research journey. When someone encounters an ad in ChatGPT, they have already spent time in a specific, multi-turn conversation that has narrowed their problem. The AI has done the educational and comparison work. The user is ready for a direct answer or a specific solution.
That intent depth is what makes ChatGPT advertising different from display or social. It also means that landing pages and creative designed for top-of-funnel traffic will underperform. The user who arrives from a ChatGPT ad is further along the decision process than most of your other paid traffic. Your messaging and destination need to match where they are.
The targeting model is also distinct. ChatGPT uses contextual matching based on current conversation topics, past chat history, and previous ad interactions rather than traditional keyword targeting or demographic signals. That combination of conversational depth and behavioral context creates a quality of intent signal that search and social cannot fully replicate.
OpenAI has been tracking ad quality closely. Fewer than seven percent of ads are currently rated as low relevance by users, and the company says improving that metric alongside user trust is an active priority. Early pilot results showed no negative impact on consumer trust metrics and low ad dismissal rates, which OpenAI interpreted as signals to move forward with expansion.
The Two Ad Formats Currently Running
Two formats are currently live inside ChatGPT. Both appear below the AI’s response, clearly labeled as sponsored and visually separated from the organic answer.
The first is a shopping product carousel with integration for checkout. This format is well-suited for ecommerce brands selling products with clear visual appeal and straightforward purchase paths.
The second is a conversational banner that includes a call-to-action and an “Ask ChatGPT about this ad” button. When a user clicks that button, they enter a conversation powered by information the advertiser has pre-loaded: product details, FAQs, and service specifics. ChatGPT answers user questions on behalf of the brand using that uploaded data. A user who asks about pricing, sizing, or features gets a direct, brand-informed answer without leaving the platform. This format is particularly powerful for high-consideration purchases and B2B categories where questions are complex and the buying cycle is long.
Where the Early Opportunity Is Clearest
The categories with the clearest early opportunity are the ones where users already turn to ChatGPT for research and decision-making. B2B software, professional services, financial products, health and wellness, travel and hospitality, and high-consideration consumer purchases all fit that profile. These are categories where the buying decision is complex, the conversation context is rich, and users are asking detailed questions across multiple sessions.
High-consideration e-commerce also performs well, particularly where users compare specifications or ask the AI to evaluate options. Brands selling commodity goods or low-price impulse purchases will find the signal-to-noise lower, at least in the early stages before format options expand.
Start by identifying the specific questions users ask ChatGPT that relate to what you sell. Use ChatGPT itself to research those queries: the language the AI naturally uses to discuss your category is a preview of the context your ads will appear in. Align your messaging with that language. Those query moments are the equivalent of high-intent keywords in early search, and right now the auction pressure around them is low.
Set a test budget and treat it as education. A modest budget in the early months of self-serve access should be viewed as learning what works in conversational ad contexts, not as a channel expected to deliver strong ROAS immediately. The data you build now will be more valuable as the platform scales.
The Bigger Picture
ChatGPT’s ad launch is part of a broader shift in how discovery works. The platform now processes 2.5 billion prompts daily from 800 million weekly active users. That is not a niche experiment. It is a mainstream consumer behavior that brands need to account for.
The parallel to early search advertising is not a stretch. Google Ads in 2002, Facebook Ads in 2007, and ChatGPT Ads in 2026 follow the same pattern: access was initially limited, costs were low, and the brands that moved early built structural advantages that compounded over time. OpenAI is targeting $2.5 billion in ad revenue for 2026, with longer-horizon projections reaching $100 billion by 2030. For context, AI-driven search ads are projected to reach $26 billion by 2029, equivalent to 13.6 percent of total U.S. search ad spend.
The window for low-competition early adoption is open now. It will not stay that way.
FAQs
Do ChatGPT ads affect what the AI says in its responses?
No. OpenAI has been explicit on this point: ads do not influence ChatGPT’s answers. Sponsored content is always visually separated from the organic response and clearly labeled. Advertisers receive only aggregated performance data. Individual conversations stay private.
Who can see ChatGPT ads?
Currently, ads are shown to logged-in adult users on the Free and Go plans only. Users on Plus, Pro, Business, Enterprise, and Education plans see no ads. That means the addressable audience is the tens of millions of people on the free version of ChatGPT.
How is ChatGPT ad targeting different from Google or Meta?
ChatGPT targets based on current conversation context, past chat history, and previous ad interactions rather than demographics or keywords. This gives you access to a deeper intent signal than behavioral or interest-based targeting can provide.
What should my landing page look like for ChatGPT traffic?
Not like a generic homepage. Users arriving from ChatGPT ads have already had a specific, contextual conversation. Your landing page should acknowledge that context directly: match the problem they were discussing, provide the specific answer or solution they are looking for, and make the next step clear.
Conclusion
$100 million in annualized revenue from less than 20 percent of eligible users in six weeks is not a modest start. When self-serve scales, the minimum spend barrier is removed, and the eligible audience expands, those numbers move fast.
Move early. Set benchmarks. Learn how conversational advertising works in your category. The cost of waiting is higher than the cost of testing.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 19:00:002026-06-15 19:00:00ChatGPT Opens Ads for All: How to React to This Shift
While managing a major B2B SaaS account, Hallam PPC Lead, Simran Harichand tightened a target CPA to improve efficiency but failed to monitor the impact. The change dramatically reduced spend, leaving the account €30,000 short of its monthly budget target.
When underspending becomes a business problem
Underspending isn’t just a media issue — it can affect a client’s future budgets. In this case, unused funds had to be returned to finance, making it harder for the marketing team to justify similar investment levels in future planning cycles.
The hardest part wasn’t the mistake
The most difficult moment came when Simran had to explain the situation to the client. Rather than making excuses, she took full responsibility for the error and acknowledged the impact it had on their goals.
Trust is built after the mistake
Although the client was understanding, trust had been damaged. Simran rebuilt confidence by introducing weekly budget pacing updates, showing transparency and proving the issue wouldn’t happen again.
Why the “brilliant basics” matter
The experience reinforced the importance of fundamentals such as budget pacing, account monitoring and conversion tracking. No matter how advanced advertising platforms become, strong basics remain the foundation of good performance.
What she’d do differently today
Looking back, Simran says she underestimated how much influence a target CPA change could have on delivery. Today, she treats any spend-related adjustment as a significant account change that requires close monitoring.
The danger of relying on AI without oversight
Simran supports testing AI-powered tools but warns against blindly adopting every new feature. She believes advertisers should balance experimentation with human oversight and strategic thinking.
Why conversion tracking remains the industry’s biggest blind spot
One of the most common issues she sees in account audits is poor tracking implementation. Inaccurate conversion data can lead to flawed optimisation decisions, making reliable measurement more important than ever.
The human side of client relationships
Strong client relationships can help teams navigate difficult moments when mistakes happen. Building trust through communication and honesty often matters just as much as delivering strong performance.
The bottom line
Mistakes are inevitable in PPC, but accountability and learning from them are what matter most. For Simran, the experience was a reminder that long-term success is built on mastering the fundamentals and maintaining trust.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/rzl5tut9h3c-RGkb8K.jpg?fit=1280%2C720&ssl=17201280Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 18:25:542026-06-15 18:25:54How a €30,000 underspend taught Simran Harichand the importance of the basics
