I recently discovered that Bing is testing a new AI Performance report within their Webmaster Tools. This has piqued my interest, especially since Microsoft has been teasing the idea of providing better insights into website performance in AI-driven Bing and Copilot searches for months.
It all started back in February 2023, and then in April 2023, Microsoft hinted at delivering data on Bing Chat and AI search impressions. Sadly, our hopes were dashed when they lumped this data together with regular web queries, leaving us still in the dark about our sites’ performance in Bing’s AI experiences. I can’t help but feel a bit let down.
Now, it seems Bing is experimenting with a new report within Bing Webmaster Tools, known as the AI Performance report. This report is in a super limited beta phase, and Microsoft hasn’t officially announced anything yet. A source shared that it showcases citation data from both Microsoft Copilot and its partners, detailing the number of citations and cited pages per day.
With this report, I can see how often Copilot cites my website and across how many pages. However, it still doesn’t reveal how many people clicked through from those citations to my site. The report also presents data categorized by “grounding queries” and “pages.” While “grounding queries” might not represent the exact query entered in Copilot, it shows how Bing interprets them, including insights into the intent behind such queries, like whether they are navigational or informational.
This new report lets me identify the specific pages Copilot cites. While there’s excitement in seeing more AI performance-related data pop up in Bing Webmaster Tools, I can’t shake the feeling of wanting click-through data. Knowing the click-through rate from AI interactions compared to regular web searches is something I, and I’m sure many other publishers and site owners, have been eagerly anticipating.
It feels like all search engines are intentionally keeping this data under wraps, and it’s frustrating not having full transparency.
I recently discovered the potential of Google AI Max and, like many of us, wondered if my account is ready to harness its power. Google AI Max promises to unlock additional conversions if set up correctly. Before jumping in, I knew I had to ensure everything was primed and in place.
Google’s AI Max is designed to transcend traditional keyword targeting by utilizing various signals to determine ad displays. It’s a game-changer for those with a history of broad match success. However, if not optimized, it could quickly deplete your budget.
One important clarification: using AI Max is not mandatory for ad appearances in AI Overviews. Broad match keywords can place ads in AI Overviews regardless of AI Max usage. I see AI Max more as a tool to expand conversions beyond mere AI Overviews.
We’ll explore the essential steps to review before testing AI Max. These insights are crucial to ensure our campaigns are fully prepared.
What to Check Before Enabling AI Max
Accurate Conversion Tracking
Having precise conversion tracking is vital. AI Max optimizes based on our defined success metrics. Inaccurate or inflated conversions can lead to poor AI decisions. This insight made me double-check everything.
Automated Bidding with a Conversion-Focused Strategy
For broad match to function optimally, a conversion-centered bid strategy is necessary. Options like ‘Maximize Conversion Value’ or ‘Target CPA’ should align with your updated strategy. My experiments indicated more consistent results with target bids than max bids.
Using max bids without watching over budget and collected data might not yield the best results. I’ve learned to keep a careful eye on it.
Conversion Volume
AI Max needs sufficient data to perform well. With over 100 conversions monthly, its reliability has been strong, provided there’s a positive history with broad match. Based on this, I aimed to test in campaigns with at least 30 monthly conversions.
No Impression Share Lost Due to Budget
If budget constraints already hinder impression share, AI Max could exacerbate this issue. Prioritize spending on top keywords and let AI Max utilize remaining funds for experimentation.
Proven Broad Match Success
AI Max treats keywords as broad match and extends beyond them. Without past success, it could be ineffective. Preparing through ad group optimization and new ad testing has been my strategy.
Should You Use URL Expansion?
Enabling URL expansion allows Google to pick any webpage for landing when AI Max triggers an ad. However, indiscriminate use can be detrimental—excluding non-conversion-oriented pages mitigates risks.
Those who created landing pages for specific geographies should carefully manage page exclusions to avoid mismatching.
Should You Try Automatically Created Assets?
I’m hopeful about automatically created assets. They can significantly enhance messaging but require caution to avoid irrelevant sitelinks and incompatible callouts. Establishing clear guidelines ensures alignment with brand objectives.
How to Test AI Max
Because of its performance inconsistencies with brand keywords, I’ve found it best to initially focus on non-brand keywords in AI Max tests. Starting with successful ad groups rich in conversion data offers the best chance to test its potential.
Operating AI Max at the ad group level via the Google Ads Editor proved efficient in my testing experience.
Is Your Account Ready to Test AI Max?
As AI Max continues to evolve, its integration into our existing systems may provide significant advantages. But, readiness involves assessing if our accounts meet all setup criteria before diving in. By following my steps, you’ll recognize its readiness and potential for success.
I’ve noticed the European Union is turning its gaze towards Google once more, scrutinizing how it handles its AI and search data. This could lead to changes that might open up its Android features and search data, ultimately reshaping the competitive landscape.
The European Commission is now formally outlining the ways Google must share specific Android functionalities and its search data with competitors, in line with the Digital Markets Act.
Tuesday marked the start of two official proceedings by the Commission, aimed at establishing a structured approach for Google to meet key obligations under the DMA. It’s fascinating to see these regulatory dialogues become more concrete.
Why I care. This move by the European Commission could alter the dynamics in mobile AI and search. With Google potentially needing to share its search data and Android AI capabilities, it could boost the competition from other search engines and AI services. Such changes might impact where advertisers allocate budgets, alter the availability of advertising inventory, and shift campaign dependencies away from Google’s platforms.
First focus — Android and AI interoperability. The regulators are delving into how Google must enable third-party developers to access Android hardware and software features as freely as Google’s own AI services, like Gemini.
– The objective is to allow rival AI providers the same level of integration with Android devices as Google’s native tools.
Second focus — search data sharing. The Commission aims to define how Google should provide anonymized search data including ranking, queries, clicks, and views to rival search engines under fair, reasonable, and non-discriminatory conditions.
– This includes specifying the types of data to be shared, how it will be anonymized, eligibility for access, and whether AI chatbot providers can use this dataset.
Between the lines. It’s not just about ticking off compliance boxes. The Commission is making it clear that AI services are under the DMA’s watchful eye, especially where data and device control could influence emerging markets.
What’s next: Within three months, the Commission plans to send Google its initial findings and recommended actions. The full proceedings should wrap up within six months, accompanied by non-confidential summaries for public input.
The backdrop. Since March 2024, Google has been required to comply with DMA obligations, having been identified as a gatekeeper in services like Search, Android, and YouTube.
Bottom line. The EU is moving from planning to action with the DMA, testing how strongly it will influence competition by overseeing Google’s AI functions and search data management.
I’ve noticed that OpenAI is introducing premium-priced ads on ChatGPT, but here’s something interesting: the data provided to advertisers is significantly limited compared to what we’re used to.
What’s happening. Reports indicate that OpenAI is offering ChatGPT ads at around $60 per 1,000 impressions. That’s about three times the rate of standard Meta advertisements! Yet, even with this higher cost, advertisers only receive basic metrics like total impressions or clicks, without insight into actions like purchases.
Why we care. ChatGPT is becoming a fresh, highly engaging ad space, but it’s not without its challenges. The hefty CPMs and limited insights mean that early advertising efforts will lean more toward enhancing brand presence and gathering learnings than achieving performance-driven efficiency.
For marketers who are open to trying new avenues, this presents a unique chance to gain insights into how ads function within AI-driven conversations before the format becomes more widespread or measurable.
The tradeoff. OpenAI is contemplating expanding its measurement capabilities in the future, yet it remains committed to user privacy. It has pledged not to sell user data or invade the confidentiality of conversations, which limits traditional targeting and attribution possibilities that platforms like Google and Meta offer.
Who will see ads. Initially, these ads will be available to those using ChatGPT’s free and lower-cost Go tiers, but won’t be shown to users under 18 or in conversations concerning sensitive topics like mental health or politics.
Between the lines. OpenAI is branding ChatGPT ads as a top-tier, trustworthy product, banking on the idea that context, focus, and brand safety can validate the higher pricing, despite the lack of detailed performance data.
Bottom line. Brands eager for prominent visibility in a cutting-edge AI-driven environment may find ChatGPT ads appealing, but those focused on performance metrics might hesitate due to the absence of detailed measurement.
Generative AI is an integral part of my search, content, and analytical workflows these days.
However, with increased usage, I’ve noticed a recurring and expensive issue: confidently incorrect outputs.
Often referred to as “hallucinations,” this problem arises not because the AI is faulty, but due to vague instructions, or more specifically, unclear prompts.
Imagine asking AI for just a “cookie recipe” without any specifics. The result? Christmas cookies in July, or a peanut-filled recipe regardless of allergies!
To mitigate this, I try to expect missteps and set clear guardrails with the help of rubrics.
In this discussion, I’ll explore how rubric-based prompting can enhance factual reliability and how you can implement it to achieve more dependable AI results.
Fluency vs. Restraint: What Matters More?
When I request polished answers from AI without specifying how to handle uncertainties, the system usually opts for fluency over restraint.
This means it prefers to continue smoothly rather than pausing or qualifying a response where information is missing, leading to potentially costly errors.
For instance, Deloitte had to refund substantial costs due to AI errors in a government report, which included fabricated citations, as reported by Associated Press in 2025.
This incident highlights the necessity of keeping AI in the loop but ensuring it’s adequately constrained — defining protocols when uncertainties arise.
Understanding Rubrics: The Guiding Hand AI Needs
Generic safeguards against AI hallucinations exist, but are often ineffective as they describe outcomes instead of a decision-making process.
This is where rubric-based prompting becomes vital, establishing a framework to steer AI behavior.
Just like an academic rubric, AI rubrics define evaluation criteria but apply it to the decision-making process during response creation.
Clear boundaries set by rubrics significantly reduce the likelihood of AI hallucinations.
Writing Better Prompts Isn’t Enough
While refining prompts can improve surface-level results, they don’t address the root cause of hallucinations: insufficient decision-making guidance.
Often, I notice that prompts ask for specific outcomes without providing rules, leaving the AI to fill in substantial gaps autonomously.
This autonomy can lead to generated outputs where fluency trumps accuracy.
Switching from inference to explicit instruction using rubrics helps align AI responses with defined goals and limits.
The Unique Strength of Rubrics
While prompts set tone and format, rubrics tackle uncertainty, defining clear decision paths and reducing ambiguity.
By supplying concrete criteria, rubrics ensure factual accuracy takes precedence over spiraling completeness.
An effective rubric guides the model on how to act if the information is insufficient, significantly improving output reliability.
Anatomy of a Robust AI Rubric
To avoid over-complication, a solid rubric must focus on a concise set of enforceable criteria addressing hallucination risks directly.
Elements such as accuracy requirements, source expectations, and uncertainty handling are essential to include.
By ensuring clarity in these areas, rubrics bolster the AI’s ability to provide truthful and trustworthy responses.
For me, prompting with purpose means shaping AI behavior effectively by foreseeing where assumptions might occur and setting parameters clearly.
With rubrics, I am able to guide AI to halt, pause, or clarify when data is lacking, fostering accurate and dependable outputs.
I’ve watched automation quietly transform PPC management over the years with rules, scripts, and API-driven workflows in Google Ads.
Like many other marketers, I’m already very comfortable with automated bidding, data-driven optimization, and a suite of other AI-powered enhancements. But there’s a new shift on the horizon that’s set to redefine how we manage and optimize PPC campaigns.
This time, I’m talking about AI agents and vibe coding. These innovations are ushering in a more autonomous mode of working where AI takes the lead in execution, allowing marketers like me to focus on strategy and creativity.
This evolution promises unprecedented efficiency and flexibility, redefining effective PPC management.
Agentic AI: Google Ads’ Game-Changing Feature
In November 2025, Google rolled out its Agentic Ads Advisor, powered by advanced Gemini models. This tool helps advertisers like me uncover insights and boost campaign performance effortlessly.
Google positions Ads Advisor as an AI partner that enhances campaign management by understanding business contexts, simplifying tasks, and learning from interactions to deliver better outcomes.
However, the pressing question remains: What functionalities should an agentic AI tool embody?
It should function as an autonomous agent, surfacing information as needed but also operating independently. It should identify opportunities for enhancing campaign setups, assets, ad copy, and more.
An ideal agentic AI wouldn’t just make recommendations but also implement essential changes on its own.
Integrating Agentic AI in PPC Workflows
Agentic AI should ideally make decisions autonomously without needing constant human input, thereby managing, adjusting, and optimizing campaigns as they run.
Beyond just advice or reporting, its real value lies in managing bidding, ad placements, and creative testing in real-time, based on live data, seasonality, and user behavior trends.
With agentic AI handling more operational tasks, I can direct my efforts toward strategic decision-making.
The competitive edge will increasingly rely on strategy rather than tools, focusing on marketing fundamentals like positioning, value propositions, and brand awareness.
Agentic AI appeals to experienced PPC marketers like myself because it scales campaigns without compromising strategic control, proving to be a true game-changer.
With real-time optimization, data-driven creativity, and reduced human error, it redefines my role by allowing more time for strategy rather than execution.
Despite its capabilities, informed oversight is essential to ensure alignment with broader marketing objectives, highlighting the need for ongoing professional engagement.
Agentic AI isn’t replacing PPC professionals. Instead, it extends our capabilities, reduces manual effort, and facilitates better outcomes with minimal friction.
Vibe Coding: Creating Your Marketing Toolbox
In tandem with agentic AI, vibe coding is redefining how I work with AI-powered platforms, allowing me to create personalized, intuitive marketing tools and campaigns.
Tools like Cursor and AI Studio have enabled me to articulate and realize specific needs seamlessly, even without being a developer.
Incorporating vibe coding led me to build an SEO schema markup generator, an SEO audit tool, and a marketing idea generator, proving its practical value in my professional life.
The possibilities expand when combining vibe coding with agentic AI, empowering marketers to engineer their AI agents tailored for PPC work.
With this combination, I integrated these tools effectively within my marketing workflows, enhancing performance and strategy development at scale.
The Future: Navigating PPC with Agentic AI and Vibe Coding
Agentic AI and vibe coding present immense opportunities to streamline PPC operations, enhance performance, and maintain competitiveness in a fast-evolving landscape.
The future is about leveraging these technologies for more autonomous, data-driven, and personalized marketing strategies that benefit both internal teams and customers alike.
As a PPC professional, it is crucial to embrace these advancements, ensuring adaptability and continued relevance in an AI-powered future.
Follow experts like Alfred Simon, Mike Rhodes, and Ales Sturala to see practical applications of these innovative technologies in real-world scenarios.
I’m excited to share that Google is introducing Personal Intelligence to its AI Mode in Google Search! In a recent Labs experiment, AI Pro and Ultra subscribers in the U.S. can now opt-in to this feature over the next few days.
Personal Intelligence was first introduced on the Gemini app last week and is now making its way to Google Search for certain users. According to Robby Stein, VP of Product at Google Search, starting today, subscribers can securely connect their Gmail and Google Photos to AI Mode, enhancing their search experiences.
This feature allows me to receive more personalized responses by connecting across my Google ecosystem, including Gmail, Photos, and YouTube history, right in Google Search. This rollout will be completed in a few days for AI Pro and Ultra subscribers in the U.S.
How to Access Personal Intelligence
This innovative feature is part of a Labs experiment, and opting in is straightforward. It’s available for personal accounts in the U.S., but currently not for Workspace business, enterprise, or education users. Subscribers will automatically gain access as the feature becomes available.
If you’d like to enable it manually, follow these steps:
1) Open Search and tap your profile.
2) Click on Search personalization.
3) Select Connected Content Apps.
4) Connect Workspace and Google Photos.
Inspiring Examples
Imagine asking Google a question like these:
Help me plan a weekend getaway with my family based on things we like to do.
Make a scavenger hunt for [partner’s name] to celebrate our anniversary, including a hint about us for each location.
I’m decorating [child’s name] bedroom; give me theme ideas and decor suggestions.
If I were the hero/heroine from a book, who would I be?
What specific era of fashion suits me best?
Recommend books that fit my current life phase.
If I were a perfume, what would my top notes and base notes be?
To truly see it in action, you can watch this video:
Why This Matters
This feature, currently available as an opt-in, could become a standard part of Google Search in AI Mode. As a result, searches will become increasingly personalized, making it more challenging to track different website citations for individual users.
AI search sentiment seems largely positive, yet there’s a real risk that isn’t in the acronyms – it’s in the volatility of the debate.
The SEO versus GEO debate has been a significant topic in our industry for the past year. New acronyms pop up almost weekly, and the sentiment can flip rapidly, with even the most reliable voices changing their stances from time to time.
This volatility isn’t confined to the periphery. It’s evident among a small group of highly visible SEO influencers who adjust their perspectives on AI-era searches in reaction to news, platform updates, and branding pressures.
My curiosity drove me to delve into how 75 leading SEO influencers discuss AI-driven search on LinkedIn. The objective wasn’t to identify the winning acronym but to gauge consistency, sentiment, and volatility in the discourse surrounding discovery shifts.
Teaming up with Danny Goodwin from Search Engine Land, I reviewed 2,025 LinkedIn posts from these influencers, examining references to various AI-related SEO terms including GEO, AIO, AISEO, AEO, LLMO, SXO, and ASO.
Each post’s sentiment was analyzed using VADER, providing a score between -1 to +1, while volatility was measured by tracking the standard deviation of sentiment over time. The data was anonymized to safeguard individual identities while retaining relational trends.
In 2025, while industry leaders engaged passionately in debates about AI-era search terms in their LinkedIn posts, they were reluctant to integrate these new terms into their personal headlines.
Our analysis reveals that 43% of SEO thought leaders still use “SEO” in their LinkedIn headlines, compared to 21% with “AI” and a mere 3% with “GEO.”
The gap is notable, indicating a hesitation to move away from the proven SEO strategies we’ve relied on for over a decade.
Well-Structured Content Hubs: Essential for Both AI and Traditional SEO
Successful digital strategies focus on creating comprehensive, persona- and buyer-journey-led content hubs that address genuine FAQs and buying intentions. By nurturing content depth throughout all stages – from awareness to decision-making, brands can provide compounded value to users and reinforce AI search algorithms.
Generate Authority with Off-site Brand Trust Signals
Publishing original research and expert insights helps earn recognition from authoritative sources, which in turn boosts your brand’s trust and recognition.
Mainstream news outlets.
Niche-relevant publishers.
Leading podcasters.
Engaged Reddit communities.
Expanding these digital footprints strengthens entity recognition and reinforces brand trustworthiness.
Leveraging audience intelligence tools like SparkToro identifies which platforms, communities, and topics should be prioritized in your digital PR strategy.
New AI Terms Gain Momentum: See the Enthusiasm Rise
Though few are updating their LinkedIn headlines just yet, industry leaders’ posts reveal growing interest in three specific terms.
63% of leaders mention AIO, with 77% positivity.
59% mention GEO, with 82% positivity.
With over 70% of posts expressing positivity, sentiment often indicates adoption likelihood. When positivity wanes, so does usage. Yet, that’s not what’s happening here.
While AEO, LLMO, and AIO attract broader audiences, GEO stands out for consistent positivity, especially among SEO influencers and LinkedIn users alike.
SEO continues as the industry’s backbone, but it’s clear: we’re witnessing the alignment phase of an emerging platform.
The focus isn’t on acronyms; it’s about accurately describing brand visibility in AI-era searches.
The Real Strategy: Timely, Value-Driven Content
Brands should refrain from over-optimizing towards any singular term, strategy, or platform. Instead, develop value-focused content, repurpose it, and engage with audiences across their existing platforms.
This adaptability ensures brands endure platform shifts, avoiding pitfalls like those seen in once-dominant platforms such as Vine and Clubhouse.
Nomenclature Volatility: A Subtle Yet Critical Indicator
Our research highlights this critical insight: less than a third of thought leaders consistently use AI-related SEO terminology with stable sentiment over the past year.
35% express positive sentiment toward these terms but lack consistency.
Just over a third are consistently positive and stable.
The discourse isn’t about being right or wrong. It’s about reframing discussions as the landscape evolves, with volatility often mirroring visibility.
By evaluating sentiment against volatility, we revealed scattered positions rather than a distinct divide.
The uncomfortable truth is that the most vocal aren’t always the most dependable. The impact of their shifting narratives is vital, as their guidance influences budgets, plans, and careers.
Leaders who maintain a balanced outlook – driven by data and tempered by experience – offer a different perspective compared to those swayed by every update.
The Key Lesson: It’s Not a Strategy Reset; It’s an Emerging Platform
Effective content marketing, digital PR, and technical SEO are the foundation for building brand visibility. AI is simply the next platform evolution, much like social media, enhancing but not replacing existing strategies.
Our analysis indicates the industry isn’t unsure about what to do. It is negotiating how to convey this rapidly evolving discovery system. This discussion is typical at this stage, but volatile shifts harm trust.
Terms like AEO, LLMO, and AIO may gain some traction, but GEO remains consistent among both practitioners and broader audiences, suggesting its potential as a stable narrative bridge as execution evolves.
Crafting a Resilient Digital Footprint: Navigating the AI Era
Market strategies shouldn’t revolve around what’s trending quarterly. Instead, focus on timeless marketing principles:
Create content that delivers real value to your market.
Repurpose and circulate it on platforms where your audience is active.
Generate citations, engagement, and trust that impact search, social, and AI systems.
In today’s era, where answers are synthesized rather than ranked, the voices that resonate won’t be the ones coining the next big label, but those that remain consistent, building trust and visibility over time.
The analysis focused on the top 75 SEO thought leaders, including agency owners, directors, industry speakers, and consultants.
Recently, I’ve been delving into the nuances of Google Search Console and its impression counts.
I learned from John Mueller of Google that when a URL shows up in both an AI Overview and the traditional blue links on SERPs, it is counted as just one impression, not two.
This clarification came to light through John Mueller, after a lively discussion among SEO experts, sparked by Jamie Indigo and publicly shared by Mark Williams-Cook from Candour on LinkedIn.
The background. Initially, Mark Williams-Cook had assumed that because of historical practices with SERP features like tweet boxes, the URL might be counted twice.
Testing this theory was challenging, but ultimately, Mueller confirmed that the Search Console treats these appearances as a single impression.
What’s happening. Google’s policy treats an AI Overview as a singular position in search results. Each link within the Overview shares that position, governed by standard impression rules.
So, when a URL appears more than once in the same search experience, the Search Console doesn’t double count these for the same query.
Why this happens. Google defines an impression based on a user’s visibility of a link within the current set of results. Multiple instances of the same URL on one results page are aggregated, not counted separately.
This approach aligns with other SERP features like knowledge panels, where scrolling past and returning, or seeing the URL in different elements, won’t create additional impressions.
Why we care. In this AI-centric era, interpreting performance metrics can be a challenge. Knowing that both AI Overviews and blue links count as a single impression clarifies how these listings influence visibility. Although the impression count doesn’t rise, appearing in both strengthens brand visibility and boosts credibility among Google users.
Recently, I was fascinated to learn that Google is taking a firm stance by keeping ads out of Gemini, its conversational AI, for the time being. As the CEO of Google DeepMind, Demis Hassabis, stated, they are focusing on ensuring trust and high-quality assistance rather than pursuing monetization right now.
What’s New. At the World Economic Forum in Davos, Hassabis confirmed that Google has “no plans” to introduce ads into Gemini just yet. He stressed the significance of improving the AI assistant’s capability and usability across various platforms before thinking about monetizing it.
The Contrast. This announcement stands in stark contrast to OpenAI’s recent decision to start testing ads in the free and budget-friendly tiers of ChatGPT. Hassabis found this move “interesting” and hinted it might be more about immediate revenue needs than a thoughtful product strategy.
Why We Care. For me, Google’s consistent decision to exclude ads from Gemini clearly suggests that monetizing AI won’t mimic the strategies we’ve seen in search or social media anytime soon. This cautious approach could initially limit ad opportunities in conversational AI. With competitors like OpenAI exploring ads, advertisers may need to experiment with these formats outside of Google’s ecosystem first.
Looking ahead, I believe Google’s approach hints at any potential future ad integration in Gemini being more restrained, prioritizing trust and taking longer to scale. This will influence how brands plan their AI-driven media strategies.
Not the First Denial. Interestingly, this is not the first time Google’s leadership has publicly opposed the idea of ads in Gemini. In December, Google Ads President Dan Taylor clarified that ads wouldn’t be a part of Gemini in 2026, indicating a unified stance on keeping it ad-free, at least for now.
Trust at Stake. Hassabis also expressed concerns about integrating advertising into such a personal AI assistant. He emphasized that maintaining unbiased and genuinely helpful recommendations is crucial to avoid eroding user trust.
Bottom Line. It’s fascinating to see Google, a company whose core business revolves around advertising, showing this level of restraint. By keeping AI assistants like Gemini free from ads, at least for now, Google aims to avoid blurring the line between help and influence as it enhances their capabilities.
