Google is giving advertisers new visibility into whether its automated recommendations actually drive performance — a long-standing blind spot in the platform.

What’s happening. A new “Results” tab within Recommendations shows the incremental impact of bidding and budget changes after they’ve been applied, allowing marketers to evaluate outcomes instead of relying on assumptions.

How it works. The feature attributes performance changes to specific recommendations, helping advertisers understand what effect adjustments like budget increases or bid strategy shifts had on results.

Why we care. Marketers can now validate whether recommendations improved performance, making it easier to decide which automated suggestions are worth adopting in the future.

Between the lines. Google has a vested interest in encouraging adoption of its recommendations, so providing performance data could build trust — but it also raises questions about how that impact is measured.

The catch. Advertisers may question whether the reported results are fully objective or skewed toward showing positive outcomes, given Google’s incentives.

What to watch. How detailed and transparent the reporting becomes — and whether advertisers see mixed or negative results alongside wins.

Bottom line. Google is moving from “trust us” to “here’s the proof,” but advertisers will be watching closely to see how impartial that proof really is.

First seen. This update was first spotted by Arpan Banerjee who shared seeing the new tab on LinkedIn.

Google is giving advertisers more control over how AI generates ad copy, making it easier to scale campaigns without losing brand consistency.

What’s happening. Google Ads is rolling out a beta feature that allows marketers to copy text guidelines from existing campaigns and apply them to new ones, eliminating the need to rewrite brand rules from scratch.

How it works. Advertisers can replicate approved tone, style and messaging rules across campaigns in one click, ensuring AI-generated ads stay aligned with brand standards while reducing setup time.

Why we care. The feature helps teams launch campaigns faster by reusing what already works, while maintaining consistency across large accounts where multiple campaigns run simultaneously.

Between the lines. This shift reflects a growing demand from marketers to “train” AI systems rather than rely on them blindly, effectively turning brand guidelines into reusable inputs for automation.

Bottom line. AI is speeding up ad creation, but control is becoming the real differentiator — and Google is starting to hand more of it back to advertisers.

First spotted. This update was spotted by Paid Media expert Arpan Banerjee when he shared spotting the alert on LinkedIn.

Was it TikTok, WhatsApp, Facebook, or WeChat?

ZeroTwo lets you access the combined capabilities of Claude, Perplexity, ChatGPT, Manus, and Higgsfield. These top AI platforms each have unique features that give them special abilities beyond their models. Now you can use all of them without paying for several subscriptions. Perplexity's agentic search, Claude's agentic connector, ChatGPT's apps, and Higgsfield's AI tools for creatives are all available on one platform.

The platform also offers deep research, canvas mode, and shared access to threads and projects. Plans include unlimited messages, expanded memory, priority performance, and team features for businesses.

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OrbitMeet is a browser-based AI meeting co-pilot that listens to your meetings in real time, surfaces questions you might miss every 75 seconds, and builds your summary as you talk with no plugins or installation.

It detects action items by speaker name, generates follow-up documents such as emails, memos, and action trackers in seconds, and works across Zoom, Teams, Google Meet, or in-person meetings. It's designed for consultants, founders, and distributed teams working in multiple languages. A free plan is available, with Pro at $20.5 CAD/month.

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Google says its AI-powered advertising tools are starting to deliver meaningful results, including major revenue gains for some retailers, as it experiments with how ads work in AI-driven search.

The big picture. Fears that AI chatbots like ChatGPT would disrupt Google’s core search business haven’t materialized, and instead the company’s ads business continues to grow, suggesting AI may be expanding how people search rather than replacing it.

By the numbers:

• Alphabet Inc. surpassed $400 billion in revenue in 2025.
• Q4 ad revenue: $82.28 billion (+13.5% YoY).
• YouTube ads: $11.38 billion (+~9% YoY).

What’s happened. Google is embedding ads into its AI-powered search experiences, including AI Mode powered by Gemini, while introducing new ad formats designed for conversational queries and tools that allow brands to shape how they appear in AI-generated answers, with a new “business agent” feature enabling companies like Poshmark and Reebok to control how their products are represented.

Driving the results. AI-driven campaigns like Performance Max and AI Max match ads to more detailed and conversational search intent, and Google says queries in AI Mode are often two to three times longer than traditional searches, giving the system more context to connect users with relevant products, as seen with Aritzia, which reported an 80% increase in revenue after adopting AI Max.

How it works. The system scans a retailer’s website and creative assets, interprets user intent from conversational queries, and dynamically matches products and messaging in real time. This is increasingly important given that 15% of daily searches are entirely new (according to Google) and cannot be predicted through traditional keyword targeting.

Why we care. Google is shifting from keyword-based ads to intent-driven, AI-matched advertising, meaning campaigns can reach consumers with far more precision at the moment they’re ready to buy. As search becomes more conversational and unpredictable, advertisers who rely on traditional targeting risk falling behind those using AI-driven formats that automatically adapt to new user behavior.

Zoom in. Google is testing new formats such as “direct offers,” which deliver personalized promotions when users show purchase intent, using Gemini to analyze conversational context and behavior, with brands like E.l.f. Beauty, Chewy and L’Oréal participating in early trials.

Commerce push. Google is also advancing its commerce strategy through a Universal Commerce Protocol developed with Shopify, which allows purchases to happen directly within AI conversations.

Yes, but. Google is not alone in experimenting with ads in AI search, and early results across the industry have been mixed, as Amazon has reportedly seen limited traction from ads in its AI shopping assistant, OpenAI continues to explore monetization models, and Perplexity AI has begun phasing out ads after underwhelming performance.

What they’re saying, Google positions itself as a “matchmaker” rather than a retailer, emphasizing that AI helps deliver more relevant and personalized ads while allowing brands to maintain control over their messaging and build user trust by showing the right product at the right moment.

What’s next. Gooogle says it has no current plans to introduce ads directly into Gemini but will continue testing and expanding advertising within AI Mode, including more personalized offers and AI-driven shopping experiences.

Bottom line. AI is not replacing search but reshaping it, and for Google that shift is making advertising more conversational, more targeted and, in some cases, significantly more profitable.

Dig deeper. Google says its AI-powered ads help some brands lift online sales by 80%.

Google Search is evolving beyond links and answers into a system that completes tasks, potentially fundamentally changing how users interact with the web. That’s according to Alphabet CEO Sundar Pichai, speaking on the Cheeky Pint podcast.

Why we care. Google is signaling a move from information retrieval to task execution.

Search becoming agentic. Traditional search behavior is already changing and will continue to, Pichai said.

• “If I fast-forward, a lot of what are just information-seeking queries will be agentic in Search. You’ll be completing tasks. You’ll have many threads running.”

Pichai also described a future where Google Search acts less like a list of results and more like a system that coordinates actions:

• “Search would be an agent manager in which you’re doing a lot of things. I think in some ways, I use Antigravity today, and you have a bunch of agents doing stuff. I can see search doing versions of those things, and you’re getting a bunch of stuff done.”

AI Mode is already changing queries. Users are already adapting their behavior in Google’s AI-powered search experiences, Pichai said:

• “But today in AI Mode in Search, people do deep research queries. That doesn’t quite fit the definition of what you’re saying. But people adapted to that. I think people will do long-running tasks.”

Search vs. Gemini overlap. Despite the rise of Gemini, Pichai said Google isn’t replacing Search with a chatbot. Instead, the two will coexist — and diverge (echoing what Liz Reid said last month):

• “We are doing both Search and Gemini. They will overlap in certain ways. They will profoundly diverge in certain ways. I think it’s good to have both and embrace it.”

The interview. The history and future of AI at Google, with Sundar Pichai

Google’s AI Overviews answered a standard factual benchmark correctly 91% of the time in February, up from 85% in October, according to a New York Times analysis with AI startup Oumi.

However, Google handles more than 5 trillion searches per year, so that means tens of millions of answers every hour may be wrong.

Why we care. We’ve watched Google shift from linking to sources to summarizing them for more than two years. This report suggests AI Overviews are improving, but still mix correct answers, weak sourcing, and clear errors in ways that can mislead searchers and reshape which publishers get visibility and clicks.

The details. Oumi tested 4,326 Google searches using SimpleQA, a widely used benchmark for measuring factual accuracy in AI systems, the Times reported. It found AI Overviews were accurate 85% of the time with Gemini 2 and 91% after an upgrade to Gemini 3.

• The bigger problem may be sourcing. Oumi found that more than half of the correct February responses were “ungrounded,” meaning the linked sources didn’t fully support the answer.
• That makes verification harder. The answer may be right, but the cited pages may not clearly show why.

What changed. Accuracy improved between October and February, but grounding worsened. In October, 37% of correct answers were ungrounded; in February, that rose to 56%.

Examples. The Times highlighted several misses:

• For a query about when Bob Marley’s home became a museum, Google answered 1987; the correct year was 1986, according to the Times, and the cited sources didn’t support the claim or conflicted.
• For a query about Yo-Yo Ma and the Classical Music Hall of Fame, Google linked to the organization’s site but still said there was no record of his induction.
• In another case, Google gave the correct age at Dick Drago’s death but misstated his date of death.

Google’s response: Google disputed the Times analysis, saying the study used a flawed benchmark and didn’t reflect what people actually search. Google spokesperson Ned Adriance told the Times the study had “serious holes.”

• Google also said AI Overviews use search ranking and safety systems to reduce spam and has long warned that AI responses can contain mistakes.

The report. How Accurate Are Google’s A.I. Overviews? (subscription required)

PeaZip 11.0 refines one of the most capable free archivers with faster browsing, smoother drag-and-drop across tabs, and a cleaner, more responsive UI. The update also improves scaling, adds flexible icon rendering, and introduces batch archive testing, alongside the usual fixes and cleanup.

Shadow OS is the first decision-making app built on 64 hexagrams, the same system Carl Jung studied for over two decades and called his most significant method for surfacing what the unconscious already knows. Other decision apps use random spinner wheels, AI chatbots validate whatever you say, and astrology apps offer forecasts open to interpretation. Shadow OS gives you one committed answer: move forward, hold, or pull back.

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BeMusic AI is a free AI music generator that turns text prompts into fully produced, royalty-free songs in under 30 seconds. Choose from 50+ genres, adjust mood, tempo, and energy, and download high-quality WAV or MP3 for videos, games, podcasts, and ads. It also offers tools to write lyrics, create instrumentals, convert audio to MIDI, edit MIDI, make AI covers, remove vocals, extend tracks, and analyze songs. Use it to avoid copyright issues and keep full ownership of every track.

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Google has begun placing sponsored ad units directly inside the Images tab of mobile search results — a new placement that eligible campaigns can access without any changes to existing keyword targeting.

What’s happening. When a user navigates to the Images tab within Google Search on mobile, they may now see sponsored units appearing within the image grid. Each unit shows a full image creative as the primary visual alongside text, and is clearly labelled “Sponsored” — consistent with how Google labels ads elsewhere in search results.

How it works. Eligible campaigns can serve into the Images tab without any changes to keyword targeting or campaign structure. The placement draws from existing image assets, meaning advertisers running Search or Performance Max campaigns with strong visual creative are best positioned to benefit. No separate image-only campaign setup is required.

Why we care. This is a meaningful expansion of Google’s paid search real estate. For product-led and catalog-heavy advertisers, the Images tab is where purchase-intent discovery often starts — and now ads can appear right in that moment. If your campaigns already use strong image assets, you may be picking up incremental impressions without lifting a finger.

The big picture. Early indications suggest this placement behaves more like a visual discovery surface than classic paid search. Expect high impression volume but lower click-through rates — more in line with display or Shopping than traditional text ads. That said, the assist value in multi-touch conversion paths could be significant, particularly for retail and direct-to-consumer brands. Treat it as upper-funnel reach, not a last-click channel.

What to watch. Google has not made a formal announcement, and there is no dedicated reporting breakdown for Images tab placements yet. Monitor your impression share and segment data closely to understand whether this placement is contributing — and whether it’s eating into organic image visibility for competitors.

First seen. The placement was spotted by Google Ads Expert – Matteo Braghetta, who shared seeing this update on LinkedIn. No official documentation has been published by Google at the time of writing.

Over 30% of outbound clicks go to just 10 domains, with Google alone taking more than 20%, according to a new Semrush study published today.

ChatGPT also relies less on the live web, triggering search on 34.5% of queries, down from 46% in late 2024.

The big picture. ChatGPT’s growth has plateaued, and its role in how users navigate the web is evolving unevenly.

• Referral traffic from ChatGPT grew 206%, comparing January 2025 to January 2026.

The details. Most ChatGPT referral traffic still goes to a small set of sites, even as more sites receive some traffic.

• Google accounts for 21.6% of all ChatGPT referral traffic.
• The next nine domains bring the top 10 to just over 30% of referrals.
• Most other sites get a long tail of minimal traffic.
• The number of domains receiving referrals expanded, peaking at around 260,000 in 2025 before settling near 170,000.

Why we care. Visibility in ChatGPT doesn’t translate evenly into traffic, and you’ll likely see marginal referral impact. The decline in search-triggered queries also limits your chances to earn citations and traffic.

When ChatGPT searches. It defaults to pre-trained knowledge and uses web search in specific cases, including:

• User requests for sources.
• Questions about recent events.
• Situations where the model lacks confidence.

Behavior shift. Most ChatGPT prompts still don’t resemble traditional search queries.

• Between 65% and 85% of prompts don’t match standard keywords, reflecting more complex, conversational inputs.
• Meanwhile, engagement is deepening. Queries per session jumped 50% in late 2025.

About the data. Semrush analyzed more than 1 billion lines of U.S. clickstream data from October 2024 to February 2026 across a 200 million-user panel, tracking prompts, referral destinations, and search usage.

The study. ChatGPT traffic analysis: Insights from 17 months of clickstream data

Google is rolling out new Google Maps features that make it easier to contribute photos, reviews, and local insights, while adding Gemini-powered caption suggestions.

Local Guides redesign. Contributor profiles are getting more visibility. Total points now appear more prominently, Local Guide levels are easier to spot, and badge designs have been refreshed.

• Top contributors will also stand out more in reviews with new gold profile indicators.

AI caption drafts. Google is also introducing AI-generated caption drafts. Gemini analyzes selected images and suggests text you can edit or discard.

• Caption suggestions are available in English on iOS in the U.S., with Android and broader global expansion planned.

Media sharing. Google Maps now shows recent photos and videos directly in the Contribute tab, making uploads faster.

• If you enable media access, Google Maps will suggest images from your camera roll that are ready to post with a tap.
• This feature is now live globally on iOS and Android.

Why we care. Google is making it easier to create and scale fresh local content, which can directly affect rankings and visibility. At the same time, stronger contributor signals may influence which reviews users trust and which businesses win clicks.

Google once attributed two of Barry Schwartz’s Search Engine Land articles to me — a misclassification at the annotation layer that briefly rewrote authorship in Google’s systems.

For a few days, when you searched for certain Search Engine Land articles Schwartz had written, Google listed me as the author. The articles appeared in my entity’s publication list and were connected to my Knowledge Panel.

What happened illustrates something the SEO industry has almost entirely overlooked: that annotation — not the content itself — is the key to what users see and thus your success.

How Google annotated the page and got the author wrong

Googlebot crawled those pages, found my name prominently displayed below the article (my author bio appeared as the first recognized entity name beneath the content), and the algorithm at the annotation gate added the “Post-It” that classified me as the author with high confidence.

This is the most important point to bear in mind: the bot can misclassify and annotate, and that defines everything the algorithms do downstream (in recruitment, grounding, display, and won). In this case, the issue was authorship, which isn’t going to kill my business or Schwartz’s.

But if that were a product, a price, an attribute, or anything else that matters to the intent of a user search query where your brand should be one of the obvious candidates, when any aspect of content is inaccurately annotated, you’ve lost the “ranking game” before you even started competing.

Annotation is the single most important gate in taking your brand from discover to won, whatever query, intent, or engine you’re optimizing for.

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What annotation is and why it isn’t indexing

Indexing (Gate 4) breaks your content into semantic chunks, converts it, and stores it in a proprietary format. Annotation (Gate 5) then labels those chunks with a confidence-driven “Post-It” classification system.

It’s a pragmatic labeler and attaches classifications to each chunk, describing:

• What that chunk contains factually.
• In what circumstances it might be useful.
• The trustworthiness of the information.

Importantly, it’s mostly unopinionated when labeling facts, context, and trustworthiness. Microsoft’s Fabrice Canel confirmed the principle that the bot tags without judging, and that filtering happens at query time.

What does that mean? The bot annotates neutrally at crawl time, classifying your content without knowing what query will eventually trigger retrieval. 

Annotation carries no intent at all. It’s the insight that has completely changed my approach to “crawl and index.”

That clearly shows you that indexing isn’t the ultimate goal. Getting your page indexed is table stakes. Full, correct, and confident annotation is where the action happens: an indexed page that is poorly annotated is invisible to each of the algorithmic trinity.

The annotation system analyzes each chunk using one or more language models, cross-referenced against the web index, the knowledge graph, and the models’ own parametric knowledge. But it analyzes each chunk in the context of the page wrapper. 

The page-level topic, entity associations, and intent provide the frame for classifying each chunk. If the page-level understanding is confused (unclear topic, ambiguous entity, mixed intent), every chunk annotation inherits that confusion. Even more importantly, it assigns confidence to every piece of information it adds to the “Post-Its.”

The choices happen downstream: each of the algorithmic trinity (LLMs, search engines, and knowledge graphs) uses the annotation to decide whether to absorb your content at recruitment (Gate 6). Each has different criteria, so you need to assess your own content for its “annotatability” in the context of all three.

And a small but telling detail: Back in 2020, Martin Splitt suggested that Google compares your meta description to its own LLM-generated summary of the page. When they match, the system’s confidence in its page-level understanding increases, and that confidence cascades into better annotation scores for every chunk — one of thousands of tiny signals that accumulate.

Annotation is the key midpoint of the 10-gate pipeline, where the scoreboard turns on. Everything before it is infrastructure: “Can the system access and store your content?” Everything after it is competition:

When you consider what happens at the annotation gate and its depth, links and keywords become the wrong lens entirely. They describe how you tried to influence a ranking system, whereas annotation is the mechanism behind how the algorithmic trinity chooses the content that builds its understanding of what you are.

The frame has to shift. You’re educating algorithms. They behave like children, learning from what you consistently, clearly, and coherently put in front of them. With consistent, corroborated information, they build an accurate understanding.

Given inconsistent or ambiguous signals, they learn incorrectly and then confidently repeat those errors over time. Building confidence in the machine’s understanding of you is the most important variable in this work, whether you call it SEO or AAO.

In 2026, every AI assistive engine and agent is that same child, operating at a greater scale and with higher stakes than Google ever had. Educating the algorithms isn’t a metaphor. It’s the operational model for everything that follows.

For a more academic perspective, see: “Annotation Cascading: Hierarchical Model Routing, Topical Authority, and Inter-Page Context Propagation in Large-Scale Web Content Classification.”

5 levels of annotation: 24+ dimensions classifying your content at Gate 5

When mapping the annotation dimensions, I identified 24, organized across five functional categories. After presenting this to Canel, his response was: “Oh, there is definitely more.”

Of course there are. This taxonomy is built through observation first, then naming what consistently appears. The [know/guess] distinctions follow the same logic: test hypotheses, eliminate what doesn’t hold up, and keep what remains.

The five functional categories form the foundation of the model. They are simple by design — once you understand the categories, the dimensions follow naturally. There are likely additional dimensions beyond those mapped here.

What follows is the taxonomy: the categories are directionally sound (as confirmed by Canel), while the specific dimension assignments reflect observed behavior and remain incomplete.

Level 1: Gatekeepers (eliminate)

• Temporal scope, geographic scope, language, and entity resolution. Binary: pass or fail. 
• If your content fails a gatekeeper (wrong language, wrong geography, or ambiguous entity), it is eliminated from that query’s candidate pool instantly. The other dimensions don’t come into play.

Level 2: Core identity (define)

• Entities, attributes, relationships, sentiment. 
• This is where the system decides what your content means:
  • Who is being discussed.
  • What facts are stated.
  • How entities relate.
  • What the tone is. 
• Without clear core identity annotations, a chunk carries no semantic weight in any downstream gate.

Level 3: Selection filters (route) 

• Intent category, expertise level, claim structure, and actionability. 
• These determine which competition pool your content enters.
  • Is this informational or transactional? 
  • Beginner or expert? 
• Wrong pool placement means competing against content that is a better match for the query, and you’ve lost before recruitment or ranking begins.

Level 4: Confidence multipliers (rank)

• Verifiability, provenance, corroboration count, specificity, evidence type, controversy level, and consensus alignment. These scale your ranking within the pool. 
• This is where validated, corroborated, and specific content outranks accurate but unvalidated content. 
• The multipliers explain why a well-sourced third-party article about you often outperforms your own claims: provenance and corroboration scores are higher.
• Confidence has a multiplier effect on everything else and is the most powerful of all signals. Full stop.

Level 5: Extraction quality (deploy)

• Sufficiency, dependency, standalone score, entity salience, and entity role. These determine how your content appears in the final output. 
• Is this chunk a complete answer, or does it need context? Is your entity the subject, the authority cited, or a passing mention? 
• Extraction quality determines whether AI quotes you, summarizes you, or ignores you.

Across all five levels, a confidence score is attached to every individual annotation. Not just what the system thinks your content means, but how certain it is.

Clarity drives confidence. Ambiguity kills it.

Canel also confirmed additional dimensions I had not initially mapped: audience suitability, ingestion fidelity, and freshness delta. These sit across the existing categories rather than forming a sixth level.

In 2022, Splitt named three annotation behaviors in a Duda webinar that map directly onto the five-level model. The centerpiece annotation is Level 2 in direct operation: 

• “We have a thing called the centerpiece annotation,” Splitt confirmed, a classification that identifies which content on the page is the primary subject and routes everything else — supplementary, peripheral, and boilerplate — relative to it. 
• “There’s a few other annotations” of this type, he noted. 

Annotation runs before recruitment, which means a chunk classified as non-centerpiece carries that verdict into every gate that follows. Boilerplate detection is Level 3: content that appears consistently across pages — headers, footers, navigation, and repeated blocks — enters a different competition pool based on its structural role alone. 

• “We figure out what looks like boilerplate and then that gets weighted differently,” Splitt said 

Off-topic routing closes the picture. A page classified around a primary topic annotates every chunk relative to that centerpiece, and content peripheral to the primary topic starts its own competition pool at a disadvantage before Recruitment begins. 

Splitt’s example: a page with 10,000 words on dog food and a thousand on bikes is “probably not good content for bikes.” The system isn’t ignoring the bike content. It’s annotating it as peripheral, and that annotation is the routing decision.

The multiplicative destruction effect: When one near-zero kills everything

In Sydney in 2019, I was at a conference with Gary Illyes and Brent Payne. Illyes explained that Google’s quality assessment across annotation dimensions was multiplicative, not additive. 

Illyes asked us not to film, so I grabbed a beer mat and noted a simple calculation: if you score 0.9 across each of 10 dimensions, 0.9 to the power of 10 is 0.35. You survive at 35% of your original signal. If you score 0.8 across 10 dimensions, you survive at 11%. If one dimension scores close to zero, the multiplication produces a result close to zero, regardless of how well you score on every other dimension.

Payne’s phrasing of the practical implication was better than mine: “Better to be a straight C student than three As and an F.”

The beer mat went into my bag. The principle became central to everything I’ve built since.

The multiplicative destruction effect has a direct consequence for annotation strategy: the C-student principle is your guide. 

• A brand with consistently adequate signals across all 24+ dimensions outperforms a brand with brilliant signals on most dimensions and a near-zero on one. The near-zero cascades. 
• A gatekeeper failure (Level 1) eliminates the content entirely. 
• A core identity failure (Level 2) misclassifies it so badly that high confidence multipliers at Level 4 are applied to the wrong entity. 
• An extraction quality failure (Level 5) produces a chunk that the system can retrieve but can’t deploy usefully. The failure doesn’t have to be dramatic to be fatal.

At the annotation stage, misclassification, low confidence, or near-zero on one dimension will kill your content and take it out of the race.

Nathan Chalmers, who works at Bing on quality, told me something that puts this in a different light entirely. Bing’s internal quality algorithm, the one making these multiplicative assessments across annotation dimensions, is literally called Darwin. 

Natural selection is the explicit model: content with near-zero on any fitness dimension is selected against. The annotations are the fitness test. The multiplicative destruction effect is the selection mechanism.

How annotation routes content to specialist language models

The system doesn’t use one giant language model to classify all content. It routes content to specialized small language models (SLMs): domain-specific models that are cheaper, faster, and paradoxically more accurate than general LLMs for niche content. 

A medical SLM classifies medical content better than GPT-4 would, because it has been trained specifically on medical literature and knows the entities, the relationships, the standard claims, and the red flags in that domain.

What follows is my model of how the routing works, reconstructed from observable behavior and confirmed principles. The existence of specialist models is confirmed. The specific cascade mechanism is my reconstruction.

The routing follows what I call the annotation cascade. The choice of SLM cascades like this:

• Site level (What kind of site is this?)
• Refined by category level (What section?)
• Refined by page level (what specific topic?)
• Applied at chunk level (What does this paragraph claim?)

Each level narrows the SLM selection, and each level either confirms or overrides the routing from above. This maps directly to the wrapper hierarchy from the fourth piece: the site wrapper, category wrapper, and page wrapper each provide context that influences which specialist model the system selects.

The system deploys three types of SLM simultaneously for each topic. This is my model, derived from the behavior I have observed: annotation errors cluster into patterns that suggest three distinct classification axes. 

• The subject SLM classifies by subject matter — what is this about? — routing content into the right topical domain. 
• The entity SLM resolves entities and assesses centrality and authority: who are the key players, is this entity the subject, an authority cited, or a passing mention? 
• The concept SLM maps claims to established concepts and evaluates novelty, checking whether what the content asserts aligns with consensus or contradicts it.

When all three return high confidence on the same entity for the same content, annotation cost is minimal, and the confidence score is very high. When they disagree (i.e., the subject SLM says “marketing,” but the entity SLM can’t resolve the entity, and the concept SLM flags the claims as novel), confidence drops, and the system falls back to a more general, less accurate model.

The key insight? LLM annotation is the failure mode. The system wants to use a specialist. It defaults to a generalist only when it can’t route to a specialist. Generalist annotation produces lower confidence across all dimensions. 

The practical implication 

Content that’s category-clear within its first 100 words, uses standard industry terminology, follows structural conventions for its content type, and references well-known entities in its domain triggers SLM routing. 

Content that’s topically ambiguous or terminologically creative gets the generalist. Lower confidence propagates through every downstream gate.

Now, this may not be the exact way the SLMs are applied as a triad (and it might not even be a trio). However, two things strike me:

• Observed outputs act that way.
• If it doesn’t function this way, it would be.

First-impression persistence: Why the initial annotation is the hardest to correct

Here is something I’ve observed over years of tracking annotation behavior. It aligns with a principle Canel confirmed explicitly for URL status changes (404s and 301 redirects): the system’s initial classification tends to stick.

When the bot first crawls a page, it selects an SLM, runs the annotation, assigns confidence scores, and saves the classification. The next time it crawls the same page, it logically starts with the previously assigned model and annotations. I call this first-impression persistence. 

The initial annotation is the baseline against which all subsequent signals are measured. The system doesn’t re-evaluate from scratch. It checks whether the new crawl is consistent with the existing classification, and if it is, the classification is reinforced.

Canel confirmed a related mechanism: when a URL returns a 404 or is redirected with a 301, the system allows a grace period (very roughly a week for a page, and between one and three months for content, in my observation) during which it assumes the change might revert. After the grace period, the new state becomes persistent. I believe the same principle applies to content classification: a window of fluidity after first publication, then crystallization.

I have direct evidence for the correction side from the evolution of my own terminologies. When I first described the algorithmic trinity, I used the phrase “knowledge graphs, large language models, and web index.” Google, ChatGPT, and Perplexity all picked up on the new term and defined it correctly.

A month later, I changed the last one to “search engine” because it occurred to me that the web index is what all three systems feed off, not just the search system itself. At the point of correction, I had published roughly 10 articles using the original terminology. 

I went back and invested the time to change every single one, updating every reference, leaving zero traces. A month later, AI assistive engines were consistently using “search engine” in place of “web index.”

The lesson is that change is possible, but you need to be thorough: any residual contradictory signal (one old article, one unchanged social post, and one cached version) maintains inertia proportionally. Thoroughness is the unlock, rather than time.

A rebrand, career pivot, or repositioning is the practical example. You can change the AI model’s understanding and representation of your corporate or personal brand, but it requires thoroughly and consistently pivoting your digital footprint to the new reality.

In my experience, “on a sixpence” within a week. I’ve done this with my podcast several times. Facebook achieved the ultimate rebrand from an algorithmic perspective when it changed its name to Meta.

The practical implication

Get your annotation right before you publish. The first crawl sets the baseline. A page published prematurely (with an unclear topic or ambiguous entity signals) crystallizes into a low-confidence annotation, and changing it later requires significantly more effort than getting it right the first time.

Annotation-time grounding: The bot cross-references three sources while classifying your content

The system doesn’t annotate in a vacuum. When the bot classifies your content at Gate 5, it cross-references against at least three sources simultaneously. This is my model of the mechanism. The observable effect — that annotation confidence correlates with entity presence across multiple systems — is confirmed from our tracking data.

The bot carries prioritized access to the web index during crawling, checking your content against what it already knows: 

• Who links to you.
• What context those links provide.
• How your claims relate to claims on other pages. 

Against the knowledge graph, it checks annotated entities during classification — an entity already in the graph with high confidence means annotation inherits that confidence, while absence starts from a much lower baseline. 

The SLM’s own parametric knowledge provides the third cross-reference: each SLM compares encountered claims against its training data, granting higher confidence to claims that align, flagging contradictions, and giving lower confidence to novel claims until corroboration accumulates.

This means annotation quality isn’t just about how well your content is written. It’s about how well your entity is already represented across all three of the algorithmic trinity. An entity with strong knowledge graph presence, authoritative web index links, and consistent SLM-domain representation gets higher annotation confidence on new content automatically. 

The flywheel: better presence leads to better annotation, which leads to better recruitment, which strengthens presence, and which improves future annotation.

Once again, better to have an average presence in all three than to have a dominant presence in two and no presence in one.

And this is why knowledge graph optimization (what I’ve been advocating for over a decade) isn’t separate from content optimization. They are the same pipeline. Your knowledge graph presence directly improves how accurately, verbosely, and confidently the system annotates every new piece of content you publish.

If you’re thinking “Knowledge graph? That’s just Google,” think again.

In November 2025, Andrea Volpini intercepted ChatGPT’s internal data streams and found an operational entity layer running beneath every conversation: structured entity resolution connected to what amounts to a product graph mirroring Google Shopping feeds. 

OpenAI is building its own knowledge graph inside the LLM. My bet is that they will externalize it for several reasons: a knowledge graph in an LLM doesn’t scale, an LLM will self-confirm, so the value is limited, a standalone knowledge graph can be easily updated in real time without retraining the model, and it’s only useful at scale when it stays current.

The algorithmic trinity isn’t a Google phenomenon. It’s the architectural pattern every AI assistive engine and agent converges on, because you can’t generate reliable recommendations without a concept graph, structured entity data, and up-to-date search results to ground them.

Why Google and Bing annotate differently from engines that rent their index

Google and Bing own their crawling infrastructure, indexes, and knowledge graphs. They can afford grace periods, schedule rechecks, and maintain temporal state for URLs and entities over months.

OpenAI, Perplexity, and every engine that rents index access from Google or Bing operate on a fundamentally different model. They have two speeds: 

• A slow Boolean gate (Does this content exist in the index I have access to?)
• A fast display layer (What does the content say right now when I fetch it for grounding?)

The Boolean gate inherits Google’s and Bing’s annotations. Whether your content appears at all depends on whether it was recruited from the index those engines draw from, and that recruitment depends on annotation and selection decisions made by the algorithmic trinity. But what these engines show when they cite you is fetched in real time.

The practical implication

For Google and Bing, you’re optimizing for annotation quality with the benefit of grace periods and gradual reclassification. For engines that don’t own their index, the Boolean presence is inherited from the rented index and is slow to change, but the surface-level display changes every time they re-fetch.

That means what you are seeing in the results is not a direct measure of your annotation quality. It’s a snapshot of your page at the moment of fetch, and those two things may have nothing to do with each other.

How to optimize for annotation quality: The six practical principles

The SEO industry has spent two decades optimizing for search and assistive results — what happens after the system has already decided what your content means. We should be optimizing for annotation. 

If the annotation is wrong, everything downstream suffers. When the annotation is accurate, verbose, and confident, your content has a significant advantage in recruitment, grounding, display, and, ultimately, won.

1. Trigger SLM routing

Make your topic category obvious within the first 100 words. Use standard industry terminology. Follow structural conventions. Reference well-known entities. The goal: specialist model, not generalist.

2. Write for all three SLMs

Clear signals for subject (what is this about?), entity (who is the authority?), and concept (what established ideas does this connect to?). Ambiguity on any axis reduces confidence.

3. Get it right before publishing

First-impression persistence means the initial annotation is the hardest to change. Publish only when topic, entity signals, and claims are unambiguous.

4. Build the flywheel

Knowledge graph presence, web index centrality, LLM parameter strengthening, and correct SLM-domain representation all feed annotation confidence for new content. Invest in entity foundation, and every future piece benefits from inherited credibility.

5. Eliminate noise when correcting

Change every reference. Leave zero contradictory signals. Noise maintains inertia proportionally.

6. Audit for annotation, not just indexing

A page can be indexed and still misannotated. If the AI response is wrong about you, the problem is almost certainly at Gate 5, not Gate 8.

Annotation is the gate where most brands silently lose. The SEO industry doesn’t yet have a vocabulary for it. That needs to change, because the gap between brands that get annotation right and brands that don’t is the gap between consistent AI visibility and permanent algorithmic obscurity.

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Why annotation matters so much and why it should be your main focus

You’ve done everything within your power to create the best possible content that maps to intent of your ideal customer profile, you have methodically optimized your digital footprint, your data feeds every entry mode simultaneously: pull, push discovery, push data, MCP, and ambient, so they are all drawing from the same clean, consistent source

So, content about your brand has passed through the DSCRI infrastructure phase, survived the rendering and conversion fidelity boundaries, and arrived in the index (Gate 4) intact. Phew!

Now it gets classified. Annotation is the last moment in the pipeline where you have the field to yourself. Every decision in DSCRI was absolute: you vs. the machine, with no competitor in the frame. 

Annotation is still absolute. The system classifies your content based on your signals alone, independently of what any competitor has done. Nobody else’s data changes how your entity is annotated.

But this is the last time you aren’t competing. From recruitment onward, everything is relative. The field opens, every brand that passed annotation enters the same competitive pool, and the advantage you carried through the absolute phase becomes your starting position in the competitive race you have to win.

That means: 

• Get annotation right, and you start ahead, with confidence that compounds through every downstream gate in RGDW. 
• Get it wrong, and the multiplicative destruction effect does its work — a near-zero on one annotation dimension cascades through recruitment, grounding, display, and won. No amount of excellent content, structural signals, or entry-mode advantage recovers it.

Warning: First-impression persistence (remember, the first time you are annotated is the baseline) means you don’t get a clean retry. Changing the baseline requires thoroughness, time, and more effort than getting it right on the first crawl.

Annotation isn’t the gate that most brands focus on. It’s the gate where most brands silently lose.

This is the eighth piece in my AI authority series. 

• The first, “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it,” introduced cascading confidence. 
• The second, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline. 
• The third, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline. 
• The fourth, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase.
• The fifth, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase.
• The sixth, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material.
• The seventh, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model. 
• Up next: “The engine’s recruitment decision: What topical ownership actually means.”

SOVOL is about to enter its multi-material era SOVOL has started teasing “something new”, a new 3D printer that promises to be both “multi-material” and “multi-colour”. Until now, SOVOL has specialised in single-colour/material 3D printing solutions, promising “open-source freedom” and a wealth of customisation options. Based on their teaser image, SOVOL’s new 3D printer appears […]

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Java 26 is here with fresh language features, faster performance, stronger security, and a wave of library and tooling upgrades. Early developer reaction has been upbeat, with many praising Java's steady pace of meaningful improvements.

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REWRITE is a 30-day interactive story and voice-first coaching platform that measures personal transformation through your voice. You follow the narrative, talk with an AI coach by text or voice, and see objective signals like stress, confidence, engagement, cognitive load, and authenticity. After the story, daily prompts and monthly voice reports track your progress, giving data you can act on. Coaches get a dashboard with client trends, attention flags, and AI-generated prep notes.

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Many of today’s PPC tools were designed to be easily accessible to ecommerce. That doesn’t mean lead gen can’t take advantage of them, but it does mean more intentional application is required.

Lead gen with AI still requires a creative approach, and many conventional ecommerce tools still apply — but not always in the same way.

Here are the priorities that matter most for succeeding with lead gen using AI.

Disclosure: I’m a Microsoft employee. While this guidance is platform-agnostic, I’ll reference examples that lean into Microsoft Advertising tooling. The principles apply broadly across platforms.

1. Fix your conversion data first

This is the single most important thing you can do as AI becomes more embedded in media buying.

Between evolving attribution models, privacy changes, different platform connections, and shifts in how consumers engage with brands, it’s reasonable to ask whether your data is still telling an accurate story.

Start by auditing your CRM or lead management system. Make sure the data you pass back to advertising platforms is clean, consistent, and intentional.

In most cases, data issues stem from human choices rather than technical failures. Still, there are a few technical checks that matter:

• Confirm conversions are firing consistently.
• Regularly review conversion goal diagnostics.
• Validate that lead status updates and downstream signals are actually flowing back.

If AI systems are learning from your data, you want to be confident that the feedback loop reflects reality.

Dig deeper: How to make automation work for lead gen PPC

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2. Make landing pages easy to ingest and easy to understand

Lead gen campaigns often have multiple conversion paths, which can be helpful for users. But from an AI perspective, ambiguity is a risk.

Your landing pages should make it clear:

• What action you want the user to take.
• What happens after action is taken.
• Which conversions matter most.

Redundant or unclear conversion paths can confuse both users and systems. If AI crawlers detect that anticipated outcomes are inconsistent, they may begin to question the accuracy of what your site claims to do. That can limit eligibility for certain placements.

Language clarity matters just as much. Avoid jargon, eccentric terminology, or internally focused phrasing when describing your services. Clear, plain language makes it easier for AI systems to understand who you are, what you offer, and how to match creative to the right audience.

A practical test: Put your website content into a Performance Max campaign builder and review how the system attempts to position your business. If you agree with the messaging, imagery, and framing, your site is likely easy to understand. If not, that feedback is valuable.

You can also paste your site content into AI assistants and ask them to describe your business and services. If the response aligns with reality, you’re in a good place. If it doesn’t, that’s a signal to refine your content.

Behavioral analytics tools, like Clarity, can help you understand exactly how humans are engaging with your site and how often AI tools are crawling your site.

Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now

3. Budget across the entire funnel

Lead gen has always struggled with long conversion cycles. That challenge doesn’t go away, and in some ways, it becomes more pronounced.

AI-driven systems increasingly weigh sentiment, visibility, and contextual signals, not just last-click performance. If all of your budget and reporting focuses on immediate traffic, you may miss meaningful impact higher in the funnel.

That means:

• Budgeting intentionally across awareness, consideration, and conversion.
• Applying the right metrics at each stage.
• Looking beyond traffic as the primary success indicator.

In many lead gen models, citations, qualified leads, and eventual revenue tell a more accurate story than clicks alone.

Dig deeper: Lead gen PPC: How to optimize for conversions and drive results

4. Clean up your feeds and map data

You may not think you have a “feed” in your lead gen setup, but that absence can put you at a disadvantage.

Feeds help AI systems understand your business structure, services, and site architecture. Even if you don’t have hundreds of pages, a simple, well-maintained feed in an Excel document can provide valuable context when uploaded to ad platforms.

Feed hygiene matters. Use clear, specific columns. Follow platform standards for text, images, and categorization. Make sure all relevant categories are represented.

On the local side, claim and maintain all map profiles. Ensure information is accurate and consistent. If you use call tracking in map placements, review your labeling carefully. AI systems may pull data from map listings or your website, and mismatches can create attribution confusion, particularly for phone leads.

Account for potential AI-driven inflation in reporting, whether you’re looking at map pack data, direct reporting, or site-level performance. Any changes you make should also be reflected correctly in your conversion goals.

5. Pressure-test your creative for clarity

Creative assets may be mixed, matched, or shortened using AI. In some cases, you may only get one headline to explain who you are and why someone should contact you.

If your value proposition requires three headlines, or a headline plus a description, to make sense, that’s a risk.

Review your existing creative and identify assets that stand on their own. You should have at least some options where a single headline clearly communicates:

• What you do
• Who you help
• Why it matters

If that clarity isn’t there, AI-driven placements can quickly become confusing.

Dig deeper: Why creative, not bidding, is limiting PPC performance

The fundamentals that still move the needle

Lead gen today doesn’t need to be complicated.

Most of the actions that matter today are things strong advertisers already do: clean data, clear messaging, intentional budgeting, and disciplined execution. What changes is how attribution may shift, and how much weight systems place on different signals.

The fundamentals still win. The difference is that AI makes weaknesses more visible and strengths more scalable.

If you focus on clarity, accuracy, and alignment across your funnel, you give both people and systems the best possible chance to understand your business — and that’s where sustainable performance comes from.

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Vala is an AI financial intelligence app that turns transactions into clear insights and practical actions. It connects bank accounts, categorizes expenses, tracks subscriptions, and helps manage shared spending for a simple, complete view of your finances.

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For most people, “Mad Men” means the TV show. But the phrase points to something more specific: Madison Avenue in the 1950s and ‘60s, when agencies grew brands through persuasion, positioning, and earned trust in a world of scarce media channels and powerful gatekeepers. If you wanted attention, you bought your way in, then made your product the obvious choice.

When the internet arrived and Google made the chaos navigable, an entire industry was built on getting brands found. Search and SEO became one of the most commercially valuable disciplines in marketing.

That model isn’t disappearing. But something new is taking shape on top of it — and most of the industry is still using the wrong language to describe what’s happening.

AI is exposing everything SEO has neglected. Brands that win recommendations from AI systems won’t do so by publishing more content. They’ll win through positioning, persuasion, and corroborated proof.

In other words, they’ll win the way Madison Avenue always did.

SEO was never really about content

One of the strangest things about the current industry conversation is how many people talk as if the job of SEO is to create content. It isn’t. Not for most businesses.

If you’re a publisher, content is the product. Traffic is the commercial engine. But for most brands, content never did what people thought.

Early on, people wrote content for customers, and it worked. Then it changed. Content became a keyword vehicle. “Get people to our site” replaced good marketing comms.

Traffic became a proxy for exposure. It worked because search rewarded retrieval: type a query, get a page, get a click. All you needed to sell that model was the belief that any traffic was good traffic. That traffic somehow led to revenue that your agency could keep delivering.

That model is now under serious pressure. 

Google and ChatGPT are increasingly taking the click. Every serious large language model is trying to satisfy informational intent before the user reaches the source. They aren’t trying to be better search engines. They’re trying to make search engines unnecessary — and that’s the entire point.

There’s too much information on the web. People don’t want to open 10 tabs and read five near-identical blog posts to find a basic answer. They want the answer. The AI systems exist precisely to give it to them.

So if informational retrieval gets absorbed into the interface, what remains? Marketing. That’s the part many SEOs are still not fully grappling with.

Dig deeper: The three AI research modes redefining search – and why brand wins

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From place to preference

The cleanest way to understand this shift is through the “4 Ps” of marketing: product, price, place, and promotion.

Traditional SEO has been, almost entirely, a place discipline. It’s been about getting your products, services, or information onto the digital shelf when people go looking.

Keyword rankings are shelf position. Paid search is just a more expensive version of the same principle. In commercial search, you pay for premium placement in a digital aisle.

That still matters enormously.

Buyer-intent search remains valuable. Google hasn’t solved its commercial transition to a fully AI-led interface, and won’t overnight. Search is too important to Google’s revenue to disappear fast. But another layer is emerging above it, and this is the layer that most agencies aren’t yet equipped to compete on.

As AI systems become the first interaction point for more users, the game shifts from being present to being preferred.

Users don’t just search. They ask. They describe a problem. They want the best CRM for a mid-market SaaS company, the best estate agent in their area, the best sandwich shop near the office. And the system responds with recommendations.

If classic SEO was about rankings, the next phase is about recommendations. If classic SEO was about digital placement, the next phase is about shaping preference. And recommendation, in practice, is advertising.

Not a display banner. Not a 30-second TV spot. But advertising in the oldest and most commercially powerful sense: influencing the choice someone makes before they’ve even consciously made it.

An AI-generated recommendation is an invisible ad unit. It doesn’t bill by impression.

Why AI recommendations hit differently

When an LLM recommends a brand, it can’t know with certainty what will work best. So it infers. It weighs signals: past success, prominence, reviews, case studies, corroborating sources, and repeated associations between a brand and a specific type of problem.

Humans do something almost identical. 

Where performance is clearly bounded, we can identify a winner. We know who won the Oscar. We know which film topped the box office.

But when performance isn’t obvious in advance, we rely on proxies. We ask friends, read reviews, and scan for authority. We use familiarity, logic, and social proof to estimate what is likely to be right.

That’s exactly the territory AI recommendation is now entering — the consideration set problem. If I ask an LLM to find me a reliable accountant for a small business, I’m not asking it to retrieve a blog post. I’m asking it to build me a shortlist. 

Unlike traditional search, the recommendation layer is invisible to brands unless they test for it actively. You don’t see the prompt or the source chain. You don’t even know why one brand made the cut and another didn’t.

But the commercial effect is real, possibly stronger than anything traditional search produced. If you’re in the recommendation set, you’re in the running. If you’re absent, you’ve lost the sale before the conversation started.

Dig deeper: Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it

Your website is now an argument for preference

The first practical consequence: your website can no longer function like a polite digital brochure. Despite being optimized for search, many commercial web pages simply:

• Introduce the company.
• Gesture vaguely at services.
• Bury differentiation under generic corporate language.
• Treat the page as an endpoint for a ranking rather than a persuasive asset.

Still, they’re weak where it matters most: actual selling.

In the Mad Men era of SEO, your landing pages and service pages need to function like sales pages, not in a cheesy direct-response way, but in the strategic sense that they must clearly answer four things:

• Who is this for?
• What problem does it solve?
• Why is it different?
• Why choose it over the alternatives?

This comes down to positioning, which is key to GEO. If seven brands do broadly the same thing, the model needs distinctions. It needs enough clarity to say: this brand is best for X kind of buyer with Y kind of problem because it does Z better than everyone else.

Your website copy must surface real performance attributes: the specific things you genuinely do better or more distinctively than competitors. Your pages must become machine-readable arguments for preference.

Copywriting is back

Actual commercial copywriting — not fluffy brand storytelling or word count for its own sake — identifies a target customer, sharpens the problem, articulates the value, and makes the offer easy to recommend.

Good copy isn’t optional.

Take a local sandwich shop. The old SEO conversation runs to “best sandwich near me,” local pack, and review acquisition. It’s useful, but limited. 

The GEO version starts with the shop’s actual performance attributes. 

• Is it the speed? 
• The handmade bread? 
• The office catering? 
• The locally sourced produce?

Those claims must be clear on the website first. Then they need corroboration everywhere else:

• Reviews that mention the sourdough specifically.
• A local food blogger’s write-up.
• Inclusion in “best lunch spots” roundups.

They’re specific, repeated, retrievable evidence of why this shop is the right recommendation for a particular type of customer.

Scale that logic to a B2B software company, and the principle holds. Pages that clearly explain who the product is for, which problems it solves, and why it outperforms rivals. Then build mentions, customer reviews, and gain trade-press coverage — the body of evidence to support recommending you to buyers — and let the AI find it.

That’s pretty much GEO in a nutshell.

Keywords don’t disappear, but they lose their throne

Keywords are a human workaround. Approximations of intent, built for a retrieval system that needed exact string matching. LLMs process fuller context, layered needs, and comparative requirements. They move from keyword matching toward problem understanding.

Keyword research still matters for classic search, paid search, and buyer-intent pages. But the center of gravity shifts.

Instead of asking only “what terms should we rank for?”, the better question is: what attributes make us the right recommendation for the buyer we actually want, and what evidence exists across the web to support that claim?

The future of SEO is starting to look like the old agency model, as the work is increasingly promotional. Once your website clearly expresses your positioning, the challenge becomes promoting that position across the wider web through credible, repeated, relevant signals.

• Digital PR. 
• Traditional PR. 
• Expert commentary. 
• Case studies. 
• Reviews. 
• Listicles.
• Awards. 
• Trade press.
• Brand mentions. 
• Conference speaking. 
• Events. 
• Creator coverage. 
• Product comparisons. 
• Original data studies that other people actually cite. 

These are the things you go after, create, and encourage. Sadly, many “AI visibility” conversations flatten this into nonsense.

The goal isn’t merely to have content cited by AI. It’s to gather enough market evidence that AI systems repeatedly encounter your brand in the right contexts, with the right associations.

The work stops being optimization and becomes maximization: building the largest possible volume of persuasive, corroborated, retrievable evidence that your brand is a sensible recommendation for a specific kind of buyer.

That’s a fundamentally different model from anything the SEO industry has been selling. It’s promotional and strategic brand marketing.

Dig deeper: How to design content that AI systems prefer and promote

Where SEO still fits

SEOs need to grow up. There’s still significant value in buyer-intent search, technical site architecture, entity clarity, internal linking, and structured data. SEOs are well placed to monitor recommendation environments, test prompts, and identify where visibility is being won or lost.

But the identity crisis is real. Many agencies were built for a world of rankings, informational blogs, and monthly traffic graphs. They aren’t equipped to lead a world defined by positioning, copy, PR, brand evidence, and recommendation science.

Tracking brand citations inside AI outputs isn’t a complete strategy. It’s a temporary metric. 

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The new agency model

Winning agencies look like hybrid commercial strategy firms: part SEO, part copywriting, part PR, part brand strategy, part technical infrastructure. They know how to protect buyer-intent search revenue today while building the fame, clarity, and corroborated authority that earns recommendation tomorrow.

This is the Mad Men model of SEO. Persuasion, positioning, and clear claims backed by public proof matter again. And the job is to become recommended by AI.

Google explains why it doesn't matter if websites are getting heavier and the takeaway has everything to do with SEO.

The post Google Explains Why It Doesn’t Matter That Websites Are Getting Larger appeared first on Search Engine Journal.

I’m getting a mid-career executive MBA. Last week, in class, we discussed the interaction between automation and advertising. The lecture covered why A/B testing in Meta is less valuable now, since Facebook can auto-optimize faster and better than marketers can on their own.

A classmate took the logical leap and asked the professor, “If digital channels have more data and more processing power, why don’t advertisers just give them a URL and a credit card and let them go wild?”

The argument has real merit. Google, Meta, and LinkedIn have access to more data than any agency ever will. Their optimization engines are improving fast. Handing them a budget and a URL and walking away isn’t entirely crazy.

But that means we’d need to have faith in the channels to optimize media in a business’s best interests, and there’s a long, proud history of that not being the case.

1. The opt-in that wasn’t

About six years ago, we met with a Google rep who pitched a product that introduced broader, more aggressive targeting and bidding. We listened to the pitch and said no. We didn’t want to try it. The reps turned it on anyway.

What happened next was what we predicted. The campaigns spent significantly more money and didn’t generate any additional conversions.

We had to comp the client for the wasted spend, which was bad enough. But what made it worse was the principle of the thing: we hadn’t agreed to this. Google made unauthorized changes to our account.

When I tried to get the money back, Google’s position was that we’d set our campaign budgets at a certain level, and they were within their rights to spend up to that amount. That framing ignores that a budget cap is a ceiling, not an invitation. 

Our agency methodology is to never hit a budget cap. We set those numbers based on the strategy we’d approved, not the one they decided to test. I hounded them for weeks, but never got any resolution. It still makes me angry.

The reps were clearly incentivized to get adoption of the new feature. When it didn’t work, there was no accountability and no recourse. We were left covering the cost of a decision we explicitly declined.

What’s being misrepresented

Budget caps were treated as implicit consent to spend. A product we declined was activated without authorization, and when it failed, the platform pointed to our own settings as justification.

The incentive structure rewarded the reps for turning it on. There was no corresponding mechanism to make the advertiser whole when it didn’t work.

Dig deeper: Google rep’s unauthorized ad changes spark advertiser concerns

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2. The profit maximization pitch

This was years ago for a successful retainer. A pair of senior Google reps sat across from us and asked what our client’s gross margin was. Around 50%, we said. They went to the whiteboard and wrote out: if overall revenue/2 – overall media cost >= 0, then we should keep spending money on ads.

On the surface, the math sounds right. In practice, it has two problems.

• It assumes the reported conversions are incremental, meaning they wouldn’t have happened without the paid ad. A substantial portion of any Google campaign’s reported conversions, particularly in brand and retargeting, are users who were already going to convert.
• The model assumes a flat cost curve, where the 500th conversion costs the same as the 50th. It does not. Marginal returns fall as you scale. The last dollars of spend are always the least efficient, but they’re exactly what this pitch is designed to help Google access. (They should have said marginal revenue/2 – marginal cost = 0 is profit maximization.)

What’s being misrepresented

The model treats all reported conversions as incremental and assumes cost per conversion is constant across spend levels. Both assumptions are wrong, and together they can justify significant overspend.

3. The ‘higher CPCs buy better clicks’ pitch

This one still happens all the time. The pitch is that if you raise your CPCs, you’ll get access to higher-quality traffic. The implied logic is that conversion rate is influenced by CPC, and that if your investment isn’t high enough, you’re missing the best clicks.

There’s a version of this that has some truth to it. Higher CPCs can mean higher ad positions, which can mean higher impression frequency against the same users. More frequency can drive higher aggregate conversion rates, because repeated exposure matters.

But the argument glosses over the other side of that equation. 

• Higher frequency has diminishing marginal returns. 
• The third impression is worth less than the first. The tenth is worth a lot less.
• The cost curve isn’t flat. You’re paying more per click at every step.

In practice, raising CPCs to chase quality traffic is almost always correlated with substantially worse overall return on ad spend.

This is a variant of the marginal return problem seen across these cases. The pitch frames the upside without acknowledging the cost curve. More spend gets positioned as access to better outcomes, when it often delivers the same outcomes at a higher price.

What’s being misrepresented

CPC and conversion rate are presented as if higher bids unlock better traffic. In most cases, the incremental cost outpaces the incremental return. The pitch frames diminishing returns as an opportunity, rather than a constraint.

Dig deeper: Dealing with Google Ads frustrations: Poor support, suspensions, rising costs

4. The learning phase as a get-out-of-jail card

“If your Meta campaigns are underperforming, it’s because the algorithm just needs more time to learn.”

“Don’t make changes, and don’t reduce budget, just give the platform more data.” 

This is sometimes true. Machine learning systems need volume to optimize effectively, and premature intervention can reset progress.

But “it needs to learn” has become a catch-all explanation that’s almost impossible to disprove in the short run. It explains away poor CPAs, delays accountability, and keeps spend flowing when a reasonable advertiser might otherwise pull back and reassess.

There’s rarely a clear definition of when the learning phase ends, which makes it a moving target. The learning phase ends when performance improves. If performance doesn’t improve, more learning is prescribed.

What’s being misrepresented

A real technical concept is being used in ways that resist falsification. When there’s no defined endpoint and no stated criteria for success, “it needs to learn” serves as a blank check for budgetary continuity.

5. The metric pivot: When conversions fail, sell sentiment

In many cases, YouTube or display campaigns aren’t driving measurable conversions. The rep’s suggestion: let’s look at brand measurement. We can measure recall rates, positive sentiment, and intent to purchase. These are real signals of brand health, and they matter in the long run.

But the shift from conversion to sentiment metrics tends to occur when conversion metrics are poor, not as a principled measurement strategy. Brand lift surveys measure awareness under controlled conditions, but they rely on self-reported intent and don’t connect to downstream revenue.

Recall is almost never translated into a cost per point of lift that can be compared across the media plan. You end up with a number that’s positive and presented as evidence of success, with no agreed-upon framework for what sufficient lift would look like.

What’s being misrepresented

A softer metric is substituted for a harder one after the harder one fails. Brand lift is a legitimate measurement tool when defined upfront as a success criterion. Introduced afterward, it functions as a consolation prize.

Dig deeper: PPC mistakes that humble even experienced marketers

6. Upper funnel combined with lower funnel for a blended average

Upper-funnel and lower-funnel campaigns serve different purposes and perform differently on a cost-per-acquisition basis. When a channel reports blended CPA across all campaign types, an average that looks acceptable can hide the fact that some portion of the media plan is wildly inefficient at the margin.

The argument for blending is that upper-funnel spend creates the conditions for lower-funnel performance. That is plausible, but plausibility isn’t the same as demonstrated causality. 

Often, it’s assumed the upper funnel is directly contributing and that, in aggregate, the system is profitable and fully incremental. This is never the case.

What’s being misrepresented

Aggregate CPA can look fine while specific segments of spend have no measurable return. Blending is a reporting choice, and it can obscure where money is and isn’t working.

7. View-through conversions: The numbers that shouldn’t count

A view-through conversion is counted when a user sees an ad, doesn’t click it, and then converts within some attribution window, often 24 hours or more. Platforms report these alongside click-through conversions by default. 

For retargeting campaigns, which by definition serve ads to people who have already visited your site, view-through attribution is particularly problematic. These users were likely going to return and convert regardless. The ad may have had nothing to do with it.

The issue isn’t that view-throughs aren’t meaningful. For a cold audience, some brand-influenced conversions happen without clicks.

The issue is that those conversions are almost never broken out proactively (you have to ask). And when you remove view-throughs from retargeting campaigns, the ROAS numbers can change dramatically. 

We’ve seen cases where removing VTAs cuts reported conversions by more than half. I would note that by moving to incremental measurement options, Meta has become substantially more transparent.

What’s being misrepresented

View-through conversions inflate reported performance, particularly in retargeting, where incrementality is already low. Default reporting includes them without flagging the methodological problem.

Dig deeper: Outsmarting Google Ads: Insider strategies to navigate changes like a pro

8. The competitor benchmark as a spending lever

This one is a pattern. A channel rep brings industry benchmark data to a meeting showing that your competitors are spending at a level above your current budget. The implication is clear: you’re being outspent, and you should close the gap.

Industry benchmarks are among the most valuable inputs a channel can provide. Knowing where you sit relative to the market is useful context for planning. The problem is how they get deployed. More often than not, benchmark data shows up as a tool to expand media spend, not as a neutral input into strategy.

And it works. CEOs and CMOs are particularly susceptible to this framing. Nobody wants to hear that a competitor is outspending them.

The emotional pull of “they’re investing more than you” is hard to counter with a measured conversation about marginal returns or strategic fit. The benchmark becomes the argument, and the argument is almost always “spend more.”

What gets lost is any discussion of whether:

• The competitor’s spend is actually working for them.
• Your business model and margins support the same level of investment.
• The benchmark even reflects an apples-to-apples comparison.

Competitive spend data without context is just a number that makes your budget feel inadequate.

What’s being misrepresented

Benchmark data is real, but it’s selectively introduced to justify budget increases rather than treated as one input among many. The framing skips over whether the comparison is meaningful and relies on competitive anxiety to sell.

9. The default settings trap

This one is hard to frame as a single incident because it’s everywhere. I’ve talked to so many people trying to break into the industry, or launch their first campaigns, and the story is almost always the same. 

They follow the platform’s setup guide, accept the default settings, and end up opted into programs that have close to zero chance of being successful.

This is true across pretty much every major channel. 

• LinkedIn defaults you into audience network inventory that runs outside the LinkedIn feed. 
• Google opts you into display inventory when you’re trying to run search. Broad match keywords are set way too far out of the box. Suggested CPCs are astronomical. 
• Google’s geographic targeting defaults to “presence or interest” rather than actual location. 

Each of these defaults, taken individually, could be defended as a reasonable starting point. Taken together, they create a setup that maximizes the platform’s revenue from day one, before the advertiser knows what’s happening.

A new advertiser following the guided setup is accepting a configuration that the platform designed, and the platform’s incentives aren’t aligned with efficient spend.

This one is genuinely difficult to solve. Platforms need to provide default settings, and they can’t expect every new advertiser to understand every option. 

But there’s something predatory about the gap between what people think they’re signing up for and what they’re getting. The defaults are revenue-optimized for the channel, not performance-optimized for the advertiser.

What’s being misrepresented

Setup guides and default settings are presented as best practices when they’re actually configurations that favor the platform’s revenue. New advertisers trust the guided experience, and have no reason to suspect the defaults are working against them.

Dig deeper: Are you being manipulated by Google Ads?

10. The tracking gap as a faith exercise

Privacy regulations and platform changes have created real limitations in conversion tracking. GDPR and Apple’s App Tracking Transparency aren’t invented problems. 

We have less visibility than we used to, and the platforms have responded by layering probabilistic modeling and modeled conversions on top of deterministic tracking.

But the tracking gap has also become a convenient shelter for underperformance. The argument goes like this:

• “The conversions are happening, we just can’t see them all yet. There’s latency in the data.”
• “There are limits to what can be tracked. We need a longer attribution window.”
• “We need more time for the modeled data to populate. And in the meantime, here are some proxy metrics that we think are directionally valid, so let’s keep pushing.”

Each of those can be true in isolation. Modeled conversions take time to appear. Attribution is harder than it was five years ago. Proxy metrics can be useful when direct measurement breaks down. 

The problem is when all of these caveats get stacked together and used to justify sustained spend in the absence of any measurable result. At some point, “the data will come in” stops being a reasonable expectation and becomes an article of faith.

The tracking gap is real, but it cuts both ways. If you can’t measure the result, you also can’t prove the spend is working. The platform’s default position is to assume it is, and keep going. The advertiser’s job is to ask what happens if the modeled conversions never materialize, and what the fallback plan looks like if they don’t.

What’s being misrepresented

Legitimate tracking limitations are used to defer accountability indefinitely. When measurement is hard, the platform’s recommendation is always to maintain or increase spend, never to reduce it. The uncertainty gets resolved in the channel’s favor by default.

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What does this mean for AI-run campaigns?

None of this is an argument that agencies are irreplaceable in their current form. We used to question tCPA, and now it’s a preferred bidding strategy. Automation handles execution-level work that used to require skilled practitioners. In-house teams are viable for more companies than they used to be.

But the argument for fully autonomous, channel-run advertising assumes the channel will optimize for your outcomes rather than revenue. Even if we imagine new profit-sharing contracts, this assumption carries real risk.

And I’m not blaming reps or the channels. They believe in their products, but they’re also measured on metrics that create a predictable drift in how they frame data. I should note that agencies struggle with misaligned incentives as well.

The advertiser’s job, with or without an agency, is to keep asking the inconvenient questions.

• What is the marginal return at this spend level?
• What percentage of conversions are view-throughs?
• What does performance look like if we exclude brand search?
• Are we measuring incrementality, or are we measuring correlation, and calling it causation?

Maybe the answer to everything is eventually full automation. But the entity building the machine shouldn’t be the one telling you when it’s ready.

For years, Salesforce Marketing Cloud was the safe choice.

Powerful. Enterprise. Trusted.

But lately, we’re hearing something different:

• “Our data is too tangled to activate.”
• “We’re locked into contracts.”
• “We’re stuck sending the same emails on repeat.”
• “Everything is Band-Aids and duct tape — I don’t know how we can move without breaking everything.”
• “We feel stuck.”

Sound familiar? If so, this fireside chat is for you.

We’ve helped dozens of brands migrate off Salesforce and into modern, composable engagement architectures built for real CRM performance. Not because it’s trendy — but because marketers needed more speed, flexibility, and innovation.

In this April 14 session, we’ll cover:

• Why brands feel stuck (and why it’s more common than you think).
• What’s happening inside the Salesforce ecosystem.
• The biggest misconceptions about migrating.
• Understanding the martech landscape.
• What life actually looks like after moving to a modern platform like Braze.
• How CMOs and martech leaders should think about platform decisions over the next 3 to 5 years.
• How to get the rest of your org on board with making a move.
• The steps to take now to set yourself up for migration success.

To be clear: this isn’t a Salesforce-bashing session.

It’s a candid conversation about innovation velocity, marketing ownership, and what the next era of marketing actually requires.

Join us

Disclaimer: To ensure a candid and open conversation, the live session is open only to brand-side marketing leaders. Registrants who are not verified brand-side marketing leaders will not be permitted to attend the live session. However, the recorded session will be made available to all registrants upon completion of the event.

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