Google is rolling out Search Live with voice features through its AI Mode Labs experiment.
You can now have natural, spoken conversations with Search while receiving web links in real-time.
The was previewed at Google I/O and is now available today for U.S. users.
How Search Live Voice Works
You can access the feature by opening the Google app on Android or iOS.
Tap the new “Live” icon under the search bar, as shown below.
Once started, you can ask questions out loud and get AI-generated audio responses. Google says it uses a custom version of Gemini with advanced voice features.
The system remembers what you talked about before, which lets you ask follow-up questions naturally. For example, you could ask about preventing wrinkles in linen clothing while packing. Then you could ask what to do if wrinkles still happen.
Key Features & Functionality
Search Live keeps working even when you switch to other apps. Your conversations continue while you check email, browse social media, or do other things on your phone.
A “transcript” button shows you text versions of the audio responses. This means you can switch between talking and typing in the same conversation.
The feature also saves your conversation history. You can go back to previous Search Live sessions through your AI Mode history.
Web links show up on your screen alongside voice responses. This gives you quick access to source content if you want to dig deeper.
Technology & Implementation
Google’s custom Gemini model for Search Live builds on the company’s existing search systems.
The setup uses what Google calls a “query fan-out technique” to find diverse web content. This aims to give you different sources and viewpoints during your search sessions.
Google plans to add more Search Live features in the coming months. This includes camera integration for real-time visual queries.
Visual search was also previewed at I/O. It would let you show Search what you’re seeing while talking about objects, locations, or situations around you.
Why This Matters
Voice-driven conversational search could be a big shift in how people use search engines.
Google’s continued focus on natural language queries means optimization must go beyond traditional keyword targeting.
Web links still appear with AI voice responses. Marketers should test it out and consider how their content appears in conversational situations. This matters more as people ask follow-up questions and explore topics through natural dialogue.
This change may also affect how we understand search intent. Conversational queries often show more detailed needs than regular typed searches.
Getting Started
To use Search Live, you must join the AI Mode experiment through Google Labs.
Once signed up, the Live icon appears right away in the Google app.
As promised, we’ll look at how AI agents can transform the SEO audit process by providing corrections and thorough analysis that would otherwise take hundreds of hours of manual work.
Traditional SEO audits are often time-consuming, involving multiple tools and manual reviews.
With Agentic SEO, however, this process can be streamlined through autonomous AI agents that identify problems and recommend and implement solutions in real time.
AI Agents For Advanced Site Analysis
Full Website Analysis With Real-Time Corrections
Agentic SEO transforms the review process by:
Comprehensive crawling:AI agents can systematically analyze entire websites, including hidden pages and dynamic content that traditional crawlers might overlook.
Intelligent pattern recognition: Unlike rule-based tools, AI agents can detect patterns and anomalies that may indicate deeper structural issues across your site.
Real-time remediation: As well as identifying problems, the agents can generate code fixes, content improvements, and structural adjustments that can be implemented immediately.
Image from author, May 2025
Example: Firecrawl Demo
With advanced AI crawling, Firecrawl can meticulously analyze HTML structures, extract microformats, and provide detailed performance metrics, revealing critical areas that need optimization and might otherwise be missed.
Image from author, May 2025
Example: Similar to tools like Cursor integrated with GitHub, Agentic SEO enables immediate application of code fixes.
When an issue is identified, the agent directly suggests optimized code changes, allowing seamless implementation through direct integration with your repository, ensuring rapid and error-free remediation.
I’m confident that OpenAI’s Codex and Google’s Jules will be equally effective for these tasks.
Image from author, May 2025
Workflow Architecture For Effective Auditing
Similar to our idea workflows, audit workflows consist of specialized components.
Image from author, May 2025
The audit workflow typically includes:
Data collection agents: These collect information from your site, competitor sites, and search engine results.
Analysis agents: These specialize in identifying technical issues, content gaps, and optimization opportunities.
Recommendation agents: They prioritize issues and suggest specific solutions based on potential impact.
A specialized knowledge base: Provide the agent with SEO best practices, Google guidelines, and industry-specific benchmarks.
Tool integration: Connect the agent to existing tools such as Screaming Frog, Moz, and Semrush, or custom APIs for comprehensive data collection.
Human-in-the-loop checkpoints: Despite automation, human expertise is still needed to validate critical recommendations.
Case Study: Ecommerce Site Optimization
In less than 30 minutes, our Agentic SEO Audit System identified 347 critical technical issues for a mid-sized ecommerce site with 15,000 product pages.
It generated optimized title tags and meta descriptions for underperforming pages.
It discovered and mapped content gaps in product categories.
It created a comprehensive action plan based on revenue impact.
Implementing these recommendations resulted in a 32% increase in organic traffic within 60 days.
Current Challenges And Limitations
Although powerful, Agentic SEO auditing does have its challenges.
Tool integration complexity: Connecting Agentic to all the necessary data sources requires technical expertise. For instance, setting up MCP (or Model Context Protocol) servers can be a challenging task.
Evolving standards: Agents require regular updates to keep pace with changes in search engine algorithms.
Tools to Build Your Own SEO Audit Agent
Here are some practical tools to help you get started:
Open-Source Workflow Automation – n8n is a powerful, open-source automation tool that allows you to create complex workflows without coding. It’s ideal for orchestrating SEO tasks like crawling, data extraction, and reporting.
Python Framework for Multi-Agent Systems – CrewAI enables the development of multi-agent systems in Python, allowing specialized agents to collaborate on tasks such as data collection, analysis, and implementation.
Agentic AI Platform – DNG.ai (Draft & Goal) is a no-code platform designed to automate complex SEO workflows using specialized AI agents. It offers features like:
Agentic Workflows: Automate tasks such as keyword optimization, content creation, and data analysis.
Multi-Agent Collaboration: Coordinate multiple agents to handle large-scale projects efficiently.
Integration with Over 20 Marketing Tools: Seamlessly connect with tools like Google Search Console, Google Ads, Google Analytics, and more.
Resources to Learn and Get Started
To improve your understanding and skills in building SEO audit agents, you can also explore these resources:
Summary: Agentic SEO Is A Fundamental Shift
Agentic SEO’s audit capabilities represent a fundamental shift in how we approach technical optimization.
By combining AI’s pattern recognition abilities with the strategic insight of human experts, we can create audit systems that are more comprehensive and actionable than traditional approaches.
In our next article, we’ll explore the final pillar of Agentic SEO: Generation. We will examine how AI agents can generate missing content, optimize existing assets, and scale content production while maintaining quality and relevance through the “SEO Expert in the Loop” approach.
Stay tuned, and experiment with these techniques to transform your SEO workflow!
Wix announced it is acquiring Base44, an AI-powered coding platform that enables users to create software and applications with natural language prompts, no coding experience necessary. The acquisition is a bold step because it reimagines what a content management system can be, enabling its users to do more with Wix than with any other platform.
Base44 provides an easy to use chat-based interface that enables users to create any kind of app without having to subscribe to third-party tools, all within the Wix platform. The acquisition is further establishes Wix as a leading platform for Internet entrepreneurship.
Maor Shlomo, CEO of Base44, commented:
“I honestly can’t think of a better fit. Wix is probably the only company that can help Base44 achieve the scale and distribution it needs while maintaining, if not accelerating, our product velocity. Our market is massive. It has the potential to replace entire software categories by enabling people to create software instead of buying it. Wix’s DNA – its customer obsession, innovation, and speed – perfectly aligns with ours, and its scale will catapult Base44 forward at exactly the right time.”
Avishai Abrahami, CEO and Co-founder of Wix observed:
“This acquisition marks a pivotal milestone in Wix’s commitment to transforming creation online. Maor and his team at Base44 bring cutting-edge technology, strong market penetration, and visionary leadership that seamlessly align with Wix’s dedication to enabling users at all levels of expertise to express their intent while intelligent agents manage execution.
Maor’s exceptional talent and innovative mindset will reinforce Wix’s mission to push the boundaries of AI-driven creation and accelerate the evolution of intuitive, intelligent tools that redefine how digital experiences are built and enjoyed.”
There are three types of marketing leaders right now:
The one worried about performance and just had enough time in leadership meetings to hear what everyone else’s opinion is on AI.
The marketing leader who is having to firefight and has spoken to their team about AI and the implications.
The marketing leader who has played with some AI tools and used a few prompts to see if it’s useful.
There should be a fourth who is planning for the future, trying to work out where AI sits in their organization and how to get ahead, so they do not get lost and can confidently say we have an internal owner.
Right now, I only know a handful of chief marketing officers who are taking it seriously enough.
The Challenge: Trust
The challenge for most is, “Who do I trust enough to do a great job in pulling the department, and often the non-technical parts of the company, on the AI journey?”
As someone who’s held both CMO and chief growth officer titles and now coaches C-Suite leaders and consults, I decided to dedicate the last six months to going deep into AI.
I have worked with companies in and around AI since 2022. However, over the last six months, we have seen AI transition from a set of tools and models to help, to starting to influence the reduction of team headcounts and being responsible for hiring freezes unless you can prove AI cannot do it first.
If you haven’t started, it will become a hotly discussed talking point in board and leadership meetings.
The promise of AI is exactly what everyone is looking for: productivity gains (not starting from zero every time), cost savings (on hiring, having to rely on data analysts or agencies), and the ability to leverage competitive advantages. We are seeing some of this play out.
The return on investment of going first and early will mean you are ahead of competitors, you will quickly understand the investment case, and you will be able to calculate ROI early.
The Solution: Two Strategic Approaches
Where to start? Most people struggle with where to start and where it makes sense to kickstart the AI agenda.
With coaching and consultancy clients, I offer two ways to tackle this:
1. Find Your AI Champion: Apply The Owner, Co-Owner, Collaborator Model To AI
You must have an owner – someone who will be there with the team and responsible for championing tech and tools and integrating them into their workflow.
When that owner says something is important, the teams treat it as such. You need an owner for when something breaks, they take control. This is a high-trust role with a lot of status attached.
You have co-owners, those who feel connected – the team members who don’t like being left behind but aren’t confident enough to own it themselves. You might say these are the ones who are likely on the fence about leaving.
Last are the collaborators, the team members who need to learn, need co-workers to help them develop and talk through what the tools have done and where they have likely used AI to get themselves out of missing a deadline or a situation where they’ve missed something.
2. Org Design/Org Redesign
This requires a strong and forward-thinking department leader who requires reshaping your teams to adapt to the new technology shift.
A proactive and visionary department leader is now essential. This leader must restructure teams to embrace and adapt to the significant shift towards new technologies.
You are not just shifting for hires and skills gaps, as most do. You are reshaping for the next two years.
You have to plan out how the next six, 12, and 18 months will change, move team members around, where there will be headcount reshuffles, and in this situation, a new technology that will reach all marketing disciplines.
The Opportunity: You will need to assign a natural long-term leader to AI. AI is not going away and will be the driving force in most businesses for the foreseeable future. You have to get ahead of when boards and C-Suites push you for your plan.
The Threat To Be Ahead Of: You must identify those who just will not naturally fit in the short to mid-term, and reshuffle your team members.
In this “do more with less” era, you will have to be at the front, leading and potentially losing headcount. AI has already seen mass layoffs, and this is unlikely to stop.
You will need to be ahead of the industry shifts. Being ahead is critical. Being close to your new owner or captain is pivotal.
The AI Owner
Who will be the owner of AI? And, how will you reshape your department?
Whether you are a marketing leader or a growth leader, you have to think about where these elements connect and who has the most exposure and muscle memory in big shifts.
Potential AI owners could come from several areas:
Social: Is it a marketer from social media? Being led by platforms to change their content types, to understand the subtle algorithmic changes, and in most cases, had to ramp up the quantity of output.
Search: Is it a search marketer? Is it a leader from organic search? If you have a strong SEO leader who understands other channels, you have someone who has seen huge changes in their industry. They have likely faced major algorithmic updates and had to adapt to a large number of changes since the early 2010s.
Growth: Are you a modern-day growth organization or an evolving marketing department? Do you have growth pros who look after paid, organic, and potential social?
You will know who fits best in your department. However, I predict it’s likely the search or growth team.
You need someone who is used to unpicking shifts – someone who can understand technical aspects and interface with product teams and engineers while teaching their colleagues.
For the top tier of SEO or search professionals, this is something they have had to do for years.
This is an opportunity for your team members, particularly in search, as Carl Hendy and I discussed in a recent podcast: It’s time to reset, mature, and take ownership from across different disciplines.
How To Find Your AI Leader
A core skill to look out for in the right candidate is having the ability to understand the importance of changes for the whole business and be able to hold their own with C-Suite executives.
The AI leader will have to hold strong, informed opinions based on knowledge of what is happening and how they assign budget and resources across the business.
Your AI lead will be a close colleague in many important meetings, so you trusting them and being able to learn and gain reverse mentorship will be essential.
The 90-Day Action Plan
Immediate: Week 1-2
Write your AI plan and create a dedicated presentation to guide the team’s success. Have a formalized kickoff: Start with the basics, explainers, examples of what success looks like, a set of approved tools and prompts, and workshops.
Assign a budget line for AI initiatives.
Address the department’s AI fears through transparent communication.
Launch formal AI kickoff workshop.
Short-Term: 30-90 Days
Department Problem Solve: List out all of your existing problems within your department and work through how you can identify existing tools, leverage, and build internal tools and processes with AI to address these.
Establish bi-weekly AI progress updates.
Begin cross-functional AI coordination and start to rebuild roadmaps.
Implement weekly team training and development sessions.
The Long-Term Plan: 6-18 Months
Develop an AI-focused hiring strategy and plan a reorg with this in mind.
Build executive presence for AI champions.
Create measurable ROI frameworks.
Remember, in an ever-evolving AI landscape, you can be on top of being proactive and be well prepared for when your business needs to be reactive.
Wondering if AI Overviews (AIOs) are stealing your clicks?
Are these AI answer engines eating into our traffic, or just changing the shape of it?
Google’s AI Overviews now appear on up to 40% of search queries, but what impact are they really having?
Stop Guessing. How To Measure AIO’s Real Impact
Get the on-demand webinar, where we explore the three main tools that can help you:
In this tactical on-demand session, Tom Capper, Sr. Search Scientist at STAT, will walk you through a practical framework for assessing AIO impact using three tools you already rely on.
You’ll learn to pinpoint if, where, and how AIOs affect your traffic so that you can respond with clarity, not guesswork.
Start Measuring the Real Impact of AIOs on SERPs Today
Don’t rely on assumptions.
Grab this free on-demand webinar now to accompany the slides below; uncover if AIOs are actually hurting your traffic, and what to do about it.
Join Us For Our Next Webinar!
Lead Local SEO: How To AI-Proof Your Rankings With Reviews
Join Mél Attia, Sr. Marketing Manager at GatherUp, as she shows how consumer trust and AI updates are reshaping Local SEO, and how agencies can lead the way.
This post was sponsored by MarketBrew. The opinions expressed in this article are the sponsor’s own.
Is Google using AI to censor thousands of independent websites?
Wondering why your traffic has suddenly dropped, even though you’re doing SEO properly?
Between letters to the FTC describing a systematic dismantling of the open web by Google to SEO professionals who may be unaware that their strategies no longer make an impact, these changes represent a definite re-architecting of the web’s entire incentive structure.
While some were warning about AI passage retrieval and vector scoring, the industry largely stuck to legacy thinking. SEOs continued to focus on E-E-A-T, backlinks, and content refresh cycles, assuming that if they simply improved quality, recovery would come.
But the rules had changed.
Google’s Silent Pivot: From Keywords to Embedding Vectors
In late 2023 and early 2024, Google began rolling out what it now refers to as AI Mode.
What Is Google’s AI Mode?
AI Mode breaks content into passages, embeds those passages into a multi-dimensional vector space, and compares them directly to queries using cosine similarity.
In this new model, relevance is determined geometrically rather than lexically. Instead of ranking entire pages, Google evaluates individual passages. The most relevant passages are then surfaced in a ChatGPT-like interface, often without any need for users to click through to the source.
Beneath this visible change is a deeper shift: content scoring has become embedding-first.
What Are Embedding Vectors?
Embedding vectors are mathematical representations of meaning. When Google processes a passage of content, it converts that passage into a vector, a list of numbers that captures the semantic context of the text. These vectors exist in a multi-dimensional space where the distance between vectors reflects how similar the meanings are.
Instead of relying on exact keywords or matching phrases, Google compares the embedding vector of a search query to the embedding vectors of individual passages. This allows it to identify relevance based on deeper context, implied meaning, and overall intent.
Traditional SEO practices like keyword targeting and topical coverage do not carry the same weight in this system. A passage does not need to use specific words to be considered relevant. What matters is whether its vector lands close to the query vector in this semantic space.
How Are Embedding Vectors Different From Keywords?
Keywords focus on exact matches. Embedding vectors focus on meaning.
Traditional SEO relied on placing target terms throughout a page. But Google’s AI Mode now compares the semantic meaning of a query and a passage using embedding vectors. A passage can rank well even if it doesn’t use the same words, as long as its meaning aligns closely with the query.
This shift has made many SEO strategies outdated. Pages may be well-written and keyword-rich, yet still underperform if their embedded meaning doesn’t match search intent.
What SEO Got Wrong & What Comes Next
The story isn’t just about Google changing the game, it’s also about how the SEO industry failed to notice the rules had already shifted.
Don’t: Misread the Signals
As rankings dropped, many teams assumed they’d been hit by a quality update or core algorithm tweak. They doubled down on familiar tactics: improving E-E-A-T signals, updating titles, and refreshing content. They pruned thin pages, boosted internal links, and ran audits.
But these efforts were based on outdated models. They treated the symptom, visibility loss, not the cause: semantic drift.
Semantic drift happens when your content’s vector no longer aligns with the evolving vector of search intent. It’s invisible to traditional SEO tools because it occurs in latent space, not your HTML.
No amount of backlinks or content tweaks can fix that.
This wasn’t just platform abuse. It was also a strategic oversight.
SEO teams:
Many believed that doing what Google said, improving helpfulness, pruning content, and writing for humans, would be enough.
That promise collapsed under AI scrutiny.
But we’re not powerless.
Don’t: Fall Into The Trap of Compliance
Google told the industry to “focus on helpful content,” and SEOs listened, through a lexical lens. They optimized for tone, readability, and FAQs.
But “helpfulness” was being determined mathematically by whether your vectors aligned with the AI’s interpretation of the query.
Thousands of reworked sites still dropped in visibility. Why? Because while polishing copy, they never asked: Does this content geometrically align with search intent?
Do: Optimize For Data, Not Keywords
The new SEO playbook begins with a simple truth: you are optimizing for math, not words.
The New SEO Playbook: How To Optimize For AI-Powered SERPs
Here’s what we now know:
AI Mode is real and measurable. ✅You can calculate embedding similarity. ✅You can test passages against queries. ✅You can visualize how Google ranks.
Content must align semantically, not just topically. ✅Two pages about “best hiking trails” may be lexically similar, but if one focuses on family hikes and the other on extreme terrain, their vectors diverge.
Authority still matters, but only after similarity. ✅The AI Mode fan-out selects relevant passages first. Authority reranking comes later. ✅If you don’t pass the similarity threshold, your authority won’t matter.
Passage-level optimization is the new frontier. ✅Optimizing entire pages isn’t enough. Each chunk of content must pull semantic weight.
How Do I Track Google AI Mode Data To Improve SERP Visibility?
It depends on your goals; for success in SERPs, you need to focus on tools that not only show you visibility data, but also how to get there.
Profound was one of the first tools to measure whether content appeared inside large language models, essentially offering a visibility check for LLM inclusion. It gave SEOs early signals that AI systems were beginning to treat search results differently, sometimes surfacing pages that never ranked traditionally. Profound made it clear: LLMs were not relying on the same scoring systems that SEOs had spent decades trying to influence.
But Profound stopped short of offering explanations. It told you if your content was chosen, but not why. It didn’t simulate the algorithmic behavior of AI Mode or reveal what changes would lead to better inclusion.
That’s where simulation-based platforms came in.
Market Brew approached the challenge differently. Instead of auditing what was visible inside an AI system, they reconstructed the inner logic of those systems, building search engine models that mirrored Google’s evolution toward embeddings and vector-based scoring. These platforms didn’t just observe the effects of AI Mode, they recreated its mechanisms.
As early as 2023, Market Brew had already implemented:
Passage segmentation that divides page content into consistent ~700-character blocks.
Embedding generation using Sentence-BERT to capture the semantic fingerprint of each passage.
Cosine similarity calculations to simulate how queries match specific blocks of content, not just the page as a whole.
Thematic clustering algorithms, like Top Cluster Similarity, to determine which groupings of passages best aligned with a search intent.
This meant users could test a set of prompts against their content and watch the algorithm think, block by block, similarity score by score.
Where Profound offered visibility, Market Brew offered agency.
Instead of asking “Did I show up in an AI overview?”, simulation tools helped SEOs ask, “Why didn’t I?” and more importantly, “What can I change to improve my chances?”
By visualizing AI Mode behavior before Google ever acknowledged it publicly, these platforms gave early adopters a critical edge. The SEOs using them didn’t wait for traffic to drop before acting, they were already optimizing for vector alignment and semantic coverage long before most of the industry knew it mattered.
And in an era where rankings hinge on how well your embeddings match a user’s intent, that head start has made all the difference.
Visualize AI Mode Coverage. For Free.
SEO didn’t die. It transformed, from art into applied geometry.
To help SEOs adapt to this AI-driven landscape, Market Brew has just announced the AI Mode Visualizer, a free tool that simulates how Google’s AI Overviews evaluate your content:
Enter a page URL.
Input up to 10 search prompts or generate them automatically from a single master query using LLM-style prompt expansion.
See a cosine similarity matrix showing how each content chunk (700 characters) for your page aligns with each intent.
Click any score to view exactly which passage matched, and why.
Today’s Memo is a download straight from my brain about the current state of Search and AI. So much happened in the last few weeks, and I haven’t had a chance to sort out my thoughts.
Until now.
I’m finishing this Memo with exclusive insight into the KPIs I measure for search right now for premium subscribers .
By noon, I’d already scrapped the slide deck I had finished the night before.
That whiplash has become routine: Each new model triggers the same loop – panic that it’s smarter than I am. Relief when I find the edges. Then, fresh panic as the cycle restarts.
When my coach, Heather, heard me vent, she dropped a killer quote that stuck with me since: “Kevin, constant change is the new normal.”
She’s right.
Releases land weekly, search interfaces mutate overnight, and the ground under every SEO strategy keeps sliding.
As we cross the midpoint of 2025, I want to freeze-frame what’s happening to search right now – and what it means for you.
Here’s the short version:
Google’s AI Overviews (AIOs) inflate impressions while suffocating clicks.
The clicks that survive carry more purchase intent than ever.
“Performance” SEO is morphing into an “influence” play that spans Google, LLMs, and every social feed your customers consult for a second opinion.
Let’s unpack each shift, starting with the calories we’ve been counting all wrong.
Empty Calories
Since Google widened AI Overviews (AIOs) in March, one pattern rules them all: impressions up, clicks down.
Google now records an “impression” the moment someone expands the overview, and every cited source is logged as position 1.
The result: visibility inflation without visitors.
2024 was the year of peak traffic.
And looking at how few people clicked on links (a few percent) in the AIO usability study makes me think it’s entirely possible clicks drop to 10% or less of what we’ve been used to in 2024. And that’s ok.
Clicks have always been empty calories anyway. They were useful as a leading indicator for conversions/revenue/pipeline/sales/etc. (But that’s about it. Clicks didn’t mean dollars, and they didn’t mean real business growth.)
Of course, to us SEO folk, losing clicks sounds grim until you look closer at user behavior:
We thought pogo sticking was bad, but it’s just normal search behavior.
The only click that matters is the one that ends the journey.
In our study, 80% of those “final answer” clicks still land on organic results, not the AIO.
When people do click, it’s to validate, compare, or buy – high-intent actions that convert.
So, yes, raw clicks are vanishing, but the ones that survive are pure protein, not empty calories.
From Performance To Influence
Clicks are collapsing, but the ones that remain are loaded with intent.
That flips SEO’s value prop on its head.
For 20 years, we sold SEO as a performance channel, whether we wanted to or not.
The standard calculation was: Search volume ✕ CTR ✕ CVR = Projected dollars.
When a keyword couldn’t survive that spreadsheet, it died in committee.
Meanwhile, those same executives drop seven figures to get a logo the size of a postage stamp on an F1 car – no attribution model in sight.
Why? Influence.
The belief that persistent visibility bends preference.
SEO is crossing the same Rubicon. In an AIO-and-LLM world, you’re not just fighting for traffic; you’re fighting for mindshare wherever prospects ask questions:
Google’s AI Overviews.
ChatGPT.
Reddit threads, YouTube comments, Discord chats.
Your brand needs to echo across all of them.
That means new yardsticks (i.e., KPIs, which I laid out in the premium section at the end of the article).
In short, SEO is graduating from direct-response to influence.
Treat it – and budget for it – like any other brand channel that shapes preference long before the buy button.
Channel Fan-Out
AI Mode turns a single prompt into dozens of behind-the-scenes queries – a process engineers call “fan-out.”
The same thing is happening at the channel level: Search itself is fanning out, escaping the browser and popping up in every feed, app, and device.
Although SEO pros have been talking about it for years, in 2025, that finally, actually matters – and for three big reasons:
1. LLMs have injected search into every app. Want a cookie recipe breakdown in Microsoft Excel? You can have it. Meta shipped a standalone Meta AI and wove it into WhatsApp, IG, and FB. YouTube and Netflix are testing AI Overviews so you can “search” for the perfect video without ever leaving their walls.1
Translation: discovery no longer begins – or ends – on Google.com. Each walled garden is now its own mini-SERP, and Google has to fight a thousand little AI search engines, not just ChatGPT.
2. People cross-check AI with humans: Our AIO usability study showed a consistent pattern: Users read the AI answer, then hop to Reddit threads, YouTube comments, or Discord chats to see whether real people agree.
Credibility now comes from echoing across both machine answers and human conversations. If you’re invisible on social or community platforms, you’re invisible in the final decision loop.
3. The pie is somehow getting bigger. TikTok, Facebook, Instagram, Threads, Bluesky, YouTube, Google, ChatGPT, Perplexity, Claude, Snapchat – the list keeps growing, and so do their daily active users.
Where’s the extra time coming from? Mostly legacy media: linear TV, radio, even mainstream news sites. Attention is being reallocated, not reinvented.
What it means:
Your brand’s “search” footprint is now the sum of every place people ask questions.
Monitoring only Google rankings is like checking the weather on one street corner.
To win budget, tie each additional platform back to concrete customer insight – ideally gathered from, you guessed it, talking to customers and using tools like Sparktoro.
Sparktoro’s channel overview
AI Mode
AI Mode is the “final boss” of search.
Sundar Pichai told Lex Fridman that “the results page is just one possible UI,” and VP of Search Liz Reid called it “a construct.”
In other words, Google’s happy to toss the classic SERP the moment the math works.
Similarweb data shows AI Mode adoption is a bit over 1% – for now (Image Credit: Kevin Indig)
But right now, the math doesn’t.
Similarweb shows AI Mode in barely 1% of queries, by design.
A single AI Mode answer can swallow 20-50 follow-up searches, erasing the ad slots those pages used to carry.
Until Google finds a new way to charge (embedded ads, pay-per-chat, who knows), rollout will stay throttled.
When that business model lands, AI Mode becomes paradise for anyone who understands user intent.
Behind each prompt, Google “fans-out” dozens of micro-queries – price, specs, comparisons, nearby, reviews – and stitches the answers together.
Those micro-queries are the very same long-tails you optimize for today; they’re just fired in parallel and reassembled into a narrative.
How to prep while the gate is still half-closed:
Map the likely fan-out set for every core topic (look at People-Also-Ask, Related Searches, Reddit threads, etc. – more in a future Memo).
Track rankings for each micro-query; gaps there equal lost citations in AI Mode.
Structure content so it’s easy to quote: tight answers, clear sub-heads, rich schema.
Do the homework now and you’ll be ready when AI Mode graduates from beta to default – at least until the next boss fight, fully agentic search, shows up.
ChatGPT Vs. Google
The twist of 2025 is that Google is meeting ChatGPT on its own turf.
AI Mode lifts Google’s results page into the same chat-first UI that OpenAI popularized – proof that Google is willing to “level down” from its ad-optimized SERP if that’s what users expect.
Last year, I shared this graphic for the launch of ChatGPT Search and got lots of questions:
Image Credit: Kevin Indig
Two Takeaways From The Latest Projection (Chart Below):
If you extrapolate the entire data set, ChatGPT overtakes Google in October 2030.
If you extrapolate only the last 12 months, the crossover happens mid-2026.
Image Credit: Kevin Indig
Important Caveats:
Growth is not destiny. Google still owns distribution (Android, Chrome, Safari deals) and can slow ChatGPT by matching its features inside AI Mode and Gemini.
The projection measures query share, not revenue share. Even if ChatGPT wins usage, Google’s ads can keep the cash register ringing longer.
A single platform tweak (bundling, default settings, carrier deals) can bend either curve overnight – think of how Microsoft pushed Bing Chat via Windows updates.
What To Watch Next:
Pay-per-chat or embedded-ad experiments: Whichever company nails monetization without wrecking UX will sprint ahead.
Default-search contracts (Apple, Samsung, Mozilla) renewing in 2026–27. Losing any of those would be a body blow for Google.
Mobile latency and offline mode: If ChatGPT can run acceptably on-device, Google’s web moat shrinks fast.
Bottom line: treat the Google-ChatGPT battle as a live A/B test for the future of search.
Your job is to be visible in both ecosystems until a clear winner emerges – and that may take years.
Conductor Mode
Image Credit: Kevin Indig
So, where does all of this leave SEO (leaders)?
Less in the weeds, more on the podium.
Your job is no longer to fine-tune a single channel; it’s to keep an entire orchestra in time as search fragments across AI Overviews, chatbots, and social feeds.
No other role sits at the intersection of so much (intent) data – and that gives you license (and responsibility) to conduct.
1. Paid Media
Pipe impression, click, and conversion data from classic SERPs, AIOs, and AI Mode back into one shared Looker Dash.
Swap keywords and creative weekly; AI churn demands shorter feedback loops.
2. Social & Community
Mine Reddit threads, TikTok comments, and Discord chats to surface the “why” behind queries.
Feed those insights straight to content so every article answers a real objection.
3. Product Marketing
Hand them the exact language users copy-paste into prompts; that’s gold for positioning.
Return the favor by baking the latest differentiators into every meta description, schema tag, and featured snippet answer.
4. Content/GTM
Package what you learn into data stories, interactive tools, and expert POVs – assets worth citing by both humans and LLMs.
Structure it so agents can lift answers wholesale: tight headers, clear claims, evidence links.
What’s Next?
Search will get even more agentic.
We could soon optimize not just for people but for the AI helpers who act on their behalf.
That means:
Higher insight density per paragraph.
Structured outputs (tables, JSON, how-to checklists) ready for zero-click consumption.
APIs or embeddings that let agents pull your data directly.
We’re not there yet, but the runway is short.
Shift from tactician to conductor now, and you’ll have the score in hand when the orchestra changes instruments again.
Google has launched Audio Overviews, a new test feature in Search Labs. It creates audio summaries of search results using Google’s latest Gemini AI models.
How Audio Overviews Work
Audio Overviews turn Google Search results into audio content. When Google thinks an audio overview might help, you’ll see an option to create a short audio summary right on the results page.
You can see how the interface looks in the example below:
Screenshot from: labs.google.com/search/experiment/ June 2025.
After clicking the button to generate the summary, Google will process the information in the SERP and create an audio snippet.
Google says the feature helps users “get a lay of the land” when searching for topics they are unfamiliar with.
Audio Overviews retains the primary value of Google Search by displaying web pages directly within the audio player. This allows users to click through to explore specific sources.
Technical Requirements and Limitations
To use Audio Overviews, you must sign up for the experiment through Search Labs, Google’s testing platform for new search features. The feature only works in English and only for users in the United States right now.
After clicking the “Generate Audio Overview” button, creation can take up to 40 seconds. Once it’s done, the audio plays directly on the page.
Google has built-in ways for users to give feedback with thumbs-up or thumbs-down ratings. This feedback will likely help Google refine the feature before making it available to a wider audience.
AI Content Considerations
Google is upfront about the technology being experimental. The company notes that “content and voices in this experience are created with AI” and warn that “generative AI is experimental, so there may be inaccuracies and audio glitches.”
While Google emphasizes that Audio Overviews direct users to source content, some publishers may see this as part of a broader trend that reduces click-throughs from search. If AI-generated summaries satisfy user intent too well, they could further shift attention away from original creators.
Google’s inclusion of visible web links in the audio player suggests an effort to maintain attribution. Still, it’s unclear how effective these links are at driving traffic compared to traditional search listings.
Looking Ahead
Audio Overviews mark another step in Google’s efforts to make Search more multimodal and accessible. By offering spoken summaries powered by generative AI, the company is testing how voice-first experiences might complement traditional search behaviors.
While the feature prioritizes linking to source content, its long-term impact on publisher traffic and content attribution remains to be seen.
As with other generative AI experiments in Search, how users respond will likely shape whether and how Google expands this format.
In an attempt to keep up with the LLMs, Google launched AI Overviews and just announced AI Mode tabs.
The expectation is that SERPs will become blended with a Large Language Model (LLM) interface, and the nature of how users search will adapt to conversations and journeys.
However, there is an issue surrounding AI hallucinations and misinformation within LLM and Google AI Overview generated results, and it seems to be largely ignored, not just by Google but also by the news publishers it affects.
More worrying is that users are either unaware or prepared to accept the cost of misinformation for the sake of convenience.
Barry Adams is the authority on editorial SEO and works with the leading news publisher titles worldwide via Polemic Digital. Barry also founded the News & Editorial SEO Summit along with John Shehata.
“LLMs are incredibly dumb. There is nothing intelligent about LLMs. They’re advanced word predictors, and using them for any purpose that requires a basis in verifiable facts – like search queries – is fundamentally wrong.
But people don’t seem to care. Google doesn’t seem to care. And the tech industry sure as hell doesn’t care, they’re wilfully blinded by dollar signs.
I don’t feel the wider media are sufficiently reporting on the inherent inaccuracies of LLMs. Publishers are keen to say that generative AI could be an existential threat to publishing on the web, yet they fail to consistently point out GenAI’s biggest weakness.”
The post prompted me to speak to him in more detail about LLM hallucinations, their impact on publishing, and what the industry needs to understand about AI’s limitations.
You can watch the full interview with Barry on IMHO below, or continue reading the article summary.
Why Are LLMs So Bad At Citing Sources?
I asked Barry to explain why LLMs struggle with accurate source attribution and factual reliability.
Barry responded, “It’s because they don’t know anything. There’s no intelligence. I think calling them AIs is the wrong label. They’re not intelligent in any way. They’re probability machines. They don’t have any reasoning faculties as we understand it.”
He explained that LLMs operate by regurgitating answers based on training data, then attempting to rationalize their responses through grounding efforts and link citations.
Even with careful prompting to use only verified sources, these systems maintain a high probability of hallucinating references.
“They are just predictive text from your phone, on steroids, and they will just make stuff up and very confidently present it to you because that’s just what they do. That’s the entire nature of the technology,” Barry emphasized.
This confident presentation of potentially false information represents a fundamental problem with how these systems are being deployed in scenarios they’re not suited for.
Are We Creating An AI Spiral Of Misinformation?
I shared with Barry my concerns about an AI misinformation spiral where AI content increasingly references other AI content, potentially losing the source of facts and truth entirely.
Barry’s outlook was pessimistic, “I don’t think people care as much about truth as maybe we believe they should. I think people will accept information presented to them if it’s useful and if it conforms with their pre-existing beliefs.”
“People don’t really care about truth. They care about convenience.”
He argued that the last 15 years of social media have proven that people prioritize confirmation of their beliefs over factual accuracy.
LLMs facilitate this process even more than social media by providing convenient answers without requiring critical thinking or verification.
“The real threat is how AI is replacing truth with convenience,” Barry observed, noting that Google’s embrace of AI represents a clear step away from surfacing factual information toward providing what users want to hear.
Barry warned we’re entering a spiral where “entire societies will live in parallel realities and we’ll deride the other side as being fake news and just not real.”
Why Isn’t Mainstream Media Calling Out AI’s Limitations?
I asked Barry why mainstream media isn’t more vocal about AI’s weaknesses, especially given that publishers could save themselves by influencing public perception of Gen AI limitations.
Barry identified several factors: “Google is such a powerful force in driving traffic and revenue to publishers that a lot of publishers are afraid to write too critically about Google because they feel there might be repercussions.”
He also noted that many journalists don’t genuinely understand how AI systems work. Technology journalists who understand the issues sometimes raise questions, but general reporters for major newspapers often lack the knowledge to scrutinize AI claims properly.
Barry pointed to Google’s promise that AI Overviews would send more traffic to publishers as an example: “It turns out, no, that’s the exact opposite of what’s happening, which everybody with two brain cells saw coming a mile away.”
How Do We Explain The Traffic Reduction To News Publishers?
I noted research that shows users do click on sources to verify AI outputs, and that Google doesn’t show AI Overviews on top news stories. Yet, traffic to news publishers continues to decline overall.
Barry explained this involves multiple factors:
“People do click on sources. People do double-check the citations, but not to the same extent as before. ChatGPT and Gemini will give you an answer. People will click two or three links to verify.
Previously, users conducting their own research would click 30 to 40 links and read them in detail. Now they might verify AI responses with just a few clicks.
Additionally, while news publishers are less affected by AI Overviews, they’ve lost traffic on explainer content, background stories, and analysis pieces that AI now handles directly with minimal click-through to sources.”
Barry emphasized that Google has been diminishing publisher traffic for years through algorithm updates and efforts to keep users within Google’s ecosystem longer.
“Google is the monopoly informational gateway on the web. So you can say, ‘Oh, don’t be dependent on Google,’ but you have to be where your users are and you cannot have a viable publishing business without heavily relying on Google traffic.”
What Should Publishers Do To Survive?
I asked Barry for his recommendations on optimizing for LLM inclusion and how to survive the introduction of AI-generated search results.
Barry advised publishers to accept that search traffic will diminish while focusing on building a stronger brand identity.
“I think publishers need to be more confident about what they are and specifically what they’re not.”
He highlighted the Financial Times as an exemplary model because “nobody has any doubt about what the Financial Times is and what kind of reporting they’re signing up for.”
This clarity enables strong subscription conversion because readers understand the specific value they’re receiving.
Barry emphasized the importance of developing brand power that makes users specifically seek out particular publications, “I think too many publishers try to be everything to everybody and therefore are nothing to nobody. You need to have a strong brand voice.”
He used the example of the Daily Mail that succeeds through consistent brand identity, with users specifically searching for the brand name with topical searches such as “Meghan Markle Daily Mail” or “Prince Harry Daily Mail.”
The goal is to build direct relationships that bypass intermediaries through apps, newsletters, and direct website visits.
The Brand Identity Imperative
Barry stressed that publishers covering similar topics with interchangeable content face existential threats.
He works with publishers where “they’re all reporting the same stuff with the same screenshots and the same set photos and pretty much the same content.”
Such publications become vulnerable because readers lose nothing by substituting one source for another. Success requires developing unique value propositions that make audiences specifically seek out particular publications.
“You need to have a very strong brand identity as a publisher. And if you don’t have it, you probably won’t exist in the next five to ten years,” Barry concluded.
Barry advised news publishers to focus on brand development, subscription models, and building content ecosystems that don’t rely entirely on Google. That may mean fewer clicks, but more meaningful, higher-quality engagement.
Moving Forward
Barry’s opinion and the reality of the changes AI is forcing are hard truths.
The industry requires honest acknowledgment of AI limitations, strategic brand building, and acceptance that easy search traffic won’t return.
Publishers have two options: To continue chasing diminishing search traffic with the same content that everyone else is producing, or they invest in direct audience relationships that provide sustainable foundations for quality journalism.
Thank you to Barry Adams for offering his insights and being my guest on IMHO.
More Resources:
Featured Image: Shelley Walsh/Search Engine Journal
