Primer on ChatGPT’s 3 Bots

ChatGPT uses separate bots for training, searching, and taking action:

• GPTBot provides training data.
• OAI-SearchBot gathers data to respond to specific prompts.
• ChatGPT-User accesses pages when requested by users.

Knowing which bot is responsible for which task is essential before attempting to disallow it.

GPTBot

GPTBot locates information to build and update training data, ChatGPT’s knowledge base for providing answers.

ChatGPT doesn’t store training URLs or track where the info comes from. Disallowing this bot will prevent the platform from using your content for training, but it won’t impact your traffic. It may affect what ChatGPT understands about your company, though external sources likely provide that information, too.

Some publishers disallow the bot to prevent ChatGPT from learning from their content and to reduce costs, as AI bots can increase hosting needs and slow down servers, especially for large sites.

I typically suggest allowing access to GPTBot to provide first-hand information about a business and thus control the context.

ChatGPT updates training data regularly, usually with each release.

OAI-SearchBot

OAI-SearchBot searches the web for current information, user reviews, product details, and more.

Opinions differ as to whether the platform indexes the URLs from these searches. (ChatGPT states it “uses a hybrid system that includes limited indexing, plus on-demand retrieval, rather than a single, exhaustive web index.”)

OAI-SearchBot searches Google, Bing, Reddit, and others for info, much like humans, and may independently crawl sites, too.

Disallowing this bot prevents it from visiting your site, but it may still cite your pages via external links. Google does this, too, incidentally. A robots.txt file can prevent Google’s bot from crawling a site, but the search giant can still index and rank its pages.

Still, disallowing OAI-SearchBot will likely reduce or eliminate citations (and traffic), which is why I don’t usually advise it.

ChatGPT-User

ChatGPT-User performs actions as requested by users. For example, a user can prompt ChatGPT to visit a page and summarize its content.

ChatGPT-User does not provide training data or citations. If your server logs include this bot, a human instructed ChatGPT to visit your site. There’s no way to block this bot because it’s user-initiated, per ChatGPT.

20 SEO Experts Offer Their Advice For 2026 via @sejournal, @theshelleywalsh

This year has been a continuation of learning and understanding about how AI impacts our industry. It’s been less about the chaos of the initial disruption and more about “how do we leverage this?”

My belief is that SEO is a practice that needs to be adaptable to the end goal and not fixed to any predetermined notions centered around Google, ranking, or keywords. The foundation of SEO is about making yourself visible online wherever your audience can find you.

“S’ is for “search engine,” but one of my favorite phrases from the year is from Ashley Liddell, who said “search everywhere optimization,” and that is the perfect approach for the mindset needed to continue in the next level of SEO.

It might be TikTok, YouTube, Google, ChatGPT, or Reddit. Most likely, it’s a combination of all of these.

For the technical side of SEO, it’s fundamental that your pages can be accessed by all search engines and machines. For the content side of SEO, you need to be creating content experiences that can be cited by search engines and machines. And everyone should be thinking about the bottom line: Does this align with the defined business outcome for my client/brand/company? Show me the money.

One critical area I wouldn’t overlook is agentic AI and the development of closed systems completing actions for users. Think booking a holiday, personal shopping for a styled wardrobe, or buying your food shop based on a specific diet. When that happens, you need to ensure you are in the game and included in those closed spaces. Start learning about this now.

As the AI future is coming fast, get ready and go with it rather than resisting it. 2026 is the watershed where you need to get on board to stay in the game.

At Search Engine Journal, we showcase some of the best SEO minds in the industry, and in our usual tradition, we asked 20 of the best practicing SEO experts, including many of our contributors, “In 2026, what should SEOs focus on to maintain visibility and achieve measurable results?”

(Editor’s Note: The following are not in any order of preference and are displayed in the order of who responded first.)

How To Maintain Visibility Online In 2026

1. Be Mentioned In the Right Places

Kevin Indig, Growth Advisor

In 2026, visibility is the result of having the right content, engaging on the right channels, and being mentioned in the right places.

The right content is a mix of hyperlong-tail articles/landing pages tailored to your audience(s) and based on your unique positioning and data stories. People prompt 5x longer than they search on Google, so you want to be the best result for their specific context. LLMs also love fresh, unique datapoints, so you want to create research-driven content.

The right channels are Google, ChatGPT, Reddit, Quora, review sites, LinkedIn, and niche forums. Those are not just the most cited platforms in LLMs but also in Google Search. But being present here takes an engagement strategy rather than an SEO approach.

The right places to be mentioned are authoritative publishers and review sites in your industry. LLMs seem to rely heavily on mentions from other (relevant) sites, so you have to be present in context (surrounding words) that reflect your positioning and market position.

→ Read More: The Alpha Is Not LLM Monitoring

2. We Have To Do More Than Just Appease Google

Cindy Krum, CEO & Founder, MobileMoxie

We have to do more than just appease Google.

Now, to get visibility in all the places where it is needed, having a good website, with high-quality, indexable content, is table stakes; it is the bare minimum, and likely not enough.

For years, Google’s algorithm focused on using content and links to a site to evaluate that particular site, and rank it. AI search utilities and LLMs work very differently. They were designed to find a consensus and synthesize it, and they are looking across all the information that they have access to, to do it.

This means, if you are just relying on your website to create your visibility online, it will not be enough. There is no consensus and minimal synthesis from just one site.

Your branding message needs to be widely distributed across the web to create a consistent but discernibly unique branding message.

→ Read More: Google’s AI Search Journeys Are Reshaping SEO With Cindy Krum

3. Optimize For Systems That Read Like Machines

Duane Forrester, Founder and CEO, UnboundAnswers.com

In 2026, SEOs need to treat visibility as something earned through retrieval, not ranking.

Focus on how content is chunked, cited, and most importantly, trusted by AI systems.

Audit what gets surfaced inside chatbots and answer engines, not just in SERPs.

Build authority signals machines can verify: structured data, consistent sourcing, and entity clarity.

Use embeddings, vector search, and retrieval testing to understand how meaning (not keywords) drives exposure.

Replace “optimize for Google” with “optimize for systems that read like machines.” Your goal isn’t a blue link anymore. It’s being the trusted source those systems turn to when humans ask questions. Trust, in 2026, is paramount.

→ Read More: Ex-Microsoft SEO Pioneer On Why AI’s Biggest Threat To SEO Isn’t What You Think

4. Be Retrieved, Cited, And Trusted Wherever Users Search

Carolyn Shelby, Founder, CSHEL Search Strategies

In 2026, SEOs need to refocus on clarity, consistency, and comprehension.

Every channel that describes your brand – your site, feeds, listings, and profiles – must tell the same story, in the same words, in a way both humans *and machines* can understand. That means cleaning up fragmented site structures, removing “hidden” or toggle-buried information, and ensuring the important facts live on the page in visible text. (Note, I did not say Schema doesn’t matter, but I am saying that there are situations where the Schema that is in the JSON-LD is NOT being read, and for those times, it is important that you have valuable product specs and data ON the page, in visible text, and not hidden behind a tab or in a toggle.)

You won’t be penalized or hurt yourself in Google or Bing by *also* optimizing for the lowest-common-denominator crawlers – but you will lose out on that extra visibility if you ignore them. Build pages that are fast (LLMs have a short attention span), crawlable, and semantically clear. Make sure your product, pricing, and positioning statements are consistent across every surface.

The goal isn’t *just* to rank anymore (though ranking is still a necessary first step in most cases). It’s to be retrieved, cited, and trusted wherever users search – whether that’s Google, Bing, or an LLM.

→ Read More: Why AI Search Isn’t Overhyped & What To Focus On Right Now

5. Visibility Will Depend On Agentic Readiness

Andrea Volpini, Co-Founder and CEO, WordLift

In 2026, we are finally designing for the Reasoning Web, where agents will read, decide, and act on our behalf, and SEO becomes the discipline of making these systems effective. Visibility will depend on agentic readiness: clean structured data, stable identifiers, precise ontologies, and knowledge graphs that let agents resolve entities, compare offers, execute tasks, and learn from results.

This is a semantic shift: not simply about being “mentioned” in AI Overviews or ChatGPT, but about exposing products, content, and services as machine-operable assets through feeds, APIs, and tools that make agents smarter every time they interact with us.

The brands that let agents run the show, safely and verifiably, will own the next chapter of search.

→ Read More: How Structured Data Shapes AI Snippets And Extends Your Visibility Quota

6. Search And Product Are Intimately Connected

Ray Grieselhuber, Founder & CEO, DemandSphere

The most important thing, in our view, is understanding that AI search is ubiquitous now across three core experiences: SERPs, LLMs, and agentic experiences.

For the first two, SERPs and LLMs, there is a lot of overlap because they rely on a shared search index (Google in most cases), but the way in which the retrieval process works across these two experiences varies widely. This is why we are hearing that everyone’s No. 1 problem is getting good data, so spend time to make sure your monitoring and data pipelines are accurate and fine-tuned.

For the agentic experience, it’s still early but you should be thinking about how your product strategy will intersect with feeds and APIs (and new, related protocols like MCP). Search and product are intimately connected going forward, and the real ones will know that they always have been.

→ Read More: AI Platform Founder Explains Why We Need To Focus On Human Behavior, Not LLMs

7. Have A Relentless Focus On Being The Best

Barry Adams, Polemic Digital

Whatever you do, don’t lose your mind to the AI hype and try to radically reinvent your SEO efforts. Yes, it will be tougher to grow traffic and revenue from search, but too many SEOs have been coasting along and relying on Google’s own growth to fuel their figures. Now that clicks from Google have stagnated, you’ll need to be smarter about your SEO.

Spend less time and effort on “busywork,” those minor little things that don’t bring any measurable improvement to your traffic. Do the stuff that actually works. Don’t compromise on quality, have a relentless focus on being the best, and make sure you capitalize on your site’s strengths and eradicate its weaknesses.

Sites that are significantly suboptimal, either technically or editorially, will simply not succeed. You have to be all-in on search, without cutting corners and “that will do” concessions. Anything less than that and you will end up on the wrong side of the zero-sum game that Google search has become.

→ Read More: AI Survival Strategies For Publishers

8. Focus On Quality And Conversion Over The Quantity Of Content

Lily Ray, Vice President, SEO Strategy & Research, Amsive

For many years, I’ve answered this question with some version of “focusing on E-E-A-T,” and believe it or not, I think this answer *still* applies in 2026 with the rise of AI search.

Why? Because being mentioned in AI search is all about reputability, experience, and trust. The more your brand is well-known and well-respected in your industry, the more likely LLMs will be to cite you as a trusted and recommended brand. This requires earning mentions and positive reviews in all the places where it matters; having a well-known and well-respected team of individuals who contribute authentic, expert insights into the brand’s content, etc.

As homogenous, AI-generated content floods the internet, users will continue to want to follow real human creators engaged in honest and authentic conversations. Also, focus on the quality and conversion potential of content over the quantity of content, as the latter can cause major SEO headaches over time.

→ Read More: The Role Of E-E-A-T In AI Narratives: Building Brand Authority For Search Success

9. Maintain A Strong Focus On Retrieval Systems And Search Overall

Pedrio Dias, Technical SEO/AI Discoverability Consultant, Visively

I believe that, in the current scenario where a significant amount of new (AI) technologies have been introduced between users and how we interact with the web, and are currently being seen through a disruptive lens, it’s more important than ever to maintain objectivity and pragmatism in our approach to organic visibility as a whole, and search in particular. As professionals, we need to understand in depth the changes that we’re being faced with, both from a technical point of view, but also (and maybe more importantly) from a behavioral point of view.

It’s tempting to cling to old habits and metrics to chase around, instead of assessing if and how we need to rework our strategies and tactics. We’re currently being bombarded with an insane amount of tools claiming to “give you insights into AI answers” and promising that they can give you directional “data” – and in some cases even bold claims of outcomes – but we haven’t even started to understand if any kind of optimization can be performed on AI, or even if inference can be influenced in any controlled and desirable way. So far, everyone is mostly just poking around, guessing, and hoping.

So, that said, in 2026, I believe SEOs should maintain a strong focus on retrieval systems and search overall. Make sure your SEO strategy didn’t get stuck in 2005 and that you’re considering all areas that contribute to consistency in visibility, be it content, branding, technical, etc.

Above all, make sure your share-of-voice strategy is omnichannel and isn’t siloed. All this while keeping your curiosity sharp and your critical thinking aimed at questioning the inconsistencies, while being cautious with a dive-head-first approach.

Watch out for overpromising claims, outdated methodologies sitting on top of baseless assumptions, and vanity metrics.

→ Read More: AI Overviews – How Will They Impact The Industry? We Ask Pedro Dias

10. Remain Focused On What Drives Impact

Montserrat Cano, MC. International SEO & Digital Strategy

In 2026, SEOs and digital marketers need to combine a deep understanding of how AI platforms work with a strong knowledge of their user base across every market.

As search becomes more personalized, AI-driven, and fragmented, visibility may also depend on understanding local search behaviors, expectations, cultural nuances, and how audiences interact with SERP features and LLMs along the purchase path, often in different ways.

The real value comes from embedding this research into ongoing internal processes such as content planning, prioritization, and testing. This ensures teams remain focused on what drives impact, e.g., the queries and content formats that matter, and the AI experiences users actually engage with.

Grounding strategies in first-party data, current market insights, and continuous learning may protect visibility and help build sustainable growth. In 2026, this becomes a core capability for effective SEO and marketing strategy.

→ Read More: Why The Build Process Of Custom GPTs Matters More Than The Technology Itself

11. Review How Content Is Organized, Linked, And Surfaced

Alex Moss, Principal SEO, Yoast

Site speed, UX, and IA are obvious and constant, but structure is something that needs to be audited and improved in the coming months, as we now need to accommodate for both agents and humans. Review how content is organized, linked, and surfaced.

Schema is essential, where in 2026, they will be utilized more to understand entities and their relationships better, which in turn reduces possible hallucinogenic responses from agents.

Also concentrate on IA, query grouping, and internal linking. These strategies have existed for some time, but also need to be revisited if you haven’t done so recently.

For brand and offsite, shift from old-hat link acquisition and instead focus on brand sentiment through third-party perspectives, including native digital PR (unlinked brand mentions are welcome).

Finally, take advantage of multi-modal content – invest in imagery, video, and platforms beyond traditional search to increase discoverability.

→ Read More: The Same But Different: Evolving Your Strategy For AI-Driven Discovery

12. Focusing On Evaluating The Revenue Impact Of Your Strategies

Helen Pollitt, Head of SEO, Getty Images

In 2026, SEOs should be focusing on evaluating the revenue impact of their strategies. Too often, SEOs fall into the trap of trying to optimize for traffic or following the newest advice or fancy tactic.

In reality, the most effective SEO strategies are those that are constantly driving towards revenue or other commercial goals. Keeping this premise front and center to your SEO strategies in 2026 will ensure you don’t get sidetracked by the latest SEO fad rather than working on a plan that drives genuine value to your business.

This means setting out your priorities based on their likeliness of success, and their revenue-generating potential. This simple calculation can help you to identify which projects or activities are worth focusing on in 2026. You will be able to identify if the latest “reverse-meta-optimization-deindexing” fad, or whatever it ends up being, is really worth your budget and resources to pursue.”

→ Read More: Ask An SEO: How Can You Distinguish Yourself In This Era Of AI Search Engines?

13. Treat The Website Like An Enterprise System

Bill Hunt, Global Strategist with Bisan Digital

In 2026, SEOs must stop optimizing solely for pages and singular phrases and start optimizing for topical understanding.

AI-driven search systems are no longer ranking documents but evaluating entities, synthesizing answers, and choosing which brands they trust enough to cite. Visibility now depends on three things: clean, authoritative data; deep topical coverage; and systems that make your content easy to retrieve, understand, and reuse. If your site architecture, structured data, and feeds aren’t aligned to these eligibility gates, you’re invisible before the ranking discussion even begins.

The SEOs who will win in 2026 are the ones who treat the website like an enterprise system, not a collection of pages. That means building durable information architecture, improving data reliability, collaborating with product and engineering teams, and creating content designed for synthesis across formats – not just the blue link.

If you’re not strengthening your site’s underlying information integrity and cross-functional alignment, you’re not competing in the new search environment; you’re just publishing.

→ Read More: Industry Pioneer Reveals Why SEO Isn’t Working & What To Refocus On

14. Develop A Distributed Revenue Strategy

John Shehata, CEO & Founder, NewzDash

In 2026, Brand Authority takes the front seat, replacing traffic volume as the primary metric. AI platforms prioritize trusted entities, so you must prove you are one. SEOs need a dual-speed strategy: a short-term strategy that maximizes today’s Google reality, and a long-term plan for a world where traffic and attention are more fragmented.

In the short term, Google is still the primary traffic driver, so optimize for multi-surface and multi-modal visibility. That means targeting AI Overviews, Discover, Top Stories, video, and short-form reels, not just traditional text results.

Convert every visitor into a direct connection through email, apps, and own communities. At the same time, double down on entity and topic authority, publish useful and unique content that is hard for AI to replicate, such as strong opinion, investigative work, and proprietary data, and strengthen technical SEO, structured data, and answer-ready formatting.

Long-term: Prepare for a post-click reality. Develop a distributed revenue strategy driven by a creator network that monetizes attention directly on social platforms and AI interfaces, accepting that success means revenue generated off-site, not just on your domain.

→ Read More: Google Discover, AI Mode, And What It Means For Publishers: Interview With John Shehata

15. Really Focus On Your Audience

Harry Clarkson-Bennett, SEO Director, The Telegraph

This is very brand and customer-dependent. My best advice is to really focus on your audience. Speak to them. Understand the impact SEO should have vs the impact it currently has. There may still be easy wins on the table. Don’t neglect it.

If you use a last click attribution system, I suspect SEO is over-valued. Work with your analytics team to trial multi-touch attribution and try to figure out the value of each channel. Then work with your PPC, social, and newsletter teams to create a proper marketing and acquisition strategy. Build your owned channels. Improve your blended CPA and solve real business problems.

This is the year you manage up more effectively and stop silo-ing channels and people. Make SEO Great Again.

→ Read More: The Impact AI Is Having On The Marketing Ecosystem

16. Transform Metrics Into Strategic Levers

Motoko Hunt, International SEO Consultant, AJPR

Audit and evolve your measurement framework. Many organizations track extensive data points without translating insights into actionable optimization strategies. The key differentiation lies not in data collection, but in strategic application.

Adapt your metrics architecture for the fragmented SERP landscape. With AI Overviews, featured snippets, and expanding SERP features fragmenting traditional organic visibility, implement granular tracking that isolates performance by SERP element. This segmentation reveals where you’re capturing attention and, more critically, where competitors are intercepting traffic before users reach your listings.

Balance emerging channels with revenue-driving fundamentals. AI search warrants monitoring – track share of voice in AI-generated responses and assess brand mention quality. However, at current adoption rates, AI search primarily serves upper-funnel awareness objectives. Your core optimization efforts should remain anchored to proven conversion pathways: traditional organic search, site experience optimization, and technical excellence that drives qualified traffic and revenue.

Transform metrics into strategic levers. Don’t just report CTR decline from position 3 to 5 – quantify the revenue impact, and identify the ranking factors at play. Connect performance gaps directly to business outcomes, then prioritize initiatives that close those gaps with the highest ROI potential.

→ Read More: Effective SEO Organizational Structure For A Global Company

17. Be Aware Of Falsehoods Which Will Continue To Circulate

Dawn Anderson, International SEO Consultant, Bertey

In 2026, SEOs should accept that we continue to have a steeper-than-ordinary SEO learning curve ahead of us. How AI is going to fully impact our industry over time continues to be largely an educated guessing game.

LLMs and agentic search provide a considerable opportunity, but it is important to not simply presume producing copy and paste AI LLM slop will make the cut for performative SEO in 2026, since this is a degenerative downward quality spiral. Instead, we must prioritize adding more authentic value beyond the norm, standing head and shoulders above competitors, and using AI predominantly for efficiency and ideation kick starting, along with prototype generation and concept testing.

Building increasingly robust reputation and authority through quality and connections should remain firmly a key priority. Particularly as the general consensus of opinion in verticals will continue to build via accumulative LLM extractions, shaping competitive narratives.

We should also be aware of falsehoods, which will continue to circulate in the vacuum of genuine knowledge that these severe industry changes create.  Don’t end up going down the wrong paths which may be very difficult to return from in the short to medium term.

→ Read More: Building Trust In The AI Era: Content Marketing Ethics And Transparency

18. Understand The User And How They Make Decisions

Giulia Panozzo, Founder, Neuroscientive

I believe that our key to achieving measurable results in 2026 is looking beyond the tactics and the new shiny tools: we need to get back to basics and really understand the user, their motivations, their frustrations, and mostly how they make decisions.

When customers decide to engage with a brand, a product, or a service, they do so by leveraging a number of micro-decisions that have very little to do with our marketing tactics and a lot more to do with their expectations and needs, their personal experiences, and the perception they have about us. A lot of those choices are made subconsciously, before they are even aware of them – and as a result, they are not visible by looking at traditional metrics.

So, focus on the bigger picture by working cross-functionally to understand not only how people get to your site, but what underlying needs and expectations they have by leveraging social listening, CX logs, and on-site behavioral metrics that will inform what they need to see and engage with before they even click on your result on the SERP.

→ Read More: The Behavioral Data You Need To Improve Your Users’ Search Journey

19. Find Ways To Differentiate Yourself From The Noise

Alli Berry, SEO Director, Marketwise

Looking into 2026 and beyond, I think SEOs need to be focused heavily on digital PR efforts and getting brand mentions and links from influential sites and people. I think we’re going to hit a point where what others say about your brand is going to have more weight than what you say about your own brand.

We’re already starting to see that with Reddit and forums, and as LLMs gain more traction, that is only becoming a more important factor in gaining visibility.

I’d also be focused on finding unique content angles that can’t be easily replicated by AI. Whether it’s telling customer stories or doing primary research, you’re going to need to find ways to differentiate yourself from the noise.

→ Read More: How To Get Brand Mentions In Generative AI

20. Have Influence Where Your Audience Is

Shelley Walsh, Managing Editor, Search Engine Journal & IMHO

During times of significant flux, go to the fundamentals and hold on: Know where your audience is finding its trusted information and have influence in those spaces.

If you embrace this core maxim, it will guide you through all the changes that Google, discovery engines, LLMs, and whatever comes next can throw at you.

However, don’t overlook the significant changes happening with technology that do influence the channels through which audiences can find us. Also, pay attention to how agentic SEO is developing so that you can consider now how you could apply it to your niche.

Don’t get caught up in pointless arguments over nomenclature or caught up in hype cycles chasing distractions. Keep focusing on what a user wants and applying your brand presence and message where they can see it. Everyone is running around like the sky is falling, but it’s all just SEO.

→ Read More: Google’s Old Search Era Is Over – Here’s What 2026 SEO Will Really Look Like

SEO In 2026

What most of our experts are saying is that what is changing is not so much the how, but the where.

Search is happening everywhere, and you need to ensure your brand narrative is accessible and consistent across all the channels where your audience is.

However, that means being mentioned in the right places, and constantly asking: “Does this move the needle for revenue, or is it just more noise?”

The future of search is being built in real time, so make sure you’re not just watching it happen, but actively shaping how your brand shows up in it.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

SEO Pulse: December Core Update, Preferred Sources & Social Data via @sejournal, @MattGSouthern

The December 2025 core update is the main story this week.

Google confirmed a new broad ranking update, clarified how often core changes happen, expanded Preferred Sources in Top Stories, and started testing social performance data in Search Console Insights.

Here’s what matters for your work.

Google Releases December 2025 Core Update

Google has released the December 2025 core update, its third core update of the year.

Key Facts

The rollout started on December 11, and Google says it may take up to three weeks to complete. This follows the March and June core updates and comes two days after Google refreshed its core updates documentation to explain smaller, ongoing changes.

Why SEOs Should Pay Attention

If you see big swings in rankings or traffic over the next few weeks, this update is probably the cause.

Core updates are broad changes to how Google evaluates content. Pages can move up or down even if you haven’t changed anything on the site, because Google is reassessing your content against everything else in the index.

The timing matters. Earlier in the week, Google reminded everyone that smaller core updates happen all the time. The December core update sits on top of that layer. You’re dealing with both a visible event and quieter, continuous adjustments running underneath.

Right now, the best move is to watch your data rather than panic. Mark the rollout dates in your reporting. Track when things start to move for your key sections. Compare this behavior with what you saw during the March and June updates. That helps you separate core-update effects from seasonality, technical issues, or campaign changes.

Over the longer term, this is another nudge toward content that shows clear expertise, purpose, and useful detail. The documentation change earlier in the week suggests those improvements can be recognized over time, not only when Google names a new core update.

What SEO Professionals Are Saying

Reactions on X focused on timing, expectations, and the kind of content that might come out ahead.

Some SEO professionals leaned into the holiday angle, joking that Google’s “Christmas update” could either deliver a gift or push sites “off a cliff” right before peak season. Others used the announcement to talk about human-written work, saying they hope this is the update where stronger, human-generated content gets more visibility.

There were also practical reads. A few people tied the update to recent delays in Search Console data, saying the backlog now makes more sense. Others pointed out that this is the third broad update in a year where Google is also investing heavily in AI systems, and that core updates now sit inside a bigger stack of changes rather than defining everything on their own.

Read our full coverage: Google Releases December 2025 Core Update

Google Confirms Smaller Core Updates Happen Continuously

Earlier in the week, Google updated its core updates documentation to spell out that ranking changes can happen between the named core updates.

Key Facts

The documentation now says Google makes smaller core updates on an ongoing basis, alongside the larger core updates it announces a few times a year. Google explained that this change is meant to clarify that sites can see ranking gains after making improvements without waiting for the next big announcement.

Smaller core updates were mentioned in a 2019 blog post, but this is the first time the concept appears directly in the core updates documentation.

Why SEOs Should Pay Attention

This answers a question that has been hanging over SEO for years. Recovery isn’t limited to moments when Google announces a core update. The new wording confirms that Google can reward improvements at any time as smaller updates roll out in the background.

If you’ve been holding back on site fixes or content work until “the next core update,” this is a good time to drop that pattern. You can ship improvements now, knowing there’s more than one window where Google might reassess your content.

The timing is interesting given this year’s release pattern. Until this week, the only named core updates in 2025 were the March and June releases, with several months between them. For sites hit early in the year, those gaps made it hard to know when changes might start to pay off. The December update adds another obvious checkpoint, but the documentation makes it clear that it isn’t the only one.

For reporting and communication, this supports a change from “wait for the next update” to “improve steadily and monitor continuously.” You still don’t need to chase every drop, but you can be more confident that sustained work has more than one chance to show up in the data.

What SEO Professionals Are Saying

Former Google search team member Pedro Dias summed up one common read, saying he thinks Google has finally reached a place where it doesn’t need to announce every core update separately. Others have connected the change to Google’s move toward layered ranking systems, where visible events are only one part of an ongoing stream of tweaks.

For you, that supports a slower, steadier approach. Instead of waiting for one moment to “fix” everything, you can keep tuning content and UX, and treat named core updates as checkpoints rather than the only chance to move.

Read our full coverage: Google Confirms Smaller Core Updates Happen Continuously

Google Expands Preferred Sources In Top Stories

Google is expanding Preferred Sources globally for English-language users, giving people more control over which outlets show up in Top Stories and similar news surfaces.

Key Facts

Preferred Sources lets people pick specific outlets they want to see more often when they browse news in Google Search. The feature is now rolling out to English-language users worldwide, with other supported languages planned for early next year. Google says people have already selected close to 90,000 different sources, from local blogs to large international publishers, and that users who mark a site as preferred tend to click through to it about twice as often.

Why SEOs Should Pay Attention

Preferred Sources gives you a direct way to turn casual readers into regulars inside Google’s own interfaces. If your site publishes timely coverage, you can now build a segment of people who have chosen to see more of your work in Top Stories.

That makes “choose us as a preferred source” another call to action you can test alongside email sign-ups and follow buttons. Some publishers are already creating simple guides that show readers how to add them and what changes once they do. You can take a similar approach, especially if you already have a loyal audience on site or through newsletters.

It’s also a signal that Google wants users to have more say in which outlets they see. For you, that means brand perception, clarity of coverage, and consistency matter a bit more, because people are deciding which sources they want in their feed instead of relying on a default mix.

What SEO Professionals Are Saying

On LinkedIn, several SEO professionals and content strategists pointed out that Preferred Sources mostly reinforces behavior that already exists.

Garrett Sussman notes that people tend to stick with outlets they trust. This feature simply makes that choice more visible and gives publishers another growth lever inside Google’s ecosystem.

If you work on news or frequently updated content, you can start treating Preferred Sources selection as its own metric. Watch how often people choose you, which articles tend to drive that choice, and how those readers behave over time.

Read our full coverage: Google Expands Preferred Sources & Publisher AI Partnerships

Google Tests Social Channel Insights In Search Console

Search Console is testing a feature that shows how your social channels perform in Google Search results.

Key Facts

Google announced a new experimental feature in Search Console that adds social performance data to the Search Console Insights report. It covers social profiles that Google has automatically associated with your site. For each connected profile, you can see clicks, impressions, top queries, trending content, and audience location.

The experiment is limited to a small set of properties, and you can’t manually add profiles. The feature only appears if Search Console detects your channels and prompts you to link them.

Why SEOs Should Pay Attention

Up to now, you’ve probably watched search performance for your site and your social channels in separate tools. This experiment pulls both into one place, which can save time and make it easier to see how people move between your website and your social profiles.

The new data shows which queries lead people to your social profiles, which posts tend to surface in search, and which markets use Google to find you on social platforms. That’s useful if you run campaigns where organic search, social content, and creator work all overlap.

The main limitation is access. If you don’t see a prompt in Search Console Insights asking you to connect detected social channels, your site isn’t in the initial test group. Still, it’s worth logging as a feature to watch, especially if you already spend time explaining how social content shows up for branded and navigational queries.

What SEO Professionals Are Saying

Reactions on LinkedIn focused on two main points. People liked the idea of a single view of website and social performance, and they quickly started asking when similar data might be available for AI Overviews, AI Mode, and other search experiences.

Others raised questions about coverage. Some practitioners want to know whether this data will stay limited to Google-owned properties or expand to platforms like Instagram, LinkedIn, and X. There’s also curiosity about how Google detects and links social profiles in the first place, and whether structured data or Knowledge Graph entities play a role.

Read our full coverage: Google Tests Social Channel Insights In Search Console

Theme Of The Week: Core Updates At Two Speeds

The common thread this week is movement at two speeds.

At one speed, you have the December 2025 core update. It’s a visible event with a clear start date, a multi-week rollout, and a lot of attention. At the other speed, you have the quieter changes around it.

Google has now said directly that smaller core updates happen all the time. Preferred Sources gives users more control over which outlets they see. Social insights start to connect website and social performance in one view.

For you, this means there’s no single moment when everything gets decided. Core updates still matter and can cause sharp movements, but they sit inside an environment where improvements can pay off gradually and where readers are making more explicit choices about who they want to hear from.

The practical response is to treat this as an ongoing feedback loop. Keep improving content and UX. Watch how those changes behave during calm periods and during core updates. Encourage your most engaged readers to mark you as a preferred source where they can. Keep an eye on how search and social interact for your brand. That way, you’re ready for both speeds.

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14 Things Executives And SEOs Need To Focus On In 2026 via @sejournal, @DuaneForrester

So many people spent 2025 arguing about whether SEO was dying. It was never dying. It was shifting into a new layer. Discovery continues to move from search boxes to AI systems. Answers now come from models that rewrite your work, summarize competitors, blend sources, and shape decisions before a browser window loads. In 2026, this shift becomes visible enough that executives and SEOs can no longer treat it like an edge case; percentages from sources will shift. The search stack that supported the last 20 years is now only one of several layers that shape customer decisions. (I talk about all this in my new book, “The Machine Layer” (non-affiliate link).)

This matters because the companies that win in 2026 will be the ones treating AI systems as new distribution channels. The companies that lose will be the ones waiting for their analytics dashboards to catch up. You no longer optimize for a single front door. You now optimize for many. Each one is powered by models that decide what to show, who to show it to, and how to describe you.

Here are 14 things that will define competitive advantage in 2026. Each one is already visible in real data. Together, they point to a year where discovery becomes more ambient, more conversational, and more dependent on how well a machine can parse and trust you. And at the end of this list is one heck of a prediction that I bet you didn’t see coming for next year! If I’m being honest, I’m sure a few of you did, but to this depth? Realizing it was all so close?

Grab a coffee or tea, find your favorite spot to read, and let’s get started!

1. AI Answer Surfaces Become The New Front Door

ChatGPT, Claude, Gemini, Meta AI, Perplexity, CoPilot, and Apple Intelligence now sit between customers and your website. More and more users ask questions inside these systems before they ever search. And the answers they get are inconsistent. BrightEdge’s analysis showed that AI engines disagree with each other 62% of the time. When engines disagree this much, brand visibility becomes unstable. Executives need reporting that reveals how often their brand appears inside these systems. SEOs need workflows that evaluate chunk retrieval, embedding strength, and citation presence across multiple answer engines.

2. Content Must Be Designed For Machine Retrieval

Microsoft’s 2025 Copilot study analyzed more than 200,000 work sessions. The most common AI-assisted tasks were gathering information, explaining information, and rewriting information. These are the core tasks modern content must support. AI models choose content that is structured, predictable, and easy to embed. If your content lacks clear sectioning, consistent patterns, or explicit definitions, it becomes harder for models to use. This impacts whether you appear in answers. In 2026, your formatting choices become ranking signals for machines.

3. On-Device LLMs Change How People Search

Apple Intelligence runs many tasks locally. It also rewrites queries in more natural conversational patterns. This pushes search activity away from browsers and deeper into the operating system. People will ask their device short, private questions that never hit the web. They will ask follow-up questions inside the OS. They will make decisions without ever visiting a page. This shifts both volume and structure. SEOs will need content designed for lightweight, edge device retrieval.

4. Wearables Start Steering The Discovery Funnel

Meta Ray Bans already support visual queries. The user points at something and asks what it is. Voice and camera replace typing. This increases micro queries tied to real-world context. Expect to see more identify this, what does this do, and how do I fix that queries. Wearables compress the distance between stimulus and search. Executives should invest in image quality, product clarity, and structured metadata. SEOs should treat visual search signals as core inputs.

5. Short-Form Video Becomes A Training Input For AI

Video is now a core training signal for modern multimodal models. V-JEPA 2 from Meta AI is trained on an unknown number of hours of raw video and images, but this still shows that large-scale video learning is becoming foundational for motion understanding, physical prediction, and video question answering. Gemini 2.5 from Google DeepMind explicitly supported video understanding, allowing the model to interpret video clips, extract visual and audio context, and reason over sequences. OpenAI’s Sora research demonstrates that state-of-the-art generative video models learn from diverse video inputs to understand motion, physical interactions, transitions, and real-world dynamics. In 2026, your short-form video becomes part of your broader signal footprint. Not only the transcript. The visuals, pacing, motion, and structure become vectors the model can interpret. When your video output and written content diverge, the model will default to whichever medium communicates more clearly and consistently.

6. Organic Search Signals Shift Toward Trust And Provenance

Traditional algorithms relied on links, keywords, and click patterns. AI systems shift that weight toward provenance and verification. Perplexity describes its model as retrieval-augmented, pulling from authoritative sources like articles, websites, and journals and surfacing citations to show where information comes from. Independent audits support this direction. A 2023 evaluation of generative search engines found that systems like Perplexity favored content that is factual, well-structured, and supported by external evidence when assembling cited answers. This remains true today as well. SEO industry analysis also shows that pages with clear metadata, consistent topical organization, and visible author identity are more likely to be cited. Naturally, all of this changes what trust looks like. Machines prioritize consistency, clarity, and verifiable sourcing. Executives should focus on data governance and content stability. SEOs should focus on structured citations, author attribution, and semantic coherence across their content ecosystem.

7. Real-Time Cohort Creation Replaces Static Personas

LLMs build temporary cohorts by clustering people with similar intent patterns. These clusters can form in seconds and dissolve just as fast. They are not tied to demographics or personas. They are based on what someone is trying to do right now. This is the basis of the experiential cohort concept. Marketers have not caught up yet. In 2026, cohort-based targeting will shift toward intent embeddings and away from persona documents. SEOs should tune content for intent patterns, not identity attributes.

8. Agent-To-Agent Commerce Becomes Real

Agents will schedule appointments, book travel, reorder supplies, compare providers, and negotiate simple agreements. Your content becomes instructions for another machine. To support that, it must be unambiguous. It must be explicit about requirements, constraints, availability, pricing rules, and exceptions. If you want an agent to pick your business, you need a content model that feeds the agent’s decision tree. Executives should map the top 10 agent-mediated tasks in their industry. SEOs should design content that makes those tasks easy for a machine to interpret.

9. Hardware Acceleration Pushes AI Into Every Routine

NVIDIA, Apple, and Qualcomm are all building hardware optimized for on-device and low-latency AI inference. These chips reduce friction, which increases the number of everyday questions people ask without ever opening a browser. NVIDIA’s data center inference platforms show how much compute is moving toward real-time model execution. Qualcomm’s AI Hub highlights how modern phones can run complex models locally, shrinking the gap between thought and action. Apple’s M-series chips include Neural Engines that support local model execution inside Apple Intelligence. Lower friction means people will ask more small, immediate questions as they move through their day instead of grouping everything into one session. SEOs should plan for discovery happening across many short, assistant-driven interactions rather than a single focused search moment.

10. Query Volume Expands As Voice And Camera Take Over

Voice input grows the long tail. Camera input grows contextual queries. The Microsoft Work Trend Index shows rising AI usage across everyday task categories, including personal information gathering. People ask more questions because speaking is easier than typing. The shape of demand widens, which increases ambiguity. SEOs need stronger intent classification workflows and a better understanding of how retrieval models cluster similar questions.

11. Brand Authority Becomes Machine Measurable

Models determine authority by measuring consistency across your content. They look for stable terminology, clear entity relationships, and patterns in how third parties reference you. They look for alignment between what you publish and how the rest of the web describes your work. This is not the old human quality framework. It is a statistical confidence score. Executives should invest in knowledge graphs. SEOs should map their entity network and tune the language around each entity for stability.

12. Zero-click Environments Become Your Primary Competitor

Answer engines pull from multiple sources and give the user a single synthesized answer. This reduces visits but increases influence. In 2026, the dominant competitors for organic attention are ChatGPT, Perplexity, Gemini, CoPilot, Meta AI, and Apple Intelligence. You do not win by resisting zero click. You win by being the source the engine prefers. Executives must adopt new performance metrics that reflect answer presence. SEOs should run monthly audits of brand visibility across all major platforms, tracking citations, mentions, paraphrases, and omissions.

13. Competitive Intelligence Shifts Into Prompt Space

Your competitors now live inside AI answers, whether they want to or not. Their content becomes part of the same retrieval memory that models use to answer your queries. In 2026, SEOs will evaluate competitor visibility by studying how platforms describe them. You will ask models to summarize competitors, benchmark capabilities, and compare offerings. The insights you get will shape strategy. This becomes a new research channel that executives can use for positioning and differentiation.

14. Your Website Becomes A Training Corpus

AI systems will digest your content many times before a human does. That means your site is now a data repository. It must be structured, stable, and consistent. Publishing sloppy structure or unaligned phrasing creates noise inside retrieval models. Executives should treat their content like a data pipeline. SEOs should think like information architects. The question shifts from how do we rank to how do we become the preferred reference source for a model.

The companies that succeed in 2026 will be the ones that understand this shift early. Visibility now lives in many places at once. Authority is measured by machines, not just people. Trust is earned through structure, clarity, and consistency. The winners will build for a world where discovery is ambient, and answers are synthesized. The losers will cling to dashboards built for a past that is not coming back.

Now, if you’ve read this far, thank you, and I have a surprise – an actual prediction for 2026! I think it’s a big, important one, so buckle up!

I’m calling this Latent Choice Signals, or these, I suppose, as it’s a grouping of signals that paint a picture for the platforms. From the consumer’s POV, this is the essential mental map they’re following: “I saw it, I felt something about it, and I decided not to continue.” This is the core. The user’s mind is making a choice, even if they never articulate it or click anything. That behavior generates meaning. And the system can interpret that meaning at scale. Let’s dig in…

The Prediction No One Sees Coming

By the end of 2026, AI systems will begin optimizing decisions around the patterns users never articulate. Not the queries they type. Not the questions they ask. But the choices they avoid.

This is the shift almost everyone misses, and you can see the edges of it forming across three different fields. When you pull them together, the picture becomes clearer.

First, operating system-level AI is already learning from behavior that is not explicitly expressed. Apple Intelligence is described as a personal intelligence layer that blends generative models with on device personal context to prioritize messages, summarize notifications, and suggest actions across apps. Apple built this for convenience and privacy, but it created something more important. The system must learn over time which suggestions people accept and which they quietly ignore. It sees which notifications get swiped away, which app actions never get used, and which prompts are abandoned. It does not need to read your mind. It only needs to see which proposed actions never earn a tap. Those patterns are already part of how it ranks what to surface next.

Second, recommender systems already treat non-actions as meaningful signals. You see it every time you skip a YouTube video, swipe past a TikTok in under a second, or close Netflix when the row of suggestions feels wrong. These platforms do not publish their exact mechanics, but implicit feedback is a well-established concept in the research world. Classic work on collaborative filtering for implicit feedback datasets shows how systems use viewing, skipping, and browsing behavior to model preference, even when users never rate anything directly. Newer work continues to refine how clicks, views, and avoidance patterns feed recommendation models at scale. It is reasonable to expect LLM-driven assistants to borrow from the same logic. The pattern is too useful to ignore. When you close an assistant, rephrase a question to avoid a certain brand, or scroll past a suggestion without engaging, that is data about what you did not want.

Third, alignment research already trains models to follow what humans prefer, not just what text predicts. OpenAI’s “Learning to summarize with human feedback” work shows how models can be tuned using human comparisons between outputs, with a reward model that learns which responses people think are better. This has been in play for years now. This kind of reinforcement learning from human feedback was built for tasks like summarization and style, but the underlying principle matters here. Models can be optimized around patterns of acceptance and rejection. Over time, conversational systems can extend this to live settings, where corrections, rewrites, and abandonments are treated as signals about what the user did not want, even when they never spell that out.

Put these three domains together, and a larger pattern emerges. As AI systems move into glasses, phones, laptops, cars, and operating systems, they will gain precise visibility into the choices people avoid. These avoidance patterns will become signals that inform how assistants rank options, choose providers, and recommend products.

This will not feel like surveillance. The model is not peeking into your private life. It is watching your interaction patterns with the system itself. It sees where you hesitate, which suggestions you skip, which tasks you hand off, which providers create follow-up questions, which prices cause users to pause, which explanations reduce confidence, and which interfaces break the chain of intent. These are all first-party behavioral signals the assistant is already allowed to use. And that platforms see these signals on a global scale.

In 2026, these Latent Choice Signals will become strong enough that they form a new optimization layer. A silent ranking system built around friction. If your brand generates hesitation, the assistant will reduce your visibility long before your analytics flag a problem. If your content creates confusion during synthesis, it will be bypassed during retrieval. If your policies trigger too many follow-up questions, the model will favor a competitor with clearer flows. The user will never know why. All they will see is the assistant presenting a different option.

This is the layer that will blindside executives. Dashboards will look normal. Rankings may appear stable. Traffic may hold steady. Yet conversions inside AI-mediated decisions will drift. Customers will stop choosing you, not because you lost traditional ranking signals, but because you introduced cognitive friction the model can detect and optimize against.

The winners will be the companies that treat avoidance as a measurable signal. They will analyze which parts of their product and content cause hesitation. They will refine policies to reduce ambiguity. They will simplify offerings. They will align explanations with how models process uncertainty. They will build experiences that reduce agent-level friction and improve confidence inside a retrieval sequence.

By late 2026, negative intent signals may become one of the strongest competitive filters in digital business. Not because users say anything, but because their silence now has structure the model can learn from. Anyone watching today’s data can see this shift forming, but almost no one is naming it. Yet the early indicators are already here, hiding between the interactions users never get far enough to complete.

This is the prediction that will define the next phase of AI-driven discovery. And the companies that understand it early will be the ones the assistants prefer.

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This post was originally published on Duane Forrester Decodes.

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