Google Lists 9 Scenarios That Explain How It Picks Canonical URLs via @sejournal, @martinibuster

Google’s John Mueller answered a question on Reddit about why Google picks one web page over another when multiple pages have duplicate content, also explaining why Google sometimes appears to pick the wrong URL as the canonical.

Canonical URLs

The word canonical was previously mostly used in the religious sense to describe what writings or beliefs were recognized to be authoritative. In the SEO community, the word is used to refer to which URL is the true web page when multiple web pages share the same or similar content.

Google enables site owners and SEOs to provide a hint of which URL is the canonical with the use of an HTML attribute called rel=canonical. SEOs often refer to rel=canonical as an HTML element, but it’s not. Rel=canonical is an attribute of the element. An HTML element is a building block for a web page. An attribute is markup that modifies the element.

Why Google Picks One URL Over Another

A person on Reddit asked Mueller to provide a deeper dive on the reasons why Google picks one URL over another.

They asked:

“Hey John, can I please ask you to go a little deeper on this? Let’s say I want to understand why Google thinks two pages are duplicate and it chooses one over the other and the reason is not really in plain sight. What can one do to better understand why a page is chosen over another if they cover different topics? Like, IDK, red panda and “regular” panda 🐼. TY!!”

Mueller answered with about nine different reasons why Google chooses one page over another, including the technical reasons why Google appears to get it wrong but in reality it’s someetimes due to something that the site owner over SEO overlooked.

Here are the nine reasons he cited for canonical choices:

1. Exact duplicate content
   The pages are fully identical, leaving no meaningful signal to distinguish one URL from another.
2. Substantial duplication in main content
   A large portion of the primary content overlaps across pages, such as the same article appearing in multiple places.
3. Too little unique main content relative to template content
   The page’s unique content is minimal, so repeated elements like navigation, menus, or layout dominate and make pages appear effectively the same.
4. URL parameter patterns inferred as duplicates
   When multiple parameterized URLs are known to return the same content, Google may generalize that pattern and treat similar parameter variations as duplicates.
5. Mobile version used for comparison
   Google may evaluate the mobile version instead of the desktop version, which can lead to duplication assessments that differ from what is manually checked.
6. Googlebot-visible version used for evaluation
   Canonical decisions are based on what Googlebot actually receives, not necessarily what users see.
7. Serving Googlebot alternate or non-content pages
   If Googlebot is shown bot challenges, pseudo-error pages, or other generic responses, those may match previously seen content and be treated as duplicates.
8. Failure to render JavaScript content
   When Google cannot render the page, it may rely on the base HTML shell, which can be identical across pages and trigger duplication.
9. Ambiguity or misclassification in the system
   In some cases, a URL may be treated as duplicate simply because it appears “misplaced” or due to limitations in how the system interprets similarity.

Here’s Mueller’s complete answer:

“There is no tool that tells you why something was considered duplicate – over the years people often get a feel for it, but it’s not always obvious. Matt’s video “How does Google handle duplicate content?” is a good starter, even now.

Some of the reasons why things are considered duplicate are (these have all been mentioned in various places – duplicate content about duplicate content if you will :-)): exact duplicate (everything is duplicate), partial match (a large part is duplicate, for example, when you have the same post on two blogs; sometimes there’s also just not a lot of content to go on, for example if you have a giant menu and a tiny blog post), or – this is harder – when the URL looks like it would be duplicate based on the duplicates found elsewhere on the site (for example, if /page?tmp=1234 and /page?tmp=3458 are the same, probably /page?tmp=9339 is too — this can be tricky & end up wrong with multiple parameters, is /page?tmp=1234&city=detroit the same too? how about /page?tmp=2123&city=chicago ?).

Two reasons I’ve seen people get thrown off are: we use the mobile version (people generally check on desktop), and we use the version Googlebot sees (and if you show Googlebot a bot-challenge or some other pseudo-error-page, chances are we’ve seen that before and might consider it a duplicate). Also, we use the rendered version – but this means we need to be able to render your page if it’s using a JS framework for the content (if we can’t render it, we might take the bootstrap HTML page and, chances are it’ll be duplicate).

It happens that these systems aren’t perfect in picking duplicate content, sometimes it’s also just that the alternative URL feels obviously misplaced. Sometimes that settles down over time (as our systems recognize that things are really different), sometimes it doesn’t.

If it’s similar content then users can still find their way to it, so it’s generally not that terrible. It’s pretty rare that we end up escalating a wrong duplicate – over the years the teams have done a fantastic job with these systems; most of the weird ones are unproblematic, often it’s just some weird error page that’s hard to spot.”

Takeaway

Mueller offered a deep dive into the reasons why Google chooses canonicals. He described the process of choosing canonicals as like a fuzzy sorting system built from overlapping signals, with Google comparing content, URL patterns, rendered output, and crawler-visible versions, while borderline classifications (“weird ones”) are given a pass because they don’t pose a problem.

Featured Image by Shutterstock/Garun .Prdt

New Google Spam Policy Targets Back Button Hijacking via @sejournal, @MattGSouthern

Google added a new section to its spam policies designating “back button hijacking” as an explicit violation under the malicious practices category. Enforcement begins on June 15, giving websites two months to make changes.

Google published a blog post explaining the policy. It also updated the spam policies documentation to list back-button hijacking alongside malware and unwanted software as a malicious practice.

What Is Back Button Hijacking

Back button hijacking occurs when a site interferes with browser navigation and prevents users from returning to the previous page. Google’s blog post describes several ways this can happen.

Users might be sent to pages they never visited. They might see unsolicited recommendations or ads. Or they might be unable to navigate back at all.

Google wrote in the blog post:

“When a user clicks the ‘back’ button in the browser, they have a clear expectation: they want to return to the previous page. Back button hijacking breaks this fundamental expectation.”

Why Google Is Acting Now

Google said it’s seen an increase in this behavior across the web. The blog post noted that Google has previously warned against inserting deceptive pages into browser history, referencing a 2013 post on the topic, and said the behavior “has always been against” Google Search Essentials.

Google wrote:

“People report feeling manipulated and eventually less willing to visit unfamiliar sites.”

What Enforcement Looks Like

Sites involved in back button hijacking risk manual spam penalties or automated demotions, both of which can lower their visibility in Google Search results.

Google is giving a two-month grace period before enforcement starts on June 15. This follows a similar pattern to the March 2024 spam policy expansion, which also gave sites two months to comply with the new site reputation abuse policy.

Third-Party Code As A Source

Google’s blog post acknowledges that some back-button hijacking may not originate from the site owner’s code.

Google wrote:

“Some instances of back button hijacking may originate from the site’s included libraries or advertising platform.”

Google’s wording indicates sites can be affected even if issues come from third-party libraries or ad platforms, placing responsibility on websites to review what runs on their pages.

How This Fits Into Google’s Spam Policy Framework

The addition falls under Google’s category of malicious practices. That section discusses behaviors causing a gap between user expectations and experiences, including malware distribution and unwanted software installation. Google expanded the existing spam policy category instead of creating a new one.

The March 2026 spam update completed its rollout less than three weeks ago. That update enforced existing policies without adding new ones. Today’s announcement adds new policy language ahead of the June 15 enforcement date.

Why This Matters

Sites using advertising scripts, content recommendation widgets, or third-party engagement tools should audit those integrations before June 15. Any script that manipulates browser history or prevents normal back-button navigation is now a potential spam violation.

The two-month window is the compliance period. After June 15, Google can take manual or automated action.

Sites that receive a manual action can submit a reconsideration request through Search Console after fixing the issue.

Looking Ahead

Google hasn’t indicated whether enforcement will come through a dedicated spam update or through ongoing SpamBrain and manual review.

What Pichai’s Interview Reveals About Google’s Search Direction via @sejournal, @MattGSouthern

Google CEO Sundar Pichai’s description of search as a future “agent manager” made headlines this week after an hour-long interview with Stripe CEO Patrick Collison.

As SEJ’s Roger Montti reported, Pichai described a version of search where users have “many threads running” and are completing tasks rather than browsing results.

But the interview covered more than that one quote. Throughout the conversation, Pichai laid out a timeline, identified the barriers slowing adoption, described how he already uses an internal agent tool, and confirmed infrastructure constraints that limit how quickly this vision can ship.

Here’s what the rest of the interview reveals for search professionals.

How Pichai’s Language Has Escalated

The “agent manager” line didn’t come out of nowhere. Pichai’s language about search’s future has gotten more specific over the past 18 months.

In December 2024, he told an interviewer that search would “change profoundly in 2025” and that Google would be able to “tackle more complex questions than ever before.”

By October 2025, during Google’s Q3 earnings call, he was calling it an “expansionary moment for Search” and reporting that AI Mode queries had doubled quarter over quarter.

In February 2026, he reported Search revenue hit $63 billion in Q4 2025 with growth accelerating from 10% in Q1 to 17% in Q4, attributing the increase to AI features.

Now, in April, he’s putting a label on it. Not “search will change” or “search is expanding,” but “search as an agent manager” where users complete tasks.

Each time the language has moved from abstract to concrete, from prediction to description.

The 2027 Inflection Point

Collison asked Pichai when a fully agentic business process, like automated financial forecasting with no human in the loop, might happen at Google. Pichai pointed to next year.

“I definitely expect in some of these areas 2027 to be an important inflection point for certain things.”

He added that non-engineering workflows would see changes “pretty profoundly” in 2027, noting that some groups inside Google are already working this way.

“There are some groups within Google who are shifting more profoundly, and so for me a big task is how do you diffuse that to more and more groups, particularly in 2026.”

He also acknowledged that younger, AI-native companies have an advantage in adopting these workflows, while larger organizations like Google face retraining and change management challenges.

The Intelligence Overhang

One of the most useful parts of the interview wasn’t from Pichai. It was Collison’s description of what he called the “intelligence overhang,” the gap between what AI can do today and how much organizations are actually using it.

Collison identified four barriers that slow adoption even when the models are capable. The first is prompting skill. Getting good results from AI takes practice, and most people inside organizations haven’t built that skill yet.

The second is company-specific context. Even a skilled prompter needs to know which internal tools, datasets, and conventions to reference. The third is data access. An agent can’t answer “what’s the status of this deal?” if it can’t reach the CRM or if permissions block it. The fourth is role definition. Job descriptions, team structures, and approval workflows were designed for a world without AI coworkers.

Pichai agreed with this assessment and said Google faces the same challenges internally.

“Identity access controls are like real hard problems and so we are working through those things, but those are the key things which are limiting diffusion to us too.”

He described how Google’s internal agent tool, which he referred to as Antigravity, is already changing how he works as CEO. He said he queries it to get quick reads on product launches.

“Hey, we launched this thing, like what did people think about this? Tell me like the worst five things people are talking about, the best five things people are talking about, and I type that.”

That’s a concrete example of the agent manager concept in action today inside Google. Pichai is using search as a task-completion tool, not a link-returning tool. The gap between that internal experience and what’s available to external users is part of what Google is working to close.

For SEO teams and agencies, the intelligence overhang is worth thinking about on two levels. There’s the overhang in your own organization, where AI tools could be doing more than they currently are. And there’s the overhang on Google’s side, where the models are already capable of agent-style search but the product hasn’t fully shipped it yet.

What’s Gating The Timeline

Pichai confirmed that Google’s 2026 capital expenditure will land between $175 billion and $185 billion, correcting a $150 billion figure that Collison cited. That’s roughly six times the $30 billion range Google was spending before the current AI buildout.

When asked about bottlenecks, Pichai identified four constraints in order.

Wafer production capacity is the most basic limit. Memory supply is “definitely one of the most critical constraints now.” Permitting and regulatory timelines for building new data centers are a growing concern. And critical supply chain components beyond memory add additional pressure.

“There is no way that the leading memory companies are going to dramatically improve their capacity. So you have those constraints in the short term, but they get, they get more relaxed as you go out.”

He said these constraints would also drive efficiency gains, predicting that Google would make its AI systems “30x more efficient” even as it scales spending.

He also noted that he personally dedicates an hour each week to reviewing compute allocation at a granular level across teams and projects within Google.

What This Means For Search Professionals

Pichai’s description of search as an agent manager changes the question that SEO professionals need to ask about their work.

In a results-based search model, the goal is to rank. In an agent-based model, the goal is to be useful to a system that’s completing a task. Those are different problems.

Consider what agent-completed search looks like in practice. You tell search to find a plumber, check reviews, confirm availability for Saturday morning, and book an appointment. The agent doesn’t return ten blue links. It pulls from structured business data, review platforms, and booking systems to complete the job. The businesses that are chosen are those whose information is accurate, structured, and accessible to the agent. The ones with outdated hours, no booking integration, or thin review profiles don’t get surfaced.

The same pattern applies to ecommerce. A shopper says, “find me running shoes under $150 that work for flat feet and can arrive by Friday.” An agent that can complete that task needs product data, inventory availability, shipping estimates, and compatibility information. Sites that provide that data in structured, machine-readable formats become part of the agent’s toolkit. Sites that bury it inside JavaScript-rendered pages or behind login walls get skipped.

If an agent can synthesize an answer from five sources without sending the user to any of them, what’s the value of being one of those five sources? That depends entirely on whether the agent cites you, links to you, or treats your content as raw material without attribution.

This aligns with the changes we see in AI Mode. Google reported during its Q4 2025 earnings call that AI Mode queries are three times longer than traditional searches and frequently prompt follow-up questions.

The 2027 timeline matters too. If non-engineering enterprise workflows start becoming agentic next year, the businesses providing the information and services that those agents draw from will need to be structured for machine consumption, not just human browsing. Structured data, clean APIs, and accurate business information become infrastructure, not nice-to-haves.

The Measurement Gap

Pichai’s insistence that AI search is non-zero-sum deserves more scrutiny than it usually gets.

He’s made this argument consistently. In October 2025, he called it an “expansionary moment”. In February 2026, he said Google hadn’t seen evidence of cannibalization. In this interview, he compared it to YouTube thriving despite TikTok.

But total query growth and individual site traffic are different metrics. Google can be right that more people are searching more often while individual publishers and businesses see less referral traffic from those searches. Both things can be true at the same time.

Google hasn’t shared outbound click data from AI Mode. Until Google provides that data, Pichai’s “expansionary” claim is an assertion, not a verifiable fact. Search professionals should track their own referral traffic trends independently rather than relying on Google’s characterization of the overall market.

Looking Ahead

Pichai’s language in this interview goes further than what Google has said publicly before. Previous statements described AI search as an evolution. This one puts a clearer label on Google’s direction for Search. Search as an agent manager is a product vision.

The timeline he laid out, with 2027 as the inflection point for non-engineering agentic workflows, gives you a window. How Google monetizes agent-completed tasks, whether agents cite sources or simply use them, and what visibility even means in an agent-manager model are all open questions that will need answers before 2027 arrives.

Google I/O 2026 is scheduled for May 19-20 and will likely provide more details on how these capabilities will ship.

More Resources:

Featured Image: PJ McDonnell/Shutterstock

Google March Core Update Left 4 Losers For Every Winner In Germany via @sejournal, @MattGSouthern

A SISTRIX analysis of German search data found far more losers than winners after Google’s March core update.

The analysis revealed 134 domains experiencing confirmed visibility losses and 32 with gains. SISTRIX determined these figures by examining 1,371 domains showing significant visibility changes, then applying filters such as a 52-week Visibility Index history, 30 days of daily data, and visual confirmation of each domain’s trend.

The update began rolling out on March 27 and was completed on April 8, 12 days after launch. It was the first broad core update of 2026 and arrived two days after Google finished the March 2026 spam update.

The SISTRIX data covers the German search market specifically. Results in other markets may differ.

What The Data Shows

Online shops accounted for the largest share of losers, with 39 of 134. Losses cut across verticals, hitting fashion (cecil.de, down 30%), electronics (media-dealer.de, down 37%), gardening (123zimmerpflanzen.de, down 27%), and B2B supply retailers. Larger German brands like notebooksbilliger.de and expert.de also declined, each losing about 11%.

Seven language and education tools lost visibility together, forming the most distinct cluster among the losers. verbformen.de fell 30%, bab.la dropped 22%, and korrekturen.de, studysmarter.de, linguee.de, openthesaurus.de, and reverso.net all declined by 7% to 15%. These sites offer conjugation tables, translations, synonyms, and study tools.

SISTRIX reports that recipe and food portals have faced pressure from Featured Snippets and, more recently, AI Overviews. The March update affected several of them. kuechengoetter.de lost 29%, schlemmer-atlas.de fell 25%, and eatsmarter.de dropped 18%. chefkoch.de, Germany’s largest recipe site, remained stable.

Among user-generated content platforms, gutefrage.net (Germany’s equivalent of Quora) lost about 24% of its visibility. SISTRIX noted that the site has been declining since mid-2025, when its Visibility Index peaked at 127. It was around 62 before this update and dropped to 47. x.com also fell 25% in German search visibility.

Who Gained

The 32 winners were dominated by official websites and established brands.

audible.de was the largest gainer at 172%, jumping from a Visibility Index of about 3 to over 8. ratiopharm.de gained 12%, commerzbank.de gained 11%, and government sites like hessen.de and arbeitsagentur.de gained 5-8%.

Four German airport websites grew in parallel. Stuttgart Airport rose 22%, Cologne-Bonn 18%, Hamburg 17%, and Munich 8%. SISTRIX described the airport gains as the clearest cluster signal among winners, which may point to a broader ranking pattern rather than isolated site-level changes.

chatgpt.com gained 32% and bing.com gained 19% in German search visibility, though both started from low baselines (Visibility Index under 5). SISTRIX attributed this more to rising demand for brand search than to algorithmic preference.

Why This Matters

The German data covers a single market, and SISTRIX’s methodology captures domains with a Visibility Index above 1, so smaller sites aren’t represented in this dataset. But the patterns are worth watching.

The language tool cluster is notable. Seven sites offering similar functionality all lost visibility at the same time. SISTRIX raises the question of whether these losses reflect Google devaluing such sites or a shift in user behavior as AI tools cover similar functions.

If you’re tracking your own site’s performance after the March core update, Google recommends waiting at least one full week after the update is complete before drawing conclusions. Your baseline period should be before March 27, compared with performance after April 8.

Looking Ahead

SISTRIX plans to publish additional market analyses. Their English-language core update tracking page covers UK and US radar data but hasn’t yet published the detailed winners-and-losers breakdown for those markets.

Google hasn’t commented on what specific changes the March 2026 core update made. As with all core updates, pages can move up or down as Google’s systems reassess quality across the web.

Featured Image: nitpicker/Shutterstock

What 400 Sites Reveal About Organic Traffic Gains via @sejournal, @MattGSouthern

An analysis of more than 400 websites by Zyppy founder Cyrus Shepard identifies five characteristics associated with whether a site gained or lost estimated organic traffic over the past 12 months.

Shepard classified sites by revisiting many of the same ones covered in Lily Ray’s December core update analysis, categorizing them by business model, content type, and other features, then measuring correlation with traffic changes. Traffic estimates come from third-party tools, not verified Search Console data.

Five features showed the strongest association with traffic gains, measured by Spearman correlation:

1. Offers a Product or Service: 70% of winning sites offered their own product or service, compared to 34% of losing sites. Service-based offerings like subscriptions and digital goods performed well alongside physical products.
2. Allows Task Completion: 83% of winners let users complete the task they searched for, versus 50% of losers. Sites don’t need to sell anything to score here.
3. Proprietary Assets: 92% of winners owned something difficult to replicate, such as unique datasets, user-generated content, or specialized software. Among losers, that figure was 57%.
4. Tight Topical Focus: Winners tended to cover a single narrow topic deeply. Shepard noted that a general “topical focus” classification showed no difference between winners and losers, but tightening the definition to single-topic depth revealed the pattern.
5. Strong Brand: 32% of winners had high branded search volume relative to their overall traffic, compared to 16% of losers. Shepard measured brand strength by examining each site’s top 20 keywords for navigational branded terms using Ahrefs data.

The effects were additive. Sites with zero features had a 13.5% win rate. Sites with all five reached 69.7%.

What Didn’t Correlate

The study also tested features Shepard expected to matter but found no correlation with traffic changes. These included first-hand experience, personal perspectives, user-generated content, community platforms, and uniqueness of information.

Shepard cautioned against misreading those findings.

He suggested these features may already be baked into Google’s algorithm from earlier updates, meaning they could still matter even if they don’t show differential results between winners and losers in this dataset.

Why This Matters

Shepard’s findings suggest that sites offering a product, completing a task, or owning harder-to-replicate assets were more likely to show estimated organic traffic gains in this dataset. The study puts specific numbers behind that pattern, though it doesn’t establish causation.

The additive pattern is the most useful finding for those evaluating their position. A site with one winning feature had a win rate (15%) roughly the same as a site with no winning features (13%). The gap only widened at three or more features.

Roger Montti’s analysis for Search Engine Journal in December identified related patterns from the other direction, noting that Google’s topical classifications have become more precise and that core updates sometimes correct over-ranking rather than penalizing sites.

Looking Ahead

The correlation values in this study are moderate (0.206–0.391), and the methodology relies on third-party traffic estimates rather than verified analytics. Correlation doesn’t establish causation.

Sites that offer products may perform better for reasons beyond Google’s ranking preferences, including higher return-visitor rates and more natural backlink profiles.

The full dataset is public, which means others can test these classifications against their own data.

Featured Image: Master1305/Shutterstock

Google’s CEO Predicts Search Will Become An AI Agent Manager via @sejournal, @martinibuster

In a recent interview, Google’s CEO, Sundar Pichai, explained how search is changing in response to advances in AI. The discussion centered on a simple question: If AI can act, plan, and execute, then what role will search play in the future?

Information Queries May Become Agent AI Search

The interviewer asked whether search remains a product or becomes something else as AI systems begin handling tasks instead of returning results.

They asked:

“What do you view as a future of search? Is it a distribution mechanism? Is it a future product? Is it one of N ways people are going to interact with the world?”

Had Pichai been interviewed by members of the publishing and SEO community, his answer may have received some pushback. He answered that search does not get replaced, but continues to expand as new capabilities are introduced and user expectations change.

He said:

“I feel like in search, with every shift, you’re able to do more with it.

And we have to absorb those new capabilities and keep evolving the product frontier.

If it’s mobile, the product evolved pretty quickly, you’re getting out of a New York subway, you’re looking for web pages, you want to go somewhere, how do you find it? So you’re constantly shifting, people’s expectations shift, and you’re moving along.

If I fast forward, a lot of what are just information seeking queries will be agentic search. You will be completing tasks, you have many threads running.”

In the first example of a person coming out of a New York subway, yes, someone may search for a web page, but will Google show the user a web page or treat it like data by summarizing it?

The second example completely removes the user from search and inserts agents in the middle. That scenario implicitly treats web pages as data.

Will Search Exist In Ten Years?

Pichai was asked what the future of search will be like in ten years. His answer suggests that the future of search will involve many information-seeking queries being handled as tasks carried out by agentic AI systems. Furthermore, search will be more like an orchestration layer that sits between the user and AI agents.

The exact question he was asked is:

“Will search exist in ten years?”

Google’s CEO responded:

“It keeps evolving. Search would be an agent manager, right, in which you’re doing a lot of things.

I think in some ways, I use anti-gravity today, and you have a bunch of agents doing stuff.

And I can see search doing versions of those things, and you’re getting a bunch of stuff done.”

At this point, the interviewer tried to get Pichai to return to the question of the actual search paradigm, if that will exist in ten years. Pichai declined to expressly state whether the search paradigm will still exist.

He continued his answer:

“Today in AI mode in search, people do deep research queries. So that doesn’t quite fit the definition of what you’re saying. But kind of people adapted to that.

So I think people will do long-running tasks, can be asynchronous.”

What he described is a version of search that manages actions across multiple steps, where multiple processes can run at once instead of returning a single set of ranked results. And yet, it’s weirdly abstract because he’s talking about queries but fails to mention websites or web pages in that specific context.

What’s going on? His next answer brings it into sharper focus.

Who Is The Flea And Who Is The Dog?

The interviewer picked up on Pichai’s mention of adaptation, made an analogy to evolution, and then asked:

“It’s almost like, does that former version or paradigm eventually go away? And what was search becomes an agent and your future interface is an agent, and the search box in ten years or n years is no longer the–“

Pichai interrupted the interviewer to say that it’s no longer possible to look ahead five or ten years because the models are changing, what people do is rapidly changing, and given that pace, the only thing to do is to embrace it.

He explained:

“The form factor of devices are going to change. I/O is going to radically change. And so …I think you can paralyze yourself thinking ten years ahead. But we are fortunate to be in a moment where you can think a year ahead, and the curve is so steep. It’s exciting to just do that year ahead, right?

Whereas in the past, you may need to sit and envision five years out, unlike the models are going to be dramatically different in a year’s time.

…I think it’ll evolve, but it’s an expansionary moment. I think what a lot of people underestimate in these moments is, it feels so far from a zero-sum game to me, right? The value of what people are going to be able to do is also on some crazy curve, right?

I think the more you view it as a zero-sum game, it looks difficult. It can become a zero-sum game if you’re innovating or the product is not evolving.

But as long as you’re at the cutting edge of doing those things, and we’re doing both search and Gemini, and so they will overlap in certain ways. They will profoundly diverge in certain ways, right? And so I think it’s good to have both and embrace it.”

What Google’s CEO is doing is rejecting the possibility of becoming obsolescent by deliberately focusing on competitive agility and embracing uncertainty as a strategic advantage.

That might work for Google, but what about websites?

I think businesses also need to embrace competitive agility and get out of the mental attitude of fleas on the dog. And yet, online businesses, publishers, and the SEO community are not fleas because Google itself is the one feeding off the web’s content.

What About Websites?

The interview lasted for over an hour, and at no point did Pichai mention websites. He mentioned web pages twice, once as something to understand with technology and once in the example of a person emerging from a subway who is looking for a web page. In both of those instances, the context was not Google Search looking for or fetching a web page in response to a query.

Given that Google Search is used by billions of people every day, it’s a bit odd that websites aren’t mentioned at all by the CEO of the world’s most successful search engine.

Google Confirms March 2026 Core Update Is Complete via @sejournal, @MattGSouthern

Google’s March core update has finished rolling out, according to the Google Search Status Dashboard.

The dashboard updated at 6:12 AM PDT on April 8 with the completion note: “The rollout was complete as of April 8, 2026.” The update began on March 27 at 2:00 AM PT, making the total rollout 12 days.

That’s within Google’s original two-week estimate and faster than the December 2025 core update, which took 18 days.

What Google Said About This Update

Google called the March 2026 core update “a regular update designed to better surface relevant, satisfying content for searchers from all types of sites.”

The company didn’t publish a companion blog post or announce specific goals for this update. It also didn’t share new guidance with the completion notice.

Core updates involve broad changes to Google’s ranking systems. They aren’t targeted at specific types of content or policy violations. Pages can move up or down based on how the update reassesses quality across the web.

Three Updates In One Month

March was unusually active for Google’s ranking systems. The core update was the third confirmed update in roughly five weeks.

The February Discover core update finished rolling out on February 27 after 22 days. That was the first time Google publicly labeled a core update as Discover-only.

The March 2026 spam update rolled out and completed in under 20 hours on March 24-25. That was the shortest confirmed spam update in the dashboard’s history.

The core update followed two days later on March 27.

Roger Montti, writing for Search Engine Journal, noted that the spam-then-core sequencing may not have been a coincidence. He wrote that spam fighting is logically part of the broader quality reassessment in a core update, comparing it to “clearing the table” before recalibrating the core ranking signals.

How The Rollout Compared To Recent Core Updates

The March rollout was the second-shortest of the past five broad core updates.

Only the December 2024 update finished faster.

Why This Matters

The completed rollout means you can now compare pre-update and post-update performance in Search Console across a full window. Google recommends waiting at least one full week after completion before drawing conclusions from the data.

Your baseline period should be the weeks before March 27, compared against performance after April 8. Keep in mind that the March spam update completed on March 25, so any ranking changes between March 24-27 could be from either update.

A drop in rankings after a core update doesn’t mean your site violated a policy. Core updates reassess content quality across the web, and some pages move up while others move down.

Looking Ahead

Google will likely continue making smaller, unannounced core updates between the larger confirmed rollouts. The company updated its core updates documentation in December to say that smaller core updates happen on an ongoing basis.

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