I’m watching OpenAI discontinue ChatGPT Atlas, its standalone desktop browser, and move its browser-based AI features into the new ChatGPT desktop app. That app brings together ChatGPT Work, OpenAI’s work-focused agent, and ChatGPT Codex.
The end of Atlas. I’m taking note of an Aug. 9 retirement date after OpenAI’s James Sun confirmed the plan on X.
I’m also noting Sun’s exact wording: “The current targeted date for deprecation is 8/9, and we’ll share more information in the upcoming days both in-app and via email.”
One desktop app. I see the new ChatGPT desktop app becoming OpenAI’s primary desktop product, complete with built-in browser capabilities. Instead of maintaining a separate AI browser, OpenAI is combining browsing, work-agent features, and Codex in one place.
Chrome users can keep Chrome. If I prefer using Chrome, I can access ChatGPT and Codex through OpenAI’s Chrome extension without switching to a dedicated OpenAI browser.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
Why I care. I see this as an important shift because OpenAI is moving AI browsing into the main ChatGPT experience, where more people can ask questions, research brands, and complete tasks. In my view, that gives ChatGPT another opportunity to influence discovery beyond traditional search results.
I first saw ChatGPT Atlas launch on Mac in October. OpenAI later released a dedicated Codex app and added an in-app browser in April. Now, I’m watching those capabilities move into the new unified ChatGPT desktop app.
I noticed that Google updated its canonicalization troubleshooting guide to clarify how long it may take for fixes to appear in Google Search results. According to the revised guidance, Google might keep pages in a duplicate cluster for up to two weeks after content issues have been fixed.
What changed. I found a new section at the top of the guide that explains the expected timeline for canonicalization fixes. Google now makes it clear that the process can take up to two weeks.
I also saw additional technical details about clustering. Google explains that pages need to be sufficiently similar before its systems can group them into a duplicate cluster and select one version as the canonical page.
Google’s updated canonicalization guidance sets expectations for SEOs: fixed pages may remain in a duplicate cluster for up to two weeks, while clearer content differences can speed reevaluation.
Here is the section Google added:
Why I care. This clarification gives me a more realistic timeline when monitoring canonicalization fixes. Once Google has processed an update, I know I may need to wait the full two weeks before deciding whether the change worked.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
That waiting period can help me avoid making unnecessary page changes while Google is still consolidating duplicate URLs and evaluating the appropriate canonical version.
I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.
The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.
What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.
Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.
Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.
For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.
Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.
I’m watching YouTube take a bigger step into conversational search by expanding Ask YouTube to signed-in U.S. desktop viewers who are 13 and older. What started as a Premium-only experiment is now reaching a much broader audience.
What is Ask YouTube? I see Ask YouTube as YouTube’s AI-powered search layer. Instead of typing a traditional keyword query and scanning a list of videos, I can ask a natural-language question in the YouTube search bar and get an AI response that may include text, video clips, long-form videos, Shorts, and suggested follow-up prompts.
Access is expanding. When YouTube announced the test in April, Ask YouTube was limited to U.S. YouTube Premium members who were 18 and older and opted in through youtube.com/new. On July 6, YouTube expanded it to signed-in U.S. viewers 13 and older using English-language searches on desktop.
Signed-out viewers and supervised accounts are still excluded for now. YouTube also said it plans to bring the feature to more devices, languages, and users worldwide in the coming months.
Standard YouTube Search is not going away. If I land on an Ask YouTube results page and want the usual video results, I can click All or return to the Home page. That means Ask YouTube remains a separate search option, not a full replacement for traditional YouTube Search.
Views still count for creators. YouTube said videos featured inside Ask YouTube responses can give creators another path to discovery. Views from Shorts, videos, and previews shown in Ask YouTube responses count toward total view metrics and YouTube Partner Program eligibility.
I also noticed that featured videos display the video title and channel name, which matters for attribution and visibility. For creators, YouTube’s guidance is clear: publish unique, high-quality content with descriptive titles and clear chapters so its systems can better match video segments to viewer questions.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Why I care. YouTube is putting conversational AI search in front of a much larger group of U.S. desktop users. If I’m creating or optimizing video content, this raises the value of clear titles, useful chapters, and segments that directly answer specific questions.
For SEO and content teams, this is another reminder that discovery is shifting from simple keyword matching toward answer-based experiences. The videos most likely to benefit are the ones that make it easy for YouTube to understand what each section covers and which viewer questions it solves.
What it looks like. YouTube shared a GIF showing Ask YouTube in action, where users can ask a question, review AI-assisted results, and continue with follow-up prompts.
I’m seeing OpenAI continue to build out ChatGPT Ads with a new round of updates for advertisers. In an email, ChatGPT Ads announced changes across ChatGPT Ads Manager and the broader ad experience, including custom audiences, a new overview tab, suggested ad drafts, a refreshed static ad card format, and expanded availability in Japan and South Korea.
Here is what stands out to me from the latest update.
Custom audiences: I can now upload audience lists with 25,000 or more users to include or suppress audiences from campaigns. OpenAI is also allowing bid multipliers for audiences at the ad group level, which gives advertisers more control over how aggressively they want to reach specific segments.
Overview tab: The new overview tab gives me a more centralized place to monitor account health, review recommended tasks that may improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.
A before-and-after look at ChatGPT's refreshed static ad card, turning a small sponsored grocery prompt into a cleaner, more readable format with larger visuals and a clear Ad badge.
Suggested ad drafts: If a campaign needs broader content coverage to improve delivery, I may see an option to select “Add new ad” from the campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description, which I can then review, edit, and assign to a campaign and ad group. Importantly, OpenAI says this does not generate new copy or imagery with AI.
Japan and South Korea expansion: ChatGPT Ads are now live in Japan and South Korea. That means campaigns can target users in both markets, giving advertisers more reach if they do business there.
Refreshed static ad card format: OpenAI is also rolling out a refreshed static ad card across web and mobile. I see this as a cleaner, more compact format designed to be easier to read while giving visuals more prominence. This format had already started appearing in late June.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Why I care: ChatGPT Ads are still new, and OpenAI is clearly moving quickly. New targeting tools, reporting views, draft workflows, market expansion, and format tests all point to a platform that is still taking shape.
My takeaway is simple: I need to keep watching these changes closely, test them as they become available, and continue refining ad creative, audience strategy, and campaign structure as ChatGPT Ads matures.
I’m adjusting how I refer to Google’s shopping platform now that Google has dropped “Next” from Merchant Center Next. Going forward, the product is simply called Google Merchant Center.
Google made the change official in a Merchant Center announcement, saying, “The platform you use today will simply be referred to as Google Merchant Center.” For anyone managing product feeds, shopping campaigns, or merchant accounts, this is mainly a naming update rather than a product change.
I remember when Google Merchant Center Next was introduced in 2023 as the newer version of the old Google Merchant Center. Over the past few years, more merchants, site owners, and advertisers moved into that updated experience.
At this point, it appears that Merchant Center Next has effectively become the standard experience. So Google is removing the “Next” branding and returning to the simpler name: Google Merchant Center.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Google said users will start seeing the “Next” branding removed from Help Center articles, email communications, and the Merchant Center interface.
Google also clarified that no action is required and that the name change does not affect existing accounts. In other words, I do not need to update settings, migrate anything, or make account-level changes because of this rebrand.
Why does this matter? When I talk about Google’s merchant tools now, I can leave off “Next” and just call the platform Google Merchant Center. Honestly, that is what many of us were already calling it anyway.
I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.
Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.
Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.
The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.
I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”
My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.
A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.
More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.
A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.
What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.
For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.
I’m seeing Google Search Console get a useful new reporting layer for social and video content through what Google calls platform properties. This gives me a way to understand how my content on Instagram, TikTok, X, and YouTube is performing in Google Search.
The big change is that I can now connect supported social or video accounts to Search Console and see how people find that content through Google. Instead of only analyzing websites I own or manage directly, I can begin looking at search visibility for content hosted on third-party platforms.
Google said this update makes it possible to track which search terms lead people to Instagram, TikTok, X, and YouTube content in Search, along with how audiences interact with those posts. I’ll be able to review this data inside the performance report, insights report, and achievements sections of Google Search Console.
A Google Search Console dropdown highlights the new platform property flow, with the rustybrick X profile appearing as a selectable property for reporting.
In the performance report, I can review total clicks, impressions, and other key metrics. I can also filter and sort the data to see which posts and queries are driving the most traffic, and if I want to analyze it somewhere else, I can export the data.
In the insights report, I can get a higher-level view of recent traffic trends, top-performing posts, and the ways people are discovering my account through Google Search.
A Google Search Console platform property view shows how an X profile appears in Search, pairing 28-day click and impression trends with the queries driving visibility.
The achievements section adds another useful angle by helping me track growth milestones, such as reaching a new threshold for total clicks from Google Search over the last 28 days.
This feels similar to the social channel details that previously appeared in Search Console insights, but platform properties look like a more direct way to verify and analyze these accounts.
A Google Search Console Insights view highlights how YouTube posts are gaining visibility in Search, with 17.8K clicks and traffic broken down by web, video, Discover, and image search.
To set this up, I need to verify a platform property inside my Google Search Console account. I can start by opening Search Console, going to the Search Console verification page, or using the property selector dropdown anywhere in Search Console and choosing “Add property.”
From there, I select one of the currently supported platforms: Instagram, TikTok, X, or YouTube. Then I follow the onscreen verification steps to securely authorize the connection.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Google said platform properties will roll out gradually over the coming weeks, so I may not see the option in my account right away. For setup details, Google points users to its help center documentation. The help document had briefly appeared a few weeks earlier before being removed, so this release makes the feature official.
What stands out to me is the access this gives marketers, creators, and SEOs. Google has not traditionally given us a clear way to see how our content performs on domains or properties we do not own. With platform properties, I can finally start seeing how my social and video content performs in Google Search, even when I do not have developer access to those platforms. That opens up a much better view of search-driven visibility beyond my own website.
I’m seeing Google expand merchant listing structured data with support for sale duration and the Product.category property. The update brings Google Search’s merchant listing structured data closer to the capabilities already available in Google Merchant Center feeds.
Sale duration. Google added a new Sale duration section to its Merchant listing structured data documentation. In that update, Google said the guidance explains how to use the validFrom, validThrough, and priceValidUntil schema.org properties to define the effective date range for sale prices.
I find this useful because Google’s guidance also covers best practices and examples for placing those properties on either Offer or PriceSpecification nodes. Google said the change aligns schema.org usage with the Merchant Center feed attribute sale_price_effective_date, giving merchants clearer instructions for handling sale price timing in structured data.
Google's sale duration guidance shows merchants how to define when a sale price starts and ends in structured data, including Offer and UnitPriceSpecification JSON-LD examples.
Here is the new sale duration section Google added:
Product category. Google also updated the same Merchant listing structured data documentation to include support for the Product.category property.
Google’s merchant listing guidance now shows how product categories can mix custom text labels with Google Product Category codes in structured data.
Google wrote that the documentation now explains how Product.category can be used with both Text and CategoryCode types. According to Google, this aligns with Google Merchant Center feed specifications for the product_type and google_product_category attributes.
From my perspective, this makes the structured data more practical for merchants because it lets them provide both merchant-defined and Google-defined category details directly in schema.org markup. Google said this can enhance product information for Google Search and Shopping.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Here is what Google added for product category support:
Why I care. If I maintain merchant listing structured data for Google, these additions are worth reviewing. Product category support can help Google better understand the products being provided, which may improve how those products match relevant queries.
I also see sale duration support as a practical improvement for planning promotions. When I update merchant listing structured data, I can now define sale price timing more clearly and align that markup more closely with Google Merchant Center feed behavior.
