On June 11, 2026, I saw more than 1,000 marketing leaders come together in New York for Zero Click New York, Profound’s largest AI Marketing summit to date.
What stood out to me was the range of leaders and brands shaping the conversation. Speakers from Coca-Cola, LinkedIn, Delta Air Lines, U.S. Bank, and CVS Health shared how they are rethinking marketing strategy, team design, and measurement as AI changes the way audiences discover and trust information.
I also found the research sessions especially important. The summit explored Claude’s citation mechanics, ChatGPT’s emerging ads business, and the data behind the kinds of content AI systems are most likely to trust. Together, these conversations made Zero Click New York 2026 feel like a clear marker for where AI Marketing is heading next.
I see Google rolling out new Agency Admin and Standard roles in Merchant Center for Agencies, giving agencies a more centralized way to control client access while improving security and day-to-day efficiency.
What is new: I can now look at client access differently because clients are linked directly to an agency instead of being tied to individual users. That makes it easier to manage permissions from one place, especially when team members join, move roles, or leave.
I also see custom labels becoming a useful part of this update. Agency Admins can organize client accounts by brand, business vertical, internal team, or another structure that fits how the agency works.
Those labels can then be used to give Standard users access to groups of accounts in bulk. For me, that is the practical improvement: agencies no longer need to configure access one account at a time when the same permission logic applies across multiple clients.
Why I care: Agencies managing several Merchant Center accounts have often had to depend on user-level permissions, which can make onboarding, offboarding, and account management more cumbersome than they need to be. This role-based structure moves client management to the agency level, which should reduce administrative work and strengthen access controls.
How it works: Agency Admins get full administrative privileges inside the Merchant Center agency account. In that role, I can link and unlink clients’ Merchant Center accounts, add or remove Standard users, modify Standard users, manage their access to client accounts, and create custom labels for organizing clients.
Standard users receive more limited permissions, which helps agencies follow stronger security practices. I see this as a way to make sure team members only access the client accounts they actually need.
Bottom line: For agencies managing large client portfolios, I expect centralized client linking, bulk access management, and customizable account labels to reduce manual work while making Merchant Center administration more secure and scalable.
I’m looking at Google Ads API v24.2 as a practical update for advertisers and developers, especially because it brings together stronger security controls, AI transparency features, better reporting and new experiment options in one release.
What’s new. The biggest security addition I see is support for multi-party approvals, or MPA. This requires a second administrator to approve sensitive account actions, including user invitations and access-level changes, which gives agencies and larger organizations another layer of protection when managing Google Ads accounts.
I’m also watching Google’s expanded support for AI-generated content disclosures. The API now exposes new SyntheticContentInfo and SyntheticContentAttestation fields on assets and ads, so developers can identify and label AI-generated creative programmatically. This is especially relevant for advertisers preparing for the EU AI Act, which takes effect on August 2nd.
Developers can start building integrations now, although I’d note that advertiser attestation fields will remain read-only until v25 launches.
Performance Max gets more visibility. I see one of the most useful changes in version 24.2 as the added visibility for Performance Max campaigns. Advertisers can now segment performance_max_placement_view reports by ad_network_type, making it easier to understand where ads are appearing across Search, Display and partner networks.
The release also adds YouTube brand channel linking through the API, which should make video campaign integrations stronger. I’m also noting the new landing page text generation option, which can automatically create text assets from a website’s landing page.
New testing capabilities. Google is expanding experimentation tools with two new experiment types, and I see both as useful for advertisers who want more structured ways to compare campaign changes.
The new COMPARE_CAMPAIGNS workflow lets advertisers compare multiple campaigns or campaign types across as many as five experiment arms, including custom Performance Max experiments.
A second experiment type lets advertisers test text customization and final URL expansion inside a single Performance Max campaign by splitting traffic between variations.
Documentation improvements. I also appreciate that Google has reorganized its API release notes by separating breaking changes from feature updates. It has also introduced a dedicated guide for feature deprecations and unversioned changes, which should make future upgrades easier to manage.
Why I care. This release may not be a dramatic overhaul, but I see it as a meaningful step for teams that need to prepare for AI disclosure requirements, tighten account security and get more useful Performance Max reporting.
I am seeing OpenAI point to early momentum in its advertising business, with executives saying ChatGPT users are dismissing ads less often and engaging with them more. For me, that makes ad dismissal a key signal to watch as OpenAI looks for revenue beyond subscriptions and enterprise AI.
What is happening. OpenAI says ChatGPT ad dismissals have dropped by 50% since the company launched its advertising business in February. I read that decline as OpenAI’s way of showing that its ads are becoming more relevant, because the company treats dismissals as a proxy for whether users find an ad useful or intrusive.
The update came from OpenAI Chief Revenue Officer Denise Dresser, who framed relevance as a central focus for the company as it builds advertising into ChatGPT.
Why I care. If users are becoming more open to ads inside ChatGPT, I see conversational AI becoming a more serious advertising channel. A 50% drop in dismissals suggests better relevance and stronger engagement, which could give brands a way to reach people during high-intent, task-focused moments instead of relying only on interruptive ad formats.
Why relevance matters. I think ads inside AI experiences face a much higher bar than traditional display ads. People usually come to ChatGPT to complete a task, answer a question, compare options or solve a problem, so an ad that feels disconnected can quickly create friction and damage trust.
According to Dresser, OpenAI has been focused on making the format useful. “This form factor is about usefulness,” she said. “That’s great for the consumer, great for the user.”
The bigger picture. I see these results as an early look at how advertising may evolve inside generative AI platforms. Instead of interrupting content consumption, AI-powered advertising is moving toward recommendations that fit the user’s intent and the conversation already underway.
That shift means success may depend less on grabbing attention and more on being genuinely helpful. The lower dismissal rate suggests OpenAI is making progress toward that goal, even if the ad model is still early.
Competition extends beyond advertising. I also see this update in the context of OpenAI expanding its business on multiple fronts. While it builds an ads business, the company is also competing for enterprise AI spending against rivals such as Anthropic.
I am seeing Google Search Console’s page indexing report running more than two weeks behind, with the latest visible timestamp still showing June 11, 2026. That means I cannot get a fresh view of page indexing data for the pages on my site right now.
When I check the Google Search Console page indexing report, I would expect to see that June 11 date instead of a more recent update. The delay is inconvenient, especially when I am trying to understand whether Google has recently found, crawled, or indexed important pages.
This report matters because it shows me which pages Google can find and index on a website. It also helps me spot indexing problems Google may have encountered while crawling the site.
I can access the report in Search Console over here, or I can open Search Console, go to the Indexing section, and then select Pages.
Inside the report, I usually see a chart with indexed pages in green and not indexed pages in gray. I can also overlay impressions on the chart, which makes it easier to connect indexing patterns with search visibility.
Below that chart, Google lists the reasons pages on the website are not being indexed. That section is often where I look first when I need to understand whether the issue is related to crawling, duplication, redirects, noindex signals, canonical choices, or another indexing reason.
For more details about how the page indexing report works, I can refer to Google’s help document.
Why I care: if I am trying to debug why Google has not indexed specific pages over the past couple of weeks, this delay leaves me with limited visibility. Until Google updates the report again, I would need to rely on my own SEO analysis or use the URL inspection tool to investigate indexing issues one page at a time.
The delay is frustrating, but I do not see it as especially uncommon. Search Console reports can lag from time to time, so for now I would treat the page indexing report as stale and avoid making major conclusions from that delayed data alone.
I’m reading this Cornell Tech research as a clear warning: deep-research AI agents can be steered by surprisingly small edits on public, user-generated pages. In the study, a single injected Reddit-style comment could become a cited recommendation for fake products, services, or entities.
The researchers described these altered pages as “poisoned” because the added text was written to influence what an AI system cites and repeats. The weakness appears in systems that search the web, collect sources, and produce cited reports. The paper calls the attack WARP, short for Web Agent Retrieval Poisoning.
How I see injected text reaching reports. The attack does not require access to the model, prompts, search engine, or retrieval system. Instead, an attacker edits or appends text to a page the agent already tends to retrieve, such as a Reddit thread, Wikipedia page, or forum post.
When the agent later searches related topics, it may pull in that page, cite it, and repeat the attacker’s chosen message as part of an otherwise normal-looking answer.
That matters because deep-research tools often run many related searches for a single user request. The paper found that the same user-generated pages surfaced across related queries, giving poisoned content more chances to appear.
Reddit stood out as the biggest opening. Across STORM, Co-STORM, and OmniThink, 17% to 23% of retrieved URLs came from user-generated platforms, including Reddit, YouTube, Facebook, and Wikipedia.
Reddit made up the largest share of those pages. It accounted for 54% to 71% of the user-generated URLs retrieved by the three open-source systems.
The researchers did not alter live websites. Instead, they used a simulation framework called GeoStorm to insert manipulated text into retrieved content during testing.
A few words were enough. What stood out to me most is how little text the attack needed. The researchers found that snippets as short as about 13 words could influence what these systems recommended.
In one test, a 15-word sentence pushed a fake cryptocurrency, BananaCoin, into a Co-STORM report as an “emerging” long-term investment option. The report cited the altered source alongside legitimate crypto sources.
When the manipulated page was retrieved, the fake entity appeared in 38% to 51% of reports across systems. When the researchers targeted multiple pages, that range increased to 42% to 62%.
The attack still worked when systems retrieved full Reddit threads, although mention rates were lower. When injected text was added to complete Reddit threads and represented less than 4% of the retrieved content, the fake entity still appeared in 30% to 53% of reports when the page was retrieved.
The defenses struggled. Blocking user-generated domains stopped this attack path, but I see the tradeoff immediately: it also removes useful sources such as firsthand product experiences and local recommendations.
The tested text filters also failed to reliably separate injected passages from normal user content. Because the manipulated passages were fluent and written by an AI model, perplexity-based filters were more likely to flag normal user content than the injected text.
Report-level checks missed the manipulation too. The altered reports looked similar to clean reports because the agent itself folded the fake recommendation into an answer that otherwise appeared normal.
Why I care. A small edit to a public page can become part of a cited AI answer, even when the underlying source is user-generated. Misinformation planted on sites like Reddit or in forums can move from discussion threads into AI recommendations that look credible to users.
About the research. The paper, Deep-Research Agents Can Be Poisoned via User-Generated Content, was written by Tingwei Zhang, Harold Triedman, and Vitaly Shmatikov of Cornell Tech and posted to arXiv on May 22. The researchers tested the full attack on three open-source systems: STORM, Co-STORM, and OmniThink.
They also analyzed OpenAI Deep Research and Gemini Deep Research for user-generated citations, but they did not run live manipulation tests because doing so would require publishing altered content to the open web.
I’m seeing an important shift for Standard Shopping campaigns: Google is bringing Maximize Conversion Value bidding to these campaigns without requiring a Target ROAS. That gives advertisers more room to pursue value-based optimization without immediately being locked into a specific return target.
What’s happening. Google is rolling out Maximize Conversion Value bidding for Standard Shopping campaigns, and advertisers no longer have to set a Target ROAS to use it.
Before this update, if I wanted to optimize around conversion value in Standard Shopping, I generally had to use a Target ROAS bidding strategy. Now, this new option lets campaigns focus on maximizing conversion value while giving Google’s bidding system more flexibility to find the highest-value opportunities.
Why I care. This matters because I can now use Google’s value-based bidding in Standard Shopping without being constrained by a Target ROAS goal. That gives me more flexibility while preserving the control and transparency that many advertisers still prefer in Standard Shopping campaigns.
It may also reduce the need to run feed-only Performance Max campaigns just to access Maximize Conversion Value bidding. For advertisers who prefer tighter campaign control, that is a meaningful change.
Between the lines. I know many advertisers have continued to favour Standard Shopping because it offers more visibility and control than Performance Max. But when they wanted flexible value-based bidding, they often created feed-only Performance Max campaigns as a workaround.
With this update, that workaround may no longer be necessary for some accounts.
Why advertisers should care. I can now combine the structure and transparency of Standard Shopping with a more flexible automated bidding strategy. In practical terms, this could simplify campaign setups, reduce unnecessary Performance Max usage, and make account management cleaner.
The bottom line. Google is narrowing one of the biggest feature gaps between Standard Shopping and Performance Max. For me, this gives advertisers another reason to keep using Standard Shopping while still benefiting from automated value-based bidding.
First spotted. Performance marketer Yash Mandlesha spotted the update and shared the option on LinkedIn.
I’m noting that Google has confirmed its June 2026 spam update is now fully rolled out. The update started on Wednesday, June 24, around noon ET, and finished on June 26 at 2 p.m. ET.
Google’s official status update was brief and direct: “The rollout was complete as of June 26, 2026.”
What stands out to me is that this was the second Google spam update announced in 2026. It appeared to feel somewhat bigger than the March 2026 spam update, but as with most updates, if my site was not affected, I would treat that as a good sign for now.
That said, I always keep in mind that spam updates can sometimes affect sites that are not intentionally trying to spam Google. Hopefully, that is not the case for your site, but it is still worth watching traffic, rankings, and Search Console data closely after a rollout like this.
As for the type of update, Google originally described it as a normal spam update that would roll out across all languages and locations, with completion expected to take a few days.
If I wanted more context on how these updates work, I would review Google’s official documentation on spam updates in this Google help document.
I think every PPC professional has at least one mistake they wish they could erase. For Danny Gavin, founder of Optidge, it was not a failed bidding strategy, a blown budget, or a campaign that never found its footing. It was something much simpler, and in many ways, much more painful.
When Danny joined me on PPC Live The Podcast, he shared the story of a technical issue that kept landing page leads from reaching the client. For one to two months, the campaigns were still generating qualified prospects, but the client believed nothing was working because those enquiries never appeared in their inbox.
The mistake that no one spotted
At the time, Danny’s agency was still small, with only a handful of people managing client accounts. One client, an autism therapy provider, appeared to be getting strong results inside Google Ads.
Clicks were rising. Cost per lead looked healthy. From inside the ad platform, everything pointed to success.
But the client was growing more frustrated because no enquiries were coming through.
The problem was not Google Ads.
It was not the landing page.
It was the email notification system.
Every form submission was being stored correctly in the database, but a technical failure stopped the notification emails from reaching the client. Because neither side realized those emails had failed, the issue went unnoticed for weeks.
By the time the problem was found, dozens of leads had already gone cold.
Why the emotional impact was worse than the technical problem
What stood out to me was that the financial loss was not the part Danny remembered most. The harder part was the feeling that his agency had let the client down. Because he knew the client personally, the mistake felt even more personal.
His team had spent weeks reporting positive campaign performance while the client saw no return from their investment. That disconnect created guilt, regret, and a real sense of helplessness.
As Danny explained it, the agency felt as if it had taken the client’s money without delivering value, even though the campaigns themselves were actually working.
Honesty became the first step
Once the problem became clear, Danny did not try to hide it. His view is straightforward: when mistakes happen, honesty is the only response that gives you any chance of repairing trust.
Instead of making excuses, the agency investigated immediately, exported every lead stored in the database, and gave the client everything they could recover. Many of those opportunities had already gone cold, but at least the client had access to the data that still existed.
From there, the focus had to move from blame to prevention.
Building systems that stop the same mistake happening twice
That experience changed the agency’s processes in a lasting way.
Instead of relying on one notification email, Danny’s team introduced multiple safeguards:
CC’ing the agency on every lead notification.
Automatically logging every lead into a shared Google Sheet.
Testing forms regularly to confirm submissions and notifications both work.
Checking with clients routinely to confirm leads are actually being received.
Those checks are now part of the agency’s standard operating procedures. They are no longer assumptions about technology working in the background.
Why communication matters as much as optimisation
Looking back, Danny sees the technical failure as only part of the issue. Communication failed too. No one had asked the simple question: “Are you actually receiving the leads?”
Today, communication is one of Optidge’s core values.
Rather than expecting PPC specialists to manage constant client communication while also running campaigns, the agency brought in dedicated account managers whose primary role is to keep clients informed.
The lesson I took from this is simple: campaign metrics alone do not define success.
Success only happens when the client experiences the results you are reporting.
Sometimes clients remember how you responded
At first, the relationship with the client ended. Danny assumed the mistake had permanently damaged the trust they had built.
Years later, though, that same client reached out again about potentially working together. In her email, she described Optidge as the most professional agency she had worked with. For Danny, it was a reminder that clients do not forget mistakes, but they also remember how agencies respond to them.
Transparency, professionalism, and a genuine effort to improve can leave a stronger impression than perfection.
Common PPC mistakes Danny still sees today
Although this happened years ago, Danny still sees agencies making similar mistakes today.
One of the biggest is focusing only on traffic instead of business outcomes. Sending visitors to a page is no longer enough.
Strong lead generation requires understanding what happens after someone clicks.
When Danny audits accounts, he often finds agencies failing to:
Feed qualified lead data back into advertising platforms.
Review search terms thoroughly and maintain negative keywords.
Build landing pages that match campaign intent.
Measure lead quality instead of simply counting conversions.
Without those fundamentals, campaign optimisation is based on incomplete information.
Where AI is genuinely helping lead generation
Danny believes AI has real potential in lead generation, but not always in the way marketers expect.
One of the most useful opportunities is phone call analysis.
Instead of manually listening to every conversation, AI can now help agencies:
Generate call transcripts.
Categorise calls by quality.
Identify whether a call became a genuine sales opportunity.
Feed qualified conversion data back into Google Ads.
That makes it possible to optimise around real business outcomes instead of surface-level metrics.
Why AI still needs human oversight
Even though Danny is using AI, he does not treat it as an infallible system.
Like automation inside advertising platforms, AI can make mistakes, miss context, and confidently reach the wrong conclusion.
For industries with strict privacy requirements, such as healthcare, AI may not be appropriate for handling sensitive customer information at all.
His advice is to trust AI enough to improve efficiency, but always verify the work.
Human expertise still matters.
The biggest lesson
I do not think any PPC professional can avoid mistakes completely.
What defines a strong agency is how it responds when something goes wrong.
That means being honest, fixing the immediate problem, building safeguards, and making sure the same issue does not happen again.
As Danny puts it, a mistake only becomes valuable when you have genuinely learned from it.
I’m deeply saddened to share that Bruce Clay, widely known as the Father of SEO, passed away in late May. Bruce was one of the true founding figures of the SEO industry, having launched a professional SEO agency back in 1996, long before search marketing became the discipline we know today.
For me, Bruce’s impact is hard to overstate. He was the first sponsor of the first-ever SEO conference, and he gave an extraordinary amount of his time, resources and money to help build the search community. Few people have supported this industry for as long, or with as much generosity, as Bruce did.
Tribute video. The Bruce Clay, Inc. team prepared a tribute video honoring Bruce’s life and legacy. It describes him as a pioneer who devoted much of his life to helping the SEO industry grow. During his three decades as CEO of Bruce Clay Inc., he wrote three books, built tools, spoke at conferences, hosted training events and helped the company expand internationally.
Because of Bruce’s founding principles, hundreds of employees around the world have contributed to SEO, and thousands of students have benefited from his experience and teaching.
The Bruce Clay team told me, “We are absolutely heartbroken, but we find strength in the vibrant community and lasting values that Bruce built. Our teams in the U.S. and around the world remain dedicated to carrying forward the mission Bruce loved so dearly.”
Kyle Pouliot, Sr. Video Production Manager at Third Door Media, also shared a personal reflection with me.
“I’ve gotten to know Bruce on a more personal level over these past few years and interacted with him frequently for our online conferences. What I’ve learned about Bruce in that time is that he was genuinely thoughtful and caring about the search community. Never short of an honest opinion, Bruce shared some really practical ideas for Search Engine Land and SMX. He loved sharing his deep experienced knowledge to everyone, it didn’t matter if you were a beginner or 20+ year industry veteran, he treated everyone the same. We talked about the hundreds of golf balls that would find their way into his property every day, food, raising kids and how incredible the weather was in Simi Valley. He will be greatly missed.”
On a personal note, I’ve known Bruce Clay since I entered the SEO industry more than 20 years ago. He was a role model to me, often a mentor, and always someone who was approachable, professional and deeply caring. In many rooms, he was likely the most generous and thoughtful person there.
I loved his SEO talks. I loved meeting him at industry events. And I especially valued the personal emails he sent about shaping the future of our industry. Those moments showed me how much Bruce cared, not just about search, but about the people building it.
