Having decided that the value of live — be it live sports, live shopping, live-streaming, live events or live conversations — is of essential importance to its clients, Omnicom is partnering with several major platforms and publishers to harness live’s power.
DIgiday has learned that the holding company today is announcing partnerships with payment platform PayPal as well as with X, in hopes of getting clients closer to influencers and content that shows a higher propensity to spur consumers to purchase – and adding valuable purchase data to show where consumers are spending.
Starting with the X partnership, Omnicom is looking to take advantage of the fact that X is working to attract more creators and influencers, given its clout as a major second-screen hub. X has already solidified its status as a real-time reflector of trends too — both elements that Omnicom is looking to tap into, according to Megan Pagliuca, Omnicom Media Group North America’s chief product officer.
“What’s really different here is the role X plays as this cultural epicenter where we’re able to take this trends data, fuse it with audiences, fuse it with our elements of culture, use it for both brand creative execution on X and then also use it for influencer discovery and influencer activation,” said Pagliuca.
Here’s how the partnership, currently active in the U.S. only, works: X’s API data and Trends API are fed into Omni, Omnicom’s open operating system. Omni then matches the signals to find moments that work for an Omnicom client to attach itself to. The audience segments and corresponding trends by audience are both used to plan activations, which are pushed back into X for influencer discovery. Finally, all the insights gleaned are fed back into Omni’s Influencer Discovery Agent, an AI-powered agentic tool that identifies influencers and creators with the most impact on consumer engagement and performance, based on multiple criteria including audience match and cultural relevance.
Similar to Omnicom’s earlier Cannes announcements this week with Meta and Walmart before it, the Influencer Discovery Agent keeps amassing more knowledge with each deal the holdco makes, because it’s collecting input from all the major platforms where influencers and creators reach their audiences, and ostensibly getting smarter with each new input.
“What I’m most excited about here is looking at what moments of culture are being engaged with on X, and how we could use that to inform the content that those influencers are producing,” added Kevin Blazaitis, U.S. president of Creo, Omnicom Media Group’s influencer arm. “You have a very active base of niche communities — identifying the right voices of those. We’re excited to have our data play a larger role, to again expand voices and have that many to many communication.”
“This partnership is a prime example of how we help marketers take advantage of key moments, conversations and live moments taking place on our platform,” said Monique Pintarelli, X’s head of the Americas.
Zaryn Sidhu, OMG’s svp of social for North America, shot down any notion that the holdco might have lingering concerns over X’s recent history of eroding brand-safety efforts, citing Omnicom’s CASA efforts at ensuring brand safety protocols. “X is actually on par in terms of controls and partner verification,” said Sidhu. “We have adjacency and placement controls even down to the keyword level. We have third-party post-bid and pre-bid verification capabilities. We have content violation reporting.”
Omnicom today also unveiled a partnership with PayPal, which centers on attaching the finance app’s cross-merchant transactional data to OMG’s streaming TV inventory curation. OMG’s negotiated deals and curated supply paths are overlaid with PayPal’s transactional and purchase data via Magnite’s and PubMatic’s SSPs, enabling Omnicom clients to bid on both live and pre-recorded streaming inventory based on purchase data as a means of connecting with their audiences as effectively as possible.
Available in the U.S. in coming weeks before rolling out internationally, the arrangement lets OMG tap a vast transaction data set that also includes Venmo and Honey — two other payment platforms PayPal has that contribute to its transaction graph. It’s estimated some 430 million consumers use one of them, giving PayPal a 45% share of the global payments market, with $1.68 trillion in total payment volume in 2024.
“Live TV has long made it difficult for brands to reach the right audience in real time,” said Mark Grether, svp and general manager of PayPal Ads. “Bringing our technologies together and connecting transaction data with Live TV inventory improves the efficiency of awareness campaigns.”
“The ability to understand what users are shopping across merchants in this first party, deterministic data set, means we can pair the scale of PayPal and their transaction graph and their data with the Omnicom Media Group negotiated rates and inventory capabilities,” said Keagan McDonnell, senior director of product innovation and partnerships at OMG North America. “We believe that’s a very powerful use case in the market.”
Cox Automotive, an Omnicom client, is tapping into the data to more carefully target its streaming ad efforts. “This collaboration allows us to reach automotive audiences built on signals from PayPal’s extensive network of merchant partners, merging precision and scale,” said Jillian Davis, director of marketing technology at the client, which includes the Auto Trader and Kelly Blue Book brands. “By pairing PayPal transaction data with Omnicom’s media curation strategies, we can reach these hyper-relevant audiences without sacrificing inventory quality.”
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Despite reviewing and testing drawing tablets all the time, there’s something uniquely exciting about getting my hands on the latest from Wacom, and after using the new Wacom Intuos Pro Medium, I can honestly say it remains the gold standard for pen tablets in 2025.
This new Intuos Pro is a precise and beautifully designed drawing tablet that will no doubt remain the go-to choice for professionals in digital art and graphic design despite stiff competition from brands including XPPen, Huion, and Xencelabs. (Read my guide to the best drawing tablets for more choice.)
You may like
(Image credit: Future / Wacom)
What you get
Swipe to scroll horizontallyWacom Intuos Pro Medium specs
Dimensions:
291 x 206 x 4~7 mm / 11.5 x 8.1 x 0.160.28 inches
Active area:
187 x 105mm / 7.4 x4.1 inches
Weight:
411g / 14.50 oz
Battery life:
16 hours (10-12 real life experience)
Keys:
10 customisable keys, 2 customisable dials
Stylus:
Pro Pen 3 (pressure-sensitive, cordless, battery-free)
Pen pressure levels:
8,192
Pen tilt:
60 degrees
Pen resolution:
5,080 lpi
Multi-touch:
No
(Image credit: Future / Wacom)
(Image credit: Future / Wacom)
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(Image credit: Future / Wacom)
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(Image credit: Future / Wacon)
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(Image credit: Ian Dean)(Image credit: Ian Dean)
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(Image credit: Future / Wacom)
Swipe to scroll horizontallyWacom Intuos Pro Medium score card
Attributes
Notes
Rating
In the box::
Everything you need, but a USB-C adapter would be welcome.
4/5
Specs:
Good sized drawing area and pressure levels..
4/5
Setting up:
Easy and comes with instructions.
5/5
Design:
Slim, lightweight, good Express Key position.
5/5
Pro Pen 3
Superb customisation and performance.
5/5
Performance
Accurate, precise and mobile.
5/5
Wacom Intuos Pro (2025): Price Comparison
This week’s Ask an SEO question about affiliate strategies comes from Mike R:
“How is AI changing affiliate marketing strategy in 2025? I’m concerned my current approach will become obsolete but don’t know which new techniques are actually worth adopting.”
Great question, Mike. I’m seeing a few trends and strategies that are changing, for the better and for the worse.
When AI is used properly in the affiliate marketing channel, it can help businesses and brands grow.
If any of the three types of businesses (defined below) in affiliate marketing use it in a way that AI and large language models are not ready for “yet,” it can backfire.
I’m answering this question in three parts, as I’m unsure which side of the industry you’re on.
For the record: The affiliate channel is not at risk (i.e., affiliate marketing is not dead) because affiliate marketing is more than a content website that creates a list or writes a review, and coupon sites intercept the end of sale.
Affiliate marketing is a mix of all marketing channels, including email, SMS, online and offline communities, PPC, media buying, and even print media.
It is not going to be as impacted by AI as SEO and content marketing – and in many ways, it will likely grow and scale from it.
Affiliates are the party that promotes another brand in hopes of earning a commission.
Here’s some of what I’m seeing regarding the use of AI and its impact on affiliate revenue.
Programmatic SEO is not new, and using LLMs to create content or lists is burning what were quality sites to the ground.
It is almost never a good idea; it doesn’t matter if AI can spin up content and get it publish-ready in minutes.
In the early 2000s, affiliates and SEO professionals would use pre-AI article spinners to create massive quantities of content from one or two professionally written and fact-checked articles, then publish them to blogs and third-party publishing platforms like Squidoo.
This is equivalent to affiliates publishing their content on Reddit or LinkedIn Pulse to rank it.
The algorithms caught up and penalized the affiliate websites. Squidoo and some of the third-party platforms managed to stay afloat as they had trust and a strong user base for a while.
Next, PHP became the go-to for programmatic SEO, and affiliates would generate shopping lists or pages with unique mixes of products and descriptions via merchant data feeds and network-provided tools. Then, these got penalized. Again, nothing new.
Media companies have been getting penalized and devalued for years for this, and plenty of content creators, too.
If an affiliate manager is telling you to use LLMs to create content, or someone is using LLMs and AI to do programmatic SEO, look for advice elsewhere.
I’ve watched multiple quality sites fall since ChatGPT, Perplexity, and others began writing and spinning their content.
In traditional affiliate marketing, if an affiliate is not making sales, even if they send quality traffic, they get ignored. LLMs have changed this 100%.
I’ve seen affiliates, including bloggers, YouTubers, forums, and social media influencers, are being sourced and cited by AI systems.
If the brand is not on the content being used for fact-checking (grounding) and sourcing, the brands begin to disappear from outputs and results. I’m seeing this firsthand.
Not getting traffic or sales, or being number seven to 10 on a list, now has value. The citations and mentions from the resources that LLMs trust can help your brand gain visibility in AI.
Affiliates can and should begin charging extra fees for these placements until the LLMs begin penalizing or ignoring pay-to-play content.
We’re likely a couple of years away from their algorithms being anywhere near that advanced, so it is a prime opportunity while Google is reducing traffic to publishers via AI Overviews.
I think coupon sites are going to take a substantial hit, as AI is starting to create its own lists of coupons that work.
It also includes where and how to save, where to shop, and current deals on specific products. For example, “I want to buy a pair of Asics Kayano 32 men’s running shoes and get them on sale. Where can I find a deal?”
Right now, Google’s AI Overviews are populating lists of where to find deals, and it is showing the coupon sites as the sources to the right. These sites are likely getting clicks now.
I’ve seen ChatGPT pull the codes directly and preventing the need to click to the coupon website and set their affiliate tracking. It does show the website it came from, though – just no reason to click since you get the code in the output.
One interesting thing is that ChatGPT may pull in vanity codes.
The output from ChatGPT featuring these could give an influencer who was sourced for the code or a coupon site credit for their sales, throwing attribution off, because it was the coupon that triggered the commission, even though the user was using the LLM.
The influencer did not have anything to do with this transaction, but they’ll be getting credit.
The brand may now pay more money to the influencer, when, in reality, it should be ChatGPT – that is where the customers are, not the influencer.
By showing where to find the deals and which deals are available by product (not brand), AI eliminates one of the deal and coupon site’s top-funnel traffic strategies to brands.
The biggest hit I see coupon sites taking is ranking in search engines for “brand + coupon” for the last-second click from someone who is already in the brand’s shopping cart.
If Google AI Overviews creates its own coupon lists as the output, like ChatGPT is doing, there is no reason to click on a coupon website and click their affiliate links.
But, don’t count deal and coupon sites out. They still have email lists and social media accounts that can drive top-funnel traffic, and they can reintroduce customers who have forgotten about you by utilizing their own internal databases of shoppers.
These are the people who manage programs by recruiting affiliates into the program, giving the affiliates the tools they need, and ensuring the data on the network is tracked and accurate so the brands being promoted have the sales and touchpoints they’re looking for.
Some managers hit the panic button because they relied on content sites and publishers who have SEO rankings, but AI Overviews is using affiliate and publisher content and not sending the same amount of traffic to the publishers.
This reduces the number of clicks and traffic. The publishers are still driving traffic, but it is coming in via Google and not the affiliate channel.
With that said, affiliate managers can shift their focus to channels not as impacted by AI Overviews, including:
From seeing this on a daily basis, it appears that high-quality publisher accounts are being created en masse as fronts for fraud and fake affiliate accounts.
I’ve had conversations with people hired by the fake affiliate account who are being paid to talk to the affiliate manager, so it makes these sites look even more legit. We’ll have back-and-forth emails, and in some cases, a call.
Once the traffic and sales start, it turns out to be stolen credit cards or program violations. In some instances, the person or websites they applied with no longer exist.
Interestingly, when they activate a year later, thinking you forgot about them, magically, the site reappears when they know you’re not checking.
Always evaluate a site, and if the content is being generated by LLMs or AI, it may be best to reject it and reduce the risk of a fake account.
AI content may rank temporarily, but this is not a long-term strategy. If your brand is being written about by AI and spun out to a site via programmatic SEO, there is a reasonable chance that the details won’t be as factual or as on-brand as they should be.
An affiliate who cannot take the time to create good content and use AI to edit, versus using AI to create and then edit, should not be trusted in your affiliate program.
When your affiliates are generating content or fact-checking via LLMs and AI, they’re not doing their jobs as your partners to promote your program factually, with correct talking points, and following brand guidelines.
There’s a reasonable chance that incorrect claims about financial products, medical treatments, or even books to buy and read will be in the content you, as a brand, are paying to have made.
Even if you’re paying on a performance basis, you are approving this content to be live and represent your brand. This is why affiliates in your program using AI to create content are a high risk.
Set rules and enforce them so that your brand cannot be included in any AI-created content, or remove the affiliate from your program until they’re ready to treat your brand or your clients’ brands with the same care as you do.
One interesting use of AI for affiliate management is merchant and affiliate matching using machine learning and AI by agencies and larger brands.
Just because a partner does well in one vertical or with one affiliate program that has a similar audience, it does not mean it is a good match for others.
One exception to using AI for matching is to build a list of potential partners from a database. But automatically approving that list because the output creates a list is problematic.
Each affiliate that is recommended still needs to be vetted by hand to make sure they meet the requirements of the new program.
Some of the best uses of AI, especially LLMs, have been building lists of potential partners.
You can train GPTs to validate the lists, remove current partners so you don’t accidentally email or call them, do a gap analysis, and even customize the recruitment email to a very strong degree.
No, it isn’t perfect, but you can save hours each week from the manual tasks of discovery, validation, and outreach.
The recruitment emails still need to be reviewed and sent manually, but it is a massive time-saver.
We manually review every email before it goes out and have to do a decent chunk of rewriting, but we’re saving large amounts of time, too.
We also pre-schedule the emails using a database tool, but we’ve slowly begun implementing new discovery and drafting methods, and they’re turning out to be fantastic.
I was a non-believer in AI for this at first, but now I’m about ready to double down, especially as the systems advance.
These are the tracking and payment platforms that power the affiliate programs.
Affiliates rely on them to accurately record sales and release payments.
Affiliate managers use them to track progress, simplify paying partners around the world, and generate reports based on the key performance indicators (KPIs) their company uses.
All of the networks we’re working on have an influx of AI-generated sites. I’ve talked to agencies and managers on the ones we don’t work on, and they’re seeing the same.
The networks would be wise to add filters and create an alert for affiliate managers to let them know if the affiliate is human or AI, meaning that AI would be a website and promotional method without quality control.
There are no advanced controls in place on any networks that I’ve seen specifically for AI affiliates. But most networks do have compliance teams to which you can report fake accounts.
From the networks I’ve talked to, they’re working on solutions to help detect and reject these sites, but it is a massive problem because they’re being generated at high volumes, and some are really hard to detect.
The spammers and scammers are getting smarter, and AI has given them a new advantage.
This is a double-edged sword. Networks have more data than any affiliate agency, and they may be best suited to try partner and program matching algorithms.
They can create a list of programs that an affiliate may want to test, or a list of partners a program manager can pay to recruit based on program goals and dimensions.
The downside is that programs spend countless hours recruiting partners for their programs. Networks doing matching and recruitment take that work and give it for free to that program’s competitors.
A second downside is that affiliates get bombarded with program requests, and this can cause that to skyrocket, making it harder to get them to open emails, including program updates and newsletters.
Once they start ignoring emails because of too many, you may not get compliance issues fixed or promotions that would normally have benefited both parties.
One of the most beneficial things a network can do, but none are currently doing on a mass scale (some are starting to, and it’s looking promising), is to use AI to create custom reports for affiliate programs. These could be charts and graphs on trends over XYZ years.
Another is a gap analysis of products that get bundled together by type of affiliate, and then which similar affiliates already in the program don’t have a specific SKU in their orders.
The manager can recommend pre-selling the SKU within the content that drives the sale, or adding that specific SKU as an upsell to any customer who came from that affiliate’s link, based on the affiliate ID passed in the URL.
It can show trends where there are cross-channel (SEO, email, PPC, SMS, etc) touchpoints and how it modifies seasonally, annually, and if the goal creates more or less sales for the affiliate channel or company as a whole.
One important thing to remember is that not all affiliate networks offer true cross-channel reporting. Multiple only offer it once the user has clicked an affiliate link.
AI is going to be amazing and horrible for each of the three entities above that make up the affiliate marketing channel.
If used correctly, it can save time, increase efficiency, and create more meaningful strategies.
At the same time, it could result in violations of a program’s Terms of Service (TOS), steal traffic from publishers, and harm multiple types of businesses.
More Resources:
Featured Image: Paulo Bobita/Search Engine Journal
In a world where attention is fleeting and culture moves at hyperspeed, brands must do more than advertise—they must emotionally resonate. As technology changes how we connect, the art of storytelling remains timeless.
In this episode of The Speed of Culture, Matt Britton speaks with Arturo Nuñez, founder and CEO of AIE Creative, a brand consultancy helping companies craft emotionally resonant, culturally authentic experiences.
Arturo shares how he builds brand love by leading with cultural authenticity. From Nike to Apple to entrepreneurship, he offers insights into how to fuel iconic brand moments.
Previously, Arturo held senior marketing roles at Apple, Nike, the NBA, and NuBank. From global expansion to brand storytelling, Arturo’s work has transformed how brands connect with diverse audiences across generations and geographies.
[03:57] Curiosity Is the Marketer’s Superpower — Arturo emphasizes that staying relevant in a fast-moving industry requires relentless curiosity. Whether adapting to AI, social media, or mobile trends, marketers must be lifelong learners. At Nike, this mindset translated into initiatives like Eakins, where insights from youth on the street shaped brand decisions. Brands that stay curious, listen closely, and adapt rapidly win.
[05:44] Culture Is Built on the Sidewalk, Not in the Boardroom — Youth no longer consume culture—they create it. Arturo reflects on how the rise of social platforms flipped the traditional power structure. Brands must now listen to and participate in conversations led by consumers, not control them. His work at the NBA showed how following players—rather than just teams—became the new path to fan engagement.
[15:07] Emotional Connection Is the Real Brand Currency — Arturo recounts how brands like Nike and Apple achieved irrational levels of loyalty—people tattoo Nike’s logo on their bodies or place Apple stickers on luxury cars—not because of features, but because of what these brands represent. At NuBank, he built Brazil’s most loved brand by crafting emotionally resonant stories that gave consumers hope and dignity. His message to marketers: product specs don’t build tribes—identity does.
[20:18] Authenticity Over Optimization: Passion Brands Win on Truth — Dante’s HiFi, Arturo’s vinyl-only bar in Miami, was created with no commercial intent, just a love for music. Yet it became a cultural destination. He explains that building something you personally believe in will always attract others who share that passion. Today’s consumers see through brands chasing trends for clout. Brands that build communities, not just campaigns, succeed by showing what they love, not just what they sell. Authenticity is your loudest differentiator in a market full of algorithms.
[33:33] Obsession, Not Perfection, Is the Path to Impact — In a powerful moment, Arturo shares a lesson from the late Kobe Bryant: greatness doesn’t come from natural talent—it comes from discipline, failure, and relentless improvement. Whether pitching a product, running a business, or chasing a dream, what matters most is obsession with your craft. Arturo warns aspiring creatives and entrepreneurs not to wait for perfect conditions—they’ll never arrive. The brands and people who win are those who jump, build the plane on the way down, and keep going no matter how often they’re told “no.”
That process really messed with my head. But I did it. I got the visa, got the awards, worked with big clients, built a studio and hired a team. I did what I was supposed to do. Even so, I didn’t feel like I fit the mould; I don’t sound like a typical founder. I didn’t grow up speaking English. I didn’t come from money. I never had training in public speaking. And I’ve tried to play the part, but it’s not really me. I’ve always felt like I had to prove myself twice – once with the work, and again to be taken seriously.
A few months ago, I was invited to close the main stage at one of the biggest design conferences in the world. It felt like a big moment. But the whole process leading up to it was more intense than what I’m used to. There were multiple rounds of content feedback, rehearsals, a lot of back and forth. I went along with it, but it didn’t feel great.
Then, a couple of nights before my talk, during a speakers’ dinner, someone from the company pulled me aside and said they wanted another rehearsal. The person hadn’t even seen my talk – they were just passing along the message. It felt off. Out of context, and kind of undermining. Like what I’d done so far wasn’t good enough.
I went back and rehearsed on my own. Not because I needed to, but because I felt like I had to prove something. Again.
This post was sponsored by MarketBrew. The opinions expressed in this article are the sponsor’s own.
Is Google using AI to censor thousands of independent websites?
Wondering why your traffic has suddenly dropped, even though you’re doing SEO properly?
Between letters to the FTC describing a systematic dismantling of the open web by Google to SEO professionals who may be unaware that their strategies no longer make an impact, these changes represent a definite re-architecting of the web’s entire incentive structure.
It’s time to adapt.
While some were warning about AI passage retrieval and vector scoring, the industry largely stuck to legacy thinking. SEOs continued to focus on E-E-A-T, backlinks, and content refresh cycles, assuming that if they simply improved quality, recovery would come.
But the rules had changed.
In late 2023 and early 2024, Google began rolling out what it now refers to as AI Mode.
AI Mode breaks content into passages, embeds those passages into a multi-dimensional vector space, and compares them directly to queries using cosine similarity.
In this new model, relevance is determined geometrically rather than lexically. Instead of ranking entire pages, Google evaluates individual passages. The most relevant passages are then surfaced in a ChatGPT-like interface, often without any need for users to click through to the source.
Beneath this visible change is a deeper shift: content scoring has become embedding-first.
Embedding vectors are mathematical representations of meaning. When Google processes a passage of content, it converts that passage into a vector, a list of numbers that captures the semantic context of the text. These vectors exist in a multi-dimensional space where the distance between vectors reflects how similar the meanings are.
Instead of relying on exact keywords or matching phrases, Google compares the embedding vector of a search query to the embedding vectors of individual passages. This allows it to identify relevance based on deeper context, implied meaning, and overall intent.
Traditional SEO practices like keyword targeting and topical coverage do not carry the same weight in this system. A passage does not need to use specific words to be considered relevant. What matters is whether its vector lands close to the query vector in this semantic space.
Keywords focus on exact matches. Embedding vectors focus on meaning.
Traditional SEO relied on placing target terms throughout a page. But Google’s AI Mode now compares the semantic meaning of a query and a passage using embedding vectors. A passage can rank well even if it doesn’t use the same words, as long as its meaning aligns closely with the query.
This shift has made many SEO strategies outdated. Pages may be well-written and keyword-rich, yet still underperform if their embedded meaning doesn’t match search intent.
The story isn’t just about Google changing the game, it’s also about how the SEO industry failed to notice the rules had already shifted.
As rankings dropped, many teams assumed they’d been hit by a quality update or core algorithm tweak. They doubled down on familiar tactics: improving E-E-A-T signals, updating titles, and refreshing content. They pruned thin pages, boosted internal links, and ran audits.
But these efforts were based on outdated models. They treated the symptom, visibility loss, not the cause: semantic drift.
Semantic drift happens when your content’s vector no longer aligns with the evolving vector of search intent. It’s invisible to traditional SEO tools because it occurs in latent space, not your HTML.
No amount of backlinks or content tweaks can fix that.
This wasn’t just platform abuse. It was also a strategic oversight.
SEO teams:
Many believed that doing what Google said, improving helpfulness, pruning content, and writing for humans, would be enough.
That promise collapsed under AI scrutiny.
But we’re not powerless.
Google told the industry to “focus on helpful content,” and SEOs listened, through a lexical lens. They optimized for tone, readability, and FAQs.
But “helpfulness” was being determined mathematically by whether your vectors aligned with the AI’s interpretation of the query.
Thousands of reworked sites still dropped in visibility. Why? Because while polishing copy, they never asked: Does this content geometrically align with search intent?
The new SEO playbook begins with a simple truth: you are optimizing for math, not words.
Here’s what we now know:
It depends on your goals; for success in SERPs, you need to focus on tools that not only show you visibility data, but also how to get there.
Profound was one of the first tools to measure whether content appeared inside large language models, essentially offering a visibility check for LLM inclusion. It gave SEOs early signals that AI systems were beginning to treat search results differently, sometimes surfacing pages that never ranked traditionally. Profound made it clear: LLMs were not relying on the same scoring systems that SEOs had spent decades trying to influence.
But Profound stopped short of offering explanations. It told you if your content was chosen, but not why. It didn’t simulate the algorithmic behavior of AI Mode or reveal what changes would lead to better inclusion.
That’s where simulation-based platforms came in.
Market Brew approached the challenge differently. Instead of auditing what was visible inside an AI system, they reconstructed the inner logic of those systems, building search engine models that mirrored Google’s evolution toward embeddings and vector-based scoring. These platforms didn’t just observe the effects of AI Mode, they recreated its mechanisms.
As early as 2023, Market Brew had already implemented:
🔍 Market Brew Tutorial: Mastering the Top Cluster Similarity Ranking Factor | First Principles SEO
This meant users could test a set of prompts against their content and watch the algorithm think, block by block, similarity score by score.
Where Profound offered visibility, Market Brew offered agency.
Instead of asking “Did I show up in an AI overview?”, simulation tools helped SEOs ask, “Why didn’t I?” and more importantly, “What can I change to improve my chances?”
By visualizing AI Mode behavior before Google ever acknowledged it publicly, these platforms gave early adopters a critical edge. The SEOs using them didn’t wait for traffic to drop before acting, they were already optimizing for vector alignment and semantic coverage long before most of the industry knew it mattered.
And in an era where rankings hinge on how well your embeddings match a user’s intent, that head start has made all the difference.
SEO didn’t die. It transformed, from art into applied geometry.
To help SEOs adapt to this AI-driven landscape, Market Brew has just announced the AI Mode Visualizer, a free tool that simulates how Google’s AI Overviews evaluate your content:
This is the only tool that lets you watch AI Mode think.
Nate Hake is right: Google restructured the game. The data reflects an industry still catching up to the new playbook.
Because two things can be true:
It’s time to move beyond guesses.
If AI Mode is the new architecture of search, we need tools that expose how it works, not just theories about what changed.
We were bringing you this story back in early 2024, before AI Overviews had a name, explaining how embeddings and vector scoring would reshape SEO.
Tools like the AI Mode Visualizer offer a rare chance to see behind the curtain.
Use it. Test your assumptions. Map the space between your content and modern relevance.
Search didn’t end.
But the way forward demands new eyes.
Book Your AI Mode Strategy Session
________________________________________________________________________________________________
Image Credits
Featured Image: Image by MarketBrew. Used with permission.
Large Language Model (LLM) agents aren’t very good at key parts of CRM, according to a study led by Salesforce AI scientist Kung-Hsiang Huang.
The report showed AI agents had a roughly 58% success rate on single-step tasks that didn’t require follow-up actions or information. That dropped to 35% when a task required multiple steps. The agents were also notably bad at handling confidential information.
“Agents demonstrate low confidentiality awareness, which, while improvable through targeted prompting, often negatively impacts task performance,” the report said.
While the agents struggled with many tasks, they excelled at “Workflow Execution,” with the best agents having an 83% success rate in single-turn tasks. The main reason agents struggled with multi-step tasks was their difficulty proactively acquiring necessary, underspecified information through clarification dialogues.
Dig deeper: 7 tips for getting started with AI agents and automations
The more agents asked for clarification, the better the overall performance in complex multi-turn scenarios. That underlines the value of effective information gathering. It also means marketers must be aware of agents’ problems handling nuanced, evolving customer conversations that demand iterative information gathering or dynamic problem-solving.
One of the biggest takeaways for marketers: Most large language models have almost no built-in sense of what counts as confidential. They don’t naturally understand what’s sensitive or how it should be handled.
You can prompt them to avoid sharing or acting on private info — but that comes with tradeoffs. These prompts can make the model less effective at completing tasks, and the effect wears off in extended conversations. Basically, the more back-and-forth you have, the more likely the model will forget those original safety instructions.
Open-source models struggled the most with this, likely because they have a harder time following layered or complex instructions.
Dig deeper: Salesforce Agentforce: What you need to know
This is a serious red flag for marketers working with PII, confidential client information or proprietary company data. Without solid, tested safeguards in place, using LLMs for sensitive tasks could lead to privacy breaches, legal trouble, or brand damage.
The bottom line: LLM agents still aren’t ready for high-stakes, data-heavy work without better reasoning, stronger safety protocols, and smarter skills.
The complete study is available here.
I’ve long waxed lyrical about the Apple Watch SE 2, and questioned the need for anyone (who isn’t mountaineering) to have any of the fancier watches on offer. It did everything I needed it to and I was happy. But then I tried the Apple Watch 10 and for some inexplicable reason, I like it more.
It’s not that I use any of the features – I don’t need to track underwater activity, and I haven’t turned any of the more advanced health alerts on. But it does actually have benefits that seem more basic yet make a big difference to the user experience. It’s currently at the lowest price ever at Amazon, by the way (only $299.99 with $100 off), so if you agree with my thinking it’s a great time to get your own.
(Image credit: Apple)
Today’s best Apple Watch Series 10, Apple Watch SE (2022) GPS + Cellular and Apple Watch Series 9 deals
Today’s Memo is a download straight from my brain about the current state of Search and AI. So much happened in the last few weeks, and I haven’t had a chance to sort out my thoughts.
Until now.
I’m finishing this Memo with exclusive insight into the KPIs I measure for search right now for premium subscribers .
In this issue, we’re looking at:
Let’s dive in!
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On April 16 at 9 a.m., OpenAI dropped ChatGPT o3.
By noon, I’d already scrapped the slide deck I had finished the night before.
That whiplash has become routine: Each new model triggers the same loop – panic that it’s smarter than I am. Relief when I find the edges. Then, fresh panic as the cycle restarts.
When my coach, Heather, heard me vent, she dropped a killer quote that stuck with me since: “Kevin, constant change is the new normal.”
She’s right.
Releases land weekly, search interfaces mutate overnight, and the ground under every SEO strategy keeps sliding.
As we cross the midpoint of 2025, I want to freeze-frame what’s happening to search right now – and what it means for you.
Here’s the short version:
Let’s unpack each shift, starting with the calories we’ve been counting all wrong.
Since Google widened AI Overviews (AIOs) in March, one pattern rules them all: impressions up, clicks down.
Image Credit: Kevin Indig
Why the gap?
Two reasons:
The result: visibility inflation without visitors.
2024 was the year of peak traffic.
And looking at how few people clicked on links (a few percent) in the AIO usability study makes me think it’s entirely possible clicks drop to 10% or less of what we’ve been used to in 2024. And that’s ok.
Clicks have always been empty calories anyway. They were useful as a leading indicator for conversions/revenue/pipeline/sales/etc. (But that’s about it. Clicks didn’t mean dollars, and they didn’t mean real business growth.)
Of course, to us SEO folk, losing clicks sounds grim until you look closer at user behavior:
So, yes, raw clicks are vanishing, but the ones that survive are pure protein, not empty calories.
Clicks are collapsing, but the ones that remain are loaded with intent.
That flips SEO’s value prop on its head.
For 20 years, we sold SEO as a performance channel, whether we wanted to or not.
The standard calculation was: Search volume ✕ CTR ✕ CVR = Projected dollars.
When a keyword couldn’t survive that spreadsheet, it died in committee.
Meanwhile, those same executives drop seven figures to get a logo the size of a postage stamp on an F1 car – no attribution model in sight.
Why? Influence.
The belief that persistent visibility bends preference.
SEO is crossing the same Rubicon. In an AIO-and-LLM world, you’re not just fighting for traffic; you’re fighting for mindshare wherever prospects ask questions:
Your brand needs to echo across all of them.
That means new yardsticks (i.e., KPIs, which I laid out in the premium section at the end of the article).
In short, SEO is graduating from direct-response to influence.
Treat it – and budget for it – like any other brand channel that shapes preference long before the buy button.
AI Mode turns a single prompt into dozens of behind-the-scenes queries – a process engineers call “fan-out.”
The same thing is happening at the channel level: Search itself is fanning out, escaping the browser and popping up in every feed, app, and device.
Although SEO pros have been talking about it for years, in 2025, that finally, actually matters – and for three big reasons:
1. LLMs have injected search into every app. Want a cookie recipe breakdown in Microsoft Excel? You can have it. Meta shipped a standalone Meta AI and wove it into WhatsApp, IG, and FB. YouTube and Netflix are testing AI Overviews so you can “search” for the perfect video without ever leaving their walls.1
Translation: discovery no longer begins – or ends – on Google.com. Each walled garden is now its own mini-SERP, and Google has to fight a thousand little AI search engines, not just ChatGPT.
2. People cross-check AI with humans: Our AIO usability study showed a consistent pattern: Users read the AI answer, then hop to Reddit threads, YouTube comments, or Discord chats to see whether real people agree.
Credibility now comes from echoing across both machine answers and human conversations. If you’re invisible on social or community platforms, you’re invisible in the final decision loop.
3. The pie is somehow getting bigger. TikTok, Facebook, Instagram, Threads, Bluesky, YouTube, Google, ChatGPT, Perplexity, Claude, Snapchat – the list keeps growing, and so do their daily active users.
Where’s the extra time coming from? Mostly legacy media: linear TV, radio, even mainstream news sites. Attention is being reallocated, not reinvented.
What it means:
Sparktoro’s channel overview
AI Mode is the “final boss” of search.
Sundar Pichai told Lex Fridman that “the results page is just one possible UI,” and VP of Search Liz Reid called it “a construct.”
In other words, Google’s happy to toss the classic SERP the moment the math works.
Similarweb data shows AI Mode adoption is a bit over 1% – for now (Image Credit: Kevin Indig)
But right now, the math doesn’t.
Similarweb shows AI Mode in barely 1% of queries, by design.
A single AI Mode answer can swallow 20-50 follow-up searches, erasing the ad slots those pages used to carry.
Until Google finds a new way to charge (embedded ads, pay-per-chat, who knows), rollout will stay throttled.
When that business model lands, AI Mode becomes paradise for anyone who understands user intent.
Behind each prompt, Google “fans-out” dozens of micro-queries – price, specs, comparisons, nearby, reviews – and stitches the answers together.
Those micro-queries are the very same long-tails you optimize for today; they’re just fired in parallel and reassembled into a narrative.
How to prep while the gate is still half-closed:
Do the homework now and you’ll be ready when AI Mode graduates from beta to default – at least until the next boss fight, fully agentic search, shows up.
The twist of 2025 is that Google is meeting ChatGPT on its own turf.
AI Mode lifts Google’s results page into the same chat-first UI that OpenAI popularized – proof that Google is willing to “level down” from its ad-optimized SERP if that’s what users expect.
Last year, I shared this graphic for the launch of ChatGPT Search and got lots of questions:
Image Credit: Kevin Indig
Image Credit: Kevin Indig
Bottom line: treat the Google-ChatGPT battle as a live A/B test for the future of search.
Your job is to be visible in both ecosystems until a clear winner emerges – and that may take years.
Image Credit: Kevin Indig
So, where does all of this leave SEO (leaders)?
Less in the weeds, more on the podium.
Your job is no longer to fine-tune a single channel; it’s to keep an entire orchestra in time as search fragments across AI Overviews, chatbots, and social feeds.
No other role sits at the intersection of so much (intent) data – and that gives you license (and responsibility) to conduct.
1. Paid Media
2. Social & Community
3. Product Marketing
4. Content/GTM
Search will get even more agentic.
We could soon optimize not just for people but for the AI helpers who act on their behalf.
That means:
We’re not there yet, but the runway is short.
Shift from tactician to conductor now, and you’ll have the score in hand when the orchestra changes instruments again.
Baton up.
1 YouTube Tests AI Overviews In Search Results; Netflix Tests New AI Search Engine to Recommend Shows, Movies
Featured Image: Paulo Bobita/Search Engine Journal
Lobby group Movement for an Open Web has issued fresh calls for the Justice Department to coordinate its proposed remedies to address Google’s monopolies in the ongoing separate search and ad tech cases.
The DOJ is developing remedy proposals to counter Google’s market power in search and ad tech, both of which were ruled monopolies during the last 12 months, with MOW now calling for conjoined technical oversight of the two cases.
Currently, the remedies in each case are under development and will be overseen by separate technical committees, according to MOW, which is now calling for these to be merged, citing the need for a holistic approach to market correction.
In antitrust cases, a technical committee is a group of independent experts appointed by the court or a regulatory body, such as the DOJ, to oversee compliance with a settlement or remedy, particularly regarding technical aspects of a case.
MOW claimed a holistic strategy is critical due to the complexity and interdependence of Google’s business lines, and that uncoordinated remedies risk missing structural issues that sustain monopoly power.
The group supports existing DOJ proposals but cites Justice Brinkema’s ruling in the ad tech case that Google’s control of AdWords demand fuels its monopoly in ad exchanges. It thus calls for additional steps to empower publishers, including control over inventory auctions, transparency in pricing data, and restrictions on Google’s use of its owned-and-operated properties to gain bidding advantages.
MOW’s Tim Cowen, also chairman of the antitrust practice at Preiskel & Co., argued that there is potential for Google to discriminate in its ad tech practices through technical means, even after divestiture, citing how its purchase of DoubleClick enabled it to leverage its position in the search market to dominate the display advertising market.
“It blocked access to the DoubleClick ID so that third parties would become dependent on it,” he told Digiday, explaining the intricacies by which the online giant was able to dominate the market. “Then what it did was to give itself the first look at bids on the exchange… when that didn’t prove to be profitable enough, it gave itself a last look as well.”
The DOJ proposes forcing Google to sell its Chrome browser, end billions in default‐search payments to handset manufacturers, license its search index and user data to rivals for a decade, and potentially divest Android and enforce a choice‑screen mandate to restore competition
Google countered these breakup demands by outright opposing the forced sale of Chrome and suggesting limited, targeted contract reforms and broad data sharing, or long-term oversight.
Meanwhile, the DOJ’s proposed remedy targets Google’s ad tech monopoly through a three-phase plan: first, mandating real-time data access for rivals via Prebid; second, opening Google’s DFP auction logic to open-source; and third, fully divesting its ad server DFP and AdX under court supervision.
Following the divestiture, Google would be prohibited from operating an ad exchange for 10 years. Additionally, 50% of AdX and DFP net revenues would be placed into escrow to support the publisher transition and independent auction development. Google’s counter-proposals recommended more limited reforms to avoid a breakup, including real-time AdX data access, ending Unified Pricing Rules, and forgoing auction advantages.
At Advertising Week New York last year, industry leaders questioned whether the DOJ’s proposed breakup of Google’s ad tech would meaningfully shift the landscape. Some argue that Google is already one step ahead of regulators, citing its dominance as a deterrent to competition and suggesting that remedies may be too late to matter.
Many are divided on whether a breakup would hurt or ultimately heal the ad tech ecosystem, with some arguing the only effective means of hamstringing Google would be a huge fine. “I think the DOJ is now fighting yesterday’s war. No matter what the result is, the damage is done, and by the time they have the remedy, the environment is going to be so different,” said Justin Choi, CEO of Nativo, on one Advertising Week panel.
MOW’s Cowen further claimed that without adequate, conjoined oversight of the technical remedies, Google can take measures to evade any effective market intervention. “The idea of the remedy is that the divested entity [either AdX, Chrome, or DFP] has no interest in Google,” he said, adding that many contractual remedies – not to mention effective ongoing oversight – are additionally required to have the desired effect.
Justice Amit Mehta, the presiding judge in the search antitrust case, is on course to offer a ruling on remedies in early August. Google may appeal the judgment within 30 days, potentially extending proceedings through 2027.
Meanwhile, the remedies phase of the ad tech antitrust case is scheduled to begin on September 22.
