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Watch this video on YouTube Rand Fishkin didn’t get into SEO because he saw the future. He got into it because he had no choice. In the early 2000s, Fishkin helped run a small web business with his mom in Seattle. They hired another company to do SEO until they couldn’t afford to pay them anymore. That moment pushed him into search marketing. More than 20 years later, Fishkin has become one of the best-known voices in SEO — and one of Google’s biggest critics. In this interview, he looks back at how search has changed, what went wrong, and what may happen next. Early SEO was wild SEO today can feel messy. But in the early days, it was even more chaotic. “There was no social media,” is how Fishkin described that era, where forums like WebmasterWorld and Search Engine Watch were the center of the industry. People shared tactics openly. Many of those tactics were risky. Buying links was common — and effective. Fishkin did it, too. Then Google’s Matt Cutts called him out in public. That moment changed how he approached SEO. He spent years focusing on “white hat” practices and following Google’s guidelines. Looking back, though, Fishkin now questions whether that shift went too far. He believes Google’s own behavior over time has made those guidelines harder to trust. The early industry wasn’t just chaotic — it was also full of strange and memorable moments. Fishkin recalled massive conference parties with huge budgets and over-the-top ideas, including a staged “retirement” of the Ask Jeeves mascot. But what stood out most to him wasn’t the tactics or the parties. “My favorite thing… is people,” he said, pointing to the relationships and friendships built over decades in search. When Google stopped sending traffic Many people think AI is the big turning point in search. Fishkin says the shift started much earlier — around 2011. That’s when the idea of “zero-click search” first appeared. Google began answering more queries directly on the results page instead of sending users to websites. At first, it was small features like weather boxes and calculators. Then it grew: Around 2016–2017: nearly half of searches ended without a click By 2018: more than half Today: more than two-thirds Fishkin emphasized that this trend didn’t start with AI — it has been building for more than a decade. Publishers had a chance — and missed it Fishkin believes publishers could have taken action early — but didn’t. “The time to fight back… was 15 or 20 years ago,” he said. In his view, large media companies should have worked together to push back against Google’s growing control. They could have demanded payment for content or limited how Google used it. Instead, they allowed Google to crawl and use their content freely. At the same time, Google expanded its influence through lobbying and policy. “Publishers just missed that opportunity,” Fishkin said. Now, he argues, the focus has to shift to adapting: Build subscription businesses Monetize attention, not just traffic Learn how to operate within platform ecosystems Some companies have already made that shift. Fishkin pointed to The New York Times as an example of a business evolving beyond traditional news consumption. Did Google change? Fishkin does not believe Google has become worse for users. “If it was easier or better to search on Bing… people would go to those places,” he said. But he does believe Google has become much harder for publishers and creators. The change, he said, was gradual. As Google grew, went public, and aligned with investor expectations, its priorities shifted toward growth and revenue. “They became the people that they spent time with,” Fishkin said. The biggest AI mistake people make Fishkin says most people misunderstand how AI works. They treat AI answers like search results — consistent and reliable. But they aren’t. If you ask the same question multiple times, the answers can vary widely. “You will get completely different answers. And if you do that 10 times, you will get 10 incredibly unique different answers,” he said. His advice is simple: don’t rely on a single response. Ask multiple times and look for patterns. If the same answer shows up consistently, it’s more likely to be trustworthy. This matters most for important decisions, like health or finance, where relying on one answer could be risky. What he misses about the early days of SEO Fishkin doesn’t miss a specific tactic or tool. He misses the level of opportunity that existed in the early web. Back then, smaller creators and independent sites had a better chance to succeed. Traffic was more evenly distributed. “The world of clicks and traffic… was so… flat compared to… today,” he said. What’s next? Fishkin believes the future of media and search may look more like the past. He expects a smaller number of powerful platforms to control most of the flow of information. At the same time, individual creators will still produce much of the content — but within those systems. Still, he hopes the web can evolve again. View the full article

If you know anything about Google Ads Asset Studio, you’ve heard the hype: “Google just killed every excuse for not running video ads.” “Total game changer! You don’t need a production budget anymore.” “Upload a few product images and get campaign-ready video in minutes.” From Google Ads > Tools > Asset Studio, you can build, manage, and scale images and videos across ad formats. The recent addition of Veo (Google’s AI video generation model) and Nano Banana Pro means you can now turn a handful of product images into full-motion video ads, for free, in no time. Apparently, video creative is no longer a constraint. But does Asset Studio actually change the game? Read on to find out if it’s worth your time. A tale of two Veos Google is its own biggest cheerleader for the power of its AI images and video. A recent Think with Google article showcases AI-generated ads for Cosmorama, a Greek travel agency. The videos are genuinely imaginative: think a flamenco dancer in the clouds, not just close-ups of headphones and sneakers. As part of learning Asset Studio, I set out to reverse-engineer their approach. I wasn’t trying to match the quality. I just wanted a proof of concept using Nano Banana and Veo. What I got instead was a series of dead ends. No scene-level control: I’d read that prompting plays a major role in video output. But there’s actually no prompt function for scenes in Asset Studio. You select an image from your Asset Library, and that’s it. Google decides how to animate it. There’s no way to direct motion, pacing, or narrative. Human performer restrictions: Video generation repeatedly failed with errors about “specific individuals.” I assumed that meant celebrities or real people. In practice, anything that resembled a human face — even AI-generated — triggered issues. The only assets that consistently worked were tightly cropped: hands, partial torsos, and abstract scenes. No real audio control: The Cosmorama video featured cinematic music. In Asset Studio, you’re limited to a small set of preloaded audio. There’s no way to upload custom music or meaningfully shape the sound layer. After so many false starts, I returned to the article. It mentioned Nano Banana and Veo by name. It never said they were used inside Asset Studio. When Veo 3 became available in Asset Studio, I didn’t realize how many limitations it would have, resulting in a completely different experience from the stand-alone version. CapabilityVeo (Full Version)Veo (Asset Studio)Control levelAdvanced control (API, model tiers, audio support)Simplified UI with fixed constraintsText-to-video promptingFull prompt control: – Scene – Camera movement – Lighting – Style – Subject/actionNoneUse casesProduction-ready pipelinesLightweight asset generationScene stitchingMulti-scene / narrative workflows (stitching and extensions)NoneHuman generationSupport (with policy constraints)Limited / often restricted What’s available may still help you create some great 10-second motion ads, but don’t go into it expecting flamenco dancing. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with Does Asset Studio actually save time and effort? That depends: Whose time? Whose effort? For years, paid search managers had one move for visual assets: push back. “I need a vertical version.” “The first five seconds need to be more engaging.” “Can you remove the text overlay?” Creative’s been a constraint, but always someone else’s constraint to solve. Asset Studio changes that. You can edit, adapt, and post YouTube video ads, even without access to the brand’s YouTube channel. But the constraint doesn’t disappear. It just changes hands. Managing creative strategy and production — even within Asset Studio — takes more time than not owning that role. Using Asset Studio, I’ve manually adapted logos to new aspect ratios, generated variations that need further edits, and written voiceover scripts I never would have been involved in creating before. And since production can’t exist without a strategy, I’m spending more time on that too. This is definitely game-changer territory, but maybe not the way you’d hoped: If you’re a brand that would otherwise need a production team: This is likely faster and more affordable than the alternative, satisfying the velocity mandate. If you’re an agency absorbing this work on top of an existing scope: You’re likely taking on a new responsibility that wasn’t priced in. It removes a bottleneck and replaces it with ownership. If that shifts what your role actually covers, it’s worth revisiting your contract scope. Will this get me in trouble? AI ad compliance explained No federal laws in the U.S. prohibit the use of AI in ads. But that’s starting to change. New York recently passed a law requiring advertisers to clearly disclose when an ad includes a “synthetic performer,” and it’s set to take effect in June 2026. (Hat tip to Sam Tomlinson for his LinkedIn post flagging this.) Asset Studio doesn’t generate a visible watermark (such as the Gemini sparkle), and there’s no way to add an AI disclosure in Google Ads. A couple of things worth knowing if you’re using Asset Studio specifically: You’re likely covered for now. Asset Studio can’t generate content with human performers. As mentioned above, anything resembling a face consistently triggers errors. That means the New York law’s “synthetic performer” provision wouldn’t apply to what Asset Studio actually produces today. There’s a watermarking layer. Google uses SynthID to invisibly tag AI-generated images. If disclosure requirements become more explicit, that infrastructure already exists to support it. Asset Studio’s limitations may actually insulate you from the most immediate compliance concerns, but if you want to proactively disclose AI use for ethical reasons, there’s no built-in way to do that. Get the newsletter search marketers rely on. See terms. AI without the slop Josh Spanier, Google’s VP of AI and Marketing Strategy, has this advice for marketers running AI-generated ads: “Stop fearing ‘AI slop.’ Humans made bad ads long before robots.” Interesting suggestion, but not all of our clients and stakeholders will be quite so enthusiastic about paying to run AI slop ads. Fortunately, tight control of Asset Studio images and video is easier than you might think. Unlike AI Max, where AI-generated assets can run before you’ve reviewed them, Asset Studio output isn’t automatically published. From your Asset Library, you choose which assets to run. The rest never see the light of day. What you can produce in Asset Studio is somewhat limited, but here are some of the non-sloppy features I’m most excited about. Image fidelity: Product images that actually look like your product Asset Studio’s Nano Banana 2 is built specifically for product integrity. Unlike general-purpose AI image tools like Midjourney, it lets you add up to five reference images and effectively “locks” the product. Only the surrounding environment is up for reinterpretation. Trim: Cut right to the action Client-produced video is rarely built for YouTube. Long intros and slow builds lose viewers before the message lands. Trim lets you jump straight to the action, without going back to the client for a new cut. Voiceovers and templates: Sleeper tools For a tool suite that promises to replace a production department, Asset Studio’s constrained voiceover and template options may seem underwhelming. Voiceover only works with audio ads or pre-existing video, and templates feel like glorified slide decks. But the more I reviewed the landscape of YouTube video ads, the more I realized: most companies struggle with messaging more than production quality. Low budget isn’t limiting sales, but bad scripts and concepts are. Templates and voice-overs let you test the right words faster than waiting for a new creative brief and a published video. In one campaign I’m running, an Asset Studio video I built in under 30 minutes using a template is already showing 10x the CTR of the client’s best-performing video. Beating the control may not be the highest bar to clear, but it’s a start. See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with The output isn’t the outcome Is Asset Studio a game changer? Not yet. But I’m not sure it needs to be. Positioning it as real competition against global creative brands sets everyone up for disappointment. The more useful frame: it’s a tool suite that makes creative faster and more accessible for accounts that couldn’t justify a production budget before. It does shift some of that strategy and production work onto the paid search manager who didn’t traditionally live in that role. But the bigger question is: what does any of this actually lead to? The point of digital marketing creative isn’t to produce more assets. It’s to drive conversions and sales. That’s still what needs to be proven. Tests are running now. I’ll share what holds up, and what doesn’t. View the full article

The European Commission proposed Google share data with rival search engines and qualifying AI chatbots in the EU/EEA. The post Google May Have To Share Search Data With Rivals appeared first on Search Engine Journal. View the full article

Many ecommerce brands misapply Meta-style thinking to Google Ads, leading to wasted spend and weak performance from poorly structured accounts. The post Winning Google Ads Campaign Structures For DTC Ecommerce appeared first on Search Engine Journal. View the full article

Shares in the space-based internet provider AST SpaceMobile Inc (Nasdaq: ASTS) are sinking this morning after a major mishap occurred with the deployment of its latest satellite from Blue Origin’s most advanced rocket, the New Glenn. Here’s what you need to know. What’s happened? On Sunday, April 19, Jeff Bezos’s Blue Origin space company launched its flagship rocket, the New Glenn, for the third time. The New Glenn is a partially reusable heavy-lift rocket aimed at directly competing with archrival SpaceX’s Falcon 9 and Falcon Heavy. (This rivalry pits two of the world’s richest people against each other: Bezos, founder of Amazon, and SpaceX CEO Elon Musk.) All three of these rockets are designed to deliver payloads into space and for their boosters to safely land back on Earth after launch, for reuse in future missions. The New Glenn, named after the U.S. astronaut John Glenn, is much larger than SpaceX’s rockets, allowing it to carry larger payloads. However, despite this benefit, Blue Origin still plays second fiddle to SpaceX, the premier private spaceflight firm that governments and companies rely on to launch their satellites. Blue Origin had hoped that the third launch of the New Glenn would begin to change this perception. For that launch, the New Glenn carried AST SpaceMobile’s BlueBird 7 satellite into space. And while the New Glenn did successfully launch, and its booster did safely touch back down on Earth, the rest of the mission—the part that AST SpaceMobile was relying on—did not go as planned. BlueBird 7 satellite was misplaced in orbit The New Glenn’s payload was AST SpaceMobile’s BlueBird 7 satellite, which is part of a constellation the space-based internet provider is building to deliver broadband internet to smartphones on Earth. When it launches later this year, AST SpaceMobile’s satellite internet service will have the advantage of working with existing, unmodified mobile devices. In other words, you won’t need a special satellite internet-capable smartphone to get satellite internet service. But before AST SpaceMobile can bring that service back to people on Earth, it needs to put about 45 satellites into space. While Blue Origin’s New Glenn successfully launched the BlueBird 7 into space, the satellite decoupled from the launch vehicle and powered on, but it was placed in an incorrect orbit, making it unusable. In a statement after the failed deployment, AST confirmed the New Glenn placed the BlueBird 7 “into a lower than planned orbit by the upper stage of the launch vehicle,” adding that “the altitude is too low to sustain operations with its on-board thruster technology.” As a result, the BlueBird 7 will be “de-orbited.” Deorbiting is the process by which a satellite is brought back into Earth’s atmosphere in a controlled reentry so that it can burn up. AST says the cost of the satellite, which is estimated to be in the tens of millions, should be recoverable by the company’s insurance policy. What went wrong? For now, it’s too early to tell what went wrong with the BlueBird 7’s deployment. Yet the fact that something did go wrong will serve as a stain on Blue Origin’s capabilities, especially just at the time when the company was hoping to start stealing some of the thunder from its main rival, SpaceX. AST SpaceMobile has not given any explanation for why the BlueBird 7 was deployed into an incorrect orbit. As for Blue Origin, in a social media post, the company confirmed that “The payload was placed into an off-nominal orbit,” while noting it was “currently assessing and will update when we have more detailed information.” Fast Company has reached out to AST SpaceMobile and Blue Origin for comment. ASTS stock drops after failed deployment After news of the failed deployment broke, AST SpaceMobile’s stock price plunged. As of the time of this writing, ASTS shares are currently down nearly 15% in premarket trading to $72.75 each. Blue Origin is a private company, so its stock is not publicly traded on any market. Until last week, AST SpaceMobile’s stock price had seen notable growth for the year. As of Friday’s closing price, ASTS shares were up nearly 18% year to date, far outperforming the Nasdaq Composite’s 5.31%. Over the past 12 months, ASTS shares have surged more than 265%. Investors have high hopes for AST SpaceMobile. If it can successfully deploy its full constellation of satellites, it could bring space-based broadband internet to billions of existing mobile devices across the world. But in order for the company to do that, its satellites will need successful launches and deployments—and not a repeat of yesterday’s mishap. The company says it is continuing with its plans, with BlueBird satellites 8 to 10 scheduled to launch in the next 30 days. It says it expects to have approximately 45 satellites in orbit by the end of the year. View the full article

Google has outlined best practices that can increase the likelihood of "Read more" deep links appearing. The post Google Lists Best Practices For Read More Deep Links appeared first on Search Engine Journal. View the full article

Trust in search is now dynamic, requiring ongoing authority building, content maintenance, and structured delivery to remain visible. The post What Search Engines Trust Now: Authority, Freshness & First-Party Signals appeared first on Search Engine Journal. View the full article

When we lift weights, do yoga, or perform exercises of any kind, there’s often an instructor chiming in to tell us to “engage our core.” But what does that really mean? It turns out there are two different ways of doing it and they produce opposite results, so it’s important to know which one you need to work on to accomplish your fitness goals. Here are the two ways, why they're different, and how to know which one you should do. Method 1: Pull your belly button to your spineThis one is probably familiar if you’ve ever done pilates or physical therapy. You’re told to pull your belly button toward your spine, or to think of “hollowing” or “drawing in” your stomach muscles. In this motion, you are still allowing yourself to breathe; you’re not sucking in your stomach, but rather, tightening it with your muscles. (If you watch in the mirror, you’ll notice your waist appears smaller when you do this. Sometimes people will also do it to pose for a picture or to create a leaner look while performing as a dancer.) The reason this is a common practice in many physical therapy, yoga, and pilates classes is that doing so activates your transverse abdominis, one of the lesser-known ab muscles. A study in 1999 found that people with low back pain were less likely to contract this muscle while moving their bodies, so physical therapists began to instruct people to contract this muscle to protect their backs from strain. Unfortunately, it turns out this move may not actually do much to protect your back after all, but it’s still popular advice. If you’re performing yoga or pilates moves this way, you’re in good company, and many physical therapists still favor this approach. But there's another way to engage your core, one that's more popular in activities like weightlifting. Method 2: Brace before lifting something heavyNow let’s talk about what to do if you’re lifting a heavy weight or preparing to perform some kind of forceful feat of strength. First, you’ll need to brace. (Bracing may also be a good alternative to hollowing your belly in physical therapy, but I’m not your PT, so talk it over with them.) When you brace for a lift, you’ll do something much like if you were expecting to get punched in the gut. If that's not an instinctive movement to you, imagine that you're lying relaxed on a bed, and you notice your cat or toddler about to jump on your belly. Try that now: you’ll probably hold your breath, contract your abs, and feel the muscles all around your waist tighten up. Rather than sucking in your belly, it may seem like you’re pulling your ribcage down toward your pelvis. This activates your transverse abdominis along with everything else. (If it feels a little like you’re bearing down for a bowel movement, you’re on the right track.) This is what powerlifters and other weight lifters mean when they talk about bracing for a lift. If you are wearing a belt, bracing will push the muscles of your midsection against the belt (not just in front, but all around). This process turns your torso into a solid, stable, pressurized column that can support a lot of weight (as in a squat), or hold its position steady as you apply force to it in another direction (as in a deadlift, where your torso is the link between your back, your leg muscles applying force, and your arms, which are supporting the barbell in your hands). Holding your breath and locking it in with a valsalva maneuver is typically part of this process. In some cases—for example, if you are pregnant or if you have certain medical conditions—your doctor may advise you not to hold your breath under pressure. You can still do your best to brace; just exhale slowly during the lift rather than holding your breath. (If you have health concerns, talk to your healthcare provider about whether this is appropriate for you.) When you’re trying to do a heavy lift in the gym, remember the distinction between these two ways of engaging your core, and do not try to hollow your belly or pull your navel to your spine, since that will have an effect opposite of the one you want. Save that motion for pilates class; when you’re under a barbell, make sure you brace. View the full article

When people discuss climate innovation, they often picture technology. Better batteries. Smarter grids. Carbon capture at scale. Those breakthroughs matter and are happening every day. But on this World Creativity and Innovation Day, I want to make a case for a different kind of innovation. One that is structural rather than technical, already underway, and quietly accelerating climate progress. It is, in a word, trust. A SYSTEM BUILT FOR FRAGMENTATION The social impact sector is filled with brilliant, committed people working on the climate crisis. It is also organized in a way almost perfectly designed to prevent the scale of impact the crisis demands. Many organizations undertaking critical work compete for the same funding. They guard their methodologies, protect their data, and duplicate efforts. They differentiate their missions so precisely that a funder reading a dozen can be forgiven for wondering whether any of them are solving the same problem. None of this is driven by bad faith. It is driven by survival. For decades, philanthropic funding has rewarded differentiation over collaboration and proprietary impact over shared learning. The result is a fragmented ecosystem applying fragmented resources to a problem that is anything but fragmented. The climate crisis does not respect organizational boundaries, and those of us working to solve it must stop acting as if it does. DESIGN FOR TRUST, NOT COMPETITION So what would it look like to redesign the system itself, not just the solutions within it? In 2023, my organization, Pyxera Global, joined an unusual experiment: the Collaborative for Systemic Climate Action. We did not know where it would lead, but something fundamental had to change. We began with 15 organizations with a combined 250+ years of experience. Three years later, the Collaborative has grown to 29 organizations, including Climate KIC, the Club of Rome, the B Team, the Green Africa Youth Organization, and the Amazon Sacred Headwaters Alliance. All are united by a shared dedication to break down the silos that have long limited what any one of them could accomplish alone. Each organization committed to driving the systems change needed to build inclusive and regenerative societies. That meant leaving organizational ego at the door. It meant rethinking power dynamics and stepping away from traditional partnership models. Most importantly, it meant sharing what is usually protected: intellectual property, business models, and even funder relationships. This level of openness carries real risk. For any single organization, it could be destabilizing. But the members of the Collaborative believe that the scale of the climate crisis outweighs institutional self-protection, and that meaningful progress requires taking risks together. PROOF THAT IT WORKS And the results are beginning to speak for themselves. Together, the Collaborative has secured significant funding from major institutional donors, including the Oak Foundation, Hans Wilsdorf Foundation, and Quadrature Climate Foundation—partners that individual organizations might not have reached on their own. It has hosted joint thought leadership and fundraising events at global convenings such as the World Economic Forum, the United Nations Climate Change Conference, and Climate Week, creating a unified platform that amplifies collective knowledge and impact. It has also seeded more than a dozen systems-change initiatives across geographies as varied as Ghana, India, Ireland, New Mexico, and Brazil. Many of these efforts would have struggled to get off the ground independently, but through the Collaborative they are now positioned to attract the additional funding needed to scale. Just as important is the infrastructure behind them. Partners convene twice a year for deep sensemaking and portfolio review, and meet weekly in virtual sessions to stay aligned, build trust, and continuously learn from one another. I have spent more than two decades working on partnerships and 38 years in the social impact sector. I thought I understood collaboration. What I have seen in the Collaborative for Systemic Climate Action is something different. The level of trust, and the results already emerging from it, go beyond anything I have experienced before. This does not mean it is frictionless. Conflicts arise. Old habits resurface. Egos occasionally reenter the room. But even with those tensions, the trajectory is clear: Something fundamentally different is taking shape. This is what structural innovation looks like. It is as disruptive in its domain as any new technology. World Creativity and Innovation Day exists to celebrate creativity in all its forms. Redesigning how climate philanthropy operates, how knowledge is shared, and how trust is built at scale is creative work. It is not a new invention. It is a new way of organizing human effort. WHY THIS MATTERS NOW That shift is becoming urgent. Public sector climate funding is shrinking. Multilateral institutions are under increasing political pressure. Corporate ESG commitments are being quietly scaled back. In this environment, the traditional nonprofit response will not close the gap. What is needed is not just more funding, but better alignment of existing resources. That is where trust becomes a force multiplier. The Collaborative’s approach is simple in concept and radical in practice: Reduce the friction between organizations that should be natural allies, so that existing resources can move faster and go further. THE REAL BOTTLENECK The Collaborative is one proof point, but the model itself is replicable. We describe it as “mycelium,” a networked system that connects and strengthens everything it touches. It requires a convener willing to do the unglamorous work of building relationships and holding space for shared ownership. It requires funders willing to invest in connective tissue, not just individual projects. And it requires leaders willing to believe that their impact will be greater within a strong ecosystem than within a weaker one they control. For companies and philanthropists looking to maximize their climate commitment impact, this is where the leverage lies. Not in funding a single organization, but in enabling many organizations to operate as one. The hardest material in climate action is not carbon. It is the institutional ego and competitive reflex that keep aligned actors apart. Building the conditions for trust at scale may be one of the most important challenges, and opportunities, in climate action today. Deirdre White is the CEO of Pyxera Global. View the full article

You’ve audited your client’s website and compiled performance data. You’ve identified what’s working, what can be improved, and your recommendations for future strategies. But how do you turn that data into a presentation that’s easy to explain and builds trust? Start with stories. Storytelling isn’t just for entertainment. It’s how people make sense of information. That’s what makes it so effective for data presentation. One of the simplest ways to structure that story is the three-act structure. It’s a familiar framework used everywhere, from Aristotle’s Poetics to Star Wars. What is the three-act structure? The three-act structure is a simple framework that shows how a story moves from beginning to middle to end. It shows how a protagonist moves from their starting point to a meaningful change. Applied to data storytelling, it helps you organize your insights, position your client as the main character (the protagonist), and clearly show what happens next. While similar to the five-point narrative arc, this framework is organized into three manageable sections: what the story is about, what happens when the main character is introduced to conflict, and how that conflict is resolved. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with Act 1: The beginning This is where the protagonist’s norm and conflict — the issue the main character is meant to face, also known as the antagonist — are established. The protagonist wants something, and the conflict is holding them back from what they want. An event or circumstance occurs that incites the protagonist into action. The background is established, the goals are defined, and the audience is invested in the protagonist’s success. Act 2: The middle The story is developed, and tension builds. The protagonist experiences roadblocks caused by the conflict/antagonist that hinder them from their ultimate goal. Conflict arises until it can no longer be ignored, causing a pivotal moment that leads into the final act. Act 3: The end The narrative is affected by the change in Act 2, bringing the story to a final showdown between the protagonist and the conflict/antagonist, ultimately resulting in a resolution. The protagonist may find closure or know what path lies ahead (this may set the stage for a sequel). The three-act structure helps you understand website data on a deeper level. It also prepares the data to be presented to your client in a way that places them at the center of the story. Using the three-act structure to identify your data’s narrative Why bother using the three-act structure as a framework for strategy analysis? It builds trust, showing your client that you’re going on a journey alongside them. You and your client are on the same team, with the same destination in mind: their success, even if the data isn’t communicating immediate results. The application of the three-act structure to data storytelling happens in three steps. Step 1: Briefly recap the existing strategies, establish previous wins, and identify the challenge currently affecting performance. This sets the baseline of Act 1. Step 2: Explain the roadblocks and how they stand in the way of the overall strategy’s success. This parallels the growing conflict found in the structure’s Act 2. Step 3: Recommend the next steps and how you plan to address the conflict. Show what success looks like by providing examples of how your recommendations fit the narrative of your client’s goals. This is Act 3, the resolution of the structure. Get the newsletter search marketers rely on. See terms. Where is your client’s story in the three-act structure? Your client is the protagonist of their story. To work more effectively together, you need to communicate to your client that you’re invested in the story of their success. At the heart of each data set is the story of how your client is impacted. When you communicate what the data is saying, position yourself as the guide who helps the main character get where they need to go. An example of applying the three-act structure framework to data analysis and presenting the data’s narrative would look like this: ActGoalScenarioApproach1Set the stage, center your client as the protagonist while introducing the challenge as the antagonist.Your client’s website has received a substantial increase in organic traffic as a result of your most recent strategy, but is experiencing a high bounce rate on select pages.Recap the strategy that led to the traffic increase and summarize the outcome from a high-level perspective.2Identify the conflict, potential roadblocks, and related stakes.The high bounce rate is preventing your website from experiencing consistent traffic flow. Explain why a high bounce rate is detrimental to overall performance, and connect the affected pages to the overall strategy.3Recommend strategies and outline next steps.Your client’s high bounce rate indicates low page speed due to large images that take a long time to load.Help the client visualize how best practices lead to better outcomes. Recommend image compression as a next step. The conclusion doesn’t always mean the end of the story Finding the story in your data — and communicating it clearly — is how you build trust with clients. Clients don’t want industry jargon. They want to feel seen, understood, and that they’ve entrusted their digital marketing success to the right person. Stories, and the connections they form, get them there. Reaching the conclusion of your data’s narrative isn’t the end, but the beginning: the start of strategy implementation, of collaborative partnerships, and of greater results. When looking at data, you and your client are on a journey together. A downward trend in your data doesn’t mean your story is over, and an upward trend doesn’t mean there’s no hope for a sequel. In either case, a new journey (your next strategy) can begin. View the full article

Google is reportedly testing opening search results in a new window. Yes, there is a setting to turn this on or off, but the setting is turned off and Google is testing opening these clicks in a new window.View the full article

The war is currently more likely to escalate than to be resolved by negotiationView the full article

Google is testing showing video ads within the local pack ads. This is also known as immersive map view videos, I am told.View the full article

A Reddit thread asks if there are any issues with Google Search handling a domain name that has a single letter change in the domain name from another domain name. John Mueller from Google said that usually this is not a problem from Google Search.View the full article

Everyone is talking about GEO. Agencies are selling it. Brands are buying it. On its face, the pitch makes sense: the search-based internet that powered the digital economy for decades is giving way to generative AI answers. The pressure to act is real. So is the trap: you’re going to start paying for another lease on someone else’s internet. GEO is a tactic for a bigger and more important strategy. That strategy is owning your audience. The problem is that this new tactic is generating too much buzz and money for brands to ignore. McKinsey recently released a report arguing that $750B in US revenue will flow through AI-powered search by 2028. If you want your brand in on that revenue, you must invest in so-called generative engine optimization (GEO) or answer engine optimization (AEO). Both are designed to convert user prompts into clear references of a specific brand. And both are important. Half of consumers already use AI-powered search engines. The shift is real. And optimizing for it isn’t wrong, it’s just not enough. The recommended response from industry leaders and agency directors is the same mistake the industry has been making for fifteen years: renting space from a landlord who can change the terms at any time. GEO is not a new strategy. It’s a new lease on the same building. And we all know how badly that can go. HubSpot is the cautionary tale nobody in this industry wants to sit with. They built one of the most sophisticated content operations in the business, dominated SEO for years, and then watched their search traffic fall off a cliff when Google changed how it surfaces results. They were renting. When the landlord renovated, they lost the apartment. McKinsey’s own data shows that a brand’s owned content comprises only 5 to 10 percent of what AI search actually references. The rest comes from affiliates, user-generated content, publishers, and sources the brand has no control over. So the optimization play — clean up your content, sharpen your headings, structure your data for LLMs — is optimizing a minority stake in the outcome. Data like that kind of gives away the game. Your GEO strategy amounts to tinkering at the margins while the real action happens elsewhere. The Infrastructure Is More Fragile Than the Pitch Admits There’s another problem nobody selling GEO is talking about. The web is fighting back. Independent developers have built tools — with names like Nepenthes and Iocaine, after a carnivorous plant and a fictional poison — designed to trap AI crawlers in infinite loops of garbage data, wasting their resources and corrupting their training sets. One developer reported eliminating 94 percent of bot traffic to his site the day he deployed one. Cloudflare and others now sell bot-mitigation and anti-scraping tools to throttle AI crawlers at scale. The resistance has gone commercial. The signal-to-noise ratio inside LLM training data is getting worse as publishers grow more hostile to being scraped without compensation. Any strategy built entirely on AI systems accurately surfacing your content is betting on a supply chain with an active sabotage problem. That is a risk the GEO consultants are not pricing in. What Building Looks Like Discoverability is not the end goal. It’s a byproduct of building something worth finding. There is a distinction between renting attention — optimizing your way into algorithms you don’t control — and building owned media infrastructure that creates direct relationships with audiences. Red Bull Media House is the most cited example because it’s the most extreme: a beverage company that became a legitimate media operation. The audience relationship it built doesn’t reset every time an algorithm updates. That’s the point of the investment. The brands doing this right are creating content tailored to how people actually search on each platform — what they’re trying to accomplish, what format serves them in that moment — and their GEO and SEO improve as a consequence of being genuinely useful. Useful first, optimized second. The Agentic Future Already Blinked McKinsey’s study ends with a prediction that LLMs will eventually act as agents making purchase decisions on behalf of consumers. Most GEO pitches build toward the same vision. So it’s worth examining what happened when OpenAI actually tried to build it. In September 2025, OpenAI launched Instant Checkout — buy products directly inside ChatGPT, Shopify and Etsy as launch partners. Users flooded in to research products. Almost none of them completed a purchase there. Out of Shopify’s millions of merchants, only a small fraction of eligible merchants went live with the integration. By early 2026, OpenAI pulled the checkout feature back, routing transactions to retailer apps instead. The agentic shopping future collided with actual human behavior and lost. People used the AI to get smarter about what to buy. Then they went somewhere they trusted to buy it. That is the whole argument. The brands that will survive AI-mediated discovery are the ones that have already built the kind of trust and direct relationship that makes someone choose them as the destination — not as the result of an optimization score, but because they earned it. No amount of schema markup builds that. No weekly GEO audit cycle builds that. Those are maintenance tasks. Infrastructure is something different. Stop renting. Start building. The landlords are getting worse, and the leases are getting shorter. View the full article

Are ChatGPT ads allowing advertisers to go after competitors with greater specificity, or is this just a topical fluke? View the full article

The folks at the Internet Archive are having issues with archiving the web. Something it has done since 1996 and is such a foundational website that it is sad to see. That being said, Fabrice Canel from Microsoft Bing wants to help.View the full article

Carroll Tower, a 194-apartment public housing development in Providence, Rhode Island, was built in 1974. For more than 50 years, residents there relied on electric baseboards for heating and their own window air conditioners, if they had them, in the summers. But now, the entire building has been retrofitted with a modern HVAC system: 277 heat pumps from Gradient, a San Francisco-based climate tech startup, will heat and cool the property. The heat pumps were installed as part of a $1.25 million public-private project between the Providence Housing Authority, Gradient, the Rhode Island Office of Energy Resources, energy consulting firm Abode Energy Management, and Envr Air, which works to accelerate HVAC electrification. It is among the largest completed heat pump installations in the United States. Installation across the entire building took just 12 days, with no drilling or rewiring required. Since heat pumps are highly efficient, the installation means less energy use and fewer emissions for Carroll Tower. The upgrade could save the building 450,000 kilowatt-hours, or about $94,500 in energy costs, a year, according to a preliminary estimate, while reducing greenhouse gas emissions by 219 tons annually—equivalent to a gas car driving about 500,000 miles. A solution for vintage buildings Residential buildings account for about 20% of the country’s carbon emissions, and heating and cooling is responsible for over half of those buildings’ energy use. HVAC systems can also leak natural gas or refrigerants, which are potent greenhouse gases. Electrifying those systems can drastically slash emissions. Heat pumps are a particularly promising climate solution; even when they rely on an electricity grid powered by fossil fuels, they still cut tons of emissions a year compared to other heating systems. In 2022, the New York City Housing Authority (NYCHA) launched a “Clean Heat for All” challenge, asking manufacturers to develop an electric, easy-to-install heating and cooling system. Gradient was one winner (along with China’s Midea) and will provide 10,000 of its window heat pumps to NYCHA buildings. Those projects are rolling, but some buildings have already received the heat pumps. A public housing development in Queens got 72 heat pumps in 2023. That led to an 87% reduction in energy use, with the development’s energy costs cut in half. Gradient has since worked with housing authorities in Boston; Chelsea and Lynn, Massachusetts; and more. Its heat pumps work especially well in “older, vintage buildings,” like those found in public housing authorities, says Gradient founder and CTO Vince Romanin. These can be buildings where extensive upgrades are difficult (such projects may require asbestos mitigation, for example), and where upgrades are sorely needed because there are no current cooling systems or the heat fails frequently—both of which can be health risks to residents. ‘You can get your window back’ Carroll Tower is one of two elderly-only buildings in the Providence Housing Authority. The average age of residents there is 71. Before this upgrade, not every resident had air conditioning; in the summers, the building would turn certain spaces into cooling stations. Now, everyone will have access to their own AC. “A lot of them are happy,” says Larry D’Alfonso, an 81-year-old resident and president of the tenant council. The Gradient units, he adds, are “very quiet.” Depending on the apartment layout or building heating system, heat pumps can have other benefits too. If someone’s apartment uses steam radiators for which they can’t set their own temperatures, a Gradient retrofit allows for personal temperature control. The units also come with a standard air filter, with an ability to upgrade it. Romanin adds that the heat pumps, which hang over a windowsill, can make a unit more comfortable because they don’t block that window like an AC unit would. They’re also quieter than clanking radiators. “Human comfort is way more than just the temperature in the room,” he says. “The idea that you can get your window back and get more natural light is actually important to someone’s comfort. The noise profile is actually important to someone’s comfort.” Easy installation Then there’s the installation. For general HVAC systems, installers may need to drill holes into walls or run refrigerant lines or ductwork through buildings. Even less elaborate systems, like ductless mini-splits, can still require the handling of refrigerants or wiring, often requiring a licensed contractor. Gradient installers don’t need to be HVAC certified. The heat pumps plug into a standard 120-volt outlet. “Our system is the first I know of that can electrify [publicly owned] buildings without having to do any work on the facade,” Romanin says. Installing 277 heat pumps throughout Carroll Tower’s 194 apartments and common areas took only 12 days, without displacing any residents. (Some units may have more than one heat pump depending on their square footage). That saves building owners on retrofit costs, too. D’Alfonso confirms that the installation was “very fast.” The installers were also “tremendous to the tenants, very courteous,” he adds. The only complaints he’s heard so far from his neighbors were that some people had to move their furniture around to accommodate the heat pumps, which stick out about 9 inches from the wall. “They gotta get used to them,” he says. “It’s something new.” View the full article

Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from the pages of Inc. and Fast Company. If you received this newsletter from a friend, you can sign up to get it yourself every Monday morning. These are difficult times for elite universities. Controversies over the handling of pro-Palestine protests on campus cost several school presidents their jobs; under the The President administration, federal research grants have plunged; and just 42% of Americans polled by Gallup in 2025 reported confidence in higher ed, down from 57% in 2015, the first year the poll was conducted. Just this month, Yale University released a report acknowledging prestigious schools’ role in losing the public’s trust. So why is Daniel Diermeier, the chancellor of Vanderbilt University, thoroughly enjoying a role that has frustrated or felled many of his peers? Diermeier, who became chancellor (the equivalent of a CEO) in 2020, believes he’s cracked the code to leading unwieldy institutions: Avoid politicization and stick to one’s core purpose. And those are lessons he thinks corporate leaders should apply to running businesses. By many measures, Diermeier’s approach is working. Undergraduate applications rose 12.6% in 2025, and the school saw a 20% jump in early-decision applicants—a metric of a school’s desirability. Indeed, Vanderbilt now admits just 4.7% of applicants, making it more selective than Cornell or Dartmouth. Under Diermeier’s direction, the Nashville-based school is expanding, with a new campus opening in New York City and campuses planned in West Palm Beach, Florida; Chattanooga, Tennessee; and San Francisco. Diermeier also has his detractors. A February Chronicle of Higher Education feature called him the sector’s “most divisive” chancellor, noting that Diermeier’s embrace of institutional neutrality has been seen by critics as a capitulation to “bad-faith critiques” of universities. Before arriving at Vanderbilt, Diermeier was provost at the University of Chicago, whose “Chicago Principles” on freedom of expression mirror Vanderbilt’s Statement of Principles. Prior to becoming provost, he was a University of Chicago dean and professor, where he taught and researched crisis and reputation management. I sat down with Diermeier to talk about how he thinks about leadership in a polarized moment—and what his experience running a major research university might mean for CEOs facing the same turbulent terrain. Edited excerpts follow: MODERN CEO: Every generation feels like it’s living in extraordinary times. How would you characterize the current environment in which you’re operating? DIERMEIER: There are [some] big forces shaping our lives. One, we’re living in an age of rapidly accelerating technology. Of course, AI is on everyone’s mind, but the next big thing is right around the corner with quantum [computing], and that is transforming sectors and industries. Number two, the changing geopolitical environment has shifted dramatically. I remember an important CEO saying, maybe 10 or 15 years ago, that the great challenge for our generation was to “get globalization right,” and now people are talking about decoupling. The third one is the erosion of trust in major institutions, accompanied by a tremendous polarization of society, which is maybe the fourth—those are the three or four things that are shaping the leadership environment, whether you’re in business, a university, or government. MODERN CEO: Trust is a big topic. How do you think about trust as a leader, and what are some of the things you’re working on to restore trust in higher education? DIERMEIER: There’s a global erosion of trust across the board, and that’s one of the big findings from the Edelman Trust Barometer. The erosion of trust in universities has been more pronounced than in most other institutions. People on the left worry about inequality and that universities are enhancing inequality. The main concern is that we’re politically biased to the left and that we’re “woke machines.” And then everybody is worried about affordability. My sense is you have to address these concerns head-on and ask yourself: Is this a communication problem or a real [systemic] problem? Universities are a large segment with different flavors, but at leading research universities, there has been a dramatic increase in financial aid. The net cost for families has actually gone down over the last 10 to 15 years, especially for families at lower income levels. If you go to Vanderbilt and your family makes less than $150,000 [a year], it’s free. So that’s a perception problem. On inequality, people who graduate from our universities who come from the lowest income segment and those who come from the highest income segment, if they both have an economics undergraduate degree, they have the same expected lifetime income. There’s a high correlation between kids’ income and parents’ income, but if you graduate from a selective, large research university, your lifetime income and the lifetime income of somebody from a very different background is the same. So, the question is, “How do you get in?” We have debates on admissions, but inequality in education doesn’t start at age 18. The problem for us is how do we make sure that we get qualified applicants from across the spectrum to apply. On the political bias question—there, I think we have a real problem. I think universities have drifted toward one side of the political spectrum, and I think there has been mission drift. The willingness for universities to take sides in political battles has clearly increased during the last 10 years, and it has really hurt them. You need to be clear about what your purpose and your values are—and don’t be dragged into other things that are really beyond the purpose of what you do. MODERN CEO: At a research university, how do you navigate an environment where [accepted science] on vaccines or climate change are politicized? DIERMEIER: It’s really critical to be very clear and explicit about the values and the purpose that you’re engaged in. I’ll give you a different example that shows how we’re thinking about it. We’re in the business of generating knowledge and then conveying it to the next generation of students and to the public. We are not in the business of telling people how to think. Not in the business of taking sides on political or policy issues as an institution. Our job is to encourage debate, not to settle it. So, let’s take an example. The Dobbs [Supreme Court] decision on abortion rights in the United States [that overturned Roe v. Wade]: Some universities issued statements saying the Dobbs decision was inconsistent with the values of the university. But in my law school, I have faculty who believe Dobbs was badly argued, and I have faculty who believe Roe v. Wade was incorrectly decided on jurisprudential grounds—even if, from a policy standpoint, they’d agree that access to abortion should be legal and safe. And we have people who say we need a compromise in the middle. The university needs to be a place where those views can be debated freely. We are a platform where questions on climate change can be freely debated and where faculty can do the work and provide decision makers with information and knowledge. MODERN CEO: You’ve said operating in hyper-polarized environments is increasingly the reality for CEOs broadly. What’s your advice to them? DIERMEIER: Be clear about who you are, your purpose, your values, and your positioning—you have to embrace that part. [Beyond] leading people and execution capabilities, managing your board—things that typically are seen as being part of the CEO’s toolkit—the ability now to operate in hyper-polarized and politicized environments is really critical. MODERN CEO: Last question: You once wrote that being a university chancellor is the best job in the world. Do you still feel that way? DIERMEIER: One hundred percent! You have to be an academic leader. Then you’re basically the CEO of a mid-sized enterprise, and it’s a pretty complicated business. It has a research and education mission, then you have an asset management component, which is the endowment. We also have a $3.5 billion real estate portfolio. On top of that, you have college athletics, which is a whole other thing. And the third piece is you really have to be a politician. You have to connect with your [municipal] mayor, with the council, with the local council, with the state, with the governor, with the federal government. These are entirely different skill sets, and makes this job challenging. Read more: the ABCs of leadership Ignition Schools drive entrepreneurship and innovation Be a better leader in today’s challenging environment How to build a leadership legacy View the full article

About 20 minutes into the Devil Wears Prada—the 2006 David Frankel film that constitutes one of the most important and perfect films ever produced (please hold all dissent)—Meryl Streep delivers a critical speech to Anne Hathaway that encompasses the plot’s primary tension. The moment, which may come up in the sequel (an Instagram post from a professional dyeing service in New York suggests this may be the case), comes as Streep’s Miranda, the frigid chief editor of a top fashion magazine is pondering items that might be featured for an upcoming issue, while surrounded by her stressed-out underlings. Also in the office is Andie (Hathaway), a comparatively disheveled new assistant who has somehow landed a coveted role at the esteemed Vogue stand-in, diligently taking notes on the runthrough. When an underling presents two blue belts to Miranda for consideration and notes that it’s tough to choose between them, Andie snortles and says, “Both of those belts look exactly the same to me. I’m still learning about this stuff.” This, of course, is precisely the wrong thing to say. Up until this point in the movie, Andie has hid, very poorly and under the guise of bubbly unfamiliarity, that she thinks the fashion industry is vain and stupid. Miranda, intelligent and ever-perceptive, has picked up on Andie’s covert derision, and uses her blue belt faux pas to delve into a crisp and critical evisceration of Andie’s attitude. You can just watch this scene online, but Streep’s dismantling of her assistant is summarized below. That “lumpy blue sweater,” she explains, is not just blue but cerulean, a color that traveled from designer collections, from Oscar de la Renta to Yves Saint Laurent, down through the market until it landed, inevitably, in Andie’s closet. What Andie shrugs off as “stuff” is a system she already participates in, albeit passively—and it’s one that generates countless jobs and millions of dollars. “It’s sort of comical how you think that you’ve made a choice that exempts you from the fashion industry,” Miranda tells a now totally silent and humbled Andie, “when, in fact, you’re wearing a sweater that was selected for you by the people in this room… from a pile of ‘stuff.’” Andie’s implicit position, throughout the film, is that while she’s interning at a fashion magazine, she sort of thinks the industry is silly, even stupid, and she’s a reluctant—though perky—observer, not a participant. Miranda, of course, thinks the fashion industry is all there is. But that’s not Miranda’s point. Her point is that we all wear clothes. To think you’re not somehow not participating in fashion, however saintly or nefarious, is inane. Here’s where AI comes in. Now, a cerulean belt is not a large language model, and Miranda is not Sam Altman, but the scene is illustrative of a reflex by some to believe that they can simply excise the influence of a billion-dollar industry on their lives—and then feel morally superior for it. A small but loud community of AI skeptics is taking the position, like Andie, that AI is not something they’re participating in, sort of stupid, and even something to look down on. This community (which tends to thrive on Bluesky) seem to believe that AI ranges from silly, uncool, stupid, and most importantly, something ignorable that they are not using. AI will either be very good or very bad for humanity, but there’s probably no universe where AI is just a silly and vapid nothingburger we can roll our eyes at and ridicule. This has even emerged into a sort of purity test, where it’s become common (among some) to believe that using AI is like a personal flaw and nothing more than dimwit tomfoolery. Even in more thought-through circles, there’s a developing emphatic sense of moral outrage over the use of AI, and urging technological renunciation instead. One problem with this attitude is that it falls into the well-worn trap of an abstinence-only approach, pushing people to restrain themselves from making a poor choice when the deluge of social pressures and personal desire shepherd toward making that exact choice. Lest we forget that most of us are workers, and many people will use AI because their bosses tell them to, not, like, for fun. (Indeed, a Gallup poll recently found half of all workers in the US now use AI). This approach also suggests that a consumer choice is the solvent to a systemic threat. Calls for ardent vegetarianism, and urging people to flick off the lights when we leave the house, did not solve climate change (which is ongoing). The same is and will be true of AI and the misguided social trend that seems to hinge on shaming people who might do so. Miranda’s monologue, though, illustrates a second problem with this line of thinking. Yes, you can decide not to use ChatGPT, and perhaps this might give you a momentary feeling of organic cognition, free of AI’s influence. And that might be worth it, alone, for preserving your ability to think clearly. But know the internet is already polluted with the output of large language models, and that you are imbibing this output everyday. It is true that you do not need to personally pay for a subscription to Claude, but the architecture of our digital system means that large language models are already a rank-and-file feature of email software, customer service bots, in media production, and so much more. ChatGPT and search engines will eventually converge into the same thing, and they are, in fact, racing to the finish line to do so. AI is reshaping our energy production systems and our politics. The question is not whether you’ll have the soup, but whether you realize you, and the rest of us, are already swimming in the soup. Considered another way, this approach appears to be the equivalent of sticking your head in the sand when the very challenge you are facing is a sandstorm. Inconveniently, systematic threats require systemic solutions, not performing purity politics. If your revulsion is to AI is that it’s corrupting our ability to think independently—which it definitely is—ridiculing those who use the consumer version of ChatGPT is a very small and more importantly ineffectual hill to die on. View the full article

AI has quickly become the most overconfident line item in the modern marketing roadmap. Budgets are shifting. Teams are being restructured. Vendors are being evaluated almost exclusively through the lens of how “AI-powered” they appear. There is a growing assumption that once the right models are in place, performance will follow. Better targeting. Smarter segmentation. Higher conversion. More efficient spend. It sounds almost inevitable. But there is a quieter reality beneath the momentum. One that rarely makes it into boardroom conversations or conference keynotes. Most organizations are not struggling to use AI. They are struggling to feed it. And what they are feeding it is far less reliable than they think. The uncomfortable truth about inputs AI does not create truth. It scales whatever it is given. If the underlying data is fragmented, outdated or manipulated, the model does not correct it. It operationalizes it. At speed. At scale. With confidence. This is where the gap begins. Marketers have spent years investing in data infrastructure, pipelines and orchestration layers. On paper, the foundation looks strong. There is more data available than ever before. There are more signals, more touchpoints, more attributes tied to every customer. The assumption is that this abundance translates into readiness. But volume is not the same as validity. A customer profile built from five disconnected identifiers is not a unified identity. An email address that exists in a CRM is not necessarily active, reachable or even tied to a real person. Engagement signals that appear recent may be the result of automated activity, privacy shielding or bot interaction. AI models are not designed to question these inputs. They are designed to find patterns within them. So, when the inputs are flawed, the outputs become convincingly wrong. Identity is the fault line At the center of this problem is identity. Every AI-driven use case in marketing depends on the assumption that you know who you are analyzing, targeting or predicting. Whether it is propensity modeling, churn prediction, audience creation or personalization, identity is the anchor. Yet identity remains one of the least stable components of the data stack. Consumers move across devices, channels and environments constantly. They use different email addresses. They share accounts. They create new profiles. They disengage and re-engage in ways that are difficult to track cleanly. Over time, what appears to be a single customer often becomes a composite of partial truths. Even within authenticated environments, identity degrades. Touchpoints go inactive. Behavioral signals lose relevance. Records persist long after the underlying reality has shifted. Most systems are not built to continuously reconcile these changes. They capture identity at a moment in time and treat it as durable. And AI inherits that assumption. Which means many models are making decisions based on identities that no longer exist in the way they are represented. The hidden impact of fraud and synthetic activity Another layer omplicates the picture further. Not all data is simply outdated. Some of it is intentionally misleading. Fraud is evolving alongside marketing technology. The barriers to creating accounts, generating engagement, or exploiting promotional systems have decreased significantly. Automated tools and AI itself have made it easier to simulate legitimate behavior at scale. Fake accounts are not always obvious. They can pass basic validation checks. They can engage with content. They can move through funnels in ways that resemble real users. From a model’s perspective, they are indistinguishable unless additional context is applied. This creates a subtle but meaningful distortion. Acquisition models begin to optimize toward patterns that include fraudulent behavior. Lifecycle strategies adapt to engagement that is not human. Performance metrics improve on the surface while underlying efficiency erodes. The result is a feedback loop where AI reinforces the very issues it should be helping to solve. And because the outputs look sophisticated, the problem becomes harder to detect. Why traditional data strategies fall short Most organizations are aware that data quality matters. Significant effort goes into cleansing, deduplication and normalization. Records are standardized. Fields are filled. Duplicates are merged. These steps are necessary, but they are not sufficient. Clean data is not the same as accurate data. A perfectly formatted email address can still be inactive. A deduplicated profile can still represent multiple individuals. A normalized dataset can still be missing critical context about behavior, risk or authenticity. Traditional data practices tend to focus on structure. AI requires substance. It requires an understanding of whether an identity is real, whether it is active, whether it is behaving in ways that align with genuine consumer patterns. Without that layer, even the most sophisticated models are operating on incomplete information. The illusion of readiness This is how the mirage takes shape. Dashboards show high match rates. Databases contain millions of records. Models produce outputs that appear precise. Campaigns are executed with increasing automation. From the outside, it looks like progress. But underneath, there are unresolved questions. How many of those identities are actually reachable today? How many represent real individuals versus synthetic or low-quality accounts? How often are behavioral signals refreshed and validated? How much of the model’s learning is influenced by noise? These are no longer rare. They are foundational. And yet they are often overlooked because they sit below the level where most AI initiatives begin. A different way to think about AI readiness True AI readiness does not start with model selection. It starts with input integrity. It requires a shift in focus from how much data you have to how much of it you can trust. That trust is built on a few critical dimensions. First, identity accuracy. Not just the ability to match records, but to ensure that those records reflect real, current individuals. This includes understanding when identities change, when they become inactive and when they should no longer be used as the basis for decisioning. Second, activity validation. Knowing that a signal occurred is not enough. You need confidence that it represents meaningful human behavior. This is where distinguishing between genuine engagement and automated or manipulated activity becomes essential. Third, risk awareness. Every dataset contains some level of fraud or abuse. The question is whether it is visible and accounted for. Without that visibility, models will absorb and propagate those patterns. When these elements are in place, AI begins to operate on a different plane. Predictions become more reliable. Segments become more actionable. Optimization aligns more closely with real outcomes. Where this creates advantage Organizations that address these foundational issues are creating a structural advantage. They are able to suppress low-value or risky identities before they enter the modeling process. They can prioritize outreach to individuals who are both reachable and likely to engage. They can detect and mitigate fraudulent behavior before it distorts performance metrics. Over time, this compounds. Models trained on higher-quality inputs learn faster and generalize better. Campaigns become more efficient. Measurement becomes more trustworthy. Perhaps most importantly, decision-making becomes more grounded in reality. This is where AI begins to deliver on its promise. The path forward There is no question that AI will continue to reshape marketing. The capabilities are real, and the pace of innovation is not slowing down. But the idea that AI alone will solve underlying data challenges is a misconception. If anything, it raises the stakes. Because AI does not just expose weaknesses in your data. It amplifies them. The organizations that recognize this early are taking a more deliberate approach. They are investing in understanding their identity layer. They are prioritizing the validation of activity and the detection of risk. They are treating data not as a static asset, but as a dynamic system that requires continuous refinement. They are not asking, “How do we apply AI to our data?” They are asking, “Is our data worthy of AI?” It is a more difficult question. It requires a deeper level of introspection. It challenges assumptions that have been in place for years. But it is also the question that separates real readiness from the illusion of it. And in a landscape where everyone is accelerating toward AI, clarity at the foundation is what ultimately determines who moves forward, and who simply moves faster in the wrong direction. View the full article

In 2021, newly relocated to San Francisco from New York City, Danielle Snyder Shorenstein went with her husband to her first Golden State Warriors game. She wasn’t a sports fan, really, and especially not a Bay Area sports fan. “I identify as a New Yorker,” she says. Having owned and run a fashion and jewelry brand called Dannijo with her sister, Jodie Snyder Morel, since 2008, and looking around at the game merch, she thought to herself how unlikely she’d be to wear any of it. Over the course of the season, Shorenstein continued to go to games with her husband and began experimenting with her own take on fanwear. She cut up a jersey, added a crochet collar, some crystal work—and wore it to games. Soon enough, players’ wives and girlfriends were sliding into her DMs. Strangers were stopping her in the arena bathrooms, all asking the same question: “Where did you get that?” Danielle Snyder Shorenstein Jodie Snyder Morel “It woke me up,” Shorenstein says. “Sport is huge. I used to think about sports like, slap a logo on a product and show off your team. But I thought, I’m going to make this chic. That was the aha moment. That was the unlock.” As Shorenstein began to cultivate her own grassroots following at the Warriors games, she and her sister, who lives in Jacksonville, Florida, got to work on a second business, DannijoPro, a fanwear brand that blends fashion and sports fandom. The effort has been an experiment in innovating across two mature industries that rarely intersect. Now, nearly two years in, the business has a full line of fan gear, from understated button downs with a tiny, offset team logo embroidered on the shirt to bespoke vintage gear with hand-sewn details, crocheted collars and rhinestone touches. They’ve even started a line they’re calling 1/won, using vintage fan gear to make bespoke pieces at higher price points. Items at DannijoPro run anywhere from $85 to $495, and are sold on the company’s website, as well as at brand-hosted pop up shops and events. At the end of April, the brand will launch on online fashion retailer Revolve. Right now, DannijoPro is growing 120% year over year, with 40% of their sales coming from DMs on social media. The brand has grown through word of mouth, with a boost from the likes of Brooke Shields, Ayesha and Stephen Curry, Selena Gomez, and Benny Blanco wearing it. The brand has also brokered a licensing agreement with the NBA. Fashion reworked The founders’ experience building DannijoPro has been entirely different from their first foray as fashion founders. “The lay of the land in sports licensing is complicated. There is no road map,” Shorenstein says. “Every league operates differently and every team is different. The distribution is complicated. It’s layered and nuanced. Relationships really matter. But, there’s a lot of opportunity to be entrepreneurial. We are creating our own path within the confines of this landscape.” And, that path, says Morel, is focused on the female consumer in fandom—a buyer that’s been largely ignored in the sports fan apparel business, favoring an infamous “pink it and shrink it” model. “We’re trying to build something from the ground up,” says Morel, noting that there’s no preexisting distribution channel for what DanniJo Pro offers. “Our stuff doesn’t live on plastic hangers in an arena. We are taking a risk, trying to cultivate and create community organically. It’s not a short cut,” Shorenstein says. When you’re trying to take up space in a market you’re new to, you have to justify your presence, which, the sisters say, is more difficult than they ever expected. They’ve had to be creative to establish a toehold in fandom, brokering relationships with players, hosting events, and focusing on the bespoke aspect and craftsmanship in what they do. Looking at DannijoPro with a wider lens, it’s clear the company is also capitalizing on a thread emerging at the intersection of business and culture wherein brands like Hathaway Hutton with its viral Boatkin, Kristin Juszczyk’s Off-Season (in partnership with Skims founder Emme Grede, Fanatics and the NFL), collegiate artwork at Axis Hats and the Clearly Collective with its college campus and city-map scarves are leveraging established, legacy IP as a sort of growth hack. Even on Etsy and Instagram, budding designers are peddling reworked Ralph Lauren button-downs and Nike sweatshirts for a refreshed, unique look. The very existence of these businesses raises a question around innovation and originality, and whether building a brand from established IP, or creating brand adjacency, is equally as wise as building one from scratch. “Licensing is a win-win-win trifecta,” says strategist and licensing expert April Beach. “It is amazing for the creator of the IP—in this case the NBA—the licensee at DannijoPro and it’s amazing for the end users. It increases profit and it gives the licensee the opportunity to take someone’s incredible work and build their genius with it.” Nicole Dolgon, partner at New York City law firm Esca Legal, says that one of the best bits of a licensing agreement like the one DannijoPro has with the NBA is that there’s exponential growth in fanbase with very little lift for the NBA. Morel and Shorenstein do the heavy lifting, maintaining constant communication and product approvals with the league. And, so long as they can jump through the right hoops and maintain that relationship in all of the best ways, everyone does well. Perhaps the most validating interaction for Morel and Shorenstein came when Divya Mathur, chief marketing officer at online clothing retailer Revolve, called. “My focus is always where our customer is spending her time,” says Mathur, noting the brand will launch on Revolve at the end of April. “On my radar was an increase in attending sporting events. I was looking at brands in this space and I came across DannijoPro. I was really drawn to very specific things: their silhouettes, the sweatshirts. None of the product out there felt like it fit our customer.” But, she says, DannijoPro offered an opportunity to play in the sports space without leaving the fashion world: “It exists beyond game da. It crosses over into everyday life, with a lot of craftsmanship and detail. These are pieces you’ll feel proud to wear. That’s the white space in the market.” Morel says filling the void has evolved into three buckets of the business: NBA-licensed pieces with Dannijo signatures (crochet collars, hand-stitching, crystal work), available for any NBA team; the Atelier (or what the sisters call blanks), where customers take Dannijo “blanks” and work with in-house artists to personalize their pieces; and then 1/won vintage, which is sourced through longtime vintage dealers in California and Florida, reworked by hand (cut, stitched, painted). Organizing the business this way, especially with its vintage 1/won line and its hand-added details, is a resistance to fast fashion and AI-generated sameness in our culture, says Shorenstein. Sports fandom is a generational loyalty that has nothing to do with trends. (You don’t send your Warriors jacket to the RealReal, says Morel.) There’s a greater emotional attachment to these pieces, especially when they’ve been altered and created by humans. The community DannijoPro builds is as much the product the brand is selling as the clothing itself. The stadium bathroom becomes as much the trunk show as the athlete’s tunnel has become the runway. Says Shorenstein: “The NBA is our window to the world. People are buying joy and community with DannijoPro. And having an eye and style matters in this space. We are going to be lending our fashion house aesthetic to other sports and leagues outside of the NBA.” View the full article

If AI can write our emails, analyze data, and generate code, then machines outperform humans on nearly everything we currently measure: speed, productivity, and task completion. Based on these measures, humans lose. Their jobs. Their dignity. Their worth. A recent management study shows that AI can help people do 12% more work, 25% faster—but it gets the answers wrong 19% of the time. That’s a telling number. And helps us to understand what we’re all experiencing. We’re optimizing for throughput while quietly accepting a compounding error rate. If we value motion and not direction, we’re like Wile E. Coyote, sprinting forward ever faster—only to realize, a beat too late, there’s no ground beneath us. The reason this matters so much right now is that AI and humans are fundamentally different kinds of intelligence. Despite its “generative” name, AI is recursive. Similar to how “social media” isn’t at all social. AI finds patterns in what already exists, optimizes what’s already been done, and accelerates what’s already been decided. That’s genuinely powerful. It can create a song for you based on your story or code a basic website for you. But—and this is important—it cannot imagine what doesn’t exist yet. It cannot dissent. It cannot empathize. It cannot hold tension or sense when a decision lacks integrity. Humans can. We humans don’t just process the world—we generate new versions of it. We take a problem no one has solved, sit with its contradictions, and tussle with it long enough to create something others missed to build something genuinely useful. That generative capacity—to imagine, not just replicate—is what fuels every meaningful innovation. And it is precisely what our management measurement systems have never learned to see. Which is to say: this isn’t a new problem. It’s a 100-year-old one that AI’s recent emergence has suddenly turned into a crisis. Sorted, Ranked, Rated In the early 1900s, Frederick Taylor gave us scientific management—the idea that human work could and should be standardized, measured, and optimized like any other industrial input. People were inputs. Efficiency was the output. Shortly after, the U.S. Army formalized rating systems to rank soldiers against each other—a tool of military hierarchy designed to sort people for deployment, not develop them. When the wars ended, corporations inherited both the logic and the form. By the 1950s, the annual performance review was a fixture of corporate life. Again, not because it developed people, ideas, or innovation. But because it sorted, ranked, and rated. Then Jack Welch locked the idea in. At GE, he made it consequential and famous: the top 20% were handsomely rewarded, and the bottom 10% were fired, every year, by design. What spread across the global business world wasn’t just a practice—it was a premise. That human beings are meant to be ranked instead of linked. And here’s what most people don’t know: the stack and rank wasn’t actually about improving performance. Welch needed a mechanism to cut people because he was managing shareholder perceptions—using it, among other tools, to make GE appear to be growing when it actually wasn’t. A trial with no jury, no defense, and few witnesses Performance reviews are, by design, performative. Think about what a performance review actually is. It happens once or twice a year—far too infrequently for feedback to be useful. It documents the past rather than addressing the present or shaping the future. It’s tied to compensation, which means everyone performs for the grade rather than the work. If you’re a team leader, you’re often told in advance how many people are allowed to receive great reviews—forcing you to distort reality, ration recognition, and turn feedback into a competition among colleagues. Performance reviews are like a trial with no jury, no defense, and few witnesses—and the prosecutor and the judge are the same person. And we know it. A recent poll I did on LinkedIn shows that people truly get that performance reviews are less about the work (14%) and more about conforming to what is expected of you. That legacy is still running our collective talent decisions today. Not because someone looked at it and thought this was a good idea, but because it’s a norm we’ve inherited and not yet interrogated. The system is working exactly as designed. To commoditize humans. Everything becomes a derivative A few years ago, Adobe decided they’d had enough. “We were a company that thrived on creativity and innovation,” said Donna Morris, then Head of HR, “and the system felt like the exact opposite of that.” Rather than patch what was broken, they replaced it entirely—introducing the “check-in,” a model of ongoing, real-time conversation focused on growth rather than judgment. Managers were coached to have these exchanges as often as the work required, with an emphasis on coaching over critiquing. The shift was concrete: eliminating performance reviews freed up approximately 80,000 hours of manager time every year—the equivalent of 38 full-time employees freed from bureaucratic ritual. And that’s just managers. Countless hours of employee time were also reclaimed—time spent on self-evaluations, on rehearsing, on the sleepless nights that rolled into crappy days. Some companies have followed Adobe’s lead. Most haven’t. Because doing so requires seeing the world differently. It requires us to admit that what the world of management has been measuring isn’t actually valuable. And that the cost is compounded into something we can’t afford. Already, 70% of global jobs require little to no creativity. With AI, that number will only rise—accelerating a shift toward speed over substance, replication over originality, isolation over connection. What was always a strategic blind spot is now an existential one. If we can’t see how human distinctiveness creates value, we won’t just lose sight of it. We’ll automate it away entirely. We’ll design it out. Everything becomes a derivative—a recursive loop of what’s already been done, instead of what we actually need next. The challenges that matter most right now—in business, in society, in every organization trying to stay relevant—require ingenuity, genuine collaboration, and the willingness to work on problems that don’t have obvious answers. That’s not a soft people-y issue; It’s an economic one. The reason every valuable business is created is to produce something useful that didn’t exist before. But then we measure time saved and cost reduced. We are standing at a fork in the road. One path automates what machines do well, freeing humans to generate what comes next—the novel ideas, the solutions we haven’t seen, the work that makes organizations worth having. The other keeps measuring humans by an outdated model they’ll always lose, and in doing so, loses the very generative capacity no machine can replace. The speed/more/output metric is giving us the wrong answer. It tells us humans should absolutely lose our jobs if AI can replace them. But that’s only true if we keep asking the wrong question. Performance reviews are a symptom. The deeper problem is that we never built the right metrics to value what humans actually do. And now that we live in a world where everything routine can be automated, that blindness is no longer just a management failure. It’s the defining risk of our moment. We need to see the norms we’re standing on, so we can stop wondering why the ground feels so thin. View the full article

Research on 68 million AI crawler visits show what SEOs should do to improve AI search performance The post 68 Million AI Crawler Visits Show What Drives AI Search Visibility appeared first on Search Engine Journal. View the full article

For decades, the business world has quietly subscribed to a myth: that cognitive performance peaks early and declines steadily thereafter. It’s a belief baked into hiring practices, promotion decisions, and even redundancy strategies. Youth is equated with innovation, speed, and adaptability; age with decline, resistance, and risk. If we ask ourselves, “Am I a better/more effective employee now than I was at 21?” most of us would say, “Yes!” Science and data prove what we already know: that many of the cognitive capabilities that matter most in today’s complex, fast-moving organizations improve with age. The wrong model of intelligence The traditional view of cognitive performance is based on what psychologists call fluid intelligence. This is our ability to process new information quickly, solve unfamiliar problems, and think abstractly. This does tend to peak in early adulthood, which is why you’ll see scores on things like numerical reasoning tests peak at around 19. Fluid intelligence however, is only a small part of the story. So much more of what predicts job performance is crystallized intelligence. This refers to accumulated knowledge, pattern recognition, judgment, and the ability to make sense of complexity. This continues to grow across the lifespan, often peaking well into our 50s. Experience is a cognitive advantage One of the more striking experiments to show the value of experience is taken from the world of chess. In classic studies, chess masters could identify strong moves almost instantly. When asked how this was possible, participants couldn’t easily articulate why. They would say ‘not sure/it ‘feels’ right/’gut instinct’. Later research showed that this “gut instinct” wasn’t guesswork, but rapid pattern recognition built from years of experience. By midlife, most professionals have encountered hundreds (if not thousands) of variations of the same underlying problems: difficult stakeholders, failing projects, market shifts, organizational politics. This exposure creates what neuroscientists and psychologists often describe as pattern recognition. The brain becomes faster not because it processes raw data more quickly, but because it recognises familiar structures and shortcuts decision-making. In practice, this looks like: Spotting risks before they escalate Making better decisions with less information Navigating complex interpersonal dynamics with greater ease Knowing when not to act It isn’t slower thinking. It’s more efficient thinking. Yet many organizations systematically undervalue it because it doesn’t look like the rapid-fire ideation associated with youth. It’s also highly likely, that alongside the expert chess players, people themselves struggle to articulate the value of their experience to employers. Emotional regulation and decision quality Another overlooked advantage of older workers is emotional intelligence (or emotional regulation). Research consistently shows that as people age, they become better at managing emotions, maintaining perspective, and avoiding reactive decision-making. In high-pressure environments, this has a direct impact on performance. Leaders and employees over 50 are often: Less prone to impulsive decisions Better at handling conflict constructively More resilient in the face of setbacks More focused on long-term outcomes rather than short-term wins In a business environment which values trust, credibility and relationships, these are not “soft” skills, they are critical capabilities. The innovation myth One of the most persistent assumptions is that innovation is a young person’s game. While breakthrough ideas can emerge at any age, many of the most impactful innovations come from individuals with deep domain expertise. Economist David Galenson’s research shows that while some innovators peak early, many of the most important breakthroughs come from “experimental innovators”—people whose ideas are built slowly through years of experience, often reaching their peak in midlife or later. Innovation is not just about generating ideas. It’s about: Connecting disparate concepts Understanding what will actually work in practice Navigating the organizational and market realities required to implement change All are areas where experience is a significant advantage. The cost of ignoring midlife talent Despite this, many organizations continue to sideline or lose talent over 50, whether through redundancy, lack of progression, or subtle cultural signals that they no longer “fit.” The cost is enormous but largely invisible because it’s hard to measure what doesn’t happen: the insight not shared, the avoided mistake, the opportunity not taken. A workforce out of sync with reality There’s also a broader demographic shift that businesses can’t ignore. People are living longer, working longer, and often needing or wanting to remain economically active well into their 60s and beyond—yet organizational practices haven’t caught up. Many career paths still assume a linear progression that peaks in midlife then declines. Development opportunities are often disproportionately focused on younger employees and hiring processes frequently filter out older candidates (sometimes explicitly: ‘don’t send me anyone over 45’), often implicitly (‘we want someone malleable with less experience’). This creates a mismatch between the available talent pool and how organizations choose to use it. Make age the asset it is Peak brain power is a concept best thought of as something that evolves with age. In the consumer world, the people with the most money are often the least targeted. In the employment world, the people with the most experience are often the least valued. The question for businesses is not whether older workers can keep up. It’s whether organizations are smart enough to recognize and utilize the assets they already have. Those that do will gain a significant competitive advantage. View the full article