Every early-stage founder I work with asks the same question inside the first thirty minutes of our first call: “Who do I hire first?”
Most founders pull up a Notion doc or a slide-deck org chart. Vice president of Growth at the top, then a paid media specialist, then a content lead, then an analyst. Maybe a designer. That chart is 2022 thinking, and most founders are still running it because nobody told them to start over.
I tell them to start over.
The build order for a growth marketing team in 2026 looks pretty different from what most founders are working off. The team-building philosophy didn’t change. The unit economics of marketing labor did. AI has made execution roughly 70% cheaper to produce, and it’s heading toward 90%+. AI hasn’t made strategic decisions any cheaper. If anything, bad strategic calls now cost more because you can compound them faster than you used to.
That one asymmetry changes who you hire, in what order, and how you actually spend the $15,000 to $50,000 a month most early-stage companies have for marketing.
The Shift Behind The New Build Order
In 2022, a generalist growth marketer was the right first hire because most of your spend went into execution. You needed somebody who could write the ads, set up the tracking, manage the agency, build the landing pages, and run a few experiments on the side. The strategic lift was real but smaller than the execution lift.
In 2026, that ratio has flipped. Most of the work the 2022 generalist used to spend a full day on (ad copy, variant testing, page builds, routine analysis) has compressed into a few hours a week with the right tooling. What hasn’t compressed: picking which channel to bet on, building a measurement model that doesn’t lie to you, telling a real experiment result from noise, and saying no to whatever shiny thing the founder saw on LinkedIn that morning. That last one is where most of the money gets lost.
What’s left is judgment work. Judgment is still expensive.
Since late 2024, the pattern across my client portfolio has held. The teams that scaled fastest had one strategically senior person plus a tooling stack, not three mid-level ICs. The teams that burned the most money had the opposite setup. Plenty of doers, nobody senior enough to choose between them.
Phase 1: First 6 Months, $15K To $25K Per Month
Hire a strategic lead. Fractional if you’re pre-seed or seed. Full-time at the director or VP level if you’re Series A and the runway can handle it.
Their job is to choose the bets. Which two channels matter. Which segment is worth obsessing over. What the measurement model should look like. What you’re explicitly not doing this quarter.
Pair the strategic lead with a tooling stack and three contractors on retainer: a paid media operator at 10 to 20 hours a week, a designer who can move fast in Figma, and an SEO/GEO specialist for a few hours a week on technical hygiene. Total burn lands between $20,000 and $35,000 a month.
I’ve watched Series A SaaS companies run this exact setup for nine months and outperform four-person in-house teams at the same stage. The contractors weren’t elite. The strategic lead was making good calls, and the contractors were executing against the right plan. That’s the whole trick.
Phase 2: Months 6 To 12, $35K To $60K Per Month
Now you hire your first full-time IC. Resist the urge to make this person a specialist.
The most common mistake at this stage is hiring a paid media manager because paid is what’s working. Six months later, the lifecycle and content gap becomes the real bottleneck, and you don’t have anyone who can move into it.
Hire a T-shaped growth marketer instead. Two or three years of experience. Has run paid, can write a decent landing page, understands attribution at a working level, has shipped at least one lifecycle program. A T-shaped hire won’t be the best at any of those functions. A T-shaped hire will be good enough to extend your strategic lead’s reach into four functions at once. That’s what you actually need at this stage.
Keep one or two of the original contractors. Cut anything that hasn’t performed.
Phase 3: Month 12 And Beyond
Phase 3 is when the specialist hire makes sense. By month 12, you know which channel is your real growth engine, so hire deep into it.
If paid is your motion, bring in a paid media lead who has scaled accounts past $200,000 per month in spend. If content and search are the engine, find someone who has built a content motion at a similar-stage company. Pay them the going rate for that specialism, which is usually higher than what you paid the T-shaped hire in Phase 2.
Once the specialist is in, stop outsourcing the work they replace. The retainers that made sense at $25K of total burn become dead weight at $60K. Cancel them.
The Tool Stack That Delivers $8K To $12K/month Of Capacity For $1.5K To $3K/month
At Phase 1, a well-chosen stack of five tool categories delivers what used to require $8,000 to $12,000 per month in headcount for roughly $1,500 to $3,000 per month in software costs.
For paid: Meta Advantage+ and Google Performance Max handle creative permutation and bid optimization. Madgicx or Smartly.io adds the analytics and creative testing the native platforms don’t give you cleanly. $300 to $800 a month.
For content and on-page SEO: Claude or ChatGPT for outlines, briefs, and first drafts. Surfer or Frase for on-page optimization. Ahrefs or Semrush for keyword and backlink work. A human writer/editor reviews every version that ships. $400 to $700 a month.
For GEO, where most of my clients are putting incremental SEO dollars right now: Profound or Peec AI to track visibility inside LLM answer engines, AthenaHQ for deeper competitive monitoring. $300 to $600 a month.
For lifecycle and email: Braze or Klaviyo on the consumer side, HubSpot or GoHighLevel for B2B, with AI-generated subject line and body copy variants tested against a control. $200 to $1,000 a month, depending on list size.
Total at Phase 1: roughly $1,500 to $3,000 a month. The equivalent capacity used to cost $8,000 to $12,000 in headcount, before benefits.
What still needs a human every single time: the brief, the measurement model, the experiment hypothesis, the kill decision, and the customer interview. Don’t try to automate those. Automating those five steps is one of the most expensive mistakes I see.
Three Mistakes That Burn The Runway
Hiring the specialist first because you’re “ready to scale paid.” You’re not ready to scale anything until somebody in the room can tell you what to scale. The specialist will optimize the wrong thing, beautifully.
Keeping the strategic lead fractional forever. Fractional works at pre-seed and seed. By Series A, if your fractional person is still your only senior marketer at month 12, that’s a leadership problem dressed up as a budget problem. Make the call.
Buying the tool stack before you have a strategic lead. Tools don’t generate strategy. Tools amplify whoever is holding the brief. Without a brief, tools amplify confusion. I’ve watched founders spend $4,000 a month on a stack nobody on the team knew how to drive.
The Takeaway
The growth marketing team in 2026 is smaller than it used to be. Not because budgets got tighter, because they haven’t. The leverage point moved. Hire for the leverage point first, build the rest around it, and a four-person budget will outperform a ten-person team pointed in the wrong direction.
SEO has been given different names in the past couple of years, usually based on whatever it’s trying to optimize for at the time: LEO (LLM engine optimization), AEO, GEO, and so on.
That is, before Google came out with new AI search guidance and said the quiet part out loud: It’s all still just SEO.
With all of these acronyms, one thing that still seems to escape our goals is, as usual, the user behind it all. One thing people often miss is that we should be optimizing for attention, not just for labels and new three-letter terms.
It’s often said that attention is the primary commodity in marketing. While I have reservations about this (trust is the ultimate mover, and without it, it’s hard to get a transaction), attention is the first gateway to our content being considered at all, and a key part of the customer journey, particularly in a world that is saturated with results that are all potentially relevant.
We have many ways to get attention at different stages of the journey (I covered this briefly in my previous article), and most of them are generally “universal,” like making sure your content is relevant and aligned with intent.
However, when we hear about “scaling internationally,” businesses operate under the (wrong) assumption that what works for people in one market will automatically work for a similar audience in another location. This hardly makes international strategy a thoughtful or efficient one.
Why Should We Care About Capturing Attention?
Getting attention is paramount because what doesn’t get seen doesn’t get consumed – and what doesn’t get consumed does not get served by the algorithm.
Humans have limited attention at their disposal, and it seems to have decreased significantly in recent years. Research by Gloria Mark, for example, suggests the average attention span on a screen is around 47 seconds (down from several minutes in earlier decades). And it’s likely even less on marketing channels, especially the ones serving short-form content.
There are, in fact, experimental studies showing that certain kinds of short‑form content can actively disrupt our ability to remember what we were supposed to do after a break. In one experiment comparing content across different platforms, people who watched TikTok during a pause were much more likely to forget their original task or intention afterward, while those who watched YouTube showed little or no such impairment.
This points to an even bigger challenge: Even when we are inherently relevant for the user, this is often not enough to make sure they pay early attention to us, especially if they’re already engaged in a task that is already taking some of their cognitive resources. In short, we can’t take attention for granted.
This makes “catching the eye” vital not only for the algorithm, which uses dwell time and engagement signals to determine what to show next, but also for humans, who might need to be quickly re‑oriented to what they were looking for after opening an app and getting sidetracked.
And beyond the algorithm, attention is also the first gateway to human persuasion. So, as budgets shrink while expectations remain the same, understanding how to capture and direct attention becomes the first step in optimizing content performance.
How Does Attention Differ Across Locales?
When you localize an experience, your goal should be going beyond basic translations: You also want to adapt it to the cultural background of the country, which includes content format preferences, shared knowledge and references, and attentional patterns. And different attention patterns shape different behaviors.
English‑speaking readers learn to read from left to right, and this shapes how they scan text and visual layouts. We tend to enter a page from the left side and top, then move rightward and downward, often skimming more as we go.
In practice, this means early elements on the left and top get more visual attention, while elements placed toward the right‑hand edge and bottom are more likely to be overlooked when people are browsing quickly.
This often results in the “corner of death“, where logos placed in the right bottom side are less likely to be seen (and thus remembered).
And Western natural reading direction is reflected in the”F” scanning pattern many of us are familiar with.
However, there are more text scanning patterns, depending on the goal of the user and the layout of the page, from a “Spotted pattern” to a “Layer‑cake pattern” and several others that describe how people jump between points of interest rather than sticking to every line in order.
So, it’s clear that if different pages call for different attention patterns, different locales (and different reading directions) most definitely do as well.
A quick study I ran as a proof of concept showed me how, on average, left-to-right readers (e.g., Spain) and right-to-left readers (e.g., Egypt) consume visual content very differently. Averaged data from 30 participants returned heatmaps where the Spanish cohort very often focused on the left side of the ad, whereas the Egyptian one largely ignored the bottom left corner.
Side-by-side perfume advertisements overlaid with eye-tracking heatmaps showing user attention patterns for Spanish participants (left) vs Egyptian participants (right). Image from author, May 2026
Why is this important?
Not only to isolate important elements that a different audience might not see at all, but also because it helps us frame the page for the goal we want to achieve.
For example, we often use the first option in a series of items as a baseline to make all subsequent judgments. Think about it: When we land on a category page, and the first item is the most expensive, it makes everything else look like a good deal.
This is a phenomenon called “anchoring,” which is widely used to direct persuasion behaviors, like in this example from the ecommerce Noon.
Image from author, May 2026
And you know what Noon does particularly well? When you change the locale to UAE, the elements flip on the page, so that the most expensive phone is now on the right side. This is a great example of correct localization, since the first item seen by an Arabic-speaking reader will be the one on the right, not on the left.
Image from author, May 2026
Beyond reading patterns, it is worth noting that certain cultures also tend to focus on different elements of a page – for example, some audiences are more drawn to bold imagery while others spend relatively more time on text and contextual details. And while I’ve focused here on left‑to‑right and right‑to‑left readers, there are also vertical writing systems where people read top‑to‑bottom, adding yet another habitual scan pattern into the mix and reinforcing that you can’t assume a single “universal” layout will work everywhere. Eye‑tracking helps you see these biases in practice, so you can decide whether to lead with visuals, copy, or supporting context for each locale instead of guessing it.
How Does Eye Tracking Work?
Traditional analytics can tell us something about attention, but it’s normally a byproxy of other metrics. Think about the heatmaps you can get from Microsoft Clarity. They’re really good at bringing out where the user scrolls, clicks, and where the journey fails – but all of this is a measure of explicit behavior. Attention patterns often don’t leave a trace in our analytics. We can infer that what doesn’t get scrolled doesn’t get seen, and that, conversely, what gets a click is something that has caught the eye.
Eye tracking goes deeper than that and isolates data that can give us an understanding of what gets seen and what does not, as well as some indication about emotional engagement and cognitive load (which is often a reason for abandonment).
It produces scanpaths and heatmaps based on metrics like:
Fixations: Moments when the eyes briefly stop and focus on a specific point, indicating where attention is actually landing.
Saccades: The rapid jumps the eyes make between fixations, showing how people move their gaze across the page or screen.
Pupil dilation: Changes in pupil size that can reflect arousal or mental effort while someone is looking at your content.
K‑coefficient: A combined measure of fixation duration and saccade length that indicates whether someone is broadly scanning (ambient) or closely focusing (focal) on what they see.
RealEye emotion and attention recording example (Image from author, May 2026)
This information can be used for optimizing the position of elements and messages, and guiding attention in landing pages and creatives.
Research-grade options are very precise (and very expensive), but there are light-weight, web-based eye-trackers that you can leverage to run attention studies at a fraction of the cost and remotely. Tools like GazeRecorder record where people look on a page or screen in real time, then turn that into scanpaths and heatmaps so you can see which elements attract attention first, and which are ignored.
RealEye combines webcam-based eye tracking with facial coding, tagging expressions such as smiles or surprise, so you can see not just where people look, but also whether their emotional response skews more positive or negative, which can be useful when testing ads or landing pages.
And if you don’t want to collect “real” gaze data from participants at all, you can also use synthetic attention predictions. Platforms like EyeQuant use trained models to simulate how a typical viewer might look at a page and generate predicted heatmaps within seconds. These aren’t a substitute for actual eye‑tracking studies, but they can be a fast, low‑effort way to spot major attention issues before you invest in full testing.
Leveraging Eye Tracking Insights To Optimize Content Internationally
The insights you get about attention from eye‑tracking studies largely surpass what we can get from explicit behavioral metrics, and they can guide how we design experiences far beyond just where we place logos and calls-to-action.
Here are some practical ways to use them:
Identify competing elements. Use heatmaps and scanpaths to spot parts of the page that pull attention away from what matters (e.g., busy background images stealing focus from the product, or a large secondary button outshining the primary CTA). You can then simplify, resize, or reposition those elements so attention flows more cleanly toward your key goals.
Strengthen the visual hierarchy. Check whether people actually look at content in the order you intended (for example, headline → key benefit → product → CTA). If their gaze jumps around or skips crucial information, adjust layout, typography, color, and whitespace until the scanpath matches the story you want the page to tell.
Refine creative per market.Run the same layout across different countries and compare where people look first and how long they stay there. Swap imagery, colors, or copy directionally (e.g., left–right, top–bottom) to match local reading patterns and visual habits, then re‑test to see whether attention on key elements improves.
Iterate on messaging and visuals. Test variations of headlines, hero images, and CTA labels while tracking not only clicks but also how quickly eyes land on them and how long they remain there. If people only skim over your main message, sharpen the copy and adjust its positioning until it earns a longer fixation.
You can even use these insights to build simple predictive models to forecast early campaign success across different markets and cultures. For instance, you might find that earlier fixations on the brand or logo predict better recall, that longer dwell time on CTAs correlates with higher conversion intent, or that emotional faces consistently drive cross‑cultural engagement.
Always keep in mind that one size does not fit all – not across cultures, and not across page types or user goals either. A product page will be scanned very differently from a Help page or a blog post, and your design should respect those intent differences.
So, next time you get asked to scale internationally, keep an eye (no pun intended) on where the attention goes, because it can tell you more than dozens of A/B tests – and help you ship experiences that work with human perception instead of against it.
Most summers, a reading list for SEO professionals is about thinking more broadly, stepping back from the day-to-day, and coming back in September with fresh perspective. This summer, it’s about keeping up. Because the gap between what you knew going into June and what you need to know by Labor Day is wider than it’s been in years.
Nobody in SEO still believes in set-it-and-forget-it. What practitioners need now is not philosophical preparation for change but concrete guidance on navigating a specific, unprecedented moment: the restructuring of search itself around generative AI. Google just completed the biggest overhaul of its search interface in 25 years at I/O 2026. The rules of content discovery, audience building, and visibility are being rewritten simultaneously.
That’s a lot to absorb. The books below won’t give you a checklist. But they’ll give you the frameworks, context, and competitive intelligence to make sense of what you’re already seeing in your traffic data, and what’s coming next.
Start Here: The Competitive Intelligence You’re Missing
AI Valley: Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence by Gary Rivlin (Harper Business, 2025) is the backstory to everything currently reshaping search. Rivlin spent more than a year embedded with founders, investors, and engineers across Google, Microsoft, OpenAI, and the firms orbiting them. He followed the story from DeepMind’s early days through the ChatGPT moment and the scramble it triggered at every major tech company.
I Am Not a Robot by Joanna Stern, the Wall Street Journal’s tech journalist, not Gerd Gigerenzer, the German psychologist, is the book that I wrote about in “White-Collar Will Be Fully Automated In 18 Months – So What Makes You Different?” Stern spent a year using AI for as much of her life as possible and documented what transferred and what didn’t. For SEO professionals and content marketers who are trying to figure out which parts of their work to automate and which parts to protect, her year-long experiment is the most practical field test currently published.
John Kaag’s review in The Boston Sunday Globe identified the book’s deepest argument: the question “I am not a robot” has transformed from a CAPTCHA formality into a genuine philosophical claim about what makes human output worth producing. That question has direct implications for content strategy in an era when AI Overviews are serving a growing share of informational queries without a click.
For Understanding Audience Behavior
The People’s Choice by Paul Lazarsfeld, Bernard Berelson, and Hazel Gaudet (1948) is the oldest book on this list and possibly the most relevant. Its central finding – that information flows from media to opinion leaders and then to followers, not directly from media to mass audiences – is the theoretical foundation of influencer marketing and the idea that reach and influence are not the same metric.
The finding is directly applicable to how brands need to think about AI search. When an AI Overview answers a query, the brand cited in that overview becomes an opinion leader in the old Lazarsfeld sense: an intermediary whose authority gives the information credibility before it reaches the end user. Lazarsfeld showed in 1948 that this is how influence has always worked. The platforms changed. The human behavior didn’t.
For The Tactical [Machine] Layer
If AI Valley explains the competitive forces that reshaped search, and The People’s Choice explains why audience behavior outlasts every platform change, The Machine Layerby Duane Forrester, is where the reading list gets specific.
His framework for what he calls machine comfort bias is worth the price of the book on its own. AI systems, he argues, naturally favor sources that prove reliable over time because verifying trust costs fewer computational resources than guessing. That’s not a ranking factor in the traditional sense. It’s a different game entirely, one where consistent, structured, citation-ready content compounds in ways that keyword-chasing never did.
This is the most practitioner-facing book on the list. It’s a working guide for teams who need to understand how discovery actually functions in a world where the intermediary between content and audience isn’t a user clicking a link.
For PPC Practitioners Who Want Leverage, Not Hype
The AI-Amplified Marketer: Digital Marketing in a GenAI World by Frederick Vallaeys is the most practically grounded book on this list for anyone managing paid search. Vallaeys was one of Google’s first 500 employees and its first AdWords Evangelist. He helped build Quality Score, conversion tracking, and the early automation capabilities that most PPC practitioners now take for granted. He has been watching AI transform paid search from the inside for two decades, which gives his skepticism and his enthusiasm equal credibility.
I heard him speak at a conference in Boston on Thursday, where he walked through how agents and MCPs are turning AI from a content generator into an actual PPC workflow layer. The book covers the same territory in depth: where AI genuinely amplifies what an experienced marketer can do, where it breaks down without human judgment to steer it, and how to close the gap between the tool demos and the messy reality of running real accounts. If you’ve spent the past year accumulating AI tools without feeling meaningfully more productive, this is the book that diagnoses why.
The Reading Order I’d Suggest
Start with AI Valley to understand the competitive forces that created the current landscape. Move to The People’s Choice to understand why audience behavior is more durable than any platform change. Use I Am Not a Robot to ground the abstract in a specific human experiment that maps directly onto content strategy decisions you’re making right now. And then read The Machine Layer and The AI-Amplified Marketer for the tactical layer.
Or reverse the order entirely. The point is to arrive at Labor Day understanding something you didn’t know in June. The web isn’t going to stop changing while you’re on vacation. You might as well be reading about it somewhere comfortable.
As an extra bonus, Rand Fishkin is currently pre-ordering for his new book, Zero Click Marketing, which will launch in the fall and will be essential reading for later in the year.
What was associated with performance was the practice of ORM. Active reputation management correlated with better business results. Not the stars, but their behind-the-scenes work.
What The Research Found
The study, published in the Journal of Small Business Strategy, tested six hypotheses regarding ORM and small-business performance using partial least squares structural equation modeling.
Five were supported. Customer orientation and Internet self-efficacy positively predicted ORM practices, with Internet self-efficacy having a stronger effect. ORM correlated with better business performance and higher Google ratings, with competitive intensity strengthening these relationships. In more competitive markets, the gap between ORM practitioners and non-practitioners was wider.
The sixth hypothesis, that Google star ratings would predict business performance on their own, was not supported.
That competitive-intensity finding is worth pausing on. The study treats ORM as a “strategic resource” under Resource-Advantage theory. The argument is that ORM works as an operational capability, not a customer service activity that produces better ratings. The performance gap widens when competition increases. In competitive markets, ORM appears to be moving from a supporting activity to a difference-maker.
The study included 251 U.S. small business owners across various industries. Performance and star ratings were self-reported, a noted limitation. Because the design is cross-sectional, it can’t establish causation.
The study doesn’t examine AI-powered discovery, but its findings on competitive intensity matter since SOCi’s data shows AI systems surface fewer businesses than Google’s local 3-pack.
BrightLocal’s 2026 Local Consumer Review Survey found that 45% of consumers now use ChatGPT or other generative AI tools for local business recommendations. That’s up from 6% the year before. BrightLocal, which sells local SEO tools, has run this survey annually since 2010.
SOCi’s 2026 Local Visibility Index analyzed over 350,000 locations across 2,751 brands. ChatGPT recommended 1.2% brand locations, Gemini 11%, Perplexity 7.4%. The same brands appeared in Google’s local 3-pack 35.9% of the time. SOCi, which offers multi-location marketing software, said this is roughly 30 times more selective than traditional local search.
The overlap between traditional and AI visibility was less than expected. In retail, SOCi found only 45% overlap between brands top in local search and those recommended by AI platforms. Strong local search rankings didn’t ensure AI visibility.
SOCi’s data showed ChatGPT-recommended locations averaged 4.3-star ratings, indicating reviews matter to AI platforms. However, ratings aren’t the whole story. SOCi views AI visibility as driven by data accuracy, reputation signals, and engagement, not just star ratings.
As Joy Hawkins, owner and founder of Sterling Sky, wrote on LinkedIn:
“Google’s AI-driven local results are showing fewer businesses and, in many cases, fewer ways for customers to contact you.”
The Multi-Location Execution Gap
The Inyang and White study examined small businesses at a single location. ORM gets more challenging when multiplied across many locations.
The gap between high- and low-performing brands is wide. SOCi’s 2024 LVI data shows low-visibility brands responded to 10.9% of reviews in 12 days, while high-visibility brands responded in 2.1 days.
It’s not that they don’t understand the importance of responding. Everyone who manages multiple locations understands that engaging with reviews is important. What we’re seeing is a failure to execute.
Robert Barrueco, founder of Webnition, which sells review automation tools, wrote on LinkedIn:
“Responding to reviews across dozens—or hundreds—of locations isn’t just exhausting… It’s almost impossible to do consistently without an automated, branded solution.”
For multi-location teams, this may require an organizational change. ORM can’t rely on scattered logins, inconsistent responses, or each location handling reviews differently. The research identifies ORM as a capability that requires shared standards, clear ownership, and operational support to ensure consistency.
This is where the word “infrastructure” earns its place. Infrastructure is what you build when the load exceeds what any single person or team can handle manually. For multi-location ORM, the load is review volume, response consistency, listing accuracy, and platform coverage across every location simultaneously.
What AI Systems Appear To Evaluate
SOCi’s analysis views AI visibility as distinct from traditional ranking, treating AI platforms as recommenders rather than sorters. The recommendation depends on the system’s confidence in the accuracy and quality of the data.
That’s SOCi’s interpretation, not a confirmed mechanism. But the pattern lines up with what practitioners are seeing.
Justin Silverman, founder and CEO of Merchynt, which sells GBP optimization tools, wrote on LinkedIn, “Your Google Business Profile is no longer just for Google.”
Meg Clarke, founder of Clapping Dog Media, was more specific, saying, “AI favors businesses that show up everywhere with aligned information.”
Review content adds location-specific context a star rating can’t carry alone. Customer reviews mentioning services, locations, or use cases are accessible to systems parsing business info. This text offers context that can improve customer understanding and AI system analysis.
NAP consistency, which SEJ has covered extensively as a key local SEO factor, now has a second audience. If AI cross-references business data, inconsistencies may undermine confidence, as SOCi warns. These discrepancies confuse customers, call into question basic business facts, and potentially affect AI visibility.
Looking Ahead
Star ratings alone didn’t predict small business success in the Inyang and White study. Active reputation management correlated with better performance, especially in competitive markets.
For multi-location brands, reviews matter, but they need systems to manage reputation across all locations and platforms. That’s more effort, but the ongoing work provides a valuable advantage, while overlooking it could lead to less visibility.
The day before, I’d been reading about Jensen Huang’s commencement address at Carnegie Mellon, where he told 5,800 graduates at one of the country’s top engineering schools to consider becoming electricians.
The same day, a philosopher reviewing a tech journalist’s new book, “I Am Not a Robot”, in “The Boston Globe” asked the question neither of them had touched – if machines can now reason, what exactly is left for us?
Huang Tells Graduates To Build Things
Moneywise reported how Jensen Huang delivered his Carnegie Mellon commencement address in the rain, to 5,800 graduates at one of the country’s premier computer science and engineering universities, and spent a significant portion of it making the case for a career in the trades.
“AI gives America the opportunity to build again,” he told the crowd. “Electricians, plumbers, iron workers, technicians, builders – this is your time. AI is not just creating a new computing industry; it is creating a new industrial era.”
He wasn’t being contrarian for effect. Moneywise reported capital spending from the largest U.S. tech companies could hit $700 billion this year in data center construction alone, and Randstad’s March analysis of more than 150 million U.S. job postings found demand for skilled trades growing three times faster than for professional desk-based roles. None of that infrastructure gets built without people pulling wire and laying pipe.
Huang also said something that tends to get buried under the trades narrative: “Yes, AI will change every job. But the task and the purpose of a job are not the same. Many tasks will be automated. Some jobs will disappear. But many new jobs and entire new industries will be created.” That distinction between tasks and purpose is the one SEO professionals should write down.
Suleyman Says White-Collar Work Has 18 Months
Microsoft AI CEO Mustafa Suleyman told the “Financial Times” that AI is approaching “human-level performance on most, if not all professional tasks.” His timeline is 12 to 18 months. The specific roles he named as vulnerable were accounting, legal, marketing, and project management.
He named marketing explicitly, and 18 months from February 2026 is August 2027.
The prediction has been circulating long enough to become background noise. That’s exactly the problem with it. Search has already changed more in the past 18 months than in the preceding five years. The practitioners feeling that change most acutely are not the ones whose jobs have disappeared. They are the ones whose workflows have been disrupted faster than their strategic frameworks have been updated.
Kaag Asks The Question Stern’s Book Doesn’t Quite Ask
Sunday morning, John Kaag’s review of Joanna Stern’s “I Am Not a Robot: My Year Using AI to Do (Almost) Everything” completed the pattern for me. Kaag, a philosophy professor at University of Massachusetts Lowell, approaches Stern’s experiment less as a technology story than as a question about what remains distinctively human once machines can imitate more and more of what we do.
He traces the arc back to Alan Turing’s famous “imitation game,” where the challenge was whether a machine could successfully pass as human in conversation. For decades, humans occupied the position of judge and evaluator. But sometime in the internet era, that relationship quietly flipped. CAPTCHA systems began asking us to prove that we were human and check the box confirming “I am not a robot.” What started as a security measure also became a cultural metaphor: machines were no longer trying to earn our approval; we were adapting ourselves to their standards of verification.
Kaag argues that Stern’s book pushes beyond the novelty of AI assistants writing emails or summarizing meetings. The deeper issue is whether human identity itself becomes harder to define once systems can convincingly simulate judgment, language, and even personality. If an algorithm can reproduce our tone, our style, and eventually much of our professional output, then the important question is no longer whether AI can think like us. It is whether we still understand what makes human thinking meaningful in the first place.
To explore that question, Kaag invokes Mary Everest Boole, the 19th-century thinker and educator married to mathematician George Boole, whose logic became foundational to modern computing. She speculated that once reasoning itself became mechanized, humanity would need to anchor its identity somewhere beyond pure rationality. Her answer was not efficiency or calculation, but qualities grounded in empathy, moral judgment, and human connection.
That idea lands differently in 2026 than it might have a decade ago. Stern’s reporting demonstrates how capable AI systems have already become at tasks once considered markers of expertise. But Kaag’s larger point is that capability alone does not settle the question of value. The more machines approximate reasoning, the more pressure there is on humans to articulate what cannot simply be automated: lived experience, accountability, intuition shaped by failure, and the ability to care about consequences in ways that are more than computational.
That is the tension running underneath Stern’s book and, increasingly, underneath modern knowledge work itself. The challenge is no longer proving that machines can imitate us.
What Makes You Different?
Three pieces, written independently, from a commencement stadium in Pittsburgh, a “Financial Times” interview, and a Sunday book review, arrive at the same argument from three directions.
Huang: The purpose of a job survives even when its tasks are automated.
Suleyman: The tasks of most white-collar work will be automated faster than most people are prepared for.
Kaag: If reasoning can be mechanized, and it can, increasingly, then the thing that defines us has to be something else.
For SEO professionals, that is the most practical question in the field right now. When your content, your strategy memo, or your keyword analysis could have been generated by a system that has learned to approximate you well enough, what makes yours different? The honest answer, Kaag suggests, is not a skill set or a process. It is the irreducibly personal quality of a perspective formed through actual experience, actual failure, actual presence in the work. That is what cannot be checked in a box.
Most high-performing marketers hit a wall they never saw coming. But this isn’t because they stop working hard or run out of ideas. In fact, their ability to execute flawlessly and quietly becomes what holds them back.
Let me explain what I mean.
The shift from executor to strategist is one of the most significant career transitions a professional can make. And almost no one explicitly teaches it.
There are no beginner’s guides or formal training programs for it. There’s just a slow and confusing process of realizing that the rules of the game have changed and that the skills that got you promoted are no longer the skills that will carry you forward.
In this article, I will try to explain why this gap exists.
Why Execution Gets You Hired But Not Promoted
There’s a reason why most leaders excel as executors early in their careers.
Execution is a way to demonstrate your competence. It’s visible, measurable, and rewarding. The problem is, execution creates a trap.
When you solve problems well, leaders give you more problems to solve. You become indispensable as a doer, which makes you invisible as a leader.
Your productivity stays high. Your strategic effectiveness remains low. And the promotion you’re aiming for keeps moving just out of reach.
This is a structural failure rather than a personal one. Organizations are designed to reward execution in the early stages of a career. Feedback loops usually look like this: publish the page, launch the campaign, fix issues, hit the target, send the report.
But somewhere around mid-career, the signals change. The work that matters most becomes harder to measure, and the people who advance are the ones who learn to work within this uncertainty.
The Invisible Ceiling Most People Don’t See Until They’ve Hit It
The tricky part of this ceiling is that it’s hidden behind appreciation and praise.
You finish a quarter, and your manager compliments your output. You complete a project, and the team celebrates. It all seems like success.
But if you pay attention, you’ll notice that the conversations at a higher level are different. And these conversations are about what should be prioritized, what sensible compromises the organization should abandon altogether.
This is precisely the level where strategy lives. And it requires a completely different way of thinking.
Executors ask, “How can I solve this problem?” Strategists ask, “Should we even be solving this problem?” The shift from “how” to “should we” represents one of the most important mental shifts a marketer can make.
It’s also one of the least intuitive, because it feels like stepping back the moment instinct tells you to put in more effort.
As one observer put it, execution success can mask the need for evolution. Clarity comes not from leaning harder, but from stepping back.
What Changes When You Shift Your Lens
Transitioning from executor to strategist doesn’t mean you’ll do less work. It means you need to think differently.
Early in a career, success is task-oriented, characterized by quick responses, clean deliveries, and long working hours. Value is created by completing tasks. But as roles become more complex, the output that matters stops being a completed task and starts being a well-framed question.
There’s also a shift in delegation that catches many high-performers off guard. Strong executors generally resist delegating tasks because they know they can do them better and faster themselves.
But this instinct, if left unchecked, will bury them in the work. Every hour spent on tasks someone else could handle is an hour not spent thinking about what you can do at your level.
I believe nobody needs direct reports to start practicing this. You can begin by creating repeatable templates that others can use, collaborating with colleagues to distribute parts of a project, or setting aside calendar time for higher-level thinking. Because strategist behaviors can also be rehearsed before the title arrives.
The Mindset Shifts That Matter Most
The gap between an executor and a strategist is simply about your way of thinking. And that makes closing the gap difficult, because changes in mindset don’t show up in skill assessments.
Here are the most important ones:
From Solving To Questioning
Executors carry out the tasks assigned to them. Strategists, on the other hand, question whether the problem is the right one to solve. Diverting resources away from the wrong priorities is more valuable than perfectly executing the tasks brilliantly.
From Urgent To Important
Execution culture rewards responsiveness. Strategic thinking rewards prioritization. Learning to distinguish between what’s urgent and what’s actually important, and acting accordingly, is a discipline, not an instinct.
From Individual Output To Organizational Leverage
The strategist asks, “What can I make possible?” and this represents a shift from doing to multiplying. This is what creates the kind of impact that is noticed at the leadership level.
None of these changes are dramatic on their own. But together, they fundamentally represent your relationship with your work and your identity as a professional.
Practical Ways To Start Making The Shift
Knowing the shifts are necessary and actually making them are two different things. The transition tends to go better when it’s approached deliberately rather than waited for.
1. Find A Mentor
The guidance of someone who has successfully moved from specialist to strategist is difficult to replicate through reading alone. They can help you see the blind spots that are hardest to identify from inside your own perspective.
2. Ask Different Questions
Strategically minded people shift their perspectives, question things, and look at things from a broader viewpoint. Good questions signal a different way of thinking and position you as someone operating at a higher level.
3. Make Your Thinking Visible
Strategists don’t just produce results; they also share the reasoning behind those results. When you point out a pattern, name a risk, or articulate a trade-off, you’re demonstrating your strategic capacity. This visibility is more important than most people can imagine.
4. Protect Time For Thinking
This one seems simple, yet it’s constantly overlooked. If your calendar is filled with execution tasks, there’s no room for the kind of reflection required for strategic thinking. Treating thinking time as non-negotiable is a structural change, and it has to happen before the thinking can.
The Transition Is The Work
Most people see strategy as the goal and execution as the means to get there. But in my opinion, this perspective misses the actual challenge.
The transition from executor to strategist is confusing precisely because it requires unlearning the behaviors that are rewarded. Habits that earn you recognition, like staying in the details, solving every problem handed to you, and being the most trusted person in the room, are habits you need to change consciously.
This isn’t an easy or comfortable process. And it doesn’t happen automatically with a title change or promotion.
Marketing professionals who successfully make this transition have one thing in common. They stop waiting for permission to think strategically and start practicing where they already are.
They ask harder questions. They make their logic visible. They assign tasks not because they have to, but because they understand the leverage it creates.
When Unilever CEO Fernando Fernández stood before investors and declared that the era of expensive corporate brand advertising was over, calling traditional TV-heavy campaigns “lazy marketing,” the shockwave through the agency world was immediate. Half of Unilever’s massive global advertising budget would shift to a “social-first” strategy. Creator collaborations would scale by 20 times. The target would be an army of over 300,000 influencers, including a micro-influencer in every postal code in key markets like India.
Traditional advertising agencies that had spent decades building relationships around six-figure production budgets and a handful of celebrity partnerships suddenly faced a client with an operationally impossible mandate. Manual sourcing, onboarding, and content approval at 300,000-creator scale simply does not exist as a human workflow. Specialized creator agencies picked up business that legacy agency-of-record relationships had assumed were locked in.
The panic was understandable. It was also aimed at the wrong target.
The More Important Question
A March 2026 Adobe Express study surveyed video creators across YouTube, TikTok, and Instagram and found that 71% have now adopted AI video generation or editing tools. Of those, 41% deploy them on a weekly basis. 56% of creators using AI tools report saving over 30 minutes per video on average, with 10% shaving more than four hours off their production time. On the performance side, they’re seeing a 19% average increase in audience watch time and a 17% boost in community engagement. Half plan to increase their AI tool spending over the next year.
So, Unilever is building an army of 300,000 creators, and 71% of creators are now using AI to produce their content. The math is straightforward, and what Unilever is actually building is a massive distributed network for the production and distribution of AI-assisted content at a scale the marketing industry has never seen.
The question that hasn’t been answered yet is whether any of it will work.
Unilever’s 300,000-creator network is generating content at a scale that makes traditional test-and-learn frameworks difficult to apply cleanly. When hyper-local micro-influencers are producing AI-assisted videos for niche audiences across hundreds of markets simultaneously, the signal-to-noise problem becomes acute. Individual pieces of content may perform well in isolation while the overall brand narrative diffuses into incoherence. Or the personalization may be exactly what audiences want, and the aggregate effect may be stronger than anything a single high-production campaign could achieve. Right now, the honest answer is that nobody knows with confidence.
Where DAIVID And ADIN.AI Come In
On April 27, 2026, two companies that many SEO professionals and digital marketers haven’t heard of yet announced a partnership that addresses the exact problem Unilever’s strategy creates.
DAIVID is a creative intelligence platform whose AI models, trained on tens of millions of human responses to ads, predict in seconds how any piece of ad creative will perform – measuring attention, 39 distinct emotions, memory encoding, brand recall, and likely next-step actions – without requiring human panels. ADIN.AI is an AI-native operating system for enterprise marketing that sits above an organization’s existing tools and provides a unified intelligence layer across channels, budgets, and decisions.
The partnership embeds DAIVID’s creative effectiveness models directly into ADIN.AI’s platform, creating what they describe as a live loop between creative intelligence and media execution. Before a campaign launches, marketers can identify which creative is most likely to succeed and allocate budget accordingly. While campaigns run, they can scale high-performing assets and pause underperformers in real time. After campaigns end, the historical performance data becomes benchmarks that guide future creative and media planning.
Ian Forrester, CEO of DAIVID, described the core problem the partnership solves: “Creative is a key driver of advertising outcomes, but for too long it has been measured in isolation, disconnected from media results.” The first live client is Ajinomoto, the global food and nutrition company.
Why This Matters For SEO And Digital Marketing Professionals
The traditional advertising agency’s anxiety about Unilever’s creator pivot was understandable but slightly misdirected. The real disruption isn’t that Unilever is working with 300,000 influencers instead of three ad agencies. The real disruption is that when 71% of those creators are using AI tools to produce content at speed, and that content is being distributed across dozens of platforms in hundreds of markets simultaneously, the evaluation infrastructure that used to separate good creative decisions from bad ones stops working.
Human panels are too slow. A/B testing individual pieces of content across a 300,000-creator network is logistically impossible. Traditional brand-tracking surveys capture what happened last quarter, not what’s working right now.
What DAIVID and ADIN.AI are building is the kind of infrastructure that makes the Unilever model actually governable – a system that can score creative at scale, link those scores to media performance in real time, and surface the signal from the noise before the budget has already been allocated to the wrong places.
Shelley Walsh made the point in her recent Search Engine Journal article on AI content scaling that enterprise brands face a specific trap: They know what they want to do (scale content production) but not how to do it without sacrificing the quality signals that make the content worth producing. The DAIVID and ADIN.AI partnership doesn’t solve the content quality problem. But it does solve the evaluation problem – which is arguably more urgent when you’re managing 300,000 creators rather than three.
For SEO professionals and content marketers, the practical implication is familiar. The distribution channels are changing, the production tools are changing, and the volume is increasing. What stays constant is the need to measure what’s actually working and make decisions based on that measurement rather than assumptions. That’s true whether you’re optimizing for search citations or creator content performance. Ground truth it, as always.
Do you know who your audience is and what they want?
Over the last 20 years or so, we used to rely almost purely on data to answer that question. But as cookie tracking and user signals declined and analytics shifted toward sampling (what we refer to as the “signal-loss era”), we’ve lost some of that superpower. On top of this, we’ve handed over control to hyper-personalized platforms with “black box” targeting algorithms to find our audiences, leaving us less able to truly understand what is going on. And in doing so, we have lost track of the user.
In a way, the abundance of data made us complacent: “Data-informed” became the standard, while “user-informed” strategies progressively faded.
The problem with that over-reliance on data is that it made it “okay” to forget we are fundamentally communicating with humans and creating connections. We focused on the outcome and lost the drive to know who we’re connecting with and what leads us to acquire certain users or lose some.
And while signal loss and AI targeting might be perceived as a constraint, in reality, it is actually a great opportunity to go back to basics of marketing. It means we can focus on really understanding the user as a person, and not as trace fragments of data they leave in our web analytics.
Ultimately, getting to know them means we can serve them better – and find stronger, long-lasting ways to connect.
The Opportunity: Understanding Users And How We Reach Them
Even if we still had the data we had before, would it even be enough? I don’t think so, because it assumes user behavior is limited to what we can observe. In reality, behavior is shaped by a series of small, automatic decisions that happen below the surface, often driving outcomes before any action is even initiated – let alone tracked.
On top of this, when we talk about “understanding the user,” this is often reduced to understanding their needs and a rough demographic, but that’s only part of the picture. Users are people, with unique needs and patterns of thoughts at every stage of their consideration journey.
Now more than ever, we need to truly know who we are talking to and interacting with. What makes them favor us over a competitor? What media and channels are they using so we can reach them? What emotional triggers are really relevant to them? What is important to them at every stage of the journey? Only after answering these questions can we claim to have at least scratched the surface.
I’ve said before that human decision-making is inherently imperfect, shaped by cognitive biases and heuristics that help us navigate complexity without analyzing every option in detail. And that’s the reason why knowing what they want is often not enough to get the full picture – you need to know how they make decisions too.
When we fully understand the user, we can shape our approach ahead of outcomes, inform testing and platform targeting, and even anticipate results before execution.
A Practical Alternative To Cookie-Based Strategies: The R.E.M. Framework
To make sure you can reach the right audience, even when data is scarce and tracking unreliable, you should work with three simple things to aim for: Being Relevant, Everywhere, and Memorable in your strategy, from creatives, messaging, and channel choices.
This is what I call the R.E.M. Framework.
Image by author, April 2026
1. Be Relevant (And Relatable)
Relevancy is the first gateway to attention. In a world saturated with competing stimuli, it’s one of the primary filters the brain uses to decide what deserves focus.
Think about it: You might be having a great conversation with a friend in a group full of other people talking, and pay attention only to what they say. And yet, if your name is mentioned by someone else in a conversation you are not listening to, it’s very likely that you will automatically start paying attention to that instead.
This is what is commonly referred to as “the cocktail party effect,” a great example of how stimuli that are relevant to our personal experience, context, and goals can automatically capture our attention even when we are engaged in another task – something that happens consistently on social media, for example.
Today, we often refer to attention as “marketing’s primary currency,” and for a good reason. In a market so saturated, we only have a few seconds to pique our users’ interest before they move on to the next thing. And any content that won’t result in early engagement is likely to be dropped by the algorithm, which won’t serve it to other users as deemed not a good fit for our audience.
This is known by the industry as “the three-second rule,” and might in fact even be optimistic for newer platforms where short-form video prevails, like TikTok videos and Instagram reels. Short-form videos tend to make people forget what they came to the platform for in the first place much faster than long-form videos, and it’s exceptionally easy to lose a viewer on these formats if the hook isn’t instantly strong enough.
But in order to understand how to capture interest early, we need to take a step back and understand how attention works.
As humans, we are consistently exposed to a lot of stimuli at the same time, and we don’t have the cognitive resources to process each one of them, so we select some of them for further processing while ignoring others. We do so via a process called “selective attention” that can be driven by internal motivations (“endogenous orienting”) or external drivers (“exogenous orienting”). In other words, we tend to allocate attention based on our own goals (for example, when we have a deadline and we need to focus on a deliverable) or on the perceptual features of the objects around us (for example, the sound of the phone ringing or a stand-out word in a sentence).
That means that we have two ways to engage someone’s attention: by connecting with their goals, or presenting them with something that stands out in a sea of other similar things.
We can argue that relevancy sits in between these processes and can engage them both. As a matter of fact, when we are researching something, we are already deciding to filter out all the results that seem relevant to our own goal. But it works the other way too: Something relevant to our needs, goals, and context will jump out when we are doomscrolling on socials, even when we are not engaged in a search.
So relevancy is a sort of “catch-all” for attention.
How do you make sure you are immediately relevant?
By identifying what your audience needs, and leading with the solution in the hook. Don’t waste time with obscure messaging or secondary angles that you can elaborate on once you’ve anchored attention.
Strong tests and creatives are the ones that don’t focus on the business, but focus on the user and what they are trying to solve instead. And hyper-personalized platforms make this even more layered. Make the audience see themselves in what you offer, and you’ll shorten the time it takes for them to recognize you as the right choice.
2. Be Everywhere (Your Audience Is)
But can you be relevant to everyone? Of course not. So it’s imperative you understand your audience and their motivations to capture existing demand. And beyond that, you need to be present where they can find you, with the message they’re looking for in that moment.
This is one of the main challenges, now that journeys are so scattered across different platforms and search experiences. There are so many channels people discover us by, that it’s virtually impossible to track where certain journeys even start from. We might get a user from an LLM query, or a social post, or a Google search. Most likely, it’s all of them. A consideration journey is not linear, and it’s in fact the result of a continuous loop of discovery and evaluation, something we know now as “The Messy Middle.” Even the best attribution models rarely capture this.
The “Messy Middle” from Google’s 2020 consumer behavior report. (Screenshot by author, April 2026)
So, the solution is to work cross-functionally to cast a wide net across different channels, because visibility builds trust. “Out of sight, out of mind”: Our brain forms associations that strengthen with repeated exposure, and drops whatever is not used. If you’re consistently present where your audience is, with relevant content, you create the perception that you are indeed everywhere – without actually having to be.
And that matters, because repeated exposure is part of how we cast a choice in a sea of options. We call this “availability heuristic,” a decision-making shortcut that makes us favor what comes to mind easily: what we’ve seen often, recently, or remember clearly. Think about recommending a movie. You’re far more likely to mention something you’ve just watched, or keep seeing suggested, than something from years ago.
So, while relevance gets you noticed, presence keeps you top of mind. That means that when someone is ready to act, you’re already part of the consideration set, often before they even start a search.
Of course, going omnichannel is a beast in itself. Creatives and messages in one platform won’t work on another – you still need to test and iterate – but if you do it from a customer lens, your work is much simpler, and the benefits are two-fold: You can target different moments in the journey and stay top of mind.
But how do you prioritize channels when resources are limited?
You can rely on demographic research, personas, and early discovery data to establish a rough baseline, although that only gets you so far. Mapping who they are doesn’t tell you what they do when they make a choice, and how those behaviors shift across the journey. That’s the piece you have to find out for yourself: How do they make decisions? Who do they rely on for information, and where do they go to find it? And just as importantly, where are they when they’re not actively looking, and how can you meet them there?
And this is where personas fall short. They might tell you what people need and who they are, but not how they feel when making a decision. Often, what gets labeled as a bad strategy is simply incomplete research.
To really understand your audience, you need all of this information, which brings us to the next part.
3. Be Memorable
Being memorable is the one variable that still carries the most weight – yet is the hardest one to achieve. Why? Because it relies on creating a meaningful connection with the audience. And what that connection looks like can vary a lot across different individuals.
The general playbook to produce an emotion in marketing has often relied on the assumption that we share the same set of basic reactions, something that is based on Paul Ekman’s studies isolating fear, anger, happiness, surprise, disgust, and sadness as the “six basic emotions.”
And while it is true that some of these can be shared, the reality of the human emotional experience is much more nuanced and is often modulated by personal context, expectation, cultural values, and much more.
While attention works similarly across different individuals, memorability relies on personal context, values, and experiences. Think about an ad that stayed with you. What was the reason why you remember it so well? Chances are, it is because of the way it made you feel. Another reader of this article will have chosen a completely different ad.
Some brands, messages, or creatives stay with us because they elicit an emotional reaction. They make us laugh, they trigger nostalgia, sometimes they outrage us. But they all make us feel a certain way. And even when we choose employing rules of thumb like going for what we already know (“familiarity bias”) or what our peers suggest (“social proof”), it’s often because these are choices that are validated and make us feel safe.
We often hear that people make decisions emotionally, then they justify them rationally. This idea is reflected in early theories like Damasio’s “Somatic Marker Hypothesis,” which proposes that emotional signals influence decision-making, and is supported by neurophysiological evidence showing that physiological arousal varies between liked and disliked brands, pointing to the involvement of emotional processes in brand evaluation.
Electrodermal activity to liked versus disliked brands. Disliked brands elicited significantly higher physiological arousal than liked brands, illustrating that emotional intensity can vary independently of stated preference (Walla et al., 2011).
And that’s important, because the way we feel about a brand determines not only our perceptions but it’s pervasive of the entire experience with them, including trust and willingness to engage with their messaging and offer. We remember experiences for how they made us feel; we connect with some brands and ethos, and we disconnect wildly from some. Once you gain that memorability with your audience, you have an easier time retaining it – as well as guiding them to choose you.
What does this mean for you? Get acquainted not only with what your user needs or what is most likely to catch their eye, but with their personal and cultural context, how they feel, and what their expectations and values are – because these are all aspects that influence the relationship between brand and consumer. A genuine connection will make the user bypass any intermediate evaluation, and make you stand out from competitors, looping us back to our R – the relevancy you aim for in the first step of this framework.
Takeaways
Catching attention isn’t the only metric of success in the signal loss and hyper-personalization era. You need to be everywhere, and to stay top of mind when your audience is looking for the solution you can offer. So it’s imperative you know your users, their motivations, and their emotional states to capture existing demand and connect with them, wherever they are.
Easy, right?
Not really, but here are some starting points:
Find what your audience needs by collating data that goes beyond search, and takes into account customer service logs, user interviews, and social scraping (both for your brands and your competitors), so that you can capture both the pre-purchase and post-purchase journeys. Use that data to inform your USP and messaging in your test and creatives. Make it all about them, not you.
Don’t take channels for granted, or ignore them just because they’re not useful to your immediate key performance indicators (KPIs). Visibility is often the result of compound actions and cross-functional collaboration. Map out your discoverability across different channels, content formats, and ways to consume content, so that you can target different moments in your audience’s journey. Let this be your guiding light when you pick your battles.
Get to know your audience at a granular level: What do they feel when they search? What are their values? What are their expectations? If they know us, how do they feel about us? Use those emotional drivers to understand what creatives, messaging, and format might be best to use as a gateway to create a meaningful connection.
In summary, start with finding your audience, learn how they decide and understand their underlying needs; all of this will inform your unique selling proposition (USP) and product value proposition, your messaging and creatives, as well as your distribution channels and the choice of formats.
It’s time we go beyond personas and start looking at the real people behind the screen.
On Friday, May 8, 2026, The New York Times published a guest essay by investigative journalist Julia Angwin with a headline that demands attention: “Meta Is Dying.” She highlights that Meta lost daily active users in Q1 2026, falling from 3.58 billion in Q4 2025 to 3.56 billion.
Angwin sees this as the beginning of a long, slow decline, comparing the company’s trajectory to AOL in 2003 and Yahoo in 2015: technically alive, still profitable, but entering what she bluntly calls the “zombie era.”
She may be right. And if she is, Theodore Levitt told us exactly why this would happen, 66 years ago.
The Lesson Meta Never Learned
In 1960, Harvard Business School professor Theodore Levitt published “Marketing Myopia” in the Harvard Business Review. His central argument was that companies fail not because demand disappears, but because they define their business too narrowly. Railroads collapsed because they thought they were in the railroad business rather than the transportation business. Trolley car companies were replaced by automobiles they could have pioneered. “People don’t want a quarter-inch drill,” Levitt wrote. “They want a quarter-inch hole.”
Now look at Meta’s six major pivots over 22 years and ask: What business did Mark Zuckerberg actually think he was in?
In 2021, he declared the answer was “the metaverse business” – a bet whose Reality Labs division has since accumulated roughly $80 billion in operating losses. Users didn’t agree. In 2023, he pivoted to generative AI and has since committed over $100 billion to building models that, as Angwin notes, currently perform worse than the competition. Q1 2026 results show record revenue of $56.3 billion, up 33% year over year, but also $33.44 billion in total costs, a 35% increase, and an AI spending outlook that has rattled investors.
The revenue looks strong. The trajectory looks like a company that keeps pivoting to new product definitions while its core users quietly disengage.
What The Traffic Data Actually Shows
This is where opinion meets evidence, and the Similarweb traffic for March 2026 is instructive.
Google leads the world with 86.9 billion monthly visits. YouTube follows with 29.3 billion. Facebook comes in third at 11.9 billion, and Instagram comes in fourth at 7.1 billion. That gap between Google and Facebook, is the data equivalent of what Levitt was describing. Google defined itself as being in the information access business. Facebook defined itself as being in the social network business. One of those definitions scales indefinitely. The other runs out of room.
The AI category data is even more pointed. ChatGPT records 5.7 billion monthly visits globally, with year-over-year growth of 28.5%. Gemini is growing sharply at 283.8% YoY. Claude.ai jumped 423.7% to 613.7 million visits YoY.
Meta.ai does not appear in the top 100 most-visited websites.
Meta spent $100 billion entering the AI race. It is not winning it.
The Squeeze Play Angwin Describes
When an aging platform’s user base starts to shrink, the immediate response is almost always the same: monetize harder. Angwin documents this clearly. Meta’s Q1 ad impressions increased 19% year over year while average ad prices rose 12%. Revenue per user jumped 27%. The company is cramming more ads onto its platforms and charging advertisers more for each one.
This is the move that maximizes short-term revenue while accelerating long-term decline. More ads mean a worse user experience. A worse experience means slower growth. Slower growth means the ad inventory eventually stops expanding. Levitt described this as the trap companies fall into when they focus on selling their current product harder rather than understanding what customers actually need.
For digital marketers and SEO professionals, this creates a near-term concern. Meta’s Advantage+ advertising suite delivers genuinely strong performance data – a $4.52 return per dollar spent, 22% higher than comparable manual campaigns, according to Meta’s own earnings reports. But those returns depend on a healthy, engaged user base generating meaningful behavioral signals. If the user base contracts and ad load increases simultaneously, signal quality degrades, and performance follows.
The Counterargument Worth Taking Seriously
Angwin’s essay is persuasive, but she is writing opinion, not analysis, and the full Q1 picture is more complicated than “dying” suggests. Year-over-year, Meta’s daily active user base still grew 4%. The quarter-over-quarter decline has a partially verifiable explanation in internet disruptions in Iran and Russia’s WhatsApp ban. Revenue growth of 33% is not the profile of a company in terminal decline.
What it is, is the profile of a company spending at a scale that requires the growth to continue, while its AI investments have not yet produced meaningful new revenue streams. As the Wall Street Journal‘s Asa Fitch observed this week, “the spending growth looks increasingly unsustainable.”
Levitt’s lesson wasn’t that myopic companies always die quickly. AOL and Yahoo lingered for years. The lesson was that once a company loses the plot on what business it’s actually in, recovery becomes structurally difficult. Every dollar spent defending the wrong definition is a dollar not spent understanding the customer.
The question Levitt would ask isn’t whether Meta is dying. It’s whether Meta has ever clearly understood what business it was actually in. Across six pivots in 22 years, the answer appears to be: not consistently.
That uncertainty is now visible in the traffic data. And traffic data doesn’t lie.
On Thursday, May 7, 2026, HubSpot CEO Yamini Rangan announced that the company was changing how it charges customers for AI agent features. Instead of charging for compute usage regardless of outcome, HubSpot would switch to outcome-based pricing. Customers would only pay when an AI agent actually resolves a support ticket or delivers a useful sales lead. The company also cut prices for its AI customer service agents and started offering a 28-day free trial.
Wall Street’s reaction was immediate. HubSpot shares closed down 19% on Friday, May 8, at $197.35, having touched $180.50 during the session. The stock has now fallen roughly 40% year-to-date and sits about 70% below its all-time high set in 2021. William Blair downgraded the stock. Cantor Fitzgerald dropped its rating to Neutral.
And yet, Q1 revenue grew 23% to $881 million, beating estimates. Customer count climbed 16% year over year to nearly 300,000. Full-year guidance was raised. The AI customer service agent resolves tickets about 70% of the time. Over 9,000 customers have activated it.
This is the kind of moment that causes people to reach a hasty conclusion. The 3,954 agencies in HubSpot’s Solutions Partner Marketplace, thousands of which specialize in SEO and website design, will be watching this closely and asking whether to double down, hedge, or quietly diversify their platform dependencies.
My advice: Before doing any of that, go watch a film.
The Counter-Intuitive Case For Quackser Fortune
Quackser Fortune Has a Cousin in the Bronx is a 1970 film starring Gene Wilder. The title character makes his living collecting horse manure from the streets of Dublin and selling it to gardeners. He is good at his job. He has loyal customers. He works hard and knows his craft. He is also watching his entire livelihood approach extinction. The Irish government is about to replace the horse-drawn delivery wagons that supply his inventory with motor vehicles. The horses disappear. Quackser has nowhere to go.
The film’s lesson is not about Quackser’s skill. His skill is real. The problem is that his skill is completely coupled to a single delivery mechanism that the world is quietly phasing out.
Now read the paragraph buried in Aaron Pressman’s Boston Globe story that most readers will skip past:
“Investors were already worried that HubSpot’s customers might start coding their own business software using AI tools such as Claude Code, cutting into sales. HubSpot Chief Executive Yamini Rangan has noted that customers have too much valuable data stored in her company’s software to abandon its apps.”
That is the entire strategic situation in two sentences. And the question it raises for HubSpot’s partner agencies is not whether the stock will recover. It’s whether their own business model is more Quackser than it looks.
The Distinction That Matters
An agency that sells HubSpot implementations is not in trouble because the stock dropped 19% in a day. Rangan is right that customers with years of CRM data, pipeline history, and contact records embedded in HubSpot’s platform are not going to rip it out because Claude Code exists. Data gravity is real, and it keeps enterprise software sticky even when alternatives look appealing.
The more interesting risk is subtler. HubSpot’s move to outcome-based pricing signals something about where the AI era is taking software broadly away from seat-based licenses and toward measurable results. An agency that has built its value proposition around configuring HubSpot, building workflows, and training client teams is in a fundamentally different position than it was two years ago. If HubSpot’s own AI agents can now resolve 70% of customer service tickets without human intervention, how much of that configuration and training work still needs to be done by an outside agency?
The question is not “is HubSpot dying?” – Q1 revenue growth of 23% does not suggest a dying company. The question is whether the work that partner agencies do is more like Quackser’s genuine craft, understanding customers and designing systems that serve them, or more like his bucket and shovel, specific tactical execution that was always a means to an end.
The professionals who have separated those two things in their own minds are in a much stronger position than those who haven’t yet asked the question.
What The Earnings Report Actually Tells Partners
Buried beneath the stock drop are several data points that matter more than the share price for agencies thinking about the next 18 months.
HubSpot’s AI customer agent now has over 8,000 active customers and a 70% resolution rate. The company is expanding its CRM architecture to allow external AI agents to connect via API, meaning the platform is becoming infrastructure for AI-native workflows rather than a destination in itself.
If that trajectory continues, HubSpot’s ecosystem needs a different kind of partner than it did in 2022. Less implementation, more strategy. Less training users on menus and workflows, more architecting the data inputs and outcome definitions that determine whether AI agents perform well or drift. That is a pivot that requires asking uncomfortable questions now, while the current business model is still working. Quackser’s tragedy was not that horses disappeared. It was that he waited until he had no leverage left.
The Practical Takeaway
HubSpot has 299,000 customers and raised its full-year guidance even as its stock fell. That is not a company in collapse. It is a company in genuine transition, and transition creates short-term uncertainty. Short-term uncertainty is exactly when the businesses that think clearly about the distinction between durable expertise and current tactics build long-term advantage.
The durable expertise in this ecosystem: understanding what customers actually need, designing systems around outcomes rather than features, and knowing how to measure whether AI-driven tools are delivering real business value or cheaper noise.
