Uphoff on Media Podcast

How B2B Marketing Lost It's Way

B2B Marketing in the Machine Age | Series Opener This is the first post in B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. This post sets the stage: the decisions, incentives, and technology bets that brought the function to where it stands today. Post 2 examines The Work: specifically why brand and positioning have become the most underdeveloped and most consequential capabilities in B2B marketing, and why agentic AI makes that gap more urgent, not less. Post 3 covers The Career. Post 4 covers The Org & Tech. Each post stands alone. Together, they make a case the industry needs to hear, and act on. There’s a meeting that happens in most every B2B company each month. You know the one. The CMO walks in with a deck. Forty, sometimes fifty slides. Multi-touch attribution models. MQL-to-SQL conversion rates. Pipeline influence by channel. Cost per attributed lead. First-touch vs. last-touch comparison. A waterfall chart that took someone three days to build. Another slide to explain the methodology behind the waterfall chart. The CEO asks: “Is the marketing working?” The CMO begins on slide four. Forty-five minutes later, nobody can answer the question. The slides didn't answer it. They replaced it. Here’s the thing: that meeting — and the weeks of work that produced it — is B2B marketing now. Not the customer insight. Not the brand positioning. Not the creative brief or the campaign strategy or the message architecture. The reporting. The attribution model. The dashboard. The deck. B2B marketing didn’t just get distracted by measurement. It became measurement. And the cost of that substitution is enormous, and about to get dramatically worse. How Accountability Became a Substitute for Thinking The original promise of digital marketing was genuine and valuable: for the first time in the history of advertising, you could actually measure what worked. Print ads in trade publications, direct mail, trade shows, these were acts of faith, educated guesses, and brand conviction all bundled together. Digital changed the equation. Clicks. Opens. Form fills. Trackable conversions. The measurement revolution was real. But somewhere between the promise and the practice, accountability as a capability became armor. I've had a front-row seat to this shift across twenty-five years: as CEO of UBM TechWeb, President and CEO of ThomasNet, and CEO of Pipeline360. In every one of those seats, I watched the same pattern play out: as marketing budgets came under pressure, CMOs reached for data as a shield. And the martech industry, never one to miss a revenue opportunity, responded accordingly. Every vendor built attribution features. The category of “marketing analytics” became a multi-billion dollar industry built almost entirely on answering one question: Can we prove the marketing worked? The measurement infrastructure grew faster than the marketing it was supposed to serve. By the time companies had full-stack attribution — first touch, last touch, multi-touch, algorithmic, time-decay — most marketing teams were spending more time configuring dashboards and preparing readouts than using the insights those dashboards generated to actually manage their marketing. This is not a technology failure. It’s a behavioral one. And it has three distinct, compounding traps. Trap One: Activity Displacement The attribution process has become the work product. Building dashboards, pulling reports, modeling multi-touch attribution, preparing presentations, reconciling data discrepancies between platforms, explaining why Salesforce numbers don’t match HubSpot numbers, this is now a significant and growing portion of what B2B marketing teams actually do each week. It crowds out the upstream thinking. Customer research. Positioning development. Message architecture. Creative strategy. The work that precedes any campaign worth measuring. You end up with teams that are extraordinarily sophisticated at reporting on marketing programs and increasingly thin on the judgment and instinct required to build them. I’ve seen this dynamic in companies of every size. The marketing operations function — which was supposed to be a support function — gradually becomes the center of gravity for the entire department. The metrics review becomes the primary artifact the team produces. And the actual marketing: the campaign, the message, the creative, the positioning, becomes almost incidental. Something that has to exist in order to have something to measure. Trap Two: The Defensive Posture Attribution-first marketing is structurally backward-looking. The question it always asks is: did we justify the spend? That's not a marketing question. It's a survival question. And it shapes everything downstream. Creative decisions get made by what’s attributable, not what’s right for the brand. Channel strategy gets optimized for trackable clicks, not for where the buyer actually is. Positioning becomes whatever produced the best CPL last quarter. The standard for a good campaign shifts from “does this change how our best prospects think about us?” to “did this generate enough attributed pipeline to survive the next budget conversation?” The result is B2B marketing that is reactive, incremental, and profoundly unambitious. And, not coincidentally, B2B marketing that buyers experience as interruptive, formulaic, and irrelevant, because it was built around what’s measurable, not what’s true. The best marketing I’ve been part of started with a question about the customer: what does this buyer actually need to know, believe, or feel in order to make a decision in our favor? That question is genuinely hard to answer. It requires research, judgment, and creative conviction. It doesn’t produce a dashboard. It produces a brief. And from the brief comes the positioning, the creative, the channel strategy, and — eventually, after the work has been done — the measurement framework. Attribution-first marketing inverts that sequence. It starts with what’s measurable and works backward. The brief, if it exists at all, is a formality. Trap Three: Attribution as Job Protection What people are thinking that doesn’t get said. Here's what nobody says out loud: the more complicated the attribution machinery gets, the harder it is for the CFO or CEO to challenge it. Multi-touch models. Platform-versus-platform data fights. Footnotes about attribution windows. At some point the complexity itself becomes the point If the marketing leader can produce forty slides of attribution data that are genuinely difficult to interpret, that require specialized knowledge to interrogate, and that no one in the room can fully refute, the budget is probably safe. Not because the marketing worked, but because the machinery of measurement is impressive enough to shift the burden of proof. This is not cynicism. It’s a rational response to a broken incentive structure. CMO tenure averages somewhere between eighteen and twenty-four months, shorter than virtually any other C-suite role. That kind of insecurity produces defensive behavior. Attribution complexity is the ultimate defensive tool. You can’t be fired for what can’t be measured, and you can’t easily be challenged on what’s too complex to fully understand. But the cost of this defense mechanism is that the incentive to simplify: to return to the customer insight, the bold positioning, the campaign that doesn’t require forty slides to explain, is almost entirely absent. Simplicity is vulnerable. Complexity protects. So complexity wins. What This Has Actually Cost The damage is visible everywhere you look. B2B creative has gotten safer and more forgettable, because every decision runs through an attribution filter that rewards the familiar over the bold. Positioning has gotten blander, because brand differentiation requires conviction that attribution models don’t measure. The CMO has become a data steward in too many companies rather than a market maker. The role now rewards students of the dashboard over students of the customer. And the buyers noticed: the B2B marketing experience has grown more formulaic and less relevant in direct proportion to how sophisticated the measurement infrastructure became. That is not a coincidence. When you build marketing programs around what’s trackable rather than what’s true, the buyer feels it. Agentic AI Doesn’t Fix This. It Exposes It. Here’s where this stops being a management problem you can solve with better habits or a more enlightened CMO. Agentic AI is about to fundamentally break the attribution model B2B marketing has built its entire operating logic around, and the industry is largely unprepared for what comes next. AI agents conducting procurement research don’t fill out forms. They don’t click display ads. They don’t register for webinars, respond to nurture sequences, or generate the trackable behavioral signals that multi-touch attribution models were built to capture. When an AI agent is tasked with evaluating a category of vendors, it conducts its research through channels that are largely invisible to the tools B2B marketers have spent a decade building attribution infrastructure around. There is no UTM parameter on an AI agent’s recommendation. The buyer journey AI agents conduct is not a modified version of the human buyer journey. It’s a structurally different process that operates on different signals. Brand reputation. Category authority. Quality of content indexed by AI systems. Customer outcomes data. Third-party validation. These are the inputs an AI agent weights when evaluating vendors. They are not well-captured by MQL counts or pipeline influence percentages. The predictable response — already visible in corners of the industry — will be to build AI-powered attribution tools designed to track AI-mediated buyer journeys. This is the wrong lesson. It is more measurement infrastructure chasing a buyer who has left the building and gone somewhere the measuring tools can’t follow. What survives in an agentic world is everything attribution-first marketing has been underinvesting in: genuine brand authority, distinctive positioning, authentic thought leadership, customer success stories that hold up to scrutiny. The very things that can’t be easily tracked are the things that will matter most. The painful irony: at precisely the moment when B2B marketing needs to pivot back to brand-building, customer insight, and creative conviction, most marketing teams are least equipped to do it, because they’ve spent years optimizing for measurement and letting the upstream marketing muscles atrophy. Where the Puck Is Going Three predictions, with conviction: * Attribution models will get more sophisticated and less useful at the same time. The martech industry will respond to AI disruption the way it always responds to disruption: by building new tools. AI-powered attribution platforms will emerge. They will be genuinely impressive. They will also be measuring a buyer journey that increasingly doesn’t exist in the form those models are designed to capture. The investment in this infrastructure will be substantial. The return will diminish. * The CMO role will bifurcate. One path leads further into the data and operations direction: essentially a marketing operations function with a C-suite title, focused on stack management, attribution governance, and pipeline reporting. The other path leads back to the original CMO mandate: market intelligence, customer insight, brand positioning, and the creative conviction to build something distinctive. Both will exist. The second path will be harder to fill and significantly more valuable. Companies that understand the difference will hire accordingly. * Brand will reassert itself as the primary B2B marketing asset Not because brand marketers won a long-running argument, but because it will be the only signal that holds up in an agentic buyer environment. The companies that have been quietly building real brand while everyone else was building dashboards will have a structural advantage that will be very difficult to close quickly. Brand takes time to build and time to move. The time to build it is not after the buyer journey has already shifted. The Entrepreneur and Investor Opportunity The dysfunction described in this post is not just a management problem. It is a market opportunity, and some of the most interesting entrepreneurs I’ve been talking to lately are starting to see it clearly. The premise is straightforward: if a significant portion of B2B marketing’s bandwidth is consumed by attribution data collection, dashboard maintenance, platform reconciliation, and reporting preparation, work that is repetitive, rule-based, and largely mechanical, that work is exactly what agentic AI is built to automate. The opportunity is to take the attribution reporting function off the marketing team’s plate entirely and deliver it as a service, purpose-built for B2B marketing organizations. Call it Attribution as a Service. The model is not complicated in concept, though it is genuinely hard to execute well: an AI-powered system that pulls data from across the martech stack, reconciles it, applies agreed-upon attribution models, and delivers clean, current reporting on a cadence the marketing team sets, without requiring a marketing operations analyst to spend three days preparing the monthly deck. The CMO gets the information. The team gets their time back. The investor thesis is real. B2B companies are not going to stop wanting attribution data; the CFO isn’t disappearing, and the budget conversation isn’t going away. But the current model, where skilled marketing professionals spend a disproportionate share of their working hours producing that data, is inefficient in a way that agentic AI can directly address. The companies that build this well will sell into a buyer who is simultaneously cost-pressured, understaffed, and increasingly aware that the way attribution currently works is not working. A few dimensions of the opportunity worth watching: The integration layer is the moat. The hard problem in this space is not the reporting interface. It is the data. B2B martech stacks are fragmented, inconsistent, and frequently incoherent. CRM, MAP, ad platforms, intent data, web analytics, ABM platforms: these systems do not talk to each other cleanly, and the reconciliation work is where most of the human hours currently go. The entrepreneur who solves the integration and normalization problem owns the defensible position. The dashboard is a commodity. The clean, unified data layer is not. The model must return time to judgment, not just reduce cost. This is the critical distinction. An Attribution as a Service model that simply makes the existing reporting machinery faster and cheaper accelerates the problem this post describes. It just produces the forty-slide deck in ten minutes instead of three days. The entrepreneurs building something more valuable understand that the real product is not efficient reporting. It is reclaimed marketing capacity. Intelligently automate the mechanical work, return that capacity to the core levers of marketing: positioning, brand, customer insight, creative strategy, and you don’t just have a more efficient marketing team. You have a more impactful one. In B2B, where the gap between average marketing and excellent marketing is measurable in pipeline and market position, that delta is worth a great deal. Agentic AI changes the addressable market. As AI agents become a more significant part of the B2B buyer journey, the attribution problem gets structurally harder for in-house teams to solve. The signals change. The trackable touchpoints diminish. The models that worked for a human buyer journey need to be rebuilt for an AI-mediated one. A specialized service provider with the technical depth to stay ahead of that evolution will have a durable advantage over an in-house marketing ops team trying to keep up while also doing everything else. The venture and PE communities have not fully priced this opportunity yet, in part because attribution has been treated as a feature of the broader martech stack rather than as a standalone category. That is changing. The market size is real, the buyer pain is acute, and the technical moment: the convergence of agentic AI capability with martech stack fragmentation, is exactly right. For B2B media and marketing technology investors, this is a space worth watching closely. The Work That Needs to Happen There’s a test I’ve applied to marketing programs for most of my career, going back to my time at InformationWeek and forward through the ThomasNet rebuild: where we did more than three hundred customer interviews with Strategyn to understand what industrial buyers actually needed, as opposed to what we assumed they needed. The test is simple: Does this start with the customer or does it start with what's measurable? Attribution-first marketing almost always starts with what’s measurable. It starts with: what did we spend, what did it generate, and how do we defend it? Customer-first marketing starts with: what does this buyer believe, what do they need to believe, and what would it take to get them there? The second question is harder. It can’t be answered with a dashboard. It requires research, judgment, time spent with actual customers, and the willingness to act on insight before you have the data to prove it worked. It produces briefs and positioning statements and creative work that might fail before it succeeds. It is also, not coincidentally, the only kind of marketing that works. Attribution is a tool. It was always only ever supposed to be a tool. A way of checking the work after the thinking had been done. The industry turned it into a substitute for the thinking itself. Getting back to the actual upstream work is not a retreat from accountability. It is the precondition for having anything worth measuring. The measurement can follow. First, do the marketing. This is the series opener for B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era, published in Uphoff on Media Next in the series: "B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix." Post 2 of B2B Marketing in the Machine Age, publishing 5/26/26. The views expressed in Uphoff on Media are entirely my own. They don't represent the opinions of any company I've led, any board I've sat on, or any investor who's had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I'll take full credit. If it turns out to be wrong, I was clearly misquoted — by myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

You Don't Know What Your Customers Are Actually Trying to Do

Uphoff on Media | Field-Tested Frameworks When I became President and CEO of ThomasNet.com, I inherited a research project already underway with a firm called Strategyn. They were conducting over 300 individual interviews with Engineers, Procurement professionals, and MROs, all focused on the jobs to be done around custom manufacturing sourcing. The core value proposition of ThomasNet was supplier discovery and custom manufacturing sourcing. The goal of the research was simple: give us user insights to inform our product roadmap, and surface intelligence we could use when marketing to our customers. We thought the number of distinct jobs would be relatively small. Maybe a dozen. Perhaps two dozen if we stretched it. We were stunned. Strategyn’s research surfaced over 200 primary jobs to be done, and another 150 secondary ones. They ranged from the mission-critical: an Engineer searching for a certified manufacturer to produce a key component for a medical device, where the wrong supplier choice could cost lives. To the granular and human: a Procurement professional simply trying to “get my global team all on the same page” about a potential new supplier. Two hundred primary jobs. One platform. One team that had been building features based on what we thought mattered. That research didn’t just inform our roadmap. It demolished it. We went back to first principles and redesigned and reengineered the entire ThomasNet platform around the actual jobs of our audience. It was clarifying in the way that only being genuinely wrong can be. And here’s what happened next: active registrations grew. Platform usage grew. Revenue followed. The JTBD research became the north star of every product decision we made, not a one-time exercise but a living operating framework. It ultimately led to an unsolicited offer to buy the company at an eye-popping multiple. I’m not telling you this to brag about an exit. I’m telling you because the causal chain matters. Understanding what your customers are actually trying to accomplish — with rigor, specificity, and humility about how wrong you probably are — is not a soft strategic exercise. It is the work. And the market eventually prices it that way. The Framework Behind the Revelation Jobs to Be Done is a theory of innovation developed and popularized by Clayton Christensen, the late Harvard Business School professor and author of The Innovator’s Dilemma and Competing Against Luck. Christensen’s core argument: customers don’t simply buy products. They “hire” them to accomplish a specific job. The analogy that crystallized it for me came from HBS marketing professor Theodore Levitt: "People don't want to buy a quarter-inch drill. They want a quarter-inch hole." I had spent decades selling drills. So had nearly every executive I knew. Strategyn, the firm we’d engaged at ThomasNet, had built their entire research and consulting methodology around this framework, operationalizing it into a rigorous process for uncovering and prioritizing the full landscape of customer needs. Not what customers say they want. Not what your product team believes they need. What they are actually trying to get done, in the specific circumstances they face, with the social and emotional dimensions that feature checklists never capture. The Milkshake Story: JTBD in Action Christensen’s most famous illustration involves something far more ordinary than industrial sourcing, a fast-food milkshake. A major quick-service chain was puzzled: the bulk of its milkshake sales occurred before 8:30 in the morning, purchased by solo customers who ordered nothing else and drove away alone. Traditional market research offered no useful explanation. Christensen’s team asked a different question. Not “what do you think of the milkshake?” but “what job did you hire it to do?” The answer was immediate: these customers faced a long, tedious commute. They needed something to keep them occupied and stave off hunger until mid-morning. Something consumed one-handed while driving, lasting long enough to make the drive bearable, without the guilt of a donut. They’d tried bananas (gone too fast), bagels (messy), and coffee (too hot, over too quickly). The milkshake was nearly perfect. It took almost thirty minutes to finish, fit the cupholder, and kept them full. The chain had been asking how to make a better milkshake. The real question was: what is a 7am commuter actually trying to get done? Two entirely different questions. Two entirely different answers. And only one of them leads to a decision that actually moves the business. What the Research Did to My Thinking After the ThomasNet experience, I took Christensen’s Harvard online course on innovation. The aha moment wasn’t just intellectual, it was visceral. I realized that I, like most business leaders, had been building and positioning products based on my understanding of features and benefits. Not my customers’ experience of them. Two entirely different things. Christensen observed that firms have never known more about their customers, yet their innovation processes remain hit-or-miss. The reason: product developers focus too much on building customer profiles and looking for correlations in data rather than on the job the customer is trying to get done. The evidence is brutal at scale: somewhere between 75 and 85 percent of all new products launched don’t succeed financially. Not because of poor execution. Because of a fundamental misread of what the customer actually needed. Jobs are multifaceted. They’re never simply about function. They have powerful social and emotional dimensions. And the circumstances in which customers try to do them are more critical than any buyer characteristic. This is why demographic segmentation so often leads companies astray. You can know everything about who your customer is and still have no idea what they’re actually trying to accomplish. Here’s the Christensen point I think is most underappreciated: if you frame your business in terms of products you’re trying to sell, you get supplanted as products and technologies change. But if you’re organized around delivering a job that’s genuinely done well, you can absorb new technologies as they emerge rather than being displaced by them. Jobs are stable. Products are not.Print that out. Put it on the wall of every product team you run. The Agentic AI Accelerant Here’s why all of this has become urgent in a new way. We are entering the agentic AI era: where autonomous AI systems don’t just assist human buyers, they act on their behalf. They search, evaluate, compare, negotiate, and in some cases complete purchases without a human ever visiting your platform, reading your copy, or clicking through your feature list. Analysts project that 90% of B2B buying will be AI agent-intermediated by 2028, driving over $15 trillion of B2B spend through AI agent exchanges. When I first encountered that number, I sat with it for a while. I've watched several major platform transitions up close: print to digital, trade directories to industrial marketplaces, on-premise software to SaaS. Each one scrambled the competitive map for companies that didn't see it coming. This one is different in speed and scope. And it has a specific implication for JTBD that I don't think enough leaders are tracking. When a human buyer visits your platform, you have the opportunity to guide, persuade, and educate. You can tell your story. A skilled sales team can build relationships and reframe a conversation. When an AI agent evaluates your platform on behalf of a buyer, none of that happens. It doesn’t read your positioning statement. It doesn’t respond to your brand narrative. It evaluates your product against the specific job the buyer has tasked it to complete. Either your product does that job — demonstrably, specifically — or the agent moves on. The AI agent is a pure JTBD machine. These agents are goal-oriented, context-aware, and ruthlessly efficient at matching products to jobs. Feature lists won’t matter. Brand stories won’t matter. What will matter is whether your product demonstrably accomplishes the job the buyer agent has been assigned. This is why JTBD isn’t just a useful framework anymore. In the agentic era, it becomes the foundation on which competitive survival is built. If you don’t know the precise jobs your customers are trying to get done — down to granular specificity — AI-mediated buying will route around you. Not maliciously. Automatically. What This Means If You’re Running a Business The implications run across the entire organization. Product: Most roadmaps are answers to questions nobody asked, built on internal assumptions dressed up as strategy. In an agentic buying environment, the gap between what you built and what the buyer needs becomes fatal. Not because a competitor beat you. Because an AI agent simply moved on. Marketing: Messaging built around your product’s attributes will increasingly fail to reach AI agents making autonomous decisions. The language of outcomes, specific, measurable jobs your product enables, is the only language agentic systems will evaluate. If your content strategy doesn’t speak to jobs, it’s speaking to no one. Sales: The human selling motion isn’t going away, but it’s narrowing to where relationships and judgment genuinely differentiate. Everywhere else, AI agents will mediate. Sales teams need to understand the jobs their customers are executing at a depth most have never been asked to develop. Strategy: The businesses that will thrive in the agentic era are those that can map their capabilities directly to the jobs of their market, with specificity and evidence. Not “we help engineers source suppliers.” But: “we help an engineer with aerospace certifications find a titanium CNC machining partner in North America within a 48-hour sourcing window, with documented quality controls for FAA compliance.” That’s a job. That’s a product. That’s a business. Do the Research When our team at ThomasNet discovered 350+ distinct jobs to be done in what we thought was a well-understood market, the response could have been paralysis. Instead, it became a forcing function for discipline. Not every job could be served equally. We had to prioritize, sequence, and build a roadmap anchored in actual user needs, not internal assumptions. That’s hard work. It requires real investment in understanding your market at a level of depth most companies never attempt. Christensen observed that for most companies, innovation remains a flawed business process — yielding failure rates consistently over 80% — because companies fail to define their customers’ needs with the rigor, precision, and discipline required to discover, prioritize, and capitalize on growth opportunities. We are not in an era where that failure rate is acceptable. The competitive margin is too thin. The pace of AI adoption is too fast. And the buyers, increasingly assisted or replaced by AI agents that will simply go find a better match, are too empowered to wait for you to figure it out. The quarter-inch drill metaphor has never been more relevant. And the urgency to understand the hole has never been more acute. We rebuilt ThomasNet around that understanding. The market noticed. So did the acquirers. The question is whether you move before the agents do. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

B2B Is Redesigning Everything. The Customer Still Isn't in the Room

There is a room I keep walking into. The faces change. The industry changes. The agenda changes. But the missing person is always the same. The customer isn’t there. I recently sat with a group of senior Procurement executives. Smart, experienced operators redesigning their workflows for the agentic AI era. The conversation was serious, these were executives working through real transformation. What processes should be automated. What decisions should remain human. How supplier engagement models need to evolve. But something kept nagging at me. The SaaS platform vendors their organizations depend on, the systems of record for procurement, sourcing, supplier management, kept appearing in the conversation as obstacles, not enablers. The Procurement teams were clearly articulating what they needed these platforms to do. New agentic AI features. Workflow modifications. Integration changes. And the vendors kept saying no. Not with technical explanations. Not with roadmap visibility. With the soft, faintly condescending resistance of organizations that don’t quite understand what their customers are asking for, or why. The vendors weren’t blocking transformation out of bad faith. They were blocking it because they don’t deeply understand how Procurement actually works. That’s a different and more troubling problem. You can fix bad faith. You can’t easily fix institutional ignorance dressed up as process. Then I offered a reframe that landed hard. Throughout the entire discussion — the workflow redesign, the AI feature debates, the vendor friction — not once had anyone mentioned the people this function actually exists to serve. The internal stakeholders. The suppliers. The customers of Procurement. The room didn’t push back. They recognized it immediately. These were experienced operators, they knew exactly what outside-in thinking looked like. They just hadn’t applied it to their own transformation. The problem had pulled them inward, and they’d followed it. That’s how it always happens. Not negligence. Gravity. The Same Disease, Different Organs I’ve been having a parallel conversation with B2B marketing, media, and technology leaders about brand trust. Same room. Same missing person. The data on this is worth sitting with. Studies consistently show that roughly 90% of executives believe their companies are highly trusted by their customers. Customers, when asked, put that number closer to 30%. A 60-point gap. And that gap isn’t a perception problem, a communications problem, or a positioning problem. It is a structural orientation problem. B2B leaders have been building brands, systems, and now AI workflows for themselves. Not for the people they serve. I don’t think this is a coincidence. I think it’s the same failure presenting in two different organs. We built a generation of B2B marketers who can optimize a CAC ratio but have never sat with a customer and asked what they actually need. Look at the last twenty years of B2B marketing. What stands out? Not branding. Not the creation of companies that buyers genuinely trust, advocate for, or miss when they’re gone. What stands out is the extraordinary rise of digital marketing, martech, and adtech. The relentless optimization of measurable things. Click rates. Conversion rates. Pipeline velocity. Cost per lead. These tools aren’t the problem. The problem is what happened when they became the entire discipline. When the metric became the mission. When a generation of B2B marketers learned to optimize data but never learned to understand buyers. When the incentive system rewarded what could be measured and quietly penalized what couldn’t: like trust, like brand equity, like the kind of relationship where a customer stays through a bad quarter because they genuinely believe in the company. Brand requires something martech can't deliver: genuine outside-in orientation. Knowing your customer as a human being making high-stakes decisions with real accountability. That got hollowed out. Completely. Faith Popcorn Named It Faith Popcorn wrote something recently that I can't stop thinking about. Faith is one of the sharpest brand thinkers I've ever encountered, and she named something I've been trying to articulate: The Heartbeat Brand. The concept is this: consumers trust brands that measure themselves in life outcomes, not transactions. Brands that show up inside the vulnerable, real moments of a customer’s life, not just at the point of purchase. Brands that ask “what does this person actually need” rather than “how do we move more product”. She writes about Costco launching an IVF program, a warehouse retailer deciding that helping its members have babies is also its business. About the USPS carrier who already stops at every door in America and could become a genuine safety net if someone had the vision to see it. About the 60-point gap between executive confidence in brand trust and what customers actually report, and how that gap is not a perception problem but a strategic blind spot of historic proportions. Her audience is primarily B2C. But the principle lands harder in B2B, where the stakes are higher, the buying decisions more consequential, and the customer has been more systematically ignored. A B2B “Heartbeat Brand” is the vendor that shows up in your worst quarter, not just your best. The platform that redesigns its workflow around how you actually operate, not how the product team imagined it. That’s the standard. And by that standard, B2B as an industry has been failing for a long time. The SaaS Problem Is the Brand Problem Back to the Procurement room. The platform vendors gatekeeping transformation they don’t understand are exhibiting the same failure mode as the B2B marketers who built twenty years of campaigns without deeply knowing their buyers. They sold into Procurement. They deployed into Procurement. They never learned Procurement. That’s not a product problem. It’s a customer orientation problem. And it shows up as friction precisely when it’s most costly; at the moment a customer is trying to evolve, trying to adopt new technology, trying to do the hard work of organizational transformation. If these vendors had developed genuine, operational-level understanding of how Procurement functions, the conversation would be different. How sourcing decisions get made. How supplier relationships are managed. How internal stakeholders experience the process. How the job of a Procurement leader has changed as the function has become more strategic. The features would exist. The roadmap would reflect what customers need. The “no” would come with a real reason or not at all. Instead, the customer is in the room and the vendor isn’t listening. The mirror image of the Procurement team that designs its future workflow without the supplier or stakeholder in the room. It’s inside-out, all the way down. This has always been a risk in enterprise SaaS. But the penalty has historically been manageable: churn is painful, switching costs are real, and inertia protects incumbents longer than it should. Agentic AI changes that calculus entirely. When AI-native tools can replicate and integrate the workflow steps that previously required dedicated point solutions, the switching cost equation inverts. The moat that protected functionally narrow SaaS vendors — “we own this step” — becomes a vulnerability. Vendors who don’t understand the function deeply enough to evolve with it won’t just face difficult renewal conversations. They’ll be architecturally bypassed. Churn in the agentic AI era won't look like churn always has: a lost renewal, a migration project, a transition cost borne by both sides. It will look like a Procurement team that rebuilt its workflow around an AI-native platform and quietly stopped needing four of the tools it was paying for. No dramatic exit. No contentious offboarding. Just a function that evolved, and a vendor that didn't. The vendors who are gatekeeping today are revealing something important: they are not positioned to guide their customers through the agentic AI transition. And their customers are starting to notice. Agentic AI Doesn’t Fix This. It Amplifies It. Here is where urgency enters the conversation. Agentic AI is restructuring workflows at the infrastructure level. Right now, organizations across B2B are redesigning the interest-to-invoice journey. They are making decisions about what gets automated, what stays human, how suppliers are engaged, how buyers are supported, how the entire operating system of B2B commerce gets rebuilt for a world where AI agents execute tasks, not just assist with them. These decisions will be very hard to reverse. You can pull a bad ad campaign. You can reposition a brand. You can’t easily rearchitect a workflow that’s been embedded into your systems of record, your supplier relationships, and your organizational muscle memory. If the customer isn't in the room during that redesign, we don't just build bad workflows. Not when B2B marketers build agentic demand generation without deeply understanding the buyer journey. Not when Procurement leaders design agentic workflows without engaging the suppliers and stakeholders those workflows serve. Not when SaaS vendors build agentic features without operational-level understanding of how their customers actually work. We automate the inside-out orientation. We encode the blind spot at scale. The companies that will compound trust in the agentic AI era are the ones that design outside-in, starting with the customer. The rest will just fail faster. Three Questions Worth Sitting With Faith Popcorn ends her piece with a set of questions she recommends sitting with over a good bourbon. I’ll offer a B2B version. * If your platform, product, or service disappeared tomorrow, would your customers miss it, or just migrate? Not the switching cost. The actual loss. There’s a difference between a product customers are locked into and one they genuinely value. Most B2B leaders don’t know which one they have. * Does your process start with what the customer needs or with what the business has already decided to build? The SaaS vendors blocking Procurement transformation aren’t doing it maliciously. They simply haven’t spent enough time understanding how Procurement actually operates. That’s a choice, even if it doesn’t feel like one. * Who is designing your agentic AI workflows, and who isn’t in the room? This is the most urgent question in B2B right now. The workflows being designed today will shape how buyers experience your brand, how suppliers experience your Procurement function, and how customers experience everything in between for the next decade. If the customer isn’t in that design process, you’re not building the future. You’re automating the past. The gap between what B2B companies think their customers feel about them and what customers actually feel has never been wider. The technology to close that gap has never been more powerful. Neither has the technology to permanently encode it. The question for every B2B leader isn't whether agentic AI changes your business. It does. The question is whether you'll finally put the customer in the room. Or whether you'll do what B2B has done for twenty years, and just do it faster. Put the customer in the room. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted — by myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

The CPO Moment

When the CEOs of Walmart and Coca-Cola announced their retirements, both cited AI disruption as a contributing factor. Let that land for a moment. Two of the most experienced executives in global business, leaders of companies with combined revenues approaching a trillion dollars, named AI as a reason for stepping back. This isn’t a technology story. It’s a leadership story. It has direct implications for every Procurement leader reading this. Three Forces Are Converging: Right Now I was recently invited to speak at Art of Procurement’s Catalyst event in San Francisco, a gathering of senior Procurement leaders from across the enterprise world. The session: a fireside chat with AoP Founder Phil Ideson titled “What Now? Leadership Choices When AI Accelerates”, was one of the more candid conversations I’ve had with a room full of senior practitioners in some time. I also facilitated a Mastermind breakout with senior Procurement leaders from across the enterprise technology sector. A smaller, candid conversation that surfaced some of the most honest operator thinking I've encountered on these themes. What came out of those sessions sharpened my thinking considerably. Not just about where the function is headed, but about the specific friction points preventing leaders from getting there. Three forces are reshaping Procurement simultaneously. Their combination creates both the highest risk and the biggest opportunity the function has seen in a generation. Force 1: Agentic AI Makes Judgment More Valuable, Not Less The narrative around AI and Procurement tends toward anxiety: automation is coming for sourcing, contracting, spend analysis, supplier evaluation. That narrative is partially right, and almost entirely wrong in its conclusion. Yes, agentic AI systems will absorb more of the transactional and analytical work in Procurement. But here’s what that actually means: the premium on human judgment goes up, not down. When an AI agent can process a thousand supplier bids in the time it used to take to evaluate ten, the competitive advantage shifts to the person who knows which variables matter, and why. Risk assessment. Relationship intelligence. Strategic trade-offs that don’t fit a model. That’s not the work AI replaces. That’s the work AI reveals. The teams that lean into this shift will separate from those that resist it. The ones that treat agentic AI as a threat will spend the next three years playing defense. The ones that treat it as an amplifier will spend those years building something that can’t be replicated. One senior procurement leader who spoke at the event put the human stakes more bluntly than I expected: “I’m trying to figure out whether we should invest in upskilling around AI or just spend the money on severance.” That’s not cynicism. That’s a genuine leadership dilemma in real time, and it deserves a real answer. The answer is neither. The right investment is in the judgment layer above the automation. The capability that makes both the upskilled humans and the AI systems more valuable than either could be alone. Force 2: Service as Software Changes What Procurement Is Evaluating Enterprise software is undergoing a structural shift, from SaaS to what’s being called Service as Software. AI doesn’t just assist the work anymore. In a growing number of applications, AI is the service. It delivers outcomes, not tools. This changes the procurement conversation fundamentally. The old evaluation criteria — features, integrations, security, uptime — don’t disappear, but they’re no longer sufficient. Now you’re evaluating performance accountability when the “vendor” is an agent. Data rights when the service learns from your proprietary information. Vendor dependency when the capability is embedded in AI infrastructure you don’t own or fully understand. These are no longer purely IT questions. They are Procurement questions. What emerged clearly from the Mastermind in San Francisco: vendors themselves are part of the problem. Multiple leaders raised a pattern I hadn’t fully framed before: vendors acting as gatekeepers, rolling out AI features on their own timeline and effectively controlling the pace of adoption within their customers’ organizations. Procurement is being throttled by the very ecosystem it manages. That’s a structural problem, not a planning problem. And it means Procurement leaders need to move the vendor governance conversation up, from contract administration to strategic posture. Which vendors are genuinely enabling your AI capability? Which ones are holding it hostage to their own roadmap? Those are not equivalent relationships and shouldn’t be managed as if they are. The function that doesn’t get fluent in this new model will watch those decisions get made without them. Force 3: The Strategic Seat Is There for the Taking Here’s what I argued in San Francisco, and what I believe most strongly: Procurement is one of the few functions in any enterprise that has a full playing field view of the business: across systems, vendor relationships, budget allocations, and business unit operations simultaneously. That vantage point is structurally unique. The CFO sees the numbers. The CTO sees the infrastructure. The CMO sees the market. Procurement sees the connective tissue that holds all of it together. In an agentic AI environment, that vantage point becomes a genuine competitive advantage for the leaders who claim it. Because what agentic AI requires: clean data, trusted vendor relationships, clear decision rights, structured governance, is exactly what Procurement is positioned to provide. No other function has that stack. COVID tested every function’s ability to respond to a supply chain crisis. The best Procurement teams used that moment to demonstrate strategic value they’d never been given credit for. This moment is different — broader, deeper, more structurally permanent — but the dynamic is the same. Disruption creates openings. The functions that move get the seat. The ones that wait get managed. But a real obstacle surfaced in San Francisco that the optimistic version of this argument tends to skip past: the attribution gap. One senior Procurement leader described it this way: “When we work with a business unit and create a more streamlined tech stack, set of vendor agreements, or workflow, the savings is something that business unit leader can reinvest, so it doesn’t show up as something Procurement did.” That’s not a small problem. It’s a structural visibility failure. The value is real. The credit never arrives. And without the credit, the seat doesn’t come. The implication: Procurement doesn’t just need to do the strategic work. It needs to get fundamentally better at narrating it. Internally. At the CFO and CEO level. In the language of business outcomes, not functional metrics. One finding from the Mastermind worth internalizing: time savings can outrank cost savings as the most valued internal contribution Procurement makes. Not marginally. As a clear priority shift. Procurement leaders still leading with cost reduction are speaking a language the organization has partially moved past. The CFO and CEO care about speed of decision, speed of deployment, reduction in organizational friction. If Procurement can show up in that conversation — and it can — the seat is available. If it keeps leading only with savings, it keeps getting managed like a cost center. What to Do Now Most frameworks name the forces and stop. So let me be direct about what I think Procurement leaders should do in the next 90 days. This Week: Get Situationally Aware * Audit every AI-enabled tool in your vendor stack. Flag which ones are moving toward agentic capability, autonomous action, not just AI-assisted recommendation. Then flag which vendors are enabling that shift and which ones are gatekeeping it. * Map where automation is already touching sourcing, contracting, or supplier management in your organization. If you don’t know, that’s the finding. * Pull your top 10 SaaS contracts. Identify which vendors are pivoting to outcome-based or AI-delivered service models. Those renewals just got more complex, and Procurement needs to lead them. This Month: Build Your Point of View * Write a one-page AI risk brief for your CFO or CPO. Cover three things: vendor dependency, data rights in AI-delivered contracts, and performance accountability when the “service” is an agent. If you can’t write it, you’re not ready for the conversation. * Rewrite your function’s value narrative. Stop leading with cost savings. Lead with time savings, risk intelligence, and business outcomes. The language you use internally shapes how leadership sees you. And the language is shifting. * Define your attribution story. Where has Procurement created real business value in the last 12 months that isn’t showing up in anyone’s performance metrics? Find it. Name it. Quantify it. Present it. This Quarter: Move the Needle * Identify two or three high-visibility business decisions in the next 90 days where Procurement can drive or materially shape the outcome. Don’t wait to be invited. Show up with a POV. * Claim a seat at one C-suite or cross-functional initiative where Procurement hasn’t historically had a voice. One is enough to start. Make it count. * Deepen three strategic supplier relationships, the kind built on trust, shared intelligence, and long-term alignment. Specifically: have a direct conversation with each about their AI roadmap, what they’re enabling, what they’re controlling, and where your interests align. No agentic system replicates that relationship intelligence. It’s your moat. The Moment Is Now I’ve spent 35 years watching disruption hit industries and functions. The pattern is consistent. The leaders who move early, who treat disruption as a forcing function rather than a threat, end up in a fundamentally different position than those who wait for clarity. One CPO pulled me aside after the session and told me she wished she'd had this conversation ten years ago. I appreciated that. But the more important point is that having it now, in this moment, is the whole game. The window in which Procurement can move from managed function to strategic asset is open. It won’t stay open forever. Agentic AI is not going to make Procurement obsolete. It is going to make the difference between strategic Procurement leaders and order-taking Procurement functions impossible to ignore. The full playing field view is yours. The attribution gap is fixable. The seat is available. The choice is whether to take it. One more thing worth mentioning: if you're a senior Procurement leader and haven't encountered Art of Procurement's Catalyst event, put it on your radar. Phil Ideson and the AOP team have built something rare, a room where senior practitioners talk honestly about what's actually happening, not what's supposed to be happening. Keep an eye out for it coming to a city near you. Worth the trip. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Wonderwall

There’s a lyric from the Oasis song “Wonderwall” that’s been living rent-free in my head every time I read another paywall story: “I said maybe, you’re gonna be the one that saves me.” That’s the emotion publishers brought to paywalls when the model went mainstream in the early 2010s. Not strategy. Emotion. Hope masquerading as a business plan. I don’t say that to be cruel. I say it because I was in the room. I watched publishers, smart operators with real businesses, latch onto the paywall narrative the way drowning people grab floating debris. The economics of digital media were already brutal. Print was collapsing. CPMs were a fraction of what the industry needed. And here, finally, was something that felt like agency. We’ll charge for content. We’ll own our audience. We’ll escape the platform trap. Fifteen years later, we have enough data to render a verdict. And the verdict is complicated, more complicated than the paywall evangelists or skeptics will admit. A handful of publishers built real businesses behind paywalls. Most didn’t. And now, just as the model was finally maturing into something defensible, agentic AI has arrived to stress-test the entire premise. This is the story of what actually happened, why it happened, and what publishers who are still standing need to understand about what comes next. Act I: The Promise (2010–2018) Salvation Narrative The Wall Street Journal had been running a hard paywall since 1996, a fact that sounds prescient in retrospect but was largely irrelevant to the broader industry at the time. The WSJ served a professional audience whose employers often footed the bill. It was a B2B subscription product wearing a B2C costume. Everyone acknowledged it worked. Everyone assumed it wouldn’t work for general-interest news. Then the print cliff accelerated faster than anyone anticipated. Between 2008 and 2012, newspaper print ad revenue dropped by roughly half. Digital advertising was growing, but it was growing for Google and Facebook, not publishers. The CPM math was unforgiving: a publisher might earn $25–$40 per thousand impressions in print and $1–$3 online. No volume of page views could close that gap. I watched this from the inside. When I was publisher of InformationWeek, we had 400,000 controlled-circulation subscribers. These readers received the magazine free because they had the right title, the right company size, the right buying authority. That verified, qualified readership built a rate base: the audited circulation number that determined what we could charge advertisers. The currency from the readers was their time, engagement, and loyalty, which was the entire benefit to advertisers. At its peak, InformationWeek generated over $175 million in annual advertising revenue on that model. The paywall era inverted that logic entirely. It assumed the reader's currency should be cash. For most of publishing history, their attention was worth far more. The New York Times launched its metered paywall in March 2011, and the industry held its breath. The conventional wisdom was that readers would simply navigate around it, that free content was too available online, that the paywall was a noble but doomed gesture. Instead, the Times started signing up digital subscribers. Not immediately transformative numbers, but real ones. Proof of concept. Between 2017 and 2020, the number of news outlets implementing paywalls nearly doubled each year. The trade press treated it as a rescue mission. By the mid-2010s, the paywall had become the dominant narrative of publisher survival. Industry conferences devoted entire tracks to paywall strategy. Consulting firms built practices around it. Publishers who hadn’t implemented one felt pressure to explain why. The enthusiasm was genuine, and in hindsight, genuinely disproportionate to the results most publishers would actually achieve. Act II: The Scorecard (2018–2023) Who Won, Who Lost, and Why Let me give you the honest operator’s scorecard, because the industry press tends to celebrate the wins and bury the losses. The New York Times won. Decisively. By February 2022, three years ahead of schedule, it hit 10 million digital subscribers and reset its target to 15 million by 2027. But the Times didn’t win because it built a better paywall. It won because it built a better product. Wordle. The Athletic. NYT Cooking. NYT Games. The paywall became a bundle gateway, not a content gate. Subscribers were paying for a suite of daily habits, many of which had nothing to do with news. The Wall Street Journal won. For the same structural reason I described earlier; it had always been a professional subscription product, and first-party subscriber data made its advertising inventory genuinely more valuable to marketers. WSJ reportedly sells 90% of its inventory direct, and advertisers using its first-party data renew at significantly higher rates. The paywall isn’t a barrier; it’s a data-collection mechanism that funds a premium ad business. The Financial Times won. Same playbook: a professional audience with employer-funded subscriptions and irreplaceable market-specific content. Everyone else? Mixed at best. The economics became clear pretty quickly: paywalls work when they protect something a specific reader cannot get anywhere else. General-interest news is available everywhere. Local news is undervalued by local readers until it disappears. Niche B2B content; if it’s genuinely proprietary, or delivered with enough utility that it functions as a workflow tool rather than a reading experience, can command real subscription prices. Bloomberg didn’t build a $6 billion terminal business by having better journalism. It built it by embedding that journalism into a platform traders couldn’t do their jobs without. Some B2B publishers followed a version of that playbook: combining content with data, analytics, or workflow integration until the product became operationally indispensable to its audience. When a reader can’t do their job without you, price sensitivity largely disappears. But that bar is high, and most publisher content — if we’re being honest — never cleared it. Most households will pay for one or two news subscriptions. That budget overwhelmingly goes to the same three or four scaled national brands. Everyone else is fighting over the remainder. The Sun tried a hard paywall and abandoned it. The Toronto Star did the same. Countless regional newspapers experimented, retreated, and experimented again. Local news operators found that the readers most willing to pay were often those most engaged. The very people who would have supported the paper anyway. The marginal subscriber who needed to be convinced often just didn’t show up. Meanwhile, a more structural problem was hardening. Publishers had signed up most of the readers who were ever going to subscribe. Growth was slowing not because the paywall model was wrong, but because the addressable market was smaller than assumed. As one Reuters Institute report bluntly noted, publishers had already captured many of those prepared to pay, and in a tight economic climate it had been hard to persuade others to do the same. Subscription growth was plateauing industry-wide by 2022–2023. Act III: The Innovation Layer (2022–2025) Smarter Walls, Shrinking Returns To their credit, publishers didn’t give up. They got more sophisticated. The blunt instrument of the early paywall — a fixed meter, same for every reader — gave way to dynamic, AI-driven systems that personalize the conversion moment. Instead of offering every reader the same number of free articles, smart meters identify which readers are close to converting and apply pressure accordingly; they identify casual browsers and leave them in the open funnel to generate ad impressions. The results have been real. Business Insider reported a 75% increase in subscriptions after implementing a dynamic paywall system. The Philadelphia Inquirer achieved a 35% lift in subscriber growth using a similar approach. By late 2024, 38% of news publishers were already using or planning to transition to dynamic paywall models. The internal debate between the subscriber revenue team (lock it down) and the advertising team (keep it open) had found a technology-mediated resolution: optimize for both simultaneously using behavioral data. This was genuinely encouraging progress. Publishers were finally applying the kind of data discipline to subscription conversion that e-commerce had been using for years. It felt like the industry was growing up. And then the floor started to move. Act IV: The Existential Threat (2025 and Now) The Wall That AI Walks Right Through The paywall was designed to stop humans. It was never designed to stop agents. In October 2025, researchers at the Columbia Journalism Review documented something that should have sent shockwaves through every publisher’s boardroom: AI browsers like OpenAI’s Atlas and Perplexity’s Comet were able to retrieve and summarize a 9,000-word subscriber-exclusive article from MIT Technology Review without triggering any of the barriers designed to block automated access. These systems aren’t scrapers in the traditional sense. They behave like humans. They load the full page, render JavaScript, pass bot-detection systems, because to the website’s infrastructure, they are indistinguishable from a person using Chrome. The scale is staggering. AI scraping attacks on streaming and media properties jumped 56% year over year. From Q2 through Q4 2025, the rate of AI content scraping grew at an average of 24.4% per quarter. In March 2025 alone, 26 million scraping attempts ignored standard robots.txt directives. The traditional defenses weren’t built for this. The paywall was designed to stop humans. It was never designed to stop agents. And the agents have arrived. But the scraping problem, as serious as it is, may actually be the second-order threat. The first-order threat is more subtle and more devastating: if AI systems can synthesize answers from publisher content without sending users to publisher pages, readers never hit the paywall at all. They never become subscribers. The loss is invisible. Zero-click search rates: queries that get answered without any click to a publisher’s page, rose from 56% in May 2024 to 69% by May 2025. DMG Media reported an 89% drop in click-through rates in September 2025, attributing it directly to AI Overviews in Google search results. The 500 most-visited publishers saw an average traffic decline of 27% year-over-year. Industry analysis estimates AI-powered search summaries reduce publisher traffic by 20% to 60% on average, with niche publications experiencing losses approaching 90%. In an industry where reach equals revenue, this is devastating. The paywall assumes a reader arrives at your door. Increasingly, they don’t. They get what they needed from the AI and never make the trip. The Framework: What Actually Works Now Post-Paywall Revenue Architecture The lesson isn’t that paywalls failed. It’s that paywalls were always a distribution fix applied to a value problem. Publishers put a gate on content that, in many cases, hadn’t earned the gate. The ones who built sustainable subscription businesses did so because they built something genuinely irreplaceable, then used the paywall to capture value from it. That logic doesn’t change in the AI era. It intensifies. Here’s what the evidence suggests actually works: * Proprietary intelligence over commodity content. If an AI can synthesize a reasonable version of your article from publicly available information, your content has a commodity problem, not a paywall problem. The publishers who will survive the AI transition are those with original reporting, exclusive access, proprietary data, or a unique analytical perspective that cannot be reconstructed from digital breadcrumbs. This is what the WSJ has. It’s what the FT has. It’s what most publishers don’t. * Memberships over subscriptions. The semantics matter. A subscription is a transaction: pay for access. A membership is a relationship: belong to something. The publishers building durable reader revenue are increasingly framing their asks around community, identity, and shared purpose. This is not AI-proof, but it is significantly more human. People don’t cancel memberships the way they cancel subscriptions. * Experiences over content. Live events, conferences, workshops, dinners — anything that requires human presence cannot be scraped, summarized, or synthesized by an AI agent. This is where B2B media has always had an advantage: the event, the roundtable, the peer-to-peer connection, is genuinely irreplaceable. Publishers who haven’t built an events business need to start building one. * First-party data as a B2B product. The WSJ model: using subscriber data to make advertising dramatically more valuable, is replicable in principle by any publisher with a real subscriber base and the discipline to develop it. And for B2B publishers who took the utility path, embedding content into workflows, combining editorial with data and analytics, the first-party data advantage is even greater. A reader who uses your product to do their job generates behavioral signals that dwarf anything a casual visitor produces. The challenge is that most publishers let this data sit underutilized, treated as a byproduct rather than a product. In an environment where third-party cookies are dead and AI is eroding traffic-based ad models, the publisher who owns rich, consented, first-party audience data — especially data generated through active, functional use — has a genuine and defensible asset. * AI licensing deals. The New York Times is suing OpenAI; a case that has escalated into one of the most consequential AI copyright battles in history, now centered on evidence that AI models memorize and reproduce copyrighted content verbatim. The FT signed a licensing deal with OpenAI. Both strategies may prove correct for their respective situations. But the larger point stands: the question every publisher should be asking is not whether to resist AI, but how to monetize their content's role in the AI ecosystem. The Song Ends the Same Way The Oasis lyric has one more line worth sitting with: “After all, you’re my wonderwall.” There’s something heartbreaking in it. The conviction that this thing, whatever it is, is the answer. Publishers felt that about paywalls. The conviction was sincere. The timing was real. The economics were genuinely dire enough to justify almost any hopeful narrative. But a paywall is not a content strategy. It is not a value proposition. It is not a relationship with a reader. It is a gate. And a gate only has value if what’s behind it is worth the price of entry, and if readers can’t get what’s behind it somewhere else for free. For fifteen years, the publishing industry argued about the gate. The conversation we needed to have was always about what was behind it. The AI era is forcing that conversation at last. Not because publishers wanted to have it, but because the old evasions have run out. The scrapers are inside the walls. The readers never arrive. The CPMs are gone. What’s left is the only thing that was ever going to matter: content so good, so specific, so irreplaceable, that a reader would genuinely feel its absence. Build that. Then put a wall around it. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]