Uphoff on Media Podcast

The most important thing Sequoia Capital published in the last year wasn’t a market map or a fund announcement. It was two sentences:“The cloud transition was software-as-a-service. The AI transition is service-as-software.”Ten words. The entire business model of enterprise software: two decades of per-seat licensing, recurring revenue multiples, and stack complexity, reframed in a single breath.I’ve been running B2B technology and media businesses for 35 years. I’ve lived through the transition from print to digital. From on-premise software to SaaS. From search to social to intent data. Each shift looked, at the time, like a feature upgrade. Each one turned out to be a structural reorganization.This one is structural. And it’s moving faster than any transition I’ve seen.The $1/$6 Ratio That Explains EverythingHere’s the number that reframes Sequoia’s thesis from interesting to urgent.For every $1 a company spends on software, it spends $6 on services.That ratio — buried in Sequoia’s “Services: The New Software” analysis — is the entire argument. The SaaS era attacked the software market. A $350 billion opportunity. Significant. Transformative. And now largely captured by the incumbents: Salesforce, Microsoft, Oracle, and their horizontal peers.Agentic AI is attacking the services market. Measured in trillions. And it’s doing it by turning software into a workforce. One that does the work, not just enables it.This is not an incremental shift. It is a category expansion that dwarfs the cloud transition by an order of magnitude.What “Service as Software” Actually MeansLet’s be precise, because this concept gets muddy fast.Software-as-a-Service was about delivery. Software moved from a box you bought to a cloud subscription you rented. The product was still a tool. You still needed people to use it.Service as Software is about execution. The software doesn’t give your team a better tool. It becomes the team. It does the job. You pay for the outcome, not the access.Sequoia’s example is Sierra, an AI-based customer support platform. Companies don’t buy Sierra seats. They pay per resolved customer issue. No resolution, no charge. The job-to-be-done gets done, or it doesn’t, and nobody pays. There is no seat. There is no license. There is a result.That model — pay for work delivered, not software deployed — is the commercial architecture of the next era of enterprise technology.And it’s already breaking the current one.The SaaSpocalypse Is Not a Prediction. It Already Happened.In early 2026, the iShares Expanded Tech-Software ETF fell more than 21% year-to-date. An estimated $2 trillion in market capitalization was erased from B2B software companies in a matter of weeks. Atlassian dropped 36% in a single month. Analysts gave it a name: the SaaSpocalypse.The market was repricing every software company built on per-seat licensing, because AI agents don’t pay per seat — and neither do the human employees whose jobs they’re replacing. Data emerging from enterprise CIOs showed that for every autonomous AI agent deployed, companies were reducing human software seat requirements at roughly a 1:5 ratio.The incumbents didn’t argue. They adapted. Salesforce and ServiceNow pivoted to outcome-based pricing, charging for tasks completed rather than seats occupied. That pivot was an acknowledgment, not a strategy. It was recognition that the per-seat model had reached its structural limit.BCG puts it plainly: 40% of buyers now cite seat reduction as their primary lever to decrease software spending. That’s not a trend. That’s a buyer revolt.The CMO Who Signed Up to Do MarketingLet me bring this down to ground level. Because the abstract inevitably becomes personal.The average B2B enterprise now runs more than 300 SaaS applications. According to Chief Martec's 2025 landscape report, the martech landscape contains over 15,000 tools. And 32% of organizations report not fully using the capabilities of the stack they’ve already built, up from 28% the year before.The average CMO did not sign up to be a shadow CTO.They signed up to do marketing. To build brand. To drive pipeline. To win markets. Instead, they’re spending a material portion of their time managing vendor relationships, integration failures, data reconciliation, and a technology stack that requires its own internal expertise to operate.This is the hidden tax of horizontal SaaS. The software works. Technically. But operating it at scale, tuned for your specific market and workflow, requires vertical expertise that the horizontal platform was never designed to provide. So you hire it, build it, or hire a partner to build it. And you pay for it twice: once in the license, again in the operating overhead.Service as Software is the exit ramp from that trap.When the unit of value is an outcome — leads generated, pipeline influenced, content produced at scale, demand signals identified — the complexity of the underlying technology becomes the vendor’s problem, not yours. The CMO’s job returns to marketing. The vendor’s job becomes delivering the result they promised.This is not a feature upgrade. It is a reallocation of responsibility.What This Means for Buyers and Buying GroupsHere’s where it gets structurally interesting, and where most analysis stops short.The shift to outcome-based procurement doesn’t simplify the buying process. It complicates it in new ways, with new stakeholders.Today’s B2B buying group already involves an average of 10 people, spanning IT, operations, finance, and end users. 79% of significant purchases now require CFO approval. Procurement professionals — historically a late-stage validator — are now identified as primary decision-makers in 53% of buying cycles, involved from the earliest stages.When you shift from buying software to buying an outcome, procurement doesn’t get less important. It gets more important. Because now they’re not evaluating features. They’re evaluating a performance contract.The questions change. “Does it have the functionality we need?” becomes “Can you prove the outcome you’re promising?” “What’s the per-seat cost?” becomes “What are the SLAs and what happens when you miss them?” “Does it integrate with our stack?” becomes “How do you measure success and how do we audit it?”This raises the analytical bar for every vendor in the room. And it raises it specifically for B2B marketing technology vendors, because marketing outcomes have historically been the hardest to attribute and the easiest to obscure.Service as Software forces accountability. That’s good for buyers. It’s clarifying for vendors who can deliver. It’s existential for vendors who can’t.The Buying System Is Now Machine + HumanThere’s a dimension to this shift that almost no one in B2B is taking seriously yet, and it changes the implications of everything above.The buyer isn’t becoming a machine. The buying system is becoming machine-human integrated.Gartner projects that AI agents will be involved in the vast majority of B2B purchases within three years, channeling more than $15 trillion in spending through automated evaluation. Forrester found that 94% of B2B buyers already use AI in the purchasing process, and that twice as many buyers now name generative AI as a more meaningful information source than vendor websites or sales teams.But here’s the critical nuance: buyers aren’t handing off to agents and waiting for a shortlist. They’re working with agents iteratively; prompting, refining, validating, overriding. The agent surfaces structured signals. The human interprets, challenges, and decides. The agent adjusts. The cycle repeats.By the time your Service as Software offering reaches a human conversation, it has already been filtered, scored, and contextualized by a machine that the human trusts enough to be working with in the first place. You are not marketing to a human audience anymore. You are marketing to a system. And that system has two components that must be satisfied simultaneously, not sequentially.The implications for how you go to market are significant.The machine component of the buying system doesn’t respond to corporate narrative. It evaluates structured, verifiable, consistent proof. “A case study stating ‘improved productivity’ is invisible to agents,” Forrester noted in its 2026 State of Business Buying report. “Reduced processing time from 14 days to 3 days is specific enough to extract and compare.”The human component, working alongside an increasingly capable agent, arrives at vendor conversations better prepared and less tolerant of information they’ve already sourced. They don’t need you to explain the category. They need you to demonstrate judgment and point of view that the agent couldn’t surface on its own.If your pricing is opaque, your outcomes aren’t expressed in quantifiable terms, or your proof infrastructure is thin, you may lose the deal before anyone picked up the phone. And you’ll never know it happened.What This Does to Partner MarketingMost channel marketing strategies were built for a licensing economy.The reseller partner who moved seats earned margin on the transaction. The systems integrator who deployed the platform earned margin on the implementation. The referral partner who brought the deal earned a percentage of ACV. Every incentive in the traditional partner model pointed at the software transaction.Service as Software decouples value from the transaction. If the vendor is now responsible for the outcome, the traditional partner’s role collapses. Moving a license to a customer who then fails to get results isn’t a win. It’s a liability.The partner that wins in a Service as Software economy is a different entity entirely: one that configures, manages, and guarantees the outcome on behalf of the buyer. A delivery partner, not a distribution partner. Accountable for outcomes, not for moving licenses. The ISV relationship becomes a managed service overlay on top of agentic capability, not a distribution arrangement.For B2B marketing leaders managing partner programs: this is not an edge case. It is the direction of travel. Your partner tiering, your co-sell incentives, your MDF programs, all were designed for a software transaction model. Most will need to be rebuilt for an outcome delivery model.The Transition Is Messier Than the ThesisI want to be clear about something, because this is an operator’s publication and operators live in reality.The SaaSpocalypse was the market pricing the transition’s messiness in real time. Two trillion dollars of repricing in a matter of weeks is the capital markets saying: we don’t know which SaaS companies survive this shift, so we’re discounting all of them until they prove it. That’s not irrational. It’s pattern recognition from investors who’ve seen this movie before.But the actual operating transition is slower and more hybrid than the market event implies.According to a 2025 SaaS Pricing Benchmark Study, only 9% of companies have fully implemented outcome-based pricing. 47% are actively exploring or piloting it. The remaining 44% are still running the old model, and will be for some time.BCG expects that most mature vendors will combine models: some subscription baseline, some agent-based pricing, some outcome-based performance fees. Pricing structures will be more complex before they’re simpler. Sales motions will be more consultative before they’re more automated. Buyers will need new RFP frameworks, new measurement practices, and new governance structures before they can fully operationalize outcome purchasing at scale.And Gartner has flagged that at least 30% of GenAI projects will be abandoned due to unclear business value. The hype cycle is real. Some of what’s being sold as “Service as Software” today is simply repackaged SaaS with a new name on it.The structural direction is clear. The pace is not. Know which one you’re operating in.What Operators Need to Do NowFor B2B marketing leaders:Buy outcomes, not access. Start by auditing your existing stack against a single question: is this vendor making my team faster, or making my team unnecessary? Those are different things, and only one of them is the future. The answer tells you which vendors survive your next renewal cycle, and which conversations you should already be having.Develop RFP frameworks that demand outcome commitments, not feature lists. Build the internal capability to measure vendor performance against those commitments. If you can’t measure it, you can’t hold anyone accountable for it.And structure your proof assets for the Machine + Human buying system, not just for a human audience. Quantifiable, verifiable, structured outcomes aren’t just good storytelling. They’re the minimum table stakes for being found and evaluated by the system that precedes the human conversation.For B2B technology vendors:The seat count is no longer a growth metric. Outcomes delivered, outcomes measurable, outcomes attributable, those are your new unit economics. If you cannot articulate what success looks like in quantifiable terms and stand behind it contractually, you are not positioned for this transition.If your go-to-market motion is still feature-benefit selling, you are selling the wrong thing to a buyer whose purchasing system has already moved on.For B2B media and marketing service providers:This is your moment. If you can execute. The demand for outcome-based marketing services has never been higher. Content syndication, demand generation, account-based programs: all become far more valuable when they’re measured against pipeline outcomes and delivered at scale with agentic efficiency.The providers who can price to outcomes and prove them will displace the vendors still selling impressions, leads, and seats.The Bigger PictureThe SaaS era gave buyers better tools. The Service as Software era gives buyers better results, or it doesn’t get paid.That accountability shift is a reorganization of incentives across the entire B2B technology ecosystem. When vendors succeed only when customers succeed, the dynamics of product development, customer success, go-to-market, and partner strategy all change. The consultative relationship that enterprise vendors aspired to becomes a contractual requirement.For the CMO who has spent five years managing a 25-platform stack, wondering when they stopped doing marketing and started running a technology operation, that reckoning is coming. And it’s going to feel like relief.The next generation of B2B marketing leaders won’t manage a martech stack. They’ll manage a portfolio of outcomes. And they’ll have a lot more time to actually do marketing.The transition is underway. The direction is clear. The only question is how fast you adapt, and whether you’re buying the real thing or the repackaged version of the old model with a new name on 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

Gartner’s latest research: buyers spend just 17% of their total purchasing time in direct contact with vendors. 83% define their requirements before talking to sales. 81% already have a preferred vendor when they finally engage a rep. The buying journey isn’t a funnel anymore. It’s a verdict with an appeals process attached.The conventional worry about all of this is disintermediation. If buyers reach shortlist without talking to sales, what exactly is sales for?That’s the wrong question.The right question is this: when a buyer arrives at 70 or 80 percent of the journey, who is carrying the risk?It’s not the rep.The Asymmetry Nobody Talks AboutConsider what a CFO, CTO, or Chief Procurement Officer has personally staked by the time they engage a vendor. They’ve built an internal case. They’ve put their credibility on the line with their board, their CEO, their peers. They’ve made a professional judgment about which vendors belong on the shortlist and why. 86% of B2B purchases stall during the buying process, and when they do, someone inside the buying organization owns that failure. If the decision goes wrong — if the implementation struggles, the promised outcomes don’t materialize, the vendor relationship deteriorates — they are exposed. Careers have ended over enterprise software decisions gone bad.The sales rep carries none of that exposure. If the deal closes and the customer struggles, the rep has moved on to the next quarter. The asymmetry is structural. And it is rarely acknowledged.This isn’t an indictment of enterprise sales. It’s a design flaw that the industry has been slow to recognize. In the old model, the one enterprise sales was built for, the human relationship formed early, developed over time, and quietly absorbed much of this risk. The buyer didn’t just trust the vendor. They trusted a person. “I know this rep. They’ll go to bat for us. They know what we need.” That relationship was a rational risk transfer mechanism. The rep’s career incentive, their account ownership, their reputation, all of it created an informal backstop for the buyer’s judgment.Strip away that early relationship, and the risk doesn’t disappear. It stays with the buyer. And now they’re carrying it alone, built on a thesis assembled from digital signals, with no human ally who helped them form it.When the Story Doesn’t MatchHere’s where it gets more complicated, and where marketing enters the picture in a way most companies haven’t fully reckoned with.During that 70 to 80 percent journey, the buying committee isn’t operating in an information vacuum. 70% of buyers say they prefer to learn about a product or service through content — blogs, videos, case studies — rather than traditional sales outreach. They are consuming executive interviews, keynote videos, thought leadership posts, product narratives, analyst briefings. 47% of buyers view three to five pieces of a company’s content before talking to a sales representative. By the time the rep gets the meeting, the buying committee has already formed a relationship. Not with a person, but with a story.What happens when the rep’s narrative doesn’t match that story?Not a jarring, obvious contradiction. Something subtler. A different emphasis. A different vocabulary. The marketing narrative positioned the platform around operational transformation; the rep leads with cost reduction. The exec videos talked about partnership and long-term outcomes; the rep is focused on closing the quarter. The content established one set of expectations about how this company engages; the rep’s behavior signals something different.The buying committee doesn’t diagnose this as a sales and marketing alignment problem. They experience it as unease. Something feels off. In a low-stakes purchase, that feeling gets rationalized away. In a high-stakes enterprise decision — where careers are on the line and 79% of enterprise deals require final CFO approval — it doesn’t. It becomes a quiet reason to slow the process, reopen the evaluation, or move toward a competitor who feels more coherent.The deal doesn’t die loudly. It just stops moving.Ground TruthThe most sophisticated enterprise marketing leaders are starting to name this problem explicitly. One framework, shared with me by a senior marketing executive at a major enterprise software company, adds a third dimension to the traditional brand and market truth model. Brand truth is what the company believes about itself. Market truth is what analysts, press, and the broader market reflect back. Ground truth is what buyers actually experience and conclude through their own research journey and customer experience.The insight is that misalignment between these three isn’t just a messaging problem. It’s a trust problem. And in a digital-first buying journey, trust failures are harder to detect and harder to recover from, because there’s no human relationship in place to spot them and absorb the friction.This is the same dynamic explored in “B2B Branding Has a New Audience” but where that post examines it from the brand and marketing side, the sales consequences are what make it existential. When agentic AI exposes messaging drift across every digital surface, the rep walking into that 80 percent meeting doesn't just carry narrative inconsistency into the room. They’re closing a gap that an algorithm has already scored against them.For enterprise marketing, the implication is structural. Every content asset, every executive communication, every campaign narrative needs to be built with the rep’s eventual conversation in mind. Not as a constraint on creativity, but as a discipline of coherence. The rep is the last mile of the content journey. 82% of B2B buyers say thought leadership from individuals, not just corporate messaging, influences their purchasing decisions. That makes every exec video, every LinkedIn post, every keynote a load-bearing element of the sales motion. If the last mile contradicts the miles that came before it, the buyer notices. Even if they can’t articulate exactly what they noticed.And that framework is about to face a stress test nobody designed it for.Agentic AI Will Make This Worse Before Anyone NoticesHere is the part of this story that isn’t being talked about yet.Agentic AI is entering the enterprise buying journey, and it is amplifying every tension described above. 90% of procurement executives say they have considered or are actively planning to use AI agents to optimize their procurement operations. Gartner projects that the majority of enterprise software applications will include agentic AI by 2028. Several major procurement platforms; SAP, Coupa, and others, have begun embedding these agents natively. The planning phase is collapsing into deployment. This isn’t a distant future. It’s the next procurement cycle.What does an agentic AI procurement system actually do? It doesn’t wait for a human to run a search. It autonomously conducts vendor research, synthesizes reviews and analyst reports, scores vendors against predetermined criteria, and surfaces a ranked shortlist. Often before a human buyer has had a single conscious thought about the category. This is the B2A shift — Business to Algorithm — and it’s not emerging. It’s here. Companies that lack clean, structured, algorithm-ready data risk being invisible in digital buying journeys before a human ever enters the picture. Salesforce’s Agentforce and a growing category of purpose-built procurement AI systems are moving this from thought experiment to Q3 budget conversation.This changes the risk calculus in a way that nobody in enterprise sales has fully processed.When a human buyer conducts self-directed research, they are forming impressions, making judgment calls, weighing intangibles. There is still a human in the loop who can be reached, influenced, and reassured. When an AI agent conducts the initial research and builds the shortlist, the process is faster, more systematic, and far less transparent. The buying committee receives a ranked list of vendors, and critically, 83% of buyers modify their initial shortlist after conducting further research, with more than a quarter making significant or complete changes. But if the initial list was built by an agent operating on criteria the rep never knew existed, the vendor who scores poorly may never understand why.The “something feels off” problem gets structurally worse. When a human buyer felt unease, a skilled rep could sometimes read the faint signals, ask the right question, and surface the concern. When an agent scores your company’s narrative consistency across fifty digital touchpoints and weights it against competitors, the friction is invisible. The deal just doesn’t progress. No signal. No conversation. No recoverable moment.This is why the Ground Truth framework matters more now than it did even two years ago. A human buyer might feel it. An AI agent will score it. And it will do so at a stage of the journey so early that the enterprise sales rep will never know the conversation was happening.How Enterprise Sales Actually Needs to EvolveThe rep entering a 70 to 80 percent buyer journey is no longer a pitcher. They are a validator…and more precisely, a risk translator.The buyer has built a thesis, often over months, increasingly with AI assistance. The rep’s first job is to understand it, confirm what’s right, and carefully address what isn’t. Not to restart the narrative from the company’s preferred talking points. The buyer has invested heavily in their own conclusions. A rep who ignores that investment creates resistance. A rep who honors it earns trust.Beyond validation, the rep needs to understand what the buyer has personally at stake. Risk qualification matters as much as lead qualification. What has this buyer committed to internally? What would a poor outcome mean for them specifically? That isn’t soft skills territory. It’s the core intelligence that determines how the rep should engage, what assurances matter most, and where the deal is actually fragile.The rep’s evolved role is risk translation: helping the buyer convert the thesis they’ve built alone into a defensible internal commitment, with a vendor partner who will stand behind it. That’s the trust transfer the old model delivered through relationship. Now it has to be earned in a fraction of the time, at the back end of a journey the rep didn’t take with them.At the organizational level, the companies that win in a digital-first world will treat sales and marketing alignment not as a coordination exercise but as a buyer experience imperative. The story the company tells; across every format, every channel, every exec communication, needs to be the same story the rep walks in with. Not identical in script, but coherent in thesis, consistent in values, aligned in vocabulary. Because that story is now being processed not just by human buyers making judgment calls, but by AI systems making scores.The Quiet Deals You’re LosingThe buyer seemed engaged. The evaluation went well. Then it stalled. Then it went quiet.Often, something felt off. The buyer couldn’t name it. The rep never knew. And increasingly, the agent that built the initial shortlist made a call no human on either side of the table ever saw coming.In a high-stakes purchase made by humans who are personally exposed, coherence isn’t a nice-to-have. It’s a closing condition. The companies that figure this out (that the rep is the last mile of a journey the buyer took largely alone, that the buyer arrived carrying real personal risk, and that AI agents are now entering that journey at its earliest and most consequential stages) will win deals their competitors don't even know they lost.Enterprise buyers have changed. The companies that change with them will define the next era of enterprise sales.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

B2B brand building is about to be restructured from the ground up.Not incrementally refined. Not optimized. Structurally inverted.Here’s the reality most B2B CMOs haven’t fully reckoned with yet: in the Agentic AI era, your brand is no longer being evaluated by a human audience with machine tools. It’s being evaluated by a Machine + Human system. Agents and buyers working together, simultaneously and interdependently, throughout the entire buying journey.The enterprise buyer isn’t handing off to an AI agent and waiting for a shortlist. They’re working with the agent iteratively, prompting, refining, validating, overriding. The agent surfaces structured signals. The human interprets, challenges, and decides. The agent adjusts. The cycle repeats. By the time your brand reaches a human conversation, it has already been filtered, scored, and contextualized by a machine that the human trusts enough to be working with in the first place.You are no longer positioning to a human audience. You are positioning to a system, and that system has two components that must be satisfied simultaneously, not sequentially.Everything about how B2B brands are built, measured, and differentiated follows from that premise.The Fuel Mix Is ChangingFor decades, B2B brand building ran on a relatively stable fuel mix: analyst relationships, trade media coverage, event presence, demand generation programs, and thought leadership content. Each of these was designed for human consumption; a buyer reads, watches, attends, engages, and forms impressions over time.That mix isn’t going to zero. But the weighting is shifting dramatically, because the audience has changed.The machine component of the buying system doesn’t respond to corporate narrative. It evaluates structured, verifiable, consistent proof. That means work most marketing organizations have never treated as brand work: schema markup, review platform completeness, certification maintenance, consistent metadata across every surface where an agent might evaluate you, is now core brand-building activity. Not IT work. Not legal work. Brand work.The human component of the buying system is simultaneously getting more sophisticated and more selective. Buyers working with AI agents arrive at human conversations better prepared, with higher expectations, and less tolerance for information they’ve already sourced themselves. They don’t need you to explain the category. They need you to demonstrate judgment, credibility, and point of view that the agent couldn’t surface on its own.Building for both components, at once, coherently, is the new mandate.Trust Becomes the Dominant Variable — But It Now Operates as a SystemIn every era of market disruption, trust becomes more valuable. When uncertainty is high and the pace of change is relentless, buyers look for the trust signal that most reliably predicts competence and consistency.This moment is no different. But the agentic era changes how trust is built and evaluated, because trust now has to work across both components of the buying system simultaneously.Machine trust is built on verifiability. Quantitative ratings, independent certifications, contractual transparency, uptime records, security documentation. Agents weight these signals heavily because they’re legible, consistent, and difficult to fake at scale. You either have them or you don’t. No amount of compelling brand narrative compensates for weak machine trust signals, the agent simply doesn’t register them.Human trust is built on authority, credibility, and resonance. Does this vendor think clearly? Do they understand my problem better than my own team does? Do I trust their judgment in a world that’s moving too fast to validate everything myself? This is the trust that gets built through thought leadership, practitioner voice, executive presence, and community standing. No amount of machine-legible proof compensates for weak human trust. The buyer ratifies the agent’s shortlist, not the other way around.There’s a reason B2B brand advertising has always underperformed. Most marketers treat enterprise purchasing as a rational process: feature sets, integration specs, pricing tiers, ROI models. They optimize for the quantitative case and underinvest in the emotional one. But B2B purchase decisions have never been purely rational. Risk is emotional. Trust is emotional. The unspoken question in every enterprise evaluation — if this goes wrong, can I defend this choice? — is entirely emotional. Nobody ever got fired for buying IBM wasn’t a product claim. It was an insight into how fear shapes procurement. In the agentic era, this dynamic doesn’t diminish. It concentrates. The machine handles the rational layer: structured proof, verified signals, consistent metadata. What remains for the human is judgment, confidence, and the emotional calculus of professional risk. Brand has always lived there. Now it’s the only place left that machines can’t reach.But here’s the critical point the sequential framing misses: these two forms of trust don’t operate in separate phases. They interact. A human buyer working with an agent is weighing both simultaneously, asking the agent to pull structured data while applying their own judgment about which vendors feel credible. A message that works perfectly for an agent but lands wrong with the human using that agent is still a failure. The signals have to be coherent within the system, not just adequate on each dimension independently.The brands that win are the ones where machine trust and human trust are strong, aligned, and mutually reinforcing. That alignment is the new core challenge for B2B marketers.Agentic AI Will Expose Every Inconsistency You’ve Been Living WithHere’s the near-term operational reality most B2B marketing leaders haven’t confronted yet: your brand is probably already inconsistent across the surfaces that matter. Not because of bad strategy. Because of organizational entropy.Your website says one thing. Your G2 profile says something slightly different. Your LinkedIn company page hasn’t been updated since the last rebrand. Your partner listings describe a product you pivoted away from eighteen months ago. Your pricing page is deliberately vague while your sales deck quotes specific numbers. Your analyst profile reflects a positioning you outgrew two years ago.In the human-only era, buyers navigated this inconsistency intuitively. They triangulated, asked questions, and gave you the benefit of the doubt. Inconsistency was sloppy but survivable.In the Machine + Human era, it’s a trust penalty. A compounding one.When the machine component of the buying system evaluates your brand across multiple surfaces — and it will — conflicting signals don’t average out. They create doubt. An agent trained to identify reliable vendors treats inconsistency as a risk signal, not a nuance to be interpreted generously. Misaligned messaging, contradictory value propositions, opaque commercial terms, these don’t just confuse the machine. They move you down the shortlist, or off it entirely. And a human buyer who sees those inconsistencies flagged, or encounters them directly, loses confidence in ways that are very hard to recover from mid-journey.The immediate tactical implication: the CMO who conducts a rigorous brand consistency audit across every machine-legible surface right now; website, review platforms, partner listings, analyst profiles, press coverage, social presence, commercial documentation, has a near-term competitive advantage that costs almost nothing to capture. This isn’t glamorous brand strategy work. It’s the operational prerequisite for everything else in this post.Do it before your competitors do.Storytelling Goes Up in Value, Down in VolumeHere’s the counterintuitive move. In a world where the machine component of the buying system handles discovery and structured evaluation, narrative and storytelling don’t become less important. They become more important, and more precisely targeted.When AI commoditizes the discovery and evaluation layer, what remains as true differentiation in the human conversation? Point of view. Intellectual honesty. The ability to frame a problem so precisely that a buyer feels understood in ways no structured data source could.Narrative becomes the currency of the final mile. The element of your brand that the machine can surface evidence of, but can’t replicate or replace.The catch: the human component of the buying system is smaller and more consequential than the broad human audience of the previous era. Fewer people are in the room. So storytelling’s value per impression goes up while its distribution volume goes down. The implication for content strategy is significant: invest in fewer, deeper, more operator-credible pieces rather than volume content designed to feed an automated pipeline. In a Machine + Human buying system, depth and credibility outperform volume every time.Where to Invest, Where to Pull Back: The Channel Mix Is Being ReorderedSome of this is already in motion. The agentic era accelerates and amplifies each of these shifts, because every medium now has to be evaluated through the lens of what it contributes to the Machine + Human buying system, not just to human awareness.↑Experiential events: Up. When the machine component handles discovery and initial evaluation, live human presence becomes the scarcest and highest-fidelity signal in the market. In-person connection is where machine-shortlisted vendors become trusted partners. Where the human component of the buying system forms the judgments that structured data can’t. The trade show model continues to struggle. The curated dinner, the practitioner roundtable, the executive cohort — these go up. Not because they’re nostalgic, but because they operate precisely in the space the machine can’t reach.↔Demand generation: Restructured, not eliminated: The form fill, the gated asset, the email drip sequence, these specific mechanics are structurally misaligned with a Machine + Human buying journey. But the underlying function of demand generation doesn’t go away. What changes is the model.B2B marketers have hit a genuine tipping point with technology complexity. The martech stack — built layer by layer over the past fifteen years — was designed for a world of human buyers moving through discrete, trackable stages. That world is ending. What replaces it isn’t more technology. It’s a fundamentally different service model.The demand generation providers who survive and lead in the agentic era will deliver something closer to Demand-as-a-Service: multi-channel engagement, pipeline impact, and analytics integrated into a single coordinated offering rather than a collection of separate tools and tactics stitched together by an overtaxed marketing team. The separation of “content agency” from “media buyer” from “analytics platform” from “SDR team” is a legacy of a simpler era. Agentic AI collapses those distinctions, or more precisely, it makes the cost of maintaining them prohibitive.Successful demand generation in the agentic era requires coordinated engagement across both the machine and human components of the buying journey, sequenced intelligently rather than executed as parallel, disconnected programs. An agent shortlists you. A human validates. A practitioner-influencer confirms. A curated event closes. That’s a coordinated buyer journey, and it requires a coordinated provider, not five vendors trying to hand off to each other.↑Thought leadership: Up, but restructured: The white paper with three industry endorsements and a PDF download is finished. What replaces it: structured POV content that’s simultaneously machine-indexable and human-resonant. Short, declarative, operator-credible, and tied to a genuine point of view about the market. The executive voice matters more now, not less, because it’s one of the few signals that works meaningfully for both components of the buying system at once. But it has to be real, not ghostwritten corporate prose.↑B2B influencers: Significant rise: Not celebrity influencers. Practitioners-as-influencers. People who have actually done the job, carry functional credibility with your buyer, and speak in a register that earns trust precisely because it doesn’t sound like marketing. This signal works across both components of the buying system: machine-indexable as third-party validation, and human-resonant as peer credibility. That dual utility makes it disproportionately valuable. Expect it to become a meaningful budget line.Academia Is Waning, And That Trend AcceleratesFor decades, academic credibility was a core pillar in B2B tech markets. Research papers, university partnerships, PhD-certified methodologies, these were proxies for rigor and third-party validation in a world where buyers had limited tools for independent verification.That proxy is breaking down, and the Machine + Human buying system accelerates the breakdown from both directions.The machine component can now source, cross-reference, and evaluate far more diverse evidence than any buyer could manually. Academic affiliation is just one signal among many, and not a particularly fast-moving one. The cycle time problem alone is disqualifying: academic research takes years; B2B markets now move in quarters. The human component, working alongside increasingly capable agents, values current, contextual, operator-grounded analysis over credentialed but dated research.What fills the gap: analyst-practitioners, operator thought leaders, and community-validated research. The rise of independent expert voices — people with track records, real stakes, and current market engagement — isn’t a trend. It’s a structural shift in how both components of the buying system evaluate what to believe and trust. The machine surfaces them. The human recognizes them. Together, they move the needle in ways institutional research no longer can.New Tools Are Coming, And the Category Doesn’t Exist YetIf brand trust now has to operate coherently across a Machine + Human buying system, the diagnostic tools have to change too.The current marketing analytics stack was built to measure human behavior. Impressions, clicks, downloads, MQLs, pipeline attribution. Those tools don’t answer the questions that matter now:* When an AI agent queries for solutions in my category, does my brand appear? With what frequency, in what context, with what sentiment?* How complete, consistent, and machine-readable is my proof infrastructure across every surface the buying system evaluates?* Does my brand tell the same story across website, G2, LinkedIn, partner descriptions, analyst profiles, and press coverage, or are there inconsistencies creating trust penalties in the machine layer?* What percentage of credible practitioner content in my category validates or references my brand, and is that content machine-indexable as well as human-credible?* And eventually, the killer metric: what percentage of agentic buying journeys in my category end with my brand on the shortlist?Nobody has built this cleanly yet.Early signals are emerging, companies like Profound are taking initial runs at AI visibility measurement, and some SEO intelligence firms are beginning to pivot from search visibility to agent visibility. The tool that measures machine trust, human trust, and the gap between them doesn't exist yet. That's not a problem. That's a market opportunity. The firm that builds a credible Agentic Brand Trust Score; something a CMO can track quarterly, benchmark against competitors, and act on across both dimensions, will define a significant new category of B2B marketing infrastructure. It’s the next essential instrument in the CMO’s toolkit, and it will emerge from AI-native analytics startups rather than from the established marketing technology vendors who built their platforms for a different world.New Firms Will Win This Market. Here's What They Look Like.I’ve written about where traditional ad agencies are headed. The creative-driven, brand-narrative firm built for human media consumption is structurally misaligned with what B2B marketers now need, because it was built for one component of a system that now has two.Five capabilities the market will pay for that barely exist today:Structured narrative architects build brand signals that work simultaneously for machine legibility and human resonance. This sits at the intersection of content strategy, data architecture, and brand thinking, and it doesn’t really exist inside any current agency or consulting model.Agentic content strategists understand how AI agents discover, evaluate, and weight vendors, and design content specifically to perform well within that process. This isn’t SEO. It’s something more fundamental: optimizing for how the machine component of the buying system thinks, while maintaining the human credibility that earns the final decision.Operator thought leadership producers help executive teams develop and distribute credible, practitioner-voice content at scale without sanitizing it into corporate messaging. The voice that works for both components of the buying system is rare. Producing it consistently is a real capability gap.Trust infrastructure consultants audit and build the proof-point architecture; reviews, certifications, structured data, third-party validations, pricing transparency, that the machine component of the buying system actually evaluates. This work currently sits in no-man’s-land between marketing, IT, and legal. The firm that owns it will be valuable.Community architects build owned audiences that aren’t dependent on algorithms, platforms, or machine discovery. In a world where automated shortlisting is the default, a trusted community of engaged practitioners is a durable moat, and a source of human trust signals no machine can manufacture.None of these exist inside a current agency or consulting firm. That's the point.The Strategic ImperativeThe brands that navigate this transition well will understand something most of their competitors won’t.This isn’t a channel shift. It’s not a new media mix or a technology upgrade. It’s a structural change in the nature of the audience for B2B brand signals: from a human audience assisted by tools, to an integrated Machine + Human system in which both components must be satisfied coherently and simultaneously.Building for that reality requires rethinking brand strategy, content strategy, measurement infrastructure, and the partner ecosystem at the same time. That’s hard work. Most organizations will do it slowly, partially, and reactively.The ones that do it deliberately, that treat the Machine + Human buying system as the design constraint for everything they build, will have a compounding competitive advantage that gets harder to close over time.The question every B2B CMO needs to answer right now isn’t whether this shift is happening. It’s whether they’re building a brand architecture designed for the buying system that exists today, or still optimizing for the one that existed five years ago.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

When Doug McMillon announced he was stepping down as CEO of Walmart after more than a decade at the helm, he didn’t blame the board. He didn’t cite personal reasons. He said something that should stop every senior executive in their tracks:“With what’s happening with AI, I could start this next big set of transformations with AI, but I couldn’t finish.”Read that again. The CEO of the largest company in the world; a leader who guided Walmart through the internet era, through supply chain collapse, through a pandemic, looked at what was coming and made a clear-eyed decision: the transformation horizon ahead didn’t match his own.Around the same time, Coca-Cola CEO James Quincey said he could lead the company through the “pre-AI” phase, but that the next wave of “AI-driven growth” needed someone with fresh energy. Adobe CEO Shantanu Narayen, after eighteen years, framed his departure around the same idea: the next era of creativity is being “shaped by AI,” and new leadership should shape it.Three CEOs. Three of the most recognized brands in the world. All saying a version of the same thing on their way out the door.This isn’t a wave of high-profile executives burning out. It’s something more significant. It’s a cohort of experienced, accomplished leaders making a rational calculation that the technology disruption now underway is categorically different from anything they’ve navigated before — and deciding, honestly, that someone else should be at the wheel for the next leg of the journey.That level of self-awareness deserves more credit than it’s getting. And it raises an urgent question for every other C-suite leader still in the chair:What’s your honest answer to that same question?The Data Behind the SignalBefore we get to frameworks, let’s establish the facts, because this isn’t anecdote. It’s a documented structural shift.The numbers tell a story that goes well beyond a few high-profile departures.CEO turnover hit record levels in 2025: a 16% spike in global departures from the prior year, more than 20% above the eight-year average. More than 1,200 U.S. CEOs left their posts in the first half of the year alone. Critically, this isn’t a story about underperformance. In the S&P 500, successions at top-performing companies jumped from 7% to 12%. Boards aren’t reacting to failure. They’re getting ahead of something.The age data is where the AI signal becomes unmistakable: The average age of departing CEOs jumped from 55 to 63 in a single year. In July 2025, the average age of exiting CEOs hit 70.3, compared to 56.2 in the same month a year earlier. A retirement wave is underway, and AI is accelerating it. Boards aren’t just accepting these departures, in many cases they’re engineering them, actively skipping Gen X and moving directly to Millennial leaders for whom AI fluency is native, not acquired.Here’s the wrinkle that most commentary is missing: not every experienced leader is heading for the exit. A recent survey found that 58% of Baby Boomers aged 62-plus said they are not currently considering retirement, a dramatic reversal from just a year prior, when only 11% said the same. The most common reason? They want to develop new skills. The best of this generation aren’t running from AI. They’re leaning into it. Which means the real divide isn’t generational. It’s between leaders who are engaging with the disruption honestly, and those who aren’t.Which brings us to the real question: not whether AI is forcing a leadership reckoning—but how the leaders still in the chair should think about it.A Disruption Unlike the OthersI’ve lived through several technology disruptions in operating roles; the internet’s first wave, the shift from print to digital, the rise of cloud and SaaS, the mobile platform shift. Each one was significant. Each one produced real winners and real casualties. None of them felt quite like this.Here’s why this is different.The internet disrupted business models and distribution. It reshuffled who owned the customer relationship and what information was worth paying for. But the core of what executives did — made decisions, managed people, read markets, allocated capital — remained fundamentally human. Technology changed the conditions. The decisions were still human.I’ve watched this pattern play out firsthand. In the early days of the internet, I sat across from publishing executives who were genuinely unconcerned. “People will always want their morning newspaper. Magazines have loyal audiences that aren’t going anywhere”. The Internet platforms looked like novelties. They were not.A few years later, while running The Hollywood Reporter, I was in meetings with studio executives exploring what digital distribution might mean for the filmed entertainment business. The response was remarkably similar. One executive turned my question about the still-nascent streaming model back on me: “Tony, theatrical box office is a $10 billion industry. Take a guess what DVD sales are. A $20 billion industry. We’re not spending much time thinking about streaming these days.” That was 2006. Today, Netflix is worth roughly two and a half times Walt Disney — the most powerful studio on the planet at the time of that conversation. The disruptor is worth more than twice the incumbent. The DVD business is a footnote. The pattern across every major technology disruption is the same: the incumbents aren’t stupid. They’re rational, defending what’s working, discounting what’s early, and optimizing for the present. The problem is that the present has a shorter shelf life each time.Agentic AI isn’t acting on the business. It’s acting on the function of decision-making itself. It’s entering the territory that senior executives have always owned: synthesis, judgment, resource allocation, workflow design, organizational structure.That’s not a disruption to the business model. That’s a disruption to the role of the leader.Consider what's now within reach:CFO planning cycles that once required analyst teams for weeks can increasingly be run — at least in significant part — by AI agents. Enterprise technology stacks that took CTOs and CIOs years to architect are being challenged by AI-native alternatives that bypass the complexity entirely. Procurement functions that were built on human relationship and institutional knowledge are being tested against end-to-end automation. Go-to-Market programs built over decades on personal relationships and market intuition are facing autonomous agents that evaluate, shortlist, and in some categories select vendors without a salesperson ever entering the picture.None of this is fully realized yet. The question isn’t whether these shifts will happen. The capability is already here and moving fast. The question is whether the leaders in these roles are getting ahead of it or waiting for it to arrive. Because the executives who frame this as a threat to their function will lose that argument to the CFO who wants to cut headcount. And the executives who frame it as a transformation of their function’s value — from process management to strategic intelligence — are the ones who will define what these roles look like on the other side.The EY analysis puts the productivity timeline plainly: the benefits of generative AI will likely arrive within three to five years, compared to multiple decades for the steam engine and roughly ten years for the computers. We are not in the early innings of a slow-moving transformation. We are in the compressed early innings of the fastest-moving general-purpose technology disruption in modern business history.Morgan Stanley’s economists reviewed five major innovation waves; from the Industrial Revolution through the internet, and found that across every one of them, disruption ultimately complemented employment rather than eliminating it. History suggests we’ll adapt. But the historical record also suggests the transition is where leaders and organizations are made or broken.The transition is now.The C-Suite Reckoning: Role by RoleThe disruption isn’t uniform across the C-suite. Each function is facing a specific version of this challenge. Let me be direct about each one.* The CEO. The pressure at the top is the most visible. The McMillon and Quincey moments are proof. But the more common failure mode isn’t the CEO who steps down honestly. It’s the CEO who stays, acknowledges the disruption publicly, and then delegates it to a Chief AI Officer or a transformation task force. Real AI transformation requires the CEO to own the conviction. Not every implementation detail, but the organizational belief that this is an enterprise-wide operating shift. Every time I’ve seen that conviction delegated, the transformation stalled. That’s more than a prediction. That’s a pattern I’ve watched repeat across every major technology shift for thirty-five years.* The CFO. Traditional financial planning — scenario modeling, FP&A, cost forecasting — is going AI-native. The CFO who built their career on Excel mastery, quarterly reviews, and headcount-based cost structures is watching the foundation move. AI can run financial models in minutes that used to take FP&A teams weeks. The CFO who leads through this will be the one who reframes their role from financial architect to “capital allocation strategist”, deciding where human judgment is irreplaceable and where AI-driven analysis should drive the work.* The CTO/CIO. Of all the C-suite roles, this one carries the sharpest irony. The executive whose entire mandate is technology leadership now faces a transformation that threatens to outrun the very playbook they built their career on. Enterprise technology stacks assembled over decades — the integrations, the vendor relationships, the architecture decisions — are being challenged by AI-native alternatives that bypass the complexity entirely. The CTO or CIO who leads through this isn't the one who defends the existing infrastructure investment. It's the one who can hold two things simultaneously: managing what the business runs on today while architecting what it needs to run on tomorrow. That's not a technology challenge. It's a leadership one.* The CHRO. Workforce planning in an era when the CEO of Anthropic has publicly stated that AI could eliminate 50% of white-collar work within five years is genuinely uncharted territory. The CHRO who is still running traditional headcount plans and competency frameworks is already behind. The CHRO who will lead through this is building what I’d call a “dynamic workforce architecture”, a continuous model that maps AI capability against human work, identifies where augmentation creates leverage, and redesigns roles rather than eliminating them wholesale. The future is about Human + Digital Labor.* The Chief Growth Officer / CMO. B2B marketing as practiced for the last decade is being structurally dismantled. Buyer journeys are being executed by AI agents rather than human researchers. The content, events, and paid media programs that generated pipeline in 2020 are producing diminishing returns. The CMO who leads through this transition will stop optimizing existing channels and start designing for a world where the first buyer in the room may not be human.* The Chief Procurement Officer. This may be the function most immediately in the crosshairs. Agentic AI is coming for sourcing, vendor evaluation, contract management, and supplier intelligence with a speed and scale that no traditional procurement team can match. The CPO who leads through this isn’t managing a cost center. They’re running an intelligence function, one where the value isn’t in the process they oversee, but in the strategic judgment they bring to decisions that AI can inform but not make.Three Archetypes for Leading Through ThisI’ve spent time thinking about how C-suite leaders are actually responding. Not how they’re describing it in the press release, but how the work is actually unfolding. What I see are three archetypes. Each has a different job to do.The TransformerThis is the leader who has the runway — in terms of time horizon in the role — and the conviction to lead through the full AI transformation cycle. The imperative for the Transformer is to stop treating AI as a department initiative and start treating it as an operating system for the enterprise.What that means in practice: every function gets reviewed through the lens of what AI can do that humans were doing, what humans must do that AI cannot yet do, and where the hybrid model creates genuine competitive advantage. That’s not a technology project. It’s a strategic redesign, and it has to be led from the top.The Transformer also has to be willing to do something most executives avoid: change the incentive structures before the transformation demands it. AI adoption stalls when the people responsible for it are also the people most threatened by it. If the CHRO’s success metrics are still tied to headcount growth, you will not get genuine AI workforce transformation. If the CPO is measured on cost savings from vendor relationships, you won’t get a procurement function redesigned for intelligence-led sourcing.The Transformer changes the metrics first. The SequencerThis is the leader who is honest — privately, at minimum — that they won’t finish the journey. They have enough runway to make real moves, but they can see the horizon. The Sequencer’s imperative is not transformation, it’s architecture.What does a Sequencer do? They build the capability scaffold that gives the next leader a genuine running start. That means: driving AI literacy into the organization at every level. Building the data infrastructure that AI-driven operations require. Making the succession bet: identifying and developing the leaders who have both the operating experience and the AI fluency to take the baton.The worst outcome for a Sequencer is the one I’ve seen too often in technology transitions: a leader who neither transforms nor prepares. Who manages inertia, makes incremental moves, protects existing structures, and hands the next CEO a business that is further behind than it looks. That’s not a neutral outcome. It’s an actively harmful one.The Sequencer who does their job well is underappreciated in real time and overappreciated in hindsight. Do it anyway.The AdvisorThis is the leader who makes the call that McMillon made. Honestly, on their own terms. They step out of the operating role, but they don’t disappear. They move into the work that their experience actually makes them irreplaceable for: board roles, advisory relationships, mentoring the next generation of operators.I’ll be transparent: this is the archetype I’m living right now. I recently stepped out of a CEO role, one where we spent two-plus years transforming a B2B demand generation business into a digital marketing services company built on a Demand-as-a-Service model. We started the AI transformation. We could see what was coming. And I made the same calculation McMillon described, at a far smaller scale. The next leg of the journey was better led by someone whose horizon matched it. That recognition is what led me to launch Uphoff Advisory and shift into board and advisory work — where thirty-five years of operating experience turns out to be exactly what organizations navigating this transition need most.The Advisor role isn’t a retirement. It’s a repositioning.The Honest QuestionLet me end where I started.McMillon said: ”I could start it, but I couldn’t finish it.”That is a remarkable sentence. It requires both confidence; the conviction that you understand what needs to happen — and humility — the honesty that the journey ahead isn’t yours to complete.Most leaders in the C-suite right now are not asking themselves that question. They’re managing the present. They’re responding to quarterly pressures, board expectations, and the urgent over the important. The AI disruption is real and they know it, but it hasn’t forced a clear-eyed personal reckoning yet.It will.The leaders who will navigate this best, whether as Transformers, Sequencers, or Advisors, are the ones who ask the question now, honestly, and then act on the answer with the same rigor they’d apply to any other strategic decision.The Transformer commits the enterprise to a full operating redesign and changes the incentives to match.The Sequencer builds the scaffold, develops the successor, and makes the architecture decisions that matter most.The Advisor doesn't exit. They redeploy, bringing years of pattern recognition into the boardroom, into advisory relationships, and into the work of guiding operators navigating a disruption they've already lived through once.None of these are failure modes. The only failure mode is the one that looks like leadership but is actually avoidance: staying in the chair, acknowledging the disruption in every earnings call, and changing nothing that actually matters.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

By Tony Uphoff · Uphoff on Media · 2026In my ongoing conversations with B2B media, marketing, and tech leaders, the subject of Vibe Coding keeps coming up.I’ve been shown remarkable engineering productivity gains — 10X+ — by multiple, experienced software developers. I’ve had several conversations with Enterprise Tech leaders who chuckle and suggest the hype is overblown and that Vibe Coding won’t be challenging Enterprise Software anytime soon…in my experience a sure sign that it likely will! And my discussions with media and marketing leaders suggest there is a ton of interest, some experimentation, and — most critically — a need for frameworks to understand the technology and how to use it. This post is that framework. Consider it a primer, a backgrounder, and a practical map for non-technical executives (like myself), operators, and investors navigating one of the more consequential shifts in how software gets built.I. WHAT IS VIBE CODING?Vibe Coding was coined by Andrej Karpathy, former Tesla AI director and OpenAI co-founder, in February 2025. His description was characteristically precise: you describe what you want in natural language, and AI writes the code. You stop thinking about syntax. You stop debugging semicolons. You stop waiting for a developer to have a sprint slot available.The human becomes the director. The AI becomes the builder. The interface between intention and output is a conversation.It’s not that the code writes itself. It’s that you describe the outcome, and the code appears.This is meaningfully different from autocomplete tools or basic code suggestions. Vibe Coding generates full working codebases from plain English prompts, iterates through back-and-forth conversation, and handles everything from the front-end interface to the back-end logic, without the operator needing to understand how any of it works at the implementation level.The tools driving this are now mature and widely available:* Cursor: the leading AI-native code editor, built for Vibe Coding workflows* Bolt, Lovable, and Replit Agent: browser-based tools for full-stack app generation with no setup required* GitHub Copilot: Microsoft’s enterprise-grade AI coding layer, now deeply embedded in developer workflows* Claude Code: Anthropic’s command-line agent for complex, multi-file codebases* Windsurf: A newer entrant gaining traction with enterprise development teamsAll of these tools are powered by large language models (LLMs) underneath. The same class of AI that powers conversational interfaces like ChatGPT and Claude. What the Vibe Coding tools add is the scaffolding, context management, and workflow design that makes LLMs practically useful for software development.Figure 1: The Anatomy of Vibe Coding: from intent to outputII. THE WORKFLOW: HOW IT ACTUALLY WORKSUnderstanding the Vibe Coding workflow is essential to evaluating whether and how it applies to your organization. The process follows four steps:Step 1: Describe in Plain EnglishThe operator; a marketing manager, a media product director, a solutions engineer, describes what they need in natural language. “Build me a lead capture page that connects to Salesforce and sends a confirmation email.” “Create a dashboard that shows subscriber engagement by content type.” “Build an event registration flow with payment processing.”Step 2: AI Generates the Full CodebaseThe Vibe Coding tool interprets the prompt and generates a working application: HTML, CSS, JavaScript, back-end logic, database schema, API connections. It’s not a mockup or a template. It’s functional code.Step 3: Iterate via ConversationThis is the step most people underestimate. Vibe Coding is not a one-shot prompt. It’s a dialogue. “Make the button gold instead of blue.” “Add a dropdown for industry.” “The form isn’t submitting, fix it.” The AI debugs, adjusts, and refines in real time, maintaining context across the conversation.Step 4: ShipThe finished application is then deployed to a web host, a platform, a CRM integration. What used to require a developer, a sprint, a QA cycle, and a deployment process now takes hours.The development backlog as a structural constraint is being dismantled. Not reduced. Dismantled.III. WHERE IT’S LANDING: B2B APPLICATIONSVibe Coding is not a consumer technology story. Its most significant near-term impact is in B2B. Specifically in the three sectors that Uphoff on Media covers most closely: media, marketing, and technology. The applications are concrete, the productivity gains are measurable, and the implications for how these organizations staff, build, and compete are substantial.The most instructive examples often come from direct experimentation. I’ve been experimenting firsthand. Using Tasklet; an agentic AI platform that automates business workflows through plain English, I built a client tracker for Uphoff Advisory and a research tool that surfaces data on Agentic AI for future posts. No developer. No code. Just a description of what I needed and an AI that executed it. Creating the two apps took a total of 5 minutes. That’s not quite Vibe Coding in the technical sense — it’s something arguably more immediately useful for business operators: agentic workflow automation. The line between the two is blurring fast.Figure 2: Vibe Coding Applications Across B2B Media, Marketing & TechnologyB2B MediaFor media companies, the most immediate impact is in the elimination of the dependency on engineering resources for tools that editorial, audience development, and monetization teams need. Newsletter templates, audience analytics dashboards, event microsites, advertiser-facing portals. All of these have historically required developer time that editorial teams rarely had access to. Vibe Coding changes the equation: the people closest to the audience and the advertisers now build the tools they need, on the timeline they need them.The deeper implication: media organizations that have always competed on content quality now have the ability to compete on product velocity as well. That’s a new capability, and it arrives without a significant increase in headcount.B2B MarketingIn B2B marketing, the impact is most acute in the gap between marketing’s ambitions and engineering’s availability. ABM campaign landing pages, lead scoring applications, content performance dashboards, ICP segmentation tools. These are all things that marketing teams have wanted for years and have had to either deprioritize or outsource. Vibe Coding puts these capabilities directly in the hands of demand generation managers, RevOps leads, and marketing ops practitioners.The signal to watch: in the companies I’ve spoken with that have begun experimenting with Vibe Coding in their marketing functions, the productivity gains are not incremental. They are order-of-magnitude. A campaign that required three weeks of engineering time and agency involvement now ships in a day.B2B TechnologyFor technology companies, Vibe Coding is simultaneously the most significant opportunity and the most complex challenge. On the opportunity side: internal admin tools, API integration prototypes, product demo environments, and MVP development are all areas where Vibe Coding is delivering demonstrable results. Solutions engineers are building custom proof-of-concept integrations in hours. Product managers are prototyping new features without waiting for sprint allocation.The complexity: for companies whose core product is software, Vibe Coding raises fundamental questions about code quality, security, maintainability, and ownership. The engineers who chuckle at the hype are not wrong to raise these concerns. They are, however, at risk of underestimating the pace of change.IV. THE PRODUCTIVITY REALITYThe data on Vibe Coding productivity is early but directionally consistent:* GitHub reports: Copilot users complete coding tasks up to 55% faster than non-users* McKinsey research finds: AI-assisted developers complete tasks 35–45% more quickly* Anecdotal operator data: including from conversations in my own advisory network — suggests that experienced developers using Vibe Coding tools are achieving 5–10X productivity multipliers on certain task typesThat last point is important context. The most dramatic productivity gains are not coming from non-technical users suddenly building enterprise software. They are coming from experienced developers who now spend less time on boilerplate, scaffolding, and syntax. And more time on architecture, judgment, and quality. The 10X+ gains I referenced in the opening of this post came from two senior developers with decades of experience. Vibe Coding amplified their expertise; it did not replace their judgment.The 10X gains I’ve witnessed came from senior developers with 35 years of experience each. Vibe Coding amplified their expertise. It did not replace their judgment.V. THE LEGITIMATE CONCERNSThe Enterprise Tech leaders who are skeptical of Vibe Coding are not simply behind the curve. Their concerns are substantive:* Technical debt and code quality: AI-generated code can be structurally sound but architecturally fragile. Code that works is not the same as code that scales, that can be maintained, or that follows security best practices.* Security exposure: Non-technical operators building applications that handle customer data, payment information, or system integrations create real security risk if the code is not reviewed by engineers with security expertise.* IP and ownership ambiguity: The legal framework around AI-generated code; who owns it, how it’s licensed, what claims can be made, is still being established.* The Dunning-Kruger risk: The most dangerous Vibe Coding practitioner is the one who doesn’t know what they don’t know. Building something that appears to work and shipping it without understanding its failure modes is a real organizational risk.None of these concerns are arguments against Vibe Coding. They are arguments for deploying it with judgment. Which, as it happens, is what every consequential technology shift has required.VI. THE FRAMEWORK: HOW B2B LEADERS SHOULD THINK ABOUT THISBased on conversations with operators across B2B media, marketing, and technology, I’d suggest the following framework for evaluating and deploying Vibe Coding:* Inventory Your Queue FirstBefore experimenting with Vibe Coding tools, map the engineering backlog items that belong to non-engineering functions: marketing’s dashboard requests, media’s editorial tools, the internal ops workflows that never make it into a sprint. That backlog is your starting point. It represents the highest-value, lowest-risk surface for Vibe Coding experimentation.* Start with Internal Tools, Not Customer-Facing ProductsInternal dashboards, reporting tools, and workflow automation are the right first applications for Vibe Coding in most B2B organizations. The stakes of failure are lower, the learning curve can be absorbed without customer impact, and the productivity gains are immediately visible.* Keep Engineers in the Review LoopVibe Coding does not make engineering judgment obsolete. It changes where that judgment is applied. For any application that touches customer data, payment systems, or core business processes, engineer review of the output is not optional. It is the risk management layer that makes Vibe Coding safe to deploy at scale.* Treat Vibe Coding as a Skill, Not a ToolThe operators in my network who are getting the most from Vibe Coding are not simply running prompts. They are learning how to structure requests, how to iterate effectively, how to recognize when the AI has gone off track, and how to maintain context across a complex build. This is a learnable skill. Organizations that invest in developing it will have a meaningful advantage over those that treat it as a plug-and-play solution.* Watch the Enterprise Software ImplicationsThe Enterprise Tech leaders who suggest Vibe Coding won’t challenge SAP, Oracle or Salesforce “anytime soon” are probably right on the one-to-two-year horizon. On the three-to-five-year horizon, the trajectory is less clear. Vibe Coding is already enabling the rapid development of lightweight alternatives to expensive, complex enterprise tools. The category of “good enough at a fraction of the cost” has disrupted enterprise software before. It will again.THE BOTTOM LINEVibe Coding is not hype. It is also not magic. It is a meaningful shift in the accessibility of software development. One that is already producing measurable productivity gains, enabling new categories of operators to build tools they couldn’t before, and beginning to reshape how B2B companies think about the relationship between their business teams and their engineering resources.For B2B media leaders, the opportunity is in product velocity and editorial tool ownership. For B2B marketing leaders, the opportunity is in closing the gap between what you want to build and what you’re able to ship. For B2B technology leaders, the opportunity is in developer productivity and the acceleration of prototyping and internal tooling.The risk in all three cases is the same: deploying a powerful capability without the judgment infrastructure to use it well.Vibe Coding doesn’t replace engineers. It eliminates the queue. Whether your organization knows the difference will matter.The pattern is familiar. The internet didn’t eliminate publishing. It eliminated the gatekeepers of distribution. SaaS didn’t eliminate IT. It eliminated the bottleneck of infrastructure. Vibe Coding won’t eliminate engineering. It will eliminate the monopoly that engineers have historically held on who gets to build.For the executives, operators, and investors reading this: the question isn’t whether Vibe Coding is real. It’s whether your team is already using it, and whether you know.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

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

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

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

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

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

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

A soon-to-be customer handed me the insight that changed how I lead. I almost missed it.We learn by hearing stories — especially ones told with authenticity, relevance, and a bit of humor. "Leadership Lessons Learned the Hard Way" is a regular feature on Uphoff on Media, drawing from my own experience: the Hollywood producer who hit me with the classic "you'll never work in this town again," the time my go-to sign-off "Best, Tony" autocorrected to "Beast, Tiny." Real situations. Hard-won lessons. For the third installment, I'm sharing one taught to me by a customer. One I've never forgotten.I was a young sales rep at the B2B publication EE Times, freshly transferred to Silicon Valley. Big opportunity. Big territory. And assigned to one of the most powerful ad agencies in the Valley. A firm that represented household names in tech and hadn’t done business with EE Times in years.The VP Media Director was legendary. She could make or break media brands. And careers. Getting a meeting with her was itself a milestone.So I did what young, hungry sales reps do. I called. I left voicemails. Detailed, articulate voicemails laying out our value proposition and requesting a meeting. Every week. For six months.Crickets.Until one evening at 6:00pm, my office phone rang. I picked up. After I said hello, the voice on the other end said:“You’re not going to go away, are you?”We met. She laid out exactly how she saw the market, what her clients needed, and how she evaluated media brands, including mine. It was a masterclass. Over the next couple of months we met several times, leading to EE Times being invited to participate in a media day for one of their biggest clients.This was enormous. My publisher, editor, and associate publisher were flying in from New York for a Friday meeting. Careers were watching.On Wednesday, she called and asked me to bring the presentation over. I told her it was still a work in progress. She didn’t care.I walked her through a printed copy of 50 slides. She sat with her arms folded the entire time. When I finished, she was quiet for a moment. Then she nodded and said:“This is good. You’re not telling me I’ve been stupid for not buying EE Times. What you’re telling me is that the market has changed, your editorial has captured that change, your readership reflects it, and now I should consider it.”I blushed beet red.Because that’s not what the presentation said at all. It was a competitor takedown dressed up as a value proposition. Slide after slide subtly — and not so subtly — making the case that anyone who hadn’t been buying EE Times must be clueless. She had just described the presentation I needed to give. Not the one I had.I gathered up the presentation, thanked her, drove back to the office, and spent the next three days rebuilding everything with my team. We threw out most of what we’d built. We reframed every slide around a single organizing idea: the market had moved, EE Times had moved with it, and here was the evidence. No competitive takedowns. No implicit verdicts. Just a clear-eyed view of a changed landscape and where we fit in it.Friday came.The room was full. Senior agency team, client marketing leadership, my New York executives. The kind of meeting where everything either opens up or closes down.We gave the new presentation.It worked.The result was the largest single sale of my career to that point, and one of the largest single sales EE Times had recorded. The agency became a real partner. The client relationship grew.But the outcome I didn’t anticipate was what happened next with the VP Media Director herself.She became a mentor.Over the years that followed, she recommended me for leadership positions. She saw something in that three-day rebuild, in the willingness to throw out work I was proud of and start over because someone wiser showed me the truth, that told her something about how I’d lead. She invested in my career the way she’d invested in that Wednesday afternoon conversation. Quietly. Decisively. Without making a big deal of it.I would not have had the management career I had without her mentorship. Here’s what she taught me, and what I’ve carried across five CEO roles since:When you’re trying to change someone’s mind, you don’t win by making them feel wrong. You win by showing them the world has changed.There’s a version of persuasion that’s really just an indictment. It says: here’s all the evidence that your current approach is mistaken, your current vendors are inferior, your current thinking is outdated. It feels like a strong argument. It’s actually a trap, because no one changes their mind when they feel attacked. They defend. They dig in. And they remember how you made them feel long after they’ve forgotten your slides.The better strategy, the strategy she modeled for me before I even knew I needed it, is to lead with the landscape, not the verdict. The market shifted. New signals emerged. A different approach may now make sense. That framing gives the buyer somewhere to go. It honors the intelligence of the decisions that got them here, while opening a door to something new.This applies well beyond sales. I’ve used this frame in boardrooms, turnaround conversations, investor updates, one-on-ones and leadership team offsites. Any time you’re asking someone to change course; a strategy, a vendor, a belief, the question isn’t how do I prove them wrong. It’s how do I show them the landscape has moved in a way that makes a new direction logical, even obvious?One more thing worth naming: she didn’t have to do what she did on that Wednesday afternoon.She could have let me walk into Friday’s meeting and fail. Instead she called, sat with her arms folded through 50 bad slides, and handed me the insight that changed everything. That’s its own leadership lesson. The most powerful people in any room often got there because they bring others along rather than let them stumble.She did that for me. More than once.I’ve tried to pay that forward ever since.Missed the first two? Start here: “You’ll Never Work in This Town Again” and here: “Beast Tiny. What Autocorrect Taught me About Leading at the Speed of Chaos”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

My standard sign-off in texts and emails has always been “Best, Tony.” Simple. Professional. Warm enough.During the years I was running UBM TechWeb: a global B2B media, events, and data business that grew to over $300 million in revenue, that sign-off had a problem. I wasn’t always “Best, Tony.” Thanks to iPhone autocorrect, occasionally I was “Beast, Tiny.”The irony is hard to miss. At 6’5” and 200 pounds, “Tiny” is not a word anyone would use to describe me. And I’ve spent my career building a leadership style and reputation around decisiveness with empathy, clarity with care. Not exactly “Beast” territory.But here’s what I learned: autocorrect doesn’t lie. It just exposes what you’re actually doing.The Pressure CookerThe years of the Beast/Tiny phenomenon were not ordinary years.The smartphone was rewriting how business got done in real time. At UBM TechWeb, we were simultaneously managing the decline of print, scaling digital, acquiring and globalizing events, including operations in Mumbai, Beijing, and Shanghai. And then the global financial crisis hit, compressing timelines, rerouting capital, and forcing us to rewire the planes while in flight.I was texting constantly. To direct reports, to global teams, to board members, to partners. From airports, hotel rooms, back-seat rides to meetings. Direction, questions, answers, decisions, all of it compressed into a thumb-typed stream of consciousness. My texts were moving at the speed of the market.And the market was not waiting for me to proof my sign-off.The Loneliest Job You’ll Never Fully ExplainHere’s what doesn’t get talked about enough: running a company at that pace, in that environment, is genuinely isolating.The data on this is striking. Research shows that half of CEOs and senior leaders report experiencing loneliness and isolation, with 61 percent believing it negatively impacts their performance. Senior leaders are twice as likely to report feelings of isolation compared to employees in non-leadership positions. I lived that statistic without knowing it existed.The isolation isn’t social, you’re surrounded by people. It’s contextual. As CEO, you’re the one everyone looks to for context. Which means fewer and fewer people can give it to you. Information flows up filtered. Perspective flows up managed. The loneliness experienced by CEOs stems not from a lack of social connections but from the heavy burden of leadership and decision-making; especially during crises, when they look to their board, senior executives, or operational managers and find that even those people are watching what they say.The faster the market moved, the more I leaned on digital communication to stay connected. The more I leaned on digital, the more isolated my actual thinking became. I was generating output — decisions, direction, responses — without adequate input. A machine running hot with no coolant.Autocorrect caught what I couldn’t. I was moving too fast. And “Beast, Tiny” was the tell.The Framework: Three Places the Gap Shows UpWhat the Beast/Tiny mistake revealed was a gap, between intent and output. That gap isn’t unique to texts. Under sustained pressure, it shows up across three domains every leader should monitor.1. Communication: What You Meant to Say vs. What LandedSpeed collapses nuance. Autocorrect made it visible; usually it isn’t.Most leaders believe they’re communicating clearly under pressure. Research suggests otherwise. Studies on managerial communication consistently find that during high-stress periods, message clarity degrades while message volume increases. A particularly dangerous combination in a global organization where context is already challenged across time zones and cultures.The discipline: Before sending anything consequential, ask one question: “What will this person do with this?” Not what you meant. What they’ll do. That five-second check is the equivalent of proofreading your texts. It costs nothing. The cost of skipping it compounds.2. Decisions: What You Intended to Signal vs. What the Organization HeardFast-moving leaders often experience themselves as decisive. Their organizations often experience them as reactive, and the difference is enormous.A decisive decision has visible reasoning. A reactive decision has visible urgency. When you’re operating in crisis mode, urgency can masquerade as clarity. People execute, but they execute on what they inferred, not what you intended.Former U.S. Surgeon General Vivek Murthy noted that loneliness “reduces task performance, limits creativity, and impairs other aspects of executive function such as reasoning and decision making.” When isolation compounds velocity, the decisions that feel sharpest may actually be the least examined.The discipline: After significant decisions, do a 24-hour check. Not to reverse, but to listen for what the organization is doing with what you said. If the actions don’t match the intent, the gap is yours to close.3. Presence: Who You Meant to Be in the Room vs. Who Actually Showed UpThis is the hardest one.At speed, leadership presence defaults. You stop showing up intentionally and start showing up habitually, running on pattern, not purpose. The “Beast” isn’t aggressive. The Beast is the unexamined version of you, operating on autopilot while you manage a hundred other inputs.Fewer peers, high external expectations, and rare honest feedback make it difficult to openly discuss one’s limitations. Most CEOs don’t get told when their presence has shifted. They find out later; in an exit interview, a board conversation, a candid moment from a trusted advisor, that people had been reading a version of them they hadn’t intended to project.The discipline: Build at least one feedback loop that is structurally honest, a peer, an executive coach, a trusted board member, someone whose job is not to manage your reaction. The Beast/Tiny moment happened because someone on my team felt comfortable enough to laugh about it with me. That comfort didn’t happen by accident.What “Beast, Tiny” Actually FixedWhen people caught the autocorrect mistake, they loved it. Initially I was embarrassed. Then I realized what it was doing: it created permission to be human.“Beast, Tiny” broke the artifice of the relentlessly-in-command CEO. It reminded my team — and honestly, reminded me — that the person sending these texts was a human being making judgment calls under real pressure, not an oracle issuing pronouncements from on high.The forcing function wasn’t just proofreading my sign-offs. It was learning to pause before I communicated, before I decided, before I walked into a room. Not slower, more deliberate. The difference matters.The market was moving at autocorrect speed. Leadership couldn’t.The Test You Can Apply TodayI now think of the Beast/Tiny Test as a simple diagnostic leaders can run in three moments:* Before you send: Does this say what I mean, or what I typed?* After your decision: Is the organization moving the way I intended, or the way they interpreted?* Before you enter the room: Am I showing up on purpose, or on pattern?Three questions. Seconds each. The cost of skipping them is what autocorrect was trying to tell me.Best,TonyThe 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