Uphoff on Media Podcast

Tony Uphoff

Uphoff on Media podcast: A practical take on how technology is rewriting the way B2B companies get built and run, and how to get ahead of it. For those founding, running, and funding B2B information, marketing, and technology companies. tonyuphoff.substack.com

  1. 3d ago

    The Forecast Has Your Name on It

    It’s 11:40 on Sunday night, and the board meets at 9:00 tomorrow morning. The deck went out Friday, and you’ve opened it again anyway. Slide six is the forecast, and the number on it is wrong. Not by a lot, but by enough. It’s the third quarter in a row the business has come in under what you told the board last time, and you’ve spent the whole weekend rehearsing your answer to the question someone will ask first: what happened? You have an answer. Two deals slipped. One went to a competitor in the last week. A renewal came in smaller than planned. All of it is true. What’s keeping you up is a different question, one nobody at that table will ask because they assume you already have: why do you keep finding out at the end of the quarter? After five CEO roles and more of those nights than I’d like to count, here is what I believe. Most repeat forecast misses are not a sales problem or a market problem. They are a process problem. The company is reporting its pipeline and calling it a forecast, and the CEO signs for the difference. A number with a name on it Pipeline and forecast are related, but they are not the same number. Pipeline is what could close. Forecast is what will close, inside a defined window. Treat them as one and the forecast comes out wrong, and someone has to answer for it. A forecast is a promise, and every promise has a name on it. Usually several: * The rep who called the deal. * The sales leader who rolled it up. * The CFO who put it in the model. * The CEO who said it out loud to the board. When the number misses, only one of those names is in the boardroom. Some of the most painful days of my career weren’t the big ones. I’ve led businesses through market collapses and lost people I didn’t think I could replace. Those were hard. Explaining a forecast miss late in a quarter was worse, especially when the miss traced back to a call one of my direct reports made. It was still my forecast. The call at Black Hat At UBM TechWeb, we had built a multi-platform business. Events, paid conferences and digital media were growing faster than legacy print advertising was declining. That was the good news. The bad news: print had become a falling knife across the industry, and our ability to forecast its decline was falling with it. I was in Las Vegas at Black Hat, one of our fastest-growing conferences, when my CFO called in the early evening. Print was going to come in $5 million short, with less than a month left in the quarter. I was standing in the middle of our best-performing business, taking the call about our worst. A sales leader had known how bad print had gotten, and held on to the hope that a few last-minute deals would come through. They didn’t. I had to explain the revenue miss to corporate. Our revenue mix meant we still beat our EBITDA target, a small consolation. It was still painful, and it put me in a difficult position. Beating the profit number doesn’t erase a revenue number you said you’d hit. What that call taught me: our process had let one person’s hope stand in for the company’s number. The decline was knowable. The forecast just wasn’t built to surface it until it was too late to matter. If you’ve taken your own version of that call, so have most of your peers. In Xactly’s 2024 Sales Forecasting Benchmark Report, a survey of about 400 sales and finance leaders, four in five said they had missed a quarterly forecast in the past year. More than half had missed two or more times. The most common roadblock they cited was reporting systems that can’t reach historical CRM data. In my experience, the data is usually fine. The problem is what the company decides the data means. How pipeline becomes the forecast Nobody builds it this way on purpose. It happens one optimistic update at a time. When a company stops keeping the two apart, the bigger, better-looking number quietly becomes the one everyone agrees to believe. It goes in the board deck because it’s the number the CRM shows, and no one wants to be the person who reports a smaller one. It isn’t a software problem. CRMs have had weighted probability fields for two decades. What’s missing is a rule for what each weight means and a rule for when a deal counts. Without those, a rep sets the probability to whatever makes the week look good, and a leader under pressure to show growth doesn’t push back. Tie every weight to something you can check I’ve used a four-stage weighting system for years: • 90%: a strong verbal commitment, waiting on signature. • 50%: a confirmed proposal the buyer is actively working through. • 10%: the decision makers are identified and have engaged back. • 0%: the account is assigned and hasn’t been contacted. A $100,000 proposal at 50% contributes $50,000 to the forecast. The system only holds if every weight is tied to something you can verify. A verbal commitment happened or it didn’t. A decision maker replied or didn’t. The moment a weight becomes a rep’s private read on how the call went, you have the same optimism with better math behind it. Weight tells you how likely. It doesn’t tell you when. This is where most forecasts break. A deal weighted at 90% that’s expected to close in eight months belongs in the pipeline. It does not belong in this quarter’s forecast, however confident the rep sounds about it. The rule is simple: a deal counts toward a window’s forecast only if its weight is earned and its expected close date falls inside that window. Both conditions, every time. Skip the second one and you’re back where you started, with a big, confident number that has little to do with what lands in the bank this quarter. That’s the number you end up defending at 11:40 the night before. The disagreement is the early warning The reps I’ve seen forecast well aren’t the ones who are always right. They’re the ones whose manager sits down with them every week, deal by deal, until the two of them land on a number they both believe. When a rep says 50% and the manager says 10%, that gap isn’t friction to smooth over before the meeting ends. It’s the most useful number in the room. Track it over a dozen weeks and you’ll see which reps run consistently hot and which run cold, a pattern no single deal review shows. That’s a read on judgment, and it’s how you avoid taking the blame for someone else’s optimism. You see it in week three, not the night before the board meeting. The same discipline applies one level up. The CEO’s weekly forecast review with the sales leader is the closest thing the company has to a live read on cash coming in. Treat it as a formality, or skip it because the CRM already shows a number, and you’ll walk into the boardroom carrying a forecast the company never agreed to. Would you sign your name to it? Here is an Uphoff Advisory Framework you can download and use in your business. It sorts every deal in the pipeline by two questions: is the weight earned, and is the close date inside the window? Every deal lands in one of four squares. * Commit: earned weight, inside the window. The only square that belongs in the forecast. * Next Quarter: earned weight, outside the window. Real pipeline, just not this quarter’s number. * Hope: inside the window, but the weight is a feeling. This is where forecasts miss, and where the $5m shortfall lived the night my CFO called. Prove it this week or pull it. * Noise: neither. Pipeline hygiene, not forecast. Run every deal through four questions before the number goes in the deck: * Is the weight earned? Name the event that justifies it: the verbal, the proposal, the reply. * Is the close date inside the window? If not, it’s pipeline, whatever the weight. * Do the rep and the manager agree? If not, record both numbers and the gap. * Would you sign your name to it? If the board asked whose call this deal was, would you be comfortable answering? Download Would You Sign Your Name to It? → View the full Uphoff Advisory Framework library → The miss you saw coming In September I wrote about what to do after a miss. This is the part that comes before it. A board will forgive… 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

  2. 5d ago

    Everyone in B2B Data Is Selling Context Now. Rebrand or Reposition?

    In April, Demandbase launched a product called Context Intelligence. Its CEO, Gabe Rogol, said: “AI without context creates noise.” In May, ZoomInfo’s Henry Schuck told investors that “go-to-market context is the ultimate competitive advantage.” Last week, 6sense, the company that defined account-based marketing, launched Contextual Intelligence and repositioned itself as a partner for agentic go-to-market. Its CMO, Kelly Hopping, said: “AI without context hasn’t made GTM teams smarter.” Three of the biggest names in B2B data. One new definition of what they are. Seven months. When a whole category redefines itself at the same moment, the old definition has stopped selling. ZoomInfo built its business on contact data. 6sense and Demandbase built theirs on account-based marketing. All three now say they sell context. That’s a claim to be a different kind of company, which is a much bigger claim than a new tagline. The category is answering the question the agentic era asked it In September I argued in If Your Moat Was Data, What Happens When AI Can Manufacture It? that the agentic era attacks both foundations data businesses were built on. Agents assemble facts for close to nothing, which erodes the cost of aggregation. Customers build their own workflow on top, which erodes the switching cost. “Context” is the industry’s answer. The claim underneath it is that the value no longer sits in the record. It sits in the interpretation: why this account, why now, what to do next. That’s the right instinct. A contact record is a commodity. A defensible explanation of buying intent is not, or at least not yet. So the question isn’t whether the vendors picked the right idea. They did. The question is whether “context” is a product or a label. 6sense says the right things, and some of it is real Credit where it’s due. The new positioning will get the headlines. Explainability is the part worth paying for: every account score and buying-stage prediction now comes with the signals behind it, the evidence a customer can check, and a confidence score. Anyone who has watched a sales team ignore a black-box score knows why that matters. People don’t act on numbers they can’t defend to their boss, and agents acting on unexplained numbers are worse. Its own research says buyers are less sure than last year how vendors use AI inside their products. Explainability answers that directly. 6sense is also delivering its data headless, through MCP and APIs, so it shows up inside whatever agents the customer is already running. That’s the channel shift I described in September, and 6sense is building for it. Now look at what else the release says. Nearly everything announced is in beta, and every piece adds something to the existing platform. Nowhere does it mention pricing or packaging. 6sense even shipped a new logo. The price list hasn’t moved, at least not in public. A repositioning without repackaging and repricing is performative, not foundational Last week I wrote about the three decisions a machine can’t make for you: positioning, packaging and pricing. They only work as a set. Change the position without changing the package and the price behind it, and what you have is a new message. A real repositioning redefines the company: what it sells, how customers pay and what its people are paid to do. That’s the line between a rebrand and a repositioning, and I’ve had to walk it from the inside. At CMP, we had to take a print media business into digital. Building the products was the easy part. The hard part was deciding how to position, package and price them, and how to pay the people selling them. Digital was a performance medium: print sold pages against a circulation, and digital got measured on response. Every one of those decisions carried a question nobody could answer in advance. Were we trading print dollars for digital dimes? Could a rep make as much selling a low-margin product as a high-margin one? Part of us saw an existential crisis. Part of us saw a head start on every competitor we had. We chose to build for where the business was going: integrated print and digital programs, a sales team paid to lead with them, and performance reporting that matched what advertisers now expected. An early product of that shift was InformationWeek Daily, one of the first commercial email newsletters, from a brand with “Week” in its name. I always loved the irony. The name didn’t change. How the business made money did. That’s a repositioning from the inside. The packaging changed, the price changed, the comp plan changed, and the core business was at risk the whole time. Anything less is a new message. That can be a perfectly good business decision. It isn’t a repositioning. ZoomInfo shows what a real repositioning costs ZoomInfo has spent this year moving customers off seat-based pricing. On its November 4 earnings call, management described the new model: customers stop “buying seats or data files” and buy credits they can use anywhere, including in agents they build themselves. Some of those customers, they said, “renew down a bit.” Revenue fell 3% last quarter, even as the company raised its full-year guidance. That’s what a real repositioning costs: you change how the customer pays, and the revenue line shows the bruise. Whether ZoomInfo’s bet works is still an open question. That it’s a real bet is not. Most of the new products serve the seller’s AI, and 6sense’s own data explains why There’s a second signal in the release, and 6sense handed it to us. Its 2026 Buyer Experience Report, which the company’s PR firm sent me after the announcement, found this: In 91.4% of buying journeys, the group picked a winner before talking to any vendor. That favorite won 82% of the time. Read that as an operator. If the decision is mostly made before the first conversation, the leverage sits in whatever the buyer uses to do that research. Now sort the announcements: Serving the seller: the BDR Agent, Explainability, the deal risk models, the headless sales intelligence. Serving the buyer: AI Visibility. AI Visibility is the only announcement aimed at the buyer’s side of the table, where 6sense’s own numbers say the decision gets made. I understand why 6sense built four products for the seller and one for the buyer. The seller writes the check, and the seller’s problems are this quarter’s problems. But it’s the pattern I keep seeing across martech: vendors and marketers focused on how they use AI, while the buying decision moves to how the buyer uses it. Here’s where 6sense’s own research gets interesting. It calls buyers’ use of AI “a tweak, not a revolution.” Follow the incentives here too. 6sense has to leap from one lily pad to the next without sacrificing today’s business to make the jump, and today’s business runs on the signals buyers leave while they research. A buyer whose agent does the research leaves different ones. I think the report underweights what’s coming. It looks back at purchases made over the past two years, so it measures where buying was, not where it’s going. Even so, a quarter of buyers already say AI significantly shaped how they saw vendors. That’s the front edge of the next buying model. The question for anyone building, running or funding in this category is how fast the rest of it arrives. Five questions separate a repositioning from a rebrand Here is an Uphoff Advisory Framework you can download and use in your business. Run it on any vendor that tells you it’s become a different company, and on your own business before you announce that you have. Download Rebrand or Reposition? → View the full Uphoff Advisory Framework library → Here is how 6sense scores today After the announcement, 6sense’s PR firm reached out with the full research study and an offer to put questions to management. I asked three things: how customers will pay, what the company will stop selling and how sales is paid to lead with the new positioning. The company answered in writing within days. Those answers, plus th… 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

  3. Nov 4

    The Three Decisions a Machine Can’t Make for You

    Go to Intercom’s pricing page and you will find the same company selling customer service two different ways. Human agents are priced the old way: $29 to $132 per seat, per month. Its AI agent, Fin, costs $0.99 per outcome, with no seats required. That page is a small picture of a large shift. When software does the work, the seat stops measuring the value, and the price has to find a new unit. The number on the page is the easy part. The unit underneath it is where the business gets decided. Here is the pattern I see across B2B information, marketing and technology companies right now. The cost of building a product is falling, and the cost of copying one is falling faster. In the businesses I work with, a feature that took a competitor a year to copy now takes about a quarter. When the product gets cheap to make and easy to compare, the value doesn’t disappear. It moves into three decisions no machine can make for you: what you stand for, what you bundle and what you charge. Positioning. Packaging. Pricing. Positioning. Packaging. Pricing. I’ve used those three to diagnose businesses for years, the ones I’ve led and the ones I advise. The buying committee has a new member Every B2B company now sells to a buying committee and to the machine that committee sends ahead. The data on the second buyer is no longer thin. In a Gartner survey of 646 B2B buyers, published in March, 67% said they prefer a buying experience with no sales rep, and 45% said they used AI during a recent purchase. 6sense’s 2025 Buyer Experience Report, drawn from more than 4,000 buyers, found that 94% used large language models during the buying process, and that the vendor a buyer favors before talking to anyone still wins 80% of the time. Read those two findings together. The shortlist is being built before your team knows a deal exists, and the vendor that leads the shortlist usually wins. The agent doing the early research applies a pass or fail test to whatever it can read about you. The committee that follows applies a different test: can I defend this choice to my CFO and my board? Positioning, packaging and pricing are how you pass both tests. They are also where most B2B companies are weakest, and for years that weakness was affordable. Sales and marketing did the alignment work live, at the point of contact. A good rep translated a vague homepage into the buyer’s problem, built a bundle on the call and found a price the buyer could take to finance. The three decisions didn’t have to line up on paper, because a person lined them up in the room. An agent doesn’t wait for that conversation. It reads what you publish, compares you with every alternative, and passes or fails you before anyone on your team knows a deal exists. Miss on any of the three and you lose the deal before it starts, without ever learning it was there. That is why the cost of getting these decisions wrong has gone up. The sales team is no longer in the room when the shortlist gets made. Putting the offer in front of the room So I’ve been building that room. I call it Buying Committee Modeling, and it is now an Uphoff Advisory solution for clients. Here is how it works. A typical model seats seven or eight roles, from the executive sponsor to finance to the team that will use the product, each an AI persona built around how that role weighs a purchase. A separate agent stands in for the buyer’s AI, running the same searches it would use to build a shortlist. Then the offer goes in front of all of them, the positioning, the package and the price, as a buyer would find them, in two to four different buying situations. Each seat answers on its own, three times in each situation, so a single offer produces dozens to more than a hundred independent responses. Every answer follows the same format: how likely the seat is to take a meeting and to shortlist the offer, a stance from Champion to Blocker, the objection that would kill the deal and what would turn it into a yes. Those answers are tabulated as medians and ranges, never as one opinion. Each committee then deliberates to a verdict: advance, advance with conditions, park or decline. Finally, a blind reviewer that never sees the committee’s answers scores the offer on seven dimensions, from value clarity to price-to-value to how easily an agent can read it, and names the one to fix first. What comes back is the conversation your sales team never gets to hear: the objections, the questions finance will ask, and whether the agent finds you at all. The modeling doesn’t predict who will buy. It guides the three decisions, showing where your positioning, packaging and pricing are working and where they aren’t, before a real buyer sees them. It also validates your ideal customer profile: put the same offer in front of different committees and you see which buyers it actually fits. And you can run it again every time you change one of the three decisions. I started with my own firm, and I now use it with clients to test positioning, packaging and pricing before they go to market. I’m not the first to build something like this, but very few companies have seen their offer the way this room sees it. I’ll share what the modeling is showing in future posts. Positioning has to survive being read literally An agent reads what you publish, not what your best rep says on a call. “Solutions that accelerate growth” passes no test, because it describes every company in the category. Positioning in the agentic era has to name the buyer, the problem and the result in words specific enough that a machine can match them to a request and a human can repeat them in a meeting. That is a higher bar than most homepages clear. It is also the first decision, because the other two follow from it. You can’t package around a promise you haven’t made, and you can’t price a result you haven’t claimed. When I was brought in to transform Thomasnet, the work started here. The company was known as an industrial directory. The value buyers actually got was sourcing: finding a qualified supplier faster, with data they could trust. Repositioning around that job, and rebuilding the business as a sourcing and data platform, is what made the rest of the transformation possible. Packaging is where most of the money hides In nearly every company I’ve been brought in to turn around or transform, I found the same thing: products sold à la carte to customers who wanted an integrated solution that worked like a recipe. The catalog described what the company made. It didn’t describe what the customer was trying to get done. At Thomasnet, moving from à la carte to solution pricing raised the average sale by 40%. At Pipeline360, we built a Branded Demand solution that combined programmatic display with data-driven demand generation at one price. Order size and retention went up, and campaign performance improved by more than 35% on average. The products didn’t change much. The package did. Agents raise the stakes on this. A buyer agent comparing vendors breaks an à la carte catalog into line items and prices each one against the market, which is how a product gets turned into a commodity. A program built around an outcome is much harder to pull apart. For digital media and marketing products, the test I use is simple: an ad, sponsorship or lead-generation product earns its place on the rate card when it sells at least half of its available inventory. Below that, fold it into a program or retire it. Most media companies I review carry several products that fail that test and are still being sold, produced, billed and reported every month. Pricing is breaking at the unit, not the number Seat-based pricing assumes a human does the work and the software helps. When an agent does the work, the number of seats shrinks while the value delivered goes up, and a price built on seats falls exactly when the product is getting better. That is the reason a company like Intercom now prices the same work two ways on one page. The fix is a new uni… 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

  4. Nov 2

    Production is Getting Cheaper. Judgement Isn’t

    Nine months after launching Uphoff Advisory and Uphoff on Media, I had a discovery problem. Seventy-four posts, a growing framework library and a suite of client tools, and not enough of it was being found in search or recommended by AI. Here’s the admission: I’d spent 20-plus years running companies with teams who did this work. I knew what good looked like. I didn’t know how to do it. So I handed it to my agents. This was my own back office, not client work. They built or rebuilt 27 framework pages, re-rendered about 30 charts and audited all 74 posts for broken links. Working alone, a skilled person would need about four weeks to do that. I spent nine hours. It’s tempting to say AI did the work. It didn’t, and a lot of operators are building their plans on that mistake. The agent didn’t take me out of the work. It moved me from production to judgment. The leverage came from that shift, not from the automation. Solo Scale was a thesis. The data is starting to come in In July I argued in Solo Scale that agentic AI breaks the constraint that has kept every services business at services margins: one expert, finite hours, revenue that scales with headcount. I wrote that the operator “sets strategy, reviews output, exercises judgment” while agents handle the production. That was an argument. This is a test of it against my own business, with the limits stated up front. It’s one project and one operator. The counts of pages, posts, links and URLs are exact. The hours are estimates, not time sheets, and nine is the high end of mine. Read them as an order of magnitude, not an exact count. The four-week figure is a line-item estimate, not a guess: writing 27 pages is the biggest piece at 40 to 68 hours, followed by building them in WordPress and re-rendering the charts. Four weeks against nine hours is roughly 18 to 1. At the low end of the estimate, 125 hours, it’s still 14 to 1. Where my hours actually went The division of labor was clean on paper. * Decisions: I made every publish call, nine rounds of three pages, plus the names, the standards and which version of a chart was accurate. The agent proposed each one with a recommendation and the trade-off. * Framework pages: For the library on my website, the agent wrote the pages describing the Frameworks I created, including How-to steps, FAQs, SEO fields and alt text. It built every page in WordPress and verified each one live. I reviewed. * Charts: The agent re-rendered and rebuilt about 30 and checked the stats against sources. I uploaded them. * Substack: The agent wrote every link line and alt text and verified every post afterward. I made the edits in 22 posts and fixed 6 links. * Search: The agent got all 27 pages to green in Yoast, audited 74 posts and checked 31 URLs. I submitted the sitemap. * Records: The agent kept the upload workbook, link map, tracker and status doc current. I didn’t touch them. Look at what I actually did on that list. About half of my nine hours went to Substack edits, and another hour or two to uploading images. That’s production, not judgment. I was doing it by hand because Substack blocks the agent from that work and getting files into WordPress’s media library stayed manual. So the division isn’t clean yet. Only two to four of my nine hours went to real judgment calls. The rest went to manual work the tools wouldn’t let the agent touch. That’s plumbing, and plumbing gets fixed. None of it needs me. The checking was worth as much as the speed The number that will get quoted is the hours. What the checking found mattered just as much. Four published facts were wrong. All four were mine, and they’d been live. * A stat credited to the wrong firm: “94% of B2B buyers use LLMs” was attributed to Forrester and CMI on two charts. It comes from the 6sense 2025 Buyer Experience Report. * A chart that contradicted its own post: One chart said mid-market B2B was “finally off premise” in 2018. The post it ran with said the opposite. * A chart that didn’t match its text: The post described seven questions. The chart showed six. * A count that didn’t add up: One framework page said “four squares” and listed five. None of these were catastrophic. Each one is exactly the kind of error a careful reader finds, and the readers of this publication are careful readers. I would not have audited 74 posts by hand. Nobody running a business has the time. The agent found the errors. It didn’t decide what to do about them. The chart that contradicted its post needed a new label that was true in both places. The seven-question index needed its scoring range rebuilt. Those were calls, and they were mine. Verification moved to the agent. Deciding what’s true stayed with me. Judgment becomes the lever, not the cost. The build was fast, not frictionless, and the friction is the most useful part of the story. The one hour I’d take back came from a call I made late. The Uphoff Value Index went from “Value” to “Valuation” and back again inside an hour. Each switch meant a chart re-render, a page retitle, a URL change and a redirect: four pieces of work, each done twice. That waste was mine, not the agent’s. Agents multiply whatever direction you give them. Make the call early and they multiply the work. Make it late and they multiply the rework. Not every mistake was a decision, and not all of them were the agent’s. One round of uploads landed on the development site instead of the live one. A stray bulk upload pulled in 29 files. All 18 rebuilt pages went to the trash once and came back with Undo. Every one of those was fixed in minutes, because the records were current and the standards were already written down. That’s the operating lesson. In every transformation I was brought in to lead, execution was where things broke, and the worst breaks traced back to decisions that came after the work had started. Agents raise the stakes. They execute so fast that a clear call made up front pays off almost immediately, and a late one throws away work that’s already done. The leverage is in the calls you make before the work starts. An Output Multiple isn’t a Scale Multiple yet Last week I wrote about the difference between an Output Multiple, how much more a person produces on one workflow, and a Scale Multiple, how much more revenue each person brings in. This build is an Output Multiple. A large one. It is not yet a Scale Multiple, and I’d be overselling it to call it one. For anyone new to the terms: Solo Scale is one expert running agents, from $250,000 to $5 million in revenue at 40% or better EBITDA. Team Scale is the next rung, a small senior team doing the same at $400,000 or more of revenue per employee and 25% or better EBITDA, from $2 million to $30 million. Both are defined by revenue per person, not headcount. For context, the median private SaaS company does about $175,000 of ARR per employee, and WPP, the largest agency holding company, about $130,000 per person. This build is evidence on the input side of that math, not the revenue side yet. Then there’s the cost. The agents ran on tools I already pay for. Four weeks of the same work, hired out at typical freelance SEO rates of $75 to $100 an hour according to an Ahrefs survey, would have cost roughly $12,000 to $16,000. One thing isn’t in that comparison: my web developer made a configuration change at no charge that let the pages hold real content. It doesn’t change the order of magnitude, but it belongs in the record. The more important point is what the comparison actually is. I was never going to hire a writer, a designer and a web producer to rebuild 27 pages of my own IP. Without the agent, the library stays half built. That’s the real Solo Scale effect. The work isn’t just cheaper. Work that would never have been funded now exists. Whether it turns into a Scale Multiple depends on what the operator does with the hours the agent gave back. While the agent was building the library, I brought on a new advisory client. One additional client isn’t a Scale Multiple, but it’s exactly where those … 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

  5. Oct 28

    It's Never Just Business

    "It's not personal, it's just business" gets said most often at the exact moment it is least true. Across a table. To someone whose job, title, or plans just changed. Nobody has ever walked out of that conversation agreeing with it. I've said some version of the opposite for close to thirty years: people who say it's not personal, it's just business are kidding themselves. Business is highly personal. And as a leader, you will need to make decisions that are in the best interest of the business and not in the best interest of someone on your team, sometimes someone you like, respect, and hired yourself. Both halves of that are true at the same time. Most of the damage leaders do to their teams, and to themselves, comes from pretending one of them isn't. Management comes with two loyalties and no instructions In Be Careful What You Wish For, I wrote about the question I've been asked every few months for three decades: should I get into management? That piece was about whether you're built for the seat. This one is about what happens after you say yes, when you discover the job comes with two loyalties and no instructions for holding both. The first loyalty is to the person in front of you. The one who stayed late during the carve-out, who covered for you when your kid was sick, who you actually enjoy having a beer with. The second loyalty is to the enterprise. That word sounds cold until you remember what it actually is: the other two hundred people, their families, the customers who bet their own plans on you delivering, the investors who funded the payroll. Deciding for the business is almost never deciding for a spreadsheet. It's deciding for a larger group of people who aren't in the room. That's the weight. Business isn’t impersonal. It’s personal in two directions at once. “It’s just business” and “we’re a family” fail the same way The first lie is the one leaders tell themselves: it's just business. It's anesthetic. It lets you make the call without feeling the cost, which is exactly why it fails. The person across the table feels the cost in full, and they can tell you've numbed yourself to it. What they remember isn't the decision. It's that you seemed not to mind. The second lie is the one leaders tell their teams: we're a family. It feels generous when you say it at the all-hands. It is a promise you can't keep. Families don't restructure. Families don't run performance reviews. Families don't sell the division. Netflix saw this clearly enough to write the opposite into its culture memo: "It's why we model ourselves on a professional sports team, not a family." Families, the memo says, "are about unconditional love." Every leader who has said "family" and then had to lay someone off has watched a normal business decision land as a personal betrayal. The word made it one. The two lies run in opposite directions, and they fail the same way. Each one makes the tension go away by pretending one loyalty doesn’t exist. The job is to carry both. What I told the team when we ended BYTE’s print edition In 1998 I was group president at CMP Media, running a division north of $300 million, when CMP bought McGraw-Hill's Information Technology and Communications Group and handed the acquired brands to me. The best known of them was BYTE, which had been the reference point for serious computing since its first issue in September 1975. From diligence onward, two groups of people were watching every move. The CMP employees in my division wanted to know what the acquisition meant for their positions and their work. The people coming over from McGraw-Hill wanted something harder to give: assurances that their brands, and their jobs, were safe. That's the moment the family lie is most tempting. A few warm promises at an integration meeting would have bought a quiet month. They also would have been promises nobody could keep. Several of the acquired brands were in decline and hadn't led their niches for a while. BYTE's circulation had slid from roughly 523,000 in 1996 to about 443,000 by the time of the deal, the New York Times reported at the time. We worked through the details, and then we made the calls. BYTE suspended its print edition after the July 1998 issue and moved online. A second acquired brand closed outright, its circulation and other assets folded into another title. Both decisions cost people their jobs. When we told the team, I said what I'd believed for a long time: "'This is business, not personal' really isn't accurate. Business is highly personal, and particularly so for the people who lost their jobs today. At times, as leaders, we need to make decisions that are in the best interest of our business and that may not be in the best interests of some of the people involved. This is one of those times." It didn't make the news any easier to hear. Nothing would have. What it did was refuse both lies in the same breath: it didn't pretend the decision was painless, and it didn't pretend the decision was still open. BYTE's last print issue still gets written about as the end of an era in technology publishing. It was. It was also the right call for the business. Both of those were true on the same day, and a leader has to be able to say both. Managers are carrying more of this, and doing it alone The numbers say this matters more than most leaders admit. Gallup's research has held for a decade that managers account for at least 70 percent of the variance in employee engagement across business units. As far as the team is concerned, the manager is the job. And managers are carrying it worse. Gallup's 2026 State of the Global Workplace report puts manager engagement at 22 percent in 2025, down from 31 percent in 2022, with five of those nine points lost in a single year. The same report found leaders feel more stress, more anger, more sadness, and more loneliness day to day than individual contributors, with loneliness running ten points higher. The person with the most influence over how everyone else experiences work is increasingly the person most alone in it. That's what happens when leaders try to carry both loyalties without a system for doing it. Give, Ration, Hold: three zones, three different rules The advice leaders usually get here is to be friendly but not friends, approachable but with boundaries. It's not wrong. It's just not usable, because it treats warmth and authority as one dial where turning up one turns down the other. They're not one dial. What a leader offers the team falls into three different zones, and each one runs by its own rule. Here is an Uphoff Advisory Framework you can download and use in your business. Use it to check which way you lean when the pressure is on. Download Give, Ration, Hold → View the full Uphoff Advisory Framework library → How each zone works Give freely. Warmth, respect, candor, your actual personality, your time when someone's world is on fire. These never get rationed, not even during a layoff, especially not then. This is the zone that keeps you yourself. The leader who goes cold to protect themselves hasn't gotten tougher. They've gotten worse at the job, because people stop telling a cold leader the truth. Ration. Access, confidences, after-hours closeness, who hears what first, which friendships deepen and how visibly. This is where boundaries actually live. Warmth stays constant; intimacy is managed. You can be the same person with everyone and still not be equally close to everyone, and on a team, uneven closeness reads as favoritism long before anyone says so. Hold. The decision criteria, the performance standard, the interests of the business. These get set before you need them and stay put when you do. When a hard call comes, it should read to the team as a standard being applied, not a friendship being broken. The standard does the work so the relationship doesn't have to absorb it. The two most common failures are mirror images. The hardened leader rations what should be given, goes guarded and formal, and end… 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

  6. Oct 26

    Your Team Got More Productive. Did Your Business?

    Every new dollar of revenue, another card on the wall. This fall I've heard the same story three times, from three very different businesses: a B2B agency, a PE-backed marketing services firm, and an AI consultancy. Each has rebuilt real work around agentic workflows, and the gains they describe don't round to a percentage. They round to a multiple. Ten times. Sometimes more. The productivity is real. The payoff isn't automatic. A multiple of output is not a multiple of anything you get paid for. If you bill by the hour and your team now does in one hour what used to take ten, you just gave your clients a 90% price cut. You worked hard for it. They didn't ask for it. And the invoice looks exactly the same as it did last year, only smaller. The number everyone quotes isn't the number that pays Two numbers are getting blurred together in almost every conversation about agentic AI in services businesses. Pull them apart and the picture gets much clearer. The Output Multiple: how much more a person produces on a given workflow now that agents do most of the execution. This is the multiple everyone quotes. The Scale Multiple: how much more revenue each person brings in: revenue per employee now, divided by revenue per employee the year before agents. $150,000 a head to $300,000 is a Scale Multiple of 2. This is the number that shows up in the P&L, and eventually in the valuation. The gap between the two is what you're handing your clients for free. Last week, in Before You Flatten the Org Chart, I argued that revenue per employee is the number to measure before you restructure around AI. This is the next question: how did the number go up? Two businesses can post the same improvement and be building two different companies. Revenue per employee is the right yardstick because it's the one software companies use to grade themselves, and services businesses are about to be graded the same way. The median private SaaS company now does about $175,000 of ARR per employee, according to Benchmarkit's 2026 data. The largest agency holding company runs well below that. WPP booked £11.4 billion of revenue less pass-through costs in 2024 with an average of 111,281 people, per its 2024 results: about £102,000, or roughly $130,000, per person by my math. Margins tell the same story. The average digital agency earned a 13% net margin last year, according to Promethean Research. Its size data is the whole problem in one line: agencies under ten people run 19% margins, and agencies with more than 50 people run 8%. In the agency business, getting bigger makes the margin worse. That's been the history of the services model, where every new dollar of revenue shows up with a new salary attached. The holding companies are raising revenue per person the old-fashioned way The largest agencies already know the labor model is breaking, and they're responding by taking people out. WPP went from 108,044 people at the end of 2024 to 97,388 at the end of June. In March its CFO, Joanne Wilson, told analysts the company intends to "move away from time and materials and decouple revenue from headcount." Omnicom, which bought rival holding company Interpublic (IPG), expects to end the year with roughly 105,000 people, down from about 120,000. The cuts are the core of a $1.5 billion annual savings target from the merger, $1 billion of it from labor. Its CFO added that the cuts "really have not impacted client-facing people very much at all." One holding company CEO, quoted anonymously in Forrester's Predictions 2026, put the plan in a sentence: "By 2028, we'll double profits and halve the people." That raises revenue per employee. It does it by shrinking the denominator, not by growing what each person produces. I've led companies through structural disruption more than once, and the pattern holds every time: cost cuts buy time, and the only thing that buys a future is a new answer to "what exactly are customers paying us for?" Publicis is the useful exception. It grew 5.0% organically in the third quarter, after 4.8% in the second, while its peers cut. That's a data business winning share, built on Epsilon and now LiveRamp, not a smaller team doing more. Scale still works for the company that owns the data layer. It stops working for everyone billing hours on top of it. Outside services, the gap is starker. On October 21st, Meta reported third-quarter revenue up 26% from a year earlier, with headcount down 15%, to 66,775. That’s revenue per employee up roughly half in a single year, from more revenue and fewer people at the same time. Meta isn’t an agency. It’s the comparison your board will make anyway. The market doesn't pay for margin by subtraction Cutting your way to a better ratio has a second problem: buyers and investors can tell the difference. Kroll's Summer 2026 software sector update makes the point with public software companies. Median EBITDA margins barely move across growth rates: 28.9% for companies that are shrinking, 25.1% for companies growing faster than 20%. The multiples move enormously, from 2.4 times next year's revenue for the shrinkers to 7.2 times for the fastest growers. The market is paying for growth, not margin. Services businesses won't be graded more kindly. A firm that gets to 40% EBITDA by cutting people is more profitable and no more valuable. A firm that gets there by growing revenue per person has built something a buyer will pay a multiple for. Where your business sits on the map Here is an Uphoff Advisory Framework you can download and use in your business. Plot your Output Multiple against your Scale Multiple and you land in one of four places. Download The Scale Multiple Map → View the full Uphoff Advisory Framework library → What each square tells you Business as usual. Neither number has moved. The agents are a pilot, a tool, or a line in the credentials deck. Giving it away. Output is up sharply and revenue per person hasn't followed. This is where most of the productivity stories live today: real gains, priced by the hour, passed straight through to the client. Margin by subtraction. Revenue per person is up because headcount went down, not because anyone produces more. This is the holding company playbook. It works until there's nobody left to cut. Team Scale. Both numbers are up, and the gain shows up in the P&L. Small senior teams, agents doing the execution, revenue per employee climbing past the software median. Team Scale is the next rung up from the model I described in Solo Scale: The New Business Model AI Just Made Possible. Solo Scale is one expert with agents, running from $250,000 to $5 million in revenue. Team Scale is a small senior team doing the same thing at roughly $400,000 of revenue per employee: $2 million to $3 million with five to eight people, up to $25 million to $30 million with 60 to 75. At the holding company rate, booking that same $30 million takes more than 200 people. Three ways firms are getting there: build it, buy it, or sell it The three conversations I opened with map to three different routes into that top-right box. I've changed identifying details; the math is theirs. Build it. The PE-backed marketing services firm built a series of agentic workflows. Before, it hired three people, on average, for every incremental $750,000 of revenue. Since then it has added about $5 million of revenue, from new clients and from expansion inside existing ones, with headcount flat. Under the old ratio that $5 million would have meant 20 new hires. It made none. Its Scale Multiple will take a few quarters to read. The twenty salaries it never added show up in the P&L today. Buy it. The agency is using an acquisition to add AI capability, with a target of taking EBITDA from 20% to 40%. That plan can go two ways, and the math shows why the route matters more than the number. Cut about a quarter of the cost base with revenue flat and 20% becomes 40%. That's margin by subtraction. Or hold the team roughly where it is, grow revenue by about 60%, and keep co… 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

  7. Oct 21

    I Asked AI Who to Hire. It Recommended Me. Once

    B2B buyers still build shortlists. They just don’t build them alone anymore. Google “Tony Uphoff” and you get about 86,000 results. Thirty-five years of bylines, press releases, bios and CEO announcements make me discoverable, and by extension the two businesses I started in February. As long as someone is searching for my name. AI is a different story. This month I asked AI the questions my audience and prospective clients ask: who to hire, who to call, what to read. 19 prompts across ChatGPT, Gemini, Perplexity and Google AI Overviews, tracked in Ahrefs Brand Radar from October 10 to 17, produced 59 answers about my two businesses and me: • Uphoff on Media showed up in 0 of 12 category answers. • Uphoff Advisory showed up in 1 of 43. • “Who is Tony Uphoff?” Every engine that answered knew the career. The models know who I am. They almost never recommend what I’ve built. That gap is where B2B buying has gone, and most companies aren’t measuring it. Search didn’t die. It changed jobs It used to be where buyers found you. Now it’s where they check what the machine recommended. In a March 2026 G2 survey of 1,076 B2B software buyers, 51% said they now start their research in an AI chatbot more often than in Google, up from 29% a year earlier. In a Semrush survey of US B2B professionals fielded in March and April 2026, the most common workflow among those who use AI at work (41%) was to start with AI, then verify its answers through traditional search. I’ve been brought in to transform two businesses built on the last era. At Business.com and ThomasNet.com, helping B2B buyers find suppliers was the product. The function hasn’t changed: connect the buyer and the seller. The form has. The machine does the finding now, and the shortlist comes back already built. That’s the new sequence. The machine drafts the shortlist. The human validates it: checks the names, reads the reviews, asks a colleague, asks the model a follow-up. In the same G2 survey, 69% said a chatbot led them to choose a different vendor than they had planned, and one in three bought from a vendor they had never heard of before. The buyer still makes the call, but from the machine’s list. If you’re not on it, you’re counting on the buyer to go looking, and those numbers show how often the machine’s list wins. For two decades the scoreboard was discoverability. SEO taught every marketing team to rank, earn the click and get found. Most companies carried that scoreboard straight into AI. They ask a model about themselves by name, get an accurate answer and conclude they’re covered. That’s discoverability in a new channel. The scoreboard that matters now is recommendation: when a buyer asks for your category, does the model put you on the shortlist? For most companies, the pattern in my results is a false positive. A well-known founder, CEO or legacy brand makes the business feel discoverable. Ask the model about the leader and the answer sounds right. Ask it what a buyer would ask, and the company rarely makes the list. Measure only discoverability and you’ll never see the gap. Answer engines describe the company you used to be All three engines that described me still call me CEO of Pipeline360. I led its carve-out and built it as a standalone business, and since February I’ve served as a board advisor. Only one of the three mentioned Uphoff Advisory, the firm I run now. The sources included org-chart aggregators, executive databases and old press releases. Answer engines default to what the web has said about your company most often, and most often usually means longest ago. If you’ve changed leadership, renamed a product, repositioned or entered a new market in the past two years, assume the models are describing your old company to your new buyers. The stale record costs you twice. The machine uses it to decide whether to recommend you. The human uses it to check the machine’s work. Fixing that is the job of answer engine optimization (AEO), and most companies haven’t started. A Corporate Ink study reported by eMarketer in May found only 29% of marketers actively track and measure AI visibility. The idea travels. The name stays behind Ask the same engines “What is Demand-as-a-Service?” and all three define it accurately, each one citing Pipeline360’s own pages. I helped create it as the foundational strategy when we built Pipeline360. None of the answers mention me, and for Pipeline360 that’s exactly right: the company owns the product and the category language. For any business that runs on a point of view, it’s a warning. The models credit whoever publishes the clearest explanation, not whoever had the idea first. AI shortlists are built from other people’s shortlists The one time Uphoff Advisory made a list, the question was narrow: “Who advises founders of B2B information and data businesses?” ChatGPT listed it second of four. The broad questions, the best strategic advisors for B2B media and who to call about a turnaround, returned nothing. The citations explain why. When the models answered category questions, they rarely cited the firms or newsletters they named. They cited roundups: “best AI newsletters” and “best B2B marketing podcasts” lists from Zapier, Content Allies, Breaking B2B, The B2B Playbook and Dupple. The AI shortlist is assembled from lists other people already published. A May 2026 study by Maryanna Franco and João da Silva, run on Friction AI’s platform, found the same mechanism at scale. They sent 14,140 queries about twelve activewear brands to five AI engines. Nike and New Balance carry the identical Google Knowledge Graph description: footwear company. Nike appeared in about 71% of athleisure recommendation answers. New Balance almost never did. In third-party content, Nike shows up alongside lululemon roughly 50 times more often than New Balance does. Equally discoverable. Only one recommended. In The Buyer Left the Building, I argued the shortlist gets built before a human enters the conversation. In The CPM Collapse, I called 2026 the agentic inflection for B2B research and vendor discovery. This is what both look like from the vendor’s side. Change the scoreboard first If you’re building, running, funding, or starting a B2B information, marketing or technology business, here’s the sequence. I’m running it on my own businesses now. Move 1 changes what you measure. Move 2 serves both sides, because the machine and the human read the same record. Moves 3 through 5 earn the recommendation. 1. Change the scoreboard. Rankings, traffic and AI mentions tell you whether you’re discoverable. Add the number that tells you whether you’re recommended: how often you make the list when a buyer asks for your category. Report it first. Write 10 to 15 prompts the way your buyer asks them: the category, the problem, “who should I hire for.” Add two or three that ask about you by name. Run them across ChatGPT, Gemini, Perplexity and Google AI Overviews and track two numbers: how often you make the list, and whether the description is right. A tool like Brand Radar does this weekly. A spreadsheet and an hour a month works too. 2. Fix the record before you chase the shortlist. Read the sources the models cite when they describe you. Update the pages you control: your site, LinkedIn, leadership bios, press page. Request corrections on the ones you don’t: org-chart aggregators, executive databases, directory listings. After any leadership change, rebrand or new pricing model, assume the models haven’t heard. This is also the record your buyer reads when they check the machine’s work. 3. Publish the canonical version of your ideas. If you coin a term, a model or a method, publish the definitive explanation under your brand, with your name on it, before anyone else writes the explainer. Then make sure the people who write about it link back to yours. 4. Win the narrow question first. Pick the most specific way a buyer describes the problem you solve and own that phrase before you chase the broad category. “A… 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

  8. Oct 19

    Before You Flatten the Org Chart, Make Sure You Can Measure What AI Delivers

    I’m a fan of radical thinking. It forces you to test assumptions you didn’t know you were making. You don’t always have to agree with it. Agreement was never the point. The point is whether it makes you think. Jack Dorsey and Roelof Botha’s essay, From Hierarchy to Intelligence, did that to me. It lays out how Block is rebuilding itself as a company organized as intelligence rather than hierarchy, and it came a month after Block cut its workforce by about 40%. Most of what gets written about AI and organizations stops at copilots, a smarter assistant bolted onto the same old structure. They go further and ask whether the organization itself still needs to exist in its current form. It pushed me past the easy cliché that AI just means fewer middle managers. Here’s where that thinking led. The oldest constraint in business Dorsey and Botha trace two thousand years of the same fix. The Roman legion built command around eight soldiers to a tent. The Prussian General Staff existed to route information up and down the chain. Daniel McCallum drew the first org chart in the 1850s to keep a 500-mile railroad from killing people. Every version since, from Taylor’s pyramid to the matrix, worked around one constraint: a human brain can only hold so much context, so organizations add layers, and layers slow down information. Spotify, Zappos and Valve tried to break the pattern. All three scaled back into hierarchy or hit a ceiling. Dorsey and Botha’s claim is that AI removes the constraint, not the org chart around it. What Block actually built Block is building a company world model (what the business knows about its own operations) and a customer world model (what it knows about the people transacting with it), and using both to compose solutions directly, without routing decisions through management. What’s left are three roles: * Individual contributors who build * DRIs who own outcomes across whatever teams a problem touches * Player-coaches who develop people instead of running status meetings No middle layer required. Money is the one signal nobody fakes Here’s the sentence that matters most in the whole piece. “Money is the most honest signal in the world.”Jack Dorsey and Roelof Botha, From Hierarchy to Intelligence People lie on surveys. They abandon carts. They ignore ads. But when they spend, that’s the truth. Block can build a company-as-intelligence because it sits on both sides of millions of transactions every day, through Square and Cash App. That gives it a signal almost nobody else has: honest, at scale, compounding. Where this breaks for the rest of us That’s also where the model runs into trouble for most of the people reading this. Very few B2B information, marketing, or technology companies have anything close to a money-grade signal. What they have instead is engagement data: opens, clicks, content consumption, lead scores, intent signals bought from a vendor. I’ve run enough pipeline reviews to know how much of that gets gamed on purpose, by the same marketing teams reporting on it. The essay’s point about people lying on surveys is more true of modern B2B marketing than its authors probably intended. A lead score isn’t a fact about someone’s business. It’s a fact about how well your content ranks. Most B2B signal sits at the wrong end of the spectrum That gap is the actual work before anyone in B2B tries to run Block’s playbook. Think of it as a spectrum, honest signal on one end and performative signal on the other. Self-reported data and form fills sit at the weak end: cheap to produce, cheap to fake. Here is an Uphoff Advisory Framework you can download and use in your business. Put every number your leadership team reviews each week on it, and see how many sit right of center. Download The Signal-Honesty Spectrum → Engagement metrics sit a notch above: harder to fake outright, but easy to optimize for the metric instead of the buyer. Usage depth and renewal behavior sit higher still, because pretending costs the customer something real. Contract value, expansion revenue, and churn sit closest to money itself. The businesses already living near that end of the spectrum are the ones actually positioned to build a world model worth trusting. Everyone else is redesigning an org chart around a signal that isn’t honest yet. Everyone else is redesigning an org chart around a signal that isn’t honest yet. This isn’t just Silicon Valley talking It would be easy to dismiss this as some crazy idea from the tech elite. Don’t. The data says otherwise, and it’s not subtle. The number of managers and executives at public companies fell 6.1% between May 2022 and May 2025, according to Live Data Technologies. Gartner predicted in October 2024 that through 2026, one in five organizations would use AI to flatten their structure, eliminating more than half of current middle management positions. We’re in the last quarter of that window. I’ve been the skeptic on AI-attributed layoffs. In June I called most of it a misattribution problem: pandemic correction and PE margin engineering, relabeled as AI. That’s still true of last year’s numbers. But manager headcount specifically, not total headcount, is the number that’s moved. Nobody announced that. They just stopped backfilling the role. This isn’t theoretical, and it isn’t just Block. GitLab and Coinbase both cut management layers this year and pointed to AI when they did. Cloudflare’s cuts reached middle management too. Google said last year it had eliminated 35% of its managers overseeing small teams. Intuit, Meta, Cisco, Atlassian, Salesforce, and Oracle all paired 2026 layoffs with AI investment or efficiency claims. Block didn’t start this. It’s just the company doing it with the strongest claim to a signal that actually justifies it. Revenue per employee is how the market keeps score Block’s reorg came with a scoreboard, and it isn’t the org chart. Run Block’s own numbers: * 2025: $10.36 billion of gross profit with 10,205 people. About $1 million per person. * The plan: $12.2 billion for 2026 with just under 6,000 people. The stock jumped more than 20% after hours. * Now: guidance raised twice, most recently in August to $12.51 billion, after second-quarter gross profit grew 25%. About $2.1 million per person, double where it started the year. The market wasn’t paying for the layoff. It was paying for the output per head. Then: a vanity stat. For most of my career, revenue per employee was a number nobody built a valuation around. A 2013 analysis of public software companies found it had almost no relationship to market value. Hiring waves, sales ramp times and company age moved it more than strategy did. Now: the cleanest read on scale. AI changed what the number measures. It shows whether technology or headcount is doing the work, and that’s the question every buyer and investor is asking. Here’s where the bar sits: * Marketing agency average: about $163,000 per employee * Median public software company: about $395,000 * Median AI-native software company past $100 million of ARR: $655,000 (ICONIQ, September 2026) * Top quartile of that group: $890,000 Download The Revenue per Employee Ladder → Why it’s hard to fake. Anyone can say AI made their team more productive. Revenue per employee shows whether the productivity reached the P&L. That’s why I added it to the Uphoff Value Index this quarter as a driver in all eight categories we benchmark. A services firm approaching the software standard earns up to a full turn of EBITDA. Two agencies with identical P&Ls, one running with 68 people and one with 40, now land nearly $1 million apart. One caution from the operator’s chair. Revenue per employee can be gamed too. Push work to contractors or offshore partners and count only full-time staff, and the number looks better than the business is. Expect buyers to start asking for revenue per dollar of total labor cost, not just per head. Measured honestly, it’s the closest thing most B2B companies have to a money-grade signal about themselves. Marketing and sales brea… 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

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About

Uphoff on Media podcast: A practical take on how technology is rewriting the way B2B companies get built and run, and how to get ahead of it. For those founding, running, and funding B2B information, marketing, and technology companies. tonyuphoff.substack.com

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  • Creator
    Tony Uphoff
  • Years Active
    2026
  • Episodes
    71
  • Rating
    Clean
  • Copyright
    © Tony Uphoff
  • Show Website
    Uphoff on Media Podcast

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