In this solo podcast, Tony Uphoff argues that companies should only restructure around AI‑driven productivity if they have a trustworthy, money‑grade signal—primarily revenue per employee—rather than relying on performative metrics like clicks, opens, or lead scores. He uses Block’s recent 40% workforce reduction and subsequent rise to $2 million gross profit per employee as a case study, showing how honest financial data enabled a successful reorganization. Uphoff introduces two practical frameworks: the "Signal to Honesty Spectrum," which maps business metrics from self‑reported, easily gamed data to transaction‑level revenue signals, and the "Revenue‑per‑Employee Ladder," which helps firms set realistic targets before flattening org charts. He highlights industry trends showing a 6.1% drop in managerial headcount at public companies and numerous AI‑citing layoffs at firms like GitLab, Coinbase, and Google. The episode then dives into the specific implications for marketing—shifting from lead‑score funnels to AI‑qualified, intent‑based leads—and sales—redefining SDR and AE roles as AI‑augmented functions. Uphoff warns that revenue‑per‑employee can be gamed via contractor accounting, suggesting future buyers will demand revenue per total labor cost. He concludes with actionable steps: map current metrics onto the honesty spectrum, assess where the business sits on the revenue‑per‑employee ladder, and only then consider flattening the org chart. Listeners are directed to his Substack for detailed frameworks and encouraged to follow the show.
Chapters
Hey, it's Tony. Plenty of people want to call this the peak of the AI hype cycle. Block's numbers say otherwise. In February, Block cut about 40% of its staff. 8 months later, it's running at roughly $2 million of gross profit per employee. That's double where it started the year. And it's raised its guidance twice. So the scale is real. That's not the problem. The problem is what comes next. Companies everywhere are flattening their org charts on the strength of productivity they're hoping for, not productivity they can measure. Block could do this because it runs on the most honest signal there is— money. Most B2B companies would be reorganizing around opens, clicks, and lead scores. So today, which signals you can actually trust, why revenue per employee just became the number buyer's price, and how do you know You're ready before you touch the org chart. Here's how to tell the difference. This episode is titled, Before You Flatten the Org Chart, Make Sure You Can Measure What AI Delivers. Okay, let's get to work. AI is delivering on the scale promise. Block doubled gross profit per employee by rebuilding around honest data. Most B2B companies don't have the signals to try it yet. 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 is not really 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 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 both had traced 2,000 years of the same fix. The Roman Legion built command around 8 soldiers to a tent. The Prussian General Staff existed to route information up and down the chain. Daniel McCollum 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 3 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. Okay, 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 3 roles: individual contributors who build, DRIs who own outcomes across whatever teams a problem touches, and 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. People lie on surveys, they abandon carts, they ignore ads, but when they spend, that's the truth. Block can build a company as intelligent 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. Look, 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 at 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. 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, but because pretending costs the customer something real. Contract value, expansion revenue, and churn sit closest to money itself. The businesses already living near the— 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. This isn't just Silicon Valley talking. It'd 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 of 2022 and May of 2025, according to Live Data Technologies. Gartner predicted in October 2024 that through this year, 1 in 5 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 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 signal— strongest claim to a signal that actually justifies it. Revenue per employee is how the market now keeps score. Block's reorg came with a scorecard that 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 bars sit. Marketing agency average, about $163,000 per employee. Median public software company, about $395,000. Median AI-native software company with more than $100 million of ARR, $655,000. Top quartile of that group, $890,000. 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 8 categories that we benchmark. A services firm approaching the software standard earns up to a full turn of EBITDA. 2 agencies with identical P&Ls, one running with 68 people and one with 40, now land nearly $1 million apart in value. 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 costs, 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 break first. If you're running marketing or sales, none of this is abstract. These are the 2 functions most exposed, and the exposure isn't only about headcount. It's about who you hire, how you coach them, and what good looks like once the AI is closer to the honest signal than your team is. Marketing. The funnel was never honest. Marketing has been running on performative signal for years, and the funnel proves it. Forrester has found that fewer than 1% of leads make it from the top of the funnel to a closed deal. The industry knows the model is broken, and vendors are already pitching a replacement. Docket calls it the agent-qualified lead, a lead qualified by an AI agent in a live buyer conversation, capturing real intent instead of inferring it from a click or a form fill. Hiring for what it— for that looks like nothing like hiring for lead gen volume. Coaching it looks nothing like reviewing MQL counts on a Monday pipeline meeting. The SDRs who survive aren't dialing more, they're coaching AI performance. Sales, the job is splitting in two. It's happening right underneath your comp plan. Reggie.ai's analysis of LinkedIn data found full-cycle AE seats are up 10.8%, while SDR headcount fell 22.7%. Two-thirds of teams cut SDRs; fewer than one in five added them. The SDRs who stayed are running AI on the accounts that matter, not working the phones. That's a different hire, managed a different way, measured against a different definition of quality. The playbooks most of us built our marketing and sales organizations on assumed a human was doing the qualifying, the outreach, and the judgment calls. Agentic AI is already doing large parts of all 3. Keep running the old playbook and you'll be coaching people to compete with a system that's already faster and can't be gamed. The real work comes before the reorg. That's the mistake I expect to watch a lot of companies make over the next 2 years, skipping straight to flattening the org before doing the harder, less exciting work glamorous work of finding or building a signal that can't be gamed. Speed is the advantage Dorsey and Botha are chasing, and they're right that it compounds. It compounds in the wrong direction just as fast when the thing moving fast is built on noise, not signal. Before anyone touches their org chart, they should answer one question honestly: What's the one number in this business that nobody can fake? If there's a real answer, the reorg is worth having, and revenue per employee will tell you within a year whether it worked. If there It isn't. That's the actual project, and it has nothing to do with headcount. Okay, that's the episode. If you want to put this to work, 2 Uphoff advisory frameworks are in the post on Substack linked in the show notes. The first is the Signal to Honesty Spectrum. Take every number your leadership team reviews each week and place it on the spectrum. If most of them sit at the performative end, That's your first project. The second is the revenue per employee ladder. Find where your business sits today, then decide where it needs to be before you touch the org chart, not after. And if you found this useful, please do one thing. Follow Uphoff on Media on Apple Podcasts or Spotify and leave a rating. It's the single best way to help another operator find it. I'm Tony Uphoff, back at it next week.
