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.

View the full Uphoff Advisory Framework library at uphoffadvisory.com/frameworks.

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

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 break first

If you’re running marketing or sales, none of this is abstract. These are the two 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 that looks nothing like hiring for lead-gen volume. Coaching it looks nothing like reviewing MQL counts in 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. Regie.ai’s analysis of LinkedIn data found full-cycle AE seats 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 quota progress.

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 three. 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 two years: skipping straight to flattening the org before doing the harder, less 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 isn’t, that’s the actual project, and it has nothing to do with headcount.

Keep going: AI Stopped Being a Story. Now It’s in Your Multiple and B2B Marketing Is Driving Forward, Looking Backward

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.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC, a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business.