45 People, $200M Revenue. The Question Nobody's Asking About AI and Your Team Size.

All those AI note-taking apps are barnacles. They're just wrong. No one is thinking about meetings correctly in the age of AI. And the meetings keep multiplying. You probably spent 12 hours in meetings last week. And that's if we're lucky because when the studies come out, it's like 12 hours on average, 16 hours for people managers, 23 hours for execs. The numbers keep going up. The standup could be a Slack message. The cross functional sync where eight people attend and two people talk. The alignment session that produces an alignment session. You get the idea. Meetings have tripled since 2020. Nobody can explain where the new ones come from. AI did not fix this problem. And AI adding notetakers isn't fixing it either. Why? Because AI broke the math on team size and not in the way that people tend to think. AI is changing how our teams operate and we're not thinking about it and we're just trying to do what we usually do. So you think you have a meetings problem, but you really don't. You have a teamsize problem. And before you jump to the conclusion that I am recommending firing people, I am not going to do that. And you're going to see why. Your teams are still three to 10 times too big though and every AI tool you adopt is making it worse. Like all those note-taking apps, it's amplifying output through a coordination structure that is fundamentally broken. Team size determines every hour of how we spend our working days. How many Slack channels we have to monitor, how many approvals we have to wait for, how many people we have to align before we can ship anything. It shapes our costs. It shapes our speed and it shapes the quality of every decision the org makes. Ultimately, it shapes what gets into customers hands. AI broke all of that and we never figured out the root cause and we're not responding to it. Well, I'm going to get into how we think about it correctly, how we think about team size and why this doesn't mean firing people. First, we're going to talk about the number five. The number of communication pathways between people in a group is defined mathematically. If it's five people, it's 10 pathways. Every person can hold the full map in their head. If it's 10 people, it jumps to 45 pathways. 20 people, 190. You think I'm joking? That is actually the number of lines that you would draw if you had to connect all of the dots in your team, each dot being a person, to all of the other dots. At a team size of 20, it becomes a joke. Robin Dunar's research on primate neoortex size established in 1992 that the human brain has layered limits on relationship complexity. This is not new. It's back in 1992. You have five for your core group, 15 with deep trust, 50 for meaningful working relationships. 150 for stable social connections. Army mathematicians confirmed the pattern empirically. Groups of five communicated most effectively with effectiveness peaking again at 15,50 and 150. The military has tested this because their stakes are so high they have to get it right. So a US infantry fire team is four people plus a leader. The layers above track Dunar's hierarchy almost exactly. So that's the squad, the platoon, the company. Later on, Jeff Bezos landed on the same number from a different direction with a two pizza team. Fred Brooks got there in 1975 through software engineering. Adding people to a project made it slower, not faster. You know what? It's been a long time since 1975 and I still see seuite executives who think adding software engineers makes it go faster. It doesn't. The communication overhead always overwhelmed the added capacity. Three disciplines, evolutionary psychology, military preparedness, software engineering, all came to the same answer in group size. The human brain can sustain deep high context coordination with about five people. What AI changes is not that number because AI didn't rewire our brains. It's the consequences of getting the number wrong that just changed dramatically. So what did AI actually change? The standard narrative that we hear all the time is that AI makes people more productive so teams can be smaller. It's true as far as it goes, but it's not far enough. Before AI, a fivep person team produced X output. Right? Adding a sixth person gave you more capacity but with diminished returns because coordination overhead grew faster than output. Toby Lutkkey of Shopify calls this a 10x loss of productivity with each addition beyond five. After AI, the same five person team produces 5 to 10x more than before. The evidence is in the revenue per employee data of AI native companies. You can look it up, but they're all stunningly larger than typical SAS companies, right? lovable hitting hundreds of millions of ARR for their employee count. Midjourney, same story. 11 Labs, same story. I can go on and on. Anthropic, same story. OpenAI, same story. I've talked about this before. The SAS benchmark per employee for revenue has been in the hundreds of thousands of dollars, usually below half a million. AI native companies are running 5 to 10 times that typically. So, here's what reframes this conversation. If each person on a fivep person team is producing that much more, say in the range of $2 to $3 million a year in value, the coordination cost of the sixth person is no longer a minor tax. It's a catastrophe. That sixth person doesn't just need to be good. They need to justify their coordination cost against a baseline where every existing member generates output that previously required an entire department. The penalty for adding a human to a team increases as the per human output increases. When each person produced $250,000 a year, the coordination cost of person number six was manageable. At $2 million per person, it's measured in millions of lost productivity. The bar didn't rise a little, it rose by an order of magnitude. This is why your meetings are killing you. Every meeting exists because someone decided coordination was worth the cost. When your per person output was $250,000, it often was worth the cost. $2 million per person, most of those meetings end up being net negative, destroying value at a rate that scales with how productive your people are. And here's where I talk about something that gets misunderstood a lot. Every conversation that I hear about AI and Teams obsesses over volume. more code, more content, faster. This leads to disastrously incorrect organizational decisions, decisions that are just plain wrong. Volume is no longer a scarce resource. So why are we talking about it so much? AI made volume cheap. What's scarce is correctness. whether the thing you shipped is actually right, architecturally sound, strategically coherent, right for the customer, polished, free of the subtle errors that look fine in a demo and compound into real failures in production. A Harvard Business School Field experiment published in 2025 tested this directly. Researchers studied 776 professionals at Proctor and Gamble on real innovation challenges. Teams using AI were three times more likely to produce ideas in the top 10% of quality. Not three times more output. You notice three times more likely to be right at the highest level. The researchers also found that AI broke functional silos. Both R&D and marketing produced more balanced integrated ideas with AI, extending each person's competence into adjacent domains. This is the mechanism that makes small AI augmented teams more powerful than large ones. Five really excellent people using AI can each operate across a broader domain than they could alone. They don't need 10 specialists in 10 narrow lanes. They need five generalist architects who use AI to extend their reach and who use each other as verification against AI's errors. But verification is the catch. Every piece of AI output requires human judgment to validate. In a five-person team, each person reviews a manageable volume against a coherent shared context. And if you get into a world like I've described in dark factories where you have agents reviewing for correctness, the team can easily layer up a level of abstraction and manage that agentic workflow. They know what right looks like because they all hold the same mental model and that enables them to scale. In a 20 person team, the AI output multiplies by another factor of four, but the shared context degrades catastrophically. So they hold meetings to synchronize. Those meetings generate more decisions, more AI tasks, more output to verify, more meetings. Wes McKini described this as the agentic tarpit. Agent sessions producing contradictory plans, AI generating technical data at machine speed. The working prototype has become trivially easy to produce. Getting from prototype to production still requires for most organizations a fair bit of human judgment. If you are one of those one in a hundred orgs that have set up agents to run the entire production tool chain, good for you. Give yourself a pat on the back. For most of us, we're still working through the journey to more agentic production. And there is human involvement on the way to prod. And the humans doing that judgment need a shared model of what they're building. The larger the team, the weaker that shared model. So a team of five optimizes for correctness and a team of 20 is optimized for volume. In a world where AI makes volume free, optimizing for volume is optimizing for the wrong thing. This is why your big teams can feel productive. They produce a lot. There's lots of jura tickets. and also why they keep shipping things that don't quite work, that need rework, that require postmortems, and that spawn follow-up projects to fix the problems created by the last project. Volume masquerades as progress and leads to pseudo work. Correctness is progress. I've started using two archetypes that I think will land for you as we talk about team sizes. Scouts is the first archetype. Scouts operate alone. One person full AI toolkit defined mission. The work is exploration. Is this technology viable? Is this market real? Can we build a prototype? Scouts move fast because they have zero coordination overhead. Their constraint is one person's judgment. That's it. No peer review, no error correction. It's fantastic for exploration, but you probably need another pair of eyes for production. Peter Steinberger demonstrated the Scout model at its extreme. In roughly 60 days running four to 10 coding agents in codec simultaneously, he built Open Claw, which is an AI agent I've covered extensively. He built it in a language he'd never used. He directed agents at the architectural level while they handled execution. So, one person, 20 years of judgment, a swarm of agents, and the output was something that the world's most valuable companies were desperate to acquire. But the solo model has limits. It works when the work is exploration. high ambiguity, low coordination, a premium on speed and individual taste. Peter's vision of Open Claw was something he could create alone. He had it in his head. He made it exist. He made it real. But anyone who's used OpenClaw will tell you, and I've covered this extensively, it shipped with lots of holes. And so, it does not work to be a team of one when correctness requires multiple perspectives. And as you discovered, and as we've seen, Peter ended up joining OpenAI in order to translate his vision at scale. It does not work when the cost of being subtly wrong is very high. It does not work to be a team of one when sustained production is the goal for a long time. The one-person model is a great scout. The fiveperson model is a strike team, and both are correct for different missions. Let's talk about strike teams. Those are teams of five people with AI executing where correctness matters. The structural advantages only become visible when you think in terms of correctness first. Every person's AI generated output passes through at least one other brain that shares enough context to catch meaningful errors at the correct level of abstraction. If you're designing agentic coding systems, you're operating at a level above the code, but you're still operating with a layer of shared context that you can use to catch real issues. So, a team of five can cover product, engineering, design, data, and domain expertise, not necessarily with five different hats, but across that team of five together. That is the real minimum surface area for a complete decision. Below five, you tend to have blind spots. Above five, you tend to have silos. And in a team of five, there is nowhere to hide, which is exactly what you want. So scouts explore, strike teams execute, scouts map territory, strike teams build the road. Most organizations currently have neither of these units. They have oversized teams that are too slow for exploration, and too diluted for precision execution, burning their best people out on coordination overhead, and lowering the overall value of their output significantly. And they wonder why AI doesn't work, and they're stuck in meetings. Here is what drives me crazy about the conversation around AI and team size. Everyone frames it as a cost story. We can do the same work with fewer people. That's the headline. That's the strategy deck. Same mission. Fewer bodies. Lower burn. It's a staggering failure of imagination for companies with strong talent forces. You have 500 people. Each just got at least potentially 5 to 10 times more capable. The correct response is not I can run my company with 50 people. The correct response is I have the capacity of 2500 to 5,000 people. What was I previously unable to do? Your 500 person company just acquired the productive capacity of say a 3,000 person company without hiring anyone, without raising capital, without building new offices. You did not get a cost reduction. You got an army. The question is whether you have the strategic vision to deploy it or whether you're going to use effectively a fleet of aircraft carriers on the same fishing route your twler used to run. The companies that get this right aren't starting with the assumption they need to cut heads. Sure, some people will deliberately not want to make the transition to AI. I've seen that happen and that's sad. But where people are excited to move, you can recompose people into strike teams. You can massively expand the number of fronts that you're building on and you can deliver an extraordinary amount of value with a better team size equipped with AI. Think of a SAS company. I know SAS is not popular, but we're going to think of them anyway because guess what? They're still a system of record. Think of a SAS company with 400 engineers maintaining one product restructured into 80 different strike teams. How much can they build? Can they build a platform with 10 products? It's entirely plausible. Think of a regional insurer. They have 200 people. They serve three states. If they were reorganized into 40 strike teams powered by AI, would they have the capacity to serve 30 states instead and build products they can outsource today? It's plausible. Now, none of these companies need fewer people. They need the same people with domain expertise reorganized into smaller teams pointed at a mission that is five times larger. So, let me reframe the org size question entirely. It's not how small can we get. It's given that every five person team now has the capacity of a 50 person department. How many teams do we need to pursue the mission we actually want? Not the mission we settled for when headcount was expensive. For some companies, the honest answer will be fewer people because they don't have that ambition. The coordination roles, the management layers that existed only because the org was too big to manage itself, those end up going away. But for many companies, the right answer is keep your people, restructure how they work together, and go after something bigger than what you're doing today. And that's the part I just don't hear people talking enough about. The ambition expansion is missing. I hear efficiency, efficiency, efficiency. I hear right sizing. I never hear people say, "We just got handed a 10x multiplier on our workforce. We're going to 10x our mission." I think the reason nobody says this is organizational inertia. Leaders like to talk a big game about building strategy, but the reality is they mostly build decks around their current mission because that's all they've ever known. Their planning processes, their budgeting cycles, and strategic frameworks all assume that headcount is the binding constraint on ambition when it's not anymore. They've spent their careers in a world where we can't do that. We don't have the people was the final answer to any ambitious proposal. That answer has expired and most leaders haven't noticed. Lovable didn't start with 45 people and build a tiny product. They built a global platform serving millions and millions of users that touches every single industry on earth and has made them a unicorn so fast. Midjourney didn't look at a 100 employees and say, "Well, we need to focus on our niche." They went after the entirety of visual creation. These companies didn't use AI to shrink. They used small teams to think really, really big. One of the questions that remains unsolved is how you compose many many strike teams into a coherent organization. The answer I think tracks the same biological constraints that give us the number five. The layers scale and done by ratios. Five people make a strike team because five people is how we naturally coordinate. Three to four strike teams share a domain coordinated by a single person focused on interteam coherence. Three to four of those domains or clusters share a strategic objective. At each level, the leader is responsible for maintaining the quality of relationship required for the coordination being asked of them. The management layer is dramatically thinned out. You don't need project managers when AI tracks projects. You don't need coordination when there are fewer humans to coordinate with. But the taste layer gets more significant. You desperately need people who get obsessed with maintaining the standard of correctness. It's what Toby look at Shopify calls the constitution. The specific principles where a reasonable competitor would choose the opposite. In an organization of federated strike teams producing at 10x, the people who define and enforce that taste standard, what makes you uniquely you in the market are the most important people in the building. There is a hard correlary to the ambition thesis. If the optimal team is five exceptional people plus AI pointed at a mission five to 10 times larger than what you're doing now, you cannot afford a single weak link on that team. In a team of five, every single person occupies one of your 10 communication pathways. Every person's judgment gets multiplied by AI. A mediocre contributor doesn't just underperform, they consume a coordination slot without providing the judgment that justifies their cost. When their mediocre judgment gets amplified by AI, they generate verification burdens on everybody else around them. That's called the AI slop tax. They make the team actively worse, not just by contributing less, but by consuming the team's most precious resource, the shared attention required to maintain correctness. This is why teams like lovable hire former founders. This is why Peter Steinberger could do what he did. Not because he could prompt an AI, but because he could architect a system at a level where agents could execute without him reading every line of code. The hiring question has transformed. Stop asking, "Can this person do the current job." Start asking, "Can this person be one of five whose taste and judgment will be amplified 10 to 100 times by AI? and can we point that team of five at a mission 10 times larger than what we're doing today? If you're wondering across your current teams who's ready, here's how you should test that. Give someone a real scout mission, a problem your company has been ignoring. Give them full AI tooling. Give them a week and give them a very clear objective. No committee, zero check-ins. What you're testing for is the cluster of traits that separates someone who can direct AI from someone who gets directed by it. Can they define the problem without being handed a spec? Do they know what right looks like at the architectural level, not the syntax level? Can they hold the whole system in their head? Do they default to action or getting permission? The uncomfortable part is that the results will probably not match your current rubric for performance reviews. Some of your highest rated people, the ones who are great at navigating large or structures, at running good meetings, at writing very clear status updates, may struggle. Those are coordination skills, valuable in a big team structure, but overhead in a strike team. Meanwhile, your most frustrating people, the ones who tend to ignore the meetings and skip them, who build things without asking, who occasionally ship something brilliant that nobody requested, may be exactly who you need. They've been fighting your organizational structure for years. And the strike team is the structure they were built for. But scout missions are diagnostic. They tell you who's ready. Now building the muscle across the org requires executive mandate. Toby lookie required every Shopify team to prototype with AI before beginning a real build. Every project, every team prototype phase AI first. He made AI fluency part of performance reviews and required teams to demonstrate why AI could not do a task before requesting headcount. The surface reading is, "Wow, this CEO is pushing AI adoption, which is frankly I saw the Toby looking memo come out in the spring of 2025." That's what the headline said. But the deeper reading is much more interesting. Toby built a systematic evaluation pipeline for his org. Every AI prototype generates a data point on what AI can and can't do in that domain. And when the next model drops, they have a pre-built test harness that reveals what's possible. So he's running these scout missions at org scale as a default practice. And every forced AI prototype ends up being a training route for the skills these strike teams need. The person who prototypes 10 times and fails seven has built more specification skill than the person who attended 10 meetings on AI strategy. You don't learn to direct agents by talking about it. You learn by doing it badly, seeing what breaks, and developing the taste to know why. The mandate also solves the cultural problem that scout missions by themselves can't fix. Some of your best potential strike team members will not volunteer for a scout type mission. They'll assume it's a trap or they'll assume it's extra work. An executive mandate that makes something like AI prototyping a default removes the permission barrier. Everybody gets the reps. The people who take to it self-identify faster than any talent review is going to surface them. Look, the reason your days are full of meetings is not that you have too many meetings. It's that you have too many people on too many teams and the coordination overhead of being the wrong size generates meeting after meeting. All of these meetings are good for startups that get AI notes on meetings and that tell you that that's the way to fix meetings. It's not. AI has just made this worse because it's increased the output per person without decreasing the cost of coordination. The structural unit of the AI era is the five person strike team. That doesn't mean that every company now needs to be five people. It means that five people is enough for a coherent mission and you should have the ambition to be able to handle multiple strike teams with multiple missions. This isn't new. What's new is that AI raised output per person by an order of magnitude, which raised the coordination cost of each additional person by the same order of magnitude. Five optimizes for the thing that matters, correctness. AI made volume free. Correctness is the scarce resource. And that's why the team of five still matters. But the right response is not to shrink to five. It's to restructure into teams that can radically expand your ambition. You didn't get a cost reduction. You got a force multiplier. The companies that will define the next decade are not sitting there cutting their headcount to protect their current margins on their current ambition. They're keeping their people. They're reorganizing into teams that make sense for AI. And they're going after missions that were impossible when each person was only producing $2 or $300,000 a year in enterprise value. And now those same people are retoled and they're producing millions. The question is no longer how do I do the same thing with less people. It's what becomes possible when every fivep person team has the capacity of a department and oh by the way that fixes the meetings because there's a lot fewer people worried about the same thing. The leaders who ask that question honestly will build the defining companies of this era and already are. The ones who don't are going to spend a lot of time in AI meetings. They're going to have a lot of AI notetakers producing very nice notes and they're going to be optimizing a structure that is already obsolete wondering why companies a fraction of their size are eating their lunch. So think about your team size. Understand the real implications of too many meetings on your calendar. take it seriously and restructure your team size so that you can maximize the value that you're getting without eating the coordination cost that used to be tolerable but that now costs everything in the age of AI. If your best people are five or six times more valuable per hour, why are they sitting in meetings? Why not put them against a mission in a small group and get them going against something that actually energizes them and dramatically expands your company's ambition? We've got team size all wrong in the age of AI and we're just not talking about it enough. I hope this inspires you to think a little bit differently about the blocks of meetings on your calendar next week and how team size can reshape all of that. Cheers.

My site: https://natebjones.com Full Story w/ Prompts: https://natesnewsletter.substack.com/p/executive-briefing-ai-raised-output?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true ___________________ What's really happening with AI and team size in your organization? The common story is that AI makes teams more productive so you can cut headcount — but the reality is more complicated. In this video, I share the inside scoop on why the five-person strike team is the structural unit of the AI era: - Why AI raised coordination costs by the same order as output - How scouts and strike teams map to different AI-era missions - What correctness-first thinking means for how you hire and build - Where the real opportunity is — expanding ambition, not shrinking headcount AI agents and LLMs didn't break your meetings problem — they amplified a team size problem you already had, and the leaders who restructure around small, high-judgment teams will build the defining companies of this decade. Chapters 00:00 Your Meetings Problem Is Actually a Team Size Problem 02:10 The Math of Communication Pathways 04:15 Dunbar's Number and Why the Military Cracked This First 06:00 What AI Actually Changed About Team Size 08:20 Why Volume Is Free and Correctness Is Scarce 10:45 The Harvard Study That Proves the Point 12:30 Scouts: The One-Person AI Strike Force 15:00 Peter Steinberger and the Solo Agent Model 17:10 Strike Teams: Why Five Is the Magic Number 20:00 The Ambition Failure Nobody Talks About 23:15 How to Compose Many Strike Teams Into One Org 25:40 The AI Slop Tax and the True Cost of a Weak Link 28:00 How to Test Who's Ready for the Strike Team Model 30:20 The Shopify Mandate and What Toby Lutke Got Right 33:00 Restructure for Ambition, Not Efficiency Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/