Tag: Building & Scaling Teams

Org design, hiring philosophy, performance in complex orgs

  • Team sizes are not design choices. They’re cognitive limits.

    Team sizes are not design choices. They’re cognitive limits.

    Team sizes are not design choices. They are cognitive limits. The recurring numbers that show up in military units, religious communities, hunter-gatherer bands, and commercial organizations are not management philosophy. They are a property of the animal doing the work, and any organizational structure that pretends otherwise pays a measurable tax in friction, communication overhead, quiet attrition, and decisions that arrive three weeks late.

    Two. Four to six. Eight to twelve. Twenty to twenty-five. Fifty. One hundred and fifty. The specific numbers recur across centuries and industries. In the Roman legion and the US Marines. In religious communities and hunter-gatherer bands. In tech companies, sales organizations, and the advice experienced managers give each other about when to split a growing team. It is not a coincidence. It is cognitive architecture. The constraint is no longer technology. The constraint has always been the brain doing the coordinating.

    Dunbar’s layers

    The research most commercial leaders eventually bump into is Robin Dunbar’s. Dunbar is a British anthropologist who, in the early 1990s, proposed that the size of a primate’s social group is constrained by the size of its neocortex. Extrapolating from primate data, he estimated the human number at around 150 — the number of people with whom any one of us can maintain a stable, recognisable, mutually-active relationship. He published it in the Journal of Human Evolution in 1992, and the number has been running through management literature ever since.

    The part that gets talked about less, but matters more, is that Dunbar’s 150 is not a single flat layer. It is the outer ring of a nested set, each layer roughly three times larger than the one inside it:

    • ~5 — your closest support group. The people you would call in a real emergency.
    • ~15 — your sympathy group. People whose loss would significantly affect you.
    • ~50 — your band or clan. People you know well enough to share deep context with.
    • ~150 — your active community. Stable, recognisable, mutually reciprocal relationships.
    • ~500 — acquaintances.
    • ~1500 — faces you can still recognise.

    These layers show up in the research almost regardless of whether the subject is a tribal society, an office workforce, or a social-network friend graph. And they map astonishingly well onto the team sizes that commercial organizations stumble toward by trial and error — not because anyone read Dunbar, but because the alternatives don’t work.

    Round-G candidate G01-HC-editorial-figure: HC1 — central figure + concentric silhouette tiers (matches #4511 aesthetic)
    A central figure surrounded by expanding tiers — 5, 15, 50, 150.

    The military got there first

    Armies have been experimenting with how to organize humans under extreme stress for two thousand years, and they arrived at exactly these numbers through pure selection pressure. Smaller was too fragile. Larger fell apart under fire. The numbers that survived are the numbers that work.

    A Roman legion’s smallest unit was the contubernium — eight soldiers who shared a tent, a mule, a mess, and most of their waking life. Eight. Right at the boundary between the 5-person inner layer and the 15-person sympathy group. The Romans knew nothing about neocortex ratios. They noticed that a group of eight held together in a way that a group of four or a group of sixteen did not.

    The modern US Marine Corps fireteam is four. The squad is roughly 13. The platoon is 30 to 40. The company is 100 to 150. The same ratios, twenty-one centuries later. The cognitive limits haven’t moved, because the brain they are about hasn’t.

    The tech industry rediscovered the same numbers

    The technology industry discovered the same structure and gave it different names.

    Jeff Bezos’s two-pizza rule — a team should be small enough to be fed by two pizzas — is a practical restatement of the 5-to-8 cognitive sub-layer. Amazon did not get there via anthropology. They got there by watching their own product teams stall every time they grew past the point where the whole group could fit around one table.

    Scrum teams are officially 7 ± 2 — the current Scrum Guide recommends 3 to 9 members — which echoes George Miller’s 1956 paper on the working-memory limit of around seven chunks. Miller was not writing about teams. The cognitive limit he found on how many things we can juggle at once maps cleanly onto how many people we can coordinate without losing track of where everyone is.

    Fred Brooks, in his 1975 book The Mythical Man-Month, observed that adding people to a late software project makes it later, because every new person increases the number of pairwise communication channels by roughly n(n–1)/2. Seven people means 21 channels. Ten means 45. Fifteen means 105. The coordination tax is quadratic, and it surfaces as “mysterious” slowdowns at exactly the team sizes where the math stops being manageable.

    W. L. Gore & Associates, the Gore-Tex company, built Dunbar’s number directly into its real-estate strategy. Founder Bill Gore had a rule: every time a building exceeded 150 employees, they built another building. He was running Dunbar’s ceiling inside his facility planning decades before Dunbar had published the paper.

    The Ringelmann effect, documented in 1913 and one of the oldest findings in social psychology, is the same story in a different register: as group size grows, the effort each individual contributes goes down. People pull harder on a rope when there are fewer of them holding it. Max Ringelmann measured it with actual rope-pulling experiments, and the finding has been replicated many times since in workplace and sports settings.

    Nano Banana round-2 variant R07-c09-overlay-A: C09-A — two-pizza with overlay text
    The two-pizza team — Bezos’s practical statement of the cognitive sub-layer.

    The role change: the first-line manager span is a cognitive limit, not a cost line

    A first-line manager’s direct-report span is not a matter of preference for most cognitive work. It sits around 5 to 7. Push it to 10 and managers stop coaching and start triaging. Push it to 15 and the role has reverted to being an individual contributor with a different title. Organizations that scale cleanly keep that first layer tight even when the spreadsheet says it is expensive — because the spreadsheet is not pricing the coordination tax that a wider span produces downstream.

    Minimalist line graph showing communication-channel count rising quadratically as team size grows from 2 to 15
    Coordination overhead grows quadratically with team size.

    The org change: 50 and 150 are hard boundaries

    The sub-team that actually owns a piece of work should be closer to 5 than to 10. Not because small teams are faster in principle, but because the communication-overhead curve gets steep fast after 7. Bezos was right about this, and almost every high-performing team of any reasonable size runs its real work through an informal group of four or five — regardless of what the reporting structure says on the org chart.

    When a function crosses 50 people, it needs an operational substructure. Tribes, chapters, pods, whatever the label — or the Dunbar sympathy layer breaks. When the people in a team stop knowing each other well enough that a death in someone’s family would visibly register with everyone, culture starts dying quietly. By the time anyone notices, six months have usually been lost.

    When an organization crosses 150, it runs two cultures whether the leadership admits it or not. The question is only whether the split is designed deliberately or happens by default. Organizations that handle the ceiling well accept it and build deliberate boundaries. Organizations that handle it poorly spend years pretending 400 people are “all one team.”

    Minimalist org-chart diagram with a horizontal dashed line labeled 150 separating a large unified structure above from subdivided smaller groups below
    Cross 150 and you either build deliberate substructure or get default fragmentation.

    The strategic consequence: org design is surrender, not construction

    Good organizational design is mostly a process of surrender. The cognitive architecture of the humans running the teams picks team sizes for you, and the only real choice is whether to build the org chart around what actually works or to fight it and pay the tax. Every commercial organization that has tried to force a bigger number — a 12-person manager span, a 30-person “small team,” a 300-person “family culture” — has either quietly subdivided itself into groups that look suspiciously like the Dunbar numbers, or lost the thing that made it work.

    AI augmentation does not move the cognitive ceiling. It moves the throughput below the ceiling. An IC managing four AI agents is still operating inside a span of four. A manager coordinating seven sub-teams of augmented ICs is still operating inside a Dunbar-5 layer. The numbers that governed organizational design before agents are the numbers that will govern it after.

    Round-G candidate G03-SEMI-fireteam: SEMI — fireteam of 4 around laptop + overlay (matches two-pizza warmth)
    Small intimate teams stay where the work actually gets done.

    So what boards should do

    Boards should design operating models around the Dunbar layers and treat AI-augmented throughput as a multiplier on what each cognitive unit can do — not as a license to stretch the unit past its ceiling. The specific actions sit at four layers: first-line spans at 5 to 7 even under headcount pressure; sub-team ownership at 5; operational substructure at 50; deliberate cultural boundaries at 150. These are not target numbers. They are discovered numbers. Every other structure is an argument with biology, and biology does not negotiate.

    The Roman legions did not know about neocortex ratios. The Marines do not design their fireteams around anthropology papers. Jeff Bezos did not cite Dunbar when he ordered the pizzas. All three converged on the same numbers because the numbers are a property of the animal doing the work, not the work itself. The job of an organizational designer is to notice this — and then get out of the way.

    References

    • Dunbar, R. I. M. (1992). “Neocortex size as a constraint on group size in primates.” Journal of Human Evolution.
    • Dunbar, R. I. M. (2010). How Many Friends Does One Person Need? Harvard University Press.
    • Miller, G. A. (1956). “The Magical Number Seven, Plus or Minus Two.” Psychological Review.
    • Brooks, F. P. (1975). The Mythical Man-Month: Essays on Software Engineering.
    • Hackman, J. R. (2002). Leading Teams: Setting the Stage for Great Performances. Harvard Business School Press.
    • Ringelmann, M. (1913). Early social-loafing experiments, Annales de l’Institut National Agronomique.
    • Gladwell, M. (2000). The Tipping Point. Popularised Gore’s rule of 150 for the management audience.
    • The Scrum Guide — current recommended team size: 3 to 9 members.
  • AI is becoming the narrative for layoffs. It is not yet the cause.

    AI is becoming the narrative for layoffs. It is not yet the cause.

    What was announced

    Through the week of February 16–22, 2026, the AI-cited layoff story moved from edge case to mainstream framing. AI was cited as the rationale for 4,680 February job cuts in the U.S. — roughly 10% of the month’s total. Baker McKenzie announced 600–1,000 layoffs (up to 10% of global headcount) framed as a pivot to AI-augmented service delivery. Dow disclosed 4,500 cuts in January with explicit AI-strategy framing. A Harvard Business Review piece in the same window argued that companies are laying off based on AI’s potential, not its measured performance. An Oxford Economics report from January concluded that many AI-cited layoffs were the consequence of past overhiring, not present AI productivity.

    What it means

    Two things are happening at once. First, AI productivity is real for specific workflows and starting to show up in unit-cost reductions. Second, “AI” is becoming the public-facing rationale for cost actions that boards and CEOs have wanted to take for other reasons — overhiring during 2021–2022, deteriorating margins in slower-growth segments, restructuring to a target operating model that was already in motion. The two stories overlap, and the public communication does not distinguish between them.

    For employees, the framing matters because “we are restructuring” and “AI is replacing your role” carry different signals about whether the function comes back. For investors, it matters because the market is pricing AI-cited cost reductions as durable while restructuring-cited cost reductions are typically priced as one-off. CEOs who choose the AI framing get a multiple uplift. That incentive structure tells you why the framing is becoming dominant.

    Andreas’s view

    My read on this: the next 12 months will see a steady drift toward AI-as-explanation in layoff communications, regardless of whether AI is the underlying driver. The reason is not deception — it is signaling. CEOs need a forward-looking story that the cost base will stay reduced, and “AI productivity” is a cleaner story than “we hired too aggressively in 2022.” The public record will eventually reconcile this; quarterly earnings will reveal which companies actually shipped the productivity gain and which simply downsized.

    I don’t think the workforce numbers are yet the right metric to watch. The right metric is the ratio of revenue per employee in the months after the cut. If revenue per employee climbs durably, the AI framing was substantively correct. If it plateaus or reverses while operational quality declines, the framing was a positioning move and the company will be hiring back inside 18 months — at higher cost and lower morale.

    The way I see it: when a CEO presents an AI-cited workforce action, the productivity model behind it should be specific enough to name which workflows, which output measures, which time horizon, and which control group. Where those answers are vague, the action is restructuring with AI vocabulary. That is not necessarily wrong, but the distinction matters — and I think it matters most at the board level, where the conversation should reflect what is actually driving the decision.

    Three things I’m watching

    Three things I’m watching as this plays out:

    1. I’ll be watching whether companies maintain a clear internal distinction between AI-driven productivity actions (with a workflow-level model behind them) and AI-framed restructuring actions (justified by other reasons). Both can be valid; conflating them confuses execution, and the ones that keep the distinction clean are more likely to deliver what they promised.
    2. The companies that track revenue per employee monthly for the 12 months following any AI-cited workforce reduction will have the clearest view of whether the productivity gain actually materialized — and I’ll be looking at that number as the most honest signal in the public record.
    3. I’ll be watching how specific companies get in their external communication around AI-related workforce changes. Vague “AI is making us more productive” framing tends to erode credibility internally faster than a precise statement of which work has been automated and which has been redesigned — and over the next year, that credibility gap will start showing up in retention and hiring data.

    References and related signals