Automation Mayhem: An AI Revolution Update

Price's Law, now on AI steroids

In many teams, output is unevenly distributed. Price's square-root rule came from work on scientific productivity; it is a heuristic to think with, not a law that tells you how many employees create half a company's value. In a hypothetical team of 100, the square-root illustration is 10. Measuring the real distribution is a separate task.

What's new is that the tail is getting heavier. The top performers in any field are now armed with tools that are themselves quietly superintelligent at the parts of their job that used to take half their week. The vital few were already vital. Give them Cursor, Claude, and an agent layer and they pull further ahead.

The small-team examples are interesting, but revenue-per-employee comparisons are easy to abuse. ARR is not recognized annual revenue, a fast-growing startup's headcount changes quickly, and contractors and infrastructure costs do not disappear because they are outside payroll. I would want matched dates and consistent denominators before claiming a new industry baseline.

A sharp engineer with a coherent product idea and good tooling can take on more work. That is my thesis, not evidence that an entire department is interchangeable with one person. Product judgment, customer trust, distribution, and accountability still have to come from somewhere.

The same dynamic plays out at the firm level. A few giant labs dominate the model layer. The companies with the most data, the most compute, and the strongest distribution have a flywheel that gets better with use. The digital era already concentrated wealth and productivity. AI is the same gradient, steeper.

Outsourcing to algorithms

Companies have been offshoring work for decades. The next move is not offshore. It's off-org. You don't hire the entry-level analyst, you point an agent at the data and it returns a report by lunch. The marginal cost of an additional "employee" is the cost of a query.

This is putting downward pressure on entry-level hiring in ways that show up in the data and even more in the anecdotes. Why hire a junior to draft the email when a model writes it in three seconds. Why hire the BPO seat when the chatbot handles the tier-one ticket without complaint. Why hire the analyst when the spreadsheet runs itself.

The result is a generation of leaner teams. Companies can stay small because most of the heavy lifting is no longer human. The Lean Startup turned out to be the warm-up act. The new ratio is whatever the senior team plus their AI tooling can sustain.

There's a related organizational question. Dunbar's research on social relationships is a useful prompt to think about coordination costs. It does not establish 150 as a universal maximum for a productive company. AI may help a small team stay effective as its work expands; whether that happens is something to measure.

The monkeysphere at work

Dunbar's number is the monkeysphere. The size of the group beyond which colleagues become "that random guy from accounting" rather than individuals. Human brains evolved to trust and coordinate in groups up to this scale. Beyond it, informal trust gives way to formal rules. Bureaucracy is what a primate species does when it grows past the limits of its own social cognition.

AI lets us route around this. Instead of one monolithic 5,000-person company, you can run a network of tight teams loosely coupled through software. Think Hollywood model. Small expert crews assemble for a project, ship, dissolve, reform. AI handles the coordination, the documentation, the institutional memory. The humans handle the relationships, the taste, the judgment calls.

Small teams can make informal coordination easier. Larger groups often need more explicit processes. There is no magic headcount at which engagement must collapse or bureaucracy becomes inevitable.

A "team of 5" with the right AI tooling can ship what used to take a 50-person org. A two-pizza team becomes a one-pizza team with a printer for second pizzas. The humans strategize and build relationships. The agents handle the support tickets and the data work and the first draft of every document.

The structural implication for large enterprises is that they should re-architect into many small semi-independent teams sharing common AI infrastructure. Internally it should feel less like a top-down org chart and more like a swarm of startups operating on shared rails. People are not wired to meaningfully connect with hundreds of coworkers. Keep the tribe small and let the machines bridge the gaps between tribes.

Automation at every level

When we talk about AI taking jobs, the image is usually a factory floor or a customer support seat. The real distribution is more interesting. Pressure is showing up at both ends of the org chart, often more aggressively at the top than people admit.

Entry-level and routine work is the easy story. AI can draft emails, summarize documents, and extract structured data. But task exposure is not the same as a job disappearing. McKinsey's July 2023 scenario estimated that activities accounting for up to 30% of US work hours could be automated by 2030. It did not say that 30% of US jobs would be fully automated. Adoption, demand, and job redesign determine how the task change reaches the labor market.

Senior leadership is being squeezed differently. AI is now genuinely useful for the work executives historically owned. Synthesizing large amounts of context. Running scenario analysis. Surfacing the option you didn't think of. There's no robo-CEO on the horizon, but there is an executive who is now competing with a tireless analyst that costs eight cents per query and never sleeps.

Middle management also contains tasks that may be automated: project tracking, status synthesis, and routine reporting. That is not a forecast that the whole role disappears. Decisions about priorities, people, and conflicting commitments still need an accountable owner. My bet is that teams will reorganize around that distinction.

What's not happening, despite a lot of noise to the contrary, is that AI is replacing human leadership. The soft skills, the moral judgment, the political navigation, the relationship work, the personnel calls, the ability to read a room and absorb risk and apologize convincingly when needed. These remain stubbornly human. The future of management is AI-augmented, not AI-replaced. The good managers will figure this out and use the tooling. The bad managers will pretend they don't need it.

So what

The shape of the company is changing. Some will be very small. Some will be vast networks of small things. Most won't last long enough to find out which they were. The labor market will sort and re-sort. The colleague in the next chair will increasingly mean either a teammate or a model. The most successful organizations will figure out how to combine the brilliant 10%, the cohesive 150-person tribe, the tireless agents, the accountable humans, and the platforms that hold all of it together.

If you're in the workforce now, the move is to bring your brain, your taste, your relationships, and your AI. You'll need all four. The companies that don't are running their primate operating system in a world that's running something else.

Sources and interpretation

• McKinsey Global Institute, July 2023: a scenario about work hours and occupational shifts, not a forecast that 30% of jobs disappear.
• Price's rule and Dunbar's number are used here as organizational analogies. The company-design conclusions are my interpretation, not measured consequences of those heuristics.
• This piece was originally published in June 2025. The October 2026 revision corrects the automation measure and removes unmatched revenue/headcount comparisons. The organizational predictions remain hypotheses.