The Human Is the New Bottleneck

Running five to twenty AI agents across every function of his company, CEO and author of the piece finds that the human is now the bottleneck, as each agent finishes in minutes and requires a decision, making attention the scarce resource. Citing research by Harold Pashler on the central bottleneck, Sophie Leroy on attention residue, and Gloria Mark on interrupted work, the author argues that the serial nature of human decision-making and the cost of task switching cannot be trained away, turning the swarm of agents into a denial-of-service attack on attention.

The Human Is the New Bottleneck AI didn't make me faster. It made me the bottleneck. Running five to twenty agents across every function of my company, the slowest part of the whole thing is the one part I can't optimize away: me. The dirty secret of running a company on AI is that the AI stopped being the bottleneck. I am. On a busy day I have somewhere between five and twenty agents working for me at once, and barely any of it is code. Agents drafting marketing copy, agents prepping sales follow-ups, agents working through an HR policy, agents pulling apart a data export, agents writing the first pass of the thing you’re reading. Every function of my job as a CEO now has a swarm behind it. Each one finishes in a couple of minutes and comes back wanting a decision. I’m the slowest part of my own company, and there’s no GPU I can buy to fix that. You might reasonably ask why I run that many. Because the economics leave no choice. An agent costs cents and there are effectively infinite of them. My attention costs a day and there’s exactly one of me. When one input is free and unlimited and the other is scarce and fixed, the rational move is to push the free one as hard as the scarce one can absorb. Running a swarm isn’t a flex, and it isn’t a mistake. It’s just what the incentives do to you. Which is the whole problem: the thing you can’t get more of is you. The story everyone tells about AI and productivity is that the machine does the work and you get more done. That’s half true. The machine does the work. Whether you get more done rides entirely on the part that didn’t get faster: the human deciding what happens next. Twenty agents, one me You feel it earlier than you’d think. At three or four agents it’s a pleasant buzz. Push past your real limit, wherever yours sits, and it stops failing gracefully. It fails four ways at once. I lose the thread and forget what the marketing agent was even drafting. I stop reviewing properly, waving through a sales email I haven’t really read. My thinking goes shallow, so the calls that need me most, the actual judgment, get the least of me. And underneath it all I’m just fried, wrung out by the switching, not the work. The word that matters is “once.” These aren’t four problems to fix one at a time. They’re one problem wearing four faces. It’s not a skill issue. It’s architecture. All four break together because they all draw on the same resource: me. And me is not something you can parallelize, however good you get at it. At the very bottom, the wiring is serial. Harold Pashler spent decades on the central bottleneck https://www.semanticscholar.org/paper/Dual-task-interference-in-simple-tasks:-data-and-Pashler/003e8d2197146d561aa5a8dc343cc55efe5dce8f : even at the millisecond scale, when your brain selects a response to one task, the selection for a second one waits. Not slows down. Waits. His experiments are about reflexes in a lab, not about juggling agents, and I’m stretching them to make a point. But the direction holds. If the brain won’t parallelize decisions at the millisecond floor, it doesn’t suddenly learn the trick when the tasks are minutes apart and a hundred times more complex. The cost doesn’t disappear at human timescales. It compounds. And at human timescales it has been measured. Sophie Leroy named what a switch leaves behind: attention residue https://ideas.repec.org/a/eee/jobhdp/v109y2009i2p168-181.html , the part of your head that stays on the task you just left. The agent you closed is still burning cycles in your skull while you stare at the next one. Gloria Mark’s work on interrupted work https://ics.uci.edu/~gmark/chi08-mark.pdf puts a number on the recovery: north of twenty minutes to fully return to something once you’re pulled off it, and you detour through two other tasks on the way. Now trigger that every ninety seconds, twenty times over. It’s a denial-of-service attack on your own attention. You can’t train this away. It isn’t discipline. It’s wiring. Not all velocity is the same Here’s the part that sharpens the claim instead of softening it. Not all agent work costs you the same. Point an agent at a big mechanical grind, set it running for hours, and walk away. It doesn’t need you every ninety seconds, so you pay almost nothing. Set a loop and go live your life. For that kind of work I referee my agents /blog/agents-write-and-review-my-code-i-referee more than I babysit them, and it scales beautifully. The tax lands entirely on design and exploration, the work where every couple of minutes there’s a fork only you can take. That’s the work that actually matters, whether it’s a product decision or a positioning call, and it’s exactly the work where you are the ceiling. Which is why throwing more agent-speed at it changes nothing: the constraint was never the agents. Make them twice as fast and they just reach your desk twice as often. Speed on their side becomes pressure on yours. Somebody already solved this, for drones The good news is that this has a literature, and an older one than you’d expect. Twenty years ago, researchers working out how one operator could fly several drones hit exactly this wall. They named it fan-out https://dl.acm.org/doi/10.1145/985692.985722 Olsen and Goodrich, 2004 : the number of autonomous agents a single human can actually run. And they reduced it to a ratio. Neglect tolerance is how long an agent keeps doing useful work while you ignore it. Interaction time is how long you have to attend to it when it needs you. Your capacity is just the first divided by the second. Yes, a coding agent isn’t a drone: it’s async, it’s resumable, a stalled agent waits politely instead of falling out of the sky. But fan-out is a ratio, not a claim about aircraft. The math doesn’t care what the agent is, or whether it’s writing code or a press release. Missy Cummings studied operators supervising multiple UAVs https://dspace.mit.edu/bitstream/handle/1721.1/90285/CummingsBruni.pdf at MIT and found the trap at the end of the road. Push automation so one person can run more vehicles. Past a point, situation awareness collapses and complacency sets in. They stop noticing when something is wrong. If you’ve ever waved through an agent’s output because nine others were waiting, you’ve lived that result without the grant funding. We’re optimizing the wrong side of the equation The ratio isn’t just a diagnosis. It’s a to-do list with two items, and almost nobody is working on either. Raise neglect tolerance: let agents stay useful longer without you. Real checkpointing, so they don’t stall on the first ambiguity. Agents that ask one sharp question before they start instead of five vague ones after. Trust levels, so proven work runs unattended and only the risky calls surface. Every one of those buys back attention. Or lower interaction time: make each hand-back cheaper. A summary you can judge in ten seconds because the agent tells you what changed and why, not a wall of output to reconstruct. The whole code review bottleneck /blog/the-code-review-bottleneck-is-you is an interaction-time problem wearing a different hat, and it isn’t only about code: the same tax applies to reviewing a campaign, a contract, or a hiring plan. And some of this isn’t tooling at all. Batching buys me more than anything I’ve installed: stop checking each agent the instant it pings, let a few finish, review them together in one pass instead of paying the switch cost twelve separate times. During real thinking I sometimes do the opposite of this whole post and run a single agent on purpose, because the fan-out that’s right for grinding is wrong for judgment. Now look at where the industry is actually spending. Almost all of it goes into making agents faster and smarter, which raises the number lining up and does nothing for the ratio. Making a bottleneck’s suppliers faster doesn’t widen the bottleneck. It just deepens the queue. The interface is the neglected variable. I’ve called AI a human-interface nightmare /blog/ai-is-a-human-interface-nightmare before, and this is the same nightmare one level up: HI, human intelligence, is the bottleneck, and you can’t optimize it away. We weren’t built to be optimized. The only move left is the handoff between the AI and the HI, the mapping from one human to many machines. That’s the actual frontier, and it’s strangely empty. You can’t parallelize a human I don’t have this solved. On a good day I hold the line around half a dozen agents. On a bad day I’ve got fifteen going and I’m underwater by noon. But I’ve stopped waiting for a faster agent to save me, because a faster agent just reaches my desk sooner. You can’t parallelize a human. You can only get better at the handoff. That’s the whole game now, and we’ve barely started playing it. Related posts How to Be a Great Software Engineer in 2026 /blog/how-to-be-a-great-software-engineer-in-2026 The framework hasn't changed. The weight of each skill has. Read more → /blog/how-to-be-a-great-software-engineer-in-2026 The Flow Is Gone /blog/the-flow-is-gone I used to hold entire systems in my head. Now I hold seven terminals. The trade was worth it, but something real got lost. Read more → /blog/the-flow-is-gone