AI Isn’t Replacing Jobs. It’s Replacing Friction
Last Tuesday at 7:30 am, I opened my laptop to a single document.
Three pages. Built overnight by an agent. Yesterday’s customer escalations with the threads attached and triage recommendations queued. Pipeline movements with the deals that stalled flagged. The two regulatory items from Canada and the US that actually mattered for our roadmap, summarized in two paragraphs each.
Six months ago, that document required two analysts roughly five hours every morning to produce. Now nobody produces it. It just arrives.
That was the morning I stopped reading the debate over AI and jobs.
The agent didn’t take anyone’s job. It killed a workflow. The two analysts are still here. They’re spending Tuesday morning doing the work that actually requires their judgment, not assembling a status document that one piece of software can build while they sleep. The headlines say jobs are getting replaced. What’s actually getting replaced is something else.
Call it friction labor. The cumulative human attention spent on tasks that exist because no system surfaces the answer automatically. Not the work itself. The maintenance contract for the work.
And almost every AI conversation in business right now is missing it.
The public conversation defaults to a frame at the job level. “X” percent of roles will be automated by 2030. “Y” million workers displaced. McKinsey produces the chart. Goldman publishes the estimate. The numbers feel rigorous, and the debate feels important.
It’s the wrong unit.
David Autor at MIT has spent more than 25 years on this question, and his core finding is uncomfortable for both sides of the debate. Automation rarely eliminates jobs wholesale. It eliminates tasks. The remaining jobs are reorganized around tasks the technology can’t handle.
The bookkeeper example is the canonical proof. Between 1980 and 2018, computers took over most of the routine work bookkeepers did. Employment in the role fell by about a third. Real hourly wages for the people still doing it rose nearly 40 percent. The job didn’t get replaced. The task mix did. The bookkeepers who survived ended up doing more interesting work and getting paid more for it.
That pattern doesn’t fit neatly into either narrative. It isn’t “AI is coming for everyone.” It also isn’t “AI changes nothing.” It’s: the unit of disruption is the task, not the job, and we’ve been measuring the wrong thing.
A loan officer’s role contains around 30 distinct tasks, going by the official task taxonomy. A processor’s looks similar in scale. Most of those are friction labour: pulling data, formatting documents, checking statuses, chasing missing pieces. A small fraction is judgment work. Saying “AI is replacing the loan officer” obscures the actual shift. The role survives. The task mix changes.
Walk through what friction labour looks like in any company, and the inventory is always the same.
Rekeying data between two systems that don’t talk to each other. Summarizing a thread of emails into a status update that somebody needs in a different format. Checking whether an upstream thing happened before triggering the downstream thing. Monitoring a queue. Reformatting an output for the third audience this week. Writing the recap nobody attended the meeting to read.
None of that is knowledge work in any meaningful sense. It’s plumbing.
Companies hire knowledge workers and load them with plumbing. The smartest people in your building spend most of their week moving water between buckets, because no system was ever built to handle it and nobody had the budget to staff a real one.
Ajay Agrawal, Joshua Gans and Avi Goldfarb wrote about this directly in Power and Prediction. Their argument: AI is a steep drop in the cost of prediction, and the real economic value isn’t in “point solutions” that speed up individual tasks. It’s in “system solutions,” workflows redesigned around the assumption that prediction is now cheap.
The implication is the part most companies miss. Every friction task in your organization exists because somebody had to predict, monitor, or reconcile something at a cost the system couldn’t absorb. The friction is a fossil. It’s the shape of what you couldn’t automate the last time you reorganized.
When the cost of prediction collapses, that fossil layer becomes optional.
I’ll tell you what the morning brief actually does, because the abstraction makes it sound bigger than it is.
It reads our Slack channels. It reads my inbox. It checks the CRM for movement on the deals I care about. It scans two regulator news feeds. Then it writes three pages and emails me a single document at 6am. That’s it.
The first version took me about four hours to build. The current version runs on a connected Cowork stack and gets better every time I tell it what to cut. It cost less than a single analyst’s monthly seat.
It replaced about eight hours of weekly analyst work spread across two people. They didn’t lose their jobs. They got their Tuesdays back. They’re now doing the work they were hired to do in the first place: the qualitative interview synthesis, the deep review of deals that stalled, the customer relationships that need actual human attention.
What got killed wasn’t a job. It was a maintenance contract on a workflow that existed only because we couldn’t afford to keep paying humans to do it manually but also couldn’t afford to live without it.
The value didn’t show up as a headcount reduction. It showed up as a clock speed gain.
That’s the part companies are mostly not measuring, and it’s where the gap is opening.
Counting jobs is easy. Counting friction is hard.
Friction labor is invisible because it’s distributed. Five minutes here, twenty minutes there, three hours on a Friday afternoon. It never appears as a line item on a P&L. Companies measure headcount and revenue per employee. Neither of those tells you anything about how fast a question gets answered, how fast a decision routes through the building, how fast a deal moves from interest to close.
That last number is the one about to bifurcate every industry.
Two companies, same headcount, same tools, same product. One operates at three times the clock speed because they’ve systematically replaced friction labor with agents over the last 18 months. The other is still in a planning offsite debating which percentage of headcount to cut.
By the time the second company finishes its plan, the first one is already in the next market.
The debate over job replacement is going to keep going. It’s the easy debate. Politicians will hold hearings. Consultancies will sell decks. Every quarter someone will publish a new number and a new chart.
The companies that win the next decade aren’t asking how many people they can cut. They’re asking what friction they can kill.
The two questions sound similar. They produce completely different companies.
Your competitors aren’t replacing people.
They’re replacing the drag.
Stop counting jobs. Start counting friction.
Chris Grimes is the founder of FundMore, an AI native loan origination platform. FundMore builds agentic mortgage and lending infrastructure for institutional clients across Canada and the US.