The Evaporation Thesis

How jobs disappear from the inside out. And what they become.

I automated a workflow last week that I had no idea how to do.

I had no idea how to do it manually. I described what I wanted to happen, an AI agent figured out the sequence, and the thing was running inside of a couple of hours. A process that would have taken me days to learn and weeks to systematize was just... done.

My first thought was delight. My second thought was: I just permanently removed something from my workload. My third thought persisted. If I can do this, and I am not an engineer, then the 78% of knowledge workers already using AI tools at work can do it too. Some of them probably already are.

That third thought became an analysis. I’ve spent the last several weeks running it through the projection framework I use for structural trend work, the same one that produced the timelines in The Last Normal Year and AI Is Coming For Your Job After All. Those pieces tracked the macro forces: corporate adoption, infrastructure buildout, the top-down decisions that lead to headlines about layoffs (I’m updating those monthly now—subscribe for first looks at what’s coming). This one tracks something quieter and, I think, more consequential.

The bottom-up disappearance of work itself.

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Your job is a label on a bundle

Here is a sentence that sounds obvious: a job is a collection of workflows.

The marketing coordinator’s job is 15 or 20 workflows bundled together and assigned to one person for 40 hours a week. Write the brief, pull the data, build the report, schedule the posts, update the tracker, send the recap. Each workflow is a discrete sequence of steps with an input and an output. The job title is just a label on the bundle.

We’ve always talked about automation as something that happens to industries, to companies, to categories of workers. A factory installs robots. A bank deploys chatbots. A CEO announces a restructuring. Those are real. I’ve been tracking those dynamics for 18 months and the causal chains are on schedule.

But something else is happening at the same time, and it runs on a completely different clock.

Individual workers are automating their own workflows. One at a time. From the bottom up. With tools they chose themselves, often without telling anyone.

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“I know kung fu.”

The first time someone automates a workflow they didn’t know how to do, it’s a moment. It’s a bit like Neo downloading kung fu in The Matrix. Shock first. Then delight. Then a question: what else can I download?

That question is the ignition point. Because once a worker automates one workflow successfully, they have more free time and more skill at automation simultaneously. The next workflow goes faster. The one after that, faster still. Each removal feeds the next. It compounds.

The data says this is already widespread. Microsoft and LinkedIn reported in 2025 that 78% of knowledge workers are bringing their own AI tools to work. Sixty-three percent said they’d continue doing so even under an explicit ban. The fastest-growing open-source project in history right now is OpenClaw, an agentic AI platform. Not a chatbot. An autonomous agent that can execute multi-step workflows on its own. That project hit 250,000 GitHub stars in 60 days, crossing adoption levels that took Linux years to reach.

And cost is collapsing. The price of running an AI agent through a complex task sequence dropped roughly 7.5x in 16 months. We are approaching the point where running an autonomous agent for an hour costs less than a cup of coffee.

The consulting layer is forming too. For every person who figures out automation on their own, there will be ten who hire someone to do it for them. Workflow architects. AI consultants. The personal trainers of the new economy, showing up with a clipboard and a subscription and rebuilding someone’s role in an afternoon. That service layer removes the technical skill bottleneck entirely. You don’t need to be an engineer to have your workflows automated. You just need to be willing to pay someone who is.

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Layoffs don’t tell the whole story

The workflows leave, one by one. What happens to the job?

Think about what a role looks like after 40% of its workflows have been automated away. The person is still employed. The title still exists. But the scope of the work has contracted. Management may not even know. The worker has a structural incentive to hide the compression. If the boss finds out the 40-hour job takes 15 hours, the role is at risk. So the automation stays in the dark.

This creates a specific dynamic. Compression builds up silently at the individual level. Dozens, then hundreds, then thousands of workers are sitting on roles that have quietly shrunk to a fraction of their original scope. The organization doesn’t see it because nobody is reporting it. The macro data doesn’t see it because BLS surveys measure jobs, not the workflows inside jobs.

Then something triggers the reveal. A round of layoffs. A performance audit. An economic downturn that forces the question. And when it surfaces, it surfaces all at once. The Thanos snap. The compression was already complete. The snap is just the moment everyone finds out.

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Experience premium = higher octane

Which roles compress and which ones transform? The answer showed up in an unexpected place.

The Dallas Fed published research earlier this year on what they call the experience premium: the wage gap between entry-level and experienced workers in the same occupation. That gap turns out to be a remarkably clean proxy for how much tacit knowledge a role contains. High experience premium means the job depends on judgment, relationships, domain expertise that can’t be written down. Low experience premium means the job is mostly defined by its workflows.

When you cross that data with actual AI exposure measurements, a pattern emerges.

Low experience premium, high AI exposure: the workflows automate, and there’s nothing underneath to sustain the role. These are the evaporation candidates. Office administrators, routine data analysts, customer service representatives, entry-level content producers. Roughly 28 to 30 million workers in the US fall into this category. About 38%-41% of the white-collar workforce.

High experience premium, high AI exposure: the workflows automate, but the residual human contribution (the judgment calls, the client relationships, the strategic direction) keeps the role alive in compressed form. These roles transform. They get smaller and stranger and more valuable per hour. Senior financial advisors, experienced software architects, seasoned project managers, creative directors. Another 22 to 23 million workers.

The entry-level pipeline is where both categories converge into something more concerning. In the high-experience-premium professions, the junior positions that once trained the next generation of experienced practitioners are themselves low-experience-premium roles. They evaporate too. The senior financial advisor keeps their job. The junior analyst who was supposed to become the next senior advisor? That position just quietly stopped being posted.

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Decentralization in increments

I guess we’re talking about movies today.

In Spike Jonze’s Her (2013), Theodore Twombly writes emotionally sophisticated personal letters for other people. That is his job. It requires voice, emotional intelligence, relational understanding. The workflows that once supported that kind of work (the research, the drafting, the formatting, the scheduling) have been absorbed by AI systems so seamlessly that nobody thinks about them anymore. The world Theodore lives in still has work. Lots of it. What it doesn’t have are jobs in the way we’ve understood them for the last century: fixed bundles of workflows assigned to a single person for 40 hours a week.

That is the end state I see in the data.

Work persists. The human contribution (judgment, creativity, relationship, taste, accountability) persists and probably becomes more valuable. What dissolves is the container. The 40-hour bundled role. The job title that was really just a label on a collection of workflows that no longer need a person to execute them.

The existing literature on the future of work has described this destination. Ravin Jesuthasan and John Boudreau wrote an entire book called Work Without Jobs that maps the organizational redesign path to get there. Their version is managed, deliberate, top-down. Organizations deconstruct roles into tasks and recombine them fluidly.

What I’m seeing is the unmanaged version. Workers automate their own workflows. The jobs shrink in the dark. Organizations discover the compression after the fact. The residual work recrystallizes around whatever human contribution remains. Same destination. Radically different mechanism. And much, much faster.

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Evaporation happens faster than you think

The honest version of the timeline looks like this. A leading edge of technical early adopters is already there. Their roles have compressed meaningfully in the last two to three quarters. A second wave, accelerated by the service layer and rapidly improving tools, reaches a broader professional population within two to four quarters. The median economy, where the macro data finally catches up to the micro reality, follows on a scale of years. But not many years.

If that sounds fast, I’ll point out that the same projection framework estimated the top-down displacement timeline at six months back in August 2025. That felt unthinkably soon at the time (consensus at the time was 3-4 years). It has tracked on schedule since. Q4 2026 feels the same way Q1 2026 felt for the original analysis. Unthinkably soon and probably right on track.

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Would I leave you hanging without an answer…?

The instinct, when looking at something like this, is to diagnose and stop. Here is the structural force. Here is the timeline. Good luck.

I don’t think that’s useful. The structural force is real, but so is the fact that the human contribution at the center of these roles is becoming more, not less, important. The workflows go away. The judgment stays. The relationships stay. The ability to see a situation clearly and make a call that matters, that stays.

But what we do with the transition period? The window between now and the moment the macro data catches up. That window is where the decisions are.

For executives: the compression is already happening inside your organization. Discover it on your terms or react to it on its terms. One of those is a redesign. The other is a crisis.

For people in roles defined by their workflows: the experience premium data is specific about what endures. Tacit knowledge. Judgment. Relationships. Domain expertise that can’t be codified. The adaptive move is to develop those capacities now, while the role still exists in a form that lets you practice them.

For everyone watching: the work doesn’t go away. The container changes. And the people who understand the new shape of work before it arrives will have built something that compounds in the other direction. Not a shrinking role, but an expanding one. One where the workflows are handled and the human part, the part that was always the point, is finally all that’s left.

That process is already underway. And I hope your awareness is catching up quickly. The changing nature of work is much deeper than layoffs. You have a couple quarters to lean in on your terms.

The best time to plant a tree is 20 years ago. The second best time is now.

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If you’re a coach working in this environment and want to see where your specific practice sits in these four chains before the pivotal Q2 window closes, I’m running a limited pilot of a self-administered diagnostic. You’d get the same structural map I build before a first session. Details here: form.typeform.com/to/QlPtNfbL