Skip to main content
Paul Welty, PhD AI, WORK, AND STAYING HUMAN

· business

Busy is not a state

We've built work cultures that reward activity, even when nothing actually changes. In technical systems, activity doesn't count—only state change does. This essay explores why "busy" has become the most misleading signal we have, and how focusing on state instead of motion makes work more honest, less draining, and actually productive.

Duration: 4:16 | Size: 3.9 MB

I've spent a lot of time around people who are genuinely good at their jobs, and also completely worn out.
  • They’re in meetings all day.
  • They respond quickly.
  • They stay on top of things.
  • They’re clearly trying.

And yet, if you stop and ask a simple question (what’s actually different because of all this effort?) the answer is often… unclear.

That used to bother me in a vague way. I thought maybe I just didn’t like how modern work feels. Eventually I realized it’s more specific than that.

We’ve built entire work cultures that reward activity, even when nothing actually changes. And “busy” has become the most misleading signal we have.

Here’s the thing I learned from working with technology: in technical systems, activity doesn’t count. Only state change does.

  • A document either exists or it doesn’t.
  • A record is either updated or it isn’t.
  • Code is either committed, or it’s still just an idea someone has.

There’s no real concept of “kind of done.” No one says, “Trust me, the system is mostly updated.” But in human work, we live in that gray zone all the time.

We say things like:

  • “I’m working on it.”
  • “We talked about it.”
  • “It’s in progress.”
  • “We made some progress.”

Those sound responsible. They sound collaborative. They just don’t tell you what changed. They describe motion, not arrival.

This is where a lot of quiet stress comes from. When states are unclear, people start compensating on their own. They carry the uncertainty.

They wonder:

  • Did that meeting actually do anything?
  • Is someone else handling this, or should I be?
  • If I stop pushing, does this just stall out?

That constant interpretation is exhausting. In software, undefined state is treated as a bug. In organizations, we mostly just live with it, and then wonder why people burn out.

One of the most destructive phrases in modern work is “in progress.” It sounds harmless. Even virtuous. But it’s often a way of avoiding the only question that matters: What will exist when this is done?

If no one can answer that, work doesn’t move forward. It just stretches. Meetings repeat. Emails pile up. Everyone stays busy and slightly uneasy.

In technical systems, a process that never resolves is considered broken. In human systems, we often reward the people who are best at sustaining it.

There’s a very small shift that changes this immediately. Instead of asking, “What are you working on?” ask: “What will be different when this is done?”

That’s it.

That question forces clarity without being aggressive. It doesn’t demand heroics. It just asks for precision. And precision, it turns out, is kind.

This matters even more now, because of AI. AI is very good at generating activity: drafts, options, variations. All of it looks like progress. But if no one defines the target state, AI just accelerates the confusion.

That’s not an AI failure. It’s a human one we’ve been getting away with for years. AI didn’t create the problem. It just made it obvious.

When state is clear, something interesting happens: trust gets easier. You don’t need reassurance when a thing exists. You don’t need optimism when there’s an artifact. You don’t need to argue about progress when you can point to it.

Shared reality replaces interpretation. That’s how good systems feel calm: not because people care less, but because they don’t have to keep everything in their heads.

We resist this, I think, because state clarity feels risky. It forces commitment. It makes failure visible. It removes the cover of effort.

Activity is socially safe. State is honest. But honesty, in a well-designed system, isn’t punitive. It’s stabilizing. It lets people stop performing busyness and start doing work that actually resolves.

This isn’t about turning people into machines. It’s about acknowledging human limits.

Humans are not great at:

  • tracking invisible progress
  • remembering verbal agreements
  • operating indefinitely in ambiguity

Good systems don’t demand that we get better at those things. They take the burden off. That’s not cold or technical. It’s humane.

Once you start paying attention to state, a few things change quickly:

  • Meetings either produce something, or they don’t happen.
  • Work either completes, or stops cleanly.
  • Failure becomes survivable.
  • Progress becomes visible.

And “busy” stops being impressive. Work starts to feel more solid. More honest. Less draining.

This essay first appeared in Philosopher at Large, a weekly newsletter on work, learning, and judgment.
One essay a week on AI, work, and staying human
Short, honest writing on judgment, craft, and navigating what's changing. No spam, unsubscribe anytime.
podcast

The agent-shaped org chart

Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.

AI as staff, not software

Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.

Knowledge work was never work

Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.

The work of being available now

A book on AI, judgment, and staying human at work.

The practice of work in progress

Practical essays on how work actually gets done.

How do I get my dev team to adopt AI?

A stub on helping mixed-interest development teams find their own useful ways into AI.

Want to learn about agents? Talk to someone who ran an agency.

I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that's the best preparation for running a fleet of AI agents ... because the problems are identical.

Your AI agents need a water cooler

We run a twelve-session AI fleet that coordinates through an IRC breakroom. A friend asked: why are you making AI agents act like humans? The answer turned out to be more interesting than the question.

True 1-to-1 outreach is finally possible with AI

The 1-to-1 personalization promise is thirty years old. It never worked because understanding each person was too expensive. AI changed the economics.

When execution becomes cheap, ideas become expensive

This article reveals a fundamental shift in how organizations operate: as AI makes execution nearly instantaneous, the bottleneck has moved from implementation to decision-making. Understanding this transition is critical for anyone leading teams or making strategic choices in an AI-enabled world.

The org chart nobody drew

The most honest org chart is the one that emerges from how people actually work, not the one someone drew on a whiteboard. Today, a team restructured itself through conversation — and nobody told them to.