The Cost of Finishing What AI Started

I was on the phone with my boss last week. He mentioned it’d be nice if a certain tool existed. Simple idea, clear use case. Before the call was even over, I had a terminal window open with a Claude Code agent building a prototype.

What would have been a polite nod and a quick addition to the backlog became an active project.

A few days later, someone mentioned it’d be great to have a public demo site for some Sigma content. I already had an idea of how to do it. We’d built some open source components that would make it easier. Another terminal window. Another agent pulling code together.

Rinse. Repeat. Within a week I had multiple desktops open, each with a little black screen of scrolling text, and I wasn’t sure what to do next. It felt like the opposite of productivity. All these partially-done projects sitting there, waiting for something. Waiting for me.

The Governor Is Gone

Starting projects used to be expensive. There was research, setup, scaffolding, a first painful commit. That friction was annoying, but it served a purpose. It was a natural governor on how many things you could have in flight at once. You couldn’t start too many things because starting things was hard.

AI removed that friction. An agent can get a project from zero to 80% in an afternoon. The problem is that the last 20% still costs the same as it always did. It still needs your attention, your judgment, your time. The 0-80% got cheaper. The 80-100% didn’t.

And here’s the part that snuck up on me: in any company setting, finishing doesn’t just require your focus. It requires other people’s focus. Code reviews. Security approvals. Design feedback. Stakeholder buy-in. I wrote about this in Architecting the Factory and Fast Work, Slow Decisions, and the same dynamics apply here at the individual level. Every project you spin up doesn’t just add to your own work-in-progress. It creates invisible WIP for your colleagues, too. You’ve manufactured demand on someone else’s calendar without asking.

Nothing Moves Faster Than the Bottleneck

This is the oldest lesson in Lean thinking, and it’s the one we keep forgetting. Goldratt’s Theory of Constraints says a system can only move as fast as its slowest point. In The Phoenix Project, the team keeps starting work because everyone wants to stay busy, and the pile of half-done projects eventually brings the whole operation to its knees.

AI just made this trap irresistible.

The temptation is obvious. Your current project is blocked, waiting on someone’s review or a decision from leadership. You could follow up, push the blocker, stay focused. But why sit idle when you could spin up something new? The agent makes it feel productive. One more terminal window. One more prototype.

The work doesn’t stop needing management once it exists, though. You’re juggling more balls now. You start checking in less rigorously on the blocker. The person responsible for that blocker is getting busier, partly because you and everyone else keeps adding things to the queue. The ball drops. The work stacks up. Like a traffic jam, everything begins to creep. Progress feels impossible, and you’re not even sure which project to push forward first.

Lean manufacturing figured this out decades ago. The ideal is single piece flow: finish one thing before starting the next. The concept isn’t complicated. The discipline is brutal, especially when starting the next thing has never been easier.

The Bragging Rights Problem

There’s a cultural dimension to this, too. Right now on tech Twitter and LinkedIn, running multiple agents simultaneously has become a kind of status signal. Screenshots of four terminal windows. “I’ve got six agents running right now.” It looks impressive. It feels productive.

But running six agents isn’t the same as finishing six projects. It’s the same as having six pots on the stove with no plan for which one to plate first. Impressive-looking kitchens don’t feed anyone.

The internet rewards the appearance of velocity. Organizations reward outcomes. Those are different things.

The Case for Slowing Down

Here’s the move that actually helped. I started using AI not to produce more, but to decide better.

Most mornings now, I sit down with Claude before opening any terminals. We review my task list together. We look at my calendar. We talk through what’s actually most important today. Not most exciting, not most novel, not the thing someone mentioned yesterday that would be cool to build. The most important thing.

Some prompts that have been useful:

• Are there any tasks that have been sitting for too long?
• Can you group tasks by topic so I can see what themes are pulling at my attention?
• What are tasks I should consider delegating?
• What should I be planning for this week?
• What would a good agenda be for my meetings today?

None of these are complicated. The value isn’t in the prompts. The value is in the act of pausing to ask them. Of forcing yourself to reflect before you react.

AI gave me the ability to start anything in minutes. That same AI can also give me the space to step back and choose wisely. The tool that creates the problem is also, it turns out, pretty good at helping solve it, if you’re willing to sit down and have the conversation instead of opening another terminal.

Intention as the Discipline

The real skill in an AI-accelerated world isn’t starting. It’s choosing. It’s the discipline to look at the list of things you could spin up today and pick the one that matters most. Then work on it with focus, push through the blockers, get it to done.

We can let AI make us busier and more overwhelmed, or we can use it to free ourselves to slow down. Both options are available. Both are easy. Only one of them leads somewhere good.

Have you found yourself drowning in half-finished AI projects? I’d love to hear how you’re managing the WIP problem. Reach out on LinkedIn or Bluesky.