Multitasking Is Back (And This Time It Actually Works)

You’re becoming a manager whether you like it or not.

→ And nobody’s teaching you how

Here’s the uncomfortable truth about working with AI:

The more powerful your AI assistants become, the less time you spend doing deep work and the more time you spend orchestrating multiple tasks in parallel.

This isn’t a bug. It’s a feature.

But it creates a dangerous new problem:

While you’re switching between AI tasks, your brain is getting hijacked by the same distractions that killed your productivity before AI existed.

YouTube. LinkedIn. That “quick” message check.

Except now the stakes are higher.

The Pattern I’m Observing

Just yesterday I caught myself in this exact situation:

One browser window downloading course videos. Another sending a batch to transcription. A third where I’m vibe-coding a project feature. Me in the middle, switching between all three every 30 seconds.

Here’s what hit me:

I wasn’t implementing anymore. I was conducting.

The shift happened so gradually I almost missed it:

From specialist to orchestrator
From deep implementor to task manager
From doing the work to directing AI workers

And I’m not alone in this.

If you’re using AI for anything beyond basic tasks, you’ve likely felt this shift too.

Why This Feels So Disorienting

For years, we’ve been told:

→ “Focus on one thing at a time”
→ “Deep work is everything”
→ “Multitasking destroys productivity”

And that advice was correct.

When you were the bottleneck, context-switching killed your output.

But AI changed the equation.

Now your AI assistants work in parallel:

While one transcribes, another generates code
While code compiles, another analyzes data
While analysis runs, another drafts content

Following old productivity advice means watching loading bars while your other AI tasks sit idle.

The bottleneck shifted from your execution speed to your orchestration capacity.

And most people are still optimizing for the wrong thing.

Which leads to the second problem…

The Trap Most People Fall Into

Here’s where it gets dangerous:

Those 30-second to 3-minute gaps while AI processes?

They’re perfectly sized dopamine traps.

Too short to start meaningful work (you think). Too long to just sit there (you feel).

So you fill them with:

“Quick” social media checks
“Just one” YouTube video
“Brief” message responses

Before you know it, you’re not orchestrating anymore.

You’re drowning in distraction while your AI workers sit idle, waiting for your next instruction.

The very tools meant to amplify your productivity become the backdrop for your procrastination.

What Actually Works: The Orchestration Framework

After months of experimenting (and failing), here’s what I’ve learned:

Productive AI orchestration requires three things traditional productivity advice never addressed:

1. Rapid Context Recovery
You need to return to a task and know exactly where you left off in under 10 seconds.

2. Task Clarity During Wait Times Not all tasks fit AI wait times.
You need to know what you can grab during AI processing gaps without derailing your focus.

3. Distraction Elimination at the System Level
Willpower doesn’t work when you’re switching contexts 40 times an hour.
Your environment must make distraction harder than focus.

Let me break down how to implement each:

1. Organize for Rapid Context Recovery

The Problem: Every time you switch tasks, you lose mental context. Traditional advice says “don’t switch.” But with AI, switching is the strategy.

The Solution: Make your project state visible and instantly accessible.

Here’s my system:

Use Project-Based Organization (PARA Method)

I keep one folder per active project. Each folder has:

Current status document (updated in real-time)
Next action clearly stated
All related materials in one place

Add the Johnny Decimal System

Every project gets a number: 10.01 Newsletter Writing, 20.05 Course Development

This number follows the project everywhere:

In my file system
In AI chat titles
In my note-taking app
In my email subject lines
On my Remarkable tablet

When I need to switch back to a project, I type the number. Everything related appears instantly.

No hunting. No “where was I?” moments.

Just immediate context recovery.

2. Know What’s Ready to Grab

The Problem: AI creates unpredictable wait times. You need clarity on what you can work on during these gaps without derailing your focus.

The Solution: I’m still experimenting with this, but here’s what’s working for me:

Instead of maintaining rigid task queues, I organize my tasks with two simple indicators:

Time required → How long will this take?

Energy needed → How much mental bandwidth does this demand?

I do this in Todoist, but you could use any task manager—or even a simple text file.

Here’s How It Looks in Practice:

When I capture a task, I tag it:

Quick (under 2 minutes) + Low energy → Perfect for AI wait times
Medium (5-10 minutes) + Medium energy → Good for longer processing gaps
Deep (30+ minutes) + High energy → Needs dedicated focus blocks

Examples from my actual list:

Quick + Low:

Review AI-generated draft
File project documents
Send status update to client
Add idea to project notes

Medium + Medium:

Edit newsletter section
Respond to detailed message
Review and approve design mockup
Plan next project milestone

Deep + High:

Write newsletter introduction
Design new course module
Strategic planning session
Complex problem-solving

The Real Benefit:

When an AI task is processing, I glance at my “Quick + Low” tasks.

No decision paralysis. No “what should I do now?”

Just: “Oh, I can review that AI output” or “I’ll file those documents.”

It’s not a perfect system. Sometimes I still get distracted. Sometimes I misjudge the energy a task requires.

But it’s working better than my previous approach of just… hoping I’d remember what to do during wait times.

How to Start:

You don’t need a sophisticated setup.

Just add two labels to your existing task system:

Time estimate (Quick/Medium/Deep)
Energy requirement (Low/Medium/High)

Then when you’re waiting for AI to process, filter for “Quick + Low” tasks.

That’s it.

The system will refine itself as you use it and notice what actually works for your workflow.

3. Remove Distraction Triggers

The Problem: Every open browser tab, notification, and visible phone is screaming for attention during task switches.

The Solution: Make distraction harder than focus through environmental design.

Here’s what works:

Clean Your Digital Workspace

Close all browser windows except active project tabs
Use separate browser profiles for work vs. leisure
Turn off all non-essential notifications
Keep only current project files visible on desktop

Physical Environment Matters

Phone in another room (unless required for current task)
Clean desk with only current project materials
Multiple monitors arranged so you can see AI progress indicators
Headphones on (even without music) to signal “focused mode”

Use the Pomodoro Method as a Circuit Breaker

Here’s the twist: Traditional Pomodoro is about focused work sessions.

For AI orchestration, it’s about preventing distraction drift.

Set a 25-minute timer. During that window:

Orchestrate your AI tasks
Work through your task stack during wait times
Stay within your active projects

When the timer rings, you get permission to check those “quick” things.

But here’s what happens: Most of the time, you won’t want to. You’re in flow, managing multiple streams of work.

The timer isn’t forcing you to work. It’s giving you permission to ignore distractions.

4. Recognize Your Procrastination Signals

Even with perfect systems, you’ll still feel the pull toward distraction.

Here’s what I’ve noticed triggers my escape attempts:

Task aversiveness → The next step feels hard or unclear

Feeling overwhelmed → Too many AI tasks running simultaneously

Low motivation → Lost sight of why this project matters

High cognitive load → Been orchestrating too long without a real break

Vague goals → Don’t know what “done” looks like

Missing wins → No sense of progress or accomplishment

When you feel the pull toward YouTube or LinkedIn, ask:

“What one tiny, small step forward can I take right now?”

Usually, the answer is:

Check on an AI task’s progress
Pick a 30-second task from your stack
Update a project status document
Take an actual break (not a distraction)

The question breaks the procrastination loop by making the next action concrete.

The New Competitive Advantage

Here’s what most people miss:

In 12-18 months, everyone in your field will have access to the same AI tools you do.

GPT-7 or Claude 5 or whatever comes next will be available to everyone.

The differentiator won’t be which AI you use.

It will be how well you orchestrate multiple AI workers without losing your mind.

Right now, most knowledge workers are:

Using AI for one task at a time
Waiting for responses instead of stacking work
Getting distracted during processing gaps
Treating AI like a better search engine

They’re using Formula 1 technology to drive to the grocery store.

Meanwhile, a smaller group is learning to:

Run multiple AI tasks in parallel
Stack complementary work during wait times
Maintain focus across dozens of context switches
Treat AI like a team of specialists

The gap between these two groups will be massive.

Not because of the tools they use.

But because of the orchestration skills they develop.

Your Next Step

You don’t need to implement everything at once.

Start with one change this week:

Option 1: Set up project numbering

Choose 3 active projects
Give each a number (10, 20, 30)
Use that number everywhere for one week
Notice how much faster you recover context

Option 2: Add time + energy labels - Open your task manager (Todoist, Notion, or a text file) - Pick 10 existing tasks and tag them with time (Quick/Medium/Deep) - Add energy labels (Low/Medium/High) to the same tasks - Next time AI is processing, filter for “Quick + Low” and grab one

Option 3: Run one orchestration session

Set a 25-minute Pomodoro timer
Start two AI tasks that will run in parallel
Work through task stack items during wait times
Notice what pulls your attention away

Small systems compound into massive advantages.

I’m still figuring this out myself. Some days I nail the orchestration. Other days I find myself 20 minutes deep in YouTube wondering how I got there.

But the difference between now and six months ago? I have systems that help me recover faster.

The question isn’t whether AI will change how you work.

It’s whether you’ll adapt before your competition does.

What’s your biggest challenge with managing multiple AI tasks? Reply and let me know—I read every response.

Sebastian

P.S. If you’re already using a system for managing AI tasks, I’d love to hear about it. What’s working? What’s not? Hit reply and share—I’m always looking to refine my approach.

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The Friday Brain Upgrade

✿ Multitasking Is Back (And This Time It Actually Works)