Artificial intelligence has made it easier than ever to stay busy. Developers can generate code, launch agents, research ideas, automate workflows, and move between projects without ever reaching a natural stopping point. Yet all this activity raises an important question: Are we actually becoming more productive, or are we simply finding new ways to stay occupied?
In this week’s Develpreneur Weekly Challenge, Rob Broadhead and Michael Meloche explore why slowing down may be one of the most valuable skills developers can develop in the age of AI. Building on their recent conversations about career growth, software engineering, and AI-assisted development, they challenge listeners to step away from constant activity and create intentional time for thinking.
The challenge sounds simple: Find two hours to disconnect, reflect, and plan.
However, in a world dominated by notifications, meetings, coding tools, and AI agents, finding that time may be harder than it sounds.
The Difference Between Being Busy and Being Productive
Many professionals measure their days by how much activity they complete. They attend meetings, answer messages, work through task lists, and move constantly between assignments.
At the end of the day, they feel exhausted.
But exhaustion does not necessarily mean progress.
Michael references Tim Ferriss’s The 4-Hour Workweek and the distinction between being busy and being productive. Someone can spend an entire day appearing occupied without accomplishing anything that meaningfully advances their goals.
The same principle applies to AI.
You can spend hours generating prompts, reviewing outputs, building automations, and experimenting with agents. However, if those activities do not help you solve a problem, improve a process, develop a valuable skill, or achieve a meaningful result, the time may not have been particularly productive.
The goal is not to do more simply because the tools allow it.
The goal is to accomplish something that matters.
AI Can Make It Difficult to Stop
One of the most interesting problems with AI is that it rarely runs out of suggestions.
Ask it to create a plan, and it may recommend additional features. Ask it to build an application, and it may suggest new architecture, additional tests, infrastructure improvements, and related functionality.
Before long, a relatively small assignment can turn into an enormous project.
Rob shares his experience with AI over-engineering solutions and pursuing improvements that were never part of the original requirements. Although the suggestions sometimes have merit, they can distract from the objective and consume resources unnecessarily.
This is where developers need to exercise judgment.
Not every recommendation deserves implementation. Not every new feature creates value. Not every technical improvement is worth the time required to pursue it.
Sometimes the best decision is to stop adding things and return to the original problem.
Give Yourself Time to Think
Rob emphasizes the importance of creating time for silence and reflection.
The constant pressure surrounding AI can make developers feel as though they are falling behind. New models, frameworks, tools, and techniques appear regularly, and there is always another article explaining why you need to adopt something immediately.
That urgency can push people toward two extremes.
Some become overwhelmed and avoid AI entirely. Others attempt to adopt everything, filling their schedules with experiments and projects they never have time to evaluate.
Neither approach necessarily leads to meaningful progress.
Taking time to think creates an opportunity to determine what actually matters.
It allows you to examine your current projects, professional goals, business challenges, and daily responsibilities without constantly reacting to outside demands.
Instead of asking what you can do next, you can ask what you should do next.
Measure the Value You Create, Not the Hours You Spend
Developers have traditionally worked in environments that emphasize hours, tickets, story points, and lines of code.
These measurements can be useful for tracking activity, but they do not necessarily describe the value of the work.
Rob suggests looking at the multiplier created by your efforts.
Consider an application that takes five minutes to complete a task that previously required an hour. The value comes from eliminating unnecessary work and allowing users to spend their time on something more important.
By contrast, an application that changes how someone completes a five-minute task without reducing the effort or improving the result may offer very little additional value.
This distinction matters for both developers and business owners.
If you are building a business, staying busy with internal projects does not necessarily create customers or revenue. If you are developing your career, completing additional tasks does not automatically improve your professional value.
You need to understand how your work contributes to a meaningful outcome.
Communication Is Becoming a Core AI Skill
Michael also connects productivity to communication.
As developers increasingly work with AI agents, they need to provide instructions that clearly describe requirements, expectations, and boundaries.
Poor communication can result in AI generating unnecessary functionality, misunderstanding the assignment, or pursuing the wrong objective.
One technique Michael recommends is asking AI to repeat its understanding of the task before beginning implementation.
This simple interaction creates an opportunity to identify misunderstandings before they become expensive problems.
It also reinforces skills that matter when working with people.
Good developers need to communicate with customers, teammates, managers, and stakeholders. Learning how to clearly explain a problem and establish expectations improves all of those relationships.
AI can help developers practice these skills, but it cannot replace the responsibility to communicate effectively.
Productivity Requires More Than Faster Development
AI has also changed how developers spend their time across the software development lifecycle.
Implementation can happen much faster, but requirements gathering, design, testing, deployment, and verification remain essential.
Rob argues that developers should use the time saved through AI to improve software quality rather than simply increase the amount of software produced.
Instead of rushing to complete more features, teams can spend more time identifying risks, reviewing architecture, testing edge cases, and gathering user feedback.
This is an opportunity to address weaknesses that have existed in software development processes for decades.
AI makes it possible to accelerate implementation. Developers still need to decide how to use that additional capacity responsibly.
Know When to Take a Break
Michael brings another practical perspective to the conversation: the importance of recognizing when constant activity becomes overwhelming.
Developers often move directly from one demanding task into another. When a project runs longer than expected or a problem becomes difficult to solve, the temptation is to keep pushing until something works.
Unfortunately, that approach can leave people frustrated, distracted, and less effective.
Michael recommends deliberately stepping away from technology when necessary.
Turn off notifications. Leave the computer. Go for a walk. Take a break between major tasks rather than immediately switching into another demanding activity.
He also mentions the Pomodoro Technique as a useful way to build focused working periods and regular breaks.
The objective is to create a sustainable rhythm rather than remaining in a constant state of reaction.
This Week’s Challenge: Find Two Hours to Think
This week’s assignment is not about building another application or learning another AI framework.
It is about reclaiming enough time to understand what you should be working on.
Step 1: Review Your Schedule
Look at your calendar, task list, or work history from the previous week.
Identify how you spent your time. Pay attention to recurring meetings, unnecessary interruptions, repetitive tasks, and activities that may not be contributing to your goals.
If helpful, use AI to review that information and suggest where you might reclaim time. The objective is to find space in your schedule, not to create another complicated automation project.
Step 2: Find a Two-Hour Block
Schedule two hours of uninterrupted thinking time.
If two hours seems impossible, start with fifteen minutes in the morning or a shorter period you can consistently protect.
The important part is beginning the habit.
Step 3: Disconnect From Your Digital Environment
Close your laptop, silence your phone, and step away from email, messaging applications, and AI tools.
Avoid filling the time with another form of digital activity.
Give yourself the opportunity to think without immediately reacting to something else.
Step 4: Go Analog
Michael suggests taking the challenge one step further by using a pen and notebook.
Writing by hand changes the pace of the exercise. Instead of quickly typing ideas and immediately editing or expanding them, you have to slow down and consider what you want to communicate.
You may find that the process encourages more deliberate thinking.
You also remove the temptation to send every new idea to AI for immediate exploration.
Step 5: Ask Yourself Meaningful Questions
Use the time to think about your current direction.
- What am I trying to accomplish in my career, business, or current project?
- Which activities are producing meaningful results?
- What am I doing simply because it keeps me busy?
- Where can AI genuinely save time or improve quality?
- What work should I stop doing?
- Which skills do I need to develop to remain valuable?
- What one change would make the biggest difference next week?
Do not worry about producing perfect answers.
The value comes from giving yourself enough space to consider the questions.
Step 6: Choose One Action
At the end of your thinking session, identify one thing you will change.
You might eliminate an unnecessary recurring task, reduce your meeting load, improve how you manage AI agents, create more time for testing, or focus your professional development on a particular skill.
Avoid turning the exercise into another enormous task list.
Choose something manageable that can create meaningful progress.
The Future of Development Requires Better Thinking
AI is changing the responsibilities of software developers.
As more implementation work becomes automated, developers increasingly need to understand requirements, design systems, coordinate work, evaluate risks, and verify results.
These responsibilities require more than technical knowledge.
They require judgment, communication, planning, and the ability to recognize what matters.
Rob points out that developers have an opportunity to become more intentional about how they approach their work. Rather than responding to AI by constantly increasing output, they can use these tools to improve the quality of their decisions and solutions.
That improvement begins with making time to think.
Your Weekly Challenge Takeaway
The challenge this week is deliberately uncomplicated.
Find two hours. Disconnect. Take a pen and paper. Think about what matters.
If you cannot find two hours, start with fifteen minutes and build from there.
You do not need another AI tool to tell you everything you should do. Sometimes you need enough silence to understand what you already know.
The most valuable work you complete this week may not involve writing a single line of code.
It may be deciding which problems are actually worth solving.
Stop measuring how busy you are. Start measuring the value you create.
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