When Fast Stops Being Useful

When The Feedback Has No Mind Behind It

Recently, I asked someone to review a draft I had written for a handbook.

They responded quickly. The feedback arrived neatly structured and grammatically clean. At first glance, it looked helpful. But as I read through it, something felt off.

The response felt like AI jello.

Smooth. Soft. Shapeless.

Technically correct in places, but lacking substance. It addressed the surface of the request without showing any sign of the person behind it. No perspective. No disagreement. No curiosity. No evidence that the person had wrestled with the ideas.

It looked like the first response from an AI system that had been passed along without reflection.

The reply was efficient.

But it was not effective.

That moment captured a growing tension in modern work: we are getting faster at producing answers, but not necessarily better at thinking about them.

The Real Problem Is Not AI

To be clear, this is not an argument against AI.

I use AI constantly in my personal and professional work. But I rarely use generic systems. Most of my interactions happen through customized projects and GPTs trained on my frameworks, my knowledge base, and the way I typically reason about specific topics.

That distinction matters.

AI can accelerate thinking. It can challenge assumptions. It can help refine ideas. It can surface patterns that might otherwise take longer to notice.

But AI should support judgment, not replace it.

The problem arises when people stop at the first acceptable response.

When AI output becomes something to forward instead of something to evaluate.

In those moments, speed gets mistaken for usefulness. Completion gets mistaken for contribution.

Effectiveness asks a simple question:

Did this work create the right outcome?

Efficiency asks a different one:

How quickly did the work move?

Both questions matter. But they do not carry equal weight.

Understanding Effectiveness And Efficiency

Efficiency reduces friction. It helps something happen with less time, less effort, or fewer resources.

Effectiveness ensures the work actually matters. It solves the right problem, creates a meaningful outcome, or moves the right person forward in the right way.

Both qualities are valuable.

But their order matters.

Effectiveness must come first. Efficiency should follow.

Without effectiveness, efficiency simply helps people do the wrong thing faster.

Why Modern Work Keeps Reversing The Order

Many organizations unintentionally reward efficiency before effectiveness.

The signals are everywhere.

• Fast responses are praised.
• Shorter turnaround times are celebrated.
• High output is interpreted as productivity.
• Tool adoption is equated with progress.

AI has intensified this dynamic. Generating a polished paragraph now takes seconds. Drafting a document can happen before the underlying idea has even been fully explored.

Speed is easy to measure. Thoughtfulness is not.

So teams optimize for what is visible.

• Fast responses.
• Full inboxes.
• Completed tasks.

But meaningful thinking tends to happen quietly. It requires reflection, questioning, and revision. It takes time to understand what truly matters.

When organizations reward only speed, they create a culture of motion instead of impact.

The AI Jello Pattern: Compliance Without Ownership

The feedback example that opened this article illustrates a deeper issue.

The person I asked for feedback did complete the task. They were responsible enough to respond quickly. But they did not demonstrate ownership of the response’s quality.

• They did not check whether the ideas reflected their own perspective.
• They did not highlight what mattered most.
• They did not question the assumptions or expand the thinking.

The response looked like something that had been produced and passed along.

This is the difference between compliance and ownership.

Compliance finishes the assignment.

Ownership improves the work.

Compliance forwards the first acceptable response.

Ownership asks:

• Do I agree with this?
• What is missing?
• What is overstated?
• What actually matters here?

Compliance gets the task done.

Ownership makes the task worth doing.

Why This Matters More Than It Seems

At first, shallow AI-assisted responses may seem like a minor annoyance.

But over time, they reveal something more concerning: people stop stretching their minds.

If someone repeatedly provides bland, unspecific feedback that never challenges ideas or deepens understanding, trust slowly changes.

You may still respect their effort. You may still appreciate their responsiveness. But you begin verifying their work more often.

Eventually, you rely on them less.

Because when someone consistently hands off work that shows little judgment or curiosity, a difficult question emerges:

What value does this person add beyond a better-maintained AI system?

The real risk is not automation. The real risk is intellectual stagnation.

When people stop questioning, refining, and expanding ideas, growth slows. Innovation weakens. Teams begin operating inside smaller and smaller thinking spaces.

What Effective Thinking Actually Looks Like

Effectiveness is not always elegant.

Sometimes it is messy. Sometimes it takes longer. Sometimes it challenges assumptions that people would rather leave alone.

But it produces meaningful movement.

You can often recognize effective thinking by a few signals.

• Someone challenges an assumption respectfully.
• Someone connects ideas across contexts.
• Someone introduces a perspective that had not been considered.
• Someone asks a question that forces the group to rethink the problem.

Effective work does not just produce output. It produces insight.

The Axe And The Maul

Consider a simple metaphor.

Imagine someone trying to chop wood with a hatchet. They can improve their swing. They can work faster. They can become more efficient with the tool they have.

But if a heavy maul is sitting nearby, the real improvement is not in swinging faster.

It is in choosing the right tool.

The effort is still required. The work does not disappear. But the effort now produces meaningful progress.

That shift represents effectiveness.

Efficiency improves execution. Effectiveness improves direction.

The Electric Car Commute

Another example makes the same point.

Imagine two electric cars.

One can travel 100 kilometers on a charge.
The other can travel 10 kilometers.

The first car is clearly more efficient.

But if your daily commute is only two kilometers, both cars are equally effective. Either one accomplishes the task.

Efficiency only becomes meaningful when the situation requires it.

In many organizations, teams celebrate efficiency improvements that do not change the outcome that actually matters.

A Lesson From Teaching

Recently, I have been spending more time tutoring English conversation.

After years of working internationally with English-as-a-second-language communities, I decided to approach tutoring more deliberately. Much as I did in photography earlier in my life, I wanted to become consistently good at teaching.

That meant accepting feedback and reviewing recordings of my sessions.

One piece of feedback stood out.

I often asked students questions like this:

“What’s your favorite animal and why?”

To a native English speaker, that feels like a simple question.

But to a young English learner, it is actually two questions.

• First: What is your favorite animal?
• Second: Why is it your favorite?

The second question requires vocabulary, connectors, and opinion language. For a learner with limited English, answering both parts simultaneously can be confusing.

The question was efficient.

But it was not effective.

The solution was simple.

Break the question into parts.

• First ask: “What is your favorite animal?”
• Then ask: “Why do you like it?”

Effectiveness meant adjusting to the audience.

Not optimizing the wording.

That lesson extends far beyond the classroom.

If the audience cannot meaningfully engage, the work is not effective, no matter how elegant it appears.

Reflection As A Working Habit

One habit that has helped me maintain effectiveness when working with AI is reflection.

I often use prompts that explicitly require multiple steps:

• Reflect.
• Critique.
• Rewrite.
• Improve flow.
• Improve structure.
• Prioritize what matters.

The point is not the prompt itself.

The point is that the first answer is rarely the best.

The real value emerges through review and refinement.

When AI produces a response, the work begins.

• Do I agree with this?
• What feels incomplete?
• What deserves more attention?
• What might need to change?

This process turns AI into a thinking partner instead of a thinking replacement.

Leadership And The Standard Of Thoughtfulness

Leaders play a significant role in shaping how teams think.

If leaders reward only speed, teams will optimize for speed.

But if leaders reward thoughtful disagreement, synthesis, and insight, teams begin to invest more effort in understanding the problem before rushing to answer it.

Effective cultures reward people who:

• Question assumptions
• Connect ideas
• Add perspective
• Improve the thinking of others

Trust grows when people demonstrate that they have genuinely considered the work before sharing it.

From Completion To Contribution

Many teams are still organized around completion.

• Did you finish the task?
• Did you respond quickly?
• Did you meet the deadline?

But stronger cultures emphasize contribution.

• Did you improve the thinking?
• Did you help clarify the real problem?
• Did you move the outcome forward?

The shift from completion to contribution changes how people approach their work.

Instead of asking how fast something can be finished, people begin asking whether the work actually matters.

The Habit That Changes Everything

If someone reading this article adopted just one habit, it would be this:

Pause before you share, and ask: Am I creating an outcome that actually matters?

That single question interrupts autopilot.

• It encourages reflection.
• It pushes people beyond compliance.
• It forces consideration of the audience and the goal.

And it turns AI into a tool that supports thinking instead of replacing it.

Living Differently In An Age Of Acceleration

My personal purpose is simple: live fully by living differently.

In the context of modern work, living differently sometimes means resisting the pressure to move faster.

It means pausing long enough to ask better questions.

It means making sure the work actually matters before trying to optimize how quickly it happens.

Because once effectiveness is clear, efficiency becomes powerful.

But when effectiveness is missing, efficiency is often just noise moving faster.

PS: My critique prompt

Please reflect, critique, and then rewrite.
Incorporate flow structuring, semantic grouping, and topic prioritization as relevant.
Think hard about this.
Before providing your final response, create an internal quality rubric, evaluate your initial draft, and iterate until you achieve excellence. Show me only the final, polished version.