The Paradox of Restraint: Why Focus Beats Frenzy in the Age of AI

Understanding why people get caught in an AI frenzy is the first step to breaking free.

I dug into the research and found that several cognitive and behavioral factors drive our distraction loop:

First, there’s a dopamine-fueled Novelty Bias. New tech is exciting because our brains literally ​reward us with dopamine​ for trying something novel. This “shiny object syndrome” leads people to leap at every new AI tool or feature, confusing new for better. The result is a constant cycle of test-driving tools that may offer little real benefit.

Secondly, there’s a Fear of Missing Out that pushes us to adopt AI tools because “everyone else is.” An overload of AI buzz convinces us that every problem needs a dedicated app, and in AI, this mentality is in overdrive. Many worry that if they’re not using the latest AI, they’ll fall behind, even if they haven’t identified a clear need for it.

Ultimately, this “chasing AI novelty” leads to a vicious cycle where teams feel busy managing a clutter of tools and experiments, but aren’t actually more productive.

Our brains and business cultures are wired to chase the latest AI shiny object, but unchecked, this leads to fatigue, confusion, and wasted effort.

(Article continues after this break)

The flood of daily headlines, product launches, and “must-read” analyses can feel like drinking from a fire hose, and for many leaders, it’s paralyzing.

On the hunt for ‘staying ahead’, curiosity is being hijacked by noise.

And while being informed is essential, being overwhelmed helps no one. The goal isn’t to know everything happening in AI; it’s to understand the few developments that truly matter to you and your organization.

When you’re bombarded by dozens of updates each day, your brain shifts from learning to triage mode. The cognitive overload makes it harder to see where AI could actually create impact.

And once you hit that point of saturation, most people simply tune out. They stop experimenting, stop learning, and lose the momentum that makes AI adoption work. Bad.

That’s why restraint in information intake is vital. Give yourself permission, even a mandate, to filter. Follow a small number of trusted sources that contextualize rather than sensationalize.

For example, we publish ​three weekly newsletters​ and host an ​active executive community​ where members discuss the most relevant AI applications for their work. But even that is too much for many.

And that’s fine. Unless you’re leading the AI effort at your company and need to be on top of absolutely everything, you can do with a more streamlined AI information diet. (Good news: we’re starting monthly ‘executive briefs’ soon, so that you can ignore the rest.)

Even I, as the CEO of this company that churns out AI insights multiple times per day, have done this by completely removing all AI content from my devices and feeds after work.

So be deliberate.

Curate what you read and watch. In the age of AI abundance, focus beats frenzy.

Just as we must be intentional in our media consumption, we also need restraint in how and where we choose to implement AI.

Adopting AI should be guided by clear objectives, not the draw of ‘great’ technology. Instead of saying “we need an AI chatbot”, say “we need to reduce customer wait times by 50%”.

In other words, we need to practice “​Tasks, Not Tools​.”

This structured filtering ensures you pursue the AI implementations that are high-value and viable, as not all use cases are equal. A few targeted applications often generate the bulk of ROI, so ​identifying those early is key​, as MIT Sloan research shows.

Then, ensure there is a clear proof of concept before committing to any platform or tool. Let people create a GPT or vibe code a small solution, and drive user adoption.

Treat AI deployments as iterative experiments with a “lean startup” approach. Rather than a big-bang rollout of a dozen AI tools, focus on one minimal viable solution, implement it, and learn from it.

Starting with a small, low-risk automation on the left side of the “AI continuum” (for example, a single-task ​AI agent​ with human oversight) to quickly get wins and feedback.

Then, iterate: refine the solution and gradually expand its scope. Only when the demand has been proven, move to investment.

Frameworks like our AI2 Investment Matrix can help rank AI use-cases, plotting potential projects by impact versus cost.

I’ve heard plenty of stories where companies went with vendors who promise the world, but fail to deliver, as in the end, the right use case didn’t exist or wasn’t defined before taking these expensive steps.

The Bottom Line: Why Focus Beats Frenzy

In the age of AI, restraint is not a weakness, but a competitive advantage.

Don’t try to ‘know and do everything.’ AI moves too fast for that.

The paradox of restraint is that by doing less, saying “no” to the majority of shiny AI distractions, you unlock the ability to do more of what truly matters.

As I’ve seen, focus beats frenzy, every time.

Until next week,

- ​Daan