When failure builds false certainty

I watched someone conclude something was impossible this week. They were wrong. But I understood completely why they believed it.

They'd been working alone for three hours trying to recover a system after a critical error. Every method they knew had failed. Database backups. VM snapshots. Everything in their toolkit.

By the time I logged in, the conclusion felt certain: nothing can be done.

But here's what actually happened: repeated failure had transformed "I don't know what else to try" into "nothing else can be done."

That shift happens so gradually you don't notice it happening.

The certainty trap

You start with uncertainty. You try everything you know. Nothing works.

• Database backup fails. That's one data point.
• VM snapshot fails. That's two data points.
• Every method in your toolkit fails. Now you have evidence.

At some point, without realizing it, the question changes.

From "what else might work?" to "see? Nothing works." Each failed attempt builds a case. A logical, reasonable case that you've tried everything and nothing is possible.

The most dangerous part is that it feels like evidence-based thinking. Like you've done your due diligence. Like giving up is the only rational conclusion.

Fresh eyes, different question

I wasn't smarter. I didn't know more about infrastructure or databases.

I just had something the person dealing with the crisis didn't: no history of failure on this problem.

Fifteen minutes exploring with AI revealed data we didn't know existed. Within an hour, the "impossible" recovery was done.

The solution wasn't hidden. It was sitting right there in documentation. But when you've built a case that nothing works, you stop looking for what might.

How many impossibles are really just failed attempts?

Think about the last time you gave up on something. Was it actually impossible? Or had you just accumulated enough failures to make it feel that way?

The bug you couldn't fix after trying five approaches but solved when someone suggested a sixth. The conversation you thought was hopeless after three failed attempts but had a breakthrough on the fourth with different framing. The problem that felt insurmountable until someone who hadn't tried and failed yet asked one question you hadn't considered.

We confuse our history with reality. When we've failed repeatedly, "I couldn't" becomes "it can't be done." Our experience becomes universal truth.

The lonely crisis effect

Working alone amplifies this. No one to challenge your conclusions. No one to ask "what else?" No one without your history of failure on this specific problem.

Distributed teams make isolation the default during crisis. You don't want to bother people. They're in different timezones. You think you should be able to handle this.

So you work alone. And each failed attempt builds more evidence that nothing else exists. Your track record becomes the only data that matters.

What actually helps

(spoiler alert: it's not trying harder with the same methods)

• Stop working alone on hard problems: Get someone else involved before your failed attempts become false certainty. Not because you're not capable. Because fresh perspective hasn't built the case for impossibility yet.
• Recognize when certainty feels earned: When you start feeling certain something is impossible because you've tried everything you know, that's exactly when you need someone who hasn't tried yet.
• Use tools that explore beyond your attempts: AI can search spaces you haven't looked in because it has no history of failure on your specific problem.

The goal isn't to never give up. Some things actually are impossible. The goal is to recognize when you're deciding something is impossible because you've failed repeatedly, not because it actually is.

What I learned

I got lucky this week. I happened to log in without any failed attempts weighing on me. But luck isn't a system.

We need ways to catch ourselves when our track record starts feeling like truth. When "I've tried five things" becomes "there are only five things to try."

The real learning is not how to solve that specific problem in the future. It's learning to recognize how often we all accept defeat simply because we've accumulated enough failures to make giving up feel logical.

And that matters far beyond any single crisis.

The question isn't whether you should give up. It's whether you're giving up because something is actually impossible, or because you've failed enough times to make it feel that way.

This piece originally appeared in my weekly newsletter. Subscribe for insights on thinking differently about work, technology, and what's actually possible.