You’re three slides into your first executive presentation. Your voice cracks. You forget your transition. The CFO is checking her phone. You can feel yourself sweating through your shirt.
Welcome to Attempt #1.
The Universal Fear of Being Bad
Here’s what nobody tells you about learning: the first time you do something, it’s supposed to be terrible.
Not “a little rough around the edges.” Not “needs some polish.” Actually, genuinely, uncomfortably bad.
Yet we act shocked when it happens. The first sales call goes sideways. The first code commit gets rejected in review. The first difficult conversation as a manager turns into an emotional mess. And we interpret this as evidence that maybe we’re not cut out for this after all.
This is backwards. The problem isn’t that you’re bad at it. The problem is you expected to be good at it on the first try.
The Science of Sucking (And Why It Matters)
Your brain is not designed for instant mastery. It’s designed for gradual refinement through repetition.
The Learning Curve Is Real
In 1885, German psychologist Hermann Ebbinghaus documented what we now call the learning curve: human performance improves logarithmically with practice. The first attempts show the steepest improvement, but they’re also the messiest.
Later research on the power law of practice (Newell & Rosenbloom, 1981) confirmed this pattern across virtually every skill domain: typing, chess, sports, surgery, public speaking. The formula is consistent: Performance = A × (Trials)^B, where early trials produce dramatic gains but start from a low baseline.
Translation: You’re not broken. You’re on the curve. Right where you should be.
Productive Failure: The Research That Changes Everything
But here’s where it gets interesting: making errors early on isn’t just tolerable it’s optimal.
Educational researcher Manu Kapur’s work on “productive failure” shows that learners who struggle through initial attempts without immediate expert guidance often develop deeper understanding than those who are “taught correctly” from the start. Why?
Because early failure forces you to build mental models. You have to diagnose what went wrong. You have to test hypotheses. You have to develop intuition about the problem space that no amount of theory can provide.
The catch: this only works if you expect the early attempts to be rough and treat them as data collection rather than performance evaluation.
The “Terrible Ten” Mental Model
Here’s the framework that changes the game: Your first 10 attempts at any new skill are not performance. They’re reconnaissance.
Attempt 1-3: The “What Am I Even Doing?” Phase
- Goal: Figure out the basic mechanics without dying of embarrassment
- Success metric: You completed the thing (doesn’t matter how badly)
- Common feelings: Panic, imposter syndrome, “why did I agree to this?”
Attempt 4-7: The “I’m Starting to See Patterns” Phase
- Goal: Identify what went wrong and form hypotheses about what might work better
- Success metric: You can articulate at least one specific thing you’d do differently next time
- Common feelings: Frustration mixed with tiny sparks of “oh, that’s how this works”
Attempt 8-10: The “This Still Sucks But Less” Phase
- Goal: Execute one improvement from your earlier attempts
- Success metric: Someone who doesn’t know you might think you’ve done this before (even if you still feel like an imposter)
- Common feelings: Cautious optimism, occasional competence
After Attempt 10: You’re no longer a beginner. You’re an apprentice. The skill stops feeling alien. You have enough pattern recognition to self-correct. You’re ready to actually optimize.
The Attempt Counter: Your Secret Weapon
The single most powerful thing you can do when starting a new skill: Keep an explicit count.
How to Build Your Counter
Create a simple tracking document (spreadsheet, notebook, notes, an app … doesn’t matter). For each attempt, log:
- Attempt Number (literally: “3 of 10”)
- Date
- What happened (2-3 sentences, factual)
- One thing that surprised you (good or bad)
- One thing you’ll try next time
That’s it. No elaborate performance reviews. No self-flagellation. Just data.
Real-World Examples
For your first 10 sales calls:
- Attempt 1: “Forgot to ask qualifying questions. Spent 30 minutes with someone who had no budget. Learned: budget question goes in first 5 minutes.”
- Attempt 5: “Used the budget question from Attempt 1. Got hung up on when I asked for the close. Learned: need smoother transition between value and ask.”
- Attempt 10: “Closed my first deal. Still awkward, but hit all the key checkpoints. They said yes anyway.”
For your first 10 code reviews as a new developer:
- Attempt 1: “12 comments on my PR. Half were style issues I didn’t know existed. Learned: read the style guide before next PR.”
- Attempt 4: “Only 3 comments, all about logic. Reviewer said code was clean. Feels less scary now.”
- Attempt 10: “PR approved with one minor suggestion. I actually understood why this time.”
For your first 10 difficult conversations as a manager:
- Attempt 1: “Tried to sandwich bad feedback with compliments. Employee looked confused about whether they’re doing well or not. Learned: be direct first, supportive second.”
- Attempt 7: “Used the direct approach from Attempt 1. Employee thanked me for clarity. This is starting to feel less terrifying.”
- Attempt 10: “Employee came to me proactively with a problem. Said they appreciate how I handle feedback. Wait, maybe I don’t suck at this?”
The Permission You Didn’t Know You Needed
The Terrible Ten framework gives you something most high-achievers never give themselves: permission to be a beginner.
Not forever. Just for 10 attempts.
After that, you can start worrying about getting good. But during those first 10? Your only job is to show up and survive.
The Questions That Change
Before the Terrible Ten mindset:
- “Why am I so bad at this?”
- “Should I even be doing this?”
- “What if people think I’m incompetent?”
After the Terrible Ten mindset:
- “Which attempt number is this?”
- “What’s one thing I learned from the last one?”
- “Am I past attempt 10 yet?”
See the difference? One set of questions leads to quitting. The other leads to competence.
Common Objections (And Why They’re Wrong)
“But I don’t have time for 10 bad attempts.”
You don’t have time not to. The alternative isn’t 10 good attempts—it’s giving up after 2 bad ones because you decided you’re “not good at this.” The 10 attempts will pass whether you track them or not. Might as well use them deliberately.
“My job/clients/boss expect competence now.”
Then set the expectation explicitly: “I’m learning this skill. I’m currently on attempt 3 of my first 10. I expect to be proficient by attempt 15, but right now I’m in the learning phase.”
Most people respect transparency far more than they respect fake competence.
“What if I’m still terrible after 10 attempts?”
First, you won’t be. The learning curve is a law, not a suggestion. But second, then you have real data. After 10 deliberate attempts with reflection, you’ll know whether this is a skill you want to keep developing or whether your talents lie elsewhere. That’s valuable information.
Start Your Counter Today
Right now, you’re avoiding something because you’re not good at it yet.
Maybe it’s public speaking. Maybe it’s cold outreach. Maybe it’s delegating. Maybe it’s giving critical feedback. Maybe it’s writing. Maybe it’s coding.
Whatever it is: You’re not bad at it. You’re at attempt 0.
Open a document. Title it “[Skill Name] - First 10 Attempts.” Write down what you’ll do for Attempt 1. Schedule it for this week.
Then do it badly. Because that’s what Attempt 1 is for.
And when you’re done, you’ll be at 1 of 10. Which means you’re no longer at zero.
Which means you’re already further than 90% of people who never start because they’re afraid of being terrible.
The goal isn’t to be brilliant. The goal is to get to 10.
The brilliance comes later.
What skill are you on Attempt 0 for? Or if you’ve already started counting, which number are you at? Share on LinkedIn. I’d love to hear what you’re learning.
References & Further Reading
- Hermann Ebbinghaus and the Learning Curve - The foundational research on skill acquisition
- The Power Law of Practice - How performance improves with repetition
- Productive Failure: Why Making Mistakes Early Helps Learning - Harvard Business Review on Manu Kapur’s research
- Learning and Instruction: Productive Failure in Learning - Manu Kapur’s original research paper