Introduction
In 2026, the most common blocker in tech job searches is no longer a lack of opportunity, intelligence, or even skill.
It is confidence erosion.
Across software engineering, machine learning, data, and AI roles, a striking pattern has emerged:
highly capable candidates consistently report feeling unprepared, even after months of study and experience.
This isn’t anecdotal.
Recruiters, hiring managers, and interviewers increasingly hear variations of the same statement:
“I know the material, but I don’t feel ready.”
What’s changed is not candidate capability, but how readiness is perceived.
Why Feeling Unprepared Is Now the Default
In earlier hiring cycles, preparation had clearer endpoints:
- Finish a course
- Practice known question types
- Master a defined tech stack
In 2026, those endpoints no longer exist.
Because:
- Roles evolve faster than job descriptions
- AI tools blur what “baseline skill” means
- Interviews test judgment, not memorization
- Hiring signals are opaque and inconsistent
As a result, candidates are stuck in perpetual preparation mode.
No amount of studying feels sufficient, because the target keeps moving.
The Confidence Gap Is Not a Skill Gap
This is the most important distinction most candidates miss.
The majority of unpreparedness today is not due to missing knowledge.
It comes from:
- Unclear hiring criteria
- Overexposure to exceptional outliers
- AI-amplified comparison
- Inconsistent interview feedback
- Preparation strategies optimized for the wrong signals
In other words, candidates are often qualified but unconvinced.
Why AI Tools Made Confidence Worse, Not Better
AI tools were expected to:
- Democratize learning
- Reduce preparation anxiety
- Accelerate readiness
Instead, they often:
- Inflate perceived competition
- Make baseline skills feel trivial
- Create unrealistic comparison benchmarks
- Encourage shallow breadth over deep ownership
When everyone has access to:
- Instant explanations
- Auto-generated code
- Mock interview simulations
Candidates begin to doubt whether their own understanding counts at all.
This creates a dangerous loop:
“If AI can do this instantly, what do I actually bring?”
The New Mismatch: How Candidates Prepare vs How They’re Evaluated
Most tech candidates still prepare as if interviews test:
- Knowledge recall
- Algorithm familiarity
- Framework expertise
But interviews in 2026 increasingly test:
- Decision-making under uncertainty
- Tradeoff reasoning
- Ownership thinking
- Communication clarity
- Failure awareness
This mismatch produces a specific psychological outcome:
“I’ve studied everything, but I don’t feel ready for whatever they’ll ask.”
That feeling is rational, not a personal weakness.
Why High Performers Feel This Gap Most Strongly
Paradoxically, the most capable candidates often feel least confident.
Why?
- They understand the depth of what they don’t know
- They see edge cases and ambiguity
- They recognize that there’s no “perfect” answer
Meanwhile, less experienced candidates sometimes feel more confident, because they mistake familiarity for readiness.
This asymmetry creates the illusion that everyone else is ready except you.
They aren’t.
Confidence Has Become a Signal Alignment Problem
In 2026, confidence is no longer about:
- Self-belief
- Motivation
- Positive thinking
It is about:
- Knowing what signals matter
- Practicing the right kind of readiness
- Understanding how hiring decisions are actually made
When candidates prepare for the wrong signals, confidence never arrives, no matter how much they study.
A Reframe That Changes Everything
Instead of asking:
“Am I ready yet?”
The more useful question in 2026 is:
“Do I understand what readiness means for this role?”
Once candidates answer that clearly, confidence stops being elusive.
Section 1: Why Modern Hiring Systems Actively Undermine Candidate Confidence
Most tech job seekers assume their confidence problems are personal, caused by procrastination, imposter syndrome, or not studying enough.
In reality, confidence erosion in 2026 is systemic.
Modern hiring systems are optimized for speed, risk reduction, and scale. Those optimizations unintentionally, but consistently, undermine how candidates perceive their own readiness.
1. Hiring Pipelines Are Built for Filtering, Not Feedback
At every stage of the hiring funnel, candidates receive less information than they need to calibrate confidence:
- Resume screening offers no feedback
- Recruiter calls provide generic signals
- Technical interviews give vague outcomes
- Rejections rarely explain why
From the company’s perspective, this is rational:
- Feedback is expensive
- Legal risk is real
- Decisions are holistic
From the candidate’s perspective, it creates a vacuum.
When outcomes are opaque, candidates fill the gap with self-doubt:
“If I failed, it must mean I wasn’t ready.”
Over time, repeated ambiguity compounds into chronic uncertainty, even when performance is objectively strong.
2. Multi-Round Interviews Fragment Confidence
In 2026, tech interview loops are longer and more fragmented:
- Recruiter screen
- Coding round
- ML/system design round
- Behavioral round
- Bar-raiser or panel
Each round evaluates different signals.
Candidates often perform well in some rounds and poorly in others, but receive only a single binary outcome.
This produces a damaging internal narrative:
“I don’t know what I’m actually good or bad at.”
Without signal-level feedback, candidates can’t adjust preparation accurately. They keep studying everything, which never restores confidence.
3. AI Screening Amplifies Silence and Doubt
AI-assisted screening accelerates early-stage filtering, but it also removes human context from rejection.
Candidates experience:
- Faster rejections
- Fewer recruiter interactions
- Less explanation
Even when humans make the final call, the experience feels automated.
This leads candidates to assume:
- They were rejected for missing keywords
- Their skills weren’t visible
- Their experience didn’t “count”
Whether or not this is true, the psychological effect is the same: confidence drops.
This dynamic mirrors what happens later in interviews, where weak signals compound invisibly, an effect explored in How Recruiters Screen ML Resumes in 2026 (With or Without AI Tools).
4. Hiring Signals Have Shifted but Prep Advice Hasn’t
Modern interviews increasingly test:
- Judgment
- Decision-making
- Tradeoff reasoning
- Communication under uncertainty
But most preparation content still emphasizes:
- Knowledge recall
- Tool mastery
- Pattern memorization
Candidates follow advice diligently, and still feel unprepared when interviews go off-script.
The result is a specific kind of anxiety:
“I studied what I was told to study… so why do I still feel behind?”
This mismatch convinces candidates that the problem is them, not the preparation model.
5. Comparative Visibility Is Distorting Perception
Candidates now see:
- Public success stories
- Viral “day-in-the-life” posts
- Interview prep bragging
- AI-generated portfolios
What they don’t see:
- The rejections
- The false starts
- The context behind success
This creates a skewed baseline.
When everyone else looks confident and prepared, feeling uncertain feels like failure, even when it’s normal.
High performers are especially vulnerable here, because they:
- Understand complexity
- See ambiguity
- Recognize edge cases
That awareness reduces certainty, even as competence increases.
6. Rejection Is Interpreted as Readiness Failure
In previous hiring cycles, rejection often meant:
- Bad timing
- Strong competition
- Limited headcount
In 2026, candidates internalize rejection as:
“I wasn’t ready.”
This interpretation is reinforced by:
- Lack of feedback
- High application volume
- Repeated silence
Over time, candidates stop trusting their own judgment about readiness. They outsource it entirely to hiring outcomes, which are noisy and inconsistent.
7. The System Rewards Risk Avoidance, Not Confidence Building
Hiring systems are designed to:
- Avoid false positives
- Minimize onboarding risk
- Prefer predictability
They are not designed to:
- Calibrate candidate confidence
- Provide developmental guidance
- Signal partial success
This asymmetry means candidates receive many reasons to doubt themselves, and almost none to feel grounded.
8. Why This Isn’t a Motivation Problem
Telling candidates to:
- “Be more confident”
- “Just apply anyway”
- “Trust yourself”
Misses the point.
Confidence erosion in 2026 is not about mindset.
It’s about signal misalignment:
- Candidates don’t know what readiness looks like
- Hiring systems don’t communicate it
- Preparation advice doesn’t target it
Until that gap is addressed, confidence won’t return, no matter how hard candidates try.
Section 1 Summary
Modern hiring systems undermine candidate confidence by:
- Withholding actionable feedback
- Fragmenting evaluation across rounds
- Automating early-stage rejection
- Testing signals candidates weren’t told to prepare for
- Amplifying comparison and silence
- Turning rejection into perceived unpreparedness
This erosion is structural, not personal.
Understanding that is the first step to rebuilding confidence intentionally.
Section 2: The Difference Between Being Unprepared and Feeling Unprepared
One of the most damaging misconceptions in 2026 job searches is the belief that feeling unprepared accurately reflects being unprepared.
It rarely does.
In modern tech hiring, the emotional signal of readiness has become detached from the factual signal of readiness. Candidates often conflate the two, and pay a steep price in confidence, momentum, and decision-making.
Understanding this distinction is foundational to closing the confidence gap.
Why “Feeling Ready” Is No Longer a Reliable Signal
Historically, readiness was easier to sense:
- You finished a course
- You practiced known question types
- You mastered a stable tech stack
In 2026, readiness has no clear finish line.
Interviews now test:
- Judgment under uncertainty
- Tradeoff reasoning
- Communication clarity
- Ownership thinking
These are contextual skills. They don’t produce the satisfying “I’m done studying” feeling.
As a result, candidates wait for a confidence signal that never arrives, and interpret its absence as unpreparedness.
The Anatomy of Actual Unpreparedness
Being genuinely unprepared usually shows up in concrete ways:
- Inability to explain past decisions clearly
- Confusion about basic tradeoffs
- Fragile reasoning under follow-up questions
- Lack of ownership over outcomes
These gaps are observable and actionable.
Candidates who are truly unprepared often:
- Avoid interviews entirely
- Rely on memorization
- Panic under ambiguity
This is not the majority case.
The Anatomy of Feeling Unprepared
Feeling unprepared, by contrast, is driven by uncertainty, not incompetence.
It commonly appears when candidates:
- Understand multiple valid approaches and can’t choose one
- Anticipate edge cases before being asked
- Recognize ambiguity in “correct” answers
- See how context could change the decision
Ironically, these are senior signals.
But because they don’t feel like mastery, candidates misinterpret them as weakness.
Why High Performers Feel This Gap Most Strongly
The more you know, the more you see:
- Tradeoffs instead of rules
- Probabilities instead of certainties
- Consequences instead of checklists
This awareness erodes the feeling of readiness.
Meanwhile, less experienced candidates may feel confident because:
- They mistake familiarity for competence
- They haven’t encountered real failure modes
- They rely on rigid patterns
This creates a false comparison baseline, where uncertainty looks like inadequacy.
It isn’t.
The Role of Opaque Hiring Signals
Modern hiring systems provide:
- Binary outcomes
- Minimal feedback
- Inconsistent evaluation criteria
Candidates then try to infer readiness from outcomes:
“I didn’t pass, so I wasn’t ready.”
But hiring outcomes are noisy:
- Different interviewers value different signals
- Hiring bars shift with headcount
- One weak signal can outweigh many strong ones
Using outcomes as readiness feedback is statistically flawed, and emotionally corrosive.
Why Preparation Increases Anxiety Instead of Confidence
Many candidates respond to uncertainty by preparing more:
- More courses
- More practice
- More breadth
But breadth without signal alignment increases anxiety.
Why?
- You encounter more edge cases
- You see more ways to fail
- You realize how little is fixed
Preparation expands awareness faster than it builds closure.
Without recalibration, this feels like regression.
Readiness in 2026 Is Probabilistic, Not Absolute
The most important reframe is this:
You are never “fully ready” for modern tech interviews.
Readiness now means:
- You can reason through unfamiliar problems
- You can make defensible assumptions
- You can explain tradeoffs calmly
- You can adapt when challenged
These abilities don’t eliminate uncertainty, they operate within it.
Feeling uncertain while being capable is normal.
Why Confidence Doesn’t Come Before Interviews Anymore
In previous eras, confidence preceded interviews.
In 2026, confidence often follows signal alignment, not preparation volume.
Candidates regain confidence when they:
- Understand what’s being evaluated
- Practice the right type of reasoning
- Stop equating certainty with competence
Until then, confidence remains elusive, even for strong candidates.
How to Self-Diagnose Correctly
Ask yourself:
- Can I explain my past decisions clearly?
- Can I choose a reasonable approach under ambiguity?
- Can I articulate tradeoffs without freezing?
- Can I adjust my reasoning when assumptions change?
If the answer is mostly yes, you are likely prepared, even if you don’t feel it.
The discomfort you’re feeling is not unreadiness.
It’s exposure to real-world complexity.
Section 2 Summary
- Feeling unprepared ≠ being unprepared
- Uncertainty often signals maturity, not weakness
- High performers feel this gap most acutely
- Hiring opacity distorts self-assessment
- Over-preparation increases awareness, not confidence
- Readiness in 2026 is probabilistic and contextual
Once candidates stop using certainty as a proxy for competence, the confidence gap begins to close.
Section 3: How AI, Comparison Culture, and Infinite Preparation Erode Confidence
By 2026, most tech job seekers are not underprepared, they are overexposed.
They see too much.
They compare too often.
They prepare without closure.
This combination creates a feedback loop that steadily drains confidence, even as competence increases.
1. AI Tools Raise the Baseline and Lower Self-Trust
AI tools were supposed to make preparation easier. In practice, they’ve raised the perceived baseline so high that candidates struggle to trust their own readiness.
When candidates can:
- Generate solutions instantly
- See perfect explanations on demand
- Run mock interviews with flawless answers
They start asking the wrong question:
“If AI can do this so easily, am I actually good enough?”
This isn’t impostor syndrome. It’s signal distortion.
AI shows idealized outputs, not the messy reasoning, tradeoffs, and ownership that interviews actually evaluate. Candidates then measure themselves against an unrealistic standard, and conclude they fall short.
The result is diminished self-trust, not diminished skill.
2. Comparison Culture Is Now Continuous, Not Occasional
In earlier cycles, comparison was episodic, limited to peers or colleagues.
In 2026, it’s constant:
- Social posts about offers
- Viral prep threads
- Public portfolios and demos
- AI-polished resumes and case studies
Candidates see highlights without context:
- Not the failed interviews
- Not the timing advantages
- Not the internal referrals
- Not the role-specific nuances
This creates a distorted reality where everyone else looks ready all the time.
Confidence erodes not because candidates are behind, but because the reference frame is broken.
3. Infinite Preparation Has Replaced Readiness
The most damaging shift is the move from preparing to interview to preparing to feel ready.
In 2026, candidates often:
- Finish a course → still don’t feel ready
- Complete a roadmap → still don’t feel ready
- Practice mock interviews → still don’t feel ready
So they add more:
- More breadth
- More tools
- More content
- More edge cases
Preparation becomes infinite because there is no clear stopping rule.
Without a stopping rule, confidence never arrives.
4. Breadth Without Alignment Increases Anxiety
Many candidates equate preparation with coverage:
- “I should know a bit of everything.”
- “What if they ask something random?”
- “I don’t want to be surprised.”
This leads to shallow breadth rather than aligned depth.
The paradox:
- You know more
- But feel less ready
Why?
- Because you’re exposed to more unknowns
- Because you see more ways to fail
- Because you haven’t practiced the signals that matter
This misalignment is a major driver of confidence loss, and it’s reinforced by prep advice that hasn’t caught up with how interviews are evaluated.
5. AI-Driven Comparison Makes Confidence Feel Fragile
AI doesn’t just raise the baseline, it makes confidence feel replaceable.
Candidates think:
- “My explanation isn’t as clean as the AI’s.”
- “My solution isn’t as fast.”
- “My answer isn’t as articulate.”
They confuse polish with readiness.
But interviews don’t reward polish alone. They reward:
- Decision-making
- Assumption clarity
- Tradeoff reasoning
- Adaptability under pushback
AI outputs obscure these signals, making candidates doubt strengths that actually matter.
6. Rejections Reinforce the Infinite Loop
Every rejection, especially silent ones, feeds the loop:
“I must not be ready yet.”
So candidates respond by preparing more.
But because rejections are noisy and opaque, extra preparation doesn’t reliably improve outcomes, or confidence.
This creates a self-reinforcing cycle:
- Prepare more
- Feel more uncertain
- Compare more
- Prepare even more
Without intervention, confidence keeps declining.
This is the same structural pattern that shows up earlier in the funnel during resume screening, where ambiguity, not ability, drives rejection, as discussed in How Recruiters Screen ML Resumes in 2026 (With or Without AI Tools).
7. Why This Hits Strong Candidates Hardest
High-performing candidates:
- See nuance
- Anticipate counterarguments
- Understand tradeoffs
These traits reduce certainty.
Meanwhile, weaker candidates:
- Memorize patterns
- Assume correctness
- Overgeneralize confidence
The system unintentionally rewards visible certainty, even when it’s shallow.
Strong candidates internalize uncertainty as unreadiness.
It isn’t.
It’s awareness.
8. Confidence Erosion Is a Systemic Outcome
No single factor causes the confidence gap.
It emerges from:
- AI showing idealized performance
- Social platforms amplifying success
- Prep culture emphasizing breadth
- Hiring systems withholding feedback
- Rejections interpreted as readiness failures
Individually, each factor seems manageable.
Together, they produce widespread, persistent self-doubt.
Section 3 Summary
Confidence erodes in 2026 because:
- AI raises perceived baselines unrealistically
- Comparison culture is constant and context-free
- Preparation lacks stopping rules
- Breadth increases awareness faster than alignment
- Rejections reinforce infinite prep loops
This is not a motivation problem.
It’s a signal alignment problem.
Section 4: What ‘Interview Readiness’ Actually Means in 2026
In 2026, the biggest mistake tech candidates make is waiting to feel “ready.”
That feeling rarely arrives, not because candidates are underprepared, but because readiness no longer feels like certainty.
Interview readiness today is contextual, probabilistic, and role-specific. Once candidates understand what interviewers actually evaluate, confidence stops being a vague emotion and becomes a practical checklist.
Readiness Is No Longer “Knowing Enough”
For years, readiness meant:
- Completing a syllabus
- Memorizing patterns
- Practicing known questions
In 2026, those signals are assumed.
Interviewers now evaluate:
- How you think when the problem is underspecified
- How you choose among reasonable options
- How you explain tradeoffs under time pressure
- How you recover when assumptions change
This is why candidates who “know everything” still feel unready, the goalposts moved.
The Five Signals That Define Readiness Today
Across roles and companies, interviewers consistently look for five signals. If you can demonstrate these, you are ready enough, even if you don’t feel it.
1. Problem Framing Under Ambiguity
Interviewers care less about the final answer and more about:
- How you define the problem
- What assumptions you surface
- Which constraints you prioritize
Readiness means you can:
- Ask clarifying questions without stalling
- Make reasonable assumptions and proceed
- Explain why you chose a direction
This is a core signal in modern interviews and explains why open-ended questions dominate, as detailed in How to Handle Open-Ended ML Interview Problems (with Example Solutions).
2. Decision-Making With Tradeoffs
In 2026, there are rarely “correct” answers, only defensible decisions.
Interviewers want to see:
- That you can compare options
- That you understand second-order effects
- That you can justify tradeoffs calmly
Readiness means you can say:
- “Given these constraints, I’d choose X over Y because…”
- “The risk here is…, and I’d mitigate it by…”
Candidates who wait for perfect certainty never feel ready, because certainty isn’t required.
3. Ownership Thinking (Even Without Authority)
Interviewers probe for ownership through questions like:
- “What would you monitor?”
- “What could go wrong?”
- “How would you handle failure?”
Readiness means you:
- Anticipate failure modes
- Think about post-launch reality
- Speak as if you’re accountable for outcomes
This ownership mindset matters even for junior roles. It signals predictability and trustworthiness, two qualities hiring teams value highly.
4. Clear Communication Under Pressure
Communication is no longer a “soft” skill.
Interviewers evaluate:
- Whether you can explain decisions simply
- Whether you adapt explanations to the audience
- Whether you stay structured when challenged
Readiness means:
- You can articulate reasoning step by step
- You can adjust when interviewers push back
- You don’t freeze when answers aren’t obvious
Candidates often underestimate how much clarity alone improves interview outcomes.
5. Recovery, Not Perfection
Modern interviews intentionally introduce friction:
- Changing constraints
- Counterexamples
- Follow-up questions
Readiness is not answering perfectly.
It’s demonstrating that you can:
- Acknowledge mistakes
- Update assumptions
- Revise your approach without defensiveness
Interviewers often rate recovery higher than initial correctness.
Why Readiness Feels Uncomfortable Now
These signals share a common trait: they operate under uncertainty.
That’s why readiness feels like:
- Mild anxiety
- Alertness
- Focus without closure
It does not feel like:
- Confidence from memorization
- Completion of a checklist
- Certainty about outcomes
Candidates misinterpret this discomfort as unreadiness.
It’s actually the opposite.
Readiness Is Role-Specific, Not Universal
Another reason confidence never arrives: candidates prepare generically.
In 2026, readiness depends on:
- Role expectations
- Seniority level
- Company risk tolerance
Being “ready” for a junior SWE role is different from being ready for a senior ML role.
Confidence improves when preparation targets the signals for a specific role, not an abstract standard.
The “Ready Enough” Threshold
You are ready enough if you can:
- Frame unfamiliar problems coherently
- Make defensible decisions with incomplete information
- Explain tradeoffs clearly
- Anticipate failure and recovery
- Communicate calmly under pushback
You do not need:
- Exhaustive knowledge
- Perfect answers
- Total confidence
Waiting for those keeps candidates stuck in infinite preparation.
Why Interviews Restore Confidence, After the Fact
Many candidates report:
“I felt unready before the interview, but fine during it.”
That’s because interviews force alignment:
- You practice the right signals
- You see that uncertainty is normal
- You realize you can reason in real time
Confidence in 2026 often follows signal alignment, not preparation volume.
Section 4 Summary
In 2026, interview readiness means:
- Reasoning under ambiguity
- Making defensible decisions
- Demonstrating ownership thinking
- Communicating clearly
- Recovering from uncertainty
Readiness feels uncomfortable, not confident.
That discomfort is not a red flag.
It’s the signal.
Conclusion: The Confidence Gap Is a Signal Problem, Not a Self-Belief Problem
The dominant struggle for tech job seekers in 2026 is not lack of intelligence, skill, or effort.
It is misaligned confidence.
Modern hiring systems:
- Test judgment, not recall
- Reward reasoning under uncertainty
- Penalize overconfidence and vagueness
- Provide little feedback
- Amplify comparison through AI and scale
Candidates respond by preparing more, broader, deeper, longer, while confidence continues to decline.
This creates the illusion that:
“If I just prepare a little more, I’ll finally feel ready.”
In reality, confidence no longer comes from completion.
It comes from alignment.
Candidates feel unprepared because:
- They’re preparing for outdated signals
- They’re using certainty as a readiness proxy
- They’re interpreting discomfort as weakness
- They’re letting opaque systems define self-assessment
Once you understand what interviews actually evaluate in 2026, problem framing, tradeoff reasoning, ownership thinking, communication, and recovery, the confidence gap becomes explainable.
And fixable.
The critical reframe is this:
Feeling uncertain does not mean you’re behind.
It often means you’re operating at the right level of complexity.
Confidence today is not about believing you’re exceptional.
It’s about knowing what “good enough” actually looks like and recognizing when you already meet it.
FAQs: Confidence, Readiness, and Job Searching in 2026
1. Is it normal to feel unprepared even after months of preparation?
Yes. In 2026, uncertainty is a feature of readiness, not a failure of it.
2. How do I know if I’m actually unprepared or just anxious?
If you can reason through unfamiliar problems and explain tradeoffs, you’re likely prepared, even if you feel unsure.
3. Why does more studying sometimes make me feel less confident?
Because awareness expands faster than closure. You’re seeing complexity, not losing ability.
4. Do confident candidates actually perform better in interviews?
Not necessarily. Calm, structured reasoning beats visible confidence.
5. Why do high performers struggle more with confidence?
They recognize nuance and ambiguity, which reduces certainty but increases judgment.
6. Should I wait until I feel confident before interviewing?
No. Confidence often follows alignment through interviews, not before them.
7. Are AI tools making job seekers less confident overall?
Yes. They raise perceived baselines and distort comparison without reflecting real evaluation criteria.
8. How much preparation is “enough” in 2026?
Enough to reason clearly, not enough to eliminate uncertainty, which is impossible.
9. Why does rejection hit confidence so hard now?
Because feedback is opaque and candidates interpret outcomes as readiness judgments.
10. Is feeling nervous during interviews a bad sign?
No. Mild anxiety often accompanies focus and engagement with complex problems.
11. What’s the biggest mindset shift to rebuild confidence?
Stop equating certainty with competence.
12. Does everyone else really feel more ready than me?
No. Most candidates feel unprepared; few admit it publicly.
13. How can I prepare without burning out?
Practice decision-making and explanation, not endless coverage.
14. What restores confidence fastest?
Clear understanding of what signals are evaluated and practicing those signals directly.
15. What should I do if confidence is blocking me from applying?
Apply anyway, with a focus on alignment, not perfection.
Final Takeaway
In 2026, confidence is not something you wait for.
It’s something you rebuild by understanding the system you’re operating in.
Once you stop preparing for certainty, and start preparing for judgment, confidence stops being elusive and starts becoming stable.
Not because you know everything.
But because you finally know what matters.
