Nov 1, 2023, 9:17 AM ETLecture, Tutorial

How to find the right career for you | The 80,000 Hours career guide (2023 edition)

Traditional advice to "discover" talent through introspection or career quizzes is unreliable because abilities are primarily constructed through decades of practice rather than pre-existing traits.
Historical figures like Darwin, Lincoln, and Oprah failed early in their careers before dominating their fields, illustrating that early performance is a poor predictor of long-term success.
Career advisors should shift focus from asking "What am I good at?" to "What could I become good at?" to avoid unnecessarily narrowing professional options.
The most successful method for finding a career is an empirical, "scientist-like" approach of forming hypotheses about fit and testing them against reality.

Personal fit is a multiplicative factor in the career success formula, outweighing impact, career capital, and supportive conditions; taking a high-impact job without fit is strongly discouraged.
Impact Skewness: In high-complexity domains like research and software engineering, the top 10% of workers contribute approximately 50% of the total output, while the bottom 50% contribute only 15%.
Extreme Outliers: In research, the top 0.1% of papers receive 1,000 times more citations than the median, highlighting the outsized impact of excellence in specific fields.
Career Capital: Success in any field generates influence, money, and connections that can be redirected to promote social impact, even in areas unrelated to one's primary expertise.
Example: Isabel Bermeky pivoted from fashion modeling to advocating for nuclear energy, using her existing platform and success to drive climate change solutions.
Satisfaction: Gaining a sense of mastery is a vital component of long-term job satisfaction, independent of external rewards.

Introspection and Gut Instinct: Intuitive decision-making is unreliable for career choices because the results take years to manifest, opportunities to practice are scarce, and the environment constantly changes.
Career Tests: Holland Type Match tests show a weak correlation with job performance (R ≈ 0.31) and job satisfaction (R ≈ 0.1–0.3).
Predictive Validity Data: A meta-analysis of 100 years of employer selection data reveals even the best predictors have low correlation with performance:
- IQ tests: 0.65 (highest correlation, but poor at distinguishing between job types).
- Structured/Unstructured Interviews: 0.58.
- Peer Ratings: 0.49.
- Job Knowledge Tests: 0.48.
- GPA: 0.34.
- Years of Experience: 0.16.
- Graphology: 0.02.
- Age: 0.00.
Hiring Reality: Employers, who know job requirements best, frequently misjudge candidates, implying that job seekers cannot reliably predict their own fit in advance.

Step 1: Hypothesis Generation: Create a broad list of career options to avoid the bias of considering too few paths or defaulting to standard trajectories (e.g., medicine, law).
Step 2: Identify Uncertainties: Rank options and isolate the key questions that, if answered, would most significantly alter the ranking (e.g., "Would I get in?", "Is the pay sufficient?", "Do I enjoy the daily routine?").
Step 3: Investigate with a "Ladder of Tests": Resolve uncertainties using a tiered approach, escalating cost only as needed:
- Level 1: Read career reviews and search online (1–2 hours).
- Level 2: Speak to one person in the field (2 hours).
- Level 3: Speak to three more people and read 1–2 books (20 hours).
- Level 4: Perform a short project or apply to a job (1–4 weeks).
- Level 5: Commit to a trial position, internship, or graduate study (2–24 months).
Iteration: Treat the first job itself as an experiment; if it does not work out after a couple of years, update the hypothesis and try a new path.

Age Factor: Exploration yields the highest return when young because there is more time to capitalize on a better option discovered early, and societal costs for switching paths are lower.
Ordering Options: Prioritize reversible paths (e.g., internships, business roles) before irreversible ones (e.g., PhDs, specialized medical training) to preserve the ability to pivot.
The "Wild Card" Approach: Include random or novel options in the exploration list (e.g., living abroad, learning a new language) to avoid settling for a local optimum.
Side Projects: Engage in short-term projects or internships during university breaks to test fit without committing to a full career change.
Upside Bias: When uncertain, choose the option with the highest potential upside (a "long shot") if the downside risk is manageable, due to the asymmetry of career outcomes.
The "Quit" Rule: If deeply unsure about staying in a role, a randomized study suggests making a change increases happiness by an average of 2.2 points out of 10, countering sunk cost bias.

Background: Graduated with a degree in math and philosophy, initially leaned toward philosophy of mind academia.
Exploration: Spent months in finance (eliminated due to lack of enjoyment), weeks in non-profits, and extensive networking in academia.
Outcome: Secured a PhD in psychology focusing on decision-making policy, while simultaneously testing public intellectual and policy roles through internships and writing.
Result: Entered the workforce with a validated understanding of fit rather than speculation.

Rank long-term career paths by the balance of impact, personal fit, and job satisfaction.
List at least five specific uncertainties regarding this ranking.
Design "cheap tests" to resolve these uncertainties immediately.
Identify the option with the highest upside potential to prioritize in an exploration sequence.
Decide on a strategy for trying multiple paths versus gaining transferable career capital before specializing.