Why are some jobs better than others?
Well, it largely depends on people’s preferences. In other words, one person’s dream job may be another person’s nightmare.
And yet, there are also clearly some universal or at least generalizable parameters that make most people accept the idea that some jobs are objectively better than others—or at least seen by most as generally preferable.
Pay and purpose
For example, jobs that pay well, offer stability, and provide opportunities for growth are almost universally considered better. A tenured professorship, a senior engineering role at a reputable company, or a stable medical position all combine financial security with long-term prospects and prestige. In contrast, poorly paid, insecure, or dead-end roles (like gig work with no benefits or exploitative manual labor with long brutal shifts and an alienating experience) are widely viewed as worse, even if a few individuals might value their flexibility or simplicity.
Then there’s autonomy. Jobs that grant people a degree of control over how and when they work (e.g., creative professionals, entrepreneurs, and researchers) tend to score higher on satisfaction than those defined by micromanagement or rigid supervision. Autonomy is a proxy for trust and respect, and it correlates strongly with both engagement and mental health. Few people dream of jobs where every move is monitored, and most aspire to roles where they can think, decide, and act freely.
Unsurprisingly, purpose matters, too. Occupations that contribute to something meaningful (whether saving lives, advancing knowledge, or building something lasting) are viewed as more fulfilling than those that feel transactional or pointless. A teacher inspiring students, a scientist developing a vaccine, or an architect designing a community space are all examples of work that confers a sense of legacy. By contrast, even lucrative jobs can feel hollow when they lack purpose or moral value. This may explain the low correlation between pay and job satisfaction, which highlights the fact that we tend to overestimate the importance of compensation when making career choices. In that sense, the “best” jobs aren’t just about rewards, but about how they make people feel about themselves and their place in the world.
What the science says
A good way to acknowledge these nuances, and yet still predict whether a person is likely to access better jobs, is to examine why some individuals have more choices than others. That is, in any job or labor market, available job or career opportunities may have different degrees of appeal or attractiveness; but from a job-seekers perspective, the more employable you are, the more likely to are to find and maintain a desirable job – whether we look at subjective or objective dimensions of desirability. With this in mind, here are some critical learnings about the science of employability that explain why certain people are better able to access in-demand jobs:
(1) Their personality
Research has consistently shown that employability is largely a function of personality. Traits such as conscientiousness, emotional stability, curiosity, and sociability predict not only who gets hired, but also who thrives once employed. Personality shapes reputation (the way others see us) and reputation determines whether we are trusted, promoted, and retained. For instance, people who are reliable, calm under pressure, and open to learning tend to be more employable than those who are erratic, avoid feedback, or difficult to work with. Moreover, personality also predicts job satisfaction: even in objectively good jobs, neurotic or disagreeable people are less likely to feel content, whereas optimistic and adaptable individuals find meaning in a wider range of roles, and are resilient if not satisfied even with jobs that make most people miserable. In short, who you are determines both the jobs you can get and how you feel about them once you do.
(2) Their social class
While most advanced economies like to think of themselves as meritocracies, the data on social mobility suggest otherwise. In the United States, only about half of children born to parents in the bottom income quintile will ever move up the ladder, and just 7% will reach the top quintile. In the UK, the “class pay gap” between working-class and professional backgrounds persists even among graduates. Privilege still buys access to education, networks, internships, and employers willing to take a chance. Sociologists call this social capital; in plain terms, it means your parents’ contacts and credentials still matter more than your own potential. The world may be trending toward meritocracy, but it hasn’t quite arrived there yet.
(3) Where you are born
Location remains one of the most powerful predictors of career outcomes. The “Where-to-Be-Born Index” ranks countries by the opportunities they afford their citizens, and being born in Switzerland, Denmark, or Singapore gives you exponentially better odds of landing a good job than being born in Haiti, South Sudan, or Bhutan. Access to education, infrastructure, technology, and basic security all shape employability. The same talent, if born in a country with weak institutions or unstable governance, is far less likely to achieve its potential. In that sense, geography is more likely than talent to mean destiny, at least until global mobility or remote work meaningfully narrow the gap.
(4) Their values, interests, and preferences
Even within similar contexts, people differ in what they want from work. Psychologists like Shalom Schwartz and Robert Hogan have shown that our motivational values (e.g., achievement, power, altruism, security, stimulation, and so forth) determine what “fit” looks like for us. Someone who values adventure and creativity will flourish in start-ups or design roles, while a person who craves structure and predictability may prefer government or finance. Misalignment between values and job environment (say, a highly independent person in a bureaucratic culture) leads to burnout or disengagement. The better your job matches your values, the more likely you are to perceive it as a good one.
Adapt, evolve, and improve
In the end, “better jobs” are not just better paid or better designed; they’re better matched to the people who hold them. Some of this is luck: being born in the right family, in the right country, with the right temperament, will simply afford you a higher range and choice of matches, so you are bound to find more options. But much of it also depends on deliberate self-awareness, namely understanding what kind of environments bring out the best in you, and aligning your career moves accordingly.
From a societal perspective, the goal should be to expand access to good jobs by improving education, reducing inequality, and helping people develop the skills and traits that make them employable. That means focusing less on pedigree and more on potential, less on connections and more on competence.
Ultimately, the world of work will never be perfectly fair, but it can be fairer. And while none of us can control where we start, we can control how we grow. The most employable people are not just those who fit the system, but those who learn to adapt, evolve, and turn whatever job they have into something better.
Below, Gene Ludwig shares five key insights from his new book, The Mismeasurement of America: How Outdated Government Statistics Mask the Economic Struggle of Everyday Americans.
Gene is the former Comptroller of the Currency and founder of th…
Amidst much confusion, polarization, and debate around how AI will impact work, the fact of the matter is that many people are concerned by automation and the prospect of AI job elimination.
For example, the simple notion that “AI is going to take my job” is a thought that has crossed the minds of 25% of workers. For some, this may be true, although the magnitude of AI-driven job displacement is still uncertain; depending on assumptions, AI-driven job displacement could potentially range from 3% to 14%. What will the ultimate figure be? It’s hard to know: nobody has data on the future, and any projection is merely extrapolating from past data and past innovation, which may or not be relevant to the AI age.
And yet, one thing is clear: for some workers, AI job displacement isn’t a distant fear—it is already their reality. Indeed, it was recently announced that Accenture is making layoffs to reshape its employees for the era of AI, exiting employees that it views cannot be retrained with AI skills. As brutal as this may sound, it could still signal a trend many organizations are contemplating (but not yet officially acknowledging).
AI can create new roles
This is not to deny the positive impact AI is having on jobs and careers. Most notably, AI is creating new roles. For example, although IBM laid off almost 8,000 employees, mostly in HR, with the aim of automating their workflows, this resulted in a recruitment drive for software engineers.
That’s not to say that the only way to avoid losing your job to AI is to become an AI engineer; IBM also invested in the recruitment of marketing and sales roles, which require human creativity and problem-solving.
Can it replace humans?
Importantly, organizations are increasingly realizing that AI is not the ultimate solution, and that it cannot replace humans’ unique skills. For instance, Klarna replaced 700 workers from its customer service team with AI agents in a move estimated to boost profits by $40 million.
Despite the agents cutting resolution time to two minutes from the previous 11 minutes, the service provided by agents was reportedly lower in quality compared to the service provided by humans. As a result, Klarna has launched a new initiative to hire more human customer service workers.
The importance of AI literacy
Despite this, Klarna is not rolling back its AI and will instead continue to invest heavily in the technology, signaling that it intends to have humans and AI work alongside each other.
This is a powerful combination, with research suggesting that workers using AI complete 12% more tasks, work 25% quicker, and have 40% higher quality outputs than those not using AI.
Using AI doesn’t automatically improve job performance, though; workers, particularly knowledge workers, must know how to use it well—they must have AI literacy.
Research has found that generative AI literacy in particular significantly impacts job performance. It also increases creative self-efficacy—the belief an individual has in their ability to be creative and innovative.
While the stronger job performance resulting from AI literacy alone isn’t enough to provide job security, research by LinkedIn suggests that AI literacy can boost career progression, and over 80% of leaders say that new worker skills are needed in the age of AI.
With several countries around the world already promoting AI literacy, it could be a lack of AI literacy, not AI itself, that puts your job at risk.
How to become AI literate
Staff AI literacy is a requirement under the EU AI Act, which governs the AI available on the market in the EU and will have global implications, but the form that literacy training must take is not specified.
Indeed, AI literacy is not one size fits all. Training must take into account the technical knowledge, experience, education, and training of staff, as well as the context the AI systems operate in and who they are used by.
At a minimum, AI literacy programs should cover the basics of how AI works, the risks involved, and how the risks can be mitigated. A sociotechnical approach is also key; AI risks are not just a technical or social problem. Using AI safely requires an understanding of the role you play as well as how the technology works.
AI literacy is not just an achievement for your LinkedIn profile; knowing how to use AI effectively could be the difference between keeping and losing your job.
Beyond survival: thriving in the AI era
However, AI literacy shouldn’t just be seen as a defensive strategy to avoid redundancy. The real opportunity lies in using AI to amplify human potential. Workers who master AI tools can automate mundane parts of their jobs, freeing up time for tasks that require judgment, empathy, and creativity—the very things machines can’t yet replicate. In other words, AI-literate employees don’t just survive automation; they lead it.
AI literacy as a new form of intelligence
Historically, each major technological revolution created a new kind of intelligence that defined success: reading and writing in the industrial age, digital literacy in the information age, and now, AI literacy in the algorithmic age. Understanding how to prompt, evaluate, and collaborate with intelligent systems is rapidly becoming as essential as knowing how to read or type. The difference between being augmented and being automated is not in the technology, but in the person using it.
A call for lifelong learning
The single best way to future-proof a career is to stay curious and keep learning. AI will not replace people who are adaptable, inquisitive, and capable of learning new tools as they emerge. But people who resist learning may quickly find themselves replaced by those who don’t. The future of work belongs to those who are not just technically skilled, but psychologically prepared to reinvent themselves—continuously.
Want to assess your own AI literacy?
Here’s a simple, practical 10-item AI literacy test designed to assess how well you may understand, use, and critically evaluate AI tools at work. It balances conceptual knowledge, ethical awareness, and applied skill, and can be adapted for self-assessment or formal training.
Instructions:
Choose the best answer (A, B, C, or D) for each question.
Each correct answer = 1 point.
Interpretation key follows below.
1. What is the main difference between traditional software and AI systems?
A. AI systems never make mistakes
B. AI systems learn from data rather than following fixed rules
C. AI systems are programmed by humans to do one specific task only
D. AI systems don’t need electricity
Correct answer: B
2. Which of the following best defines “Generative AI”?
A. AI that predicts stock prices
B. AI that can create new content (text, images, code, etc.) based on training data
C. AI that generates electricity
D. AI that manages databases
Correct answer: B
3. If you ask ChatGPT for help writing an email and then edit it to fit your tone, this is an example of:
A. AI replacing human work
B. Human–AI collaboration (augmentation)
C. Algorithmic bias
D. Deepfake creation
Correct answer: B
4. Which of the following is a major ethical risk of AI?
A. Too much human empathy
B. Algorithmic bias leading to unfair outcomes
C. Faster decision-making
D. High energy efficiency
Correct answer: B
5. What does “AI hallucination” mean?
A. AI creating false or made-up outputs that sound plausible
B. AI visualizing data
C. AI having emotions
D. AI overheating due to overuse
Correct answer: A
6. Which of the following statements is TRUE about data privacy and AI?
A. AI systems never store your data
B. Data used to train or run AI may contain sensitive personal information
C. AI makes all data anonymous automatically
D. Data privacy laws don’t apply to AI systems
Correct answer: B
7. What is the best way to ensure reliable AI output?
A. Accept all AI answers as correct
B. Verify and fact-check outputs using trusted human or data sources
C. Use AI only for creative writing
D. Ignore the AI’s sources
Correct answer: B
8. Which of these professions is least likely to be fully automated by AI?
A. Graphic design
B. Customer service
C. Psychotherapy
D. Data entry
Correct answer: C
9. “Prompt engineering” refers to:
A. Writing code to create AI models
B. Crafting precise inputs or questions to get better AI responses
C. Building robots
D. Programming hardware chips
Correct answer: B
10. The EU AI Act requires organizations to:
A. Replace humans with AI wherever possible
B. Ban all generative AI
C. Ensure staff have adequate AI literacy and training
D. Only use open-source AI
Correct answer: C
Scoring & Interpretation
- 0–3: AI Beginner — You’re curious but need to learn the basics. Try a short AI literacy course.
- 4–7: AI Aware — You understand the concepts but need more practical experience. Start experimenting with AI tools.
- 8–10: AI Fluent — You can work effectively with AI and critically assess its risks and benefits. Keep refining your skills.
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