There’s a saying: you can’t control the world, but you can control yourself.
This perspective is critical when navigating an uncertain economy.
I learned this lesson the hard way, right out of college, when taking my first steps into the full-ti…
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When I first started my freelance writing business, I assumed I should find clients who would put me on retainer. The appeal seemed obvious: steady income for me, predictable working relationship for the client. I even knew how to structure retainer agreements based on my prior roles at marketing agencies.
But a few months into a solo career, I was willing to take any work that came my way. Which was primarily project-based work, not retainers. I quickly built a business based on ad hoc assignments from many clients, rather than relying on a few.
The conventional wisdom would say that I was “doing it wrong.” Every solopreneur forum, coach, and freelancer community says the same thing: Lock in recurring clients. But after three-plus years of running my solo business on almost entirely project-based work, I’ve found the opposite to be true. Chasing retainers isn’t the only path to a stable solo business … and it might not even be the best one.
The case against putting all your eggs in the retainer basket
Retainers feel stable, but they can create real risk in your business. If one or two retainer clients make up the bulk of your income, losing one creates a giant hole. And, depending on your work, that hole might not be easy to fill immediately.
The “stability” of retainers is often an illusion. You’re dependent on a small number of clients continuing to renew, and the decision might be outside your control. Budgets get cut. Leadership changes. Priorities shift. None of that has anything to do with the quality of your work.
Retainers can also be a harder sell. When budgets are tight, asking a potential client to commit to a six-month engagement is a bigger ask than scoping a single project. That’s how I found myself with almost entirely project-based work. It lowered the barrier to entry: It was easier for a potential client to say yes to one deliverable than to an ongoing commitment. Clients can flex up or flex down how much work they send me, depending on their current needs.
How project-based work builds a stronger foundation
When you don’t have retainers to fall back on, you’re forced to create habits that actually sustain a solo business, including:
- Consistent marketing. I post on LinkedIn, nurture referral relationships, and stay visible because I’m always on the lookout for my next project. I can’t afford to go quiet for three months and hope the work shows up.
- Pipeline management. Retainer-dependent solopreneurs often stop marketing once they’re “full.” Then a client leaves, and they’re scrambling. (I’ve seen it happen many times.)
- Client diversification. With more clients at any given time, losing one is rarely catastrophic. Losing one project out of six is manageable. Losing one retainer out of two is stressful.
I maintain all of these habits even when I’m busy. I have to trust that my next project is on the horizon—so I have to do the work to make sure it is.
Project-based work doesn’t mean more hustle. On the contrary, if you build repeatable systems, it means that finding the next project isn’t a crisis every time. Doing these things is just part of running your business.
Prioritize the habits that keep your income stable
None of this is anti-retainer. Some solopreneurs offer services that naturally lend themselves to ongoing work or long engagements. If retainer clients are part of your business, that’s great.
But I’d encourage you to maintain your marketing and pipeline habits as if those retainers suddenly don’t renew—because that’s always a real possibility. The solopreneurs who build sustainable businesses are the ones who’ve built the habits to keep a steady stream of work coming in.
Below, Liz Tran shares five key insights from her new book, AQ: A New Kind of Intelligence for a World That’s Always Changing.
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During the last decade, digital innovations have produced a range of recruitment and evaluation tools: now, whenever you first apply for a job, you are less likely to be judged by humans and more likely to be assessed by AI. Before you can even get the opportunity to impress a human interviewer, you will first need to impress the algorithm!
More recently, AI has also been used to assist current employees in doing their jobs and then to help their employers evaluate how well employees are performing in those jobs. In fact, AI adoption is now the norm across knowledge economy jobs, with estimates indicating that at least 70% of people use AI regularly at work (a figure that is probably an underrepresentation of the reality, since much of AI use at work is clandestine and undisclosed) and an increasing number of organizations are using AI to evaluate employee performance.
Meritocratic or Orwellian?
Traditional performance evaluations (often an onerous, annual ritual based on subjective, “noisy,” and unreliable or invalid manager feedback) are indeed being disrupted by algorithms capable of analyzing workflows, communication patterns, and even “relational analytics” (mining the digital footprints of your exchanges with coworkers) in real-time, which critics lament as a form of “surveillance capitalism.”
To be sure, these tools put unprecedented power in the hands of organizations to pursue data-driven management decisions which, at their best, can make workplaces fairer and more meritocratic, but at their worst, seem uncomfortably close to an Orwellian big brother dystopia and can erode trust and morale.
To make sense of AI in performance management, it helps to imagine a simple matrix with four quadrants or scenarios, which echoes the classic negotiation model by Roger Fisher and William Ury on win-win outcomes, as well as decades of behavioral science differentiating integrative from zero-sum approaches to conflict. In one scenario, the company and the employee both win. In another, only the company wins. In a third, employees learn to game the system to their benefit but not to the company’s. And in the worst case, nobody benefits at all.
First scenario: AI helps both the company and the employee. Let’s start with the best quadrant of the matrix. Used well, AI can make feedback fairer and more useful. Anyone who has ever received a vague appraisal knows the problem, and meta-analytic studies show that only 1/3 of feedback is typically useful, 1/3 is useless or irrelevant, and 1/3 actually worsens employees’ performance! Add to this the typical unreliability of performance evaluations, which are usually highly subjective: one manager loves your enthusiasm; another thinks you talk too much; a third simply remembers the last mistake you made; a fourth has no idea who you are, and so on. In other words, performance evaluation has historically been closer to subjective wine tasting than to objective science.
AI, if properly used and validated, can anchor feedback in observable behavior rather than impressions. A sales manager might see which client interactions actually led to repeat business on her sales team. A project leader might learn that delays happen when approvals pile up on his desk. Instead of impatiently waiting for an annual review to learn how their performance may be perceived, employees get real-time feedback and suggestions. The process becomes closer to coaching than judging. This is where the promise of AI is most compelling. It democratizes the collection and distribution of feedback and suggestions. It replaces guesswork with data. It never forgets and it can make employee evaluation performance-driven rather than political.
Second scenario: AI helps the company but harms employees. The same tools can quickly slide into surveillance. Algorithms now analyze workflow, communication patterns, tone of voice, and even what some vendors call “relational analytics.” A decrease in typing speed may be interpreted as disengagement. A change in Slack sentiment might flag someone as “skeptical” or “cynical.” Tracking and penalizing irregular working hours could covertly disadvantage parents or people with health issues. Voice or facial analysis might infer emotional states or physical conditions that employers may actually be legally prohibited from determining or diagnosing. What begins as an effort to measure performance may become a digital panopticon. Employees feel watched rather than supported. Trust erodes over the long term, even if productivity appears to rise in the short term. As is often the case, European countries are among the first to provide legislative protections (https://natlawreview.com/article/ai-news-italy-sets-rules-ai-workplace) for employees in order to safeguard against this scenario.
Third scenario: Employees benefit but the company loses. People are not passive. When employees realize they are being judged by an algorithm, they learn to reverse-engineer it to their benefit. Anyone who has worked in a call center (or even just called in to one) has seen this dynamic. If the AI rewards a cheerful tone, everyone becomes artificially upbeat even if doing so creeps out callers with the service reps’ saccharine affect. If AI rewards high email volume, outboxes and inboxes fill up with unnecessary messages.Teachers teach to the test. Students memorize without understanding. In offices, people optimize for metrics instead of outcomes. Real collaboration moves to private channels, and official data becomes less truthful than before. AI ends up measuring performative theatre rather than real value added, and employees learn to create perfect performance reviews and fake productivity signals leveraging AI to deceive or fool employers, taking progress back decades.
Fourth scenario: Nobody benefits. The worst outcome is multilateral mistrust. Managers hide behind dashboards that they cannot explain. Employees treat feedback as noise. Performance reviews become bureaucratic “check the box” exercises completed with minimal attention. “We pretend to work, and they pretend to pay us” was a cynical workers’ slogan decades ago in the Soviet Union. Perhaps “We pretend to evaluate our performance and they pretend to evaluate us” could be the contemporary equivalent when appraisals are essentially “AI Slop.” When a manager says, “the system gave you this rating,” leadership has effectively abdicated responsibility. Organizations can collect terabytes of data that predict nothing useful. Employees disengage. Trust and morale decline. We have seen versions of this before with poorly designed assessments or unvalidated tools. Technology does not eliminate bad management. It can scale it. And in this scenario, even as “the system” remembers anything that is fed into it, managers and employees quickly disregard and forget everything that comes out of it.
What to do
What, then, should leaders do? The principles are simple, though not easy. Validate before you automate. Ask whether a metric predicts real performance or just activity. Be transparent about what data is used, how and why. Ensure that the system can be audited for how it maps inputs into outputs and is not an inscrutable “black box.” Keep humans in the loop so context is not lost. Do not collect or consider private information, even if technology can infer it. And don’t just optimize for operational metrics or output, but also for morale and engagement. And finally, let the AI provide feedback not just to employees, but also to managers and HR about what can be done to create the foundation for enhanced employee success in the future.
During the last decade, as algorithms and AI have become central to talent decisions and as estimates suggest that the overwhelming majority of people use AI at work, the temptation to measure everything has grown. As the line often attributed to Einstein reminds us, not everything that counts can be counted, and not everything that can be counted should count. AI can make performance management either more like good coaching or more like constant surveillance. The difference lies not in the technology, but in how wisely managers, employees and organizations choose to use it. AI should not just be used to evaluate employees within an organizational system, it should also evaluate the system in which the employees are working and come up with constructive observations and recommendations that can enhance individual, team, departmental and company success.
Importantly, there is still much to preserve from the art of good performance appraisals, which long predate AI and often work precisely because they are human. When a manager and employee co-create clear, measurable goals at the start of the year, everyone gains clarity about what success looks like (and makes a cognitive and emotional investment in achieving that success) and fewer surprises or disappointments emerge later. When feedback is specific, timely, and anchored in real achievements or failures, such as a difficult client negotiation, a failed product launch, or a junior colleague you coached getting promoted, employees learn what to repeat and what to fix, and managers see capability rather than just output. And when reviews include a forward-looking development plan, perhaps rotating someone into a new market, funding an additional training program, or pairing them with a mentor, the organization invests in future value while the employee sees a credible path for growth. These simple practices succeed not because they are high tech, but because they align incentives, create shared holistic understanding, and turn managers into competent people-leaders. When used properly, AI can enhance and accelerate the successful co-evolution of systems and all of their stakeholders.
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In 2025, New York Times columnist Ezra Klein and Derek Thompson from The Atlantic released a book called Abundance, which posited that America had developed a culture of scarcity. Overregulation and overall risk aversion from the government, the authors argued, were stifling the development of infrastructure and housing in the country. To remedy this, they proposed an “abundance agenda,” one that focused on a growth mindset among elected officials that would help foster long-term prosperity. Although the provocation has its challenges, it got me thinking: What if we applied the idea of abundance to our work?
For over a decade, I’ve occupied two worlds simultaneously—one foot in the world of practice as an advertising executive and one foot in the world of academia as a marketing professor. On an average week, I’d work at the ad agency from 9 to 5 and then teach a night or two at the university from 6 to 8. This duality made me better at both jobs. I taught what I practiced, which made course concepts more applicable for students, and I applied what I studied, which made my practice more rigorous. I didn’t have the language for it then. I just thought, Why do I have to choose?
Essentially, I had adopted an abundance agenda for my career. I removed the confines that restricted my career to a “one-job-at-a-time” approach to navigating corporate America. I was going to do both—not a side hustle, not a moonlighting gig, not a hobby, but a portfolio career that prioritized my development in both arenas. Before long, I found myself in a university class, but this time as a student studying consumer culture theory and pursuing a doctorate. In doing so, my career has prospered. But what if we applied an abundance agenda to our organizational culture? Apparently, Bing Chen, the cofounder and CEO of Gold House and managing director of AU Holdings, was one step ahead of me.
As YouTube’s global head of creator development and management from 2010 to 2014, Chen helped build the platform’s multibillion-dollar creator economy. In 2018, he launched Gold House, a nonprofit that embraces an abundance mentality and is dedicated to accelerating the socioeconomic equity of Asians and Pacific Islanders. While experiencing all the accolades and praise from his many achievements at Google, he wanted something more. In fact, during our interview with Chen for the latest episode of the From the Culture podcast, he revealed that his very first word as an infant was “more.” And this idea of “more” resonates throughout the organization and Chen’s leadership style.
If Gold House was going to succeed at supporting Asians and Pacific Islanders, it first had to succeed at supporting itself—its people. But that doesn’t mean merely supporting their work, safety, and well-being; that’s just table stakes for Chen. Instead, at Gold House, this means supporting your dreams. So, the first question Chen and his team ask candidates when they interview for a job centers squarely on getting to know the candidate’s dreams. Not their skill set. Not their experience. But their dream.
Chen contextualizes the questions with a scenario: “If I gave you $10 billion and the world’s biggest Rolodex, where the person on the other end of the line will say yes to whatever you ask, what would you do?” According to Chen, if the job the candidate has applied for is not related to or accelerates that dream, he will end the interview right there. Why? Because he wants to support the long-term ambitions of his team members, just as he does his clients and his constituents.
The lesson for leaders that Chen realized when he adopted an abundance agenda for Gold House was that the organization will get more out of its employees when the organization understands its employees’ greater ambitions and helps advance their trajectory to achieving them. This shift toward a culture of abundance has paid off handsomely for Chen because this approach has enabled the Gold House company to prosper.
Not only is the organization behind paradigm-bending shifts to cultural narratives about the Asian Pacific community through its successful marketing campaigns and strategies for movies like Crazy Rich Asians and Everything Everywhere All at Once and the Netflix series Beef, but it has also accelerated the dreams of over 100 Asian Pacific founders with the $30 million Gold House Venture Fund. For Chen, this is the power of thinking audaciously enough to desire more and commit ourselves to it. It’s not a skill so much as it is a mindset; one that we all can adopt.
Check out our full conversation with Bing Chen and how he developed an abundance mindset on our latest episode of the From the Culture podcast.
We live in a world, especially in Western cultures, that relentlessly promotes positive thinking and celebrates self-belief to the point of sidelining reality—that inconvenient thing that does not disappear simply because we ignore it.
Self-help advice and pop-psychology slogans urge us to stop worrying about what others think, to believe in ourselves no matter what, and to focus on our strengths. They rarely stress the value of acknowledging our flaws and limitations, even when this requires revising, if not abandoning, our childhood ambitions.
It may sound harsh, but science shows clear benefits to confronting our shortcomings, aligning our self-assessments with our actual abilities and, when necessary, adjusting them downward.
What the research says
Consider some key findings from academic research.
1. Metacognition is a key enabler of learning. In any domain of skill or competence, practice improves performance, but practice without feedback, or feedback without self-awareness, leads to stagnation. Progress depends on our ability to track our own development, to know what we do well and where we fall short. Metacognition, the capacity to think about and evaluate our own thinking and performance, is the mechanism that makes this possible.
Whether you are learning a language, a musical instrument, or a sport, believing you are better than you are removes the incentive to improve. And when the gap between how good you think you are and how good others perceive you to be becomes too wide, the result is not confidence, but credibility loss. When others are of the opinion that you suck, and that you are totally unaware of the fact that you suck, they will think even more poorly of you than if you were aware.
2. Excess persistence can be more damaging than insufficient persistence. We admire stories of spectacular success built on grit and determination, but we forget that these winners usually combined persistence with talent, timing, and opportunity. They are vivid anecdotes, not representative data, and the plural of anecdote is not data. The far more common stories of people who persist heroically and still fail rarely make it into biographies or Netflix documentaries. Psychologists describe this as the false hope syndrome: People set unrealistic goals, overestimate the speed and ease of progress, and double down when reality resists. The result is wasted time, sunk costs, burnout, and foregone alternatives that might have suited them better.
Sensible persistence is evidence-based. Just like competent scientists abandon hypotheses that do not replicate, rational investors cut their losses, and effective leaders know when a strategy has stopped working, recognizing when your effort is no longer productive is not a weakness, but a sound judgment call. In careers, as in research, the goal is not to try harder at everything, but to try longer only where feedback suggests improvement is plausible and success has a high-enough probability of occurring. As Daniel Kahneman noted: “Courage is willingness to take the risk once you know the odds”—but if you are blind or delusional about the odds, you are simply displaying wishful thinking or self-destructive recklessness rather than courage.
3. While deliberate practice and effort matter, talent and potential are key. The popular reading of deliberate practice implies that anyone can achieve elite performance with enough hours of focused training. Yet the evidence is more nuanced. A large meta-analysis found that deliberate practice explains about 26% of performance differences in games, 21% in music, 18% in sports, 4% in education, and only 1% in typical professions (knowledge work jobs) once other factors, such as prior talent or expertise, are considered. In other words, practice matters, but far less than we think. And the best predictor of performance is not practice, but how much talent and potential someone has to begin with.
Baseline cognitive ability, personality traits such as conscientiousness and openness, access to coaching, health, and simple luck also play substantial roles. The implication is not fatalistic. It is practical. Improvement is possible in almost any direction, but not infinitely and not equally for everyone. Ignoring aptitude leads to wasted effort and frustration; aligning ambition with talent or potential allows effort to compound. As in investing, the goal is not to bet everywhere, but to double down where the expected return is highest.
The power of positive illusions
To be sure, there are short term benefits to ignoring weaknesses. Overconfidence can help you charm an interviewer or deliver a confident presentation, provided the audience is not too discerning. Positive illusions can protect self-esteem and reduce anxiety. Optimistic distortions (which, by the way, are the norm) sometimes encourage experimentation that reveals hidden strengths. Entrepreneurs often start companies precisely because they underestimate the odds. Yet these advantages are temporary. Eventually markets, colleagues, and customers provide brutally honest feedback. In the long run, reality excels at fact-checking.
There is also a moral case for recognizing our limits. When leaders refuse to confront their blind spots, others pay the price. History is full of confident incompetence—from failed mergers to catastrophic political decisions. A little humility would have saved billions of dollars and countless careers. In organizational psychology, we often say that integrity is inferred from behavior over time. Acknowledging your weaknesses is a signal of integrity because it shows respect for evidence and for the people affected by your decisions.
Intelligent self-awareness
So how should we practice intelligent self-awareness without slipping into paralysis or cynicism? Three habits can help here.
1. Seek high-quality feedback. Peer ratings, 360 reviews, and objective performance data are better guides than intuition. As Rob Kaiser’s work shows, colleagues often detect strengths and flaws that individuals cannot see. Treat feedback as market research on your behavior, or a way to crowdsource your reputation or internalize your professional brand.
2. Experiment at the edges. Try roles, projects, or skills that stretch you but remain diagnosable. Instead of announcing a career reinvention on social media, run small pilots. Measure results. Iterate. Learn to fail well or fail smart, as Harvard’s Amy Edmondson notes. This is the scientific method applied to personal development, and it is far more reliable than inspirational quotes.
3. Cultivate curiosity about your own limitations. Ask why certain tasks drain you, why some colleagues energize you, why your best work appears in particular contexts. Patterns will emerge. Those patterns are clues about where your comparative advantage lies. The point of recognizing weaknesses is not self-punishment. It is efficiency: Time is finite, attention is scarce, and life is too short. In an age when generative AI promises to do everything faster, the scarce resource is thoughtful human judgment about where to invest effort. Honest self-knowledge helps allocate that effort wisely, and even gen AI can help you improve that.
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