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When I first ventured into self-employment a few years ago, I received a lot of advice from fellow freelance writers: Know your worth. Don’t take low-paying work.
The advice was valid, as too much low-paying work is a recipe for burnout. But to the newly self-employed, I would say: Know your worth. And also, there are very valid reasons to take low-paying work, if it can help launch your business.
You can open the right doors without selling yourself short.
The project is good for your portfolio
Potential clients will expect “proof” that your work is good—especially if it’s the type of work that can be displayed in a portfolio (design, video, writing, or other creative work). Portfolios don’t grow overnight. One good client at a lower rate will lead to a better client who pays more.
Even now, several years into freelance writing, I’ll still take projects below my normal rate if I think the work will be a standout in my portfolio. The payoff comes when clients approach me and say, “I saw your work for XYZ company—I love that publication!”
Sometimes projects can earn you far more in the long run than your short-term payout.
The project will connect you with important people
Some of my best clients are referrals, even when the original project was low-paying, boring, or short-term. I’ve even had clients rehire me when they move on to their next gig.
You’ll quickly learn which people in your industry are “movers and shakers.” By working with them, you could get a glowing recommendation or countless referrals.
You can also say yes to speaking on panels, podcast appearances, and writing guest posts for publications if you feel like the work will get you in front of the right audience or make good industry connections. These are often a much lower lift than a full-blown paid project and can have a similar impact.
You can learn new skills
If you need it, here’s your permission to say “yes” to a project that’s slightly outside of your skill set. Slightly being the operative word. You need to be confident that you can meet the client’s expectations. But it’s also an opportunity to try something new and get paid for the work.
Don’t ever, ever overpromise and under-deliver. However, sometimes the only way to gain new skills as a solo business owner is to take on the work, wow the client, and get the project into your portfolio so you can take on future projects that require the same skill set.
It’s OK to say “no”
For many self-employed people, money is a primary factor in accepting projects. But just like there are valid reasons beyond money to take on new work, there are also valid reasons for declining work—even if the money is good.
Bad clients can cost you. They can absorb too much of your time and mental energy. You may also reach a point in your business where you don’t need the money, even if you have the bandwidth.
One of the best things about running your own business is that you get to make those decisions. When you work for an employer, you’re forced onto projects or stuck with colleagues you’d rather avoid. Self-employment is different. Taking on clients is a business decision—and you should get comfortable basing your decisions on factors other than money.
Tell me: Do things like this ever happen to you?
You have clarity of purpose. You know what you need. You walk into another room to get it. Then, distraction hits, and you forget entirely what prompted you.
Or else, you search the house for you…
“Let’s circle back when we have the bandwidth to touch base on whether we need to hop on a call to tackle the low-hanging fruit.”
(If this corporate buzzword bingo sent a shiver down your spine—apologies.)
In the world of professional com…
When my teenage son developed mysterious symptoms, I followed the same path anyone else would: I put his health in the hands of a team of medical professionals. Multiple myeloma is a rare blood cancer. It is so uncommon in 17-year-olds that it doesn’t appear on diagnostic checklists. Despite having no clear starting point to work from, my son’s doctors worked their way to an accurate diagnosis through a process of trial and error, bouncing ideas off each other and testing and discarding hypotheses until they could tell us what was wrong. The process felt inefficient and uncertain at a time when I wanted fast answers and cast-iron guarantees. But this messy and distinctively human approach saved my son’s life.
AI promises to improve processes like this, replacing the fallible and unpredictable human mind with the analytic power of trained and tested algorithms. As someone who helps organizations implement AI technology, I know just how much potential it has to make processes and workflows more efficient. But before we start replacing human judgment at scale, we need to think carefully about the hidden costs that can come with productivity gains.
A recent study in The Lancet Gastroenterology & Hepatology presented some sobering findings for AI maximalists. Physicians who spent several months working with AI support in diagnostic roles showed a significant decline in unassisted performance when the technology was withdrawn. This kind of “deskilling” effect isn’t unique to either medicine or AI. We have known for years that extensive GPS use leads to a decline in spatial memory and that easy access to information reduces our ability to recall facts (the so-called “Google effect”).
Most people are willing to accept these cognitive losses in exchange for convenience. And that is a trade-off that individuals need to decide for themselves. But when it comes to organizations and institutions, things are more complex.
The first concerns that leap to mind are worries about losing access to our AI tools after outsourcing our skills to them. What if the system crashes or performance drops off? While this is a real problem, it is nothing new. We can design backup solutions where necessary, just as we always have with technology.
But there is another set of problems that cannot be resolved simply by putting guardrails in place. Human skill sets are important not just because they let us act on those skills, but also because they let managers and decision-makers understand and supervise what is happening on the frontlines. If physicians lose their diagnostic chops, who will validate or audit the output of the algorithms? Who will notice that the edge cases—the patients with statistically implausible diseases—are not being diagnosed correctly? And, perhaps most importantly, who will take responsibility for the algorithmic judgments, whether they are right or wrong?
For most organizations, maintaining public trust is a core part of their relationship with society. Just as we won’t eat in a restaurant if we don’t trust the kitchen to deliver safe food, so we avoid products and services that we believe may harm us. Without accountability, trust is impossible.
As an IBM training manual put it nearly 50 years ago: “A computer can never be held accountable, therefore a computer must never make a management decision.” The same principle holds true for AI. Without a clear accountability trail that leads to a human decision-maker, it becomes impossible to hold anyone responsible for any harms that arise from the AI’s behavior. And this accountability deficit can destroy the legitimacy of an institution.
We can see these dynamics at work in the U.K.’s 2020 exam grading debacle. At the height of the COVID pandemic, with normal exams cancelled, the U.K. government used an algorithm to assign grades. The algorithm imported biases and systematically favored children from wealthy backgrounds. But even if it had worked perfectly, something critical would still have been missing: institutions that can justify their decisions to those affected by them. Nobody will be satisfied by an algorithmic explanation for a result that might have lifelong effects. Ultimately, the government reversed course, replacing the AI judgment with assessments made by each student’s teachers.
What this means for your organization
The challenge isn’t whether to use AI—it’s how to implement it without creating dangerous dependencies. Here are specific actions leaders, managers, and teams can take:
- Implement AI rotation schedules: Ensure that teams rotate periodically from AI-assisted work to manual work to maintain core competencies.
- Create skill preservation protocols: Document which human capabilities are mission-critical and cannot be outsourced.
- Establish accountability chains: Specify which decisions require human sign-off.
- Institute “analog days”: Schedule regular sessions where teams solve problems without AI tools.
- Design edge case challenges: Create exercises focusing on unusual scenarios AI might miss.
- Maintain decision logs: Create institutional memory of the value and role of human judgment by documenting when and why you override AI recommendations.
- Practice explanation exercises: Regularly require team members to explain AI outputs in plain language—If they can’t explain it, they shouldn’t rely on it.
- Rotate expertise roles: Ensure multiple people can perform critical tasks without AI support, preventing single points of failure.
Warning signs your organization is too AI-dependent
Watch for these red flags that indicate dangerous levels of dependency:
- Teams can’t explain AI recommendations
- Acceptance of AI results without validation has become the norm
- Staff miss errors or outliers that the AI overlooks
- Employees express anxiety about performing tasks without AI assistance
- Simple decisions that once took seconds now require AI consultation
If you spot any of these signs, you need to intervene to restore human capability.
The path forward
My son’s cancer was successfully diagnosed thanks to structured redundancy in his care team. Multiple specialists approached the same problem through different lenses. The bone specialist saw what the blood specialist missed. The resident asked the naive question that made the senior doctor reconsider. This kind of overlap can look like inefficiency at times, but if we don’t work to retain it, we lose something vital.
We should not shy away from the advantages AI can offer when it comes to analytical speed and pattern-recognition. But at the same time, it is essential that we shield the decision-making process from being overwritten by a single algorithmic voice. We must keep humans in the loop both because they can look beyond statistical likelihood and because they can be held accountable for their final decisions.
Yes, maintaining human capabilities alongside AI will be expensive. Training tracks that preserve human skills, AI-off drills, and rigorous human audits all cost money. But they preserve the institutional muscle memory that holds the whole edifice up. The cost of losing the human perspective is one we cannot afford to bear.
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