A lot of people go out on their own after a layoff, especially in the current economy. And when they do, they tend to focus on what they don’t know: how to find clients, how to set pricing, how to market themselves. But a long corporate career also builds some core competencies that translate directly into running a solo business.

I spent 15 years in a corporate environment, including a role on an executive team. I pivoted to a new career, and then found myself laid off 18 months later. I made the snap decision to start my solo business the next day.

While a lot of aspects of starting a solo business were intimidating, there were a few things I knew I could do well, based on my corporate experience. If you find yourself in a similar situation, here are a few corporate skills that might lend well to your solo career.

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The other day, someone asked me if my clients would start demanding that I lower my prices “because of AI.”

The primary way I earn money is by working as a freelance content marketer for B2B SaaS companies. It’s a question that a lot of people are facing right now — freelancers, employees, anyone whose work has gotten faster or more efficient thanks to AI tools. The assumption baked into that question is that if something can be produced faster, it must be worth less. Or that workers should be producing significantly more of it for the same price.

The pressure to lower prices or produce exponentially more “because AI” rests on a specific assumption: that the value of work is measured by how long it takes to produce it. If something gets done faster, it should “cost” less in some shape or form.

That assumption was always wrong.

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The “do more with less” trap

The demand that AI should make workers cheaper or exponentially more productive is the latest version of a pattern that has been squeezing workers for years. Automation did it before AI. “Lean” management did it before automation. CEOs have long demanded “more with less” — AI is just the latest mechanism.

The basic way economists measure productivity is in output per hour. If you produce more output in the same number of hours, that makes you more productive. No wonder CEOs are salivating.

I had written almost this entire draft and was ready to publish when the CEO of ClickUp (a project management software) decided to further illustrate my point. He wrote on X/Twitter:

Today we reduced headcount by 22%. The business is the strongest it’s ever been. So I think it’s important to be direct about what I’m seeing and why.

This wasn’t about cutting costs. Most savings from this change will flow directly back into the people who stay. We’ll be introducing million-dollar salary bands. If you create outsized impact using AI, you’ll be paid outside of traditional bands.

The primary change is that we’re reconstructing around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.

The 100x org is actually heavily dependent on people — infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.

(There was a lot more after this about the 100x org… it was a long post.)

Translation: “This isn’t about cutting costs. I just don’t give AF about the people who work for me.”

He doesn’t care about the former employees and families of nearly 25% of people who are now unemployed at the whim of the CEO.

He doesn’t care about the employees left behind, because 100x output is insanity. And even though he says that they will be increasing the salaries to match the impact… is it 100x? No. The difference between the impact, what flows to the company, and what flows back to the employee is what’s outsized.

He said the quiet part out loud (at least, according to a CEO’s way of thinking): AI isn’t about freeing up anyone’s time, as it promises. It’s about creating and requiring more work.

The infuriating part of his word salad of a statement was saying that the 100x org is “heavily dependent on people.” Just… not the people that the company had already hired, apparently.

How to think about your value at work

100x org aside, productivity gains that flow entirely to the employer or client have absolutely no benefit for the worker. What is the point of a 100x org? It’s a means of extracting more output for the same money (or the same output with fewer people, or for less money).

The conversation about AI and compensation — whether that’s salary negotiations, freelance rates, or even workload expectations — needs reframing now, before this intense and unrealistic focus on increased output becomes the new baseline.

For employees: When a manager suggests that AI should allow the team to take on twice the workload, that’s a change in the requirements of the role — and workers need to treat it as such before it becomes the default expectation. 100x is ridiculous. He’s probably blowing smoke, but it’s smoke he chose to blow in public. I know for many people, quitting is not an option, but I hope the CEO’s post scares away any new talent.

If you take AI out of the picture and were asked to take on more responsibilities, you are being asked to do more for the same pay (which isn’t right). Even ClickUp’s promise of higher salary bands isn’t aligned with the 100x output the CEO is expecting.

I’ve long advocated that people research a company before applying for a job, and public statements like this are self-filtering. Don’t apply unless you plan to create value only for the company, and not for yourself.

For the self-employed: Thinking about client expectations in a world of AI, the frame is simpler (though the stakes may feel high because your livelihood depends on clients). The deliverable is the deliverable. If the quality hasn’t changed, the price hasn’t changed. No “extra output” for the same cost, and certainly not a lower cost. How you produce the work is your business — literally.

I’ve used AI in many parts of my business. I used to have a manual process (copying/pasting) from client briefs to my own project management tool. Automation and AI now do that formatting for me, saving time with every deliverable. I have one client who requires heavily researched articles. Claude now does the bulk of the research, and I check the results to make sure it’s correct and relevant to what I’m writing. These are process improvements, not a devaluation of my work.

If you’re navigating these conversations right now, don’t concede the premise that “faster” means “cheaper.”

This latest wave of “do more with less” doesn’t have to be the one that pushes people over the edge.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

At any given time, I’m juggling multiple clients. That means I’m juggling context for multiple projects, background information on various companies, and a lot of deadlines. Some of my clients give me a steady stream of work each month, while others pop in with a request every few weeks.

Whether you’re coaching, doing creative work, or have long-term retainers, most solopreneurs eventually find themselves managing multiple clients simultaneously. The number of clients you take on directly impacts your income, but more clients also means more complexity.

In my corporate life, I worked as a product manager at a software company. Even though my work is very different now, much of the project management follows the same basic concepts.

When running a solo business, you need to estimate your capacity, plan for future work, and not lose track of 100 moving parts.

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In 1944, the CIA published the Simple Sabotage Field Manual — a guide for ordinary citizens in occupied countries to disrupt their workplaces from within. The tactics are delightfully mundane: refer all decisions to committees, bring up irrelevant issues in every meeting, and haggle over the precise wording of every communication. The genius was that it all looked like normal organizational dysfunction. Sabotage was indistinguishable from business as usual.

Eighty years later, the tactics are different, even if they have the same end result.

In mid-2022, 70% of workers said it was a good time to find a quality job. By 2026, a Gallup survey found that number had flipped: only 28% agree, with 72% saying it’s a bad time. “Don’t quit” has become the default career advice.

Labor economists call this era “The Great Stay.” But that framing makes it sound like a choice, when really, it’s more like being stuck. The next company will be the same or worse. Or the fear is that the job market is so frozen there’s nowhere to go at all. People are living with career discomfort, and wondering if it’s the new baseline, rather than a temporary rough patch.

So what do people do when they can’t leave?

They resist from the inside.

The new performance theater

Think back a few years, and remember quiet quitting? Resistance showed up by doing the job as described, nothing more, and definitely nothing extra. But the resistance has evolved into something more active and more creative. The driving factor is forced AI adoption, and workers are pushing back in ways that would make the CIA’s sabotage manual proud.

Companies are mandating AI use, tying it to performance reviews, and treating skepticism as an undesirable trait in its workers rather than a legitimate response. Meta created internal AI leaderboards, and CNBC reported that almost every Fortune 500 company is now tracking overall AI usage. The message to employees is unequivocal: use the tools, or else.

But token usage is a terrible proxy for productivity — and workers know this. A term has emerged for gaming the system: tokenmaxxing. Running up token spend with bad prompts and producing volume without value. Workers see through the metric and are playing the game while knowing it’s absurd. On top of that, AI usage can cost companies thousands — if not millions — of dollars.

And it goes beyond gaming metrics. A Fortune report from April 2026 found that 29% of workers were intentionally sabotaging their company’s AI rollout — out of genuine fear that their roles will become obsolete. Sabotage ranges from putting a company’s proprietary data into an AI tool to intentionally generating low-quality work to make AI appear less effective.

One of my friends saw a job posting recently for a role to come in and train a company’s AI systems. She said, “So I’m training the thing that will replace me?” She wondered if she should go in and intentionally sabotage it.

The response from leadership is always the same: workers should just adapt. But adaptation requires trust — trust that the tool helps the employees, not just the company. When AI fluency is tied to performance reviews while layoffs are happening simultaneously, workers are interpreting the message in front of them: they’re being asked to build the thing that replaces them.

Nilay Patel put it well on a recent Decoder episode: the lack of worker empowerment is “causing a specific kind of nihilism” — and the people pushing these mandates have “all greatly contributed to” it. It’s a rational response to a culture of fear.

An alternative that used to seem riskier

Some workers reach a different conclusion: if no company will listen, leave the corporate system altogether.

At a minimum, the math has changed. The current job market is terrible for finding another corporate role. But that same instability makes the solopreneur path look less risky by comparison. If both options come with uncertainty, at least one of them comes with some control.

Sabotage and disengagement might provide a temporary outlet, but they’re unfulfilling. Most people want to feel like they’re contributing something that matters — not just running out the clock until the next round of layoffs.

And companies are (inadvertently) making this transition easier. The same organizations cutting full-time roles? They’re increasing contractor budgets. They want flexibility without commitment, a workforce they can scale up and down on a whim. It’s a model that self-employed people can use to their advantage. You want to treat labor as interchangeable? Fine. But the arrangement works both ways. Contractors can bounce from client to client, and losing one contract isn’t the same as losing an entire salary overnight.

As a solopreneur, I purposefully don’t require retainers or minimum commitments from my clients. While logistically a bit trickier to juggle, it makes my work an easy “yes” because clients know they can come to me when they need me.

The labor market is genuinely bad, and most people can’t just walk away. But for workers who’ve already mentally checked out — who’ve already stopped caring — the question becomes: if the anxiety about job security is going to be there either way, would they rather be anxious while building something of their own?

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

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The general public’s discomfort with AI is well-documented and growing. The tech industry thinks we should all be clamoring for what it’s offering, and most people… aren’t. A Gallup poll found 31% of Gen Z is angry about AI, and 51% are anxious about it.

I use AI every day. It’s found its product-market fit as a business tool. That’s how I interact with it: in a work context. I’m sure it would be the same, even if I were working for a company and not self-employed.

I’m not naive about the risks. The environmental concerns are real. Data centers are wildly unpopular, expensive, and may have unknown health risks. The over-hype is real, and a bubble may still be looming. But the tool itself is useful as a business tool. The genie isn’t going back in the bottle, and my stance is that we have to figure out how to live with it — like every other technology revolution.

This relationship between me and technology goes back more than 20 years. I’m also not new to people’s pushback. It was literally my job to convince people to try something new for a large portion of my career.

Yet I feel significant tension in writing and talking about something that is genuinely useful and also poses enormous threats to people, both their jobs and potentially all of humanity. I’m angry beyond belief at how companies are rolling it out and how it’s impacting employees.

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And so I wanted to explore my longstanding career that’s been built on finding ways to use technology and how AI is both “just another chapter” and “fundamentally different.

Then and now: what’s changed

My first job was working as a bank teller at a community bank in my hometown. I stayed at the job through college, eventually working as a mortgage underwriter in the 2003/2004 era. Interest rates had dropped, people were refinancing like crazy, and the loan officers were overwhelmed. I offered to help, which involved learning a bunch of new software.

The following year, the bank decided to digitize all of its paper loan files. At the time, this wasn’t common. Most banks still relied on enormous file cabinets of paper files. But I, along with a few other college students, was assigned the task of sending paper documents through a desktop scanner.

It was unbelievably tedious work. And at first, it seemed like brainless work. But I quickly realized a problem: inconsistency. Each college student organized the digital files in different folders, with different names. Loan officers couldn’t find anything in the digital version. As a result, the loan officers wouldn’t use the digital copy, and would pull out the paper file.

And I set out to fix the problem. I came up with an organization system and naming convention. Once the already-scanned files were fixed, I advocated that the paper files be moved so that the officers couldn’t rely on paper: they’d be forced to use the digital version. Eventually, everyone accepted the change.

After college, I went to work for the company that made the bank software I’d been using. I helped other banks implement digital loan files, along with other loan management tools. One of the final products I worked on (later as a product manager) was a tool that could automatically recognize the data from a bank customer’s tax return and analyze it, saving bank staff from repetitive data entry.

I saw software as a way to solve a problem: large banks have nearly unlimited resources for all types of tasks. Small banks don’t. Automation lets smaller organizations compete with larger ones. They don’t have to spend time on the boring, repetitive work and can instead focus on relationships with customers.

That core principle came with me when I started my own business. I knew I could do more if things simply hummed along in the background. I could focus on client work instead of things like “organizing files” or “manually creating checklists of fifteen items.” One of my friends joked, “I didn’t know how you could do the work of ten tireless humans. Then I realized all the systems that you have running in the background.”

And then along came AI.

AI: Different and yet the same

The current framing of AI is dominated by either hype (”AI will replace everything!”) and backlash (”AI is terrible, and I refuse to use it, ever”). There’s a third position that doesn’t get a lot of attention: people, like myself, who have found ways to make work easier.

Here’s a specific example: One of my clients gave me a spreadsheet of 27 rows. Each row contained some changes that they wanted to make to blog posts, with the changes embedded as bullet points within a cell. The final deliverable back to the client wouldn’t be a spreadsheet: it needed to be a Google Doc of the changes. So I had Claude extract the data from the spreadsheet and put it in a Google Doc for me. That took about one minute. If I had to do it manually, it would have taken at least an hour, maybe more, to do all of the formatting.

That’s the “genuinely useful” category. There’s no glory or anything to be gained in me manually formatting a giant Google Doc. The client is paying me for the writing and editing, not my ability to copy/paste from a spreadsheet.

That’s where I see similarities between the anti-AI position and what I heard throughout my career implementing software. Refusal to use AI is giving up legitimate ways to make work easier. I compare it to the example of extracting data from tax returns when I worked on bank software. The process of keying in numbers was just repetitive, manual work. Interpreting the numbers was part that required a human.

Where I feel the tension most, as I write about AI, is that my experience is not the same as most of corporate America. I have the freedom to explore and find what works for me (and what doesn’t work). Many companies are taking an iron-fist approach and insist that employees “use AI”… without providing any guidance.

And that is the disconnect. I know from experience that a free-for-all doesn’t work. Not everyone has a “software brain” and can immediately see how to use software to do work differently. They need to be given step-by-step instructions.

This is further compounded because AI has a much less obvious path than automation tools. Automation typically follows a specific process: if one thing happens, then this other thing happens. AI is much more open, like my spreadsheet example. That’s a thing I needed one time, and will probably never need again. It’s not a repeatable process. Yet I reached for AI as a tool in my toolbox, knowing that it could do what I needed.

What companies should be doing is providing specific training. That’s always the key to successful software implementation. I used to train a small group of “cheerleaders” within a bank. They would, in turn, figure out how to apply the software to their specific processes. And then they would train everyone else on the specifics.

Instead, CEOs are yelling, “Figure it out!” while simultaneously saying, “You will be evaluated on your use of AI on your upcoming performance review!”

On an episode of Decoder, Dr. Adam Dubé said the following about AI in education:

There’s some research that looks at school climates and teachers who get demotivated for their use of generative AI in education and what causes demotivation. And for them, it was being forced to use these systems when there was a top-down rule that you had to use generative AI…That is demotivating for educators. They don’t like being told which tools to use because it feels like it’s removing their autonomy. And so whenever we remove workers’ autonomy or their own sense, basically their control over their own work environment, people get demotivated.

Automation very clearly removes boring and tedious work. But AI is often a push (from the top) to replace creative work. Or to simply “create more!” without answering the question, “But why are we creating more…?”

That’s where companies are getting it wrong. They’re applying AI to the wrong use cases, and why people working with these scenarios are resisting (and demotivated). It shouldn’t be used to replace the parts of work that people find fulfilling. Yet that’s the push in the attempt to squeeze every last drop from worker capabilities.

This is why my feelings around AI are complicated. I’ve seen exactly how it saves time and effort. And I think most companies are doing it wrong, and workers have a right to feel frustrated by the threats to their jobs because their employers are trying to apply AI to everything, instead of the right things.

I think a “never AI” stance is going to leave some people — especially small or solo businesses — struggling to keep up. They simply won’t be able to keep up with people who do things like “use AI to save a few hours formatting a spreadsheet.”

If you want to learn real-life use cases for automation and AI at work, sign up to attend one of my free live sessions.

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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 sustainable solo business… and it might not even be the best one.

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In 2021, I left my career at a tech company after 15 years and decided to pursue content marketing and journalism. The first marketing agency that hired me assigned writers to levels — 1 through 9 — based on experience. I came in at a level 2, the second-lowest. Fifteen years of product management, executive leadership, and deep domain expertise in financial technology didn’t “count” for anything. In this new world, I was a beginner.

That part of career pivots is often uncomfortable. We hear about the bold decision to quit. We hear about the eventual success story. But there’s a space in between where everything you knew in your prior career doesn’t quite translate yet.

It’s humbling in a way that catches people off guard, especially at mid-career, when you’ve spent years not having to prove yourself. I went from being the most knowledgeable person in the room to the least. And for a while, that felt like starting over from zero.

But it wasn’t zero. Not even close.

Nearly 70% of U.S. workers considered changing careers in early 2025. That number is a reflection of the current economy more than anything else — people can’t find new jobs in their current industries, or the industries themselves are shifting underneath them. More than 38% of new freelancers in 2025 were previously laid off.

Those numbers suggest something that the fear of starting over tends to obscure: the gap between “I left” and “I landed” closes faster than most people expect. The expertise from a previous career doesn’t evaporate. It’s waiting for the right context.

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What actually transfers

The narrative around career pivots tends to focus on what gets lost: title, salary, status, and the expertise that took years to build. But the more interesting story — and the one I’ve lived — is what transfers to a new career path.

After I quit my fintech job, I worked at two different content marketing agencies. Eighteen months later, I was laid off. The job market had tanked. I decided to go out on my own as a freelance writer, something I’d been considering. The layoff forced me to make a decision: try to find a job in a crappy job market, or make the leap.

What I didn’t expect was how much my fintech background would become a competitive advantage. I assumed that most clients would see me the way the first content agency did: a level 2 writer, with very little experience.

But most writers can’t speak the language of banking technology. Most fintech experts can’t write. The combination turned out to be rare and valuable.

The same pattern showed up in other ways. The systems thinking I’d developed as a product manager — implementing automation, streamlining processes, evaluating software — transferred directly to running a solo business. I’d spent years at a small company finding ways to make processes faster and better for my team. As a solopreneur, those same instincts kicked in. Except now the team was just me, and I reaped all the benefits.

The broken promise and the new path

Over the past few years, something has shifted in how people think about career risk. It used to be common sense: a corporate salary equals security. Self-employment is the risky path.

Like many people, I grew up believing that if you stick with your employer, stay loyal, and put in the time, you’re going to be rewarded. I think people just don’t believe that anymore. The social contract between employer and employee has broken. If the loyalty won’t be reciprocated, the job becomes unfulfilling. On top of that, people are constantly nervous about layoffs. Or restructuring. Or the next round of cuts.

Here’s how I think about it now: both paths carry risk.

With a traditional employer, one day you have a salary, and the next day you might have nothing. When I was laid off from the marketing agency, that’s exactly what happened. No warning, no safety net beyond a piddly amount of unemployment. The U.S. has almost no protections for employees (whereas other countries have things like mandatory notice periods and mandatory severance).

Working for myself, if I lose a client, that’s a fraction of my income — not my entire income. I purposely work with a couple of core clients and then take on additional projects as they come. My income is more variable, sure. But it’s never 100% to zero. I feel more secure now than I would if I were working in a corporate job (And I realize how counterintuitive that sounds to someone who grew up thinking self-employment was the riskier path.)

Self-employment isn’t a consolation prize for people who couldn’t make the traditional path work. It’s increasingly what sustainable careers look like — especially for people who’ve already navigated one or two major shifts. And the assumption that any particular industry is safe? That’s gone.

If you’re sitting in a job that no longer serves you, wondering if you have what it takes to try something else: you probably do. The expertise you’ve built isn’t wasted. It’s more portable than you think. It’s only a question of how long before your old career catches up with your new one.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

If you want to support my work as a writer, you can subscribe to receive additional issues I publish.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

In 1996, a physics professor named Alan Sokal submitted a paper to Social Text, an academic journal of cultural studies. The paper, titled “Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity,” proposed that quantum gravity is a social and linguistic construct. The journal published it.

Three weeks later, Sokal revealed that the paper was entirely made up. He’d written the paper to test whether an academic journal would publish anything that sounded good and confirmed its editors’ ideological leanings. It did. The “Sokol affair,” as it came to be known, kicked off a debate about intellectual rigor in academia that lasted for years.

A Planet Money episode from February 2026 explored what’s known as the “replication crisis” in social science: the pattern where published studies can’t be reproduced when other researchers try to verify them. Economist Abel Brodeur, a professor at the University of Ottawa, has been organizing events called “Replication Games,” where teams of social scientists audit published papers by re-running the original code and data.

What they’re finding isn’t always fraud. Sometimes it’s honest errors in coding or data handling. But sometimes it’s something more uncomfortable: researchers who massaged their datasets until they got a statistically significant result. Brodeur admitted to doing exactly this himself as a master’s student. He ran analysis after analysis on data about smoking bans until he finally got a result worth publishing. He later decided to publish the more accurate (and less exciting) null result instead — and went on to build the Institute for Replication to address the problem at scale.

Today, the packaging has gotten a lot more sophisticated, and answering the question, “What is real?” is even more difficult to answer.

The problem is older than AI

There’s a tendency to talk about AI-generated misinformation as though we were living in some golden age of accuracy before large language models arrived. We weren’t.

This problem is as old as research. Sokal proved that we’re willing to believe what we want to believe, even from seemingly credible sources. Brodeur shows us that research is sometimes manipulated. That’s to say nothing of the endless spree of disinformation on The Internet.

Now consider what happens when AI enters the process, which is already our reality. In December 2025, Sam Rodriques, CEO of FutureHouse and Edison Scientific, claimed to accomplish six months of doctoral-level research in a 12-hour run with his AI agent, Kosmos. Rodriques walked through how the tool identified a genetic mechanism for type 2 diabetes — connecting a variant, a binding protein, and a gene involved in pancreatic function — by analyzing massive amounts of raw data that would take a human researcher much longer to sort through.

Stories like what Rodriques shared are genuinely impressive. And it’s easy to imagine how tools like this could accelerate scientific discovery in ways that matter (drug development, disease research, climate modeling, etc).

But the same qualities that make AI useful for research also make it dangerous. AI models hallucinate and present hallucinations with the same confidence as factual information.

A Stanford RegLab/HAI study found that general-purpose AI models hallucinate between 69% and 88% of the time on specific legal queries, using state-of-the-art models. The researchers noted that these models “often lack self-awareness about their errors and tend to reinforce incorrect legal assumptions and beliefs.”

The lack of self-awareness is the alarming part. A human researcher who massages data is making a conscious choice (even if it’s a rationalized one). A journalist who spins a story knows the angle they’re taking. AI has no clue that it’s wrong. It presents fabricated information with the exact same tone it uses when presenting accurate information.

The Sokal hoax was discovered because Sokal himself revealed it. Academic replication errors can take years or decades to surface. AI can generate plausible-sounding misinformation instantly, at scale, and no one is around to reveal the errors. The same dynamics that made any research vulnerable — confirmation bias, incentive structures, lack of verification — now operate at the speed of typing into a chatbot. And these systems that claim to “democratize access” also make it easy for misinformation to propagate (like the guy who claimed that he cured his dog’s cancer with ChatGPT).

We’re right to be skeptical

None of this means AI is useless. But it does mean the question of “what is real?” now applies to virtually every piece of information we encounter — including (maybe especially) the information that sounds the most authoritative.

Cory Doctorow is a science fiction writer and tech journalist, and is well-known for coining the phrase “the enshittifcation of the internet.” He put it bluntly on Offline with Jon Favreau:

“The big problem with AI is that it’s just not real. No one’s ever lost as much money as they have on AI. AI is the losingest proposition in business in the history of the world.”

AI companies are selling a story — that AI can replace human workers — because that story is what investors want to hear. Whether or not AI can actually do the work is almost beside the point. The narrative has become as important as the product.

Companies are making claims about AI that are extraordinarily difficult to verify. When a company says “AI replaced 10 people,” what does that mean, exactly? What’s the output comparison? What’s the error rate? What’s the timeline? In most cases, we have no idea, because the data either doesn’t exist or isn’t shared. A Harvard Business Review analysis from early 2026 laid it out clearly: companies are laying off workers based on AI’s potential, not its actual performance.

The question of “what is real?” has always required effort to answer. Academic papers require peer review (but it might be lacking). News stories require fact-checking (but may still have bias). Corporate claims require scrutiny (and rarely get it). What’s changed isn’t the need for verification. It’s that the effort required has increased exponentially, because AI can produce information with such speed and at scale. The tools for manufacturing a wholly convincing unreality have gotten exponentially easier to use.

When the people making the tools say one thing, and the people using the tools experience something else entirely, it fuels this credibility problem with AI.

I think far too few people (and even fewer corporations) share real, tangible, honest examples of how AI has made their work better. Even in examples of scientific research, we’re right to ask, “Can those results be trusted?”

Personally, I use AI a lot. I try to share specific examples of my use cases, because I realize that I’m fighting the “AI can do everything! It’s amazing!” narrative and a proliferation of slop. But I’m also one person, and I don’t claim anything at the scale of “AI has changed my life and made my work 10,000% better.”

The best defense is the same one it’s always been: question the source, verify what you can, and be especially skeptical of the claims from people who have an incentive to demonstrate a specific result. That’s the lesson from Sokal, 30 years later.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

At one point in my life, I managed a team of seven. My days consisted of 1:1 calls, performance reviews, and running interference between the team, other departments, and customers.

I thought that’s what I wanted: the perceived power and responsibility of being a manager. But in reality, it was very stressful.

Today, I have been a solopreneur for three years. The assumption is that solo businesses are a starting point. You launch alone, build momentum, hire employees, and scale. That’s the entrepreneur’s playbook, right?

But over 80% of small businesses in the U.S. have no employees, according to the U.S. Small Business Administration. For many of us, that’s not a limitation. Staying solo is a deliberate strategy that prioritizes control and flexibility over growth for growth’s sake.

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I saw a thread online recently about a pattern that probably sounds familiar: the employees who raise concerns, suggest improvements, or push back on bad decisions are frequently rebranded by their managers as “having a bad attitude.” The label sticks whether or not the feedback had merit.

It’s worth pausing on who these employees usually are. The people who complain are often the ones who still care. They want to be successful at their jobs. They want the workplace to be better, too. Complaints — real ones, not venting — are a form of participation. They’re evidence that someone still believes the company is capable of improving.

So what does it mean when those same employees stop complaining?

In the U.S., employee engagement has dropped to a 10-year low of 31%, with 17% of workers actively disengaged. Globally, 1 in 5 employees now report feeling trapped in ongoing job dissatisfaction. Companies tend to treat the absence of pushback as a sign that things are working. But silence is often the last sound before employees walk out the door.

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I worked at a software company for 15 years. For a long time, I advocated for fixing anything that I perceived as broken: processes, messaging, shortcomings in the product itself. As I moved up the ranks and eventually was promoted to an executive role, I hit the ceiling of what could be fixed. Management simply wasn’t willing to address some pervasive underlying issues.

I stopped complaining because it was useless. I stopped caring. And I quit.

AI is the newest thing employees are being told to stop complaining about

Disengagement is accelerating right now because companies are pushing AI adoption on employees who have real, valid, and specific concerns. Yet CEOs are dismissing those concerns as resistance to change.

On top of how AI is used at work, there’s a whole additional layer of concern about AI as an industry. A Pew survey found that 73% of AI experts believe AI will have a positive impact on how people do their jobs. Only 23% of the American public agrees. That’s a 50-point gap between the people building AI and the people living with it.

The tech journalist Nilay Patel captured the broader frustration in a recent episode of The Vergecast:

“The AI industry is staring at these polls that say everyone hates them. And it’s because they are asking for so much. They’re asking for a lot of power. They’re asking for a lot of land to build data centers. They are asking for every stick of RAM that has ever existed in the history of the world. They’re asking to scan every book without payment.

Whatever it is that they’re asking for, they’re doing it without permission and they’re asking for a lot and they have not given back a product that makes people feel the way that the internet made them feel or the smartphone made them feel or YouTube made them feel. It just doesn’t exist yet.”

To be clear: AI is a useful tool. I use it every day. The critique here isn’t about AI itself. It’s about what happens when companies deploy it in a way that ignores legitimate concerns and how the vast majority of people feel about the technology.

Most executives haven’t grasped that there are three layers of mistrust stacking on top of each other. Employees don’t trust AI as a technology, for reasons that are well-documented in public surveys. Separately, employees don’t trust the way their employer is rolling it out — often with mandates, with performance reviews tied to “AI fluency.” Thirdly, the social contract has long been broken with sweeping layoffs, so employees don’t trust that their employer won’t replace them with AI at the first chance they get.

You could dismiss the workplace mistrust as resistance to change. But when the same people are also skeptical of AI in their personal lives, “resistance to change” isn’t a satisfying explanation. What’s being resisted is something more specific: being told to adopt, quickly and without question, a technology that the general public is nervous about and that has not yet proven itself to be “life-changing” in the way that AI leaders have promised.

What happens after silence

Most of the writing about employee disengagement focuses on what companies lose: productivity, revenue, and institutional knowledge. That framing is aimed at executives and misses the other side of the equation entirely.

When companies mislabel that skepticism as a bad attitude, employees eventually stop voicing it. It probably won’t lead to a mass exit, because, at present, employees think, “Where else can I go? Will the next company be the same way?” Instead, they start to wonder, “What else can I do with my career? How can I regain control?”

When you stop trying to fix a system that doesn’t want to be fixed, you start seeing the potential exit paths more clearly. The mental energy you were spending on advocacy becomes available for something else — a job search, a side project, or a plan to leave entirely.

If you’ve stopped speaking up because no one was listening, that’s information worth taking seriously. Not every company deserves your energy, and the decision to stop pushing isn’t a failure on your part. Sometimes it’s an accurate read of the situation and a deliberate move to protect your mental health.

In the current labor market, the realization that no one is going to listen is often less specific to their current employer. It’s not, “This company won’t listen.” It’s “No company will listen” (at least, not in a way that matters). There’s no “grass is greener” outlook.

Final thoughts

If you’ve given up on the idea that they can change, you have to think about your next steps. Is it laying low and doing the best you can to avoid a layoff? Or is it something else entirely?

A lot of entrepreneurs and solopreneurs can trace their exit from corporate life back to a moment like this. Not a dramatic blow-up. Not a single bad boss. Just the realization that nobody was going to listen, no matter how well they made the case.

If you’re reading this and recognizing yourself in the silence, pay attention to what it’s telling you. Silence at work is rarely the end of the story. It’s usually the start of a transition.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

In 2015, Paul Ford published a 38,000-word essay in Bloomberg called What Is Code? It’s an explainer for non-technical people who may find themselves in technical meetings with software developers and engineers. Recently, on an episode of The Vergecast, Ford says, “I wrote What Is Code? because I really did believe that this was a good way into the middle class, and it had been for me.”

In the same episode, he acknowledges, “There’s somebody out there… who is counting on their tech job. That somebody like me told them 15 years ago was the safest possible bet. And they went and got a certificate in AWS management. And now people are telling them, ‘Why would I ever do that? I’ll just deploy by using Claude.’” These jobs are existentially at risk.

For a generation, the career advice given to displaced workers, uncertain graduates, and anyone looking for a stable path was essentially the same: find a way to get into tech. Learn to code. Become a product manager. Work for the sales or marketing department at a tech company. Join the industry that is the disruption, not the one getting disrupted.

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I’ve worked in the tech industry (and now, tech-adjacent) since 2006. It was well-paying and, until late 2021, felt safe. I left my tech job voluntarily and went into content marketing for tech companies. 18 months later, I lost my job. A lot of factors played into that turn of events, but one was that the tech company was being decimated. Companies no longer had the budgets for marketing, impacting the marketing agency I worked for.

Over the past few years, I’ve watched the ground shift beneath the feet of so many people I know. That assumption that tech is “safe” has crumbled into dust. And the people who believed it most are the ones feeling it hardest.

The decade that didn’t deliver what was promised

There’s a cohort of workers — mostly elder millennials — who arrived at what was supposed to be the “payoff phase” of their careers at the exact moment everything started to change. Mid-to-senior titles. Accumulated equity at startups. Stable (even growing!) compensation. The decade was supposed to deliver on the promise that patience and hard work in tech would lead to security.

But then the pandemic hit. Tech layoffs hit. Generative AI hit. Just as this cohort reached the positions they’d been climbing toward, the industry started dismantling itself.

As of early April 2026, tech layoffs this year alone have impacted over 91,000 workers. In 2025, that number was approximately 246,000 across 783 companies. While some layoffs have been from startups struggling to survive, others are from companies that aren’t strapped for money. Amazon reported record revenue of $716.9 billion in 2025 while cutting 30,000 corporate roles. Block — the fintech company behind Square and Cash App — cut 40% of its workforce, roughly 4,000 people, explicitly because of AI. CEO Jack Dorsey said it wasn’t driven by financial difficulty. After the announcement, the company’s stock went up.

What makes the current wave of layoffs structurally different from what came before is what’s driving it. The first wave of tech layoffs (2022–2024) was a post-pandemic correction. Companies had to dial back the hiring sprees they went on during COVID. Painful, yes, but cyclical. The thing that’s happening now is something else entirely. Companies are explicitly stating that they are replacing human roles with AI systems. In a Resume.org survey, 44% of hiring managers anticipate AI will be a top driver of layoffs in 2026.

And here’s what I keep thinking about: the executives making these workforce decisions are acting on a capability that, by most reports, hasn’t fully materialized. Microsoft’s AI CEO, Mustafa Suleyman, predicted that most white-collar tasks — lawyers, accountants, project managers, marketers — will be “fully automated” within 12 to 18 months. Meanwhile, a randomized controlled trial by METR (published July 2025) found that experienced developers using AI tools took 19% longer on tasks than those working without them, even though they were faster.

The gap between what executives promise (or what they believe will happen) and what workers actually experience is enormous. Employees are bearing the cost of a bet they didn’t place, when we were promised something different.

The decision to pivot

Not everyone who’s leaving tech is being pushed out. Some are choosing to go.

There’s a steadily growing sentiment among the cohort that was supposed to be entering the highlight of their careers. It’s less about the dramatic exits of The Great Resignation and more about a widening gap between what tech promised and what it actually delivers at this stage of life. Kids entering adolescence. Parents aging. Bodies changing. Energy levels changing. Perspectives changing. People already juggling more “life outside of work” are rightfully questioning the constant grinding of their careers.

And the incentive to retool themselves again — this time at the breakneck speed that AI requires inside a corporate structure — just isn’t there. Think of it this way: why apply yourself to learning AI, when all of the benefit goes to the company, not the individual? The company gets more output. The individual gets more work.

Many are choosing something else, such as consulting, starting a business, or even learning trades. They’re turning long-held hobbies or passions into second careers. It’s intentional.

Part of what’s driving this shift is that people want more from work itself. More meaningful work instead of more money. More flexibility and control instead of a prestigious title at a company that might lay them off next quarter. RTO mandates, constant restructuring, AI-related pressures at every performance review… the culture of tech has changed. For some workers, the industry that used to feel generous and exciting doesn’t feel that way anymore.

And then there are the golden handcuffs. The math used to be simple: the paychecks from working for a tech company are often big enough that leaving feels irresponsible. But the equation changes when those paychecks come with layoff anxiety, RTO mandates, and the persistent feeling of dread, thinking that you’re one AI deployment away from being replaced. At some point, the handcuffs aren’t golden anymore… they’re just handcuffs.

I left my work at a tech company years before AI arrived, and before the mass layoffs hit. But the desire for “something else” was my motivator. I felt the thrill of building something — a product I truly believed in — early in my career. As I reached my 40s, with older kids and a post-pandemic lack of tolerance for bullshit, my personal calculus changed.

Was tech ever the destination?

Here’s what’s worth knowing if you’re in that space of “what do I do next?” right now: career pivoters are overwhelmingly happy on the other side. I’ve talked to many, many people over the years. And sure, it’s a small sample, but I feel confident that it’s representative of how people feel overall. They’re relieved, even if the initial “getting through change” phase is hard.

I’ve written before about the portfolio career replacing the career ladder, and about the different types of career pivots people make. What’s happening in tech right now is a collision of several of those pivot types at once: forced pivots from layoffs, anticipatory pivots from people who see the writing on the wall, and boredom pivots from workers who’ve mentally checked out of an industry that no longer resembles the one they signed up for.

Non-linear paths are not consolation prizes. They’re increasingly what sustainable careers actually look like — especially for people who’ve already navigated one or two major industry shifts. They can build on what they’ve learned, instead of clinging to something that’s disappearing.

We don’t fully know how this plays out. But the assumption that tech was a career safe harbor? That’s gone. So if you’re in tech and uneasy, considering leaving, or already have one foot out the door, keep this in mind: your experience is more portable than it feels right now. Systems thinking, project management, data interpretation, navigating ambiguity…. those skills don’t evaporate when you leave the industry that taught them to you.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

I’ve worked remotely since 2006 (way before it was common). However, my days were filled with calls to colleagues and DMs to chat about everything from work to what we had planned for the weekend.

Now I’m a solopreneur. I have occasional calls with clients, but they’re rare. Most of my days are spent working alone. In many ways, this is great since I have the freedom to work however and whenever I want. But staying motivated when it’s just me requires me to be really thoughtful about how I work.

According to a 2025 report by Leapers, nearly half of self-employed professionals feel lonely occasionally or some of the time. One in five feels lonely or isolated often or always. It can be really hard to stay motivated when you’re working in isolation. You have to create your own structure and find ways to keep going without other people around.

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This week’s Perspectives is an interview with Carrie Olsen, a voice actress, voiceover business coach, and online course creator. Carrie left a corporate career in instructional design to pursue voiceover full-time, and now helps others explore creative careers through her coaching practice and courses.

You can check out Carrie’s 3-step guide to building something that’s yours here:

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This interview has been edited for length and clarity.

Anna Burgess Yang: Tell me about the work that you do.

Carrie Olsen: My primary work is voiceover. Anywhere that you hear a voice but don’t see a face, that’s voiceover. That could be anything from animation to TV and radio commercials, the self-checkout line telling you “Are you ready to pay?”, the voice on the bus saying “Next stop, Broadway Street.” Audiobooks, podcasts, promos. I do a lot of promos for TV shows and live announcing. When you’re listening to the Oscars or the MTV Movie and TV Awards — which I’ve live announced — that’s voiceover work.

Through coaching other people on how to get into voiceover, I found that some people are truly passionate about voiceover. But others are like, “I just want something that’s creative, that’s out of the ordinary. Tell me about this. Is this something that might fit that mold?”

Over the years, I’ve started tailoring my coaching more toward a creative outlet. If you don’t know what your thing is, we’ll figure out what that is. And if you do know what it is, it’s usually psychological barriers that are keeping people from pushing forward. We’ll work through those things.

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ABY: You were at a corporate job and discovered voiceover through a podcast you were listening to. What was it that made you think, “Yes, I could do that”?

CO: I was listening to a podcast, and a voiceover coach was on it. Basically describing a day in the life. I had just had my first daughter, so I was looking for something where I could be at home with her more. And it was checking all the boxes. She would say, “I get up in the morning, do my workout, check my emails, go record a little bit, then go back to the booth.” And I was like, she’s doing that from home, and she’s making a living, and she’s supporting her kids. That sounds like something I could do.

It’s kind of the impetus we all have. When you see someone doing a fun job, you’re like, “Oh, that looks fun and easy. I’m sure I could do that.” That was me being naive going into it. But then, after I had my first coaching session with her and started learning what it takes to build a voiceover business, I got more excited. It was combining my creative side with my entrepreneurial side and building a business from scratch.

ABY: You went from a corporate job with clear structure and a clear title, and now you’re in a field that a lot of people don’t even know exists. How did that change how you thought about yourself professionally?

CO: I’ve always thought about my corporate job as feeling like I was wearing a mask. There are some people who are really good at corporate, and they’re bringing their full selves. But for me, it was being in a stuffy office and having to wear clothes I didn’t choose and sitting in inefficient meetings.

The job I had right before I got into voiceover, as far as corporate jobs go, was fantastic. I liked the work, I liked my coworkers, but it wasn’t ideal. I was still away from my daughter, and it wasn’t what I would have chosen.

When I started my own business, how I thought about myself became less defined. I was creating what I was doing. Everything was my call. When people ask what I do, I always have to explain it. It’s not like saying “teacher.” People are like, “What is that? What do you do?”

I’m a quiet person. I’m introverted. But voiceover became this space where I had to be bigger and louder. I was still getting to explore those other sides of me that you would never get to at a corporate job or in a boardroom. It was really neat to get to explore that creative side.

ABY: Did that exploration change your sense of identity, or did it reveal who you already were?

CO: I think it was the latter. It was always there. But there were parts that I didn’t even know were there, or you’d have to probe really hard for me to admit it. It seems silly for an adult to be like, “I want to be an actor someday.” Responsible people don’t say that. Realistic people don’t say that. So it was this thing that was buried really far underneath, and voiceover opened that up.

ABY: You’ve built a business with multiple income streams: voiceover work, coaching, and an online course. Was that intentional, or did it evolve over time?

CO: That definitely evolved. Getting started in voiceover, I didn’t know what to expect. When I started booking work, it was this leap of faith that my husband and I took. I was booking work on weekends, working after work with a newborn at home. We had this moment where we said, “What would happen if you put full-time hours to this work?” We did that, and it worked out. I matched what I made at my corporate job in my first year of voiceover work.

My corporate job was instructional design. I was a content design specialist for the e-learning department of a construction company. I really love adult learning theory. When people started asking me, “How did you build a voiceover business in a year?”, my natural answer was, “Let me build a course about it.”

It started off as a blog post, and people were reading it. I started building my email list, I thought, “Now I have this asset. What do I do with an email list?” So I started doing webinars to send people to my course. It all just stacked on. I didn’t have a master plan from the very beginning. I just kept seeing what was offered next and saying yes.

ABY: What’s the hardest part about running a creative business?

CO: There’s no steady paycheck. You have to learn how to budget on an irregular income. I’m okay with that. My husband is happier when things are expected and normal. Balancing our two different preferences for how money comes in has been a challenge the whole time I’ve been a solopreneur.

But I think in general, it’s getting over the hump of saying, “I can do this. It’s okay for me to do this. It’s actually good for me to explore something that is fulfilling.” I think that’s a part that responsible people have a hard time with, because we feel like we need to take care of everyone else. And a lot of times that means putting our own desires on the back burner, indefinitely. That barrier is hard to overcome.

ABY: What would you tell somebody in a corporate job who has a creative passion and wants to turn it into a real career? How do they get past that barrier?

CO: You don’t have to quit your job right away. There’s a way to do it responsibly. That’s a barrier for some people, but it’s also kind of the easy barrier that’s hiding other barriers. It’s the easy one to say: “I want to be responsible. I can’t risk losing my job.”

I have a three-step guide that talks about this. The first step is to see the lie: the stories you’ve been telling yourself about the reasons why you can’t write a book or start painting or whatever. Those stories aren’t true. Things like, “I’d be abandoning my family,” or “I’d be doing something irresponsible,” or maybe it’s something your parents instilled in you.

It’s important to recognize that your inner critic is trying to keep you safe. But you’re actually going to be okay. You don’t have to quit your job right away, and you don’t have to jump in with both feet. You can practice putting yourself out there in small ways. That helps you build the muscle so you get more used to what it feels like.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

A recent Entrepreneur headline proclaimed: “Dell Shrunk Its Workforce By 10% for the Third Year in a Row — Without Layoffs.”

Dell has about 97,000 employees. That’s according to its latest federal filing, published this week. In February 2023, it had 133,000. That’s a reduction of nearly 30% of its workforce in three years.

Except... Dell did lay off 12,500 employees in August 2024. That was less than two years ago. Someone on the editorial team at Entrepreneur failed at the most basic task of “accurate information.” (The article itself notes the layoff, which is a direct contradiction to the headline.)

On top of the layoff, Dell has employed other methods of reducing its workforce, such as hiring freezes, “employee reorganizations,” and a five-day return-to-office mandate. The end result is the same: reducing the size of the workforce. They’re layoffs without negative publicity.

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I wrote about Dell in March 2024, when the company announced that remote employees would be ineligible for promotion. At the time, Dell’s own internal data showed the policy disproportionately impacted women. I said then that it looked like Dell was trying to force employees to quit rather than go through another expensive round of layoffs. When employees quit due to a change in working conditions, the company can shrug and say, “Not our fault that they quit.” Two years later, the numbers confirm exactly what I predicted would happen.

What Dell and other companies are doing is part of a broader shift toward what Glassdoor calls “forever layoffs“: continuous, small-scale workforce reductions that fly under the radar. But they create lasting damage to the people left behind.

The playbook for disappearing workers

In these “forever layoff” scenarios, the work doesn’t disappear. It gets redistributed to the people left behind.

The high-profile layoffs grab attention: Block cut 40% of its workforce in February 2026, with Jack Dorsey explicitly pointing to AI. Meta is reportedly planning to cut 20% of its staff. Atlassian cut 10%. But plenty of companies are taking the same approach as Dell and handling a reduction in force (RIF) much more quietly, to avoid attention.

But the cumulative effect is the same. And the impact on the people who stay is significant. Glassdoor’s research from September 2025 found that after a layoff, employee sentiment among remaining workers takes more than two years to recover. Repeated layoffs have double the impact on sentiment, with the biggest drops among key talent, managers, and new hires. Employee mentions of “layoffs” and “job insecurity” in Glassdoor reviews are now higher than they were in March 2020, at the onset of the pandemic.

The near-constant layoffs-that-aren’t-layoffs are demoralizing in a very specific way. Workers never know when the next “reorg” will eliminate a few people in their department. They absorb more work without more pay. They watch colleagues deactivated in Slack in real-time. Rolling layoffs breed cultures of anxiety, insecurity, and resentment.

“Just use AI” isn’t a workforce strategy

The CEO mantra of “just use AI” as a solution to a smaller workforce doesn’t sit well with the people actually doing the work. AI doesn’t replace a whole human, and certainly makes the remaining workers further wonder if their jobs will be next on the chopping block.

Zuckerberg said in January that he was seeing “projects that used to require big teams now be accomplished by a single very talented person.” That framing — one talented person replacing an entire team — is becoming the justification for cutting headcount across the industry. Though Meta’s plans to cut headcount are in part to offset the $135 billion in infrastructure costs of AI (which is wild, considering how bad Meta is at AI compared to the frontier models). You’d think Meta could spare some of that investment for its human employees.

But how much of Zuckerberg’s claims of employees’ use of AI are actually happening on the ground? Technology reporter Kevin Roose wondered this on the Hard Fork podcast: “The data that we have is largely self-reports. And I think some firms have exaggerated how much they are doing with AI because they want to appear to be cutting edge and futuristic, and look how transformed we are.”

Even if it is the case that humans are now overseeing multiple AI agents, as Zuckerberg suggests, researchers have found that “AI brain fry“ is on the rise. This is defined as a type of cognitive strain and mental fatigue related to the excessive use of, interaction with, or oversight of AI — beyond one’s cognitive abilities. “Doing the work” alongside human colleagues is a far different experience than overseeing a bunch of AI agents.

At the other end of the spectrum, with employees less comfortable with AI experimentation on their own experience a fundamental training gap. Companies are telling workers to “just use AI,” but they’re not investing in teaching them how. If companies aren’t even providing the basics of AI training, expecting workers to use it as a substitute for human colleagues is setting them up for failure. Especially as many employees now report that AI usage is a part of their performance reviews.

It’s no wonder that employees are resentful.

Why the narrative isn’t fooling anyone

Dell, Block, Meta, and others like to claim that AI is so capable that companies genuinely need fewer people. In reality, these companies have decided that workers are the most expendable line item — and are using the current AI hype cycle as cover. When Jack Dorsey announced Block’s layoffs, its stock price jumped more than 20%. When reports of Meta’s potential layoffs surfaced, Meta’s stock climbed too. The market is rewarding companies for cutting people.

So they’re optimizing for short-term gains and framing it as “strategy.” Nobody seems to be asking what happens when the remaining workers burn out, suffer from “AI brain fry,” or when institutional knowledge walks out the door.

Thinking about a career change? Download my guide: 5 Types of Career Pivots.

If you want to support my work as a writer, you can subscribe to receive additional issues I publish.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

One of the things that I love about working for myself is that I don’t need to ask anyone’s permission before making a decision. If I want to make a change, I go for it, on whatever timeline makes sense for me.

But the freedom of solopreneurship can be a double-edged sword. Since you don’t need approval from other people, nothing is stopping you from chasing every shiny tool, course, or strategy that promises to solve your problems.

The ability to say no to distractions is an underrated skill for solopreneurs. There’s a difference between making strategic business decisions and letting yourself be pulled in a million directions. You need to master the former and resist the latter.

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A few weeks ago, I vibe-coded a new website for myself. If you’re not familiar with vibe coding, it’s using natural language (“Hey Claude, we’re going to work on a new homepage for me”) to code something. An article in Microsoft Source describes it this way:

Coding has long been limited to the realm of software engineers who studied it in school. But now there are so-called no-code, low-code and pro-code AI tools. That’s broadening who can build apps, helping everyone from non-technical business professionals to experienced developers solve problems, save time and boost creativity.

It’s addicting… after building my initial website, I spun up several more. I’ve spent hours fiddling with Claude CoWork. It’s exciting and genuinely a big deal for people who like to “tinker.” You can now build the tool you’ve always wanted — either because no company was going to make it (because the use case is too specific to you), or because you don’t want to pay a monthly subscription for something you’ll only partially use.

All of this has a real impact on people’s lives, both at work and personally. But the ability to build doesn’t mean you should build everything. And the line between building something useful and building for building’s sake is thinner than most of us realize.

The new math of building

Companies have always wrestled with the “build vs. buy” decision. If they wanted a tool to do something, they could either build it themselves (which takes time, money, and resources) or buy it (which lacks full control and customization). Now the calculation is murkier, and it can rely on individual decisions instead of a big corporate buy-in. You want something? You can build it yourself. The question is whether you should.

What used to require hiring a designer, a developer, or a consultant can now be done in a few hours with an AI tool. That’s a real shift, and for many people, it’s the first time they’ve been able to bring an idea to life without depending on someone else.

Regular people are building personal websites, scheduling tools, custom dashboards, automations that connect their apps — things that weren’t possible a year ago. The potential is enormous.

But the potential also has a way of expanding to fill all available time, rather than saving time.

Katie Parrott wrote a piece called “AI Was Supposed to Free My Time. It Consumed It.” She describes staying up until 1 a.m., building and rebuilding with her AI assistant. Her piece captures something important: a compulsive quality of AI-assisted building that many people find once they get started. It doesn’t feel like work. It feels like momentum… and that’s what makes it hard to stop. (You can read Katie’s guest essay about productivity for this publication here.)

There’s a version of this playing out on a much larger scale. On a recent episode of The Verge’s Decoder podcast, Hasbro CEO Chris Cocks described how AI-assisted design has transformed the company’s product development. Where Hasbro’s teams used to take one or two toy concepts to the full prototype phase, they can now take 10 or 20. More ideas, faster, at the same cost.

That sounds like pure upside. But it’s also a perfect illustration of Parkinson’s law, a concept based on a satirical essay that contained the line, “Work expands so as to fill the time available for its completion.” When AI makes it possible to generate 20 prototypes instead of two, you don’t stop at two and go home early. You generate all 20. And then you review all 20. And then you iterate on the five most promising ones. The capacity to do more becomes the expectation to do more.

This pattern shows up in survey data, too. The Upwork Research Institute surveyed 2,500 workers in 2024, including executives, employees, and freelancers. 77% of employees said AI tools had actually added to their workload. Workers reported spending more time reviewing AI-generated output, more time learning the tools, and being asked to do more work as a direct result of the technology. More recently, Upwork’s 2025 follow-up study found that of the workers reporting the highest productivity gains from AI, 88% of them experienced burnout.

This cycle of constant building with AI only benefits workers if they were previously overworked, and AI actually reduces the load. If the “newfound time” just gets filled with more stuff (more prototypes, more iterations, more output), then the gains flow to the employer. The worker doesn’t get any time back.

Building with intention

This isn’t an argument against AI tools. I’m a fan, trust me. But it’s an observation that the low friction to get started doesn’t mean you should build everything. It’s no longer a “build vs. buy” question, but instead a “build vs. ‘what do I gain from building?’” question.

The temptation to build everything you can build is real. And the people who benefit most from these tools are the ones who build the right things: the things that actually improve their lives rather than just consuming their attention.

I could stay up allll night (like my friend Katie) and build new things in CoWork or Claude Code. Instead, I’ve got to decide how to meaningfully apply my time and attention. If I were working for an employer, it would be the same.

Before you sit down to build something with AI (whether it’s a custom app, an automation, or a new workflow), a few questions are worth asking:

What specific problem does this solve? If you can’t name a real use case — something that’s actually costing you time, money, or frustration on a regular basis — it might be a distraction posing as a productivity gain.

What’s the real cost? Not in dollars, but in hours. How much time will you spend getting this up and running versus how much time it will actually save you? A tool that takes 10 hours to build and saves you 20 minutes a week will take a while to “pay for itself” (though that certainly compounds if you save 20 minutes a week forever).

When will I stop? Deciding on an end result before you start is fundamentally different from trying to stop once you’re in the flow. (The flow is a trap!)

What will I do with the time I get back? This is the question that keeps the whole building process in check. If AI takes something off your plate, the benefit only exists if you’re intentional about what fills that space. Otherwise, you’ve just replaced one type of busy with another.

Honestly, I’m haven’t reached the point where building gives me more time… yet. I’m too deep into changing processes. Some are things I’ve had in place for years and are forcing me to fundamentally change how I do work. Will it pay off, eventually? That’s the bet I’m making: that this upfront work will be worth something.

Want to build a life-first business? These reflections will help you determine your priorities.

If you want to support my work as a writer, you can subscribe to receive additional issues I publish.

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Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

It’s Friday afternoon, and a potential client just emailed asking about your services. You scramble to find your pricing (where did you save that document?) You dig through old emails for a proposal you sent six months ago that you could adapt. You piece something together and curse your past self for not being more organized.

This scenario plays out constantly for solopreneurs. Most chalk it up to the chaos of running a business alone. But constantly scrambling will start to cost you as your business grows — and eventually hold you back.

Most solopreneurs think that “operations” is something only real companies need: businesses with employees, office managers, and HR departments. But the absence of basic systems wastes your time, causes unnecessary stress, and makes you look amateurish to potential clients.

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This week’s Perspectives is an interview with Sarthak Dhawan, co-founder of Turbo AI, a notetaker and study tools app for students. In 2025, Sarthak and his other co-founder, Rudy Arora, dropped out of college to focus on Turbo AI full-time.

I’ve written a lot about AI and its impact on work, so it was interesting to hear Sarthak’s perspective as an “industry insider” working for an AI company.

This interview has been edited for length and clarity.

ABY: Tell me a little bit about your background and how you ended up starting Turbo AI.

Sarthak Dhawan: I started coding in elementary school because my brother got me into it. I got a lot of joy from building things and seeing people use them. It slowly evolved into creating random projects. I met my co-founder, Rudy, in middle school.

When I was going into 9th grade, my school had this portal that students would use to check their grades. But it was really slow and didn’t work half the time. I said to myself, “This seems like an opportunity for me to go ahead and build a nice, pretty mobile app for students to check their grades.” I did that, and it ended up going pretty viral, just by word of mouth. I remember going to my school, seeing people use my app, and they were liking it!

I got into Duke University, and Rudy went to Northwestern. We started Turbo AI with $300 we had saved up from an internship, and used that to create the LLC.

About eight months ago, we chose to drop out of college because the business was doing so well. We have over 8 million signups and are earning more than a million dollars per month.

ABY: What was it like, making the decision to drop out of college and focus on this full-time, rather than finish your degree?

SD: The first time I mentioned dropping out to my parents, they had a terrible reaction. Like, “What are you thinking? Duke is a good school. We’re paying so much in tuition. You worked so hard to get to this point. Why not just finish and do the startup stuff later?” At the time, I thought, “You know what? That makes sense.”

It was only when we were making a sizeable amount of money, growing the team, and needed more employees that I couldn’t justify the amount of time I was spending on school. It wasn’t like I said, “Hey, I think this is going to be big. Let’s drop out.” It was more like, “This is already big.” It was a natural progression. And when it came to that point, my parents realized that their kid was going to be ok.

ABY: What was the transition like from “this is my side hustle” to “this is my full-time job”?

SD: Mentally, there was never really a transition, if I’m being honest. I wake up every day because I find this work enjoyable, and I want to grow the company.

One of the biggest reasons I didn’t want to leave school was actually the social environment. Duke is awesome. When we formed the company, we decided to make the social part a priority.

ABY: When you think about creating a work environment for your team, what’s important to you?

SD: When I think about hiring and building a culture, I think the most important thing is having autonomy in what you do. No one wants to be told, “Do this task,” and be micromanaged the whole time. That makes it painful.

We’re very small for the scale we’re at. When we’re hiring someone, we want them to own that sector of the business, whether it’s an engineer or a marketing person — so they feel like they’re making decisions and having an impact. I think that’s what people derive value from.

With the actual logistics of things, we’re very flexible. If you want to work some days remotely or choose your own hours, it’s fine. I’ve never really understood why that matters. I’m of the perspective that if the work gets done and it’s high quality, I don’t care what you do.

ABY: What do you see in your users’ adoption of AI — mostly students — in how they approach trying new things or how they include AI in their work?

SD: We’ve had a rapid shift in the past few years where people’s attention spans are smaller and smaller, because of social media. That shortened attention span is affecting people in all areas of their lives.

So we’re seeing a problem in the usage of our application where students’ attention spans are so short that even if you take a 100-page PDF and put it into our tool to make four pages, they’re not willing to sit down and read those four pages. Four pages are still too much.

AI is catalyzing this even further because instead of reading a 10-page article, you can ask for a summary and read a few words. It’ll give you exactly what you want.

The awesome part about consuming information before AI was that you were forced to consume a lot of information and synthesize it yourself. And the process of consuming information you don’t necessarily need is really valuable. In this world of AI, you’re not consuming any extraneous information. It’s sort of like tunnel vision that’s been exacerbated by social media being exacerbated even more by AI. What we’re trying to do with Turbo AI is make learning more fun, more enjoyable, and more interactive.

ABY: How do you think about AI’s impact on the workforce as a whole?

SD: When I look at AI from two years ago, when it wasn’t nearly as good, compared to my productivity now, it’s maybe 100 to 1,000X. I think it’s very complementary. We’re not at the stage where AI can autonomously do things. You need a human utilizing these tools.

Historically, when the Industrial Revolution came around, jobs were lost, but many new jobs were created. There was a lot of technology early on replacing jobs that people didn’t necessarily want to do.

The problem we’re seeing now is that AI excels in areas that are highly intellectual. For example, right now we’re seeing that AI is amazing at coding, math, and physics. When jobs at the higher end of desirability start to get taken away, I don’t know what sort of macroeconomic shifts that would cause. It’s a little bit concerning to me, but hard to say.

ABY: What would you say to someone who is uncertain about the job market right now?

SD: Knowledge across the board has never been more democratized. Fifty years ago, if you wanted to understand certain concepts or improve your job prospects, you might have to go to a prestigious college. But now, AI levels the playing field in terms of how fast, how easily, and how cheaply you can upskill yourself.

So the bottleneck is just, are you willing to put in the time to learn something? But that’s a solvable problem, and very realistic.

Want to build a life-first business? These reflections will help you determine your priorities.

If you want to support my work as a writer, you can subscribe to receive additional issues I publish.

Subscribe now

Have a work story you’d like to share? Please reach out using this form. I can retell your story while protecting your identity, share a guest post, or conduct an interview.

I had to submit my resume for a role. Then I went through three interviews, with nearly identical questions each time.

The biggest problem? The role was for a freelance writing position. Not an employee of the company. I got all the way to the third interview only to learn that the role paid a fraction of my usual rate — even though I’d provided my rates upfront.

I’m experienced enough as a solopreneur to know that going through three interviews was a bad sign. The potential client wasn’t communicating internally (as confirmed by the fact that my rate had been overlooked). Repeated interviews are incredibly uncommon in my line of work, and indicated to me that the company didn’t know how to work with a freelancer.

When you’re a solopreneur, bad clients cost you time and money. They also crowd out better opportunities and put a strain on your bandwidth. Client selection is a core business skill. And if you’re not in a position to turn down work, you at least need to know how to handle sticky situations when they come up.

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Ever feel like the amount of work you have to do is running you into the ground? Most of us — whether working for an employer or working for ourselves — don’t have the luxury of handing off tasks to a team. Everything lands on your plate, and there’s never enough time.

AI won’t run your life for you (despite what some of the big AI companies would have you believe). But it can give you back hours every week. Some tools are AI-first, meaning their primary job is to perform an AI-driven task. You can also look at adding AI features inside tools you’re already using.

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These days, I rely heavily on AI. I can get more done in less time, without sacrificing quality in any of my work.

Here are a few AI tools that can make a huge difference in your work.

Meeting notetakers

An AI notetaker was the first AI-first tool I added to my business. My notetaker auto-joins my calls, records the conversation, transcribes everything, and sends me a recap with action items. Instead of scrambling to remember what a client said three months ago, I have a searchable archive of every meeting.

This solves a real problem: you can be fully present during the conversation rather than taking notes by hand. You also don’t risk missing something important, which can happen with manual note-taking.

Tools: Otter, Fireflies* [affiliate link], Fathom

Knowledge systems

Over time, we accumulate a mountain of valuable material: proposals, client emails, blog drafts, research notes, and random thoughts. Most of it gets buried in folders (or notebooks), which makes it hard to track through your thinking or find related ideas.

A personal knowledge system changes that. It creates a searchable “second brain” – like your own Wikipedia. Add AI into the mix, and you can “chat” with your own content instead of digging through your notes and files. Think of AI as a personal research assistant who has read everything you’ve ever written.

Tools: Google Notebook LM, Tana, Notion AI* [affiliate link], Reflect

Standard operating procedures

Even if you work alone now, you might eventually bring on help (like a virtual assistant, a subcontractor, or a specialist for a specific project). When that happens, you’ll need documented processes. The problem is that writing step-by-step instructions for everything you do is tedious. Most people never get around to it.

AI tools solve this by recording your screen as you complete a task and automatically generating written documentation. You walk through a process once, and the tool creates a standard operating procedure (SOP), complete with screenshots and written instructions — without any extra effort on our part.

SOP tools are uncannily good. I usually only need to make small tweaks to the written version, and sometimes don’t need to make any edits. I store them on my Google Drive so I can easily share them if needed.

Tools: Loom AI, Scribe* [affiliate link], Tango

A business coach

AI chatbots can serve as an on-demand sounding board. They won’t replace your judgment, since they can’t understand the nuance of the real world and human relationships. But they’re useful for thinking through options, drafting difficult emails, or walking you through the different angles of an idea you might have.

In Claude, I’ve created a “Business Coach” project. I’ve uploaded a lot of files so Claude has context, including information about who I am, the work that I do, my brand, and the potential clients I’m targeting. When I’m trying to think through something, Claude asks me questions. By responding, I clarify my own thinking.

You can also do this if you’re working for an employer by providing context about your role, team, or projects. Or your “business coach” might help you think through the next phase of your career, if you’re looking to make a change.

The key is prompting well. The more context you give about your situation, and any constraints (like your time or finances), the more useful the output.

Tools: ChatGPT, Claude, Gemini

AI features embedded in existing tools

Every company has been rushing to add AI features to its products. Some are good. Some are included with your existing subscription, while others treat AI as an add-on.

For example, I rely on Airtable* [affiliate link] to run the “back-end” portion of my business. AI-powered “field agents” have been able to accomplish a lot of tasks I used to do manually.

A few other ideas:

• AI-powered transaction matching in accounting software like QuickBooks or Kick can categorize your expenses and spot anomalies

• AI scheduling assistants in tools like Motion or Reclaim* [affiliate link] can help you plan your day and protect your calendar from too many meetings

• AI email features in apps like Superhuman or Spark can draft replies or prioritize your inbox

The tools you or your employer already pay for are getting better. If AI has been added since you originally signed up, the features are worth exploring.

Start with one new tool

AI fluency is becoming a baseline skill, like knowing how to use a spreadsheet. And it’s becoming ubiquitous: apps will keep adding AI features to make work easier and faster.

But you don’t need to master everything at once. Pick the tool that solves an obvious problem or can complete a task that drains a lot of time from our day. Figure out how to get the most out of it before adding the next thing.

Want to build a life-first business? These reflections will help you determine your priorities.

* Affiliate link: If you sign up, I may earn a small commission, at no additional cost to you.