For more than seven years, I was a product manager at a software company. Product managers guide the direction of the product based on feedback from customers, sales, and leadership teams. They keep the company updated on the progress of projects and initiatives. And they constantly juggle tasks and deadlines when projects go off track or something unexpected comes up.

It often felt like herding confused cats out of a building that’s always on fire.

A significant amount of my time was spent buried in documentation. Preparing reports for the leadership team, reviewing spec documents for new features (or writing them myself), and preparing release documentation for customers that explained new features and bug fixes.

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I look back at the amount of time I spent on much of this work and how it limited what I could get done during the day. Today, that role would be incredibly different, thanks to AI.

David Pierce, Editor-at-Large at The Verge, said in an interview:

The thing that I am hopeful for, and frankly already grateful for in the technology, is that it can do a lot of the busywork I don’t want to do. It’s not creative. It’s not even thoughtful. It’s just administrative.

We can all find better things to do than turning an Excel file into bar graphs in a PowerPoint. So if we can automate that out of existence, I’m actually extremely hopeful for what we can all go find to do instead.

Product management is a critically important role in many companies, but also one that is drowning in tasks. The sources of data are scattered, like customer data, the upcoming project plans, a calendar of due dates, analytics, and more. It’s a chore to pull everything together.

What I would hand over to AI

The same job, today, would be almost unrecognizable. I’d have more time for the things that make a product manager role interesting (thinking about the direction of the product) and spend less time on “paperwork.”

For example:

• AI could spot patterns. I used to pore through hundreds of customer support tickets, looking for things that customers were complaining about or asking for. Those with the most requests often received higher priority, but identifying them was an intensely manual process.

• AI could create a design prototype. For new product features, developers would work from a mock-up that was painful to create. AI could create this in seconds.

• AI could write reports. I produced a monthly report on each project’s status for the leadership team, but the data lived in multiple systems. On top of that, the CEO liked to see the report in a very specific format. All of that could be handed over to AI. I could have even produced more frequent reports with the time saved.

• AI could write release notes. AI could determine from the project management and coding tools which features and bug fixes actually made it into a release and draft the release notes into a format suitable for customers. This process used to take hours (if not days) to compile.

What I could have done with the extra time

At a guess, I think the current version of AI tools could have saved me at least 10 hours per week. Maybe more, during the weeks when I was creating design prototypes or writing release notes.

And none of these required human creativity.

• Spotting patterns is simply compiling data. The judgment for “What should we build?” is still human.

• Design prototypes were based on well-established product specifications, just applied to a new feature (like “This button always goes in the upper-right corner, and when you click it, it does XYZ thing”). Developers simply needed a visual reference so they could code the new feature.

• Reporting was also mainly compiling data, with the addition of formatting. Explaining the project’s progress to the leadership team would still come from a human.

• Release notes were taking a description of a bug fix written in “developer speak” and translating it into something a customer could understand.

Without the hours spent on these tedious (but necessary) tasks, I could have:

• Talked to customers. Product managers at bigger companies with larger teams often talk to customers to understand their pain points. As the sole product manager, I rarely had time for this and had to rely on support tickets written by the customer service team.

• Made better product decisions. Since my process of looking through support tickets was so manual, customer needs and pain points were often missed.

• Fixed things myself. The company had hundreds of outstanding bugs to fix at any given time. Some were incredibly minor, so they were never prioritized. But with vibe coding, it’s likely that I could have learned to apply a fix myself and let a developer review it. Fewer outstanding bugs, happier customers.

I’ve always tried to automate repetitive tasks throughout my career. I did as much as I could back then, but there were simply limits to what I could do — especially with some of the most time-consuming tasks. Generative AI has changed that.

Of course, I recognize that this also blurs the lines for different roles. Should a product manager be expected to code bug fixes? Maybe not (though I would have enjoyed just fixing something rather than letting it linger).

My point is that I would have found the job much more fulfilling if I had been able to spend more time on the work that required human judgment. Talking to customers would have been incredibly valuable, but it wasn’t a requirement to move forward. So it got pushed to the bottom of the list. Creating a report for the CEO — while tedious — was an expectation of the job.

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In June 2026, two major publications ran stories with a similar message: remote work might be hurting you. NPR published “People Love Working From Home. But Does It Love Them Back?“ The New York Times followed with “We Liked Remote Work. Then We Looked at the Data.“ Both cited the same study — Emanuel, Harrington, and Pallais, published in Science — which surveyed 588,000 Americans and found that workers in remote jobs experienced a 58% rise in hours spent alone and a 72% rise in spending their entire day with no human contact. This led to more anxiety, more depression, and more medication to treat these things.

The data is real. And the findings around isolation absolutely deserve attention.

But these articles treat isolation as though it’s baked into remote work itself, rather than examining what companies and workers can do about it. The headlines imply remote work is the villain — not the way companies have implemented it (or, more accurately, failed to implement it). Nor do they look at the broader question of whether the office was ever providing the social connection it’s now being credited with. And certainly not in the context of the “loneliness epidemic” that many experts agree is prevalent in American society as a whole.

The arguments against remote work are so predictable at this point: find a downside and frame it as “proof” that the whole model is broken. Meanwhile, there’s no discussion around whether the tradeoffs are worth any downsides. And all of these arguments pretend to put the employees’ interests first in RTO mandates, rather than looking at the company’s motives. (Spoiler: they’re not thinking about employee well-being at all.)

It was never about your loneliness

Every argument about remote work isolation rests on the assumption that in-person work provides positive social connection. The office is where meaningful human interaction happens!

But what about the interactions people are relieved to avoid? Microaggressions. Hostile managers. Office politics. The energy required to context-switch all day. The NPR piece quotes a researcher lamenting that remote workers miss out on casual interactions: “not even a wave to a barista.”

That’s the bar we’re supposed to use to measure wellbeing?

These articles pretend to care about people. We’re looking out for your loneliness. Or perhaps the reporters and researchers truly care about people and the impact of loneliness. But that’s not at all aligned with what CEOs have in mind when they order people back to the office.

The business case for bringing everyone back also doesn’t hold up. Studies consistently show that return-to-office mandates don’t have any impact on productivity or financial returns.

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Maybe these companies believe that they will benefit. Maybe they’re truly concerned about employee isolation (though doubtful). But research published in 2026 found that the only personality trait that consistently predicted objections to remote work was narcissism. The more self-important and entitled the company leaders, and the more they coveted power and status, and the more they favored return-to-office mandates.

Psychologists have long suggested that narcissism operates like a drug. Narcissists crave a regular supply of attention and validation. Remote work deprives leaders of that supply. These leaders see any kind of remote work as a threat to their authority and attention, and ordering people back to the office full time is a power and status move.

Loneliness is a convenient argument for something much simpler: leaders perceive remote work as a loss of control.

What remote work actually supports

“But people need human connection — the data is clear!” Yes. But conflating “human connection” with “office presence” is a faulty argument. People who study the loneliness epidemic in the United States find that the leading causes are technology, insufficient time with family, people being overworked, and living in a society that’s too individualistic. Remote work is not one of them.

In fact, if “being overworked” is a cause of isolation, that’s an argument in favor of remote work. Removing a lengthy commute from the day leaves more time to interact with other people. People can also get more rest if they’re working from home.

The loneliness framing fails to ask a basic question: what is gained? Remote work provides structural support for the people who need it most. Women choose remote work because remote work makes it possible to manage caregiving responsibilities (that still disproportionately fall on women). People with disabilities may find commuting draining or offices inaccessible. People face discrimination at the office (racism, misogyny, microaggressions) that drains energy and erodes mental health over time. The disparate impact of return-to-office mandates falls hardest on these groups.

Even if companies are well-meaning (and the narcissism research suggests many are not), the argument assumes that “loneliness” outweighs accessibility, safety, caregiving, and basic equity. When my kids were little, I had to drop them off and pick them up at day care. I had to take them to doctors’ appointments during the day. Even if I’d felt lonely, I wouldn’t have traded the flexibility that remote work gave me. The logistics of my life would have been exponentially harder if I were working in an office.

Some in-person connection with coworkers is valuable. But full-time office attendance is unnecessary. Atlassian found that a quarterly gathering does more for connection and belonging than daily commutes to the office.

So instead of asking “Is remote work bad for workers?” companies should be asking what they’re doing to support remote workers. The answer, for most, is: very little. Culture requires intentionality from leadership — whether the team is in one office or spread across ten cities.

If remote work is what allows you to be present for your family, manage your health, or do your best work without the overstimulation of an office — that’s reason enough. It’s worth being aware of the risks of isolation (as a whole, not just tied to remote work). But no one should pretend that isolation is the only — or most important — factor in someone’s work life.

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Early in my career, I had to travel a lot. I was working for a financial technology company. A bank or credit union would buy the software, and I would travel to the customer’s location. These were mostly really small banks, with maybe only a few branches, in small towns.

Getting to them was an entire production, often involving a flight to a major airport, then renting a car and driving for a few hours. I started working for the company in 2006, before the iPhone. Heck, I wouldn’t even own a smartphone for another four years.

As such, I had to carefully prepare how to get around. I’d print stacks of Mapquest directions. Airport to hotel. Hotel to bank. Exploring the city was too risky (what if I got lost)? But there usually wasn’t much to see anyway. Food was limited to whatever was in my line of sight between the hotel and the bank.

I traveled heavily for about three years. Once I had my first baby in 2009, the company changed its overall method of delivering training to a web-based approach. My boss realized that she wouldn’t be able to retain a lot of the company’s talent with roles that required 50% travel. After the change, I only traveled a few times per year.

The towns I visited were sometimes so small that there were only a few main roads. Even so, I simply wasn’t brave enough to explore. I’d get done working for the day, grab a sandwich from Subway, and collapse in my hotel room with CNN and my laptop.

I thought about this while I was in San Francisco this past week. I bounced all over the city, relying on a combination of Google Maps, Waymo, and public transit. I looked at Yelp reviews of local restaurants, eager to try a little dive dinner in Haight-Ashbury or a walk-up dim sum place in Chinatown.

I wonder about everything interesting I probably missed in these small towns. Navigating the roads, restaurants, and landmarks was just so different back then.

On one of my first trips, as a new employee, I was headed to western Kansas from the company’s headquarters in Topeka. Another employee was with me to show me the ropes of working on-site at a customer’s location. He drove, and we sat in silence for most of the trip. We barely knew each other and had little to talk about (though we later became friends). He would often call his girlfriend on his cell phone and talk to her for hours at a time while I sat, listening to his half of the conversation.

We were driving along the roads of Nowhere, Kansas, when he announced that we were taking a detour to visit the world’s largest ball of twine. There was nothing I could do but agree. We got out of the car and walked around the “landmark.” In today’s world, I would have been snapping photos. But back then, I didn’t have a smartphone, and it never occurred to me to bring a digital camera on my trips.

Part of me is sad that I have no documentation of my travel. I never wrote anything down, even though I experienced a lot. The only time I took photos was when I was sent to Kalispell, Montana, and planned ahead to visit Glacier National Park.

I don’t even remember what the world’s largest ball of twine looks like... only that we made the stop.

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In February 2013, Yahoo CEO Marissa Mayer banned all remote work. The move surprised everyone: Mayer was a new, young CEO, and Yahoo had a well-established remote work policy. The stated reason was collaboration and innovation, which Mayer thought was lacking.

The real context was much simpler: Yahoo was losing the competition with Google and Facebook, and Mayer needed to look like she was doing something. The RTO mandate was the “something.”

Fast-forward thirteen years. The mandate didn’t save Yahoo. Verizon acquired it for $4.5 billion in 2017, merged it with AOL, then wrote down the value by more than it paid. At the time of acquisition, Forbes reported, “The biggest story is how Yahoo squandered its massive head start and let each wave of new technology in search, social, and mobile pass it by. Yahoo remains largely the same company it was a decade ago.” Yahoo was sold again in 2021, and the Forbes sentiment remains true.

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When companies are struggling, they change the rules workers operate under and call it “strategy.” But these moves are almost always a cover for a business problem leadership can’t figure out or won’t name. And, over and over, we’ve seen that the companies that abandon their core principles under pressure don’t save themselves. They just lose what made people want to work there in the first place.

When the company you joined stops existing

The company formerly known as Facebook (now Meta) debuted at number one on Glassdoor’s Best Places to Work in 2011 and held the top spot three times. By 2023, it had fallen off the top 100 list entirely.

In April 2026, Meta deployed what it calls the “Model Capability Initiative”: software that captures employee keystrokes, mouse movements, clicks, and periodic screenshots. CTO Andrew Bosworth confirmed there is no opt-out. (European employees are exempt, because GDPR won’t allow it.) Employees have said that the initiative makes them really uncomfortable.

In June 2026, Meta laid off 8,000 employees, and several thousand more were reassigned to the Applied AI Engineering unit — and had no option to transfer elsewhere. Some have reportedly taken to calling themselves “draftees.”

The company that once topped every “best workplace” list is now tracking every keystroke in some type of nightmare dystopian workplace and telling thousands of employees they can either accept a role they didn’t sign up for or leave. The company still exists. But the company people joined? That’s long gone.

Starbucks has drifted from its own identity, too. For years, Starbucks built its brand on being the “third place” — a community space between home and work. But Starbucks has had an identity crisis over the years, amidst sagging performance. Should it optimize for mobile orders? But then that detracted from the “third place” vibe. Should it add more seating inside the stores? But then that made the stores more crowded. Employees have been dragged along for every iteration, and meanwhile, the company has taken a strong anti-union stance, trying to prevent workers from organizing to protect themselves.

The Starbucks experience changed, and so did the working conditions. What was once a company built around the idea of community became, for many of its workers, a volume operation that happened to sell coffee.

Nothing tells you more about how a company’s values than how it treats its employees when it is struggling. Webflow, once a company known as a great place to work, laid off employees in May 2026 by locking them out of their laptops at 7 am with no advance notice. Employees discovered they’d been impacted when their Slack and email access vanished — before any email from HR arrived.

And I’ve personally experienced this. I worked for a company that proclaimed to be “people-first.” The company began to struggle during my year there, leading to me losing my job unceremoniously. I was shocked by how it unfolded, since I had believed that this company cared about its employees — that’s what it had proclaimed, every step of the way.

Your values are the only constant in your career

Roger Lynch, CEO of Condé Nast, was recently on the podcast “Channels” with Peter Kafka. In 2020, the employees indicated that they wanted the company to focus more on diversity initiatives and sustainability. The global media conglomerate put together internal teams to address both. In the interview, Lynch said:

“It’s discouraging to see all these other companies dropping these initiatives, but it only says one thing: that it never was a core value, it was a convenience.”

“Companies have to adapt to survive — you can’t expect them to stay the same forever.” That might be the claim of Meta, Starbucks, or even my former employer. True. But there’s a difference between adapting your strategy and abandoning the principles that attracted your workforce. Yahoo opted for command and control of the office. Meta turned to surveillance.

You can’t get attached to a company’s stated principles. Unfortunately, there’s no way to predict which companies will abandon everything when the market gets rough or the industry changes. Companies may last decades and go through several iterations, and new leadership may usher in entirely new values (just look at Twitter, now X).

Every company will put itself first. The question is whether you’re building a career that depends on a company continuing to be the company you joined. Because if there’s one pattern that holds, it’s this: you can’t trust the company not to change in ways that you fundamentally disagree with.

The only thing you can truly depend on is what you build for yourself. That’s not saying you should quit when there’s company upheaval (that’s not realistic in the current job market). But it’s worth asking whether the stability you think you have is real, or whether it’s contingent on a company continuing to honor the values it used to recruit you. And it’s worth thinking about what you’re willing to tolerate to stay, even if the company has changed.

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Every promotion moves you one step further from the work itself. First, you’re doing the work. Then you manage the people who do the work. Then you manage the people who manage the people who do the work.

At each step, the daily reality of the job gets a little further from your line of sight. You may no longer be aware of the tool that no longer meets the team’s needs, or the client requests that have started absorbing entire afternoons.

I went through a few iterations of this throughout my career. I moved from a role in enterprise software implementation to the company’s product manager. Though I was no longer talking directly to customers, I was still “touching” the product constantly as I tried to figure out how to make it better.

Then I moved to an executive role. Though I was still the product manager (dual role), I no longer had time to tinker with the product. I was meeting with my team of seven people, preparing reports, and organizing vast swaths of data for the development team. The longer I was in the role, the more I realized that I no longer understood what the new features of the product did. I was also removed from the pain points that customers were experiencing — the kind that the customer service team had to deal with every day (and made their work harder).

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The further you move up in an organization, the easier it is to lose sight of what the people below you do all day. It’s even true of self-employed people who may start to outsource some work. It’s imperative to stay in touch with the work in some shape or form. Being too removed from the work is what causes bad decision-making.

The disconnect happens because of distance

Each rung up the ladder adds a layer of abstraction between you and the work.

When I was an individual contributor, I lived inside the daily friction of customers. As a manager, I was only hearing about it. My boss (COO) and the CEO only heard the summaries I provided. At larger companies, these are reduced to quarterly metrics.

By the time the information reaches the top, it’s been compressed so many times. The context is gone, and the context is how people actually understand the problems. (This is part of why so many people feel trapped in the middle as a middle manager.)

When I moved into a product manager role, I was responsible for reviewing issues reported by customers. I wasn’t receiving them directly, but I looked at the tickets. The tickets would contain notes from the customer support rep (if a phone call) or the direct emails from the customers. I could get a sense of the urgency or level of pain that the issue was causing. But the other members of the executive team (the COO and CEO) only saw numbers: how many customers reported the issue? It was my job to convey urgency, but this was often stacked against other priorities.

In Glassdoor’s Worklife Trends 2026 report, mentions of “disconnect” in reviews that reference leadership rose 24% year over year. “Miscommunication” rose 25%, and “misaligned” jumped a whopping 149%. Employees are well-aware of the structural distance — and when they feel like they’re not heard, they may check out entirely.

Part of the problem for managers is the endless barrage of meetings and reports. Your calendar is filled with strategy reviews, daily standups, and quarterly planning, and you stop having time to understand the work itself. You spend your days staring at dashboards and tracking outputs instead of staying tuned in to how work actually gets done.

A manager who only sees the metric can’t tell the difference between “the team is behind schedule” and “the team is behind schedule because something in the process is completely broken and the workaround takes three times as long.”

Freeing up time to stay involved in the work

I remember the intensely uncomfortable feeling that I no longer understood the product that I was supposed to manage. My days no longer had any time built in to get my hands on the product and play around.

But the fix isn’t to swing to the other extreme and do everyone’s job for them, which is what some managers do. That’s a failure mode all on its own: the leader who refuses to let go and hovers over every decision. (And founder mode is the worst version of this.)

Stepping back so people can do the work they’re good at is the entire point of building a team — you hire smart people precisely so you can let go.

The ideal is informed distance: close enough to understand how the work actually gets done, far enough to let people do it without you breathing down their necks. You step aside on every single detail, but stay connected to the how.

I’m an advocate for AI when it’s actually useful (i.e., not slop), and it has a place here: buying back in a manager’s day. I think back to the hours spent prepping for meetings, compiling information scattered across five different systems, and turning raw numbers into a summary for the CEO — a lot of that is now work that can be done with AI.

And the point isn’t more output. If done correctly, that reclaimed time flows back into understanding the work itself. In an episode of the podcast How I AI, host Clare Vo says:

I know so many people who feel like they’re in meeting after meeting after meeting. And when they’re not in meetings, they’re preparing for meetings… You would much rather your managers be hands-on doing the work, filling a creative impulse, than prepping for meetings.

Management roles can often suck the fun out of work. There’s creativity in being an individual contributor that disappears with a promotion to a manager role. Imagine if managers actually had the time for creativity. They’re more connected to the team and get to keep work interesting.

Most of the conversation about AI at work is about cutting headcount or increasing output. But for managers specifically, the most valuable thing added is giving them more time for attention — the time to actually watch (or put their hands on) the work again.

For managers

The structure of most managerial roles causes a disconnection that most managers feel. The system moved you away, then handed you responsibilities that make it impossible to stay connected.

The best leaders stay curious about what the work feels like, not just what it produces. They know the difference between a number on a report and the context behind it. They get that story by talking to the people living it. That only happens when you have time in your day for curiosity. The management team above you won’t give you that time, so you’ve got to find ways to claim it for yourself.

For self-employed people

The same management scenario applies the moment you stop being a team of one. When you hand off work — to a contractor, a VA, or a subcontractor — you need to understand more than just the end result.

You should absolutely hire people more capable or more specialized than you, but it’s too easy to forget about the work altogether. Distance creates the same blind spot for a solopreneur, the same way it does for a VP. You stop thinking about the “why” or “how,” and the work loses meaning.

So as you hire help, resist two urges: the pull to micromanage (let the person do their thing!) and stepping so far back that you lose sight of the work. Stay close to the process even when you’re no longer the one doing it.

The goal is always to keep your eyes on the work without constantly putting your hands all over it. And to build time into your day that lets you do just that.

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A few days ago, Webflow laid off a significant portion of its workforce with no warning. Employees found out when they couldn’t log in to their work laptops or Slack for the day.

It was so bad that one employee posted this on LinkedIn, tagging the company CEO Linda Tong:

Webflow, a tool for designing websites, was once considered a great place to work. Now, it finds itself in the spewing the same BS that we hear from startups every few days: “Today, we’ve made the difficult decision to reduce the size of our workforce.”

It happens over and over. Abrupt layoffs repackaged as “restructuring” or “adapting to the world of AI.” Every time, the response follows the same script. We share the LinkedIn posts, we express outrage, we say, “This is horrifying.” Then we move on until the next company finds itself in the temporary spotlight when it makes a similar announcement.

And yet we keep not asking the obvious question: why does this keep happening?

The answer is brutally simple. The only reason this keeps happening in the U.S. is because nothing prevents it. Outside of unionized workers — which account for just 10% of the U.S. workforce — American employees are largely at-will. They can be fired at any time, for any non-discriminatory reason, with no notice and no severance required. We’ve built an entire employment system around the assumption that workers are disposable. All the power is held by the employer.

I’ve written before about how we underestimate the risks of traditional employment. The never-ending barrage of tech layoffs has proven to be one of the starkest examples of how this can play out.

What worker protections actually look like

I’ll frequently share screenshots like the LinkedIn post above. Without fail, someone will comment, “If you don’t like the way these companies function, start your own business.” (I did, as a matter of fact, but that’s not the point here.)

The power employers hold is a uniquely American experience, as is the idea that employees are entitled to nothing when their employer decides to cast them aside.

As part of my client work, I’ve spent a lot of time researching employment laws in other countries — particularly around how termination works. The contrast with the U.S. is striking.

Here are a few examples from my research.

Japan

Japan takes worker protections very seriously. Employees can only be dismissed for “objectively reasonable grounds.” Employers have to demonstrate they’ve done everything possible to avoid termination — document issues, offer performance improvement opportunities, consult with labor unions, consider reassignment — before resorting to it.

The standard notice period is 30 days (or 30 days’ pay in lieu). Japan even offers Employment Adjustment Subsidies to help companies retain workers rather than lay them off. (Imagine if that existed here.)

Netherlands

The Netherlands doesn’t allow at-will employment. Employers typically need permission from a government agency or a court to dismiss someone, and they need a valid reason. Notice periods range from one to four months depending on tenure, and employers are required to pay mandatory “transition compensation.”

Philippines

The Philippines has enshrined “security of tenure” in its labor laws. Employees can only be dismissed for legally recognized causes. For business-related reasons like redundancy, the employer must give 30 days’ written notice to both the employee and the Department of Labor. The employee is paid severance based on the length of their tenure.

South Africa

South African labor law requires dismissals to be both “substantively fair” (valid reason) and “procedurally fair” (proper process). For redundancy, employers must go through a formal consultation process — notifying affected employees, applying fair selection criteria, and considering alternatives to termination.

Turkey

Turkey requires valid reasons for all terminations, with notice periods ranging from two to eight weeks based on tenure. Employees with at least one year of service receive one month’s gross salary per year of service in severance. Pregnant employees, those on parental leave, and union representatives are protected from dismissal except in cases of gross misconduct or company closure.

This wouldn’t fly anywhere else

Every one of these countries requires a valid reason for termination. Most mandate weeks to months of notice. Most require severance. Several give employees the right to challenge their dismissal and be reinstated with back pay.

This is the polar opposite of U.S. at-will employment, where most workers can be let go at any time with no notice, no reason, and no recourse.

Japan’s framework is particularly striking when held up against what happened at Webflow. Japanese courts allow redundancy as a valid reason for dismissal, but with strict restrictions. Employers have to demonstrate a genuine business necessity. And they have to prove they did everything in their power to avoid a layoff first.

Now think about Webflow. Webflow said it was restructuring “because AI.” This is a company that has raised hundreds of millions of dollars in venture capital funding and had a $4B valuation in 2022. If AI truly was the reason (and that’s a generous reading), they could have chosen to do more with the same people. Instead, they chose to do the same with fewer people.

Under Japan’s labor laws, that argument would fall apart. Under the Netherlands’ system, they’d need government permission first. Under Philippine law, the dismissals could be invalidated for procedural failures alone (no notice period).

But in the U.S.? Nothing. No mandatory notice, no required severance, no mechanism for employees to fight the layoff. Hundreds (if not thousands) of employees have their lives upended. And once the company gets past the bad PR, they get to operate as they were before.

Power is 100% in the hands of the company. And when fear of a layoff becomes the driving force in how people think about their jobs, that’s not an individual problem. It’s a structural one.

These companies deserve to be publicly dragged. Because right now, nothing else is holding them accountable.

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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.

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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?

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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.”

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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.

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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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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.

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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.

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.

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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.

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.

An executive says out loud what many executives believe privately: “I’m successful because I put work above everything else in my life.” The room — full of C-suite leaders — nods along.

A Wall Street Journal opinion piece by a young entrepreneur took it even further, proclaiming that “Work-life balance will keep you mediocre.” He wrote, “In a winner-takes-all economy, extreme efficiency during your peak physical and mental years becomes a baseline for building wealth that lasts a lifetime.” Sacrificing time with family and personal relationships in college was, by his definition, necessary for success.

These aren’t fringe opinions. They’re the standard playbook of most people in corporate leadership. Ambition should hurt. The hardest worker wins.

This belief system treats sacrifice as proof of commitment — that the only way to earn your success is to give up everything else in pursuit of it. But what if it’s not a requirement for success? What if it’s just a story we’ve been told so many times that it feels like a fact?

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The cost of the sacrifice narrative

I remember nights early in my career, furiously typing away at the keyboard late into the night. I had to be the best. I had to prove how hard I was willing to work. It was effective, because I received promotion after promotion in my corporate life. Work was everything — until I had kids. And thank goodness I did, because kids fundamentally changed my perspective about what was important.

You can’t have it all, as the saying goes. You’ve got to make choices about how you spend your time. But in addition to damaging relationships, the “sacrifice everything” model has measurable health costs. A study co-authored by researchers at Wharton and UC Berkeley examined the long-term effects of stress on CEOs’ health. The findings were stark: CEO lifespans decreased by 1.5 years after an industry-wide downturn. When insulated from intense market pressure, their lifespans increased by two years.

While the study focused on CEOs, the implications extend to other workers. The same pressures — long hours, high-stakes decisions, chronic stress — are present at every level of management. Middle managers, in fact, report the highest rates of burnout of any group, with 71% saying they feel burned out. The people on the path to becoming those corner-office leaders are already paying the price.

There’s a well-known adage that people don’t lie on their deathbeds thinking, “I wish I had worked more.” They wish they’d spent more time with the people they care about and doing the things they love. And, according to the research, intense overwork doesn’t just diminish the quality of life. It actually shortens its length.

Thankfully, the next generation is already opting out. There’s a term for what’s happening: “conscious unbossing.” According to DDI’s Global Leadership Forecast 2025, Gen Z is 1.7 times more likely than other generations to step away from leadership roles to protect their well-being.

This is a rational response to watching their managers burn out and deciding the trade-off isn’t worth it. Younger workers have observed the sacrifice model up close — the missed family dinners, the 10 p.m. emails, the manager who hasn’t taken a real vacation in years — and they’re saying, “I don’t want that life.” They’re refusing to accept that suffering is a prerequisite for achievement.

And research consistently links work-life balance with higher job satisfaction and job performance — not lower. There’s no real evidence for the assumption that balance breeds mediocrity.

Redefining success on your own terms

The appropriate reframe to “success requires sacrifice” is that success requires clarity about what matters to you — and the discipline to protect it.

For employees:

The executives who wear sacrifice as a badge of honor are modeling a particular version of success. It’s not the only one. If your company’s culture treats burnout as a badge of honor, that tells you something about the company — not about what success requires.

If you’re being asked to sacrifice everything and told that’s just “what it takes,” consider whether the system is designed for your success... or for the company’s. After all, companies benefit from your overwork.

For self-employed people:

The sacrifice narrative can follow you out of corporate life. When you’re building something on your own, it’s easy to slip back into corporate habits — working around the clock because that’s what “dedication” looked like for years.

It’s also easy to compare yourself to others. I hear entrepreneurs brag about their revenue or how many hours they put into their business. I have to remind myself that another person’s definition of success is not the same as mine.

The advantage of working for yourself is that you get to define what success looks like, from top to bottom.

Final thoughts:

The people who say “you can’t succeed without sacrifice” may genuinely believe it. But their belief is self-reinforcing. The people who’ve made sacrifices point to their own sacrifice as evidence that it’s necessary. They made it to the top, and the cost was enormous, so the cost must have been required. It’s a circular argument.

What if the question isn’t “how much are you willing to sacrifice?” but “what kind of success are you building — and will you have space in your life to enjoy it?”

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

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You quit the 9-to-5 to have more control over your time. You wanted flexibility, autonomy, and the freedom to structure your days around your life instead of someone else’s schedule.

Yet here you are, apologizing to a client for not responding to a message immediately. Feeling guilty on a Tuesday afternoon when you’ve only worked for four hours that day. Checking Slack at 9:00 PM because that’s been your routine for most of your working career.

Many solopreneurs don’t realize they’ve inadvertently recreated corporate life until they’re already living it. You traded a demanding boss for a dozen demanding clients. You swapped mandatory meetings for back-to-back Zoom calls. That freedom you craved? Doesn’t exist in your solopreneur world.

To find actual freedom as a solopreneur, you have to recognize that you’re following a corporate playbook — and make a conscious decision to change.

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I recently vibe-coded a new website for myself. Even though I worked at a software company for many years, I’m not a developer. But a few hours with Claude Code, and I was able to create a website just by “talking” to Claude with phrases like, “Can you move that to the center?” or “Can you change the color here?”

To pay someone to create a website for me would have likely cost thousands of dollars. Now, it’s a few hours with an AI coding tool — even without any specialized skills.

This is a small, personal example of something much larger happening across the labor market. AI is changing work from two directions at once. It’s changing how people get hired (AI screening, AI interviews, AI deciding whether your resume ever reaches a human). And it’s changing whether companies need to hire at all, as AI agents start replacing headcount entirely.

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Both of these trends are quietly hollowing out the foundation of how careers have traditionally been built. Workers are rightly nervous because they see the seismic shift coming... but don’t yet know how it will impact them.

AI’s role in hiring: gatekeeper or equalizer?

AI is now embedded in the hiring process, but the results are a mixed bag. Sometimes, AI reduces human biases, while other times it reinforces discrimination that has always made hiring problematic.

Derek Mobley, a Black man over 40 with a disability, applied to more than 100 jobs through Workday’s platform. He was rejected every time, often within minutes, sometimes in the middle of the night. He’s now suing Workday, alleging their AI screening tools discriminate based on race, age, and disability.

The numbers are staggering: Workday disclosed that 1.1 billion applications were rejected using its software during the relevant period. The class action lawsuit could potentially include hundreds of millions of members. Even though the case hasn’t settled yet, the message is clear: companies may be liable for the actions of their AI systems if those systems violate existing laws.

But there’s another way to think about how AI can impact the hiring process.

PSG Global Solutions developed an AI interviewer called “Anna” and had researcher Brian Jabarian at the University of Chicago Booth School of Business run an experiment on 70,000 job applicants. Applicants interviewed by Anna were 12% more likely to get a job offer and 18% more likely to stay in the job for at least a month. Reports of gender discrimination were cut nearly in half compared to human-led interviews.

Surprisingly, when given a choice, 78% of candidates chose the AI interviewer over a human. Why? The theory is that people felt the AI was “less judgy.” For example, a human interviewer might penalize a candidate for being nervous (whether consciously or unconsciously). The AI also captured more job-relevant information: an average of nine topics per interview compared to five for human interviewers.

So which is it? AI can reduce human bias, but it can also perpetuate biases at scale. We don’t fully know the answer (yet), but the difference seems to be in how the AI is designed and what data it’s trained on. For the time being, job seekers have no way of knowing whether that’s working for them or against them.

AI replacing humans: the appeal and the problem

In January 2025, I wrote “No, AI isn’t coming for all the jobs.” I truly believed what I wrote at the time. But it seems more and more that AI can replace some of the work of humans, in ways we couldn’t have predicted even a year ago.

Jason Lemkin is the founder of SaaStr, the world’s largest community for software founders, and a veteran SaaS investor. After two of his salespeople quit, he made a decision that fundamentally changed how his company: he replaced his entire go-to-market team with AI agents. SaasTr now has 20 AI agents managed by 1.2 humans (one full-time, one part-time) doing the work that used to require 10 sales development representatives and account executives.

The result? Same revenue, 24/7 availability, able to handle greater volume, and instant response times.

His framing is really telling: “The job that would have gone to a new rep now goes to an AI agent. The headcount just never gets added.”

But there’s a problem with this approach. You need seasoned, senior people to train AI agents. It worked for SaaStr right now because the senior people already exist. But what happens five to ten years from now when there are no more junior people becoming senior people?

According to data from Revelio Labs, entry-level tech hiring has declined about 35% since January 2023. While this data is specific to tech workers, the pattern will easily extend to other industries as AI tools become more capable and accessible.

It’s a classic case of short-term thinking with long-term consequences. Companies are solving today’s labor struggles by creating a future talent crisis.

So, as workers, what can we do?

I see people online who are still opposed to AI. I get it: there’s still a lot to figure out. In addition to biases and job displacement fears, many of the ethical and environmental questions are very real and unresolved.

And yet... AI isn’t going anywhere. Continuing to resist will likely be a losing strategy.

Early in my career, I helped banks with digital transformation. Customers had bought enterprise software to replace a lot of manual internal processes. There were always employees who insisted that the “old ways” were better. But, after a period of time, the employees who were most successful were the ones who figured out how the software could help them do their jobs better.

AI is going to become as ubiquitous as a calculator or a spreadsheet. The question isn’t whether it will change your work. It’s whether you’ll be ready when it does.

For employees:

If you work for an employer, you’re probably going to need to learn on your own. Part of this is because companies consistently fail to train employees properly. If you wait for formal training, it may never come.

Lemkin’s advice is practical: “Pick an AI tool today. Train it yourself. Become the person at your company who knows how to make AI agents productive. That skill set is the new job security.”

For self-employed people:

AI can extend your skills without extending your costs or the amount of effort you put in. My vibe coding example is proof of that. But it also means your competitors can do the same. Staying ahead means staying curious and constantly testing new tools and new ways of working with AI.

Final thoughts:

The labor market is being reshaped, and we don’t fully know the outcome. The workers who protect themselves are the ones who see this uncertainty and prepare themselves as best they can.

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.

In 2022, I was working at a marketing agency. My manager was... not good at his job. In my prior role at a tech company, I’d been a manager for many years and recognized all the ways the company was spiraling under his indecisiveness and lack of experience.

After a few months of struggling under his leadership, I admitted to him that the job wasn’t working for me. I wanted to come up with some type of exit plan that could allow me to transition out, something I thought he’d be open to as a purported “people-first” company.

His response? He fired me the next day. The role had changed dramatically from what I was hired to do, and we both knew that its current iteration was not the right fit for me. When I said the words out loud, it gave him permission (in his mind) to cut me loose.

As someone who cares deeply about words, I found myself stuck in a strange linguistic limbo. When people asked what happened, I didn’t know what to say. “Fired” implies wrongdoing — like I violated company policy or didn’t perform. But I hadn’t done anything like that. But “laid off“ wasn’t the right phrase either, since it implies business decisions, like restructuring or budget-driven factors.

What actually happened was different: I lost my job because the environment was dysfunctional. The management was bad. The fit was off, not because of some failing on my part, but because the company didn’t know what it wanted from the role and then failed to set me up for success.

I found myself struggling to describe how the job ended, because we don’t have words for that scenario. In the end, I usually say simply, “I lost my job.” Sometimes I’ll say, “I was laid off.” But I refuse to say, “I was fired” because I didn’t do anything wrong.

Without the right language, employees absorb shame that doesn’t belong to them. And employers face no accountability for their role in the situation.

We don’t have employment protections

In many countries, employers can’t just fire someone because the situation isn’t working out. They have to prove cause.

In Japan, “at-will” employment doesn’t exist, and arbitrary dismissals can lead to serious legal consequences. Japan’s Labor Contract Act explicitly states that if a termination lacks “objectively reasonable grounds and is not found to be appropriate by general societal terms,” the termination is considered void. The Philippines takes a similar approach. Employees have a legal “right to security of tenure” and can only be dismissed for “just” or “authorized” causes as defined in the Labor Code. The burden of proof falls on the employer to justify why the termination was necessary.

The United States, by contrast, offers almost zero protection outside of organized labor unions. According to the OECD’s indicators of employment protection legislation, the U.S. is the least regulated country when it comes to dismissing individual workers. At-will employment — which is the default in 49 states — means you can be fired for almost any reason, or no reason at all, as long as it’s not explicitly discriminatory.

Americans have internalized a uniquely individualistic narrative around job loss. We assume that if someone lost their job, they must have done something wrong. The absence of structural protection becomes an invisible weight we carry. Institutional failures become personal shame.

How to reframe your story

Bad management is one of the leading causes of employee departure. According to data from BambooHR, 58% of people leave their jobs because of bad managers. Yet somehow, if the employer makes the decision to part ways instead of the employee, it’s the employee’s fault.

Employers often lean on vague language like “not a good fit.” It sounds neutral, but carries an implicit accusation: that the employee was somehow wrong for the role, rather than the role (or the management, or the culture) being wrong for the employee. The vagueness leaves you holding the bag (and on the financial hook) for a situation you didn’t create.

If you’ve been in this situation — or you’re in it now — here’s how to think about moving forward.

Document what actually happened. You may not need to use it, but having a clear-eyed record of the dysfunction helps you own your story. Write down the specific incidents, the patterns of bad management, and the moments when you tried to make it work. When you write it down, you have clarity. You’re less likely to rewrite history with yourself as the villain.

Don’t use their language. If “fired” doesn’t capture the truth, don’t use it. You can say “the role ended,” or “we parted ways,” or “the job wasn’t sustainable.” You don’t owe **anyone **an explanation that makes you the bad guy in a story where you weren’t.

Recognize the shame isn’t yours to carry. When a job wasn’t a “good fit,” that’s a problem with hiring. You were brought into a situation where you couldn’t possibly succeed. It’s not your fault that you got mixed up in it.

The standard career advice world tells us to never badmouth a former employer. There’s some truth to that. Bitterness isn’t a good look, and interviewers don’t want to hear you trash-talk.

But maybe the real problem is that we’ve made it taboo to call out dysfunction when we see it. We’ve created a system where employers can create toxic environments, make poor hiring decisions, and provide inadequate management — and then frame voluntary or involuntary departures as the employee’s fault.

Until we have language for “I lost my job because the company sucked,” employers will keep escaping accountability. And employees will keep bearing the brunt of those decisions.

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.