There’s something almost beautiful about the audacity of it.

We’re told that artificial intelligence has made us ten times more productive, that the old constraints on human output are falling away, that we’re entering an age of abundance, where work that once took days can be accomplished in hours, and work that once took hours can be accomplished in minutes.

We’re told to embrace the opportunity. To reinvent ourselves. To accelerate.

And then, before we’ve had so much as a moment to enjoy this miraculous new prosperity, we’re told we need to accelerate some more.

Faster. More efficiently. With fewer resources, if possible. Ideally without complaining about it.

But hey, I’ll grant us a quick breath to ask a question on our behalf: where the hell is our pie?

I’m not talking about a slice of the pie, no, nor some modest increase in the percentage of company profits allocated to the people whose labor produces them. We’re not after a pizza party, an employee appreciation luncheon, or an additional $3.50 in annual bonuses if we somehow clear another year’s ever-rising expectations.

I’m talking about a whole damn pie.

If the gains are really as extraordinary as we’re being told, there should be pies everywhere. Pies stacked in the break room. Pies lining the hallways. Pies being wheeled into the office on carts because there are simply too many pies to carry.

Instead, all we seem to be getting are demands to bake them faster without daring to have ourselves a taste along the way.

The Miracle of Doing More for Less

To me it’s clear that AI has transformed how we work. I see daily what these tools can do. I use them to accomplish things that would have been prohibitively time-consuming only a few years ago, so I’m not going to pretend the technology is useless just because I have concerns about how it’s being deployed.

What irks me—and what should irk all of us—is that there’s a difference between a technology making certain tasks dramatically easier and an entire workforce becoming ten times more productive. Why? Because the evidence, as it turns out, is considerably less extravagant than the rhetoric.

A study of more than 5,000 customer support agents found that access to a generative AI assistant improved productivity by about 14% on average, with substantially larger gains among less experienced workers. That’s meaningful. It’s also a long way from tenfold.1

More recently, researchers studying the adoption of AI chatbots in Denmark found that workers reported productivity benefits, but those benefits had not translated into measurable increases in earnings or hours worked two years after ChatGPT’s introduction.2

Then there’s a 2026 review from the International Labour Organization that concluded that productivity improvements are real but uneven, frequently unverified, and have yet to translate consistently into higher measured output or earnings.3

It seems to me the pies are increasing in size, even if not tenfold. And yet, here we are years later, with only unevenly distributed gains to show for it.

In other words, when businesses want employees to embrace AI, its productivity benefits are practically supernatural, but when it comes time to compensate employees for those benefits, the gains become rather difficult to locate.

Now, I’ll allow that maybe the promised gains haven’t materialized yet. Maybe they’re being consumed by implementation costs, quality issues, and the enormous expense of making these systems work reliably. Maybe companies are taking a long-term view of an investment whose returns won’t arrive for years.

Fine. Those are legitimate possibilities.

But if that’s the case, perhaps we could dispense with the breathless declarations about how much more productive everyone has supposedly become; we shouldn’t declare a productivity revolution when setting expectations and then plead uncertainty when discussing how its gains should be distributed.

To put it plainly: Pick a lane.

The Productivity Dividend

There’s a historical dimension to all of this that’s worth understanding.

The Economic Policy Institute has documented a divergence between the growth of productivity and the compensation of typical American workers since 1979. While both measures rose at roughly comparable rates in the decades following World War II, productivity subsequently began growing much faster than typical workers’ pay.4

That doesn’t mean, however, that workers have received none of the benefits of technological progress. Real compensation has risen, and the relationship between productivity and wages depends in part on how each is measured. But the gains haven’t been distributed evenly, and productivity growth has not guaranteed comparable improvements for the typical worker.

The lesson should be obvious: Greater productivity doesn’t automatically produce greater prosperity for the people doing the work, and that distribution is a choice.

And now we’re being required to take part in another technological transformation without much discussion of whether the people making it possible will share proportionately in its rewards.

Consider what happens when a worker figures out how to accomplish eight hours of work in four. There are several possibilities.

Their employer could pay them the same salary for a shorter workday, recognizing that the value of the work hasn’t changed simply because it takes less time to produce.

The employer could maintain the eight-hour day but increase compensation to reflect the additional value being created.

The employer could share a portion of the savings through bonuses, profit-sharing, or other benefits.

Or the employer could congratulate the worker on their ingenuity, assign them another four hours of work, and begin planning next year’s productivity targets around the assumption that they’ll maintain their new pace.

Now, you tell me: Which of these feels most familiar?

Granted, that last option isn’t unreasonable from a certain perspective. Businesses exist to create value, and there’s nothing wrong with using new technology to serve more customers, develop better products, or pursue opportunities that weren’t previously economical. The problem arises, however, when the additional value flows exclusively in one direction.

When every improvement in efficiency becomes the new minimum expectation. When yesterday’s extraordinary performance becomes today’s baseline. When an employee who discovers a better way to do something is rewarded primarily with more things to do.

Eventually, innovation begins to look less like an opportunity and more like a trap, and why wouldn’t it? If becoming more efficient only earns you more work, what incentive do you have to become more efficient?

We Were Supposed to Be Buying Back Our Time

For all the talk about AI transforming the workplace, there’s remarkably little imagination being applied to what a transformed workplace might actually look like.

We seem perfectly capable of imagining software that writes code, drafts contracts, summarizes meetings, analyzes data, and handles increasingly complicated administrative work, but the moment someone suggests that employees might work fewer hours as a result, imagination deserts us.

Forty hours is forty hours, apparently—a number as immutable as gravity.

Never mind that it’s a convention rather than a law of nature. Never mind that the entire premise of a productivity-enhancing technology is that we can accomplish more with less effort. Apparently, the one thing we’re not allowed to accomplish with less effort is our jobs. Infuriating, to say the least.

And we’re not merely talking about money here. We’re talking about human lives.

We’re talking about the hours people spend away from their families. The hobbies they abandon because they’re too exhausted to pursue them. The books they don’t write, the gardens they don’t tend, the communities they don’t participate in because work has consumed so much of their available time and attention.

We’re talking about the parent who could pick up their child from school. The caregiver who could be home for another afternoon. The person who could spend a Friday hiking instead of answering emails.

If AI genuinely allows us to accomplish in thirty hours what previously required forty, why is a thirty-hour workweek treated as a radical proposition? Why is the assumed outcome another ten hours of productivity rather than ten hours of life?

And before someone starts explaining the importance of competition, let me acknowledge the obvious: Yes, businesses compete. Yes, reducing costs and increasing output are essential to remaining viable. Yes, sometimes productivity gains must be reinvested just to stay in business.

I’m not arguing that every minute saved should automatically become a minute of paid vacation. I’m arguing that the possibility—or something like it—should be on the table. I’m arguing that the people whose labor and expertise turn technology into actual business value deserve a voice in determining what happens to that value. I’m arguing that the conversation about productivity should involve more than how much additional output can be extracted from an employee before they collapse.

Because if the purpose of technological progress isn’t to improve human life, what the hell are we progressing toward?

Who Actually Made the Pie?

There’s another peculiar feature of the AI productivity narrative that bothers me, namely that we’re regularly encouraged to think of the technology itself as the source of all this newfound value.

AI wrote the code. AI drafted the proposal. AI found the answer.

But who identified the problem? Who knew what the customer needed? Who recognized that the generated code was wrong? Who understood the organization’s constraints, made the judgment calls, corrected the mistakes, and took responsibility when the system produced something that looked convincing but wasn’t remotely fit for purpose?

Who learned to use the tools in the first place?

Who figured out which workflows could be improved, which ones shouldn’t be automated, and how to integrate these capabilities into systems that already existed?

We did.

Businesses are investing in AI—and those investments deserve returns—but buying access to a tool isn’t the same thing as creating value with it. An employer can purchase the most extraordinary technology in the world and still accomplish very little without people who know what they’re doing, yet the story we’re increasingly invited to believe is that the technology deserves credit for the gains while the workers fortunate enough to use it should simply be grateful they remain employed.

What a convenient arrangement.

Capital provides the tool. Labor figures out how to make it useful. Capital claims the returns. Labor inherits a new baseline.

And if labor objects, someone will invariably point out that the technology makes their work easier—as though easier work were inherently less valuable, as though the amount of suffering required to produce something ought to determine how much its creator deserves to be paid.

What an absurd conception of value.

If someone invents a more efficient way to manufacture a product, we celebrate the innovation. We don’t insist that the product must now be worthless because producing it requires less effort.

Why, then, should an employee’s contribution become less deserving of compensation simply because they’ve learned to produce it more efficiently?

The relevant question, then, has little to do with how difficult the work has become and everything to do with how much value the work creates and how that value ought to be distributed among the people and institutions responsible for creating it.

Show Us the Math

This is where I’d like corporate leaders to start being considerably more specific.

If AI is making your employees dramatically more productive, quantify it. Show us the costs. Show us the savings. Show us the increased output, the improved quality, the additional revenue, and the return on the investment.

Then show us where the gains are going.

Are they funding additional hiring? Better products? Lower prices for customers? Higher wages? Shorter workweeks? Increased retirement contributions?

Or are they flowing into executive compensation, shareholder distributions, and financial targets that seem to become more ambitious every time they’re achieved?

Some mixture of these outcomes may be perfectly defensible. Shareholders take risks. Companies need capital. Customers should benefit from innovation, too.

But workers aren’t merely an expense to be minimized in pursuit of those other interests. We’re participants in the enterprise. Our knowledge, judgment, adaptability, and effort are often the very things converting a technological possibility into an economic reality, and we deserve more than a congratulatory email about how much we’ve accomplished.

I want to see companies establish meaningful mechanisms for sharing productivity gains. Profit-sharing tied to measurable improvements. Compensation that reflects expanding responsibilities. Paid time returned to employees. Clear opportunities for frontline workers to participate in decisions about automation.

I want to see this not because every company has unlimited resources, but because productivity should be a source of shared prosperity rather than an endlessly escalating obligation.

And if the gains aren’t sufficient to support any of those things? Then stop telling us we’re ten times more productive. Stop using the language of abundance to justify the expectations of scarcity.

The Whole Damn Pie

What frustrates me most is that I actually believe in the promise of this technology.

I believe there are extraordinary possibilities in giving people tools that allow them to create more, learn faster, and overcome limitations that once constrained their ambitions.

I believe there’s something genuinely exciting about making complex work more accessible and giving people capabilities they didn’t previously possess.

But I don’t believe that excitement obligates us to surrender the benefits of our ingenuity to the organizations that employ us, and I certainly don’t believe we should be expected to respond to every technological advance by making ourselves available for even more work.

At some point, the question has to change.

Not: How much faster can we go?

Not: How much more can we produce?

Not: How many additional responsibilities can we fit into the same number of hours?

But: When do we get to enjoy the benefits of what we’ve already accomplished?

Because if we really are producing twice as much, or three times as much, or ten times as much value as we were before, there ought to be a corresponding improvement in our lives.

Maybe that means better compensation. Maybe it means more time with our families. Maybe it means some combination of financial security, professional autonomy, and the freedom to pursue things that have absolutely nothing to do with generating returns for somebody else.

Workers deserve to participate in deciding how rewards are distributed, and until that happens, I’m not particularly interested in hearing another inspirational speech about how AI is going to make us all superhuman.

If these are the greatest productivity gains in generations, if we’re truly entering an unprecedented age of technological abundance, then there ought to be enough prosperity to go around.

I’m not asking for a bigger slice of the same old pie.

According to the people announcing this revolution, we should have enough ingredients, equipment, and newfound capacity to open a whole damn bakery.

So quit telling us how much faster we need to work.

Show us the pies.

Tell Me in the Comments

1. When a tool makes your work faster, what actually happens to the time it saves?

2. If your workplace became substantially more productive, would you rather see the gains in pay, shorter hours, or something else? Who should help decide?

3. What’s the strongest case for putting those gains back into the business instead of returning them to workers? Does it persuade you?

Death. Taxes. The speed of light. These are but some of the universal constants.

Of all the universal constants, though, change is perhaps the most pressing: stalking us daily at work, at home, and everywhere in between.

Given our depth of experience with change, one would think we’d be well equipped to tackle it—and to be fair, we are, or at least we are in terms of thinking strategically, finding new efficiencies, and, hopefully, updating our priors to suit the new environment in which we find ourselves.

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That said, there’s a key component missing from that list.

The missing piece? People. Rarely do we discuss change from the perspective of the people experiencing it. Too often, we focus instead on urgent language like adapt or fall behind. Disrupt or be disrupted. Reinvent yourself. Reskill. Rebrand.

In the right contexts, that’s reasonable advice, but there’s a difference between understanding why change is necessary and the experience of that change, particularly when it’s a change that takes something from you.

And ever since the earliest days of the industrial revolution, one of the most destabilizing examples of this has been the threat change poses to hard-earned skills and craft.

Craft Under Threat

The Luddites are largely remembered as a group of yokels who simply hated technology, but the truth is far more complex. The industrial revolution turned the Luddites against specific machines not because the machines were new, but because those machines threatened their livelihoods, independence, and status.

Most of the Luddites were skilled textile workers whose craft mattered to them immensely. They’d spent years developing expertise within a formal system of apprenticeship and had a deep understanding of their work. As a result of their dedication and consumer demand, these skills had economic value. More importantly, however, this combination of factors granted these textile workers control over their work lives and a sense of identity within their communities.

That is, until the machines came along.

With textile production reorganized around machines over manual labor, skilled workers came increasingly under threat, ultimately leading to ways of work in which individual craftsmanship mattered less and less.

Sure, the Luddites may have been unusually willing to raise hell about putting a stop to this change of circumstances1, but despite their best efforts, the change they were reacting to didn’t disappear—it repeated. Industrialization continued to break complex crafts into smaller and smaller tasks, and it, along with other types of automation, continues to do so to this day.

In case no examples come to mind, think of the assembly line. This way of reorganizing work led to the production of goods at a scale that would have been unimaginable to earlier craftspeople, many of whom underwent this social transformation from highly skilled workers to meat appendages for highly repetitive, low-skill tasks.

Then there’s the mechanization of agriculture, the automation of factories, globalization, outsourcing, and software’s elimination of entire categories of clerical activity.

We’re seeing this again now, too, with artificial intelligence2 imposing on forms of knowledge work that, until very recently, seemed relatively insulated from that history.

The point is that the technologies may be different, but the experience is surprisingly familiar, leaving the displaced or to-be-displaced with a frightening pair of thoughts: I spent years learning how to do this. Does that still matter?

That question deserves more attention from anyone responsible for leading people through change—no matter the scale—because a threat to craft is rarely a threat to someone’s task list alone.

These changes threaten autonomy. They threaten belonging. They often fundamentally undermine competence, at least in the short term.

Autonomy, relatedness, and competence are the three psychological needs that underpin self-determination theory, a framework I’ve returned to before when thinking about meaningful work and craftsmanship.

In the workplace, I tend to think of them as something even simpler:

Autonomy. Belonging. Competence. The ABCs of finding your way at work.

Periods of upheaval, unfortunately—particularly when the upheaval is sudden or unending—have a habit of undercutting one’s sense of all three.

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Autonomy: Do I still have a say in my own work?

Becoming a master of your craft traditionally offers you control. The more expertise you possess, the less you generally need to be told exactly how to perform every part of your job.

A craftsperson understands the materials. The tradeoffs. The exceptions. They have the hard-earned judgment necessary to outperform their lesser-experienced peers. Expertise creates discretion, or at least it can, which is one reason technological and organizational change can feel threatening even when someone’s employment itself is not immediately at risk. Change can alter not just what someone does, but how much authority they retain over doing it.

Here, too, we find a long history supporting this notion. Industrial systems have often improved output in part by moving decisions away from individual workers and into processes, and scientific management3 only pushes that idea further by studying work, determining the optimal method, standardizing it, and organizing workers around the system.

In these types of work environments, employees are told which technologies to use, how often to use them, and perhaps eventually judged on whether they’re using them enough. At that point, a technology that might otherwise have expanded human capability begins to feel like something being done to people instead.4

That distinction matters because what was originally an attempt to increase an individual’s autonomy by, say, permitting them to “focus on higher leverage tasks” ultimately removes some meaningful agency those individuals previously had over their work.

At times of change, then, the managerial question should not be limited only to whether people are using a new technology, but whether they are still experiencing meaningful agency over their work despite this new technology.

That said, no leader can preserve perfect autonomy during every change. Sometimes systems need standardization. Sometimes workflows genuinely improve when they’re redesigned. Sometimes employees will have to learn tools they would not personally have chosen. I get all of this; I’m a huge fan of standardization, efficient processes, and improved workflows. My career is riddled with examples of pursuing just that!

But when pushing for change myself, I always try to think carefully about the impact it will have on people. If the change in question is going to remove some amount of control from those with whom I work, it’s important to make sure those people experience an increase in control elsewhere, even if it’s in the implementation process for the change itself.

The people closest to the work are worth consulting about where automation helps and where it doesn’t precisely because they’re closest to the work. Even if that means they may have blind spots of their own, their insider knowledge is invaluable and should be the cornerstone of any conversation about change.

In other words, to ensure autonomy isn’t undercut during times of change, it’s incumbent on leadership to increase ownership over higher-order decisions when lower-order ones become automated.

Change creates opportunities for management, too. Some leaders will see disruption as an opportunity to consolidate control while employees are already on their heels; others will see an opportunity to create new forms of ownership and participation.

Change is an opportunity; the type of opportunity management sees in it is often very telling about the relationship they have with their reports.

And if circumstances are already taking autonomy away from your people, that is certainly not the time to unduly take more.

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Competence: What happens when the thing you’re good at matters less?

The effect of change on competence is where craft under threat becomes especially personal.

Just think, for decades—if not since time immemorial—we’ve given people a particular kind of career advice: Learn valuable skills. Develop expertise. Build mastery and become excellent at something.

I’ve made versions of that argument myself.

The “craftsman mindset” is compelling precisely because competence can produce so many other things we want from work. Mastery creates pride. Scarce skills create leverage. Expertise produces career capital that eventually buys greater flexibility, interesting opportunities, and autonomy.

But—and this is a major but—there’s an uncomfortable implication to that advice.

What happens when someone does exactly what we told them to do—say, go to college and take out massive loans to get an in-demand degree—and then the world changes what their skill is worth?

Yeah, not great, and this has happened repeatedly.

The weaver becomes less economically valuable when a machine can produce cloth at dramatically greater scale. The factory craftsperson loses some of the importance of holistic knowledge when production is decomposed into standardized steps. The office worker becomes less necessary when software automates the process.

And here, now, we watch as experienced knowledge workers are left gobsmacked as artificial intelligence performs, in seconds, a task they spent years learning to do.

True, none of this means the machine can necessarily replace the person. Psychologically, on the other hand, replacement is not the only thing that matters. Competence is more than the possession of skills, it’s the experience of being capable, of knowing what you understand has value, and being able to look at a difficult situation and think, I know how to handle this.

When technological change undercuts that feeling, telling someone to simply “reskill” is an incomplete response. Of course people should learn new things, and of course skills evolve. No craft has ever been completely frozen in time.

But a leader who responds to someone’s professional disorientation with little more than “learn the new tool” risks sending a brutal message: What you spent years becoming good at no longer counts.

Yeesh.

The better response is to help people see both discontinuity and continuity, including the new opportunities that present themselves as a result of these changes.

Yes, parts of the work may become automated, but judgment still matters. Context and experience still matter, and knowing when an output is wrong still matters.

Knowing which problem to solve—perhaps the highest-order task available—still matters.

So, when competence is undermined, give your people harder problems. Give them responsibility, let them mentor, and let them learn. Their expertise is still valuable, it may just need new opportunities to demonstrate its flexibility across domains and new processes.

We can’t confuse the decline of one task with the disappearance of the person who used to perform it. That distinction is important because our rhetoric around change often blurs it.

When someone hears they’re now a “10x employee” and that “one person can now do the work of an entire team,” there are nine other people on that hypothetical team receiving that message, too.

Leaders should be thoughtful about what that message does to someone’s sense of competence.

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Belonging: Am I part of whatever comes next?

Then there’s the question beneath almost every major period of workplace upheaval: Is there still a place for me here?

This is where change becomes bigger than craft. People can be led to ask themselves this question as the result of a merger or acquisition, a reorg, a new leader.

And layoffs will certainly have people asking themselves whether there’s still a place for them here. Even if they survived a first wave, what if there’s another?

The circumstances can vary enormously, but the underlying question doesn’t.

People want to know whether they remain part of the group and whether the group still has a future for people like them. This is why I prefer the word belonging in workplace conversations even though self-determination theory traditionally uses relatedness.

Belonging gets directly at the organizational experience and, even more importantly in my view, ties into something deeply human: the need to belong.

And whether organizations realize it, they’re constantly answering the question of whether their members are still wanted.

Think about the language of AI transformation today. We hear a lot about the future of work, AI-first organizations, smaller teams accomplishing more, automated agents, flattened organizations, efficiency, and headcount leverage.

It’d be hard to argue that employees are acting irrationally when they listen to all of that and wonder which category they belong to: are they the future or the inefficiency being removed from it?

Now, this doesn’t mean leaders should promise permanence. To do so would be deeply irresponsible when one considers that despite any workplace’s best intentions, roles will sometimes disappear. Sometimes organizations genuinely need fewer people doing a certain kind of work. Sometimes external circumstances force painful decisions that no good manager can prevent.

Belonging doesn’t require dishonesty, no, it requires people to believe that they’re being treated as participants in change rather than as inputs to be optimized.

There’s an enormous difference between nothing about your job will ever change and we want you to help us build what comes next. The first may be impossible to promise, but the second is often entirely within a leader’s control.

What change takes away—and what leaders can put back

This, I think, is where the ABC framework for leading through change becomes most practical. Change will sometimes diminish autonomy, belonging, or competence. That’s inevitable. The mistake is assuming that because the loss is unavoidable, nothing needs to be done about it.

To put it in terms even the most data-only executives would appreciate, imagine instead that leaders treated these needs as a kind of psychological balance sheet.

If a change takes something away, ask where you can put something back. For example, if automation removes discretion from part of someone’s work, can you increase their ownership elsewhere? If a new technology makes an existing skill less central, can you give that person opportunities to develop or demonstrate competence in a different way? If a reorganization creates uncertainty about someone’s place in the organization, can you increase communication, participation, investment, and connection?

The goal in doing this isn’t to ensure that nobody ever loses anything. That would make meaningful change impossible. The goal is to recognize the loss and, just as importantly, avoid compounding it.

If someone is already questioning their competence, that’s not the time to start micromanaging them. If people are already unsure whether they belong, don’t communicate with them exclusively through the language of efficiency. If a transformation is already changing someone’s work in ways they can’t control, don’t strip away the autonomy they still enjoy.

In other words, don’t stack deprivation on top of deprivation.

This principle applies far beyond AI, with COVID providing an obvious recent example.

During the pandemic, people lost enormous amounts of control over their working lives almost overnight: teams lost familiar ways of connecting, and employees had to learn unfamiliar technologies and new ways of doing jobs they already knew how to do.

Some managers responded to that upheaval by increasing flexibility, trust, communication, and compassion, while others responded to the loss of physical visibility with more surveillance and more control.

The external event was the same, but the managerial response was not—and that’s an important distinction. Leaders can’t control every disruption, but they do control whether their response restores something that’s been lost or takes even more from their people during challenging times.

Resistance is information

This also changes how we might think about “resistance to change.”

Yes, people can be stubborn, but “resistance” describes behavior without explaining its cause. This is yet another reason the Luddites remain such a useful cautionary tale.

Here we are, two centuries later, their name synonymous with all things technologically averse, but, like we highlighted earlier, their objection wasn’t simply machine bad. The behavior was resistance, but the cause of that resistance had its origins in the fact that their world of work was changing. Their expertise was being devalued. Their autonomy was being undermined. Their economic and social standing became less certain.

Talk about a hit to autonomy, belonging, and competence.

Some may argue this doesn’t justify everything they did—and they’re welcome to that opinion—but no matter how you look at it, these facts do make their behavior more intelligible.

The lesson here is that modern leaders should be careful not to make the same interpretive mistake: when people resist a change, ask what they believe they’re losing. Autonomy? Belonging? Competence? Some combination of the above?

The answer to those questions may not mean the change should stop, but it may tell you a great deal about how the change needs to be led.

The business case for self-determination theory is stronger than it looks

There’s another reason asking ourselves these questions as leaders matters: people who feel safe, capable, and valued have much better incentives to help an organization change.

Let’s take AI as an example again. Suppose I discover that a tool can automate thirty percent of my current work. If I trust my leaders, believe my expertise is valued, feel I have a future in the organization, and expect to have some say in what happens to the time that automation frees up, that discovery is exciting.

But in an organization where leaders are indifferent toward reductions in headcount, make unilateral decisions about work, treat employees as a line on an expense sheet, and describe AI mostly in terms of how many people will no longer be needed—that same discovery is no longer exciting. It’s a threat.

If I, or anyone, really, were in the latter organization, keeping that tool to myself wouldn’t necessarily be resistance to a full-throated embrace of technology. It might just be rational behavior.

All of this leads to a strange irony: the organizations most capable of benefiting from automation may be the ones where employees feel safest helping automate their own work.

In this way, we can see trust is a prerequisite for transformation.

And with autonomy, belonging, and competence our fundamentals to building trust, they should be treated as prerequisites to transformation, too.

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The ABCs

Periods of upheaval come in many forms.

Some are global, some are organizational, and others, still, are intensely local. The scale of the change can, well, change, but the human questions are remarkably consistent.

To get ahead and stay ahead when leading people through change, then, it’s critical that we ask ourselves the below.

• Autonomy: Does this leave people with more or less meaningful agency over their work?

• Belonging: Does this give them more or less reason to believe they have a valued place here?

• Competence: Does this help them experience themselves as capable and useful, or does it make something they spent years learning suddenly feel irrelevant?

The answer may not always be positive. Change sometimes takes more than it gives, and leadership isn’t the art of preventing that; it’s the practice of understanding what change is asking people to give up, deciding which losses are truly necessary, and being intentional about what can be restored elsewhere.

Because while the history of work is in many ways a history of changing tools, systems, and forms of craft, the people doing the work have remained consistent in what they need. People need agency. They need connection, control, and a sense of mastery—our ABCs of autonomy, belonging, and competence.

Work will change. It always has.

Our obligation to the people doing that work should evolve with it.

Join the Conversation in the Comments

• Think of a change you were asked to navigate at work: what did your leaders do well, and where did they lose you?

• Which matters most to you during a period of change—autonomy, belonging, or competence—and why?

• What is one thing leaders consistently misunderstand about resistance to change?

Some ideas ask to be written. Others demand to be spoken.

Since its inception, R: On Everything has been exclusively a written project, a place for me to think through questions about technology, work, creativity, leadership, culture, and the connective tissue that binds them to one another. The essays we share here give me room to research, revise, reconsider, and develop a deeper understanding of the topics that return to me time and time again.

Those who have been following me for years know, however, that writing is only one way to explore an idea.

That’s why I’m launching the R: On Everything podcast, a new way for us to examine the forces reshaping our lives.

Listen to the first three episodes ➡️

Why a podcast?

I’ve spent a good portion of my creative life behind a microphone. Before R: On Everything, I hosted and produced podcasts through the Writescast Network, where I spoke with authors, editors, publishers, and other creative professionals about the craft of creative writing and what it took to build a sustainable and sustaining creative life.

In 2020, though, I stepped away from podcasting altogether. This was the right decision at the time: the conversations I wanted to have were expanding beyond publishing, and, as I juggled other significant life challenges, I needed the space to figure out what connected this new, more diverse set of interests.

Years later, R: On Everything became that space.

More recently, however, I’ve found myself hearing some of these essays as I write them. Certain arguments have seemed to ask for cadence, emphasis, and the occasional pause. Some ideas became clearer when I imagined explaining them to another person rather than arranging them on a page. Others opened doors to stories, asides, and connections that didn’t belong in the written version but felt entirely natural when spoken aloud.

This podcast gives those ideas somewhere to go.

It also creates a more human kind of connection. Writing allows me to choose each word carefully; audio lets you hear the uncertainty, humor, frustration, curiosity, and conviction behind those words. Neither form is inherently better. They simply reveal different parts of the thought—and, inevitably, different parts of the person doing the thinking.

This isn’t an audio version of the newsletter

The podcast and the essay feed will overlap, but neither will be a perfect mirror of the other.

Some episodes will begin with essays published here, particularly when I think an argument would benefit from being adapted, expanded, or reconsidered in another medium. Even then, I don’t plan to simply sit down and read the post into a microphone. The audio version may follow a different structure, incorporate new material, or arrive somewhere the original essay did not.

Other episodes will be created specifically for the podcast, and others still will give us room to revisit older ideas, connect themes across multiple essays, and respond more quickly when something happening in the world demands attention.

Likewise, some essays will remain essays. I don’t intend to force every idea into every format merely because both are available. Rather than produce the same material twice, we’ll let each idea find the form—or forms—that serve it best.

If you read and listen, the two feeds should complement one another. If you prefer only one medium, each should still stand on its own.

What you can listen to today

The short podcast teaser is live now, along with the first full episodes, “AI Isn’t Inevitable. We’re Choosing It.,” “Stop Following Your Passion. Build It Instead.,” and “Why People Resist Change—and What Leaders Keep Getting Wrong.”

The first of these examines the way technological change is often presented to us as something automatic and unstoppable: a future that simply arrives, leaving ordinary people with no meaningful role except to adapt.

It should likely go without saying that I don’t believe that story. Technology may create possibilities, but people and institutions still decide which possibilities receive funding, legitimacy, and power.

In other words, the future doesn’t simply arrive. People build it.

That argument captures much of what I want this podcast to do. R: On Everything will take seriously the enormous changes unfolding around us without treating their consequences as foregone conclusions. It will resist both reflexive techno-optimism and reflexive despair. Most of all, it will remain interested in the choices hiding behind whatever we are told is inevitable.

The other two episodes that I’m releasing today establish the range of subjects and approaches you can expect from the show, and after that, new episodes will be released every other week.

That biweekly rhythm is intentional. I want enough structure to make the podcast dependable without manufacturing episodes simply to satisfy an unforgiving content calendar (I learned a lot from my time running the Writescast Network). These ideas deserve time to develop, and you deserve something more considered than another voice filling space.

One project, more ways to explore it

Although the format is new, the central curiosity behind R: On Everything remains the same.

What happens to people when systems change around them? Who benefits from the stories we tell about progress? How do we preserve agency, dignity, creativity, and meaning in institutions increasingly shaped by automation and scale? What can technology help us become—and what might it quietly train us to surrender?

Those questions can’t be contained within a single category. They lead from artificial intelligence to management, from publishing to surveillance, from mental health to craftsmanship, and from the future of work to the way each of us decides what constitutes a worthwhile life.

That is the territory of R: On Everything—not literally everything, but everything connected to the questions that matter.

The essays will continue; the podcast now joins them, not as a replacement or a replica, but as another way of thinking in public.

I hope you’ll listen.

Listen now and follow the show ➡️

Onward, upward, and keep curious,
Ryan

Not on Apple Podcasts? Tune in on one of the other platforms below.

Find your platform and listen here

I’ve been looking for a new job lately.1

After years in software engineering, I’ve been looking for opportunities to bring more of my interests together: building things—after figuring out what’s worth building, of course—and helping people navigate change. Every so often, I find a posting that makes me stop scrolling.

Recently, I found an especially good one.

The responsibilities lined up remarkably well with the work I’ve done and, perhaps more importantly, with the kind of work I’d like to do next. Technology deployed at scale. Product strategy. Complex systems. Real-world consequences. Remote work. It was the kind of posting where you get several paragraphs in and begin unconsciously translating each bullet point into a story you could tell during an interview.

There was only one problem.

The job was at Flock Safety—and I couldn’t do it.

For the unfamiliar, Flock describes itself as a public-safety company. Its careers page talks about “safer, stronger communities,” eliminating crime, helping families feel secure, and doing “meaningful work that makes communities safer.”2 I suspect many of the people working there sincerely believe in that mission. Helping find a missing child or identify a vehicle involved in a shooting would give someone good reason to feel proud of their work.

That’s part of what makes this worth talking about: some of the most dangerous systems a society builds are created by smart, earnest people solving narrowly defined problems.

• How do we find stolen cars?

• How do we identify a vehicle involved in a shooting?

• How do we find a missing child?

Those are good questions, but the problem begins when the answer is to build an infrastructure capable of quietly recording the movements of virtually everyone else, twenty-four hours a day, seven days a week.

No matter how useful that infrastructure may be, I couldn’t reconcile helping construct it with my own values.

Because Flock Safety isn’t merely selling cameras, it’s constructing something America should have learned to fear a long time ago: a surveillance network.

You may already have been Flocked

You’ve probably seen a Flock camera, even if you didn’t know it at the time.

The most recognizable versions are small black devices mounted on poles, often paired with rectangular solar panels and positioned along roads, at intersections, or near entrances to neighborhoods.3

They’re touted as automated license plate readers, or ALPRs, but “license plate reader” dramatically undersells what they can do.

Flock’s systems can collect a vehicle’s plate, location, time, make, color, body type, and other visible characteristics.4 The company’s own marketing boasts about searches based on what it calls a “Vehicle Signature,” allowing investigators to use vehicle attributes even when they don’t have a complete plate number.

Drive past one and, absent any suspicion that you’ve committed a crime, your vehicle can become another timestamped data point. Then another. Then another.

The individual photograph is almost beside the point.

The power is in aggregation.

One camera seeing your car at 8:03 a.m. provides a single observation. A network seeing your car at 8:03 a.m., 8:27 a.m., 12:14 p.m., 4:52 p.m., and 7:31 p.m.—across days, communities, and jurisdictions—can reveal patterns in your life.

Where do you sleep? Where do you work? Which church, medical clinic, union hall, or protest do you visit? Whose house?

And how often?

Now, I should say that Flock emphasizes its ALPR product does not use facial recognition and collects vehicle information rather than biometric information. Okay, fair enough, but I invite you to perform a simple thought experiment:

Imagine that I know where your car has been every day for a month. Now tell me with a straight face that I know nothing about you.

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Look around

If you want to understand how widespread this infrastructure has become, don’t take my word for it. Go to DeFlock.org.

DeFlock is an open-source project through which volunteers map automated license plate readers throughout the United States. As I write this, its database contains nearly 140,000 documented readers nationwide.5 The database includes Flock and competing systems—an important distinction because Flock is only the most visible participant in a much larger surveillance industry.

August reporting puts Flock’s own deployment at roughly 120,000 cameras, collectively performing around 20 billion plate scans per month.6 It’s difficult to picture what twenty billion of anything looks like, which is where the map becomes useful.

Zoom into your city. Follow your commute, look around your neighborhood, and find the road you take to the grocery store. Then zoom out.

The point of doing this is to connect those enormous numbers to something familiar: the routes we take while going about our lives. A camera outside a neighborhood might seem like a neighborhood matter. Once its observations can be searched alongside those collected elsewhere, the implications extend well beyond that entrance.

“But I’m not doing anything wrong”

Few phrases have done more work on behalf of surveillance than this one.

Why should I care? I’m not doing anything wrong.

I understand why that feels reassuring. Most of us are doing fairly unremarkable things most of the time. We’re getting groceries, going to work, dropping the kids off at soccer practice. Why would that be of any interest to anyone outside us or our families?

But a plate-reader sighting can become part of a searchable record regardless of how ordinary the trip was. Collect enough sightings and someone with access may be able to infer a family’s routines. The absence of wrongdoing doesn’t prevent the information from being collected; it’s precisely what makes collecting it so objectionable.

The question is what the system makes possible—and who gets to decide what to do with that possibility.

We have a tendency to evaluate surveillance through its nicest imaginable use case. It’s true that Flock’s systems have helped recover stolen vehicles, locate missing people, and advance criminal investigations. Those benefits deserve acknowledgment, but so do the consequences when safeguards fail.

Police officers have been accused of using license-plate databases for personal surveillance,7 including tracking romantic partners or acquaintances, leading departments around the country to open investigations into alleged misuse. In Lufkin, Texas, for example, one officer was indicted after investigators alleged tens of thousands of improper searches.8 At that point, reassurance about the system’s intended purpose does very little for the people whose information was searched.

Now, Flock maintains that customers control their data and sharing settings, that searches are logged, and that misuse violates its policies. The company has also responded to the backlash with new safeguards, including a move toward a seven-day default retention period, mandatory case codes, additional auditing, and stronger misuse detection.9 Those are meaningful changes.

But notice what had to happen first:

1. The network had to exist.

2. The abuses had to occur.

3. Citizens had to discover them.

4. Journalists had to report them.

5. Activists had to organize.

6. Municipalities had to revolt.

Only then did stronger safeguards become urgent. That’s backwards.

Before building a system capable of population-scale tracking, the government should have to demonstrate why it needs that capability and what limits will govern it. The people being recorded shouldn’t have to reverse-engineer those answers years later.

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This is an American issue

Opposition to mass surveillance is sometimes treated as though it belongs to one political tribe. That shouldn’t be the case.

After all, the Fourth Amendment doesn’t say that people shall be secure against unreasonable searches and seizures only when the people running the government are from the “correct” party.

Eventually, someone you do not trust will hold power. You don’t even have to agree with your neighbor about whether that person is in office already to appreciate the need for limits. Even decent people can do tremendous harm when institutions grant them powers whose consequences they haven’t adequately considered.

This is precisely why the emerging backlash against automated plate readers has become so politically interesting.

Civil-liberties organizations have sounded alarms. Privacy advocates have organized. Local activists have protested, and prominent conservatives have begun expressing the same concern.

You don’t have to agree about immigration, abortion, firearms, policing, Donald Trump, Alexandria Ocasio-Cortez, taxes, Gaza, gender, unions, or much of anything else to recognize that building a machine capable of reconstructing everyone’s movements is an extraordinarily dangerous way to run a free society.

Today’s surveillance target may be someone you dislike. Tomorrow’s may be you. Civil liberties have to survive both possibilities, or all we’ve secured is a temporary advantage for whoever currently gets to choose the targets.

Wisconsin is not outside the network

Here in Wisconsin, an investigation reported that at least 221 Wisconsin law-enforcement agencies were using Flock cameras in 2025. Since then, communities including Dane County, Fitchburg, Oshkosh, and Sturgeon Bay have moved away from the company’s systems amid privacy and misuse concerns, and the UW–Madison Police allowed its Flock contract to expire this summer.10

Those decisions, along with Milwaukee County’s directive limiting Flock camera operations on county property, sound encouraging. There’s an important detail, however, that a headline about canceling a contract can leave out.

Despite kicking Flock to the curb, UWPD simultaneously said it remained supportive of automated license plate readers and would look for another vendor,11 which brings us to the next problem.

Killing Flock isn’t enough

Flock has become the Kleenex12 of the American ALPR backlash: recognizable, everywhere, and synonymous with the function it serves. As a result, public anger has attached itself to the brand, and competitors have noticed.

Axon, Motorola Solutions/Vigilant, Verkada, and numerous other vendors sell technologies capable of performing similar forms of vehicle surveillance,13 and as communities turn against Flock, those companies have an extraordinary business opportunity: become the acceptable surveillance company.

The ACLU has warned about exactly this dynamic: replacing one vendor while leaving the underlying surveillance architecture intact doesn’t solve the civil-liberties problem. In fact, in some ways it makes the problem harder.

Flock cameras are conspicuous once you learn to recognize them. There’s a pole, a solar panel, and a black box pointed toward traffic.

That visibility has become a liability. People photograph them, map them, and organize around them.

And, increasingly, it turns out, people destroy them.

Some people have decided to fight the cameras themselves

Across the country, surveillance cameras have been spray-painted, obstructed, knocked over, cut from poles, and otherwise disabled.14 In August, The Guardian documented at least 33 apparent acts of anti-surveillance vandalism across 23 states.15

I understand the impulse. I really do. When a camera appears on your street without meaningful public debate, records everyone’s movements, feeds data into systems you barely understand, and exists because a municipal procurement process treated surveillance like another piece of road equipment, anger is a rational response.

But smashing cameras presents two problems.

The first is obvious: damaging equipment can be a crime, and people have been arrested and charged for doing it. The second is more strategically important: Destroying the device doesn’t destroy the policy that put it there.

A camera can be replaced, a pole repaired. A city can change vendors. Meanwhile, if the public debate becomes consumed by vandalism, officials get to spend their time condemning damaged property instead of answering why they authorized the collection of innocent people’s movements in the first place.

I worry, too, about what happens when residents understandably tire of repeating this fight. A controversial installation becomes a replacement contract, then a routine renewal. The information keeps accumulating while public attention moves on.

That’s a path to permanence we should be working to interrupt.

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So what do we do?

We make surveillance politically expensive.

Start by knowing where the cameras are. Use DeFlock to locate and document them. Show your neighbors, then ask who owns them, which agency receives the data, how long it’s retained, and who can search it.

The sharing arrangements deserve particular attention, too. Which other agencies have access, including federal agencies? Is a warrant required for a historical search? Does someone have to supply a case number? Will the department publish audit logs?

Oh, and was the community ever consulted before the cameras appeared?

These questions give us somewhere concrete to begin. Submit public-records requests, attend police-and-fire and common-council meetings, and write your alderpersons, county supervisors, and state legislators. Ask them to explain what they approved—or what was purchased without their approval—and what they intend to do about it.

We can demand surveillance-impact reports and public hearings before new technology is purchased. We can push for warrants for historical location searches, short retention periods, and bans on immigration, reproductive-health, political, religious, and other sensitive tracking.

Demand independent auditing and penalties for misuse. Demand that surveillance contracts expire unless elected officials affirmatively renew them in public. Safeguards should create obligations that institutions have to meet, with consequences when they don’t.

And when a city says it has “heard the community” and is dropping Flock, ask the follow-up question:

What are you replacing it with?

Because the goal isn’t to DeFlock America only to re-Axon it; the goal is to decide what powers we’re willing to grant the state.

There’s reason to believe this work can succeed. As of late August 2026, the Institute for Justice was tracking 92 local governments that had canceled ALPR contracts since the beginning of 2025.16 Every cancellation deserves scrutiny of what comes next, but those decisions demonstrate that contracts can be challenged and institutions can be made to reconsider.

Usefulness is not the only value that matters. Citizens have been making that clear, and officials have begun responding.

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Safety is not the only thing worth protecting

I keep coming back to that job posting. I’m sure I could have made a compelling case for myself to get the job. I’m sure there are interesting technological problems to solve at Flock, and I’m sure there are employees there who go home proud of what they’ve helped accomplish.

I also keep thinking about how much can disappear from view when we focus on doing our particular part well. The engineer improves the search. The salesperson secures the contract. The department adds another tool. Each can point to a job done successfully while the people whose movements make the whole thing useful remain outside the conversation.

The surveillance state doesn’t need soldiers installing cameras beneath enormous banners reading THE SURVEILLANCE STATE HAS ARRIVED. A thousand reasonable procurement decisions will do.

A stolen-car initiative, a public-safety pilot, a neighborhood camera. Then a database integration, a contract renewal, another capability enabled because the infrastructure already supports it. We can arrive at a system that surrounds us without ever having had a public conversation proportionate to the power we’ve assembled.

That is how freedoms disappear in technologically advanced societies—not necessarily through conquest, but rather convenience, indifference, and defaults. Freedoms disappear through people deciding that because they have nothing to hide, nobody else needs somewhere to hide either.

So look for the cameras. Map them. Talk about them. Ask uncomfortable questions about them, and show up when contracts come before your elected officials.

On this issue, we should all be pointing at the same thing: the state should not know where everyone is simply because technology has made knowing possible.

Freedom sometimes requires accepting that dangerous people will possess freedoms too, and privacy sometimes requires accepting that investigators will have to investigate. Rights that exist only when their exercise is convenient for the government aren’t rights.

The cameras are already here, and the network is already enormous, but its continued expansion is not inevitable.

The officials approving these contracts answer to us.

Act like it.

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Entropy has a bad reputation.

Ordinarily, we associate entropy with decay: the ramshackle barn collapsing into the earth, the neglected water heater finally giving out, the once-simple workflow accumulating exception after exception until no one remembers how it achieved such discombobulation.

From this perspective, it seems like that bad reputation is well deserved: given enough time, everything drifts toward disorder.

To be fair, this distillation of entropy is near enough to the scientific concept to be recognizable, but it wouldn’t be precise enough to satisfy a physicist.

In thermodynamics, for example, entropy concerns the number of possible microscopic arrangements available to a system. Over in information theory, it measures uncertainty or unpredictability. Elsewhere, neuroscientists use the word in still other ways, often to describe the diversity or complexity of brain activity.

These meanings aren’t interchangeable, but they share an idea useful beyond their original disciplines, namely that a system can occupy more possible states than the one it occupies now.

This is a frightening prospect. We build routines, institutions, identities, and explanations partly to reduce the number of possibilities confronting us. As we accumulate experience, we become increasingly capable of relying on top-down processing: we have enough data to form reasonably reliable hypotheses about the world, allowing prior knowledge to guide how we interpret what we encounter.

Often, this comes as a relief. Order makes the world manageable. It helps us coordinate with one another, preserve what works, and avoid reconsidering every decision every day.

But even order has its shortcomings and modes of failure.

One example of this is the notion that a system can become so committed to maintaining its present arrangement that it loses the ability to imagine another. Eventually, what was once stability transforms more meaningfully into rigidity. Consistency becomes repetition. The system’s coherence becomes a prison.

Sometimes, the problem is not that everything is falling apart—it’s that everything is being held together too tightly.

The Order of Depression

Mental illness is often approached as though it were a form of chaos. Someone is disordered—their thoughts are disorganized, their emotions out of control.

I can attest to the fact that mental illness can feel chaotic. But depression, at least in my experience of it, can also feel oppressively orderly. The same thoughts travel the same routes and arrive at the same conclusions. The future is interpreted through a negative outlook on the past. New information is admitted only long enough to be translated into evidence for what the mind already believes.

Nothing will change. You will always feel this way. You have tried before. This is simply who you are.

Depression reduces an enormous world to a handful of fixed propositions. Its conclusions may be painful, but they’re also remarkably stable. The depressed mind does not necessarily lack order; it may suffer instead from too much of a particular kind.

This—and prepare for a sharp left-turn here—is part of what makes recent research into psilocybin-assisted therapy so intriguing.

Psilocybin is the psychedelically active compound found in certain species of mushroom. After it enters the body, it’s converted into psilocin, the active metabolite primarily responsible for its psychedelic effects. In clinical settings—generally accompanied by preparation, psychological support, and follow-up care—psilocybin has shown promise as a treatment for depression, including some cases in which conventional treatments have failed.

I should say here that the collection of evidence is ongoing, and psilocybin doesn’t help everyone. Its risks are real, and tightly controlled trials shouldn’t be confused with unsupervised use1, but the results are significant enough to demand serious attention.

What’s most fascinating about the positive results of these therapies isn’t whether psilocybin and psilocin can help, but how they help.

The research suggests psilocybin temporarily alters the brain’s usual patterns of communication. Networks that ordinarily operate within relatively stable boundaries become less segregated. Established hierarchies of activity loosen, and some measurements show an increase in the diversity or unpredictability of brain signals—a rise in what researchers call brain entropy.

One influential framework known as REBUS (“relaxed beliefs under psychedelics”) proposes that psychedelics reduce the authority of our high-level assumptions. Related research has examined changes to the default mode network, a collection of interconnected brain regions associated with self-referential thought, autobiographical reflection, and our internally narrated sense of self. It’d be too tidy to simply call this Freud’s ego, but there’s enough conceptual overlap to make the comparison tempting.

Another way of looking at this is through the lens of what Aldous Huxley described as the mind’s “reducing valve.” Huxley’s phrase was philosophical rather than neuroscientific, but the metaphor is useful: the brain filters an overwhelming supply of stimuli, admitting only what it deems necessary for our evaluatory consciousness to, well, evaluate.

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As a result, we’re constantly using prior experience to predict what we’re encountering. This is normally efficient. Without these filters, every moment would overwhelm us with waves of sensory input.

However, the same machinery that makes our experiences manageable can also make them inescapable. When our highest-level beliefs become too rigid—I am worthless; the world is hostile; nothing I do matters—they shape the interpretation of everything beneath them. Contradictory evidence can be filtered out before it has a chance to register.

In other words, Huxley’s reducing valve has become hyperactive, minimizing any evidence that might challenge the narratives in which we’ve become trapped.

Psilocybin, by altering communication among areas of the brain that ordinarily operate within more stable boundaries, may temporarily loosen those beliefs. To borrow Huxley’s metaphor, it opens the reducing valve, allowing information that might ordinarily be suppressed to rise into awareness. Familiar mental paths lose some of their gravitational pull. The mind becomes capable, at least for a time, of occupying states that its existing organization had made difficult to reach.

I should clarify here that the treatment doesn’t replace one belief with another; it weakens the certainty that the present arrangement is the only possible one.

In other words, it introduces entropy.

Disorder Is Not the Cure

It’d be tempting to stop there and conclude that disorder itself is healing.

But, for as much as entropy creates possibilities, it does not choose among them.

A psychedelic experience can be beautiful, frightening, clarifying, confusing, or all of these at once. A person may encounter an insight without knowing how to incorporate it into ordinary life. A mind can become more flexible without automatically reorganizing itself into a healthier form.

This is why integration matters.2

The experience may interrupt an old pattern, but interruption is not transformation. The moment of revelation must eventually become a conversation, a boundary, a habit, a changed environment, a repaired relationship,3 or a different choice. Otherwise, the old system remains waiting. Once the disturbance passes, it can pull everything back into its familiar arrangement.

The same is true of nearly every disruption we experience.

A crisis can reveal that our way of living is unsustainable. A loss can expose what we had mistaken for permanence. A period of burnout can make it impossible to continue honoring commitments that have long since stopped honoring us.

But breakdown is not inherently a breakthrough.

It would be cruel to insist that suffering is valuable simply because something can sometimes be learned from it. Many forms of disorder are only destructive. A collapsing bridge doesn’t become wiser. A person in crisis doesn’t owe the world a redemptive interpretation of what happened to them.

Still, when disruption has already occurred, restoring the old order is not always the healthiest possible objective. Sometimes the old order helped produce the crisis.

The work, then, isn’t to celebrate the collapse. It’s to notice what the collapse made visible—and to resist rebuilding the very structure that could no longer hold.

The Organizations That Cannot Change

Organizations have their own methods of reducing uncertainty.

They create policies, processes, hierarchies, job descriptions, strategic plans, performance measures, approval chains, and governance structures. The existence of these policies and procedures does not make an organization inherently dysfunctional. On the contrary, much of this work is simply how groups of people coordinate work at a scale that goodwill and improvisation alone cannot support.

The trouble begins, however, when maintaining the structure becomes more important than attending to reality.

A process that once solved a problem becomes the way things are done. A metric becomes a substitute for the outcome it was meant to represent. A reporting structure preserves the authority of people whose knowledge is increasingly distant from the work.4 Employees learn which truths the organization can comfortably absorb and which ones must be translated into more acceptable language.

Despite this decay, the organization may remain extremely orderly. Meetings happen. Reports circulate. Dashboards—so many dashboards!—stay green. Every required box is checked.

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None of this means the system remains capable of responding to the world outside it, though. This is why organizations need some amount of entropy; they need places where existing assumptions can weaken and new possibilities can enter.

A pilot program introduces entropy. So does a dissenting employee, a cross-functional team, a prototype, a reorganization, an unexpected customer behavior, or a technology that makes yesterday’s process newly absurd. Each disturbs the established arrangement. Each creates information the existing system may not know how to accommodate.

Even workplace resistance can perform this function.5

When leaders introduce automation or artificial intelligence, they often regard employee discomfort as a communications problem, and they’re not always wrong about this. Sometimes their communication is genuinely shoddy, and this is a problem.

It’s too easy, though, to treat pushback as a communications problem. When it’s assumed that restlessness is a result of failed or insufficient comms, the solution is another presentation about the future, another assurance that the work will become more efficient, another reminder that adaptation is inevitable.

But resistance is information.

It may reveal that employees are defending not merely a collection of tasks, but a craft through which they have built competence, identity, community, and pride. It may expose risks the people closest to the work can see more clearly than the people commissioning its transformation. It may tell us that the organization has mistaken faster production for better work, or reduced human judgment to an inefficiency waiting to be engineered away.

That does not mean resistance should always prevail. It means a healthy organization permits resistance to disturb its assumptions.

Leaders who are too eager to restore order may eliminate the very feedback that could help the system adapt intelligently.

The Edge of Chaos

A system with too little order can’t coordinate itself. A system with too much order can’t change. This tension appears everywhere.

A creative practice needs discipline, but discipline without experimentation produces repetition. A relationship needs stability, but stability without honesty becomes performance. A career needs continuity, but continuity without reconsideration becomes inertia. A self needs coherence, but coherence without flexibility becomes confinement.

The goal isn’t chaos. It’s a system capable of approaching disorder without being destroyed by it—a system able to loosen, learn, and organize itself again. Some might see this as a definition of resilience.

We often describe resilience as the ability to return to normal. The resilient person gets back on their feet. The resilient organization resumes operations. The resilient community rebuilds what was damaged.

But returning to normal is not always enough. Normal may have been brittle. Normal may have distributed suffering invisibly. Normal may have depended on assumptions the disruption has now disproved.

With that in mind, perhaps resilience is better understood as the capacity to establish a new order from what disorder reveals. That requires both openness and discipline. We have to permit just enough uncertainty for new information to reach us, then do the harder work of deciding what to build from it.

Entropy without integration leaves us scattered. Order without entropy leaves us stuck.

Growth requires both.

Building Again

We spend much of our lives trying to get everything into place.

We establish routines. We find explanations. We decide who we are and what the world is like. We build organizations around what’s worked before, and every successful adaptation becomes part of the architecture through which we meet the future.

This is necessary. We can’t live in a state of permanent reconsideration.

That said, no arrangement remains adequate forever.

The identity that once protected us may eventually constrain us. The process that enabled growth may later prevent it, and the belief that helped us survive one season may keep us from recognizing that the season has changed.

Something must occasionally disturb the system.

Sometimes we get to choose that disturbance: a new experience, a difficult conversation, an experiment, or a deliberate departure from routine. Sometimes it arrives without permission: illness, loss, technological change, institutional failure, or the sudden realization that a life we have successfully maintained is not one we still wish to inhabit.

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We don’t have to romanticize these disruptions to learn from them.

We can instead ask what possibilities have become visible now that the old arrangement has loosened. What information can reach us now that certainty has weakened? Which beliefs deserve to be rebuilt, and which should be allowed to remain in ruins?

The lesson of entropy, then, isn’t that everything falls apart. We already knew that.

The more hopeful lesson is that falling apart is not the only thing systems do. Given energy, openness, and care, they also reorganize. They discover configurations that were previously unavailable to them. They produce new forms of order—not by preserving themselves unchanged, but by responding to what change has taught them.

In this way, wisdom may lie not in choosing order over entropy, but in learning when to preserve a structure, when to let it loosen, and how to build again from what disorder reveals.

Join Us in the Comments

• Where in your life have you mistaken a natural cycle for a problem that needed to be solved?

• What helps you recognize when it’s time to restore order—and when it’s better to let things fall apart?

• Do you find the idea of entropy reassuring, unsettling, or both? Why?

Earlier this year, an engineering leader and colleague of mine posted something online that immediately raised my hackles.

The subject? Agentic AI. The specifics? AI workflows that carry out the full software development lifecycle without any human involvement.

Developing this capability, he argued, was “non-negotiable.”

Now, before we get into what troubled me so much about this comment (if it isn’t already obvious): I use AI every day as a software engineer. With that experience, I can say firsthand that these tools can eliminate—or at least accelerate—some of the drudgery that accompanies knowledge work.

So my angst at reading this post wasn’t the result of an ill-informed, general AI-aversion; it was what non-negotiable seemed to imply: that removing human labor from the software-development lifecycle was not merely something we should explore, but a pre-ordained outcome.

When I pointed out this potential interpretation1—and that it might be wise to avoid posting as such in public channels—it touched off what I can only categorize a minor theological dispute with the responses coming quickly and from a number of directions.

“This is inevitable.”

“This is progress. This is capitalism.”

Eventually, it became clear that the devout and the apostates weren’t going to resolve anything by pinging one another in Slack, which led to the original poster and I having a direct conversation.

Despite this not-so-subtle admission that we need humans in the loop to hash things out (irony machine go brr), the conversation quickly spiraled, instead centering largely around how this engineering leader should be allowed to express their opinion at work.

This was a peculiar defense, to be sure: I never suggested opinions were the problem. Instead, I’d asked for caution in the contents and context of what we post, particularly at a time when professional uncertainty and insecurity run amok. What’s more, the real crux of the issue seemed to be that I had expressed my opinion in response to theirs.

So it wasn’t that I challenged their right to believe something. It was that a leader’s beliefs about the future somehow became exempt from scrutiny once that future was declared inevitable.

And here, our keyword once more: inevitability.

The Gospel

I get the allure of declaring something inevitable. If something is inevitable, there’s no need to decide whether it’s good. No one has to ask who benefits from it. No one has to ask who loses.

Oh! And it means we don’t have to consider alternatives, build consensus, earn trust, or persuade skeptical colleagues (heretics!) that the change we want to make is one worth making. Inevitability also shields everyone from taking responsibility for the consequences. After all, what choice did we have?

I’ve come to frame this as the Gospel of Inevitability: the belief that because a technological, economic, or organizational change is likely to happen, we’re absolved of responsibility for deciding whether, how, and to what extent we participate in its manifestation.

How can we identify the Gospel of Inevitability? For one, proponents of it tend to speak in the passive voice (or something perilously similar to it).

This is where the industry is going. Jobs are going to change. This is just what we do now.

What they’ve conveniently set aside in leaning into this linguistic jiu-jitsu is that industries, on their own, do not move. Jobs do not change themselves. “This” does not become what we do through immaculate conception. People make these choices.

People allocate budgets. People set incentives. People define strategy, decide what to automate and what to preserve. People determine whether a new technology augments skilled workers or replaces them. People decide what kind of work is valuable, what kind of experience employees ought to have, and which costs are acceptable in pursuit of a particular vision of progress.

Inevitability as an Argument-Killer

In the end, it’s not that conversations featuring differing viewpoints should be avoided or seen as frustrating by their very nature. People are going to disagree. Making cogent arguments for what we believe in—how something should be built, who should own it, which features should be considered in or out of scope—is an everyday part of the job.

I’ll take this one step further and suggest healthy organizations require disagreement. It forces us to articulate what we believe, test our assumptions, confront evidence that makes us uncomfortable, and occasionally experience the character-building indignity of discovering someone else had a better idea.

This particular disagreement played out in a bothersome fashion, though, because the Gospel of Inevitability was deployed in place of any attempt to intellectually engage with the argument. There was no argument to be had; “inevitability” was invoked to see to that.

If full-scale AI-driven automation is inevitable, there’s no need to discuss whether it’s actually desirable. If it’s simply “progress,” there’s no need to define what progress means. If it’s simply “capitalism,” there’s no reason to talk about leadership, culture, employee trust, customer outcomes, professional craft, organizational mission, or human flourishing.

I’ve seen this play out far too often this year. At times, it’s produced a genuine cultural schism in which anything short of wholehearted enthusiasm is treated as resistance to progress itself.

And, as I’ve stated, when nuance becomes apostasy and dissent becomes heresy, consensus-building is no longer necessary. Why persuade when you can proclaim? Why cultivate buy-in when you can mandate? Why explain how a technology serves your organization’s mission when you can simply announce that this is what we do now?

That is how the Gospel of Inevitability becomes a self-fulfilling prophecy; a particular vision of AI adoption begins to look inevitable once enough people with authority agree to treat it that way. Its acceleration, as a result, becomes evidence for the inevitability they asserted in the first place.

Is this tautology? 💁‍♂️🦋

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A Quote Finds Me

This conversation and its aftermath stuck with me for months. There was something about the inevitability aspect of the argument I couldn’t shake, but I couldn’t quite discern why.

Then I encountered the below passage while reading Attensity! A Manifestation of the Attention Liberation Movement, quoting sociologist Barrington Moore Jr.:

“People are evidently inclined to grant legitimacy to anything that is or seems inevitable no matter how painful it may be. Otherwise, the pain might be intolerable. The conquest of this sense of inevitability is essential to the development of politically effective moral outrage.”

There was more to the passage, but those lines spoke to the anger I’d felt for months. I wasn’t angry about AI in general, but rather something much older and, in my view, more sinister: the transformation of human choices into supposedly immutable laws of nature.

Inevitability does not merely engender surrender. It legitimizes it. If the future can’t be changed, resistance is irrational. And if resistance is irrational, then those who drive the change no longer need to defend their choices.

The uncomfortable thing about rejecting inevitability, meanwhile, is that responsibility rushes back in to fill the vacuum. If our choices matter, we own them. If another path is possible, we become accountable for the path we take.

If automation is not a force of nature but a set of decisions made by people, then we have to ask whether those decisions serve the people and institutions for whom we are responsible. That is a much heavier burden than inevitability.

This is, I suspect, why some people are so eager to put it down.

The Work of the Heretic

The Gospel of Inevitability asks us to surrender our judgment, which is why resisting it requires more than reflexive opposition. We need a way to decide which changes are worth pursuing and what form they ought to take.

This is where I think organizations most desperately need to rediscover something profoundly unfashionable: purpose.

What are we here to do? Whom are we here to serve? What problem have we been entrusted to solve?

Long-time readers will be unsurprised to see me coming back to the idea of purpose. I’ve written about it plenty before, about how mission statements—good ones, anyway—give us a lens through which to evaluate nearly any pursuit in an organization, and that includes technology.

If AI helps us better serve our customers, colleagues, communities, or organizational mandate, fantastic. Go ahead and use it. If it helps people do better work, learn faster, make wiser decisions, reduce drudgery, expand access, or create something previously impossible, pursue it!

But if adopting a technology becomes the mission itself, we have confused means with ends, and when leaders chase a technological paradigm without continually connecting it back to the actual purpose of their organization, they are not demonstrating vision. They are abdicating it.

Leadership is not pointing toward the loudest trend on the horizon and shouting “Forward!” It’s deciding where we ought to go and bringing people with us.

That requires conversation. It requires persuasion. It requires enough humility to entertain the possibility that the person raising their hand in the back of the room might not be standing in the way of progress. They might be trying to prevent you from walking off a cliff.

This is why somebody has to keep raising their hand. That, I think, is the role of the heretic.

Heretics ask who decided this? Why? Who benefits? Who carries the risk?

What are we trying to accomplish? How does this serve our mission? How does it serve our customers? How does it serve our colleagues?

What becomes possible because of this change—and what might become impossible? What human judgment are we preserving?

What would we choose if we refused to begin from the assumption that the choice had already been made?

None of this requires rejecting technological change. It requires something harder: participating in change without surrendering our judgment to it.

Knowledge, Power, Peace

At the beginning of August, during a period of acute uncertainty and anxiety, I did a druidic drawing of lots using Coelbren. For this reading, I opted to draw three: one for knowledge, one for power, and the last for peace.

The first of these, knowledge, delivered a message I interpreted as You have the strength to finish the job. My second drawing conveyed that There’s power to be found in uncertainty and imbalance.

And the third, the one for peace? Learn to accept what you can and cannot control.

Needless to say, I’ve carried these ideas with me ever since—particularly that last one. Why? Because accepting what we can’t control is not the same as accepting everything that happens to us.

True, there are institutions I can’t control. I can’t control the people around me, nor can I control their opinions. There are technologies whose development I can’t stop, economic incentives I can’t redirect, and decisions others will make regardless of how passionately I argue against them.

Peace requires acknowledging this, but agency requires something else. We must recognize what remains ours: our judgment, our voices, our work, our values. We must maintain our willingness to say no, to say yes, but not like this. We must hold fast to our willingness to build something different.

There’s no contradiction in accepting that the world will change while insisting we retain a role in deciding what that change should mean. In fact, I’ll argue the latter is our responsibility.

After all, inevitability is rarely as inevitable as its evangelists would have us believe. The future does not simply arrive; people build it.

And if people are going to build it, then the rest of us retain every right—perhaps even an obligation—to probe what they are building, to ask whom it serves, what it costs, and whether there might be a better way.

The Gospel of Inevitability asks us to surrender that responsibility, and I, for one, refuse.

Consider me a heretic.

In the world of passionate craftspeople and deliberate mastery, the allure of ambition often overshadows the necessity of strategy. Far too many people think (and I’ll admit to being one of them in the past): If I just work hard enough—if I pour every ounce of passion into my craft—success will follow.

But as my painful experience suggests (and as Cal Newport warns in So Good They Can't Ignore You and Richard P. Rumelt emphasizes in Good Strategy/Bad Strategy), effort without focus is not enough. To thrive, we need more than passion and practice—we need strategy.

In this post, we'll examine how Rumelt’s insights in Good Strategy/Bad Strategy can guide us to align our passions with mastery, while making deliberate, effective choices in our careers as authors or engineers.

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Expected Takeaways

• Passion without strategy is just effort without direction. Diagnose challenges, set a guiding policy, and take coherent actions to ensure your work moves you forward.

• A good strategy isn’t just about working hard—it’s about working smart. Focus on high-leverage actions that align with your goals and amplify your impact.

• Avoid the traps of bad strategy. Fluff, ambition without focus, and ignoring reality can derail even the most passionate pursuits.

Diagnosis: Knowing What You're Up Against

A key tenet of Rumelt's kernel of good strategy is the importance of diagnosing the problem. Without a clear understanding of the challenges we face, even the most heartfelt efforts—like frantically writing that book you’re sure is the one that will get you noticed—risk being misdirected.

For those of us pursuing passion-driven mastery, this means asking:

• What are the real obstacles to progress in my craft or career?

• Are there industry norms, market trends, or personal skill gaps holding me back?

• Have I fallen into the trap of mistaking busyness for meaningful progress?

Action Step: Conduct a "self SWOT analysis"—assess your Strengths, Weaknesses, Opportunities, and Threats. Use this as a foundation to clarify where you stand currently and then focus your efforts on the most pressing challenges.

Guiding Policy: Crafting a Strategy that Fits

Rumelt's second component of a good strategy is a guiding policy—a clear, overarching approach to addressing the diagnosis. This isn't about setting generic goals like “write more” or “level up my coding skills.”

Those aren’t bad goals if you value only quantity or leaning into the abstract notion of what it means to “level up,” but neither of them move the needle in the direction of creating a framework for action that aligns with your circumstances and values.

For example, an aspiring author whose main challenge is staying consistent and building an audience might develop a policy like the below.

Focus on establishing a disciplined writing routine while sharing excerpts and engaging with readers through a personal blog.

This version of “write more” not only gives direction but also ensures that one’s creative and promotional efforts work in tandem.

Similarly, an early-to-mid-career software engineer looking to stand out in a competitive field might lay out the following guiding policy.

Specialize in a high-demand area like cloud infrastructure or artificial intelligence and machine learning (AI/ML) and contribute to open-source projects that showcase my expertise.

Bam! This framework focuses their energy on building both depth and visibility in their field.

Action Step: Write your guiding policy in one sentence. Let it act as your compass, ensuring every choice you make contributes to a coherent long-term vision.

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Coherent Actions: Aligning Effort with Purpose

The third component of a good strategy—coherent actions—ensures that your efforts align with the guiding policy and build toward your goal. These are the tangible steps you'll take—not in isolation, but as part of a cohesive plan.

For the author whose guiding policy is to establish a disciplined writing routine while engaging with readers through a personal blog, coherent actions could include:

• Set a daily writing target (e.g., 500 words or one hour of focused work).

• Schedule weekly blog posts, using them to share insights, character sketches, or reflections on the writing process.

• Engage with online writing communities by offering thoughtful critiques or participating in discussions to build connections and grow visibility.

For the software engineer aiming to specialize in cloud infrastructure or AI/ML and contribute to open-source projects, coherent actions might include:

• Take advanced courses or earn certifications in AWS or TensorFlow to deepen expertise in the chosen specialty.

• Dedicate a portion of your weekly schedule to contribute to open-source projects related to cloud or AI, showcasing one's skills to a broader audience.

• Present your work by writing technical blog posts or giving talks at meetups, demonstrating thought leadership in your area.

Notice how each action directly supports the guiding policy? They're not just a random collection of tasks—they work together to build momentum and reinforce progress. This is called leverage, and the more you can generate, the better.

Action Step: List three to five specific, repeatable actions that align with your guiding policy. Ensure they fit together like puzzle pieces to form a unified strategy.

Bad Strategy: Avoiding the Passion Pitfalls

While Rumelt outlines what makes a good strategy, he also, thankfully, highlights the dangers of bad strategy. Many of these pitfalls are similar to the passion hypothesis traps Newport critiques, some of which I detail in the Passionate Craftsman series:

• Fluff: Mistaking inspirational slogans (“Do what you love!”) for actionable strategy.

• Ambition Without Focus: Trying to do too much, too soon, with too little direction.

• Failure to Face Reality: Ignoring obstacles or skill gaps in favor of wishful thinking.

Action Step: Take a hard look at your current goals. Are they grounded in actionable plans, or are they lofty but vague aspirations (e.g. get a literary agent, or land a job at one of the MAANG companies)?

If it reads in the vein of either of the examples above, reframe your goals using Rumelt's kernel.

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Strategic Passion: Bridging the Gap Between Passion and Mastery

The craftsman mindset emphasizes deliberate practice, but even if you embrace that mindset, that on its own isn’t enough; it's like sailing without a map. Rumelt's strategy framework helps us direct our energy effectively, ensuring we don't just work hard but work smart.

By diagnosing challenges, creating a guiding policy, and executing coherent actions, you can forge a path that not only aligns with your passions but also maximizes the likelihood of sustainable success.

And as someone who knows a thing or two about unsustainable success, I really hope you take the importance of sustainability to heart.

To sum it all up, then, mastery isn't an accident—it's the result of consistent, strategic effort.

So let's not chase our dreams blindly. Let's build them—deliberately, strategically, and with an unwavering focus on what matters most.

Tell Me in the Comments

• What’s one passion-driven goal you’ve pursued that could have benefited from a clearer strategy?

• Have you ever mistaken busy-ness for meaningful progress? What helped you shift your approach?

Where we last left off, we’d survived the Triassic and Jurassic, including the rise of the dinosaurs, the initial splitting of the continents, and one or two site migrations.

Perhaps most harrowingly, we survived Blogger.

Science has truly given us so much.

Let’s continue our tour of our planet’s geologic history, then, including the lessons learned from oh-so-many prehistoric website migrations and domain name accumulations.

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Hold on to Your Butts: a Geologic History of Accumulation, Evolution, and Extinction (cont’d)

As in our last post, we’ll break our timeline up into eras, epochs, etc.

The Cretaceous

Why This Was Well-Intentioned

Check out the current price for a WordPress business plan and try to tell me I should have stayed with them.

Seriously, as of the date this post has gone live, a WordPress business plan is $25/month (that’s $300/year), and all it gets you (or all I really used it for) are some plugins and the ability to stand up an online store.

Those plugins and the online store are why I initially pursued the WordPress business plan; I needed a way to host and publish my podcast episodes to Apple Podcasts, and for a time I did sell signed copies of my books through my website.

But that $300/year covered only my author website.

When you account for the cost of that plan plus the premium plans I was using to stand up writescast.net and the sites specific to my books, I was dropping nearly $600/year on WordPress plans that didn’t even include a free annual domain name renewal.

So, yeah—in the same way the continents finally got serious about their split during the Cretaceous, I’m glad I, too, started to get serious about my split from WordPress.

The key word there being “started.”

Where I Beefed It

Did my moves during the Cretaceous save me some cash? Yes. Was I still leaving a bunch of money on the table each year? Yes.

How?

Because though I had consolidated writescast.net and my individual book and series sites, I was still forking over far too much (at least $300/year, remember) for the WordPress business plan that stood up my author website.

“Now, Ryan,” someone might have said if other humans had existed during the Cretaceous, “couldn’t you just downgrade your plan to either the premium or personal tier?”

And I could have, yes, if I wanted the Writescast Network to go dark and to have to redo my entire website.

The trick with the top-tier WordPress plan is that those plugins you pay for quickly balloon; no matter how much I might have diminished their value when I brought them up earlier, there’s a lot of power and functionality to be added to your site by taking advantage of them. Downgrading your plan shuts plugins down entirely.

That’d have meant no more Writescast Network on streaming platforms. No more online shop. No more Writing Prompt Generator or Google Analytics. You know, the good stuff!

None of this is to mention that in the absence of the Divi plugin by Elegant Themes, my author site would have looked like a pack of Anklyosaurus had taken their clubbed tails to it. A real smash-up job, and not in the good way.

Now, could I have pushed myself during the Cretaceous to do the work I would later do in the Paleocene and get off WordPress entirely? Yes, but if I was off doing that, who would have massaged the tired arms of those poor T-Rexes from all the waving they did as the continents split?

Lessons Learned

• Make incremental improvements where you can

• Keep an eye on accumulating costs over time

• Pride and elevated standards incur costs of their own

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The Paleocene

What Made the Paleocene Worth It

Despite the sarcasm in the recap paragraph above, the moves I made during this epoch get a rating of mostly worth it.

Remember, there was huge financial upside in pushing myself to get away from WordPress altogether. On top of those savings, by creating my own site from scratch and hosting it with anyone other than one of the cookie-cutter website services, I could use the new site as a solid portfolio piece for my work as a software engineer.

The pains taken to facilitate this (described in the next section) also made for good engineering challenges to take on, even if I would have preferred to pursue them without a tight, gotta-get-it-down-before-my-plan-renews deadline looming over me.

Where This Came Up Short

⚠️ Note: This section gets a little wonky, tech-wise.

If this makes you nervous, find the nearest consenting adult and ask them to hold your hand while you read.

The downside of getting away from WordPress once and for all was the amount of time I had to spend building my new website from scratch and the extraordinary pains that were required to keep historic blog posts online given how WordPress makes your content available to you when you export it.

It’s almost like they don’t want you to leave.

As an example, here’s what the full slate of my historic blog posts looks like when I exported them using the WordPress mass export tool.

Yes, it’s largely just a lot of HTML strewn about in there at content.rendered, but, if you’ll recall, I’d started using the Divi UI engine at some point to further customize my site. This introduced additional layers of what I’ll call gobbledygook into my posts, and that gobbledygook, unlike HTML, does not go gently into that good night and become JSX without a hitch.

The journey to render a historic blog post in the new experience (tell me in the comments if you want a separate post that covers the code itself) wound up looking like this—

• A reader hits a URL on ryanrcampbell.com that matches the /:year/:month/:day/:slug/amp? format.

• If the year in the URL matched the years I wrote on WordPress or earlier, a nearly 70,000 line object was combed over to find a match for the remaining URL parameters of month, day, and slug.

• Once a match was found, the information the reader actually wanted (content.rendered) had (most of) the Divi gobbledygook stripped from it, was parsed as JSX, and rendered in the browser.

Even then, this wasn’t perfect. A number of media assets just… wouldn’t show up in the new experience. It was all very strange, and with hundreds of blog posts to account for, this quickly became a mess in its own right.

But at least I wasn’t paying (as much) for it! Monetarily, anyway.

Even if frustrating at times, however, the algorithmic gymnastics required above made this move something to feel good about from an engineer’s perspective—just not a blogger’s.

Lessons Learned

• Even the best intended moves come with trade-offs

• Outcomes are rarely clean

• Find value where you can, when you can

The Pleistocene

The Positives of the Pleistocene

You know what they say: nothing keeps you warm during an ice age like starting—or, in my case, restarting—a blog.

Yeah, I don’t know why they say that, either.

Aside from cold, I’d gotten itchy during this epoch; I often found myself wondering what now or what next?

It was at this time that I felt I’d finally adjusted sufficiently to a new career in a new city, and I’d at long last returned to reading in my free time. With all of these new thoughts in my head and experiences under my belt, getting back into blogging felt only natural.

And, having put in that work during the Paleocene to create my own website from scratch, I had a place to do it (mostly) rent free! I wrote a blog post every month or so for a while, but then… that momentum fell off.

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Where The Momentum Went

⚠️ Note: The tech wonkiness returns in this section. You’ve been warned.

The eagle-eyed reader might have noted that my work in the previous epoch only supported historic blog posts. It didn’t address, in any capacity, how I might go about blogging in a sustainable way going forward.

Given the metaphorical Rube Goldberg machine I’d created to ensure historic blog posts were still available—and wanting to minimize my barrier to entry back into blogging—I leaned into a blogging “solution” that would let me use the infrastructure I already had in place without having to write entire posts in HTML at content.rendered.

If you’re curious to know how this all worked, well, it was a real cowboy job.

First, I created a new file, new-posts.js, that held all new post metadata. Then, in a neighboring set of directories, I would blog in JSX.

When a reader hit a URL ending in something like /2024/11/26/strategic-passion-a-playbook-for-aligning-passion-with-mastery, the year (2024), being greater than 2023, would have the site looking for a non-legacy post with a slug of strategic-passion-a-playbook-for-aligning-passion-with-mastery in a directory located at post-jsx/2024/11, as the URL indicated the post went live in November.

How much do you hate that? I can guarantee you don’t hate it as much as I did.

This was, again, a fine technical exercise, but it missed the point. Why would I lean into embracing a unique, yes, but more frustrating way of blogging when I simply… didn’t… have to?

Oh, and remember how I was trying to minimize my barrier to entry back into blogging?

Right. I’d missed the mark entirely.

Lessons Learned

• Future opportunities should be on equal footing with backwards compatibility

• Take advantage of existing infrastructure when you can (to a point)

The Holocene

What (I Think) Makes This Work

Ah, the Holocene. The current epoch, at last!

I have to say, I’ve really enjoyed Substack so far; by cleaving the blogging functionality away from my bespoke site and moving it here, I’ve unlocked a number of (wince) synergies I wasn’t taking advantage of previously or that simply weren’t available back in the Pleistocene or earlier epochs—

• Low barrier to entry for blogging

• Full integration between historic mailing list and blog posts

• Opportunities for compensation

• Engaged, platform-agnostic readership

First off, as had been the case since the last ice age, I wanted to get back into blogging, and here I am. Mission accomplished!

Or, well, hopefully. I don’t plan to unfurl a banner or anything. 😬

Even as the work continues, there’s a lot of potential to tap into on Substack. That it provides writers with a single-solution platform to reach their mailing lists and interact with their readers while finding new ones, well, that’s the stuff of dreams.

Where This Might Come Up Short

It’s still early days and I’m encouraged by how matters have played out so far, but I’m keeping a diligent watch out for a couple of potential problem areas that might arise as a result of my migration to Substack.

I do find it a bit odd that new blog posts will be decoupled from what has traditionally been my author website. As I go, though, I may make updates to ryanrcampbell.com that provide links to my latest posts while making it clear that visitors will be redirected to R: On Everything to read them.

Another concern is far more fundamental, namely that I’ll fail to embrace the “on everything” declaration I’ve made in this publication’s very name. I suspect it will be all too easy for me to return to blogging about topics that other authors find interesting, but, as I wrote about in my series on mission statements, it’s important I hold myself accountable to embracing the curiosity-centric mission of R: On Everything.

My aim is to use that curiosity to cultivate an audience that gathers here not for writing and publishing tips—not that there’s anything wrong with that—but instead finds itself intrigued by the topics and themes that will wind up in my works of fiction as they are published and, in some cases, re-published.

There’s some worry, too, that my return to online engagement will lead to social media burnout. I love writing and engaging with readers, but there’s a temptation to fall into a scroll hole whenever one adds a new social media platform to their digital diet, and even Substack’s algorithms appear to incentivize polemic content that isn’t great for trying to go about one’s day without their jaw clenched.

Into the Future

Only time—and, God forbid (or God willing?), another mass extinction event—will make it clear whether my latest migration to Substack is the one, to the extent a one can exist at all.

And with the bullet-point lessons dropped along the way here and in the previous post, there’s surely so much more to explore in this space. For example:

• What lessons might we distill from the experiences I’ve outlined over the two posts in this series so far?

• How might those lessons inform not only how we got about establishing and maintaining an online presence, but other matters in our personal and professional lives as well?

We’ll answer those questions in the third and final installment of this series, but to do that, I need your help.

Tell me in the comments: what lessons have you learned in platform juggling over the years? Or, if you’re relatively new to the notion of platform, how have you gone about making your decisions so far? Are you feeling positive, overall, with where that’s led you?

*These savings would have been valuable if the concept of paper currency had existed. Or the United States.

You may not know this about me, but I’m a collector.

From the flagship ryanrcampbell.com to the lesser-known (but fun!) realfakebirds.app, I’ve accumulated quite the cache of dot-coms, dot-apps, and dot-nets over the years, and oh-so-many of them have endured migrations that built them up, spun them off, or reconsolidated them over time.

The catch? I didn’t become a blog shepherd by design.

To tell the tale of how this collection came to be—and the harrowing migrations that led to its advent—we’ll have to go back in time. Way back in time.

As we work our way from the distant past and toward the present, we’ll expound on the lessons learned from these migrations, domain name accumulations, and brand extinction events known heretofore only to the fossilized remains beneath our feet.

Domain In the Membrane: a Geologic History of Accumulation, Evolution, and Extinction

Let’s begin, then, with a timeline separated into epochs and eras. A tour of eras, one could call it.

Oh, and note that the below only accounts for a subset of blog migrations and domain names acquired across millennia.

Yeah, I have a problem.

The Late Triassic

A Brief History of the Epoch

Highlights of the Time

After a great deal of deliberation and consternation, I realized the only way I was likely to fulfill my dream of becoming a published author was—what a revelation—to start writing.

In pursuit of this goal, I attended a seminar about how the publishing industry works, and one of my takeaways from it was that a solid manuscript alone would not be enough to catch the attention of a literary agent and sell one’s book to a publisher. Platform! You need a platform, they told me, so I built a platform.

After blogging into the abyss for some time, I at least had the wherewithal to get myself a domain name. I told myself this was to make my blog look more official—and it did!—but it had the secondary consequence of providing the makings of a brand around which content could be organized.

Much like how the dinosaurs didn’t know an asteroid would eventually wipe them from the face of the earth, I didn’t know at the time that establishing this brand—one that emphasized the craft of writing fiction—would improve SEO and provide the foundation for offshoots of my writing career down the road.

Sometimes, everything comes up Melanosaurus!

Where Evolution Went Wrong

I’ll say there were a few blog posts from this epoch that survived in some form or fashion—a couple would even become some of my most popular of all time after a great deal of re-work—but the content otherwise failed to connect with a significant audience. Why? Because it was mostly full of personal updates about word counts for manuscripts that would never see the light of day.

Thought I had attempted to embrace the conventional wisdom of “build a platform,” I had done so without ever asking who I wanted to reach and creating content that audience would find engaging.

The consequences of this misstep way back in the Late Triassic would echo throughout eras to delightful and devastating effect.

Lessons Learned

• Trust (some) conventional wisdom

• Build a brand

• Focus on content relevant to one’s audience

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The Early Jurassic

A Brief History of the Epoch

Why This Worked

Even homo habilis would have recognized moving from Blogger to WordPress as the right choice at the time. The problem? Homo habilis wouldn’t exist for a few hundred million more years, which left it to me to make this choice on my own.

By pivoting to WordPress—a platform that offered the chance to make my home on the web something more than a blog—I kept my options open for the future. Admittedly, I was at the time still years from seeing my goal of becoming a published author come to fruition, but migrating my content to WordPress before I truly needed to made it much easier to create unique hubs in a central location for my books, editing services, and other extensions of my platform.

Like Blogger, WordPress was free to get started with, and though I ended up paying for premium versions of the latter once I added the Writescast Network and an online shop to the WordPress site, the initial choice to move came with very little overhead and a lot of upside.

At this time, too, I really leaned into creating content that would resonate with other writers. By posting fewer personal updates—or at least by drowning them out with writing-related content informed by my relentless reading about the craft of writing fiction—I was able to find and engage with a community of similarly dedicated writers.

In sum, unlike the choice our single-celled ancestors made to squirm out of the primordial soup, this migration remains a good move to this day.

Lessons Learned

• Keep options open

• Build for the future

The Middle Jurassic

A Brief History of the Epoch

Highlights

In the same way evolving hollow bones did wonders for the avian dinosaurs and their descendants, landing a few publishing contracts in this epoch did wonders for me and my career. It would be poor form to suggest, however, that blog migrations and amassing domain names on their own put me in a position to sign on the dotted line, so to speak.

None of which is to undercut the value of years of dedication to blogging regularly, engaging with a community, and focusing on my work. If I hadn’t been putting in that time, I surely wouldn’t or couldn’t have learned all the lessons I’d need to learn—or met the people I’d need to meet—to position myself and my manuscripts for the attention and trust of industry professionals.

Spinning off the Writescast Network to its own home online was also the right move for the time; by establishing it as an entity in its own right—rather than as an extension of me, the host, back when it was the R.R. Campbell Writescast—I further embraced the vision I had for it as a community-focused enterprise.

Lowlights

Goodness, did lowlights abound during the Middle Jurassic. By creating unique websites for all of my titles, I intended to create content that would resonate with the diverse audiences those books aimed to capture.

The spirit of that idea was righteous. In fact, one could argue this was the correct approach and that it was the timing of this pursuit that led me astray. In other words, by blogging about the topics, themes, and research that guided my manuscripts’ paths only after they were slated for publication, I’d left myself with too little runway to cultivate an audience for the novels via blog.

Had my main blog instead targeted readers specifically interested in the genres of my books instead of other writers with an interest in sharpening their craft, I’d have had no need to maintain multiple websites and assume all of the overhead that comes with keeping them online.

Lessons Learned

• Unlock brand synergy wherever possible

• Avoid liquidating your brand by doing a little of this here, a little of that there

• Know your target audience; engage them early

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The Late Jurassic

Why This Was Wise

Just as Pangaea was cleaving itself in twain, I was bringing all of me back together. By pivoting away from a pen name and embracing myself more fully—even if only in a seemingly superficial, digital capacity—I created a permission structure to be all of me.

If ever there were a healthy way to take on a crisis of identity, this was it, or at least it felt like it at the time.

Separation had been one of the issues I faced in the Middle Jurassic, if you recall: I had attempted to keep separate the parts of me that wrote for disparate audiences, and all I received in return for my dedication to separation were ballooning bills for an increasing collection of domain names.

One might argue I was falling into the trap of bygone epochs by creating a website for Cedarbrook Books, but remember this site simply redirects to my primary hub online. It’s not domain- or cybersquatting, per se, but rather a protectionist, strategic choice.

Where This Came Up Short

It was—is—contrarian of me to pursue this path. One of the lessons learned from the Late Triassic was to trust the conventional wisdom, but there I was, rejecting the notion of emphasizing highly targeted content for a highly targeted audience.

Or so it seemed on the surface.

I wouldn’t recognize this until years later, but at the time, I was laying the foundational philosophy that would lead me to R: On Everything, which asserts I can’t be the only person whose interests are far more diverse than any single-topic blog could ever aspire to be.

In other words, I’m not targeting everyone, I’m targeting—without liquidating my brand—everyone who’s up for a little bit of this and a little bit of that.

Polymaths, essentially.

Lessons Learned

• Be yourself; no one will do it for you

• A separation of concerns can work if done strategically

A Stop Along the Way

This is not the end for our timeline, nor for our lessons learned. We still have four epochs to traverse, in fact, though we’ll save them for another time, along with a final analysis of all the lessons learned since our little planet came to be.

Have you ever migrated content from one place to another? What lessons did you learn before, during, or after undertaking this effort for yourself? Be sure to tell me in the comments.

And don’t forget to subscribe to be updated when the second installment in this series becomes available.

Key Takeaways

• Partnership in Writing Takes Many Forms. A conversation partner—digital, physical, or otherwise—can help refine creative ideas, clarify story elements, and push past writer’s block.

• Ask for Engagement, Not Answers. Relying on a digital duck as an answer generator alone undermines opportunities for enriched world-building and inhibits unexpected creative insights.

• A Duck is Still a Duck. Digital ducks are useful for brainstorming and problem-solving, but they can’t replace your unique voice or creative vision.

Planking. The Ice Bucket Challenge. Yanny versus Laurel. The Grimace shake.

Yeah, a lot has happened since 2011, which is also known as the year I first started writing what would become the EMPATHY sci-fi saga. As I’ve waded deeper into rewrites of that series, the time that’s passed—and the change that’s come with it—have been top of mind.

The way I’ve gone about my work as an author, for example, has changed tremendously. No longer do I track my daily word count. No longer do I beat myself up for missing a morning of minding my manuscripts. I’ve swapped in Scrivener for Word and have given side hustles the bird, and I’m certainly no longer maintaining massive spreadsheets of agents to query.

But the biggest change has to be the duck.

The talking duck. The talking duck who lives in my laptop.

This may sound unusual, but I’ve not lost my mind (yet), I promise.

Questions for Readers

Consider the following as you read, and be sure to join the conversation in the comments!

• Have you ever experienced a creative breakthrough by talking through a problem—either with a person or an object? What was that moment like?

• How do you balance efficiency and originality when engaging third parties in your creative process? Are there specific boundaries you set for yourself?

• In what ways could a back-and-forth dialogue help uncover blind spots in your storytelling?

Rubber Ducking: The Original “One Weird Trick”

Blockers are, definitionally, a pox on progress. Whether in the form of a dry well of ideas, the inability to recall a specific word, or a plot knot that isn’t going to untangle itself, these frustrating moments of how, exactly, am I going to move forward can upend advancement before words even hit the page.

The same is true in software engineering, where mischievous bugs and descents into callback hell can quickly mar even the most well-planned hands-on-keys code jams.

Fortunately, software engineers everywhere (or at least those who have been around for a minute) have long since had a strategy for combatting these moments of molasses.

Enter—the rubber duck.

Yes, the yellow floating bath time toys of yore figure into engineering outfits everywhere, crouching on desks in anticipation one-way conversation.

The theory is that, when plagued by programming paralysis, one can pick up the duck and describe one’s challenge out loud. Doing so tends to force clarity, stir step-by-step thinking, and reduce mental blocks by shifting one’s perspective and jogging the brain into recognizing previously unforeseen solutions.

It also encourages you to stop taking whatever you’re working on so seriously, which I find particularly beneficial.

Overall, this strategy works surprisingly well, but for as useful as it’s been, historically, the duck has never been able to talk back—until now.

Quacking Up: Taking Rubber Ducking Up a Notch

The digital duck who lives in my laptop is truly a wonder. He’s particularly adept at offering starting places for research, helping me recall words that are on the tip of my tongue, and identifying why a particular turn of phrase reads so oddly, even if it made sense when I first wrote it out.

“Now, Ryan,” one might say, “if the digital duck is such a savant with respect to words, couldn’t you simply ask it to rewrite your book for you?”

I could, yes—and some people use their digital ducks to do just that—but the thing about my digital duck is that he’s got a certain style when it comes to wordsmithing. It’s almost as if he consumed the whole of the internet and was trained to write in a style that I’ll call lowest common denominator; by design, it’s meant to be approachable, but it’s at times clunky and somehow, at others, overwrought.

It’s also, you know, not my voice, and my stories are certainly not the digital duck’s to tell.

Ducking and Weaving Through Rewrites

I recently engaged my digital duck when working on the rewrite for EMPATHY: Imminent Dawn. In this particular instance, I realized I had to backtrack to a chapter I had already rewritten once over1 after noticing I had left opportunities for verisimilitude on the table.

By this, I mean one of my goals in rewriting this series is to conjure a world that feels more lived in. Not only that, but it needs to be darker: more grim, more gritty, more grievous.

For example, in the first point-of-view chapter for Imminent Dawn’s intrepid journalist character Meredith, I stitched in the following detail in the context of Chief Executive Gleason’s Civilians In Arms program, which provided willing citizens with government-funded firearms to ostensibly fend off a land invasion of the west coast.

Never mind [the Civilians In Arms program] birthed countless militias, including the one that carried out the massacre in question. Never mind it led to vulnerable communities being overrun—effectively ruled—by armed neighborhood gangs who demanded protection money to keep real or perceived threats at bay.

Aha! New details have emerged in this draft of the second edition, namely those in the second sentence: that some communities in the North American Union are now under the yoke of unaccountable neighborhood crime syndicates.

Taken on its own, this is a fine detail to include, but, I realized, one of Meredith’s defining pain points is that she herself is not particularly well-to-do. Might she live in a neighborhood that’s been overrun by gun-toting, jackbooted, military wannabes?

I didn’t make this connection for myself until after I’d rewritten the first chapter in which we see Meredith returning home from work, and readers of the series might recall that she previously lived in a fairly nondescript suburb of ranch homes on the outskirts of Austin, Texas. Well, no longer!

Calling for the Duck

Given Meredith’s financial situation—and given other macroeconomic and social trends in the world in which she lives—I ultimately concluded she wouldn’t live in a neighborhood run by gangs, but rather one that has taken some level of precautionary measures to make would-be neighborhood domineers think twice before striding into town.

This required a significant re-imagining. What might her neighborhood look like as she approached by car? Who might she encounter? What would the mood be just on the outside of—and especially within—the perimeter of her community?

Inspired by something approximating the living situation of the main character in The Parable of the Sower, I could see it all vividly in my head: a patchwork fence, scattered debris, abandoned vehicles.

I could also see, uh, metal. Like the kind that, dang it, you’d see in a shanty town. Not that she lives in a shanty town, but more like, you know, it’s the wavy kind of metal that…

Yup, I got tongue-tied when it came to this detail, and it was really bumming me out. I felt like I could see precisely what I intended to write, but without the right word, I felt as though I couldn’t move forward.

Fortunately, I had a digital duck to discuss this with.

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The Duck Quacks

In a pre- or mid-apocalyptic world, I began, in a neighborhood that has been built up to offer its residents makeshift protection in the form of a fence line, what might one see the fence line made out of? Feel free to ask five clarifying questions before you respond.

One may notice something interesting here. I didn’t come right out and ask, “Hey, duck, what’s that metal that you find laying around construction sites? It’s kind of wavy, maybe made of aluminum or tin. You see it used as roofing material in areas of fewer resources. You know what I mean, right?”

That would have been a fine approach, but my goal was to open up a dialogue here, not nab a single detail and hit the road. By zooming out a bit—and by prompting my digital duck to feel free to ask clarifying questions—my thought was I might uncover additional opportunities to broaden and enrich Meredith’s world.

The digital duck, ever dutiful, subsequently took me up on the opportunity to ask those five clarifying questions.

• What level of apocalypse are we dealing with? (E.g., slow societal collapse, zombie outbreak, nuclear event, resource scarcity, etc.)

• What kind of neighborhood is it? (Urban, suburban, rural? Wealthy, working-class, or impoverished?)

• How much time have the residents had to build the fence? (Days, weeks, months, or years?)

• Do the residents have access to construction equipment or skilled laborers, or are they relying on scavenged materials and basic tools?

• Are they more concerned with keeping threats out or signaling that the area is occupied and defended?

Now these really got me going. By forcing me to slow down and examine my priors, as they say, I realized there were a number of other opportunities I’d overlooked to flesh out the scene as I’d previously imagined it in my head.

For example, the mention of “skilled laborers” in one of its prompts immediately brought to mind a guard at a gate of sorts, possibly a retired contractor who would take it upon himself to offer protection—or at least the appearance of it—to his neighbors by manning the gate after having also played some role in its construction.

That my digital duck had the savvy to probe whether threats to the neighborhood were active or anticipated also encouraged me; this mirrored the very question I’d confronted when embracing the notion of Meredith’s neighborhood as one that was determined to stave off criminality rather than one actively under criminal control.

With these questions fresh in my mind, I returned to the page and typed out a few more sentences, including an introduction of this neighborhood watchman.

But I still couldn’t remember what the heck that thin, wavy metal was called.

So, again, rather than just ask the duck outright, I responded to each of its questions to see if doing so would help me uncover further details, as well as to see what it might have to offer by way of reply.

Its response, I have to say, was fantastic, because—there, bam!—in the second of eight suggestions for what the patchwork fence might include were the words I’d been looking for all along: sheet metal.

Good. Great. A win, right? Yes, but there was more value to be had. My digital duck now had an understanding of what I was trying to achieve, and, recently unblocked, I set out to draft Meredith’s approach of and entry into her neighborhood in full.

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Some fifteen minutes later, Meredith approached the patchwork fence line of chain-link, sheet metal, and plywood that surrounded her neighborhood. A volunteer group had built it out of scrap from abandoned construction sites or upcycled material from their own DIY projects in recent years, and though it didn’t make her neighborhood particularly welcoming from the outside, it wasn’t meant to be—assuming its browned lawns and warped siding could have ever achieved welcoming in the first place.

Antonio, a retired contractor who minded the gate most work days, tipped his frayed ball cap as she slowed. “Afternoon,” he said. Woodward, his head halfway out the back window already, leaned into the scratches Antonio gave him behind the ears.

“You keeping hydrated out here?” Meredith said. “Can never be too careful on days like these.”

He nodded over his shoulder. Under an umbrella squatted a duct-tape-sturdied plastic table, on top of which rested two empty bottles of beer. In his free hand, he shook what remained of a third.

“Retirement seems like a blessing,” Meredith said.

Antonio laughed. “La vida es bendición.”

“Así la es,” Meredith replied. “Though some days more than others.”

“If you say so, vecina.” Antonio stepped back and slid the gate open. “Pasa ya before I get any ideas about taking that dog of yours home with me.”

Meredith hurled him a half-salute and inched forward through the gate, thanking him before she rolled up her window.

An older neighborhood, Meredith always loved twisting her way through its winding streets of ranch homes, even if the last ten years had seen dusty, abandoned vehicles begin to pop up along the curb. It was the neighbors that made a neighborhood, in the end, and all things considered, it wasn’t a terrible place to live—rolling brownouts and all.

Now, is this perfect? No, but first passes rarely are. Does it introduce a number of additional details that bring new life to the world in ways that were absent from the first edition of Imminent Dawn? Absolutely.

The introduction of this new character and the fence in general will also have significant spillover effects for my rewrite of Meredith’s arc. I now have to address questions related to how, exactly, someone manages to sneak in and drop mysterious envelopes at her front door. I have to determine how government thugs, other reporters, and friends alike manage to visit Meredith at home given the security measures now in place.

These are challenges I’m excited to take on, and I have a combination of my own guile and the prompting of my digital duck to thank.

Whose Duck Is It, Anyway?

So, I have a digital duck, yes, and I wish I’d had one going all the way back to 2011. And though it has, in a sense, largely just replaced2 my use of Google, its ability to deliver quickly and with more detail than competing tools—and that I can engage it conversationally rather than by keyword—have made it an invaluable copilot in my writerly suite of tools.

But here’s the thing: there’s nothing particularly special about my digital duck. In fact, you have one, too—or at least you have access to one.

This is because, if you haven’t figured it out already, my digital duck comes in the form of OpenAI’s ChatGPT3, one of the many digital ducks now just awaiting your engagement should you so choose.

How you use your digital duck is also entirely up to you, but, as we’ve covered in a number of other posts4, digital ducks aren’t perfect. It’s worthwhile to fact check them when using them for research, particularly if you’re using them to source information about fields and topics with which you have little familiarity. And, as I mentioned earlier, if you do plan to use them as a tool that does the actual writing—which I personally don’t recommend—you’ll likely want to be mindful of the fact that sometimes the writing is, at least as I see it, not particularly good.

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Digital Duck Ponds

If you’ve not employed a digital duck yourself, you may be wondering where, exactly, you can find one. I have personally gravitated toward ChatGPT, but Anthropic’s Claude5 is a favorite amongst many others as well.

Other models have come into the fray recently, too—including DeepSeek, which I won’t link to here—and more are popping up every day, but the investment in the models I’ve named thus far makes them likely contenders to hang around for a while.

Feed the Duck and Take Flight

Whether one chooses to chat with a digital duck once a month, once a week, or once a day6, for now I’m of the belief that there are advantages to be had in engaging them. So long as one does so with their eyes wide open about what they are and, more importantly, what they’re not, digital ducks can quickly become a crucial tool in one’s workflow—for now, at least.

Because who knows where we’ll be in another fifteen years?7

Tell Me in the Comments

• Have you ever experienced a creative breakthrough by talking through a problem—either with a person or an object? What was that moment like?

• How do you balance efficiency and originality when engaging third parties in your creative process? Are there specific boundaries you set for yourself?

• In what ways could a back-and-forth dialogue help uncover blind spots in your storytelling?

Key Takeaways

• Large Content Models Represent a Paradigm Shift. Unlike Large Language Models (LLMs), which generate text probabilistically word by word, Large Concept Models (LCMs) operate at a higher semantic level, potentially mirroring human cognition more closely.

• Generative Grammar vs. Cognitive Linguistics. The emergence of LCMs aligns more with Cognitive Linguistics, emphasizing meaning and context over strict syntactic rules, though both paradigms may still complement each other.

• AI Evolution vs. Human Evolution. While human cognition preceded language development, AI is being developed in reverse—starting with language (LLMs) and progressing toward conceptual cognition (LCMs), raising philosophical questions about intelligence and communication.

The Break Up

Since ChatGPT’s public debut in 2022, Large Language Models (LLMs) have been the dominant tool in the artificial intelligence space. The novelty, perceived efficacy, and ever-accelerating rate of improvement of LLMs have made it such that one can hardly roll out of bed without being bombarded by AI-generated or AI-related content.

At R: On Everything, for example, we’ve explored what ChatGPT 4o knew about my work as an author and the what and how of what it got right and wrong. We’ll soon even rig up a whole experiment that challenges an LLM, our audience, and yours truly to test our abilities to compete with and identify AI-generated content.

But LLMs have also been a bit of a drag:

• They’ve gaslit us about the number of Rs in the word “strawberry.”1

• They refuse to acknowledge David Mayer. An ex, maybe? Seems sus.

• They’ve spent endless hours hallucinating, and they refuse to see a doctor.

They’ve also—not once!—offered to do the dishes after dinner.

So, yeah: I think a break up is in order.2

Conveniently, Meta recently published the results of their initial foray into architecting artificial intelligence driven by what they’ve dubbed a Large Concept Model, or LCM.

According to Meta, Large Concept Models distinguish themselves from LLMs by mirroring more closely the inner workings of the human mind, operating at “explicit higher level semantic representations.”

In other words, they’re smart. They’re cool. They’ve got that look in their eye that says, “You want to get to know me, don’t you?”

And I do. I really do.

The true contrast between our ex and our new best friend is that rather than generate responses token-by-token like LLMs do, LCMs do so at the “concept” level, which Meta defines as “language- and modality-agnostic” representations of a “higher-level idea or action in a flow.”

What they mean, basically, is sentences. Rather than generate their outputs word by word3, LCMs first construct a high-level, conceptual representation of meaning that is then realized one sentence at a time4.

This is a fascinating approach with potentially widespread spillover effects for other fields, including linguistics and our understanding of human cognition. To tease out these prospective consequences, I’ll offer two assertions, both of which we’ll explore in further detail below.

1. The advent of LCMs creates further tension between longstanding linguistic paradigms, namely Generative Grammar and Cognitive Linguistics.

2. The evolution of artificial intelligence is mirroring that of humankind’s capacity for thought and language—but in reverse.

Questions to Consider

As you read, consider the following and answer in the comments.

• Do you think the shift from LLMs to LCMs will fundamentally change how we interact with AI, or is it just another incremental improvement?

• If AI had been cognition-first instead of language-first, how might that have shaped its role in our society?

Meet the Parents: Generative Grammar vs. Cognitive Linguistics

Generative Grammar and Rules-Based, Syntax-Over-Semantics Outputs

At some point in any relationship, it’s time to meet the parents.

In the case of LLMs, there’s a lot to love about their parentage—at least with respect to the field of linguistics, where I see a great deal of overlap between Noam Chomsky’s Generative Grammar and the inner workings of LLMs.

Generative Grammar has been the dominant force in the field of linguistics going back to the 1950s, when Chomsky made the initial arguments that would later define decades of research. The core tenets of his Generative Grammar relate to the innateness, modularity, and rule-based structures of human language, which manifest or are represented in the following ways5.

Language as an Autonomous Module

Language is distinct from general cognition and, as a faculty, is an innate mental structure.

Universal Grammar (UG)

All human languages share an underlying grammatical structure, no matter how unique they might appear on the surface.

Syntax Sets Direction

Syntax operates independent of meaning. Deep linguistic structures are transformed into surface structures via the formal rules one comes to understand when acquiring a language.

There are immediately a number of interesting parallels between Generative Grammar and the inner-workings of LLMs.

When an LLM (or, hey, generative AI) delivers human-consumable outputs, it does so in a way that is distinct from general cognition; it’s just making probabilistic guesses about what should come next in a string of text.

In the linguistic sense, then, LLMs appear to be productive, but they only mimic human language generation and resemble grammatical, rule-following behavior; they’re not doing anything one could call thinking under the surface.

This decoupling of language from thought has direct tie-ins to the three tenets of Generative Grammar described above. For an LLM, language (or, truthfully, token generation) is a modality unto itself, informed by scraping the internet for books, blog posts, comment sections, and message boards. In doing this, it develops the appearance of understanding humankind’s underlying grammatical structures and their surface-level manifestations.

What I’m saying is LLMs are, in a way, sophisticated, try-hard content generators.

They’re so last summer, and we are never, ever getting back together.

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Cognitive Linguistics: Meaning, Usage, and Abstraction

Forged in the 1970s, Cognitive Linguistics is essentially a rebel’s response to the formal, rules-focused approaches of the 1950s, when Generative Grammar first emerged.

In other words, if Generative Grammar came back from the war to settle in the suburbs and have five kids, Cognitive Linguistics is those kids.

And those kids? Well, they became the counterculture their parents feared. In our primary metaphor, they’re also the parents to the Large Concept Model—our nouvelle belle, as it were.

To demonstrate the divergence in these paradigms, consider that where Generative Grammar declares meaning is a byproduct of underlying syntactic rules, Cognitive Linguistics treats meaning as central to language. For Cognitive Linguistics, it’s semantics, not syntax that makes language worth studying, which has a significant impact on the defining principles of Cognitive Linguistics, described below.6

Language as a Cognitive Phenomenon

Unlike in Generative Grammar, language is not distinct from general cognition and is instead closely tied to—and an extension of—one’s experience of the world.

Usage, Not Universality

Rather than accept that language is governed by innate rules, Cognitive Linguistics argues our understanding and production of language is driven by conceptual metaphors and exposure-dependent statistical generalization.

Schema as Semantic Structures

Instead of treating them as distinct modalities, Cognitive Linguistics views syntax and semantics as intertwined. Conceptual metaphors and mental representations of our experiences become part of internalized schema that are used to derive, create, and share meaning.

In a way, Cognitive Linguistics and, as an extension, Large Concept Models ”zoom out” relative to Generative Grammar. By emphasizing structured conceptual representations instead of tokenized language-oriented associations, LCMs should theoretically be encoding meaning at a more semantic and functional level instead of a modularized, rules-based, probabilistic one.

This zooming out is also what would unlock an LCM’s ability to embrace conceptual metaphor. The very notion of context—which I’ll argue is necessary to create and sustain meaningful, metaphorical thought—is far more available to an LCM by virtue of its operation at the higher level of abstraction its creators aim to instill within it.

The consequences for this prospective pivot to LCMs aren’t exclusively theoretical, either. Practically speaking, a shift to response generation at the concept level should reduce hallucinations and make AI tooling more reliable for research, writing, and coding.

Their improved capacity for maintaining context over long conversations or longer bodies of text should also ensure their responses are more likely to remain internally consistent, minimizing the frequency with which the LCM contradicts itself or “forgets” an instruction or vital bit of its own response history.

In keeping with our break up metaphor, what I’m trying to say is LCMs are smart. They’re cool. They just get it.

They just get me.

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LCMs and Linguistic Lore: A Penchant for Tension

What LCMs don’t get, however, is that their advent would seem to suggest Cognitive Linguistics should win the day.

As AI moves toward more meaning-driven, usage-based models, the principles of Cognitive Linguistics become increasingly relevant, potentially drowning out the perceived need for or utility of the Generative Grammar paradigm.

That said, Generative Grammar has survived as long as it has for a reason. Its formal structures have had an immense impact on the field of computational linguistics, and its deductive power—that it can explain how diverse linguistic phenomena can be derived from a core set of principles—has made it indispensable in understanding how humans acquire language.

Ultimately, then, we may see both Generative Grammar and Cognitive Linguistics playing well together, with the former providing the foundations of AI-generated language at a deep, structural level, rather than in a merely probabilistic capacity. The latter, meanwhile, would subsequently improve outputs by incorporating context-dependent, conceptual models.

Huh. Maybe one’s ex and one’s new fling can be friends.

Maybe.

Digital Evolution !== Human Evolution

This is where we get glonky, by which I mean speculative. It’s the philosophical jazz of the LLM-LCM interplay, if you will.

Anyway—

What struck me about the notion of LCMs and how they are positioned to succeed LLMs is that the former is an attempt to go from a language-focused model to one that closer resembles thought, or at least what we believe to be the stuff of thought.

This is the opposite of how we humans went about it evolutionarily7.

Think about it: what need would we have had for language if there weren’t anything going on between our ears worth sharing? By this, I mean for humans, thought preceded language. Humans’ machines, however, will have first had language, then thought, to the extent that they ever truly have either.

I’m of the mind that this demonstrates the extraordinary influence language has on how we navigate and shape our world. One wonders, for example, at the form these tools might have taken if humans communicated with one another by directly sharing our thoughts with each other as they exist in the abstract.8

Might this have led us to first develop a model that one could engage with by non-linguistic means? Might we have then secondarily pivoted to a model that relied on language in order to communicate over longer distances? Or would the need for long-distance communication have necessitated an LLM equivalent first anyway?

As with any thought experiment, there’s truly no way to know, and the answers to these questions are likely to rely on the parameters one establishes.

All of this does make me curious, though, so let’s keep up the conversation in the comments.

Tell Me in the Comments

• Do you think the shift from LLMs to LCMs will fundamentally change how we interact with AI, or is it just another incremental improvement?

• If AI had been cognition-first instead of language-first, how might that have shaped its role in our society?

In the first installment in this two-part series, we explored the mission-driven (or not so mission-driven) outcomes of two enterprises, namely the Writescast Network and Kill Your Darlings Candle Company. This was, as a reminder, in service of the mission statement we laid out for ourselves for this discussion:

We’re here to sharpen our sense of purpose to achieve success with intention.

Now, having developed an understanding of the differences between success and success with intention, we can finally explore how to sharpen our sense of purpose and unlock success with intention.

Before we do, though, check out the expected takeaways from this article below, along with questions to consider as you read.

Expected Takeaways

• A mission statement provides focus and direction. It should be a clear declaration of purpose that guides decisions and goals.

• Prioritization is key. Rather than juggling multiple priorities, a strong mission statement centers around a singular, overarching objective.

• Mission statements should be concise and meaningful. Avoiding generic values and excessive wording helps ensure they’re memorable and actionable.

Questions to Consider

• What is the single most important priority that drives your project or personal mission?

• How can you refine your mission statement to make it more specific, actionable, and unique?

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Mission Statements, Defined

A quick glance about the web will confirm what many of you might already suspect: there are about as many definitions for mission statement out there as there are get-rich-quick schemes.

It’s my observation that there are common threads that run through these definitions, which converge on the notion that a mission statement is a declaration of purpose that provides focus and direction for an organization, project, or person. It’s a reference from which one’s goals will spring and, as we saw in our examination of KYD Candle Co. in the last post, against which decisions can be made and measured.

Mission Statements, Declared

So, how can we knit it all of this together?

We’ll start here: I see mission statements as boiling down to a handful of key components: priority, values, goals, and how.

Priority (Not Plural)

The word priority came into English via Old French (which itself picked up the word from Medieval Latin) sometime in the late Middle English era. In fact, it wasn’t until the 1930s that a plural form of the word came into widespread use, especially compared to the historic use of the singular form.

Historically, then, the very notion of priorities in the plural is a relatively recent phenomenon, and its advent is made even more clear when one looks at the explosion in use of the word prioritization. After all, there’d be no need to prioritize if there were only one priority, right?

What I mean to drive home here is that, in the context of a mission statement, it behooves us to be singularly minded: what is the absolute number one priority that our business, project, or person is focused on? Once this has been determined, you’ll have taken a tremendous step forward in crafting an effective mission statement.

Value(s)—Plural If You’d Like!

Defining values to undergird your mission is a critical step in developing an effective mission statement. Remember the word sustainably in KYD Candle Co.’s mission statement to sustainably illuminate the lives of readers, writers, and their communities? That word on its own is a communication of values, and so is the mention of communities at the end.

I point this out because, after making a list of our values1, it can become far too easy to drop a laundry list of them into our mission statements. This ballooning makes it far more challenging to recall our mission statements when we need them most, and if we can’t readily recall them, we’re far less likely to embody them.

When it comes to values, then, I encourage you to include only those values that will set your organization, project, or personal brand apart. Think, for a moment, about some of the more tiresome mission statements you have seen in the past. How many of them mentioned integrity, excellence, and innovation? These are great values, but they don’t really tell us anything. After all, who doesn’t want to do novel things well?

Think of it this way: if the nature of your business, project, or brand would imply a particular value, it doesn’t need to be mentioned explicitly in your mission statement. As in creative writing, let context do the heavy lifting for you whenever possible.

Goal

It should come as no surprise that one’s goal is an extension of one’s priority. The overlap between these is significant enough, in fact, that it can be hard to tell them apart. I do this by considering the goal in a mission statement to be a distillation of the priority; goal is a statement of what you’re doing right now to drive the embodiment of your priority.

In this way, then, goal in a mission statement is not some future state that you’re aiming to achieve. Instead, it’s a nod to the ongoing embrace of your what.2 It’s this interpretation that also leads me to be a strong advocate for the removal of words like “aim” or “strive” from mission statements. Don’t tell me what you’re trying to do; tell me what you do.

A Suggestion of How

Earlier, I mentioned the word sustainably in the KYD Candle Co. mission statement was a declaration of (one of) the company’s values.

It was also a suggestion of how it went about its work.

Here, we see that the elements of a quality mission statement can overlap. There is a relationship between a suggestion of how and values in the same way there’s a coincidence between priority and goal.

Furthermore, KYD Candle Co. managed to include a suggestion of how in a single word. There was no COMPANY does THING by EXTREMELY LONG COMMA-SEPARATED LIST OF NONSENSE formula at play.

Mission Statements, Done

Let’s build a mission statement now. Or, rather, let’s see if we can use the mission statement I’ve articulated for R: On Everything and your responses to the challenges I’ve left you along the way to put this all together.

Priority (but Also Values!)

In creating a mission statement for this blog, I started by asking myself what the priority would be. I wanted to make sure it would be something that guided me forward with an emphasis on quality, community, curiosity, and value, which ultimately made me realize I wasn’t starting with priority at all. I was starting with values!

So, then, I had to distill those values down into a singular notion, and hopefully one I could articulate using a strong verb. The word curiosity stood out to me as one worth tying to that verb, which led me here as a starting point.

R: On Everything encourages curiosity…

I liked this. The verb encourage comes with a positive connotation, in my view, and I avoided the trap of saying I try to encourage and instead stated flat-out that this blog encourages curiosity.

Focusing on Values

I had a start to the mission statement, but I also had other values to account for—and, hopefully, not by simply slapping them in my mission statement as part of a comma-separated list.

I turned to community next, as it would help answer the question of for whom the blog exists. In other words, whose curiosity are we trying to encourage?

First of all, I knew it had to stimulate my own curiosity; it’d be hard to keep up if I were researching and writing about topics I found uninteresting. But, given my long-term goals for this project, it also couldn’t be only for me.

This led me to expounding on what I’d written earlier, landing me here:

R: On Everything encourages curiosity amongst its author and readers…

Hmm. Author and readers? That is a community, but there could be a stronger articulation of who those readers are, especially.

With that in mind, I tried again.

R: On Everything encourages curiosity by cultivating a community of like-minded polymaths…

This increased specificity paints a much stronger picture of what this blog values and who it is for, namely people (like me) whose interests are wide ranging. One could argue that this is casting too wide of a net, but I would argue the community of individuals who are genuinely interested in all of the topics I cover (more on that in a moment) is a smaller, tighter-knit group than one might expect.

And on those topics, well, those form (part of) our how.

Encouraging Curiosity: The How of It All

First off, I’d already started to articulate the blog’s how with the phrase by cultivating a community. As they used to say in those cable TV call-in commercials, though, wait, there’s more!

Cultivating a community is a good how, but in the same way that author and readers read as too generic, cultivating a community could, on its own, land a little flat. I aimed to avoid becoming ensnared in the land of the generic by ensuring I made mention of what topics I intended to cover.

R: On Everything encourages curiosity by cultivating a community of like-minded polymaths through the thoughtful exploration of topics at the intersection of technology, publishing, leadership, and culture.

This bought me a few things, including the inclusion of one of the values that got left out of the previous draft. The word thoughtful makes a nod to the notion of quality, and the list of topics—though broad—is actually a winnowing in the context of polymathy.

The Ghost of a Goal

We seem to have arrived at a fully fleshed out mission statement without having focused specifically on incorporating a goal.

Despite not having developed a goal overtly, we wound up with one anyway. Cultivating community is a demonstration of how we serve our priority, yes, but it’s also a goal.

When we pair that thought with our earlier discussion of the interplay between goal and priority, we see that it didn’t prove necessary to focus specifically on forcing a goal into the mission statement; it arose instead as a result of the confluence of the elements that seemed most important for us to focus on first.

This goes to show that though the components of a mission statement can be described in a discrete fashion, they don’t need to be (and really shouldn’t be) put forward in a paint-by-numbers fashion.

It may be necessary to start with a formulaic-sounding mission statement, and if yours reads as one, pause for a time and reflect on what overlap exists between its elements to sharpen it further.

Time Sharpens All

On that note, I should point out that, since initially writing this post, revisiting it before publication has me reflecting further on the mission statement I’ve written for R: On Everything above.

Though it does cover all the bases, after a couple of months of letting it sit, I’ve come back to it now with fresh eyes. My first impression is that it’s more verbose than it needs to be. To address this whenever it comes up—and it does more often than one might think—the first thing I do is look for redundancies or overlaps, rather than focus on shaving off a word or two here or there.

If I unsheathe my word machete, I can slash out a significant chunk of the mission statement’s middle.

R: On Everything encourages curiosity at the intersection of technology, publishing, leadership, and culture.

Now that’s a sharpened mission statement.

This version does lose the suggestion of how and the inclusion of two of our values, namely thoughtfulness and community. However, once one knows what R: On Everything is—and that the comment sections on these posts are meant to be as interactive as possible—context does a lot of heavy lifting for us.

Public-Private Missions

The above winnowing of the R: On Everything mission statement does have me seeing the notion of context in a new light. For the first time, I’m considering perhaps one can have a more thorough mission statement behind the curtain, so to speak, while a shortened version of the same mission is available to those who have more context into the endeavor.

But be careful: I don’t mean to suggest that one should have unique mission statements on either side of the private-public divide. Recall that our missions have a single priority. This, along with our goals, suggestion of how, and values should be the same both publicly and privately; it’s simply what we choose to emphasize in our outward declaration that varies.

Regardless of whether one has two versions of the same mission, a quality mission statement will convey to anyone who reads it what an organization, project, or person is all about. In the end, though, the mission serves to sharpen the purpose of the individual(s) assuming responsibility for the verb at the heart of the statement’s priority.

In other words, your mission is for you.

And as we’ve seen with the diverse outcomes of the Writescast Network and Kill Your Darlings Candle Company, carefully crafting a mission statement makes all the difference between achieving success and success with intention.

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Tell Me in the Comments

• What’s your mission statement, personally or for a project you’re part of?

• What is the single most important priority that drives your project or personal mission?

• How can you refine your mission statement to make it more specific, actionable, and unique?

Let’s see if we can help each other do what we set out to do at the start of this series, which was, as a reminder, to sharpen our sense of purpose to achieve success with intention.

Key Takeaways

• A mission statement provides clarity and direction. Without a well-defined mission, even a successful venture can lose focus, become misaligned with long-term goals, and ultimately become unsustainable.

• Success on paper is not the same as success with intention. Some endeavors may achieve strong metrics, but a lack of a guiding purpose can lead to their downfall. Meanwhile, other projects thrive due to a clear and actionable mission.

• A strong mission statement influences decision-making. Mission statements can be used to guide product development, marketing, and community engagement, ensuring alignment with one’s core purpose.

Setting the Stage

Far too many eager entrepreneurs, ambitious engineers, and enthusiastic authors have seen their sure things stumble out of the gate or, after an initial wave of enthusiasm, quickly prove themselves unfit enough to make it to the finish line.

And I would know—I’ve been that eager entrepreneur. I’ve been that ambitious engineer. Lord knows I’ve been that enthusiastic author.

The question is why? Why do our best knit plans unravel so quickly?

The answer to that why is, in my view, another why: it’s that we’ve not asked ourselves why we’re doing what we’re doing. We’ve not adequately explored the purpose behind our projects, businesses, and brands. We’ve not explored whether what we’re doing—or considering doing—is truly aligned with our values, goals, and vision.

I’ve learned the hard way how costly this mistake can be, but enduring those shortcomings—and spending a great deal of time reflecting on them—has helped me better understand that understanding our why can better inform what choose to pursue, as well as how we do it.

To that point, what’s our purpose? What will we cover in this discussion?

We’re here to sharpen our sense of purpose to achieve success with intention.

To do this, we’ll compare the journey and outcomes of two endeavors, both of which I will argue were successful, but only one of which was successful with intention.

Then, we’ll tease out how each endeavor wound up in either bucket, as well as what we can do to urge our own undertakings toward success with intention by exploring how to develop and embrace a sharpened sense of purpose.

Questions to Consider

As you read, consider the following and answer in the comments.

• Have you ever worked on a project that was successful on paper but lacked a clear sense of purpose? How did that affect its long-term sustainability?

• What’s one way a well-defined mission statement has (or could have) helped you make better strategic decisions in your own work?

Success with Intention: A Case Study

Let’s examine two sets of metrics. Which one of these ventures was successful with intention, do you think?

Enterprise A

• Profitable in third year (44.98% margin)

• Customer base in the thousands

• 10,000s of yearly transactions

• Ceased operation after four years

Enterprise B

• Profitable in second year (28.7% margin)

• Customer base in the hundreds

• Hundreds of yearly transactions

• Ceased operation after two years

The greater scale and longevity of Enterprise A would make solid arguments in favor of it having been the one to succeed with intention.

But let’s not rely on first impressions. The details matter!

A versus B: The Reveal

Enterprise A: The Writescast Network

From 2016 to 2020, I ran the Writescast Network, “a podcast collective for writers, by writers.” The network featured five unique shows that, over the course of four years, garnered tens of thousands of downloads across 146 episodes.

As the network’s founder and the host of its flagship program, I interviewed dozens of authors, literary agents, and other publishing industry professionals. Doing so positioned me to speak at writing conferences in-person and online, which had a multiplier effect in my ability to reach new audiences.

Successful? Yes! But let’s see if we can tease out whether it was successful with intention by examining the network’s mission statement.

The Writescast Network’s Mission Statement

Network. Learn. Grow.

Hmm.

Did the Writescast Network lead to opportunities for networking? Yes.

Did it help me and others learn more about the craft and business of writing? Yes.

Did it help me and others grow as individuals and as authors? I’d like to think so.

Despite having fulfilled that mission, I’ll argue that the mission statement itself leaves a lot to be desired. It is concise, which I appreciate, but it has a few critical weaknesses that wound up costing me, the network, and its listeners in the long run.

Unpacking the Writescast Network’s Baggage

Let me be clear. That mission statement above? I never actually articulated this or any other mission statement for the network while it was around.

And, even if I had, the mission statement I’ve retconned into place for it doesn’t adequately articulate a why. It also fails to give us any insight into the network’s how.

This lack of specificity and purpose led to a number of issues throughout the network’s lifecycle, particularly once I began to enjoy some success as an author in my own right. Why? Because once I had books getting published, the network’s mission shifted to one that emphasized helping me sell more books.

And can you blame me? Once one knows their books are to be in print, the next step is to ensure those books are read, and all the better if readers are willing to pay for them.

Looking back at it, the mission statement above about networking, learning, and growing is one that could have helped me get a crucial leg-up in a crowded marketplace.

And yet, it didn’t. Why?

A Mission-less Launch, A Hard Pivot

My failure to articulate a quality mission statement in advance—coupled with the shoe-horned pivot to focusing on book sales—led to a virtuous cycle that exposed the network’s foundational issues.

These included having been overly-focused on the network’s what (producing podcasts), failing to target the proper audience for my work as an author, and broadcasting content that wasn’t in alignment with my long-term goals of shutting down my freelance editing business and focusing on my own writing.

This is what ultimately led to my scrapping the network altogether despite its success in terms of downloads, listenership, and the opportunities it afforded me.

Had I articulated a stronger sense of purpose for the network before I started interviewing authors and posting episodes willy-nilly, I might have identified earlier the issues that led to its unwinding, thereby transcending success and arriving at success with intention.

What does this mean, then? It means that the mysterious Enterprise B is the one that achieved success with intention.

Enterprise B: Kill Your Darlings Candle Company

My wife and I founded Kill Your Darlings Candle Company (KYD, or KYD Candle Co.) in 2020. In the 18 months before we sold its intellectual property, we received 688 orders (more than one per day, on average), took in nearly $20,000 in revenue, and raised more than $800 for values-aligned non-profit organizations. Our customer base stretched across three countries on two continents, and we reached profitability in our second year of operation.

We may have also started a cult, but more on that later.

Kill Your Darlings Candle Company’s Mission Statement

For purposes of exploration and comparison, let’s see if the KYD mission statement gives us any insight into its success (with intention!).

Kill Your Darlings Candle Company sustainably illuminates the lives of readers, writers, and their communities.

Clear. Concise. Instructive. It served to remind us—and the world—what we did, who we did it for, and why we did it. The inclusion of a word like sustainably also made for a suggestion of how we went about our work, which led to a more complete mission statement and offered insight into our values as an organization.

The KYD mission statement further distinguishes itself from the Writescast Network’s because it was crafted with care before we launched the business. As a result, it served as the guiding light that informed our decisions, our marketing, and our operations throughout our time at the helm of the enterprise.

Mission-Guided Products and Marketing: Choosing Our Pursuits

For example, the brand’s initial success—and requests from consumers—led us to consider expanding our product line to include bath and body products. We did some prototyping but, after asking ourselves whether a line of soaps would sustainably illuminate the lives of readers, writers, and their communities, the answer very quickly revealed itself to be no.

Soaps don’t illuminate, after all.

Contrast that with our choice to partner with other authors to support their work. Doing so had a direct tie-in to the notion of illuminating the lives (and livelihoods, we hoped) of writers and readers by bringing the two of them together in small features on social media or by crafting limited runs of custom candles that complemented their books.

Okay, but What About the Cult?

One of the more fascinating outcomes of embracing a strong mission statement is that it positioned us to engage with a highly targeted audience in an authentic way. Clarity of purpose attracted customers who were aligned with our values and, it seemed, an appreciation for the work of others in our communities.

So began the cult of Becky at the Post Office.

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The Legend of Becky at the Post Office

It wasn’t uncommon for me to post on social media about the process of boxing up orders on the weekend. I’d then follow up with additional posts during the week about how I was headed out for the weekly post office run and how patient Becky at the Post Office had been as I brought in an ever-growing stack of packages each week.

This led to a craze I could not have anticipated.

Our followers on social media started to ask about Becky each week. They told me to say hi, to thank her on on their behalf, to ask if they could buy candles for her.

At one point, we even designed Becky at the Post Office merch, and though it never made it to our online store, that it even escalated to that point is a testament to the power of community, something we were able to cultivate only because we had a clear sense of purpose that centered around readers, writers, and their communities.

KYD Candle Co.: Not Without Its Challenges

It would be poor form to suggest that a mission statement on its own permits an operation to overcome all obstacles. In the case of KYD Candle Co., we were never able to scale the business in a way that would have been sustainable for us as its owners. Our respective day jobs (or, in my case, side hustles) on top of the time and energy required to keep up with orders, marketing, and product development eventually snowballed into more responsibility than we were capable of assuming.

Despite those challenges, KYD was—especially compared to the Writescast Network—successful with intention. Running KYD may have been chaotic at times, but it was controlled chaos. Why? Because we knew who we were, what we were about, and why we were doing what we were doing. This made for a vastly better experience as business owners and, I believe, better outcomes for the community we served.

As a final testament to the success of KYD relative to that of the Writescast Network, our sale of the candle company’s intellectual property in 2021 made for a lasting affirmation that what we were selling and why we sold it had a significant impact on our target market.

Crafting an Effective Mission Statement for Your Enterprise

We’ve explored, now, the difference between success and success with intention by comparing and contrasting the outcomes of the Writescast Network and Kill Your Darlings Candle Company.

Though the podcast network achieved success on paper, it proved to be misaligned with other medium- and long-term goals, and it failed to ascertain the same levels of engagement that the candle company did in far less time.

In the next installment in this series, we’ll take what we’ve learned and explore what sets a good mission statement apart, as well as how we can use that knowledge to sharpen our sense of purpose and achieve success with intention.

In the meantime, tell me in the comments—

• Have you ever worked on a project that was successful on paper but lacked a clear sense of purpose? How did that affect its long-term sustainability?

• What’s one way a well-defined mission statement has (or could have) helped you make better strategic decisions in your own work?

So what if I AI myself once in a while? What I do in the privacy of my own home is my business.

Well, I guess it became your business, too, starting with this post from last week.

If you haven’t read that one, it explores the outcome of asking a generative AI tool to expound on a topic about which I am an expert: namely, my own work as an author.

What it got right and what it got wrong are noteworthy, and I catalog both in that post, which I encourage you to check out before we get into analyzing—

• What the results of our experiment tell us about how large language models (LLMs) work

• What this means for the trustworthiness of generative AI and LLMs

• How we might still make good use of them as authors and technologists

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Mistakes Were Made

For the sake of re-familiarization, let’s list some of the mistakes that came out of my conversation with ChatGPT, which started when I asked it whether it was familiar with the works of author Ryan R. Campbell. I’ve grouped these mistakes into two categories: one of which I’ll call data misinterpretation and one I’ll refer to as mistaken token generation.

Errors from Data Misinterpretation

• describing me as a fantasy and horror author

• calling my sci-fi series Imminent Dawn

• referring to that series as a trilogy

• claiming Event Horizon was published in 2020

Mistaken Token Generation

• calling that same sci-fi series referenced above EMPRESS

• Dr. Wyatt “Halberd”

Okay, but what the heck do those categories of mistake mean, and why did I divide them up that way? We’ll rely on what we know about how LLMs work in the next section to offer up some hypotheses.

How Those Mistakes Were Made

Data Misinterpretation

With respect to the errors I chalk up to data misinterpretation, these all have their origins in the relatively—emphasis on relatively—sparse or ambiguous data available to the LLM about my work.

Even though I went on a real blogging streak from 2016-2020 (especially in 2019 and 2020) and my author website was linked to via countless other sites and blogs, the corpus of data available to the LLM specifically about me and my books is infinitesimally small relative to the full sum of online conversation about related topics during that window.

That isn’t to say no one was talking about my work, but rather that compared to the most popular or most blogged about authors in my genres, the LLM simply didn’t have as much data to pore over to increase the probability of correct assertions on a consistent basis.

Generic Genre Errors

Looking at the errors in this bucket more specifically, incorrectly including fantasy and horror as genres in which I write is likely due to educated guesses the model is trying to make based on 1) other authors it recognizes and 2) larger linguistic forces.

With respect to the former, science fiction and fantasy are genres generally viewed as complementary; there are a number of writers and readers who produce or consume both to some extent.

Then there’s the linguistic angle: there’s something satisfying about the phrase “science fiction and fantasy” compared with “fantasy and science fiction.” This has its origins in cultural and linguistic phenomena like order of precedence and binomial ordering. We won’t go into what each of those are here (in this post, anyway), but you can learn (a lot) more at the links provided.

The reason I bring this up is because, as we’ll discuss more in the section below about mistaken token generation, LLMs are probabilistic; they attempt to guess or predict what “should” come next in a string of text based on observations they make about their training data.

As a result, it’s not terribly surprising that after writing “science fiction” in a list, it would simply follow up with the word “fantasy,” which it did in the list of genres it spat out about me. Probabilistically, “fantasy” isn’t a bad guess.

Where including horror as a genre in which I write is concerned, though, that’s another matter. I imagine it included this as there may be some mentions out there about elements of body horror in the EMPATHY series or because I did host some author spotlights back in the day that welcomed in authors who write firmly in the horror camp.

The LLM may therefore have had just enough of an association of my work with horror that it felt confident to erroneously include it as one of the genres in which I write.

Serial Series Errors

Referring to the EMPATHY series as the Imminent Dawn series is an easy enough mistake to make when we consider, again, the relative lack of data available to the LLM about my work or this series.

What happened to the EMPATHY series?

Out there on the interwebs, the word “series” appears often next to the title of the first book in the series, Imminent Dawn, as when it was released, and subsequently after its release, I really wanted to drive home the fact that this was a book meant to provide a foundation for much more to come.

In fact, take a look at that last sentence and how adjacent “series” is to Imminent Dawn. The sentence as written is how it came to me naturally, and I have little doubt that I did this regularly when blogging about the book in the past.

In this case, then, the LLM just seems to have made a bad—but not unreasonable, all things considered—assumption.

Where referring to EMPATHY as a trilogy and claiming Event Horizon was released in 2020, these are actually pretty fair mistakes to have made. I wouldn’t blame a human for arriving at these conclusions because, well, I know some humans who have.

As far as the internet—and therefore, the LLM—knows, only three books were ever formally announced as under contract and due for publication. By way of contrast, there was a relatively very small handful of blog posts and pages on which I laid out the full scope of the series (5-7 books).

And it is true that Event Horizon was meant to be published in 2020. When I pulled that book before publication, however (more details on this in the previous post in this series), I don’t think I made much in the way of a formal announcement outside of maybe a social media post or two.

Given this, I can’t blame the LLM—or any human—for, at a quick glance, arriving at these conclusions. It’s what the data would suggest if you only took a cursory glance and had to make snap assumptions probabilistically.

Mistaken Token Generation

The mistakes that fall into this category are, for me, far more fascinating than those described above. Though these errors were far fewer compared to those in the other bucket, referring to the EMPATHY series as the EMPRESS series and misnaming one of its characters as “Halberd” instead of “Halman” actually give us great insight into how LLMs make their probabilistic assertions.

Chokin’ on a Broken Token

Despite what one might guess, LLMs do not generate their assertions word-by-word.

Instead, they generate tokens, which are smaller units of language not dissimilar from the linguistic concept of a morpheme. That is to say, a token could be a whole word like “over” (a word in its own right) or “overconfident” (in which “over” also appears, but instead as a prefix that changes the meaning of the word by virtue of agglutination).

As an LLM builds a response, it evaluates the likelihood of what the next token in a string might be, and it’s clear—to me, anyway—that the EMPRESS and “Halberd” mistakes are examples of misattributed assertions that stem from this tokenized response generation.

The EMPRESS Makes One “Hal” of an Error

With EMP and “Hal” as the initial tokens in their respective words, the LLM had to calculate what the likelihood of the next token in the string would be based on the data available to it.

In my observation, it would not be unreasonable to conclude that EMPRESS is a viable title for a book series, especially if the model already mistakenly believes the author to be one who writes in the fantasy genre!

For “Halberd” versus “Halman,” the explanation is even more simple. “Halberd” is a word that likely appears an impossibly greater number of times than “Halman” does in the LLM’s training data by virtue of the fact that a halberd is a real thing.

In fact, “Halman” is probably only significant in the context of the EMPATHY series and the Arthur C. Clarke reference on which I based Wyatt and his family’s last name (how’s that for an Easter Egg?).

What These Mistakes Mean Going Forward

Look, you don’t need me to tell you that you can’t trust an LLM to get everything exactly correct. Heck, OpenAI even tells you to double check ChatGPT’s outputs, especially for important information.

Now, whether my work is important… that’s another matter, and it’s one I don’t think it fair for me to opine upon. Ha!

With all of this in mind, though, it’s another reminder to double check ourselves—lest we wreck ourselves—when it comes to relying on LLMs to inform our opinions or guide our research.

As for what all of this means in the context of how authors and technologists can use LLMs going forward, well, the experimentation will continue. I have a number of ideas I’m tinkering with for how we might probe these technologies further, and we’ll get into that in subsequent R: On Everything series.

For now, though, how will you use what you’ve learned about how LLMs work to make better use of them going forward? Tell me in the comments below!

I can’t believe you caught me. It isn’t what it looks like.

I can explain.

It’s the first time, I promise, and I don’t know what came over me. I just got this idea, and, well, there’s no sense in hiding it, is there? Fine. I’ll come clean.

I admit it. I AI’d myself.

There! Happy now?

Even if you’re not, I actually was. Am! I learned something about myself, or at least about myself as the internet might see me, and maybe that will prove useful in better understanding my books from an outsider’s perspective. Maybe it’ll even inform how I approach marketing going forward. Who knows?!

Now that we’ve got the unpleasantness out in the open, let’s at least explore what ChatGPT 4o thinks about a topic about which I am a subject-matter expert, including—

• What it got right

• What it got wrong

• What this tells us about how large language models (LLMs) work

• What this means for how trustworthy we should find LLM outputs

• How we might still make good use of what LLMs do well

To do this, we’ll walk through the full text of the conversation, step by step. The transcript we’ll be using for this discussion can be found here.

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First Impressions

Before we dive in too deep, I should say I was surprised ChatGPT knew about me at all, not that I necessarily should have been. ChatGPT 4o can browse the web for current information, an improvement over the up-to-September-2021 dataset its predecessor models were trained on.

Given those predecessors were trained on data through September of 2021, I really shouldn’t have been surprised it knew about me, as the bulk of my digital footprint as an author was between 2016-2020.

Yes, it’s been a few years. Please don’t think me washed up.

Or do. I’ll be over here in the meantime telling myself in the mirror that I’m good enough, I’m smart enough, and gosh darn it, people like me.

What It Gets Right

Here’s a list of where ChatGPT went right—

• Wisconsin author

• Speculative and science fiction

• Pen name R.R. Campbell

• Imminent Dawn

• Contributions to community

• Topics of speculative fiction work

Where It Flatters Me

I’ll be honest: it was nice to see a nod to genre blending, complex characters, and thought-provoking themes. If this is what the internet really thinks about my books, that’s a win, and one I’ll take.

Where It Goes Wrong

For as much as I might want to be reassured by its observations about genre, character, and theme work, well, it gets harder to take as gospel given what ChatGPT got wrong, which were far baser facts. A couple of notes—

• Imminent Dawn is not a series; it’s the first book in a series

• EMPRESS is not the name of that series; it’s EMPATHY

• EMPATHY (or, if you’re ChatGPT, EMPRESS) is not a trilogy

I’ll expound on why I think it got wrong the facts it got wrong below, but my first thought about what it got wrong was actually, Dang, can you imagine trying to use this thing as a student to write papers about people or their works?

Not that I expect anyone to be writing papers about me or my books, but… it was a thought I had.

What? ChatGPT may be capable of flattery, but no one flatters me like I flatter me.

Needless to say, given these errors, I thought I’d call one of them out and see what ChatGPT had to say about it.

First, it was reassuring to see ChatGPT get it right when challenged, though that patterns with how it ordinarily responds to pushback, at least in my experience.

That said, I do still get nervous when ChatGPT is so accommodating; this has meant historically that it might have overcorrected and become a citizen of hallucination nation, but, thankfully, that doesn’t appear to be the case here.

I could quibble with the characterization of mind-to-mind communication, but it is technically true. I was a little surprised it had gotten this far into describing the series without describing how that mind-to-mind communication occurred, namely via an internet-access brain implant.

At this juncture, though, I became interested not only in the objective, but the subjective as well. To do this, I responded to the question at the end of its most recent message with—

Yes, please tell me more about the series and how it has been received. Is it popular?

ChatGPT responded with a wall of text to this one, so let’s take it section by section.

This improves on mind-to-mind communication by getting more specific with direct neural communication, in my opinion, but it drops a big oof on us by listing Event Horizon as having a publication year of 2020.

More on that later, though. Here’s the next chunk of text it sent along.

This is actually a fantastic summary of the key themes and storyline, especially for the first book.

The problem? It gets the last name wrong of the only character it chooses to name.

It is close, however. The character’s actual name is Dr. Wyatt Halman, and the error ChatGPT makes here is in the same vein as the error it made earlier by calling it the EMPRESS series instead of the EMPATHY series.

I’ll expound on the likely reason for this mistake later, but for now, let’s look at the next bit of its response.

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This is, again, an exceptional summary overall. It does err in continuing to refer to EMPATHY as a trilogy, but I haven’t had a chance to correct it on that yet; remember, this is all still in response to a single question I asked it earlier.

I also can’t say whether EMPATHY compares to The Circle. I’ll admit I’m not familiar with Eggers’ work (should I check it out?), and this was the first time I’d seen my work compared to his, which means this is either a hallucination or there’s some media out there in which this comparison is made.

Maybe I’m being harsh on myself—or, well, the series—but I was surprised to see it be so generous with respect to EMPATHY’s popularity. I thought for sure it’d take a look at metrics like the number of reviews on the most common platforms and go, “Meh, looks like it didn’t do as hot as the author might have liked, sales-wise,” but no! Instead, it focused on a loyal readership and that it is—and this is according to ChatGPT, mind you—a standout in its genre.

The more I thought on it, though, I do think it’s fair to say EMPATHY has a loyal readership. People do still reach out from time to time to ask if I have more planned for the series (more on that here), which isn’t nothing.

But okay, back to the chat. ChatGPT has asked me a question, and I have an answer for it.

Okay, well, I have a question for it, and it’s one based on the glaring error it made much earlier in its most recent response.

When challenged, ChatGPT again does get it (mostly) right, which is reassuring.

It is true that Event Horizon was announced as the third installment in the series, and, to be fair, it was originally slated for publication in 2020. The rub is that, as a result of disagreements between me and my publisher with respect to how the book’s contents should be presented (they wanted to do a Martin-esque A Feast for Crows-A Dance With Dragons split given the number of POV characters and amount of ground covered), I ultimately pulled the book from publication.

To be fair, I don’t know if I ever shared that information publicly. This all happened in February and March of 2020, so one might understand how an announcement would have gotten lost in the fray. Confusion and delay, indeed!

ChatGPT does continue to insist on the bit about it being a trilogy—it was (is?) planned to be a pentalogy—but I don’t need to keep picking that nit.

What happened to the EMPATHY series?

Where does this leave us?

We’ve had a chance to review the conversation, to see where ChatGPT got it right, mostly right, and where it got me plain wrong.

In the next installment in this series, we’ll take a closer look at how ChatGPT likely made the mistakes it made, what that tells us about how LLMs work, and how we might use this knowledge to good effect going forward.

In the meantime, if it’s not too personal of a question, tell me in the comments: have you ever AI’d yourself? What did the LLM get right about you, and where did it come up short?

Current Status

As of December 2024, the EMPATHY sci-fi saga is out of print, with Imminent Dawn and Mourning Dove being de-listed by their original publisher at my request.

I’m grateful to NineStar Press for the opportunity they were willing to give this series when I initially signed with them in 2018, and I’m excited about what this reversion of rights means for the series going forward.

History of EMPATHY

What started as a short story I wrote in the early 2010s would, over the course of the coming years, grow into a multi-perspective novel-length manuscript titled Imminent Dawn. Here are the highlights of its progression thereafter.

2018

• Imminent Dawn is brought aboard for publication with NineStar Press.

2019

• Mourning Dove, the sequel to Imminent Dawn, is similarly offered a publishing contract with NineStar.

• Imminent Dawn and Mourning Dove are published in January and April, respectively.

• Event Horizon, the third book in the series, is drafted and likewise offered a contract for publication.

2020

• After considering the best way to present the series going forward, I ask that Event Horizon’s March 2020 release be postponed indefinitely.

• Imminent Dawn is named as a finalist in the International Book Awards by American Book Fest.

2024

• At my request, NineStar Press reverts to me the rights to the first three books in the EMPATHY series.

• I begin to rewrite Imminent Dawn to account for changes in the current technological landscape as well as the evolution of my approach to storytelling.

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What Comes Next

I’m determined to see through the rewrite of Imminent Dawn before I progress to doing the same with Mourning Dove and Event Horizon.

It is my intention to re-release the first two books of the series in conjunction with Event Horizon in 2026.

Bear in mind, this will require revisiting and reworking hundreds of thousands of words, and it is my aim to ensure that the subsequent books in the series are also outlined in full before the first three books are published once again.

I’ll provide updates on the rewrites as they progress. Be sure to subscribe below to ensure you get the latest news about the EMPATHY series going forward.

It’s been a minute.

In fact, it’s been 1,342,080 minutes, more or less, since I last sent out an author newsletter.

Since then, I’ve had time to adjust to a new career in a new city, to find my footing and feel, at long last, home once more.

I’ve had time to pause, too, and to reflect: on who I was as an author, on how it affected me as a person, on what it would mean for me—and how I’d go about it—if I ever got back in the game.

Today, finally, I do.

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Introducing R: On Everything

Together, we’ll elevate one word above all others. It will serve as our north star, our guiding principle in choosing what we explore and when we explore it. We will follow that word where it leads—not blindly, no, but with a sense of purpose, with a defining philosophy in place.

And that word? Curiosity. That purpose? Community.

So, let’s learn together. Let’s explore the intersections of publishing and technology, of leadership and culture and everything in between. Let’s explore everything that keeps us curious.

It won’t always be easy, but it will be worth it.

With that worthiness in mind—and with our curiosity carrying us forward—let’s get started. You’ll get an update on the EMPATHY series tomorrow, followed by the first of a new series of weekly posts beginning on Saturday.

In the meantime, happy new year, everyone. Keep curious.

I have come and gone a number of times over the years.

A tide of enthusiasm accompanies the comings. Unrelenting inspiration swells, manifesting in proclamations of a renewed sense of purpose, of a commitment to a new project, a new vision, a new sense of self.

The goings, though? The goings vary.

Some are announced. They come in the form of blog posts, of newsletter declarations, of social media mentions.

Others, still, are quiet, are little more than suggestions of an absence felt before it is confirmed, assuming it’s confirmed at all.

No matter how the goings have arrived, however, they have always arrived, and though today is a day for going, it is not, as has often been the case, a withdrawal.

Today is a day for going forward, for uniting these comings and goings in a movement guided not by aspirations of more, but by curiosity.

So, this time, on everything. This time, all of me.

More to come. Keep curious.

In the first two parts of this series, we explored the pitfalls of the passion hypothesis and the strengths—and limitations—of the craftsman mindset. Along the way, we examined how passion alone can lead to burnout and frustration, while an over-reliance on skill-building can erode fulfillment if pursued without regard for personal autonomy or joy.

Now, in this final installment, I'd like to propose a new way forward: a marriage of passion and craft that leverages the best of both approaches to create a sustainable, fulfilling career. Below, I'll lay out five principles for integrating passion into a craft-driven framework, drawing on lessons from both my experience and Cal Newport's So Good They Can't Ignore You.

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1. The Confluence of Mastery and Passion

The allure of “following your passion” can be intoxicating. Who wouldn't want to leap out of bed every morning, driven by the idea of doing what they love? But as we've seen, passion alone is often an insufficient foundation for a career. Without mastery, passion is like relying on kindling alone to fuel a fire—it may burn brightly for a time, but it will fizzle without fuel to sustain it long term.

Mastery, on the other hand, creates value. By developing rare and valuable skills, you accumulate what Newport calls “career capital,” the currency that allows you to negotiate for the autonomy, flexibility, and meaning that self-determination theory argues many of us rely on for fulfillment in our work.

And though passion may nudge us down a particular path, it doesn't always have to lead the way; it can follow you—complementing your pursuit of mastery to become that sustaining fuel—as you burn more brightly, becoming so good they can't ignore you.

2. Choose Objective Fields When Possible—Or Redefine Success in Subjective Ones

Some careers offer clearer paths to success than others. In software engineering, for example, the criteria for success are often objective: does your code work? Does it scale? Is it secure?

By contrast, fields like publishing thrive on subjectivity; success can depend on timing, taste, and luck as much as skill or effort.

This doesn't mean you should abandon subjective fields entirely—especially if they hold your interest—but it's important to redefine success in those areas. Instead of focusing on external sources of validation (e.g., book deals or landing on a bestsellers list), pivot instead to embracing the pride that comes from creating the work itself or connecting with a community of like-minded creators. In all cases, understanding the dynamics of your chosen field will help you align your efforts with achievable outcomes.

3. Balance Craft and Personal Fulfillment

The craftsman mindset teaches us to double down on skill-building, but skill alone doesn't guarantee satisfaction. As I've learned, investing in the wrong opportunities—no matter how lucrative or well-aligned with your skillset—can erode autonomy and leave you feeling unmoored.

To counter this, balance your pursuit of craft with personal fulfillment. This might mean saying no to projects that don't align with your values or carving out time for passion projects that reignite your sense of purpose.

With respect to my work as a software engineer, I've found immense value in blocking off Friday afternoons for what I call Exploration Afternoons, where I work on pet projects that excite me and expand my skills in directions that feel meaningful.

Meanwhile, in my capacity as an author and blogger, I still find value in reading, writing, and reflection in the mornings as I drink my coffee to start the day. Whether what I write or read ultimately leads to content that is consumable by others is irrelevant; it's in the notion that I've taken the time to stimulate my curiosity that I find the most value.

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4. Integrate Passion into Craft

While passion may not be a reliable career compass on its own, it can complement and enhance your work when thoughtfully applied. By bringing your unique perspectives and external passions to your field, you can stand out from the crowd and create a career that feels authentically yours.

For example, my love of storytelling has shaped how I approach software engineering, particularly when presenting technical ideas to colleagues or advocating for user-friendly solutions. Your passions don't have to align perfectly with your job description to add value—they just have to be integrated in a way that feels natural and impactful.

5. Leverage Autonomy, Competence, and Relatedness

At the heart of self-determination theory lie three psychological needs: autonomy (control over your work), competence (mastery of your craft), and relatedness (connection with others). The craftsman mindset aligns well with these principles, but it's up to you to ensure your work supports all three.

Take autonomy, for instance. I regained a sense of control over my time and focus by leaving behind ventures that demanded more than they gave and transitioning into a field where deliberate practice yields tangible results. Competence followed as I honed my skills, and relatedness grew as I found peers who shared my interests and values.

Whether you're in publishing, tech, or another field entirely, designing your career around these principles can maximize both satisfaction and sustainability.

The Path Forward

So, where does this leave us? Should we abandon passion altogether in favor of pure pragmatism? Absolutely not. Passion has its place—it just shouldn't be the sole guide for your career. By combining the deliberate skill-building of the craftsman mindset with the joy and individuality of your passions, you can create a career that offers both purpose and satisfaction.

And if it's true that the best things in life come from balance, then perhaps the real secret isn't to follow your passion, but to shape it—and yourself—along the way.

Why Skill Triumphs Over Passion: The Craftsman Mindset

“Ask not what your career can do for you. Ask what you can do for your career.”

This, in a nod to the Kennedy inaugural address, is one way to distill the craftsman mindset. Rather than expecting to follow one's passions to the ideal job—which focuses on the value one's career can offer them—those who adopt the craftsman mindset instead sharpen their craft through deliberate practice in order to accumulate career capital, which they can later exchange for greater autonomy and fulfillment.

In part two of this multi-part series, we'll consider examples of the craftsman mindset in action before examining its results and identifying the gaps it leaves behind.

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Craftsman at Work

In So Good They Can't Ignore You, author Cal Newport lays bare the craftsman mindset and how it better aligns with what actually brings people joy in their careers, namely autonomy, competence, and relatedness.

If these terms seem familiar, you're likely already acquainted with self-determination theory, which posits that autonomy, competence, and relatedness are the psychological needs that “underlie growth and development,” as the University of Rochester Medical Center describes it.

Autonomy, one might point out, is precisely what those who pursue the passion hypothesis garner for themselves. After all, if one follows their passion, will they not find themselves with a career that empowers them to do what they will, when they will, as they see fit?

This was not my experience as a full-time author, nor is it the experience of thousands of entrepreneurs whose work lives have long since eclipsed whatever might have remained of their personal lives—none of which is to mention the lack of autonomy they might experience in their work itself.

For example, though I had cultivated—or at least believe I cultivated—a great depth and breadth of competence in the realm of creative writing and, in doing so, tapped into a strong sense of relatedness via the expansive and engaged writing community, I had to take pains to ensure I had time to focus on my own writing day to day.

Between an obligation to social media algorithms (platform, platform, platform), an always-on-time production schedule for the Writescast Network, and my freelance editing clients, there was always someone who depended on me to deliver for them. In time, these pressures consumed me as they have so many others—including those detailed in the early pages of So Good They Can't Ignore You.

For my part, I felt the only way to reclaim a sense of self was to discontinue my editing services and shut down the Writescast Network. In other words, I had to trade relatedness and public-facing demonstrations of competence to reclaim the autonomy I had lost in my adherence to the passion mindset. It's only thereafter that I used this rediscovered autonomy to tactically invest in upskilling alongside others who were looking to do the same.

From Craft and Passion to Craft and Craft Alone: A Risky Proposition

Having successfully followed my passion to a career in publishing, it felt dangerous to abandon that which vocational awe had led me to believe was an extraordinary privilege. It took little reflection to recognize that casting that privilege aside to enter a highly-competitive, ever-changing field did not always seem like the best proposition.

After all, I'd made a years-long investment in upskilling in the world of all things writerly, which, in its own way, suggests that the craftsman mindset isn't all it's cracked up to be. Simply put, an investment in sharpening my craft in one field had once led me astray; why did I believe I would find a better balance in a field not my own?

There are a couple of reasons for this: differences between the fields themselves and my own poor investments of career capital.

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Fields of Play, by Which I Mean Work

Working in tech distinguishes itself from publishing in a number of ways, one of which is along objective-subjective lines. By this, I mean there is almost always something subjective when it comes to evaluating the outputs of authorship (read: books, short stories, query letters, etc.).

Contrast this with working as a software engineer, where, at the end of the day, your code either satisfies acceptance criteria or it doesn't. If your code works and you're on a tight delivery schedule, congratulations! Let's merge it and begin the delivery cycle anew. If you're not up against a deadline, yeehaw. You've got plenty of time to take that already-working code and fine-tune it further for future-proofing and performance.

These contrasts are of course simplifications. One could make the argument that there is a sense of the objective that emerges when evaluating a manuscript. For example, imagine someone pitching their work as a capital-R romance when it's clearly contemporary fiction with a romantic element. This does happen, and there are objective (or close to objective) criteria for defining the lines between genres.

And in the world of software, there are clearly countless ways to engineer one's way to solutions. When those solutions are later reviewed by one's peers prior to acceptance into the greater codebase, subjective matters of style and approach can arise. However, everything that undergirds one's code is ultimately ones and zeroes the deeper down one drills, so the arguments to be made in favor of or against some approaches have their bases in the immutable.

The takeaway here is that engineering tends toward the objective; the same cannot be said for publishing. As a result of this difference, as one upskills with respect to the former, there is a greater likelihood of correlation between skill and success.

The appeal of this, to me, made taking the risk of starting from close-to-zero in a new field more than worth it. There was always the chance that it wouldn't work out, but with enough dedicated study and self-advocacy, I could see a way forward in engineering in ways that were far more uncertain in publishing.

Bad Bet

I'd be remiss to not count my own poor investments as among the contributing factors to my demise as an individual who worked in publishing full time. When I signed my first publishing contracts and left my job in regulatory affairs to focus on writing, I suddenly found myself facing a dilemma I had not yet encountered: I had to—or felt I had to—justify my choice to abandon my career by making money from writing-related work as soon as possible.

This is a natural fear to have had, but I went about addressing it in ways that weren't in alignment with my long-term goals. The quickest way to generate income was to trade in the career capital I'd been accumulating (industry and craft knowledge) and expand my client base as an editor. This required me to increase my reach as a podcaster, which required me to record more episodes, which required me to spend more time on social media to promote them, all of which was, again, in service of expanding a client base that would require more of my time.

The end result of this cycle was one that pressed me to spend more and more time away from the very thing I'd left outside-the-home-employment to do: write. In other words, I'd made a bad bet with my career capital; rather than focusing exclusively on my own work in order to increase the likelihood of sustainable success, I surrendered nearly all of the autonomy I had earned for myself in those years of honing my craft as a writer and establishing myself as a trusted voice in the writing community.

The cycle that underlies this series of events is discussed in So Good They Can't Ignore You. It is also, affirmingly, not an unusual circumstance for those who adopt the craftsman mindset to find themselves in. When one has become sufficiently skilled as to attract the attention of others, the advice the author gives is to embrace opportunities that give you more autonomy and reject those that do not.

It should go without saying that I really wish I'd read this book years ago.

Knowing what I know now—and given the industry differences described above—my belief that I could find a better balance in a field not previously my own should be much more understandable.

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Autonomy and Control: Lessons Learned and Applied in Engineering

On that notion of embracing that which grants one more control, though: I did lose some autonomy by returning to more traditional employment. I'm beholden to direction of the organization and my department, yes, but this was to be expected. The career capital I'd cultivated in publishing was not, unsurprisingly, wholly transferable to this new-to-me field of software engineering.

But, in the three years since I made this career transition, I've already found ways to accumulate industry-specific career capital for myself, which has translated to greater autonomy and, as self-determination theory would have it, an increased sense of job satisfaction.

It may seem that I've just contradicted myself. How can I be beholden to an organization's mission while still having opportunities to pursue that which appeals to me while on the job?

Because I make time to do the latter.

Every Friday afternoon, I have my calendar blocked off with a big red do-not-disturb meeting that reads Exploration Afternoon. During that time, I work on pet projects whose purposes range from making me or my team more efficient to building apps that ensure customer support teams are able to resolve client inquiries as quickly as possible.

Despite the success these Exploration Afternoons have brought me—and how they've helped me further my technical skills—I know I'm not, nor do I believe I will ever become, the kind of engineer for whom reading textbooks about Typescript design patterns is an ideal weekend. Those engineers have a unique marriage of craft and passion for this specific field, and it's these individuals to whom I can turn when highly specialized knowledge is required.

At the same time, it's also these folks one must compete with for promotions.

This could engender a sense of embitterment or fear: if my relative lack of passion for the close-to-the-metal technical aspects of my day job is unlikely to position me for long-term success, have I perhaps chosen the wrong career path? This is where the craftsman mindset comes up short. I'll argue that the craftsman hypothesis, when taken to its extreme, embraces the notion that only most highly skilled individuals in a field can ever expect to succeed in that space.

As we've explored here, skill is deeply important, yes, but one's passions need not be aligned with an industry's stereotypes to enjoy success. In fact, as I'll argue in the next post in this series, bringing one's unique, external passions to a position that would not ordinarily emphasize them can create an ideal environment to stand out from the crowd. In the long run, this helps one further develop a sense of autonomy, feel a sense of relatedness, and demonstrate one's competence all at once.

And wouldn't acolytes of self-determination theory take solace in that? I know I do, and I'm hopeful you will as well after reading part three in this series.