“I'm dying. Of overwork.”

When I read those sentences by Kache, I thought of all the late nights I spent creating just yet another product idea, a research deep-dive, a short story. I indulge with exhilaration but also feel the creeping exhaustion. It’s a good type of exhaustion. Almost romantic and evocative of the Renaissance. The hunger to unravel the world's secrets transformed sore wrists and weary eyes into marks of devotion—almost blissful in their ache.

This is the paradox of our time: the very tools designed to free us from labor are trapping us in an endless cycle of escalating work. As our productivity increases, our standards and expectations rise even faster, creating a psychological Jevons Paradox that threatens to consume our humanity in the pursuit of ever-greater output. We become victims of our own efficiency.

I sent the essay to some of my most prolific, creative friends, and they all resonated deeply. Faces materialized in my mind like photographs developing in solution. The indie hacker from Paris who has built four bootstrapped micro SaaS businesses with Loveable. Researchers in China taking apart viral closed-source products and launching open-source versions at record speed. The game designers from Prague who, with the integration of ThreeJS, can now build indie-games with a Blender MCP, all playable within a browser, hosted on SpacetimeDB. The writer from LA that now, armed with Comfy and Midjourney, could storyboard their vision in high fidelity without pitching first to studios.

What happens when the consensual vision around how AI minimizes work time and maximizes leisure time is in fact, wrong. And what happens in reality, when those that are already productive, only work so much more? And as individuals become much more empowered, how does one stop oneself from working, that now the opportunity cost of not working becomes almost infinite?

Jevon’s Paradox: from coal to cognitive labor

If you work in tech, you’ve been hearing about the Jevon’s Paradox applied to the supply and demand of AI and compute. Investors and executives justify the skyrocketing valuation of Nvidia stock with such dynamic.

The term arrived in 1865 when William Stanley Jevons noticed something peculiar about coal: make it more efficient to use, and people burned more, not less. This paradox has shadowed every technological leap since. Efficiency doesn't tame our appetites; it whets them. Our innovations become trampolines for our desires.

Compute and AI exhibit a strong version of Jevons' Paradox as technical leaps in efficiency rapidly drive down the cost per calculation. Potential demand for computation continues to expand with new AI capabilities and innovations, and no obvious saturation point appears in sight. As computing becomes cheaper, possibilities multiply. Each new capability spawns a thousand use cases, and our consumption of resources blossoms, radiant and hungry.

But what happens when we apply this same logic to human labor — our time, our energy, and our creativity?

Traditional economics might predict that AI-boosted productivity would reduce working hours, a four-day weekend for tasks that once took five days. But reality has different plans. We're witnessing what I call the "labor rebound effect"—productivity doesn't eliminate work; it transforms it, multiplies it, elevates its complexity. The time saved becomes time reinvested, often with compound interest.

When your productivity increases, several mechanisms kick in simultaneously:

Leisure's opportunity cost skyrockets. When an hour of work generates what once took days, rest becomes luxury taxed by your own conscience. Every pause carries an invisible price tag that flickers in your peripheral vision.

Productivity breeds new demand. Like efficient engines creating new energy uses, AI can create entirely new work categories and expectations.

Competition intensifies. The game theory is unforgiving: when everyone can produce 10x more, the baseline resets, leaving us all running faster just to stay in place.

Today, I see a psychological version of Jevons Paradox reshaping our relationship with time itself. As our tools amplify each hour's potential yield, our internal expectations don't just keep pace, they outrun our capabilities like shadows lengthening at sunset. There’s an exquisite tension between what we can theoretically accomplish and the stubborn limitations of being human. When every hour holds tenfold possibility, rest feels like surrender, like watching gold-threaded potential slip through your fingers into a river.

The anxiety manifesting across industries reveals the central paradox of the 10x future: as our tools enhance our capabilities, they simultaneously deepen our sense of inadequacy. When speaking with founders and creators, a common thread emerges: the fear is no longer about meeting standards, but about optimizing every variable in an increasingly complex equation. A senior engineer at a major tech company recently noted that his team's productivity metrics had improved, yet satisfaction scores had declined. The tools that promised liberation have instead created a new form of constraint.

To navigate our path, we benefit from reflecting on how our measures of progress have evolved and what values we might want to preserve along the way. Suppose we don't consciously redefine what our systems optimize for. In that case, we risk falling into what Scott Alexander might call a Malthusian trap, where competitive pressures force everyone to sacrifice everything else at the altar of productivity, creating a collective outcome no individual wants.

Measures of progress

The very metrics we use to measure progress have evolved in ways that accelerate this treadmill effect. Looking back through history, we can track how our definitions of productivity and success have shifted, each time recalibrating what we optimize for:

Our metrics of progress have continuously abstracted: from the tangible bushels of wheat in agricultural economies (with natural physical limits), to industrial-era efficiency metrics (units/hour, machine uptime), to the nebulous productivity measures of knowledge work. Each evolution has moved us further from human-scaled, naturally bounded metrics toward increasingly arbitrary optimizations without clear stopping points. And now AI accelerates this abstraction, transforming the constraint from output capacity to our ability to evaluate infinite possibilities.

Each evolution in how we measure progress has pushed us further from human-scaled metrics toward increasingly artificial optimizations. We've moved from "enough food to survive the winter" (a natural limit) to "maximum possible output" (a limitless treadmill).

I recalled the brief time I spent in the Beijing's tech scene as a college student. The infamous "996" work culture (9am to 9pm, six days a week) emerged not from necessity but from the recursive application of productivity metrics in a hypercompetitive environment. When everyone could theoretically work more hours, everyone eventually had to. The West's fear that "all our code will be written in Bangalore or Shenzhen" missed the point entirely. What actually happened was more complex: technology democratized software creation globally, creating new categories of work faster than we could name them. The phenomenon fundamentally transformed the nature of work itself, reshaping expectations around output, availability, and the boundaries between work and life. Workers everywhere faced boundless possibilities, and relentless pressure.

“Measuring what matters”

Walking through San Francisco's SoMA district, past endless rows of startups all promising to make something 10x faster, I couldn't shake the feeling that we're missing the real story. The question isn't how to optimize the current system, but whether we need to redefine what we're optimizing for.

Integrating AI into our workflows has created a "meta-optimization problem." When everyone suddenly gets 10x more powerful, the hard part isn't doing things—deciding what's worth doing in the first place. My friend at a high-profile AI startup told me recently that their biggest challenge isn't training better models, but figuring out which problems these models should address, and how their solutions integrate with human values and needs. In other words, “measuring what matters.“

Some experimental attempts at new measures I’ve seen:

• Time affluence over output volume. Companies like Basecamp and Buffer have deliberately optimized for employee time sovereignty, measuring success not by maximizing productivity but by creating sustainable workloads that allow for deep thinking and creativity.

• Well-being indices over growth metrics. New Zealand's "wellbeing budget" and Bhutan's Gross National Happiness index represent national-scale attempts to optimize for human flourishing rather than simply economic expansion.

• Impact depth over efficiency. The most thoughtful AI implementations I've seen don't ask "how can we do more?" but "how can we go deeper?"

This shift in what we optimize for doesn't mean rejecting productivity gains. Rather, it means capturing those gains as genuine improvements to human flourishing instead of feeding them back into an endless cycle of escalation. For example, Cursor has created the potential for developers to spend more time on creative problem-solving and less on mechanical implementation. But realizing that potential requires consciously redirecting the productivity dividend toward something other than simply writing more code.

An individual’s choice in the Malthusian Trap

My more self-aware friends resist the productivity treadmill with deliberate rituals of restraint, imposing artificial boundaries against endless optimization. While a true Malthusian Trap is difficult for individuals to escape (the very nature of such dynamics operates at a system level), we can still create personal boundaries that preserve our humanity within the larger game.

Some of the early strategies that seem promising:

Intentional constraints. Some of the most interesting creative work happening now involves deliberately limiting AI's role. An artist I know uses AI for initial concepts but insists on manual execution, embracing the "beautiful mistakes" that emerge from human imperfection. By imposing artificial constraints, she creates work that feels distinctly human.

Open-sourcing ideas. Projects that focus on collective flourishing (network building) rather than individual optimization can partially escape the treadmill effect. As technology moat of software decreases, open sourcing software or knowledge that creates positive externality to the surrounding community ended up creating (and eventually capturing) more value than close-sourcing ideas.

Redefining success entirely. The most radical approach involves changing the metrics completely. I was at dinner with founders last month when the conversation turned unexpectedly vulnerable. "When everyone can do everything," one founder confessed, staring into his untouched wine, "doing anything starts to feel meaningless." After a thoughtful silence, another suggested that perhaps meaning was never about output at all, but about connection, presence, and the quality of our experience.

A better vision

A few weeks ago, I watched a performance at a small theater in Brooklyn. Her voice cracked during the climax of her song. In that moment of vulnerability – that beautiful imperfection – the audience leaned in. I felt my chest tighten with recognition. I heard whispers in the crowd afterwards:"That wasn't my best performance technically, but it felt really real."

That's the paradox we face: in a world where AI can generate flawless execution, our humanity shines brightest through our limitations, not despite them.

The contours of a 10x society are already visible in pockets of cyberspace. In the Midjourney's Discord, we see glimpses of this future: artists, developers, and enthusiasts fluidly moving between roles, their enhanced capabilities allowing them to operate as both creators and curators, teachers and students, often simultaneously. The traditional boundaries of profession and practice blur not because they're being forcibly eroded, but because they're becoming irrelevant to how value is actually created.

When technical execution becomes trivially easy, when anyone can spin up a startup, design a fashion line, or produce a film, the scarce resource becomes knowing what's worth doing in the first place. And what’s worth doing is typically deeply subjective.

Many have asked me recently, somewhat anxiously, about how I see the future of work unfold. I start asking them: close your eyes and think what amplifies your soul?

The answers I’ve gotten were about overcoming some really hard thing like finishing a marathon, experiencing a mind-blowing hospitality experience provided at a hole-in-the-wall bar, or an imperfect but emotionally-filled performance on Broadway. I thought of this Joan Didion quote on self respect:

“To do without self-respect, on the other hand, is to be an unwilling audience of one to an interminable documentary that details one's failings, both real and imagined, with fresh footage spliced in for every screening. (...) To live without self-respect is to lie awake some night, beyond the reach of warm milk, phenobarbital, and the sleeping hand on the coverlet, counting up the sins of commission and omission, the trusts betrayed, the promises subtly broken, the gifts irrevocably wasted through sloth or cowardice or carelessness. However long we postpone
it, we eventually lie down alone in that notoriously uncomfortable bed, the one we make ourselves. Whether or not we sleep in it depends, of course, on whether or not we respect ourselves."

Art collectors instinctively know this. The most successful shows aren't the ones displaying technical virtuosity but those that wrestle with distinctly human questions.

This may be the good news for those that didn’t dare to fully lean into what they love and want to do. What if the most game-optimal play in the new system is actually to become relentlessly, unapologetically you?

It’s not just “you can just do things,” but that “you can just be you, doing things.”