Unpredictable Patterns #111: The Great Bifurcation of Time

In defense of biological tempi, AI as temporal interfaces, the bifurcation of time and judgment/action economies

Dear reader,

This week’s note will be focused on a subject that I have been long interested in - technology and time. I have had a series of conversations with people about this over the years, and recently a conversation with Tom Rachman reignited my interest in the subject (do read his excellent essay on AI and behaviour over at AI-policy perspectives). If you are interested in my earlier thinking about this, this paper on law, technology and time as well as these earlier posts on time machines, the production of now and rhythm. This week we will explore the relationship between technology and time more in detail - with a focus on artificial intelligence. (The reading time for this is 25 minutes, and so here is a playlist that lasts 26 minutes for your enjoyment! We aim to please.)

The Production of Time

Humanity's relationship with time has been fundamentally transformed by technology. What was once experienced through natural cycles—the rising and setting of the sun, the waxing and waning of seasons—has become increasingly mediated through technological interfaces. This technological production of time changes us in important ways, opening up new ways for us to act and exist.

The history of timekeeping technology reveals our evolving relationship with temporality. Early civilizations relied on sundials, water clocks, and hourglasses—devices that measured time through natural phenomena like shadows or flowing water. These instruments chunked the day into rough increments, sufficient for agricultural societies governed by seasonal rhythms.

As Lewis Mumford notes in Technics and Civilization (1934), this changes when the medieval monastery introduced the mechanical clock. Invented to regulate monastic prayer schedules, the mechanical clock would eventually transform human consciousness. Mumford argued that the clock, rather than the steam engine, was the key machine of the industrial age, as it created the concept of measured, abstract time. "The clock," he wrote, "is a piece of power-machinery whose 'product' is seconds and minutes."¹

This technological production of time eventually allowed for the synchronization of human activity, making possible the coordinated labor of factories and the precise scheduling of trains. In Heideggerian terms, technology made time available as a standing reserve (Bestand) to be used. In his essay The Question Concerning Technology (1954), Martin Heidegger argued that modern technology does not merely utilize time but fundamentally alters our relationship to it. Through the "enframing" (Gestell) of technological systems, time becomes a resource to be optimized and exploited, divorced from natural rhythms and authentic human experience. The clock transforms time from something we dwell within into something we track, manage, and consume - it shifts time from a private experience into a public resource.²

Technological innovation has since progressively accelerated human experience. The French philosopher Paul Virilio developed what he called dromology—the study of the logic of speed—to understand this acceleration.³ For Virilio, speed is not merely a phenomenon produced by technology but the defining quality of modernity itself - each technological revolution recalibrates our relationship to speed and consequently to time.

Consider the compression of distance: walking gave way to horseback riding, which yielded to steam railways, automobiles, and eventually supersonic flight. Meanwhile, communication technologies followed a similar trajectory, from written letters to telegraphs, telephones, and finally instant digital messages. Virilio puts it well: “here no longer exists, everything is now”.⁴

Fast-forward to our digital age, and Judy Wajcman's Pressed for Time (2015) challenges the simplistic narrative that technology merely speeds everything up. Wajcman argues that digital technologies haven't just accelerated time—they have provided interfaces to time that allow us to choose different temporal tactics. Consider how your smartphone simultaneously creates time pressure (the expectation of immediate email responses) while offering new temporal flexibility (the ability to work from anywhere) - there is, even under pressure, a choice for us here as to how we engage with the new temporality constructed by the technology.

German sociologist Hartmut Rosa offers perhaps the most comprehensive theoretical framework for understanding these dynamics in his seminal work Social Acceleration: A New Theory of Modernity (2013). Imagine time as a three-layered system constantly speeding up: Rosa identifies technological acceleration (faster transport, communication, and production), social acceleration (more rapid turnover of institutions and relationships), and life-pace acceleration (the compression of actions within smaller time units). It's not just that your phone is faster than last year's model—it's that the entire social world now churns at a quicker pace, forcing you to adapt by cramming more activity into each hour. These dimensions, Rosa argues, form self-reinforcing cycles that drive modernity's accelerating tempo.

But Rosa also observes something else, almost in passing, that turns out to be very important as we move to think about AI and time. He writes: “To the contrary, many things slow down, like traffic in a trafficjam, while others stubbornly resist all attempts to make them go faster, like the common cold.”

Why do some things slow down or refuse to move faster? The answer is that we are increasingly living in a world with two major forms of time.

Computational time and biological time

Computers are different from clocks, and computational time is different from clock time - and they are both very different from biological time.

Computational processes operate according to their own internal sequence logic—executing one instruction after another—but at speeds that have increased by orders of magnitude over decades. A calculation that might have taken hours in 1980 happens in microseconds today - but do you know why? Why is it that computers are faster now than they were in the 1980s? The answer is surprisingly simple: the physical substrate of computation (silicon chips) allows for this acceleration by shrinking distances between transistors, reducing the time needed for electrical signals to travel.

Sure, architectural and material changes also matter - but it is to a significant degree through the compression of space that acceleration is produced.

Biological processes, however, follow chemical and physical pathways that cannot simply be "sped up" by miniaturization. A cell divides at a rate governed by complex biochemical reactions, a bone heals through stages that require a certain temporal unfolding, and a human pregnancy proceeds through developmental stages that resist compression. These processes have their own intrinsic temporality.

Another way to think about this is to consider it from the perspective of physics. Physical systems tend toward entropy (increasing disorder) over time—the second law of thermodynamics. A cup falls and breaks; it doesn't spontaneously reassemble. This entropic direction creates what physicists call the "arrow of time"—the fundamental asymmetry between past and future.

Living systems, however, temporarily reverse this trend through what Ilya Prigogine called "dissipative structures"—open systems that maintain internal order by exchanging energy with their environment. A plant absorbs sunlight to organize simple molecules into complex ones; your body maintains its complex structure by consuming energy from food. These processes are negentropic—they create order rather than disorder, swimming against the entropic current.

Computational systems represent something new: they're designed to process information without degradation. Digital data doesn't wear out with repeated use; algorithms don't become less effective through repetition. This creates a form of time that's neither purely entropic nor negentropic, but informational—governed by the manipulation of patterns rather than energy flows.

The clash between entropic physical time, negentropic biological time, and informational computational time helps explain why some processes resist acceleration. Biological healing, for instance, requires the coordinated negentropic activity of billions of cells responding to environmental cues—a process that can't simply be "computed" faster. The common cold remains stubbornly tied to the rhythms of viral replication and immune response, operating according to negentropic principles that computational acceleration can't override

The musical tempo of institutions, policy and politics

Why can your laptop perform calculations billions of times faster than it could thirty years ago, while a broken bone still takes roughly six weeks to heal? This question reveals a profound divide between computational time and biological time—a divide that shapes our experience of the modern world in ways we're only beginning to understand.

Imagine peering inside a computer chip. What you'd see is a race against distance itself. Unlike the steady pendulum of a clock marking uniform intervals, computation involves signals sprinting between transistors. The dramatic acceleration of computing over decades stems to a large degree from one achievement: we've made these signals run shorter and shorter races. By shrinking the physical space between transistors from micrometers to nanometers—a 1000-fold reduction—we slowly push computational processes to approach the ultimate speed limit: the velocity of light. Sure, this is a simplification - we have also seen new parallel architectures and the introduction of new materials, but the compression of space has been a key factor in acceleration!

Biological processes work differently. A cell doesn't divide faster when we wish it would—it proceeds through an intricate biochemical choreography that unfolds in its own time. A broken femur knits itself back together through stages that cannot be rushed: inflammation, soft callus formation, hard callus formation, bone remodeling. The nine months of human gestation represent not an arbitrary timeline but a necessary sequence of developmental events, each building upon the last. Even our consciousness operates at speeds determined by neural transmission rates and biochemical cascades that haven't changed since Homo sapiens first appeared.

What's the fundamental difference? While computational systems follow the logic of information processing, biological systems engage in a constant battle against entropy—the universe's tendency toward disorder. Your body maintains its complex structure not by storing information but by continuously harnessing energy to create order from chaos. This negentropic activity—what physicist Erwin Schrödinger called "feeding on negative entropy"—operates according to chemical and thermodynamic principles that set hard limits on how quickly biological processes can unfold.

Now, this abstract set of observations have some very real consequences, because human institutions mirror these biological constraints. Consider justice and markets as pieces in society's symphony, each with its natural tempo. Justice performs as a sostenuto—a slow, sustained movement requiring deliberate pacing and thoughtful development. Speed a sostenuto beyond recognition, and you destroy the very qualities that give it meaning. Markets, by contrast, perform as an accelerando—quickening naturally as they process information and reallocate resources. Forcing markets to play adagio often leads to stagnation and distortion.

The technological acceleration of our era tempts us to make everything as rapid as computation itself. We grow impatient with the deliberate tempo of democratic deliberation, ethical reflection, or meaningful relationship-building. We schedule our days in smaller and smaller increments, squeezing activities into time slots that barely accommodate them. We even grow frustrated with our bodies' stubborn adherence to biological rhythms—needing roughly the same amount of sleep, recovery time, and digestive processing as our ancestors did millennia ago.

But what happens when we try to force institutions to operate at computational speeds? Imagine taking Bach's Cello Suite No. 1—a piece whose profound beauty emerges through its deliberate unfolding—and speeding it up a thousandfold. At such speeds, the music wouldn't just sound different; it would cease to be music at all, becoming an incomprehensible burst of noise. Similarly, justice compressed into microseconds isn't merely quick justice—it's no longer justice. Democracy conducted at processor speeds isn't accelerated democracy—it's something else entirely, stripped of the deliberation, reflection, and human connection that give it meaning.

The question that emerges from this tension is one of the more intriguing political and policy issues facing us: the political organization of time and tempo. But rather than end up predicting collapse due to the increased tension between computational and biological time, we can actually explore a much more hopeful scenario - because of the emergence of artificial intelligence.

The Coming Bifurcation of Time - AI as temporal mediator

The preceding analysis suggested a fundamental incompatibility: as computational systems accelerate while biological rhythms remain stubbornly constant, humans face an insurmountable temporal divide. We appear destined for increasing friction between silicon speed and cellular patience, with our institutions caught in the crossfire.

But this conclusion misses something profound: the emergence of artificial intelligence as a temporal mediator—a technology uniquely positioned to bridge computational and biological temporalities.

Consider what happens when you interact with a large language model like ChatGPT or Claude. Computational processes are operating at astronomical speeds—billions of operations per second—yet the interface doesn't overwhelm you with this velocity. Instead, it presents information at a pace you can metabolize, often mimicking human conversational rhythms. The AI serves as a temporal step-down transformer, converting the nanosecond world of computation into the second-by-second world of human cognition.

This mediation works bidirectionally. When you step away from a conversation with an AI for hours or days, the system doesn't experience this as "waiting"—it exists in a suspended computational state, ready to resume instantly when you return. Your biological need to sleep, eat, or reflect doesn't create friction because the AI operates in a fundamentally different temporal framework where such pauses have no inherent meaning.

This temporal brokerage has profound implications, pointing toward what may be the most significant sociotechnological transformation of the coming decades: the great bifurcation of time.

We are entering an era where computational and biological temporalities will increasingly decouple rather than collide. Instead of human institutions racing to match computational speeds—a race they cannot win—AI systems will negotiate between these temporal domains, allowing each to operate according to its indigenous rhythms.

Consider what this might mean for knowledge work. Rather than humans attempting to process information at computational speeds (a losing proposition), AI systems will increasingly serve as asynchronous collaborators—working continuously through problems at computational speeds, then presenting solutions when the human is ready to engage. This is what we already see with deep research modes in chat agents, to take a simple example. The human provides direction, judgment, and values at a biological pace, while computation proceeds at electric speeds in parallel.

Financial markets already hint at this bifurcation. High-frequency trading algorithms operate at microsecond scales utterly inaccessible to human traders. Rather than forcing humans to trade at algorithmic speeds (an impossibility), the market has bifurcated into multiple temporal layers: algorithms interacting with algorithms at one timescale, while human investors make decisions at very different ones, with various AI systems mediating between these layers.

This pattern will extend across domains. Consider:

• In healthcare, AI systems will continuously monitor vital signs and medical data at computational speeds while in parallel ingesting the latest research, any new data from medical macro monitoring and then present actionable insights to doctors and patients at human-comprehensible intervals.

• In education, adaptive learning systems will analyze student performance at millisecond resolution while delivering personalized guidance at pedagogically appropriate paces.

• In governance, AI systems will process vast quantities of data and model outcomes at speeds no human could match, while presenting options to policymakers in formats that support thoughtful, ethical deliberation - and these systems could even explore the possible space of negotiated agreements at the same time - converging on possible equilibrium points.

Perhaps most significantly, this bifurcation will enable what we might call a personal temporal space—individualized relationships with time itself. When AI systems mediate our relationship with accelerating information flows, we gain the capacity to control our temporal experience in unprecedented ways.⁵

Imagine working with an AI that shields you from the tyranny of immediate response, aggregating messages and information into batches delivered at intervals you specify. Or consider how AI might let you engage with rapidly changing fields at your own pace, continuously synthesizing developments while you're away and presenting only what's relevant when you return. No longer must you choose between staying "current" (racing to match computational speeds) and preserving your sanity (honoring biological rhythms)—AI creates a third option: remaining connected while maintaining temporal autonomy.

This suggests a profound revision to Rosa's acceleration theory. Rather than technological acceleration inevitably forcing humans to accelerate likewise, AI creates the possibility of computational processes continuing their exponential speedup while human experience potentially slows down. The economy, information flows, and technological systems might operate at ever-faster speeds, yet human experience could become more deliberate, more aligned with biological rhythms.

This bifurcation brings both challenges and new openings. On one hand, it might free humans from the acceleration treadmill, enabling a renaissance of temporally-appropriate activities: deep reading, contemplation, craftsmanship, and relationship-building. We might witness the emergence of "slow thought" movements that reclaim biological temporality while still benefiting from computational acceleration where appropriate.

On the other hand, temporal bifurcation risks creating new inequalities—between those who can afford AI mediation and those forced to race against computational speeds directly. It also raises profound questions about agency and power: who controls the parameters of these temporal interfaces? When AI systems decide what information to present and when, they make value judgments about temporal importance that have deep implications.

It will require that we learn to think in time in new ways. Just as learning to maneuver a car requires new physical techniques, working with temporal mediators will require learning new concepts and ideas and new ways of exercising our vastly augmented agency.

What's emerging is something like a fundamental reorganization of temporal experience—perhaps the most interesting since the mechanical clock synchronized human activity in the medieval era. The coming bifurcation of time could reshape our relationship with temporality as dramatically as the clock did for our ancestors, but in the opposite direction: not by forcing human activity into mechanical synchronicity, but by creating multiple, parallel temporal registers mediated by artificial intelligence.

The grand reversal is that technology—having accelerated human temporal experience for centuries—may now enable its deceleration. As computation approaches physical speed limits and AI systems mature as temporal mediators, we face an unprecedented opportunity: to reclaim human temporal sovereignty even as computational processes continue their headlong race toward the speed of light. Where technology used to be force for synchronization, it now creates a new asynchronicity.

There will be new breaking points - the computational / biological temporal transformation has limits - but the possibility here to vastly expand our agency in time is incredibly promising.

An Example of Temporal Mediation

To truly grasp how AI functions as a temporal mediator, consider the evolution of computational support in a doctor's diagnostic process—a transformation that reveals the profound shift from simple acceleration to complex temporal orchestration.

Yesterday: Acceleration of Reference

A decade ago, Dr. Chen might have used a medical database to check symptoms against possible diagnoses. The computer accelerated what was essentially a reference task—allowing her to search medical literature more quickly than flipping through textbooks. The temporal relationship was straightforward: the computer performed a single task faster than a human could.

This represents what we might call "first-generation temporal assistance"—simply speeding up discrete tasks that humans would otherwise perform sequentially. The doctor remained the orchestrator of all diagnostic processes, with the computer serving merely as an accelerated reference tool.

Soon: Massive Parallelization and Temporal Mediation

Now, imagine Dr. Chen in 2027 examining a patient with puzzling symptoms. As she conducts her interview, an AI medical system engages in what would be impossible for any human physician—not just acceleration, but massive parallelization across multiple temporal domains:

• While Dr. Chen asks her first question, the AI has already analyzed the patient's electronic health record, identifying patterns across decades of medical history that might escape human notice.

• As the patient describes their symptoms, natural language processing assesses subtle linguistic markers that might indicate depression, cognitive impairment, or pain levels the patient hasn't explicitly mentioned.

• Simultaneously, the AI queries epidemiological databases to determine whether the symptoms match patterns of disease currently circulating in the patient's geographic region or demographic group.

• In parallel, it runs simulations of how different treatment protocols might interact with the patient's existing medications and genetic profile as well as their personal life and circumstances.

• It cross-references the latest research papers published globally within the last 24 hours that might relate to the symptoms, synthesizing findings from hundreds of studies.

• Analyzing video of the consultation, it detects micro-expressions indicating patient anxiety about particular topics, flagging these for the doctor's attention if the patient has agreed to this.

• It compares this case against Dr. Chen's previous diagnostic patterns, identifying potential cognitive biases she tends to exhibit with similar presenting symptoms.

Each of these processes operates in computational time—milliseconds to seconds—while the human conversation unfolds over minutes. What's remarkable isn't just that these processes happen quickly, but that they happen simultaneously, in parallel temporal streams that would be impossible for a human mind to coordinate.

Yet Dr. Chen isn't overwhelmed by this computational hurricane. The AI doesn't flood her with the raw output of these parallel processes—that would simply transfer the temporal burden from computation to cognitive processing. Instead, it performs a sophisticated form of temporal mediation:

• It determines which insights require immediate attention and which can wait until natural breaks in the conversation.

• It translates complex statistical patterns into intuitive visualizations that Dr. Chen can grasp quickly.

• It arranges information hierarchically, presenting the most relevant possibilities first rather than overwhelming her with an exhaustive list.

• It adapts to Dr. Chen's cognitive rhythm, learning when she tends to pause for reflection and delivering insights accordingly.

This isn't mere filtering or prioritization—it's a fundamental reconciliation of computational and biological temporalities. The AI creates a temporal interface that allows massively parallel computational processes to support, rather than overwhelm, the necessarily sequential nature of human cognition.

The Decisive Moment

The power of this temporal mediation becomes apparent when Dr. Chen faces a critical decision point. The patient's symptoms suggest either a common condition or a rare but serious alternative. In the past, the fear of missing the serious diagnosis might have led to defensive medicine—ordering excessive tests "just to be sure."

But now, as she contemplates her options, the AI has already:

• Calculated a Bayesian probability of each condition based on population data, regional epidemiology, and this patient's specific profile.

• Simulated the likely outcomes of different diagnostic and treatment paths, including risks, costs, and recovery trajectories.

• Identified a subtle constellation of three symptoms that, together, strongly indicate one diagnosis over another.

• Flagged a newly published case study from Singapore reporting an unusual presentation of exactly these symptoms.

• Generated a decision tree highlighting key decision points where additional information would most effectively narrow the diagnostic possibilities.

When Dr. Chen engages with this synthesized knowledge, she's interfacing with what would have been months, or years, of sequential human research compressed into seconds—yet presented in a form that respects her biological need to process information at a human pace. She maintains her decisive role in the judgment economy while delegating vast swaths of information processing to the action economy.

The AI doesn't replace her clinical judgment; it expands what that judgment can encompass—extending her cognitive reach across far more information, possibilities, and connections than any unassisted human mind could manage. The doctor continues to operate in biological time, but her decisions are informed by processes that occurred in computational time.

Beyond Acceleration: The New Relationship with Time

This example reveals something important about AI as temporal mediator: it doesn't merely make existing processes faster—it transforms our relationship with time through orchestration and synthesis.

The medical AI doesn't just help Dr. Chen work more quickly; it allows her to work differently, embracing the inherent pace of human-to-human conversation and clinical judgment while simultaneously benefiting from massive computational parallelization. She can be fully present with her patient—maintaining eye contact, building rapport, observing subtle cues—because the AI handles the vast information processing that would otherwise compete for her attention.

This represents a key shift from first-generation digital tools. Early computers accelerated singular tasks, forcing humans to adapt to their limitations. Advanced AI systems orchestrate complex arrays of parallel processes and then mediate between computational and biological temporalities—adapting to human cognitive rhythms rather than demanding humans adapt to silicon speeds.

The result isn't the frenetic acceleration that technology critics fear and preach. Instead, it creates the possibility for a more human pace in domains where biological temporality matters most—like the doctor-patient relationship—while computational processes handle what they do best: rapid, parallel information processing. The doctor doesn't need to rush the conversation to accommodate the computer; the AI accommodates itself to the natural rhythm of human interaction.

This example illustrates why temporal mediation matters so profoundly: it allows us to reconcile the exponential acceleration of computational capabilities with the constants of human cognition and connection.

The Economics of Bifurcated Time

As AI systems increasingly mediate between computational and biological temporalities, we are witnessing, in tandem, the emergence of a fundamentally bifurcated economic landscape—one that separates along temporal lines into what we might call the "judgment economy" and the "action economy."

The judgment economy operates primarily within biological time. It encompasses activities that require human deliberation, ethical reasoning, creative insight, and interpersonal wisdom—processes that resist acceleration because they're fundamentally tied to our embodied experience as biological beings. Judgment involves weighing values, considering context, imagining possibilities, and making decisions that reflect human priorities.

The action economy, by contrast, operates increasingly within computational time. It encompasses the execution of well-defined processes, the gathering and processing of information, the implementation of decisions, and the optimization of systems according to established parameters. These activities can be dramatically accelerated through computational means precisely because they can be reduced to algorithmic procedures.

This division reflects an interesting reorganization of economic activity along temporal lines. Consider how this plays out in various domains:

• In financial services, investment advisors operate in the judgment economy (understanding client goals, risk tolerance, and life circumstances) while trading systems operate in the action economy (executing transactions at microsecond speeds).

• In healthcare, diagnosis increasingly spans both economies—physicians exercise clinical judgment while AI systems rapidly process test results, medical images, and research literature.

• In legal practice, attorneys formulate strategy and negotiate settlements in the judgment economy while document review, case research, and regulatory compliance shift toward the action economy.

AI serves as the critical mediator between these economies, translating between their different temporal registers. An attorney using a legal AI doesn't need to process thousands of cases personally; the AI does this computational work and presents relevant precedents when the attorney is ready to exercise judgment. The judgment remains human, while the action becomes computational.

The New Economic Geography

This bifurcation creates a new economic geography—not of physical location but of temporal position. Economic actors increasingly find themselves situated in relation to this judgment-action divide. Some will specialize in judgment, others in designing and managing action systems, and many will occupy hybrid positions that bridge these temporal domains.

What determines position in this new economic geography? Three factors emerge as crucial:

1. Judgment quality - The ability to make discerning, contextually-appropriate decisions that reflect human values becomes a primary form of economic value.

2. Temporal orchestration skill - The capacity to effectively coordinate between biological and computational timescales—knowing when to accelerate and when to deliberately slow processes.

3. Mediation design expertise - The knowledge needed to create effective interfaces between human judgment and computational action.

These factors are reshaping labor markets in ways that traditional automation narratives miss. Rather than simply replacing jobs, AI is redistributing economic activity across the judgment-action spectrum, creating new forms of work at the boundaries between these economies.

If we believe that this temporal bifurcation is an interesting model to explore, we also will need to explore how economic value is created and captured. In the action economy, value increasingly derives from speed, scale, and precision—computational virtues that can be continuously improved through technological advancement. The ongoing exponential improvements in computational capability translate directly into economic value in this domain.

In the judgment economy, value derives from qualities that resist technological acceleration—discernment, wisdom, creativity, and ethical reasoning. These capabilities aren't necessarily improved by moving faster; often they benefit from deliberate slowness.

As technology accelerates action, it potentially increases the value of deliberate judgment. When execution becomes essentially instantaneous, the limiting factor in value creation becomes the quality of the decisions being executed. In a world where anything can be done, what should be done becomes the essential question.

The bifurcation of economic time creates new forms of capital and, consequently, new dimensions of inequality:

• Attention capital becomes increasingly precious—the capacity to direct sustained, high-quality attention toward judgment tasks without fragmentation or diminishment. Those who can cultivate deep focus gain advantage in the judgment economy.

• Temporal autonomy emerges as a key political good—the freedom to operate according to biological rhythms rather than being subjected to computational tempos.

• Judgment leverage becomes a source of outsized returns—the ability to pair high-quality judgment with high-speed computational action allows individuals to create value at unprecedented scales. When judgment can be instantly executed through computational systems, a single decision can have massive impact.

These new forms of capital aren't originally distributed equally - and our conception of a just society might be explored by imagining how we would allocate these forms of capital under a Rawlsian veil of ignorance.

Interestingly, this bifurcation could our fundamental economic metrics. For centuries, productivity—output per unit of time—has been the north star of economic progress. But productivity as traditionally conceived belongs primarily to the action economy; it measures how efficiently we can execute known processes.

In the judgment economy, the relevant metric isn't productivity but something closer to discernment—the quality of decisions per unit of attention. This shift requires new economic indicators that value wisdom, foresight, and ethical reasoning alongside efficiency and output.

Organizations that thrive in this bifurcated landscape will be those that effectively balance biological and computational temporalities—accelerating action while creating protected space for judgment. They'll recognize that some processes should be optimized for speed while others require deliberate pacing to maintain their integrity and value.

The Future of Work in Bifurcated Time

As this economic reorganization advances, we can anticipate several shifts in the nature of work:

1. Judgment-intensive roles will be increasingly valued - Positions requiring sophisticated ethical reasoning, creative insight, interpersonal wisdom, and contextual understanding will command premiums

2. Pure action roles will continue to automate - Tasks that can be fully specified and don't require human judgment will increasingly shift to computational systems

3. New hybrid roles will emerge at the boundaries - Much work will involve orchestrating the relationship between judgment and action economies, requiring fluency in both temporal registers

4. Temporal design becomes a core business function - Organizations will need specialists who design appropriate temporal frameworks for different activities, knowing which processes benefit from acceleration and which require deliberate pacing

5. Work will be evaluated on new temporal dimensions - Beyond simply measuring time spent or output produced, work evaluation will consider temporal appropriateness—whether activities unfolded at the right pace for their purpose

This transformation represents not just a change in what work we do, but in how work unfolds in time. The winners in this new economic landscape won't simply be those who move fastest, but those who move at the right speed for each task—accelerating where possible while preserving the deliberate temporality required for meaningful judgment.

One economic challenge of the AI era thus becomes not merely increasing efficiency, but orchestrating a robust relationship between the judgment and action economies—between biological and computational temporalities. Those societies that manage this relationship effectively will not only create material prosperity but foster human flourishing in a world of increasingly bifurcated time.

Now, I think the judgment-action framework provides a useful starting point for understanding the economics of bifurcated time, but what we have here is but a rough sketch. Several refinements merit further exploration.

• First, we need a more granular taxonomy of both judgment and action—recognizing different categories of judgment (ethical, aesthetic, strategic, interpersonal) and different types of action (creative execution, routine implementation, embodied performance).

• Second, we must better understand the continuous feedback loops between judgment and action—how decisions shape implementations and how actions generate information that informs subsequent judgments.

• Third, temporal variations within each category deserve attention: some judgments occur rapidly while others require extended deliberation; some actions unfold over long periods while others are instantaneous.

• Fourth, the collective dimensions of judgment—how communities, democracies, and cultures make decisions together—need integration with individual judgment frameworks.

• Finally, sectoral differences in temporal bifurcation require investigation, as industries will experience this transformation unevenly based on their particular blend of judgment and action requirements.

And all models of the world - including this one - are of course wrong. But I do believe that temporal models are consistently undervalued and so can be useful. More than anything I think AI is likely to mean a rethink of many of the temporal modes of interaction our society is built on.

Thanks for reading,

Nicklas

P.S. A few other things that you may find interesting. Here is a note that Petri Kokko and I wrote about how boards can think about AI, a small new entry in a series of hypotheses, about how AI might accelerate trade shifts and geopolitical differences and an essay I co-wrote with some colleagues about AI and science.

1. See latest ed. Mumford, L., 2010. Technics and civilization. University of Chicago Press.

2. See Heidegger, M., 1977 (1954). The question concerning technology. New York.

3. See Virilio, Paul. Speed and Politics. Translated by Mark Polizzotti. New York: Semiotext(e), 1986.

4. See Virilio, P., 2005. The information bomb (Vol. 10). Verso.

5. This may also be challenging - it is easy to see how those arguing that we have information bubbles would see something like personal temporal spaces as problematic. I disagree - I think temporal autonomy is a key pre-requisite for human flourishing.