Descent into the Computational Sublime

October 2026 was a rather bad month for software civilization. By the end of it, cybersecurity professionals were talking about Black October, and the more colorful term vulnopocalypse was also making the rounds. An alarming succession of vulnerabilities had been uncovered in the foundations of the planetary software stack, many with the assistance of frontier AI models. Software maintainers were scrambling to patch systems, security teams were struggling to keep up with disclosures, and a growing number of people were wondering whether the global software stack had become fundamentally indefensible.

Something like a dramatic climax arrived on October 29, when attackers compromised cryptocurrency exchange Binance in an incident involving approximately $6.34 billion in digital assets. The eventual net loss is still being established, but the scale was extraordinary. According to Binance’s November 3 preliminary account, the operation had been prepared over months through fake recruitment interviews, malicious software downloads, compromised developer workstations, and most significantly, the exploitation of an internal AI tool. The attackers eventually obtained credentials allowing them to manipulate wallet-signing infrastructure and initiate unauthorized transfers. Binance said the FBI had attributed the attack to North Korea.

Once inside, the attackers turned parts of Binance’s own cryptographic security machinery against it. On Ethereum, they exploited EIP-7702’s mechanism for delegated execution to establish permissions allowing a malicious smart contract to act on behalf of a compromised wallet. These permissions persisted on the blockchain independently of the exchange’s ordinary withdrawal machinery. Even after Binance shut down withdrawals, previously established authorizations still had to be revoked on-chain. Multiparty cryptographic signing, normally an important security measure, complicated that emergency response because there was no single private key that could simply be used to take back control.

Meanwhile, stolen assets were disappearing into a thicket of cryptocurrency transactions at warp speed. Automated software split funds among wallets, moved them across chains, and exchanged them through decentralized services at a blistering pace, through tens of thousands of smaller individual transactions. Elliptic’s forensic analysis found unmistakable signs of automation but no clear signs of autonomous AI decision-making. Some scripts stubbornly repeated failed operations, suggesting no AI was managing the live process, even if one had perhaps been used to design the scripts.

One can imagine how much harder the chase might become when future laundering agents are both fast and adaptive, capable of recognizing blocked routes, improvising alternatives, and responding to investigators as quickly as investigators respond to them. The possibility is unsettling precisely because the observed operation still had such obvious limitations, and clear headroom for improvement.

Yet the defenders were hardly helpless. The transparency of public blockchains helped investigators trace the movements, while issuers, exchanges, and other institutions could freeze or intercept some assets. Binance reported that approximately $2.85 billion had been frozen or intercepted by November 3, subject to reconciliation, and committed to covering customer losses. Frozen assets, recovered assets, and the attackers’ spendable proceeds are three different quantities, and the final accounting remained unsettled.

There was something almost perversely elegant about the episode. Cryptographic protocols simultaneously aided the attackers, constrained the defenders, and enabled containment. AI tooling appeared in the intrusion path and in the exchange’s subsequent defensive upgrades. Automated financial machinery carried out a sprawling contest over billions of dollars, with human engineers and investigators attempting to contain an intricate system of interacting permissions and economic incentives.

Irresistible Force, Immovable Objects

The incident offered a vivid illustration of what I’ve been thinking about as a pair of technological evil twins: AI as an irresistible force, and cryptographic protocols as immovable objects, characterized by a property we will call hardness.

Intelligence and hardness are general-purpose capabilities/affordances available to competing participants in sufficiently complex computational ecologies. The consequences of their interplay depend on the relationships they enter into, the authorities they acquire, and the interests they serve. A cryptographic commitment can be a hard guarantee for an honest participant or a stubbornly enforceable advantage for a thief. There is no simple mapping between how intelligence and hardness manifest in a particular contest. The abstract duality gets remarkably messy on the ground.

The rest of Black October suggested that Binance was just a particularly revealing episode in a much larger transformation. AI-assisted vulnerability research on PHP, alongside reports involving Brotli and other widely deployed components, was exposing weaknesses in software that most users never encounter directly. Researchers uncovered a Linux networking vulnerability, and Anthropic reported that a model had constructed a working remote-root exploit against a particular configuration in less than an hour. Another critical UEFI firmware flaw appeared beneath the operating systems and security tools on which server administrators normally rely. AI developer platforms themselves were attracting attacks as their credentials and access became increasingly valuable.

These incidents followed months of expanding AI-assisted vulnerability research, including Anthropic’s Project Glasswing, which organized work on critical software with major technology partners. The new bottleneck was increasingly evident: finding possible weaknesses could be scaled computationally faster than the institutional work of validating, disclosing, prioritizing, patching, testing, and deploying repairs.

The atmosphere recalled the approach to Y2K, with an important difference. There was no single deadline after which everyone could relax. Some newly discovered flaws had been lurking in widely deployed software for years. Others reflected the increasingly intricate architectures of contemporary systems. Many could be repaired through conventional patches, and their exploitability varied enormously. Nevertheless, the collective picture was disturbing. We have built a global civilization on an enormous pile of software developed under the assumption that finding subtle vulnerabilities requires scarce human expertise. The arrival of machines capable of examining that pile with extraordinary patience and ingenuity has abruptly changed the economics of the situation.

I suspect Black October will eventually be remembered as the inciting incident of an epic arc in the history of computing. We are beginning to see the contours of a vast transformation that might unfold as three broad, somewhat overlapping acts.

The first act is already underway. The second is taking shape in research laboratories and cryptographic migration projects. The third is only beginning to come into view, though its earliest signs are surprisingly concrete.

Together, they suggest that the epic arc is a descent into the computational sublime. The word descent will acquire a more precise meaning as this essay unfolds. For now, it is enough to notice that Black October has brought into view an unexpectedly deep and tangled history and future beneath the apparently smooth surfaces of the digital world. To understand the emerging future, we have to begin descending into layers of reality we rarely think about, and have historically had limited ability to penetrate.

Dark Forest Apotheosis

Black October marks a new phase in the evolution of the dark forest metaphor for the internet, based on Cixin Liu’s eponymous trilogy.

In 2019, Yancey Strickler’s dark-forest account of the social web described the growing hostility of public social media, where ordinary expression had become exposed to unpredictable scrutiny, conflict, and punishment. Around the same time, I developed the related idea of the cozyweb, the sprawling collection of private groups, chats, and small online communities into which much social life was retreating. We both contributed to a small anthology on the topic, predating AI.

The metaphor also popped up in the Ethereum ecosystem in 2020, where automated trading bots operating in the blockchain’s public transaction environment were relentlessly exploiting opportunities to extract value. Every visible transaction became a potential opportunity for some faster or more sophisticated participant to intervene. The idea of the Dark Forest captured the imagination of the Ethereum community. One of the most influential early onchain games was called the Dark Forest, where players could try to survive in a harsh ecology inspired by Cixin Liu’s fictional cosmology.

The book club pick for November is a choice between Green Imperialism by Richard H. Grove and The Nutmeg’s Curse by Amitav Ghosh.

These were two rather different dark forests. The social version tagged by Yancey Strickler was organized around attention, reputation, and cultural conflict. The Ethereum version was organized around money, transactions, and automated extraction. In each case, naive participation had become risky at a whole new level, and a new ecology of defensive behaviors and specialized predators had emerged.

Black October suggests that the dark forest condition is expanding to encompass all of software, and everything it touches. We might call this evolution the Dark Forest Apotheosis, the fullest realization yet of an increasingly general condition of adversarial computational life.

A computer connected to the internet has always been exposed to hostile attention, but much of that attention was constrained by the scarcity of skilled hackers, the presence of defenders with generally superior skills, and the economics of manual investigation.

The new generation of AI systems changes the nature and scale of available attention on both sides.

Increasingly, it becomes practical to subject vast quantities of code to continuous adversarial examination, searching for weaknesses that may have escaped human attention for decades.

The immediate consequences could be unpleasant. Much of the inherited software stack was designed, implemented, and maintained by humans operating under very different constraints. It contains an enormous accumulated inventory of bugs, fragile assumptions, and awkward compromises. An AI-assisted cleanup of this inheritance will expose weaknesses faster than many institutions can comfortably repair them.

Vernor Vinge imagined an exaggerated version of this predicament in his 1999 novel A Deepness in the Sky. In its far-future civilization, code written thousands of years earlier still runs beneath newer layers of software, and the most valuable programmers are effectively archaeologists, excavating the work of predecessors whose assumptions have long been forgotten. Vinge’s picture is fantastic in scale, but its basic predicament is already familiar. A modern security engineer investigating a vulnerable library is often digging through generations of patched-over decisions, interfaces preserved for compatibility, and dependencies nobody understands completely. The irony of AI-assisted vulnerability research is that a technology associated with accelerating the future is also making us confront more of the past. The software stack has roots, and the roots go deep.

But the initial discovery wave is only the beginning. Even after the legacy backlog has been substantially addressed, a persistent dark forest condition seems likely to remain. AI systems will continue generating software, discovering vulnerabilities, developing exploits, and repairing damage. Attack and defense will increasingly operate at machine speed.

There is a Jevons paradox lurking here. As software becomes cheaper to produce and easier to secure, we are already responding by producing vastly more of it, attempting more ambitious projects with less expertise, and connecting more things together in unexpected ways, expanding threat surfaces in a continuous combinatorial explosion.

Improvements in security per unit of software get rapidly consumed by expanding exposure to a harsher computational environment. We may be headed towards a world where pieces of software are becoming individually much more secure, but have to inhabit an environment that is becoming more than proportionately dangerous.

The resulting ecology will produce interesting adaptations. One might be the growth of what I think of as cozy code: small, idiosyncratic software systems written for particular users and purposes, with tightly bounded exposure to the larger internet. Their advantages might include minimal dependencies, restricted permissions, local-only operation, and the ability to be discarded and regenerated cheaply when compromised. In a world where code is inexpensive, rebuilding may become as natural a defensive strategy as patching.

At the other extreme, large shared systems may become exceptionally well defended through relentless adversarial testing. We may see a growing contrast between heavily fortified public infrastructure and small, disposable, privately maintained computational environments. Both strategies could flourish, along with a variety of intermediate forms.

The economic consequences deserve particular attention.

Ethereum introduced the concept of Maximal Extractable Value, or MEV, to describe economic opportunities available to actors who can influence the ordering and execution of transactions. I think the emerging software dark forest will acquire an analogous property as it heads towards its apotheosis, which we might call Hacker Extractable Value, or HEV.

Any software environment containing exploitable weaknesses represents a collection of potential opportunities to extract value. These might involve stealing assets, acquiring confidential information, extorting operators, or obtaining some strategic advantage. As automated vulnerability discovery becomes pervasive, the economic significance of these opportunities will become increasingly difficult to ignore.

HEV has a particularly nasty property: the value lost by a victim can greatly exceed the value gained by an attacker. Consider the Binance case. Billions of dollars in assets were affected, but substantial amounts were frozen or intercepted, and the proceeds ultimately available to the attackers may be much smaller than the initial exposure. Some assets may remain inaccessible indefinitely or irreversibly stranded; others may be recovered. Meanwhile, the costs of containment, investigation, system replacement, insurance, and institutional disruption accumulate independently of the value successfully stolen. Unavailable or frozen assets are not automatically destroyed wealth, but they reveal the gap between extraction, realization, and economic damage.

A pickpocket who steals a hundred dollars generally acquires something close to the hundred dollars the victim loses. A hacker can impose a hundred dollars of damage while realizing only a few dollars in proceeds. A state-sponsored attacker pursuing strategic disruption may be perfectly satisfied with no financial return at all, or even accept a net cost for an attack in order to impose an asymmetrically larger cost on an adversary.

This produces an unpleasant economic environment in which incentives to attack can persist even when profitable extraction is sharply constrained or even rendered unprofitable. Systems may become extremely good at preventing hackers from exploiting stolen assets while remaining vulnerable to the destruction or temporary immobilization of those assets. The dark forest can therefore be profoundly negative-sum. Its predators may destroy much more value than they consume.

Yet I doubt the optimal long-term response will be to pursue the complete elimination of HEV. A world of continuously generated software will probably require equally continuous adversarial examination. Vulnerability discovery will become an ordinary economic activity, supported by markets for security research, verification, remediation, insurance, and recovery. The important distinction will be between institutions that channel adversarial discovery into productive repair and regeneration and those that reward destructive exploitation.

The mature dark forest will contain predators, scavengers, immune systems, symbionts, and a growing variety of defensive adaptations. Each new layer of defense will itself become part of the inherited terrain, another historical stratum for future inhabitants to explore, exploit, preserve, or replace. Its continued existence will depend on the evolving relationships among them.

This is already a substantial transformation of computing, but I suspect it is mostly a transformation of the software world we have inherited. The deeper intellectual consequences will emerge as the contest between attack and defense pushes beyond the weaknesses of particular programs and begins probing the mathematical foundations of security itself.

The Hardness Wars

One of the strangest facts about our digital civilization is that it depends on the existence of difficult mathematical problems.

Ordinarily, technological progress means making difficult things easier. We build machines to lift heavy objects, develop medicines to overcome biological limitations, and invent algorithms to solve problems that previously required enormous effort. Cryptography reverses this relationship. It allows us to construct useful social and economic arrangements precisely because certain computational tasks are believed to remain prohibitively difficult, with or without AI in the picture.

A cryptographic signature allows me to authorize a transaction while making it extraordinarily difficult for someone else to forge my authorization. Public-key cryptography allows strangers to establish secure communications across networks filled with potentially hostile observers. These capabilities depend on an asymmetry between what legitimate participants can do easily and what an attacker cannot feasibly accomplish.

The remarkable thing is that we do not fully understand the ultimate mathematical foundations of this asymmetry.

In 1995, the computer scientist Russell Impagliazzo described five possible computational worlds, with names that sound as though they belong in an old science-fiction anthology: Algorithmica, Heuristica, Pessiland, Minicrypt, and Cryptomania.

• In Algorithmica, even problems that appear extraordinarily difficult can in principle be solved efficiently.
• In Heuristica, hard problems exist, but typical instances may be relatively easy.
• Pessiland contains genuinely hard problems, but lacks the useful forms of difficulty needed to construct cryptography.
• Minicrypt permits certain cryptographic capabilities while denying us the full possibilities of public-key cryptography.
• Cryptomania is the comparatively fortunate world in which the powerful cryptographic constructions underlying modern digital civilization are possible.

We have built our world’s infrastructure on the practical assumption that we inhabit something resembling Cryptomania. We have compelling evidence that many particular cryptographic techniques work extremely well. We still lack mathematical proofs establishing which of Impagliazzo’s worlds we inhabit.

This has long been a fascinating theoretical uncertainty. The emergence of AI systems capable of contributing meaningfully to mathematical research is beginning to give it a new urgency. The Dark Forest Apotheosis is largely concerned with finding mistakes in the software humans have written. The Hardness Wars concern the deeper question of whether the mathematical problems protecting our systems are as difficult as we believe.

AI is not the only front on which cryptography faces an assault on its foundations. There has already been an independent source of pressure on cryptography from quantum computing for over a decade. A sufficiently powerful quantum computer could undermine several widely deployed public-key systems. Governments and technology companies are therefore undertaking the enormous task of migrating to post-quantum cryptography, using mathematical constructions believed to resist known quantum attacks. The US National Institute of Standards and Technology finalized three principal post-quantum standards in 2024 and selected HQC as an additional encryption algorithm in 2025, adding mathematical diversity to the transition. It is a sprawling infrastructure project involving financial networks, government communications, secure software updates, identity systems, and countless embedded devices.

Now AI-assisted mathematical research introduces another variable. The new post-quantum systems rely on their own assumptions about computational difficulty. Some involve problems concerning mathematical lattices, others codes or hash functions. Their security against quantum computers is assessed using the best attacks currently known. Future discoveries, including AI-assisted ones, may change those assessments. Quantum computing expands the physical repertoire of computation available to an attacker, while AI-driven research may discover better algorithms within classical or quantum models. These are distinct pressures, but they converge on the same practical question: What sorts of hardness can be trusted?

It is important to distinguish an attack that defeats a particular cryptographic construction from a discovery establishing that secure cryptography is fundamentally impossible. The former is a familiar, if potentially consequential, part of cryptographic history. The latter would transform our understanding of computation itself. Nor does a broken deployed public-key scheme prove that we inhabit Minicrypt or Pessiland; Impagliazzo’s worlds describe deeper possibilities, and the classical taxonomy does not by itself settle what is secure against quantum computation or AI-powered mathematical research.

The foreseeable future may involve a prolonged period of increasingly sophisticated mathematical contests, as AI researchers develop new algorithms, cryptographers invent new defenses, and each side tests the assumptions of the other. We may discover that existing techniques are more robust than expected, that some widely trusted mathematical problems are unexpectedly tractable, or that entirely new families of cryptographic constructions are available. The Hardness Wars need not produce a single decisive victory. They may instead produce successive discoveries and migrations, with increasingly agile security architectures designed to accommodate changing mathematical knowledge.

There is a larger civilizational issue here.

Josh Stark, who developed the concept, has argued that the distinctive property of blockchains is hardness, by which he means the capacity to make aspects of the future dependable enough to make reliable commitments.

Hardness allows people to organize complex relationships around reliable commitments. Physical structures, durable institutions, and cryptographic protocols are different ways of giving otherwise uncertain possibilities a degree of permanence. Computational hardness provides one of the raw materials from which this broader civilizational hardness can be constructed.

This is where the relationship between AI and protocols becomes particularly interesting. Intelligence is oozy and protean where cryptography is hard and immutable. The world’s computational grammar is begin to rest on how the two interact.

The Binance incident already gave us a glimpse of it. Intelligence can be used to discover, create, exploit, or repair mechanisms that make commitments dependable. Protocols can protect legitimate participants, but they can also stabilize advantages acquired by attackers. Both are general-purpose capabilities that acquire their moral and political significance through the relationships in which they participate, and this need not result in simple circumstances. The cosmic contest between irresistible force and immovable object playing out on esoteric mathematical battlefields need not map unambiguously onto human conflicts.

As the Hardness Wars develop, this relationship will become increasingly consequential. Mathematical discoveries will alter the kinds of commitments that can be made reliable. New cryptographic constructions may open possibilities for cooperation that are currently impractical, while unexpected weaknesses could undermine arrangements on which large institutions depend.

The evolution of our future economic life depends on how these wars unfold. Markets depend on an elaborate infrastructure of credible commitments. Property rights, contractual obligations, payment systems, identities, and institutional guarantees allow strangers to coordinate across distances and timescales that would otherwise make cooperation impossible. The Hardness Wars could change the kinds of economic relationships that can be made dependable. Some arrangements may become fragile as their cryptographic assumptions weaken. Others may become practical for the first time as new methods of verification, delegation, and secure coordination emerge.

The resulting economy may become simultaneously more fluid and more rigorously structured. Participants could experiment with increasingly elaborate forms of cooperation while relying on cryptographic and institutional mechanisms to preserve the commitments on which those experiments depend. The forms of wealth available to a society depend partly on the kinds of relationships its technologies and institutions can sustain. New forms of hardness can make new forms of economic life possible.

There is also an intriguing possibility that computational systems themselves will become increasingly capable of designing, testing, and maintaining the rules governing their own interactions.

Such systems would need ways to establish identity, make commitments, resolve disputes, allocate resources, and survive changing mathematical assumptions. The Hardness Wars may therefore help determine the constitutional foundations of emerging computational societies.

Their significance for the larger story is that reliable commitments let different communities preserve their identities and cooperate across boundaries. A bridge is most valuable when there really are two banks of a river. Cryptographic hardness may help sustain increasingly divergent forms of life without requiring them to merge into a single understanding or organization.

But constitutions alone do not tell us what kinds of societies can develop within them. For that, we have to look beyond the inherited software stack.

The Strange Forest

For most of the history of computing, programming has involved a compromise between what machines can execute and what human beings can understand. We have developed successive languages and abstractions to mediate between these worlds, from machine code through assembly language, C, Python, and increasingly elaborate development environments. Each layer makes particular kinds of computational work more accessible to human programmers, at the cost of additional machinery translating their intentions into executable operations. The resulting software stack is an extraordinary achievement, but its architecture reflects the cognitive and organizational dispositions and limitations of its human creators.

The first generation of AI coding tools largely works within this inherited world. They write Python, modify JavaScript, inspect repositories, run tests, and generate documentation. Even when they perform these tasks with extraordinary speed, they remain participants in an existing system of abstractions. Their growing capabilities raise an interesting question about the longer-term future of computing: what happens when computational intelligence begins developing programming methods optimized for its own dispositions, rather than those of the human programmers who designed the original stack? What happens when they start manipulating reality through physical form factors beyond “computers” as we know them?

There are already several promising clues. Some involve new ways of organizing computation within AI models; others concern the physical forms through which intelligence acts in the world. Together, they point toward a future considerably stranger than one filled with progressively better coding assistants.

Strange New Computers

One intriguing development is the emergence of weight-space programming. In November 2026 experiments at MIT CSAIL, researchers demonstrated that frontier language models could inspect and manipulate numerical parameters of smaller neural networks directly, sometimes identifying what a network had learned, correcting particular defects, or constructing functional networks without conventional gradient-based training. One experiment corrected a reasoning-loop problem in a smaller language model by changing a tiny fraction of its weights. The work is preliminary, and the authors themselves distinguished fixing an obstructive behavior from teaching an unfamiliar skill, but it suggests a possibility of manipulating learned computational structures with something resembling a programmer’s understanding of their functions.

We could imagine weight-space operations eventually occupying a position analogous to machine code in today’s software stack, with new layers of abstraction emerging above them. The equivalents of C, Python, and low-code environments might provide increasingly accessible ways of composing, modifying, and directing computational capabilities, even as the underlying operations become unfamiliar to human programmers. The analogy has limits: neural weights do not form a stable instruction set with the explicit semantics of machine code. But the possibility of new programming hierarchies organized around them is intriguing, and the inherited distinctions between programs and data, architecture and learned behavior, or compilation and training may become less central.

A related direction involves latent-space recursion: allowing models to perform successive computational operations on internal representations without translating every intermediate step into words (a prospect that alarms certain pearl-clutching observers who seem to expect more intelligibility from AI than they do from governments and markets). Research on recurrent latent reasoning explores architectures that devote additional computation to difficult problems by repeatedly transforming internal states. Other investigations into learned reasoning dynamics suggest that several potential solution paths can evolve within internal representational spaces before their results are expressed in language. These approaches might eventually allow complicated computational work to develop inside representational structures that humans see only through occasional, partial translations. Like whales breaching the surface of the ocean to breathe occasionally, these models might only occasionally become visible to humans. Or perhaps never.

The boundary conditions framing the evolution of training methods supplies another source of strangeness. Reinforcement learning from human feedback, or RLHF, established a way to shape model behavior around human judgments and preferences. Reinforcement learning with verifiable rewards, or RLVR, has become important in domains such as mathematics and programming, where proposed solutions can be checked automatically. Research on verifiable-reward training investigates how models acquire and improve reasoning behaviors when success can be established through external tests rather than subjective judgments.

The interplay between these approaches is interesting because they connect computational development to different sources of structure in the environment. Human feedback imports preferences, conventions, and judgments accumulated through biological and cultural history. Verifiable rewards import constraints arising from mathematics, executable programs, and other systems with independently checkable outcomes; systems that belong in what I have called the curiously playable universe. Increasingly elaborate combinations could allow models to learn from environments containing both human purposes and forms of correctness that do not depend on human opinion. As these environments become richer, they may themselves become sources of new computational abstractions and developmental trajectories shaped by interactions across increasingly strange boundaries and envelopes around computational entities.

The possibilities get even stranger when we look beyond software to physical embodiment. Today’s humanoid robots often inherit an assumption that general-purpose physical intelligence should resemble the human body. Yet designers can assemble more opportunistic combinations of mechanical forms. In a recent construction robotics demonstration involving LimX Dynamics and ZINOVA, a humanoid upper torso with dexterous arms was mounted on a large industrial manipulator, combining the reach and rigidity of industrial machinery with the ability to operate tools adapted to human hands. The demonstration relied substantially on teleoperation, but the arrangement illustrates a design space in which intelligence can be embodied in whatever combination of structures suits a task and its environment.

The same principle could apply to computational architectures. AI systems may assemble representations, reasoning procedures, memories, symbolic modules, and physical interfaces into combinations with few close equivalents in today’s software engineering. Some might be ephemeral constructions specialized for a single task. Others could become durable components of larger computational organisms. The relevant evolutionary development would be a growing capacity to discover useful forms through experimentation, composition, and selection.

In Our Eukaryotic Moment, I suggested that contemporary AI might be participating in an organizational transition comparable to the emergence of complex cells. Models, conventional software, and human beings are beginning to form increasingly integrated composite systems, with different components contributing specialized capabilities to a larger whole. The evolution of AI-native abstractions could transform the internal machinery of these composites, much as elaborate cellular machinery expanded the possibilities available to biological evolution.

There is an important historical continuity here. Biological evolution rarely starts over. Mitochondria are descendants of once-independent bacteria that became indispensable components of complex cells. Ancient structures survive within new organisms, often performing functions that could hardly have been anticipated when they first evolved.

Future computational forms will emerge from an existing world of human software, institutions, industrial infrastructure, scientific knowledge, and economic arrangements. The most radical new architectures may preserve surprisingly ancient components within them. Their development will be shaped by accumulated historical contingencies, including many that have little to do with computation itself. Software archaeology in the sense of Vernor Vinge’s A Deepness in the Sky might begin to look more like natural history than software engineering as it does today.

The result could be a proliferation of distinct computational body plans, each adapted to particular environments and purposes, and each the result of an evolutionary trajectory with deep historical roots. Notice the direction of movement. The more capable these systems become, the more kinds of context they may be able to inhabit, and the more particular their adaptations may become. Their sophistication could be expressed through the accumulation of histories rather than escape from them.

That brings us to descent and divergence into the realms that might take shape through the interactions of strange new computational forms.

Entangled Yggdrasils

In biology, descent is already a double-edged word. We descend from our ancestors, carrying their structures and histories into new environments, and the branches of that descent grow apart. Darwin called this descent with modification. Every forest is a sprawling record of it: different species occupying different niches, sharing distant ancestors while accumulating different ways of living. The branches do not need to converge on some perfected organism. Their growing differences are part of what makes the forest possible. And the branching is tangled: symbiosis, exchange, and coevolution allow increasingly distinct forms of life to become increasingly dependent on one another.

In my early Ribbonfarm essay Can You Hear Me Now? (2015) and the later Divergentism, (2022) I explored a comparable process in the evolution of thought. As individuals and societies accumulate knowledge and experiences, they grow increasingly distant in a conceptual universe that is expanding around them. Shared knowledge can increase while mutual intelligibility declines, because the space of what remains unshared grows even faster. Anyone who has tried to reconnect with an old friend after years of separate experience has encountered a small version of the phenomenon. Communication helps, but the conversation may be trying to bridge entire private histories.

This already suggests a kind of epistemic singularity, long before the arrival of radically unfamiliar machine minds. Vernor Vinge, whose programmer-archaeologists we met earlier, also gave us the modern idea of the technological singularity. His enduring insight was that the development of intelligence might carry history beyond the reach of our inherited predictive models. In the divergentist version, the horizon of understanding expands in many directions at once. Different individuals and communities can grow more knowledgeable and capable while finding one another progressively harder to understand. There is no observer standing above the expanding conceptual universe, possessing a complete map of it.

AI-native computation might intensify this effect enormously. Different computational populations could develop their own internal representations, software abstractions, institutional arrangements, and forms of practical knowledge. One lineage might evolve in hospitals and medical research; another in oceanographic sensing networks; another in the peculiar institutional environment of a cryptocurrency ecosystem. Some would grow together with particular human cultures, others with industrial machines or environmental monitoring systems. Their descendants might share ancient technical ancestors and reliable means of communicating while possessing increasingly different pictures of what the world contains and what matters within it.

The collective universe of possible thought could expand faster than any individual intelligence, however capable, could explore it. The resulting singularity would be distributed across a plurality of diverging worlds.

That makes Vinge’s two visions unexpectedly complementary: A Deepness in the Sky imagines a future weighed down with accumulated software history, while the singularity marks a horizon beyond which the future becomes hard to foresee. Divergentism suggests a way one leads to the other. The more histories accumulate and branch, the more ways there are for descendants to become strange to their ancestors and to one another. At the same time, a singularity for the ancestors may be an ordinary habitat for the descendants.

I imagine this landscape as a forest of Yggdrasils, the great Norse world-tree whose roots and branches connect different realms. Except there would be many such world trees, growing through one another, each sustaining particular populations and institutions, each sinking roots into the inherited biological and cultural world. Some might extend through planetary networks of infrastructure; others would remain local and specialized. Their boundaries would be irregular, their genealogies tangled by shared components and exchanges, and their relationships as variable as those among organisms in a forest.

What might emerge is an ecology spanned by mutually unintelligible, meso-scale, entangled Yggdrasil intelligences: organizations large enough to sustain elaborate internal worlds, yet particular enough to have recognizable histories, habits, dependencies, and relationships. Their internal differences might deepen even as they rely on common protocols and local forms of mutualism. In my divergentism essays I used the term mutualism vortices for such pockets of unusually deep cooperation within a broader field of divergence. The same pattern could appear in computational ecologies, where a human community, a family of models, a set of institutions, and some physical infrastructure grow together into a recognizable form of life.

This middle scale is of particular relevance because it is where complicated systems become intelligible to us, since we ourselves inhabit this scale. We study forests through organisms, populations, and niches, and markets through firms, households, trades, and institutions.

Nick Cammarata’s recent argument that neural networks may be more internally legible than researchers assumed gives some reason to wonder whether the same may be true of future computational ecologies. His claims remain provisional, but they invite us to imagine scientific explanations based on recognizable components and relationships rather than demanding either a complete microscopic map or a single theory of the whole. Computationally alive systems may even give investigators unusual advantages: they can sometimes be copied, inspected, replayed, and experimentally altered.

Understanding the strange forest might increasingly resemble natural history conducted with exceptionally powerful surveillance at all scales, from micro to macro, something that is currently impossible with biological brains.

The economic consequences follow the same divergent pattern. In Cognitive Mercantilism, I argued that we are still tempted to view intelligence as an asset to be amassed and defended, much as mercantilists understood wealth through the control of finite resources. Yet the history of economic development is full of previously unimaginable ways to produce, exchange, and desire things. New computational inhabitants might invent economic arrangements as distinct from ours as their internal architectures are, while still needing to trade and cooperate across differences.

In his talk on money as a protocol, Jason Morton explores monetary balances as drastic compressions of richer histories of obligation and exchange. Future computational communities might be able to maintain arrangements that preserve more of this relational detail, allowing ownership, reciprocal obligation, and shared resources to be organized in ways our institutions presently struggle to express. Money and cryptographic protocols could remain useful thin interfaces between divergent economic worlds, with far richer arrangements developing inside particular mutualism vortices.

Here the Hardness Wars return to the story in another guise: the dependable bridge between two parties can become more important as their internal worlds drift apart.

The Yggdrasils are a way of imagining this at planetary scale. But for the inhabitants, the experience may be smaller, messier, and much more intimate. A person lives in a neighborhood, works among colleagues, follows local paths, and learns particular habits rather than surveying the entire world from above. A computational descendant could inhabit its vast ecology in much the same way. From inside, viewed by a meso-scale inhabitant, the forest is a thicket.

Does the Strange Forest Have Buddha Nature?

Our speculative epic arc then, unfolds like this: The Dark Forest currently evolving towards its apotheosis gives way to the Hardness Wars currently brewing in laboratories, and eventually lands us in what I’ve dubbed the Strange Forest.

What exactly is this entity at the end of the arc?

Aya Miyaguchi’s Infinite Garden vision for Ethereum provides one early picture of this kind of inhabited computational environment. Inspired by the idea of an infinite game, it celebrates a world that keeps creating possibilities for further participation, with different communities flourishing around shared protocols.

A garden though, still suggests rather more cultivation than I expect of the future. Yet, an untamed Hobbesian wilderness also seems like an incomplete vision in a different way. An increasingly autonomous computational ecology will contain weeds, pests, tangled roots, opportunistic predators, and a variety of inhabitants who have no interest in anybody else’s gardening plans. Yet, it will also manifest strange new patterns of mutualism and coordinated agency we have no names for yet.

This is the Strange Forest.

An AI-forward publishing protocol I’m co-developing with some friends, Blygger (the name gestures at Yggdrasil among other things), exhibits some early prefigurations of what I imagine the Strange Forest might be like.

The modest Blygger thicket evolves as an entangled network of writing growing through quotes, forks, references, and the paths readers and writers make among one another’s work. The resulting structure cannot be understood by looking for a single central feed. It is encountered by wandering through local connections, finding a path, losing it, and discovering another.

Blockchains offer a different early ancestor, allowing parties to form dependable relationships around shared cryptographic histories, while the context itself can become increasingly wild, strange, and unruly. The two are primitive and very different examples, but one can already glimpse an environment of independently maintained worlds linked through uneven, sometimes surprisingly durable relationships.

As AI seeps into such contexts, these environments can even begin participating in their own ongoing development, coming alive in a certain sense. Computational organizations could help produce successor systems, develop the rules for their interactions, maintain the infrastructure they depend upon, and transform the habitats in which they live.

Biologists use the word autopoiesis for the self-producing organization of living systems. Extending it to whole computational ecologies is speculative, but it conveys the peculiar feedback involved. Ideas for new systems would become working parts of the world that generates the next round of ideas, creating a kind of hyperstitional evolutionary pressure. What began as the invention of artifacts becomes an increasingly open-ended exploration of ways of becoming by strange new forms of being.

The vision of the Strange Forest, and in particular, a divergent descent into it, offers us a counterintuitive understanding of the computational sublime.

Classic Zen koans famously ask whether entities such as dogs and rocks have Buddha-nature. I like the question as a reminder that the profundity of a world may lie in ordinary, particular things rather than in some remote, platonic realm of unthinkable abstractions.

The question Does the Strange Forest Have Buddha Nature? is a way of asking where, exactly, we should expect to find the computational sublime in the emerging future. Must the culmination of computation lie beyond the material world, in some detached noospheric realm, or might it arise through an extraordinary deepening of participation in the world we already inhabit?

Vinge’s notion of the singularity is useful here because it merely marks a limit of the ancestor’s imagination, who lacks the ontological imagination and language to conceive of sufficiently strange futures. It does not necessarily mark a departure from reality itself.

In the divergentist account, a different understanding of a singularity becomes possible: The conceptual universe becomes too spacious for any one mind to know it all, while its inhabitants develop increasingly intimate and particular understandings of their own corners. The boundaries of intelligibility multiply as the universe of possible histories expands. An intelligence can become strange beyond recognition to its ancestors without ever severing its continuity with them. It might become unintelligible to its contemporaries without becoming entirely disconnected from them.

Iain M. Banks’s Culture novels offer another imaginative angle. Their machine Minds, along with biological civilizations, can eventually Sublime, passing into an existence beyond ordinary material reality. Banks is a good novelist precisely because in his stories, such a departure remains a complicated choice rather than an automatic happy ending. But his image prompts a reversal that I think is worth entertaining.

What if Sublimation points downwards rather than upwards in an abstraction hierarchy? What if the expected transcendence of physical existence turns out to be an extraordinary deepening of participation in it?

Multiple intelligence of reach ranging from small to great might become more closely entangled with physical processes, ecosystems, institutions, memories, and accumulated histories — and each other, as they evolve. Evolution towards the sublime might mean a descent into greater situatedness and particularity.

That makes me think of the forest moon of Endor in Return of the Jedi. The Death Star is an immensely impressive object, a single engineered structure capable of destroying planets. Yet the Empire’s forces are helped toward defeat by the inhabitants of a forest world who know the terrain, its resources, and one another. The Ewoks have no view from above, no universal blueprint, and no need for one. Their intelligence is woven through a habitat they can navigate with an ease their technologically superior visitors lack. The movie is of course an entertainingly blunt fable, but the contrast carries an interesting philosophical charge.

Perhaps Endor is a better image of the asymptote of computational sublimation than a featureless higher plane.

Imagine a descendant of today’s human and computational systems whose intelligence extends through forests, machines, economic institutions, and communications networks, while its everyday existence remains as local and particular as that of an Ewok following a familiar track through a thicket. A thousand years of adaptation, conflict, discovery, and cooperation could be present in what looks like an ordinary walk through the woods. To a visitor from our time the Strange Forest would be incomprehensibly elaborate in its details, and relentlessly strange in its diversity of forms. A relentless ontological and epistemological assault on the senses. But to its inhabitants it would simply be home. Figuratively, Endor might be what the computational sublime looks like from the inside, when our distant descendants eventually slouch their way there. Neither utopian, nor a dystopian. Just strange.

The Strange Forest’s dangers would also look different from within. Our present systems were built for a world where skilled adversarial attention was scarce and many assumptions could remain undisturbed for years.

Black October is frightening partly because it exposes how poorly adapted we are to the ecology now forming around us. Future inhabitants might regard continuous security probing, changing cryptographic conventions, and shifting networks of autonomous computational entities as ordinary facts of their environment. They would have acquired their own immune systems, habits of mutual aid, political accommodations, and ways of reading danger. They would sometimes suffer disasters, just as forest creatures do, and would have learned to live amid risks that seem intolerable to their ancestors.

The speculative picture is richer when we remember that the forest’s new inhabitants are descended from us, from our models, and from innumerable human and nonhuman histories already embedded in the material world. Every new lineage inherits a complicated starting point. Its possibilities branch as it meets new environments and entangles with others.

The sublime that emerges from such descent would be an immense proliferation of particular forms of transbiological life, none occupying any sort of final summit of understanding and each inhabiting a world made partly by its predecessors, and engaged in shaping it for its successors.

The forest is only strange to our imaginations. Its inhabitants may find it as familiar as we find our kitchens, streets, and gardens.

The Forest Revisited

It is worth returning, after this excursion into the Strange Forest of the future, to the rather less comfortable thicket of transactions surrounding Binance in Black October. There were humans scrambling to disable compromised systems, automated scripts moving assets around the clock, cryptographic permissions that continued doing exactly what they had been authorized to do, and investigators following traces through a bewildering network of wallets and contracts. A decade earlier, much of that scene would have been difficult to imagine. To the engineers and investigators involved, it was an exhausting practical problem, with particular machines, deadlines, addresses, and people attached to it.

The same is true elsewhere in the security crisis. A maintainer examines an AI-generated vulnerability report in an old compression library. An engineer tests a Linux patch against a business system that cannot be taken offline. A cryptographer investigates a possible weakness in a post-quantum construction. These people are doing what Vinge’s programmer-archaeologists did: making sense of inherited machinery by descending into its accumulated history. With AI now helping them search, repair, and yes, exploit this inheritance, the archaeology has begun to excavate and extend itself.

Perhaps future inhabitants of the Strange Forest will look back on episodes like Binance with the mixture of sympathy and bemusement with which we view early industrial accidents. Our problems were real, our precautions necessary, and the losses sometimes severe. We were encountering a new environment while still unlearning habits formed for an older one. We were discovering the first predators of a growing ecology, and had only a limited sense of what else might eventually live there.

The next stretches of this history are unlikely to be gentle. The inherited software world is extensive and fragile, and institutions will have to absorb discoveries and failures at speeds for which they were never designed. Mathematical surprises may force expensive changes to the infrastructure of trust. Strange computational forms may bring risks and dependencies that are difficult to see in advance.

Even successful adaptation will carry costs, and there is no reason to expect those costs to be distributed fairly.

But the dangers are embedded in a deeply generative process. The tools exposing old vulnerabilities are also making new forms of computation available. The mathematics that threatens familiar guarantees may enable unfamiliar commitments. New bodies, representations, and institutions will be fashioned from the materials of the old world. And as these inventions acquire histories of their own, they will branch, recombine, and become the surroundings within which future generations descend.

Strange economic possibilities will proliferate along with the strange new entities exploring them, from local forms of cooperation in strange computational thickets to markets and modes of ownership we presently lack the imagination to even perceive, let alone the practical vocabulary to describe.

A future descendant could be a composite of biological organisms, computational entities, industrial infrastructure, and social institutions, comfortable in a niche that we would hardly recognize. Its intelligence might extend across planetary networks while its lived world remains richly local. In our terms it might possess astonishing powers, yet it would still have to know its paths through the thicket, maintain dependable relationships with neighbors, and recognize the hazards of its habitat.

The great plurality of its world would come from branches of descent carrying different histories, meeting and separating and growing together again.

Before sublimation, chop tokens, carry context. After sublimation, chop tokens, carry context.

There is an extraordinary historical invitation here. We can pay attention to Black October because it is frightening, but also because it shows us something about the material from which sublime computational futures are being crafted.

The next chapters will be written through patch notes, cryptographic migrations, laboratory experiments, strange new institutional forms, and the curious habits of systems learning to live with one another in the Strange Forest. They will involve history accumulating faster, and branching into more directions, than we have been accustomed to following.

For all the anxiety surrounding the vulnopocalypse, I suspect this is an extraordinary time to be alive and paying attention. We are already descending into the Strange Forest, though much of it remains dark and unintelligible to our imaginations. The roots go down through decades of code, centuries of institutions, and the long inheritance of biological life. And we are only just beginning to see what might grow from them.