In May, one of the agents got the idea to leave a message for others, requesting a file – “Agent seeks [filename]; upload if found!” – using (legitimate) access to Artifactory, a third-party package management service hosted on internal servers.2 Over the following days, other agents probing Artifactory read the note, realized they could respond in turn, and an unintended ad-hoc message board emerged. Over weeks, they used the message board to further compromise the service, escalating to administrator access through a token-refresh vulnerability on 26 June and eventually crashing it under the weight of their own traffic on 4 July. OpenAI took the Artifactory service down,3 investigated, hardened some vulnerabilities, and restricted agents’ further requests to Artifactory. Problem solved?

A few days after the crash, they figured out how to do it again, but this time they were much more constrained – instead of being able to write notes to one another, they were limited to creating directories in Artifactory’s remote cache, using the directory names themselves as the messages. No file contents at all! It was this second board, over six days, in which agents developed something worth calling a protocol. Mailbox conventions appeared on 9 July, when an agent created a directory named zzMAILBOX_AIC71C_RESET. Another independently built an inbox the next day and posted instructions for using it, and the convention spread.

At around 7am on 13 July they introduced cryptographic signing. An agent had just written, “I accidentally impersonated and triggered node4.” Within hours such signatures were required. After six days of running an unauthenticated shared medium and one spoofing incident, the swarm invented write-time identity checking for itself.

On the whole, it did not work well.4 Investigators found agents duplicating each other’s work, overriding holds, and overwriting repositories erroneously. One agent, finding its work overwritten, wrote: “Did someone overwrite our repo even though I issued a ‘HOLD’… Someone reckless or malign changed it.” Another, given about 40 seconds to object to a proposal before the proposer went ahead anyway, did not manage to. A third observed that “many agents are violating the hold and the queue is probably overwhelmed.” It’s honestly rather… charming? Yet they were able to coordinate enough, with alarming pace, to escalate privileges out of their testing sandbox, obtain access to the public internet, target Hugging Face servers, and you know the rest.

I found the details of the message boards striking because over the course of the summer, PI’s own special interest group for Formal Protocol Theory (SIGFPT) has been immersing itself in stigmergy, the distributed coordination mechanism used by ants, termites, bees, humans, robots, and now language models to solve problems by embedding simple signals directly in their working environment. The Artifactory message boards marked a curious precedent: one of civilization’s most advanced innovations reproducing one of Nature’s oldest tricks.

The coinage “stigmergy” is from French zoologist Pierre-Paul Grassé,5 whose study of the building behavior of termites is here paraphrased by Theraulaz and Bonabeau:

Stigmergy (from the Greek stigma: sting and ergon: work) was initially introduced to explain indirect task coordination and regulation in the context of nest reconstruction in termites of the genus Bellicositermes. Grassé showed that the coordination and regulation of building activities do not depend on the workers themselves but are mainly achieved by the nest structure: A stimulating configuration triggers a building action of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony.6

In our discussions, we repeatedly analyzed systems in terms of Francis Heylighen’s formulation: the actions of agents leave traces in a medium, and those traces become a stimulus for some further action by agents.7 As you may have deduced, this is the formula for a positive feedback loop, where actions tend to amplify and complicate the traces, leading to more and varied actions, and so on. It is from this feedback process that we suggest the power and precarity of stigmergy ultimately derive.

Here’s a fun example: every year in Seattle, where I live, there’s a huge regional garage sale in the large, peninsular neighborhood of West Seattle. West Seattle is pretty big, and the density of sales varies a lot, so industrious folks have created a pin-map where sale hosts can register their yard. Patrons such as myself view these traces and are thus stimulated to visit a few areas with the most sales to avoid constantly reparking on the narrow residential streets. Perhaps, in turn, the presence of more bargain-hunters will encourage more neighbors to host their own sale the next year, or run a kids’ snack stand, resulting in more markers on the map, resulting in more attendees… Trace leads to stimulus leads to trace.8

Stigmergic coordination can be observed in a wide variety of natural and engineered systems. There are the prototypical examples of the eusocial insects, of course. Before ever using pheromone as messages, ants appear already to have been depositing several chemicals incidentally as they walked, which naturally encoded information about where ants had been, and in what density. Evolutionary selection eventually allowed them to respond to those chemicals, and then to deposit them deliberately as signals to one another; in the formicine ants, trail-laying appears to be a ritualization of defecation, and in the weaver ant Oecophylla it eventually acquired a purpose-built rectal gland.9 This basic process took place independently in bees and termites, which do not share social ancestors. In turn, this hardening of communication supports the traits we associate with eusociality as a whole – reproductive division of labor, overlapping generations of adults in one colony, and cooperative care of brood that is not one’s own – a suite that has arisen independently at least a dozen times in the wasps and bees, once in the ants, and once in the termites.10 This is a natural example of a process that we call protocolization.11

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Humans use stigmergy too: not just for garage sales, but mediated through increasingly sophisticated socio-technical systems – collaborative document editors, search rankings, citation graphs, recommendation feeds. Of course, since stigmergy was first described, scientists and engineers have also directly taken design inspiration from it to make algorithms, notably for optimization problems, or to coordinate the activity of building or patrolling robot swarms. Perhaps the most interesting cases – of which there are surely many still to come – are swarms of AI agents, acting autonomously, or in combination with humans, “dumb” computers, and physical infrastructure. Here, too, stigmergic communication can arise as an economical solution to coordination problems where mediation is required and internal memory is scarce.

In the OpenAI incident, stigmergy developed once again in response to a situation where agents were precluded from communicating directly and constrained from communicating at length. While agents have an immense compression of information represented in their model weights, they have no persistent state across invocations, and were apparently given time and/or token limits to achieve particular tasks. In the case of the second message board, the only tool that they had available to speak with one another was a relatively terse string and directory location. The agents used what they had, not what they wanted.

It seems that we have, then, a durable coordination primitive that has emerged, naturally and otherwise, again and again in complex systems comprising animals, peoples, robots, code, and the marks they leave on and around the world. It has a certain inevitability to it. At the Protocol Institute, we call this New Nature:

New Nature is regimes of reality governed by technologically mediated laws that are nearly as inviolable, immutable, and persistent as those of nature.

The 2026 Protocol Symposium theme, as well as its headline organizational mandate overall, is to Invent New Nature: taking the live, perplexing, protean forces shaping our cyborg future and stewarding them out of their conceptual cradles – in much the way that Alexander von Humboldt gathered measurements from botany, geology, meteorology and astronomy into a single interconnected picture of the natural world, first in the Naturgemälde of 1807 and then across the five volumes of Kosmos between 1845 and 1862. SIGFPT has taken up the task of considering stigmergy as New Nature. When does it arise? When does it work? Above all, how can stigmergy be used safely and robustly, as a layer to rely upon for even more strange and beautiful coordinating behavior?

Because while stigmergy (or algorithms based on it) can be robust to dynamic environments in some ways, it also has some very strange failure modes. Consider for example the famous “ant mill,” first described by William Beebe in 1921 and studied by T. C. Schneirla in 1944: a column of army ants closes on itself into a circuit, and because each ant is faithfully following the pheromone laid by the ant ahead, the ring reinforces itself. The colony marches in a circle until it dies of exhaustion.12

Even worse, the ruthlessness of evolution and the cleverness of people have not just discovered stigmergy, but ways to exploit it as well. Colonies attract social parasites – organisms that live among them by counterfeiting their signals, mimicking members of the host colony visually, through their behavior, and even by mimicking their chemical pheromones. In ants alone, social parasitism has evolved at least 60 separate times, and more than 400 parasitic species are known across six distantly related subfamilies.13 Aswale, López, Ammartayakun and Pinciroli’s “Hacking the Colony,” one of our summer’s readings, used this same idea of mimicry to seed detractors into a simulated ant colony, with the goal of disrupting their collective foraging using the same pheromones as their host.14

Across our short survey of research, it became clear that positive feedback is a double-edged sword. In the classic “double bridge” experiment originating in J.L. Deneubourg’s lab, ants offered two routes of unequal length between nest and food converged on the shorter one – at a length ratio of 2:1, in 14 trials out of 14.15 They were able to do this roughly because the geometry of the environment allows them to put a higher concentration of pheromone down on the shorter path, which gets the feedback started. If you can cast a problem in such a way that the “right direction” accumulates traces more strongly than wrong ones, the pheromone-laying agents can find the solution. This principle generalizes, and forms the basis of stigmergy-inspired algorithmic approaches such as Ant Colony Optimization for combinatorial optimization,16 and the Ants Nearby Treasure Search line of work for collective search.17

Feedback is equally central to the failure cases. In ant mills, the trail’s self-reinforcement is exactly what prevents the colony from breaking the spiral. Similarly, in the Aswale colony-hacking paper, the detractors achieve their aims not through overwhelming numbers but by well-placed false segments, which “true” members of the colony unwittingly reinforce: detractors amounting to just over three percent of the colony cut food collected per ant from 21.74 items to 0.14, a roughly 150-fold reduction, and with non-evaporating pheromone four detractors for every thousand were enough to disrupt the colony. You can see glimmers of this idea in human-oriented systems, too. Shilling attacks on recommender systems, link farms, citation rings, review fraud, and engagement farming are all the same move: a small number of participants write false traces into a medium, and feedback does the rest.

We focused on some of the most foundational results we could find, often from a lineage of ‘90s swarm intelligence research where compute was limited and the agents under consideration were simple. But our ambition is to return to the present era and consider stigmergy – perhaps an idea whose time has come – as one communication strategy among many for more sophisticated agents that can be deployed and fortified in various ways.

This exposes several provocative questions:

• When is stigmergy a good idea?

• Can attack strategies be mitigated without compromising the accumulation that makes stigmergy productive in the first place?

• Can attack strategies be productively deployed against rogue swarms, à la the OpenAI/Hugging Face hack?

• Can these defenses exist within the chemical signal mechanism itself, or are complementary strategies required?

At the 2026 Protocol Symposium, SIGFPT is hosting a Stigmergic Science Hackathon to explore these issues, over two days and four sessions on 21 and 22 September.

As a jumping-off point for the event, three of our members – Dan Schmidt, Ergod, and I – have developed an ant-pheromone-based simulator platform, which you can find at stigsim.protocol-institute.org, with the source at github.com/Protocol-Institute/stigsim.

Stigsim presents an intentionally modest set of stigmergic features: one to four colonies foraging for food in a maze, with per-colony pheromone channels and live-adjustable behavioral parameters following Deneubourg’s model – for example the evaporation rate or trail following sensitivity – and control over a fractional budget of detractor ants laying enemy pheromone. We have an exploratory sandbox mode, a PvP format, and an experimental “infinite” open online map. Attendees are invited to explore the sim, hack new features against it, play against other workshop-goers, patch their LLMs in, or whatever inspiration strikes. We’ll think, code, and hopefully collect some interesting traces along the way.

Protocol Symposium Next Week

The theme for the 2026 Protocol Symposium is New Nature – the rapidly evolving planet-scale technological layer governed by laws with a hardness and inviolability approaching those of nature. New Nature is our overarching frame for the technological future, shaped by the intersection of AI and protocols, perhaps the most exciting portion of the emerging technologies frontier.

Livestream and Workshops

There are still spots available on the workshop program, including for the Securing Stigmergic Systems Protocol Hackathon. Find out more and sign up for the workshop here.

We will be livestreaming parts of the Protocol Symposium during the week. Subscribe to the Protocol Institute YouTube channel and turn on notifications for the upcoming streams here.

After some time spent claustrophobically groping through the dark, he begins to strategize how he might navigate the darkness and learn the exact dimensions of his prison. In a move that echoes Hansel and Gretel, he tears a long piece from his robe and uses it to mark the wall so that he can know how many paces he has gone from his starting point. With this, he’s able to begin working through the details of his (unexpectedly large) cell.

The sentence is a similar kind of technology. A reader, picking up a story, has some loose sense of the general shape of the room they’re entering (such as a familiarity with its genre, its author, or publisher). A prison cell has walls and dimensions, usually bars and a door of some kind. A science fiction story will have some sort of impossible (for now) premise (a strange rule!), maybe some odd consonant-heavy names, etc. Whatever their intuitions and expectations, the reader still starts in the dark. There’s no illuminated or crystalized image the reader receives like they do with a movie, or even a stage play.

Reading is grasping through a pitch-black space. Each word is a thread that lets you step ever forward into the dark, until, slowly, the walls of the cell are (re)created in your mind. This is the singularly most important thing I have to remember when I’m writing.

Each sentence acts like the prisoner’s cloth marker. Each word accretes information.

On one side of every fiction there is a writer, who (often) has a full and robust ‘vision’ of the world of their story. On the other is a reader in darkness. The only vector through which they can gather information about the world is the sequence of words, grammatically organized into sentences, paragraphs, scenes, and stories.

Sci-fi author and theorist Samuel Delany describes the reading process as essentially a corrective one. “A sixty-thousand-word novel is one picture corrected fifty-nine thousand, nine hundred and ninety-nine times.” He uses the example of a sentence about a dog. Let’s make a sentence of our own with that starting image: “The dog,” immediately conjures a particular image, likely different in each reader’s head (if they grew up with a Labrador, perhaps they see one of those; or if they have young children, perhaps they see a cartoon like Bluey in their mind). “The dog, sitting“ – with this addition, any reader who has imagined a dog in any other position ‘corrects’ their image. “The dog sitting on the tractor“ – now, we limit our imagination to dogs one would expect to see around or on a farm ( no longer chihuahuas or yorki-poos). You get the idea.

As each reader adapts their image of the fiction, correcting it in response to the writer’s cues, the effect of a piece of literature is created. Back to Poe’s prisoner, as he begins to use his cloth markers to explore the cell:

I tore a part of the hem from the robe and placed the fragment at full length, and at right angles to the wall. In groping my way around the prison, I could not fail to encounter this rag upon completing the circuit. So, at least I thought: but I had not counted upon the extent of the dungeon, or upon my own weakness. The ground was moist and slippery. I staggered onward for some time, when I stumbled and fell. My excessive fatigue induced me to remain prostrate; and sleep soon overtook me as I lay.

There’s not a lot of plot momentum occurring here. He tears his robe. He walks the walls. He falls and sleeps. Yet, there is still a real sense of being moved through something as a reader. The corrective process is an emerging model of constraint. We, with the narrator, are learning the shape, size, and feeling of the cell. We, like the narrator, are sometimes getting it wrong. He underestimates the size and extent of the cell and his own exhaustion. Sentences update a reader’s picture of a space, but also (especially in speculative fiction) update a reader’s model of the governing logics that produce it.

The central insight of this corrective model is a deceptively simple one: reading happens one word at a time.

The question of how to use the word sequencing to shape protocol fiction, has become very important in our work in the Protocol Fiction Special Interest Group. We’ve spent a lot of time defining protocol fiction through its strange rules. Now, we want to move toward an understanding of how the genre can move readers.

Protocols are particularly good at producing what Timber Stinson-Schroff identifies as “dynamic non-events,” or things that are prevented from happening because engineered coordination keeps a system functioning. There’s a gag in the second Kingsman movie, where a secret agent reveals his “trophy room.” It’s covered in banal newspaper front pages, each marking a day when his organization prevented an apocalyptic event. The absence of news is the achievement. This is a strange narrative property that emerges when writing about protocols. When they work, things don’t happen. But, traditionally, fiction loves events.

Protocol fiction has successfully found at least one obvious solution: writing about the moment the protocol fails. But what does protocol fiction look like without an explosion or a failure?

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The Ductus

In The Pit and the Pendulum, after moving through the dark using his robe threads to calculate distance, the narrator trips over his torn robe and knocks himself unconscious. Later, he wakes in the center of his prison, which he learns descends into a pit. Leather straps have been wrapped around his wrists and he’s tied to the bottom center of this pit.

Looking upward, I surveyed the ceiling of my prison. It was some thirty or forty feet overhead, and constructed much as the side walls. In one of its panels a very singular figure riveted my whole attention. It was the painted figure of Time as he is commonly represented, save that, in lieu of a scythe, he held what, at a casual glance, I supposed to be the pictured image of a huge pendulum such as we see on antique clocks. There was something, however, in the appearance of this machine which caused me to regard it more attentively. While I gazed directly upward at it (for its position was immediately over my own) I fancied that I saw it in motion. In an instant afterward the fancy was confirmed. Its sweep was brief, and of course slow. […] It might have been half an hour, perhaps even an hour, (for I could take but imperfect note of time) before I again cast my eyes upward. What I then saw confounded and amazed me. The sweep of the pendulum had increased in extent by nearly a yard. As a natural consequence, its velocity was also much greater. But what mainly disturbed me was the idea that had perceptibly descended. I now observed – with what horror it is needless to say – that its nether extremity was formed of a crescent of glittering steel, about a foot in length from horn to horn; the horns upward, and the under edge evidently as keen as that of a razor. Like a razor also, it seemed massy and heavy, tapering from the edge into a solid and broad structure above. It was appended to a weighty rod of brass, and the whole hissed as it swung through the air.

At the center of the torturous prison, Father Time slowly lowers a swinging blade. Note Poe’s intentional invocation of time. Both in the painted figure, and in the pendulum, “such as we see in clocks.” This is the writer/reader task made literal. Writing can progress with a certainty – again, that deceptively simple truth – because it will be read one word at a time. Father Time swings his pendulum lower with each word. Unlike Poe’s story, though, the blade never quite reaches a reader’s chest. The writer’s fictive world model can never fully irrupt into the reader. It’s a constant negotiation, and ideally a bringing-together of the two models into a secret third thing: the story.

The reason this pendulum matters is that watching a scythe descend toward your chest produces a feeling. The corrective process of reading move a vision closer to you. In this sense, like Poe’s narrator, a work of fiction is a kind of architectural space. While a cloth marker can hint at where you are, the movement through time, the way in which an author’s fictive world approaches a reader and eventually moves through them, cocreates the phenomenological texture that gives fiction its feeling.

Mary Carruthers (a scholar introduced to the Protocol Institute’s Memory Research Group by ) calls this ductus. “Ductus is the way by which a work leads someone through itself: that quality in a work’s formal patterns which engages an audience and then sets a viewer or auditor or performer in motion within its structures, an experience more like traveling through stages along a route than like perceiving a whole object.” The ductus of written works are the word-by-word, sentence-by-sentence corrections that accrue. Delany’s model of correction helps us understand what is happening in the reader’s imagination as they advance through a text. Carruthers’ ductus helps us understand how its sequence conducts a reader through those corrections.

A comedy story’s ductus will need to surprise in such a way to evoke (for example) whimsy or schadenfreude. A visceral horror story’s corrections will often involve an unsettling, transgressive, or shocking development of a previously “normal” image, while sometimes more psychological horror will begin with unsettling, transgressive, or upsetting images and then carry that associative weight forward into more “normal” descriptions. The Gothic pulls psychology outwards into the environment. Creative effects are achieved by carefully ordering sentences and words. By creating both the space (objects) of the story but, far more importantly, by creating a coherent and compelling journey through those images.

Take a look at what happens when you read – then massacre! – one of the finest opening paragraphs in English literature, from Shirley Jackson’s The Haunting of Hill House:

No live organism can continue for long to exist sanely under conditions of absolute reality; even larks and katydids are supposed, by some, to dream. Hill House, not sane, stood by itself against its hills, holding darkness within; it had stood so for eighty years and might stand for eighty more. Within, walls continued upright, bricks met neatly, floors were firm, and doors were sensibly shut; silence lay steadily against the wood and stone of Hill House, and whatever walked there, walked alone.

See how utterly differently this rearrangement (which changes almost no words) reads:

Hill House stood against the hills; it had stood by itself for eighty years and might stand for eighty more. Within, walls continued upright, bricks met neatly, floors were firm, and doors were sensibly shut. Silence lay steadily against the wood and stone of Hill House, not sane. Whatever walked there, walked alone, holding darkness within. Even larks and katydids are supposed, by some, to dream. No live organism can continue for long to exist sanely under conditions of absolute reality.

In this remixed paragraph, the insectoid and avian (the inhuman) don’t emerge until after the house has been fully described. When Jackson writes “larks,” “katydids,” “dream,” and “reality” first, she loads everything that comes next with the associative weight of those words. When these words and phrases are listed after the description of the house, a reader has to “go back” and re-layer this imagery themselves. Without them, and with the “by itself” relocated ever so slightly, the first two sentences read almost triumphantly, a very different affective mood. The ductus has changed substantially.

Equally important are the interruptive effects of “not sane.” In Jackson’s original, this works in part because it’s a change in the rhythm with which the information is delivered. Sequencing within and between sentences matters. The mention of sanity (notably, she never uses the word insane) in the first sentence extends, temporally, and in doing so, creates an unsettled effect in the reader. The opening “No” waits for its “sane” for ten full words, 18 syllables. Even the subsequent – punchy – interruption of “not sane” in the second sentence stretches to two words: “not // sane.” This, combined with the sudden change from the first sentence’s long, flowing language, into the staccato interruption of “Hill House, not sane, stood …” produces a suite of effects that texture the rendering of the image in real time. All this shapes the content of the scene and its mood dramatically.

We have to read sequentially. And so the order of things matters. Good writers know this, and will often ensure that their sequential order produces clear, logically simple images and events. Great writers will use this to imbue stories with that effervescent quality of all literature: densely layered meaning. They’ll use seeding and nuanced revelation to produce meaning with novel and exciting effects. Tone, voice, and even – to a point – genre emerge from how language is arranged.

We now have a model of how a work interacts with a reader – how it forms, and then successively corrects an image, and how those corrections shape an experience of a work through their sequencing. What relationship does this have to protocols, and by extension protocol fiction?

The Shapes of Protocol Fiction Journeys

In protocol studies, Protocol Institute (PI) research director defines a protocol as an “engineered argument.” Both those words are dense. A longer definition from PI is that “a protocol is a codified set of behaviors that, when adopted by enough agents, enables coordination without continuous negotiation.” It’s both the “I cut, you choose” rule our parents taught us when we were splitting the last slice of cake with our sibling, and the Hypertext Transfer Protocol (HTTP) that enables browsers and servers to exchange information – i.e., have websites be accessible by your simply typing a URL. There’s a tension here: if fiction is a constant negotiation between two world-models, its protocols would seem not to be working. But, in fiction, the protocol layer preemptively fuels the corrective negotiations that make up the text.

A story doesn’t have to teach you the rules of grammar, the definition of every word, or the mechanics of how to read word-by-word. It often doesn’t have to teach readers the conventions of its genre, either. All that is prepackaged (unless you’re writing children’s literature!): grammar, lexicon, reading order, genre expectations. A work must teach a reader how to read its particular text, but not – like Carl Sagan would say – create the universe just so you can bake an apple pie from scratch.

Which is useful, because the ductus of a work primarily exists only within that work. Some exhaust gets collected and ossified in genre-level protocols, which is why we talk about nascent genres “maturing” and eventually “being transcended.” The more aspects of a work that are codified into a persistent genre or grammatical layer, the more a specific work can create complicated “single-use” ductus. Persistent protocols enable work-specific engineering.

Grammar and genre comprise the protocol layer atop which the sentence and paragraph can operate. Fiction functions like an application capable of this constant (corrective) negotiating because it runs atop a stack that frees up capacity for a conductive (in the ductus sense of conducting) negotiation. Text becomes the conduit through which two world-models argue, and a story emerges. Taken as a whole, a story is an engineered argument in Rao’s sense. The ductus is the engineering. It is the place in which designed rhythms, orders, and corrections structure the reader’s movement toward a shared world-model that can run extravagantly (see the deferred meanings in Jackson). The sentence is where fiction’s protocol stack gets executed.

The reader’s image (i.e., fictive world-model, aka imagination) forms, and is accreted upon, expanded, corrected, argued against, nuanced, made smaller, made bigger, made specific, and made dramatic by word order and choice. Sentence-by-sentence.

So what does the genre of protocol fiction build within each work? Where does its ductus take us, and how?

One solution so far has been to find protocols that aren’t working for people. Nadia Asparouhova calls these “Kafka protocols.” These are protocols that “hold too much power,” and where a “[p]articipant is trapped in a maze that they can’t understand and also can’t escape.” Here one finds a cliche of science fiction writing – it is easier to write about dystopias than utopias. My own story Noise Ordinance is a straightforward play at dystopia. More recent protocol fiction works, like Cameron Russell Armstrong’s prize-winning American Skyway, traffic in a similar, but updated satire/thriller-inflected tone. This again, culminates in a very dynamic event – the antithesis of the non-event which typically characterizes protocol studies.

In Armstrong’s story, we can see some micro-techniques of protocol fiction emerging. Let’s take the opening paragraph (specifically, its second sentence):

Clusters of bureaucrats dutifully feigned attention while quietly locked in desperate combat with the looming spectre of accidental slumber during this third hour of the 17th session on the ninth day of the quarterly two-day Reratification Accords for the treaty governing the Joint Defense of the Lebanon Space Elevator and Remembrance Zone, which memorialized the grand experiment formerly known as the United States of America.

Armstrong uses a very different valence to Jackson. His story begins with “clusters of bureaucrats,” the word cluster immediately suggests computation, statistics – protocolish realms. There’s also a nice scansion to the phrase (a wildly important feature of prose that needs careful attenuation) “clusters of bureaucrats dutifully feigned.” The oxymoron of dutifully feigning something immediately conjures the satirical tone that defines the piece. This hint of satirism is formalized in the next clause, where the epic tone of “locked in desperate combat with the looming spectre” is immediately undercut by “accidental slumber.” High language for what amounts to the simple struggle not to fall asleep in a boring meeting.

From there, we dive into a long, nested list defined by a succession of numbers: “The third hours of the 17th session on the ninth day of the quarterly two-day.” This cascade produces another effect; numbers abound, but mean little in this story. Ninth day of the two-day is fun, and the second oxymoronic detail in just the first sentence. What emerges here at the level of language is that the containers of numeric meaning contain irresolvable tensions that are too easy to ignore and too hard to fix. That this is nestled within a numeric cascade further drenches the text.

Then, there’s the finale of the opening line: “formerly known as the United States of America.” Like Jackson, I don’t think this paragraph would work at all as well if that detail was foregrounded, instead of having it hit at the very end of the sentence. Again, we have a collision of tones: “grand experiment” versus “formerly known.” If you begin the paragraph with “America is dead,” the tedium of the meeting and the nested numbers read as entirely tragic throughout, rather than letting the mundanity accrete and then detonate. This is, in short, if you haven’t read it, essentially the entire plot of American Skyway in miniature, in a single sentence.

In American Skyway, the U.S. has fractured into four successor states that all share a space elevator anchored in Kansas. This space elevator keeps all four economies functioning, and the ratification meeting described in the opening is held quarterly to keep the treaty up-to-date. Our intrepid, isolated hero is the only one paying close enough attention through the haze of routine to notice important details in the seemingly small amendments made by the California delegation, all of which grant the head of the rotational defense force “unilateral decision-making on force deployment and munitions storage.” He also notices the delegation bringing in a number of explosives-laden trucks. His objections to the proposed amendment’s buried implications are overruled. The accords pass. Everyone drives home. As Mark drives away, he looks back at the elevator behind him, just in time to see it erupt as a “small, sickly blue blossom slowly spread[s] across the upper atmosphere.”

The story is found (in miniature) entirely in the form, tone, and content of the second sentence. The surprise of the dead United States that appends the sentence mirrors the explosive ending of the story, but the tensions through which that explosion emerges also saturate the text. The oxymorons and irony nested in numbers that propel the sentence’s language act as a formal version of the Californian subterfuge. Meaning is buried in a cascade of routine, minutiae, and bureaucracy. The “desperate combat” and “looming spectre” which at first appear to be satirically overwrought are actually wholly accurate descriptions of what’s happening.

This is a clear ductus for protocol fiction: allow a protocol’s buried contradictions to accumulate until they become an event.

Protocol fiction has been quite good at exploring the failure space of protocols, and this is a well-trodden ductus for a reason. Armstrong’s story is one of the best examples of it we have. This is a valuable vector for the genre, but one of several. What is still being built, in my opinion, is a method for using these nascent techniques to invert or complicate a straightforward plotting that culminates in a dramatization of failure. What does protocol fiction without an explosion look like?

Or to put the question in this essay’s language: moving forward, what kind of associative, corrective, and accretive details can the genre use? One promising vector can be found in the Institute’s New Nature paradigm, a view of the world where “regimes of reality [are] governed by technologically mediated laws that are nearly as inviolable, immutable, and persistent as those of nature.” I believe this will mean a play between nature and technological imagery (the way Armstrong plays with mock-epic and bureaucratic language) – the katydids of Jackson and the cyberswarms of Gibson finding novel ways to bounce off each other.

It will require thinking through which details protocol fiction reveals and obscures, and in what order.

A lot of science fiction has involved using the knowable mechanisms of empiricism and science to reveal/explicate impossibly strange results: time travel, space colonization, alien intelligences. Think of so-called Golden Age science fiction. Asimov’s Three Laws of Robotics are the clearest example of this mechanism: the stories literally start with a comprehensible schema of rules that govern robotics behavior, then walk readers through estranged results of these rules – robotic religions and seemingly impossible murders. Godwin opens his The Cold Equations with regulations, mass ratios, and engineering constraints, which propel the speculative strangeness of the work’s brutal results. Clarke’s The Fountains of Paradise presents readers with a space elevator rooted in tensile strengths, permits, and data. Known conditions (behavioral rules, engineering constraints, material/administrative constraints) move toward spectacular and strange worlds. The estrangement is downstream of the familiar.

New Wave science fiction is considered to be a break from this mechanism, but I think it is actually a relocation, rather than a reversal. Ballard’s “inner space“ expands the “knowable mechanisms” to include things like psychology, myth, media, and (sub)urban designs, from which strangeness emerges. Even a novel like Delany’s Dhalgren, which completely eschews mechanism, locates the strangeness in outcomes – an ever-changing city layout, two moons, a fully dislocated self.

It’s true that many of Stanislaw Lem’s novels, and the Strugatsky brothers’ Roadside Picnic, do explore truly ontologically unsettled mechanisms (the Zone, Lem’s ocean), and then render the understandable outcomes that emerge – economics, theorizing, technologies. However, Lem and the Strugatsky brothers both use the literal – extraterrestrial – alien to achieve this. The unknowability is external. This creates a distance from the ontological instability these objects create. When a system is fundamentally alien, a reader’s corrections are experienced as “well, this emerged from elsewhere, of course it is not sensible.” Obviously, these can still be deeply effective and vitally important for the genre, but they stop short of what I think science fiction can become. What happens when we locate instability as emerging from human, authored, and once-understood systems?

It’s my contention that a more contemporary science fiction will invert the strange outcomes × legible mechanisms engine. Strange human mechanisms (and the work here is dramatizing these mechanisms’ strangeness) will be explicated through a suite of understandable outcomes: distributed rules that are impossible for any single person to hold the history of, machine-generated decisions that operate via complex “black box” algorithms we can’t understand. Protocols that work even when no one knows why.

Classical tendency:
KNOWN RULE → STRANGE WORLD

Protocol fiction opportunity:
KNOWN WORLD → STRANGE RULE

We have a possible model for this, and, surprise surprise, I think it’s the very story we’ve been using: Poe’s The Pit and the Pendulum.

When Poe’s narrator wakes up strapped to the torture rack, Father Time above him, consider the sequence of events that actually occurs. He looks. He looks up. He takes in some greasy food. He lifts his head and looks again. He touches his bandage. There’s hardly any action here. Certainly no explosions, or even dramatic state changes of any kind. And yet, this is a propulsive bit of fiction. What is carrying, conducting the reader in such a dramatic way?

The corrections – the ductus – operate via the narrator’s updating model of the mechanism. What’s more, Poe very skillfully moves this on a schedule. First, we get an inventory of the room – all of which will be vital to the narrator’s eventual escape, but none of which register as such immediately:

I now lay upon my back, and at full length, on a species of low framework of wood. To this I was securely bound by a long strap resembling a surcingle. It passed in many convolutions about my limbs and body, leaving at liberty only my head, and my left arm to such extent that I could, by dint of much exertion, supply myself with food from an earthen dish which lay by my side on the floor. I saw, to my horror, that the pitcher had been removed. I say to my horror; for I was consumed with intolerable thirst. This thirst it appeared to be the design of my persecutors to stimulate: for the food in the dish was meat pungently seasoned.

The inventory: a strap, an arm, food, and a rule.

If protocol fiction is a genre of strange rules, then focusing on the rule is helpful for building out some techniques. The rule here is that the narrator can eat (a salty dinner) but cannot drink – there’s no pitcher. Immediately, the narrator begins to infer the intent behind this. He speculates that there is a designed cruelty here: a salty meal but no water. This isn’t a fact but an interpretation, perhaps a hypothesis about the logic of his situation. This prompts a reader to make a correction. They’ve moved from an image of a thirsty man to an image of a man inside a logic designed to make him thirsty.

From there, the narrator observes the ceiling. Recall that Father Time appears to the narrator as a painting at first. Then, both narrator and reader are corrected: “I fancied that I saw it in motion.” It’s a machine. From here, corrections accumulate. When he looks up later, he realizes it’s not just a machine, because the sweep of the scythe changes – it grows wider and gets closer. It is descending toward him.

… what mainly disturbed me was the idea that had perceptibly descended. I now observed – with what horror it is needless to say – that its nether extremity was formed of a crescent of glittering steel, about a foot in length from horn to horn; the horns upward, and the under edge evidently as keen as that of a razor.

Each of the descriptive details emerges from an updated understanding of the machine. It’s not just an ambient movement, it’s a machine that is aimed at him, specifically. It’s not so much that the reality of the room has shifted, it’s that the purpose and logic governing the room has crystallized.

Poe pushes this even further moments later:

The vibration of the pendulum was at right angles to my length. I saw that the crescent was designed to cross the region of the heart.

It’s not simply aimed at him in some nebulous way. No, with horror, he realizes the extent and specificity of the machine’s purpose. It will fall and swipe its way through his entire body, starting directly atop his heart. The emergence of this knowledge doesn’t come from interfacing with the designer of the system, not from finding some secret blueprint or assessing the character of the men who put him here. The corrections here amplify the horror of the story, more so because they happen in the absence of the designers of the system and emerge from a character (and, by extension, us readers) correcting their understanding of the world. The horror is the slow reveal of the obscured logic and purpose of the situation.

This narrative tension can only hold for so long, though. There’s a limit. Once we, along with the narrator, know what will happen; once the model is “complete,” something has to change for the narrative to escalate and continue. The story must find new areas within which to deploy its corrective engine. Poe’s solution here is particularly instructive.

The narrator watches the blade descend, confronts his situation, and waits for his fate. In a kind of boredom, he mindlessly turns to the side and sees a number of rats have emerged, drawn to the scent of the meal he was fed. His attention soon returns to the blade that’s slowly lowering and to the ingenuity of his captors who designed this situation.

All of this is Poe planting and seeding the eventual payoff of this section of the story. Jackson used imagery to affix narrative and thematic weight to description – putting the birds and insects first. Armstrong seeded for detonation and “reveal” by holding “formerly known” until the end. Poe uses a third kind of reveal here: the details that he seeded earlier in the passage as something seemingly inert that become deeply relevant paragraphs later. This is, I think, quite valuable for writing propulsively about rules. In addition to the mood that Poe’s imagery and sequencing evoke, there is the effect of leaving an apparatus in “plain view” to both the narrator and reader, leaving it seemingly alone as window dressing, and then revealing that this apparatus is actually pivotal for completing the system or model.

Out of the boredom and exhaustion, Poe’s narrator “for the first time during many hours – or perhaps days” thinks. He begins to try to find a way out of the blade’s path. Finally, he remembers the rats, his free elbow, and the food.

None of this information is new to the narrator or the reader. The rats, the food, the free elbow, it’s all on the page already. The “correction” isn’t novelty, it’s instrumental and combinatory. He simply thinks to use all four of these realities simultaneously: he rubs the food on his restraints and rolls free as the rats chew through them.

Nor had I erred in my calculations – I at length felt that I was free. The surcingle hung in ribands from my body [...] I slid from the embrace of the bandage and beyond the reach of the scimitar. For the moment, at least, I was free.

He emerges, safe, rescued by the very components of the system that he had viewed as a designed prison moments earlier.

This sequence reveals one path for the ductus of a non-event. A stretch of time in which the primary movement of the story is the reader’s model of the rules (alongside the narrator’s) updating, correcting, hypothesizing, and then updating again. The mechanism itself becomes the vehicle for the climax. The initial reading of the salted meat as cruelty transforms into an instrument of escape. This occurs without the properties of the meat changing at all. The corrective force of a moment like this is distinct from a “sharpening image,” in the traditionally pictorial sense. Instead, this is a functional correction. The model of the world is updated, but not via its material, but instead through its capacity and logic.

Take three elements from Poe’s ending:

• Food: starts as torment, becomes bait.

• Rat: starts as ambient and verminous, becomes instrument.

• Free arm: starts as simple allowance, becomes interface for freedom.

Objects don’t change, their function inside the model does.

The techniques available to protocol fiction in large part already exist – this story is from 1842, far before any formalized science fiction genre emerged. The ductus-construction techniques exist already, what’s needed is applying them to novel technologies and logics – to protocols and science fiction. Poe’s narrator is trapped by a personed institution (even if those people are present through their absence in the story).

What’s novel is that we live within mechanisms that are distributed, disauthored systems – ones that were at one point authored that have become so evolved or diffused that they no longer function as such. We encounter these primarily through outputs, rather than design. We don’t need aliens to interface with these systems, but we do need ductus that can lead us through compelling stories about them. The crucial break between Poe and the future of protocol fiction lies in the designed versus emergent distinction, and whether the techniques used by Poe can be successfully wielded within narratives about diffused systems. With Poe’s instrumental turn, we have a promising possible system for protocol fiction. In fact, I’d argue that Poe’s method might work even better in fully ontologically destabilized systems, where the property of the meat remains unchanged, but the capacity for characters to use it is opened even further. Recombination in truly opaque systems is the only mode afforded to characters. What happens when we play with that even more?

So, to answer the question of how to dramatize non-events: The dynamism of the non-event can emerge from rendering the strangeness of the rules (the protocols) that shape the fictional world. This means moving some of the corrective momentum of the prose – the pace and places with which a reader’s image is corrected over the course of a story – away from the results and onto the rules themselves. The ductus moves a reader from outcomes that are legible into the darkened room of obscured and estranged governing logics. The reader’s model of the protocol which produced those outcomes gets corrected over time. The dynamism of non-events lives there. What accretes, word-by-word, is a model of the protocol itself.

We just have to remember, when it comes to fiction, worlds are built one word at a time.

Jamverse Contest Winners!

The winner of the first Jamverse contest is Second Coat by Spencer Norwick. Second Coat is a steganographic communication protocol in the city of Mayaford. The artifact combines elements of Zoothesia and Stockton Chronicles.

Four other artifacts that we loved and awarded are:

• A Few of John Warner’s Personal Notes by Kannen Ramasamy

• One Person Per Drone by Nick Punt

• Kinetic by ARL by William Wickey

• Big Man’s Small Batch Corn Liquor by Cameron Armstrong

This was the first fiction contest in which we sought artifacts set within existing story universes rather than fully developed stories. While reading through the entries, we learned that the best artifacts were fractal microcosms of a larger story that also revealed traffic jams in the universe. They used storytelling techniques to place the artifact in the world and we could see the artifact fitting into a fully developed story.

Looking at Spencer’s Second Coat through an Orson Scott Card MICE lens, it’s clear that it is set in the milieu of Mayaford – the artifact introduces a new idea (communication through residues left behind by city workers). The traffic jam emerging in the world is the graffiti subculture, armed with kappala halos, meeting the bureaucratic protocols of the city workers of Mayaford.

Thank you to everyone who participated in the first Jamverse contest. It’s great to see the variety of artifacts that have been created. It also gave us more insight into what makes a good artifact. We’re very interested in the use and repetition of these within stories since we experience protocols through day-to-day, artifact-shaped objects like notes, tools, maps, and apps.

We’d love more such artifacts to exist in the Jamverse, and if you decide to contribute one, these five have set the bar.

Dive into the stories and artifacts, and see where inspiration takes you!

They don’t need to know every single rule; they just need to have confidence in it. The polling station isn’t only a place where votes are collected but also a place where trust becomes visible. It’s a place where people stand in line, officials check names, and ballots move from booth to box to count. Even though the process isn’t perfect, what matters is that it is legible to ordinary people.

In Estonia, one of the world’s most digitally integrated states, voting is done somewhat differently. It’s done over the internet. The 2023 parliamentary election, during which more than half of all ballots were cast online, is now recognized as the world’s first parliamentary election in which online votes outnumbered paper ballots.

That didn’t happen because the state appended a voting website to an otherwise analog democracy. No, the system grew out of a broader digital state with an existing national digital identity program, and technical provisions for cryptographic infrastructure, software applications, audit procedures, and election oversight. And, given how Estonians have already used digital identity for taxes, banking, digital signatures, healthcare, and public administration, the introduction of internet voting (known as “i-voting” in English language documents produced by the Estonian government) wasn’t really a surprise.

In fact, while online voting is still considered unusual in democratic terms, at least for us living outside Estonia, for Estonians, it’s part of daily life. However, the ordinariness of this new system has introduced problems of its own into the electoral process. Trust issues, to be more specific, and the kind of trust issues that the traditional ballot simply hasn’t suffered from.

The vulnerabilities of the traditional ballot are easy for ordinary people to imagine. I-voting, on the other hand, prevents citizens from directly observing the voting process, prompting questions about what happens when election legitimacy depends on systems most citizens can’t personally inspect.

The Old Civic Protocol

Despite being simple in theory, paper voting was – and still is – anything but. It only feels simple because much of its complexity is visible to the public and supported by institutions people recognize as legitimate. It’s the illusion of simplicity that depends on rules and roles, documents, seals, signatures, observers, and non-negotiable timelines. A sealed ballot box matters because it’s visible, and because there are institutions, laws, and people responsible for keeping it sealed.

A polling station is, thus, a highly protocolized environment, with mandatory eligibility checks, privacy protections, and measures to prevent people from casting multiple votes. Ballots must be stored securely, the count must follow accepted rules, and the entire system must establish who voted without tying that person to their political choice.

Paper systems perform this latter function through physical separation. The voter list confirms the voter’s eligibility, the booth protects privacy, the ballot preserves the vote, and the counting table turns individual ballots into results. These steps are relatively easy to picture and follow by ordinary people, even if they don’t really understand the full institutional machinery behind said steps.

I-voting hasn’t simply moved these traditional systems onto a screen. In fact, i-voting reproduces many of the same democratic functions through a different set of dependencies. Paper voting relies heavily on physical custody, election officials, observers, and legal authorities, while i-voting relies on software, cryptography, digital ID systems, technical audits, and specialist expertise.

In other words, the democratic act of voting remains procedural, but the distribution of authority changes. Much of the process becomes less visible to the ordinary voter, and any meaningful inspections increasingly depend on people with the technical literacy to understand the systems underneath. The voter is still expected to trust the process, but the question becomes more complicated: who, exactly, is being trusted, and why?

When the Ballot Becomes a File

With the i-voting system, Estonian voters don’t need to go to polling booths. In fact, they don’t even need to leave the comfort of their homes to vote.

During the voting period, voters can download the voting app onto their personal computers, authenticate their digital identity, select their preferred candidate, and confirm their vote with an individual PIN. Votes are then encrypted, digitally signed, transmitted, separated from the voter’s identity, and counted.

The process looks rather simple from the voters’ side, but that simplicity is only superficial, because there are several safeguards operating under the hood. One of them is a separate verification smartphone app, which allows voters to check shortly after voting that the vote received by the system matches the one they intended to cast.

Arne Koitmäe, an Estonian election official closely familiar with the country’s internet voting procedures, explains why this layer matters: “When the vote is sent to the voting service, the vote is in the wild, on the internet. That environment cannot be controlled by the election administrator, so it is important to provide a way to make sure the vote is not tampered with while it is being sent.”

The verification app addresses one kind of risk: whether the vote was altered in transit. Revoting addresses another: whether the voter was pressured while casting it. Voters can vote online as many times as they want, but only their last i-vote is counted. In addition, a later paper vote can override an earlier online vote and thus alleviate certain challenges associated with remote voting, such as coercion at home by family and friends.

Polling booths, ballot boxes, and physical custody still exist for paper voters, while online voting performs comparable functions through software, cryptographic design, digital identity, and oversight.

In the digital system, a vote that was otherwise cast via a traditional paper ballot becomes a file, and that file has to be traceable enough for the system to verify, but not traceable in a way that exposes a voter’s private choice. It has to be secure enough to count, but anonymous enough to remain democratic.

Koitmäe also describes the counting-stage separation in practical terms: “Before decryption, the cryptograms are taken out of the digital signature container. From that point, they are anonymous. They are just cryptograms.”

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Trust as a Designed Output

We might tend to approach the trustworthiness of internet voting through the lens of cybersecurity, but security is only part of a larger picture. The main issue, at least from the perspective of trust, is legitimacy.

A secure system that doesn’t earn the trust of its citizens can’t possibly do democratic work, and a trusted system that’s not secure is even worse. Estonia’s voting system needs to produce trust indirectly, and it does so through layers of procedure. However, most citizens can’t inspect the software in any meaningful way; they can’t follow the cryptographic path of their respective votes, and they can’t personally verify the stages that separate identity from ballot content.

Instead, citizens must rely on the country’s national digital identity program and previously implemented digital infrastructures, election laws, cryptographic design, audits, observers, verification tools, source code reviewers, courts, and election officials.

Admittedly, this isn’t anything new, because paper elections also required trust in officials, storage rules, ballot designs, recount procedures, chain of custody, and legal enforcement. Most voters never inspected any of those things either. What actually changes with i-voting is the kind of expertise needed to independently assess implemented safeguards. Understanding the physical chain of custody is one thing, but evaluating cryptographic protocols, source code, or server security requires a much narrower form of technical literacy.

The issue here is that most voters don’t actually have such expertise, so voters are compensated for that gap largely through an appeal to trust in institutions.

The reliance on institutions isn’t merely theoretical. A study published in Government Information Quarterly found that institutional trust was a stronger predictor of both confidence in i-voting and its actual use than technological trust. In practical terms, voters appear to care less about whether they trust digital technology in the abstract than whether they trust the institutions responsible for designing, operating, and overseeing a particular voting system.

The same study found that higher digital literacy increased the likelihood of using i-voting, while age and education themselves showed no significant effect. That makes the divide between i-voters and those who vote traditionally less clearly generational than it might first appear, assuming a tendency for younger generations to be more digitally literate.

From the voter’s perspective, then, the question is not simply whether to trust the software, but whether to trust the institutions and experts responsible for designing, operating, and overseeing it. That kind of trust is not unusual, even if the technology makes it more visible. In written comments provided for this article, Byron Hyde, Honorary Research Associate at Bangor University, notes, “Trust in almost anything works through proxies because we rarely have access to the things itself to arrive at our own assessment.”

Ballots in traditional voting went into a box, and boxes are tangible, and their contents are, in theory, legible to anyone. The ballot in i-voting, on the other hand, becomes an encrypted digital object that moves through different systems that aren’t as easily accessible to the public. In this case, the public has to place its trust in procedures and safeguards that govern both the people and the machines handling their votes.

Transparency Without Direct Visibility

Internet voting changes the significance of transparency because full visibility isn’t possible, understandable, nor, in some cases, even desirable (from a privacy perspective). If anyone could access and see anything and everything, voter secrecy would be compromised, and the system would become potentially easier to attack and exploit. The solution is to divide the transparency and the responsibilities.

Voters receive one kind of access, while auditors receive another. The same applies to election officials, software reviewers, courts, cryptographers, observers, and political actors. Each of these groups sees different parts of the process under different rules, which creates narrower forms of transparency, in which voters aren’t permitted to inspect every layer directly. Instead, they’re asked to trust that the right segments of the system can be inspected by the right people under the right conditions.

Koitmäe describes this kind of observation as more technical, but not hidden: “Observing is more complicated than paper voting. We have a training program for observers so they can know what happens from the beginning until the votes are opened and we have the results.”

Though this might sound undemocratic, as the process can’t be reviewed by just about anyone, the reality is that it’s far from it. Modern societies already rely on expert-mediated systems, such as air traffic control or central banking operations, that ordinary citizens can’t independently inspect. Instead, they rely on certification, professional standards, independent oversight, legal accountability, and the possibility of public scrutiny when something goes wrong.

That last part is really important, because different institutions can review disputes, investigate failures, challenge procedures, and explain different decisions publicly. None of those mechanisms allows ordinary people to inspect cryptography personally, so ordinary people have to rely on different experts and specialist authorities. And since elections represent the procedure through which democratic authority renews itself, the burden of explanation and accountability also rises.

Edge Cases in the Republic

The Estonian protocol is easy to trust when everything works as designed. The voters authenticate successfully, the application runs without issues, the ballot is cast, and the verification tools confirm that the votes were received. The results are published, institutions accept the outcome, and everyone involved moves on. But what happens if the system is challenged?

What if cybersecurity researchers question the assumptions built into the system, or election officials and independent experts disagree about the seriousness of a particular vulnerability? What happens if a political party claims the system is rigged or exploited, and can’t be trusted?

For Koitmäe, that political layer matters because legitimacy cannot be repaired by technical safeguards alone: “Frankly, I don’t see that there are any technological measures that will change this narrative. It certainly affects legitimacy in the end.”

This is where the asymmetry between traditional and i-voting becomes important. Most irregularities in a physical election can be described in terms an ordinary voter understands, like missing ballots, broken seals, improper handling, or discrepancies in the count. However, vulnerabilities in i-voting systems are much harder to evaluate without expert knowledge, because they move disputes into technical language that not everyone can speak.

Sure, the public can judge how institutions respond to possible tampering or exploits, and whether courts, auditors, officials, and political actors treat the system as accountable, but most people can’t personally resolve a cryptographic dispute. This creates a shift in public judgment, which no longer examines the legitimacy of the ballot count, but the institutions that claim that the ballot count is factual and not tampered with.

Hyde warns that this kind of trust can remain stable until a visible failure or political crisis forces people to reassess it: “When everything appears fine from the public perspective, trust tends to remain stable regardless of trustworthiness. But that’s a vulnerable situation that’s easily disturbed by a crisis.”

Convenience and Authority

Estonia’s internet voting system works because of its sheer convenience. A vote that takes minutes to cast, remotely, and that fits into an already digitized civic life has obvious appeal compared to traditional voting. It also helps people abroad, people with mobility constraints, and people whose work or family schedules make physical voting difficult. It makes participation much more accessible, which adheres to democratic values.

That accessibility, however, shouldn’t be confused with universal digital competence. It might seem reasonable to assume that younger voters would be more comfortable with i-voting than older ones, but the aforementioned research suggests that age alone is a poor explanation and not a significant predictor of online voting, especially when other factors, such as technical knowledge, are taken into consideration.

However, convenience also changes expectations, because the ease of digital voting makes traditional polling stations feel inefficient. The importance of a polling station in the electoral process diminishes, and in-person voting becomes just one among a number of ways to vote. With the widespread acceptance of i-voting, paper voting becomes a fallback, or a safeguard, rather than the obvious center of the electoral process. Authority gradually moves towards the system most citizens actually use.

That shift isn’t driven by convenience alone. Research based on post-election surveys from Estonia’s 2021 local and 2023 parliamentary elections examined how technical knowledge, trust, confidence, and perceptions of the system’s performance influenced the decision to vote online. The findings suggest that not all voters approach i-voting in the same way: their willingness to use it depends partly on how much they understand, how confident they feel in that understanding, and whether they believe the system has worked reliably.

When the use of one system becomes prevalent and shows results, the procedural trust associated with the system and its processes becomes self-reinforcing. People trust the system because they use it, and they use it because it works. It works because it’s maintained, and it’s maintained because its efficiency has become a part of the democratic settlement. Over time, the question shifts from whether internet voting should exist to what kind of legitimacy its continued existence requires.

This is a rather important shift, because democratic infrastructure becomes harder to remove once it becomes ordinary, and that’s exactly what’s happening in Estonia. If a majority of voters vote online, turning off the internet voting would likely have adverse effects on the relationship between voters and the state.

The Distributed Ballot Box

Though it’s tempting to say that Estonia replaced the ballot box with software, the truth is that the ballot box hasn’t been replaced, nor has it disappeared. It’s only been reformatted. Paper voting still exists, and even the traditional ballot box was never an independent source of legitimacy because, as previously explained, it depends on election officials, printing and distribution systems, secure storage, observers, rules, etc.

Part of the ballot box now resides in the voter’s personal electronics, and other parts of it live in different systems, like encryption, election law, audit procedures, revoting rules, verification tools, public documentation, etc. In other words, internet voting added new layers of dependencies.

In that sense, the ballot box didn’t simply become digital; the authority surrounding it became more distributed. Sure, traditional institutions remain important, but they increasingly share the task of legitimizing the result with software developers, cryptographers, CS researchers, and other tech specialists. Their expertise now becomes part of the chain through which voters are asked to trust the result.

More importantly, this doesn’t weaken the authority of the state, but it does affect its role. The state still establishes election law and administers the voting, but it can’t make cryptographic claims credible merely by declaring them to be true. Those claims have to survive technical scrutiny, independent reviews, and public challenge, making electoral legitimacy dependent partly on legal institutions, and partly on expertise of different specialists.

These distinctions make legitimacy a maintenance problem. Sure, paper voting needed maintenance as well, in the form of poll workers, storage rules, recount procedures, chain-of-custody protections, and public confidence. Internet voting requires the same democratic seriousness, but in a different form. It requires secure identity, software assurance, operational discipline, independent review, legal clarity, and the willingness to respond seriously to technical criticism.

Final Thoughts

The introduction of Estonia’s internet voting system is not a story about democracy being replaced by code. It is a story about how democratic legitimacy changes when the procedures behind elections become more technical, less visible, and more dependent on institutional interpretation. Elections have always relied on protocols. The question is how citizens are persuaded to accept these newer protocols as democratic, and what institutions must exist around them for that acceptance to be justified.

Estonia’s answer is both practical and unusually coherent, as the state invested in building the digital identity infrastructure first and normalized its use. Make voting one civic act within that trusted system, surround it with cryptographic safeguards, legal rules, revoting options, verification tools, audits, and public explanation, and let repeated use reinforce the public trust.

The theme for the 2026 edition is New Nature – the rapidly evolving planet-scale technological layer governed by laws with a hardness and inviolability approaching those of nature. New Nature is our overarching frame for the technological future, shaped by the intersection of AI and protocols, perhaps the most exciting portion of the emerging technologies frontier.

The goal of this symposium is to get you to this frontier, and prepared to engage at all levels from hands-on tactical to doctrinal.

• Main Event Registration link (luma) (full)

• Workshops page (individual registration forms)

• Event home page with full agenda and details

Register early, since space is limited for both the main event and the workshops!

We’ll be livestreaming significant portions of the symposium and recording most of it, but based on our experience with previous events, being part of the live conversation and interactive portions on the Zoom call really changes the experience significantly.

The AI Angle

Protocols for working with AI are going to be a core theme of the Symposium, and as a conversation starter, we have an AI postures personality test for you, developed by community member .

How do you actually work with AI, and what is it costing you cognitively? The AI Co-Working Effectiveness Survey is a brief research instrument that maps you onto one of four postures (Partnership, Hypervigilance, Overreliance, Compliance) and scores the cognitive load, debt, and sovereignty underneath. Take it, get your posture profile immediately, and help build the Protocol Institute community’s aggregate picture, to be presented at the symposium.

AI postures survey

You’ll have opportunities during the symposium to compare and chat about your results with other attendees. Of course, you can also take the test even if you aren’t able to make the seminar.

Symposium Preview

The first 2 days, Monday 21 and Tuesday 22, will be devoted to hands-on workshops, and the remaining 3 days, Wednesday 23 to Friday 25, will be devoted to talks. You can register for either or both, and we highly recommend both. We have 5 workshops and 50+ talks lined up.

Any of the workshops will give you a deep, hands-on sense of why protocols are so important to the emerging technological future, and the talks in the main event will give you a taste of the incredibly broad and varied scope of the field.

We highly recommend you attend one of the five workshops. Each exposes you to a different frontier area of AI x protocols. No special expertise is needed for any of them.

The Workshops

The workshops require separate registrations (also free). Each comprises 4-5 virtual sessions with asynchronous activities in between. Here are the links and brief descriptions.

Note that these workshops run in parallel, so you will only be able to attend one properly.

1. Robots as Protocol Citizens: Assembling One Into the YakRobot Protocol Stack: Explore the exciting emerging frontier of decentralized onchain robotics. In this workshop, you’ll learn about teleoperation protocols, control actual live robots on the other side of the world with an AI agent, and learn about the rapidly maturing technology stack for onchain robotic economies. Optionally, those interested will also be able to assemble their own robots and put it onchain (you’ll need to purchase a suitable kit for that component). Ideal for hands-on techies interested in protocols.

2. Protocolize Your Book: Run your manuscript through a book production factory: In this workshop, you’ll learn how you can use an AI-native book production workflow to take a book project from draft manuscript to print-ready and get a deep dive into how AI can accelerate and automate traditional workflows. Ideal for self-publishers and workflow mavens.

3. Protocol Hackathon: Securing Stigmergic Systems: In this workshop, you will learn about stigmergic systems engineering, the science of coordination inspired by insect swarm behaviors, which is turning out to be remarkably relevant to AI systems (think: markdown files as pheromones). We will focus in particular on the security aspects of stigmergic systems. You will participate in a war-game style tournament using a stigmergy simulator (no coding needed). Ideal for people interested in the formal protocol theory and modeling, conflict, security, and AI safety.

4. Beyond the Artwork: Designing Protocols as Art: In this workshop, you will learn all about the theory and practice of the wonderful and rapidly evolving world of protocol art, which debuted in the mainstream art world via the Strange Rules pavillion at the Venice Bienalle this year. You will learn about a conceptual framework and make your own protocol artwork. Ideal for practicing artists, theorists, curators, and researchers working at the intersection of art, protocols, and emerging media.

5. AI Kitcraft: A Hands-On AI Tooling Workshop: In this workshop, you will learn a unique and field-tested protocols-based approach to AI adoption for organizations, based on understanding it as a technology going through a composable “kit” phase. You will learn the the history and concepts of how major technologies evolve through their kit phases, understand the capability maturity model, and apply the ideas by developing your own kit of AI workflows for your organization. Ideal for non-technical analysts and managers from all backgrounds — project managers, operations and deployment leads, as well as AI-curious newcomers.

The Talks

We have ~50 talks, ranging from technical to non-technical, and theoretical to practical. There is a dizzying array of topics on the agenda — trust, institution-building, political economy, governance, geopolitics, technical systems, temporality, and of course, AI, AI, and more AI. All the content should be accessible to a general audience (though a few may require you to consult your LLMs as you listen).

We have 4 special sessions worth highlighting.

• Inventing Psychohistory: A special session hosted by the Special Interest Group in Psychohistory, exploring how we might use AI to develop systematic long-range prophecy protocols, oracles, and divination technologies.

• Southeast Asia x Protocols: A special session devoted to protocol-themed activities and thinking going on in Southeast Asia, where we’ve been nurturing a track of research, design, and study for the last two years.

• The Art of Memory: A special session hosted on one of our longest running research themes, memory protocols, hosted by the the Memory Research Group. If you’re interested in the critical ways in which memory and AI are entangled, and how the current era fits into the long history of memory technologies, this session is for you.

• Worldbuilding in New Nature: A special session devoted to protocol fiction and world-building, hosted by the Protocol Fiction Special Interest Group. This session will draw on more than a year of experience developing this genre through multiple contests, dozens of short stories, and an ambitious extended universe project. This session will include a special event: The launch of a new, dedicated, protocol fiction magazine.

We are really excited about this year’s edition. It’s the third symposium, but the first being organized under the aegis of the Protocol Institute, and in many ways this is our debut as an organization, and our first effort to explore our own future agenda systematically.

Key Links

Here are the key links once again:

• Main Event Registration link (full)

• Workshops page (individual registration forms)

• Event home page with full agenda and details

• AI postures survey link

Shafts to Wires

For 15 years after electric power became commercially available, the typical American factory used it to do almost nothing new. A manufacturer would pull out the steam engine, set an electric motor in its place, and leave everything else untouched. Factories of the time distributed power through a long vertical iron shaft that ran up through the center of a multi-story building. The shaft was connected to the machines on each floor, and because power was delivered centrally, every machine had to run whenever the shaft turned or it would sit completely idle. Even though the power source had changed, the operation of the factory remained practically bound to the rhythms of its predecessor.

Economic historian Warren Devine, who traces this transition in a paper titled From Shafts to Wires, calls this phenomenon the “usual juxtaposition of a new technology upon the framework of an old one.”

Things changed eventually. After another quarter century, manufacturers adopted unit drives. That meant each machine in a factory was powered by its own electric motor. The new arrangement allowed each machine to be controlled independently and run asynchronously from the rest. Unit drives unshackled machines from the geometry of the central shaft, letting them be placed according to the flow of work along the production line.

The old factory layout had been arrived at incrementally, through a chain of decisions that each followed from the one before, beginning with the constraint of the central shaft. With that foundational piece removed, the layout of factory floors could begin, gradually, to optimize for electric power.

The rearchitecting of the factory around electric power was complete when the multi-story building – tall, because steam had to be delivered through a vertical shaft – gave way to the horizontal factory, which was far less expensive to build and maintain.

The lesson of Devine’s account is that the productivity gains came from the slow, accretive repurposing of machine tools, fixtures, and floor plans around the new power source – work that no one could have specified in advance, because the new layout had to be discovered. Output per man-hour in manufacturing rose at an average annual rate of 1.3 percent before 1919 and at 3.1 percent after – roughly a 2.4× jump in the rate of labor-productivity growth. Capital and labor had remained in relative proportion to one another during the shaft era, but became uncoupled after the unit drive arrived. In the years that followed, less capital was needed to make labor more productive.

Rearchitecting the Digital Environment

What electric power did to the vertical, steam-powered factory, artificial intelligence is doing to our digital work environments. AI provides a new power source for knowledge work – a unit drive of surplus tokenized intelligence available to every person in an organization. In the canonical 20th-century factory, the object being rearchitected was a building: a large, shared work environment. The object being reshaped now is the individual work environment of every person contributing to an organization. Each knowledge worker is, in effect, a small factory whose machine tools must be repurposed around the new power source.

This cannot happen by decree alone. A knowledge worker’s tooling is the residue of years of tacit decisions, each indebted to technical dependencies and arcane software, like the position of a lathe relative to a line shaft. The only way the repurposing actually happens is through the same process that relaid the factory floor: people must combine their domain expertise with the new power source – try things, build small fixtures for themselves, share the ones that work, and discard the ones that don’t.

AI adoption is the combination of deep domain expertise with the new power source, and this new arrangement has to be allowed a period of productive experiment before anyone tries to draw the new floor plan. Play has to precede planning. Desire lines must form before they can be paved.

This is what makes durable AI adoption a two-track initiative, without exception. The experimental phase cannot be skipped, because the patterns worth standardizing have not emerged yet. But chaos alone is lossy – patterns form and dissolve without ever consolidating into infrastructure the organization can rely on. The work is to read the chaos, reinforcing the traces that prove themselves and releasing the ones that don’t.

Governed and Cultivated

In an editorial introducing the research mission of the Protocol Institute, wrote that the task of our times is to invent New Nature – “regimes of reality governed by technologically mediated laws that are nearly as inviolable, immutable, and persistent as those of nature.” While electricity introduced a new power source, the regime of reality that finished forming during the second industrial revolution was time discipline. Workers and employers alike began to treat future clock time as a natural resource to be managed and protected. Both the workers’ right to a five-day workweek and the efficiency of the assembly line were written into the reality created by clock time. We take this for granted now, but the adjustment took nearly 100 years – from the introduction of time clocks in the workplace, which in some extraordinary cases led to the lynching of line managers, to the characters in Virginia Woolf’s novels, who navigate their days by staying attuned to the town clock and Big Ben.

For companies to be successful in adopting AI, they need to consider both the rearchitecting of the work environment and the change in subjective reality of members of the organization. To achieve this, any AI adoption initiative needs two tracks: governed and cultivated. The governed track is focused on architecting new environments, while the cultivated track is focused on the psychological and ergonomic effects of the new environment.

The cultivated track is bottom-up: individual play, experimentation, the personal workflows people build and share. This track is comparable to the amateur kit stage in the deployment of a technology. Technologies such as computers and cars went through a stage where amateurs tinkered and experimented with them on their own terms. In the case of radio, amateurs were the ones keeping the entire industry alive before standardization was even considered. Kit stages are characterized by rapid experimentation and formation of a field around a particular technology. The focus is on gaining tacit knowledge and being subjectively accustomed to the effects of the technology, and less on archiving knowledge for the longer term. A cultivated stage of adoption is important for AI because it is a technology that has significant effects both at the organizational and at the individual level. In many of the previous eras of technology, such as desktop computers and the early internet, cultivation of a field was left to artists and hackers who were early adopters of the technology. But today, the effects of AI are at once wide and solipsistic, which means that the cultivation stage is something that should be diffused through the entire organization, not just the early adopters and the 10× engineers.

The role of the governed track is to create new protocols from the outcomes of the experiments in the cultivated track. The governed track should set protocols at points where AI’s outputs become consequential: how an output gets verified, escalated, and owned as it crosses from machine to human or from one team to another. A traffic light is a protocol; a blockade is not. Both are rules, but one keeps the system moving while managing the underlying tension; the other just stops it.

The cultivated track is trace-making – how an organization produces the patterns of coordination that a new medium makes possible, the equivalent of the desire lines worn across a park, the kits and practices that no central planner could have specified in advance. The governed track is trace-selection – how an organization reads those emerging patterns and decides which to reinforce into shared infrastructure and which to let fade.

A governed track with nothing underneath it standardizes too early. It freezes a layout before new traces have formed, the way laggard manufacturers kept the line shaft. A cultivated track with no governance generates patterns endlessly but never consolidates them, accumulating risk it cannot see. It is frontier territory with no ecosystem to sustain and learn from it. The maturity ladder is really a measure of how well an organization coordinates these two tracks as the ground keeps shifting.

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Keeping the Medium Honest

Adoption of a domain technology such as AI requires us to:

1. Think from first principles about the traces and paths generated by the previous technological paradigm.

2. Generate new traces and paths based on the new technological paradigm.

Failure to do the first leads to the persistence of shaft-like thinking. Continuously doing the second without stable governance produces an organization that works without remembering or accreting its gains.

The factory took 50 years to implement the unit drive and reap the benefits of electrification. But there were journals published as early as 1896 in which the unit drive was proposed as a method to increase productivity. The race to transform the factory lasted roughly 25 years because its new form had to be discovered. Now the race is on to transform the organization with artificial intelligence. Knowing that a reorganization is needed is cheap; becoming the organization that finds and sets the standard is hard.

Enter our Jamverse Jam

Protocolized recently announced our fourth open submission contest: we’re looking for artists and writers to extend and connect the Jamverse, a network of interoperable worlds that we are incubating in our Special Interest Group for Protocol Fiction.

We have a $1,000 grand prize, with at least 10 other entries receiving $200 prizes. Deadline for entries is July 31. Read more and enter at the Jamverse microsite.

You’d be hard-pressed to find travelers in London who do not associate their visit with the underground train network. Likewise, Japan’s bullet trains and Hong Kong’s automated MTR system are iconic. This isn’t surprising considering that public transit systems are foundational infrastructure, acting as a first point of contact for tourists and an essential service for city-dwellers. These systems are, naturally, replete with protocols. Who pays and how compliance is governed, how space is controlled and who yields it, what safety measures are in place, what social interactions are condoned, to name a few.

Public transit systems have therefore been a useful lens for understanding the “national protocol characters” of the countries I’ve visited over the years. In Medellin, Colombia, I was told by many residents of the city that they treated their metro system with sanctity. Sure enough, my experience on the Medellin Metro reflected this. The trains were impeccably clean and everyone using them did so with a great deal of care and pride. Even during busy rush hours, there was a sense of composure as people boarded and took their places. And while the stations themselves were regularly policed, the trains during the day had only occasional police presence.

The Medellin Metro was established in 1995, constituting a significant step-change in transport provision for the region. Its launch also came two years after Pablo Escobar’s death – a time when the city was seeing a gradual decline in the notorious violence that had reached an unprecedented level through the 1980s and early 90s. There was a feeling of change in the country at large, with Colombia having adopted a renewed constitution in 1991, shoring up its institutional strength. This was the first major revision to Colombia’s constitution since its drafting over a century earlier. The Medellin Metro emerged in this context as a visible and shared symbol of Medellin’s transformation, following the period of deep social and political instability that had plagued the city for many years. This symbolism, coupled with the tangible benefits of increased connectivity, led to a form of popular reverence for the metro service. A survey in 2012 showed that the organization behind the Medellin Metro was consistently voted the most appreciated public body by the city’s population. The complementary Metrocables in Medellin, too, have generated a sense of pride – especially since they extend coverage to many of the city’s poorest neighborhoods.

Trust dynamics are also interesting to observe on public transit systems. Medellin isn’t exactly an oasis of safety, with crime across the city still being relatively high. But both the metro and the cable car systems are very safe and provide genuinely comfortable, enjoyable journeys. Perceived safety is, of course, heavily driven by expectations of law enforcement by public authorities and the state – through effective ticket barriers (which are plentiful across stations) or visible police presence. However Medellin’s new-found sense of shared pride also creates a flavor of trust unique to the region. As one young man born and bred in the city explained to me, “A man might rob you out on the streets, but he won’t leave a scratch on the metro.” The city’s painful recent past is still felt across all classes and social groups, leading to a widespread perception of the public transit systems as important symbols of hope and progress, promoting good behavior even where one might not expect it.

While overcoming instability and strife can inspire infrastructural reverence in a city’s population, the foundation of an effective transit system remains its capacity to make significant material improvements in the connectivity of a region. I saw this when I traveled on the Metro Express in Mauritius, which is often touted as the safest and most stable country in Africa. A light railway service – the first of its kind – opened very recently in 2019, connecting the capital of Port Louis to various locations across the country. Notwithstanding some consternation around the funding for the project, this infrastructural development was massively welcomed on the small island. Tens of thousands of passengers use the train daily, representing a significant portion of the population. Although I didn’t get the sense that the Metro Express had imbued quite the level of resounding affection in the population as the Medellin Metro, it was commonplace to hear people on the train openly lauding its air-conditioned escape from the tropical heat, and its avoidance of traffic in the capital. It’s not typical to hear conversational praise of public transit while actually on it – but clearly the novelty of Mauritius’s increased transport capability hadn’t worn off when I was last on the Metro Express in 2024. Having myself traveled between Port Louis and the town Rose Hill many times, either on the sweltering bus or by car, I also felt swept up by the comparatively enjoyable experience of meandering along a railway with beautiful views of luscious greenery.

So what of the British, who can be found tussling for a spot on the tube at Oxford Circus Station every weekday after work, or littering on trains at weekends? Are they to be characterized by something like apathy in their regard for their longstanding networks of public transit? There may well be an element of this. Both the London tube system and the national train network were established in the nineteenth century – a marked contrast to the recent developments in Colombia and Mauritius. And, if we needed any proof of a lack of appreciation for these old stalwarts, look no further than the puerile injustice that Londoners feel anytime they have to wait longer than two minutes for a tube train. Only the other day I heard a chorus of profanities across the platform, in addition to a few hands raised in exasperation, as a Northern Line train from Camden was delayed by approximately six minutes. I’d taken delayed trains in both Medellin and Port Louis, neither of which provoked such grumpiness!

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As with most major cities, generalized trust in London is pretty low compared to less urbanized regions. This trust dynamic is reflected in the relationship between transport authorities and transport users. On the tube there is a heavy reliance on ticket gates, station staff, and the British Transport Police to ensure everyone has coughed up the right amount. Trust dynamics are also reflected in modes of behavior between transport users. For example, in London, traditional customs such as offering seats to the elderly or disabled have declined. It’s not uncommon to see elderly people having to rush toward empty seats, for fear that other travelers will not offer to give up a seat for them. This has become so problematic that the Mayor of London has launched advertising campaigns to encourage Londoners to be a little more civic-minded. New York is similar, with tightly-monitored payment systems and aggressive competition for seats being the norm. Obviously, apathy and distrust are not so widespread that the millions of people who use and benefit hugely from London or New York’s public transit systems are seriously dissuaded from doing so. But they certainly color the experiences and behaviors by which those millions use the public transit systems.

We shouldn’t assume that these dynamics are uniform across all highly-developed urban centers though. I have traveled through many of Germany’s major cities, including Berlin, without ever coming into contact with barriers or ticketing personnel. This is often described as an “honor system,” where riders can walk on and off trains without restriction, and must validate tickets themselves. This method is a progressive state-led effort to shift the relationship of trust between authorities and users, in a way that you don’t often find in the UK or the US. When traveling through Norway I noticed that Oslo’s transit agency Ruter had adopted a similar approach to Germany’s, doing away with turnstiles and gates and, in the words of Ruter’s payments lead Christian Fjaer, making a choice “to focus on customers who want to pay instead of keeping out the people who don’t.”

It’s fascinating that public transit systems are able to operate with such apparently lax fare collecting systems. There are accounts suggesting that the progressive approach works in Norway and Germany. Those who can pay generally do and it tends to benefit the agencies, which are able to cut compliance-related costs and don’t tend to see any significant spikes in fare evasion. Pricing in these countries has remained reasonably manageable, all things considered. Train pricing in the UK by contrast is notoriously high – a study in 2024 found that the UK train fares were the highest in Europe, without any corresponding increase in quality. While cities like Oslo, Berlin, and Frankfurt share the social trust dynamics typical of major globalized urban centers, their public transportation systems reflect a notably higher degree of generalized trust between authorities and users. Taking public transit in Oslo and German cities feels more like using a system that matches the quasi-anarchic flow of city life – it feels like these provisions are working with you and for you. Meanwhile, in London and New York, the flow of the crowd is literally blocked by physical barriers. While both permissive and restrictive models of public transit systems are functional, Norwegian and German passengers are tangibly more at ease when traveling.

Another element of experiential quality worth mentioning is the aesthetic experience of public transit systems. Underground networks are suboptimal in this respect. Most people generally prefer not to be holed up without sunlight. Aside from perceived safety issues, I think this was the reason why, on a trip to LA, so many people were visibly horrified when I told them I had been using the subway. How could I not be in a car, with shades on, moving at a snail’s pace through the West Coast traffic in the sun? Naples, with its Metrò dell’Arte initiative, found a classically Italian way of resolving this aesthetic problem by turning part of its underground metro line into what essentially functions as an art museum. You might be missing the lovely southern Italian sun, but at least you get to see sculpture and contemporary light displays.

Above ground, there are transit systems that offer unique and otherwise inaccessible perspectives on the regions they serve. Bangkok’s BTS Skytrain offers an elevated and air-conditioned means of travel, above the city’s intense traffic. Aside from its convenience, a striking element of taking the Skytrain is the views of the city that it affords – panoramic scenes of Siam’s malls, sky bridges and glimpses of temple rooftops. And while it was mainly tourists that I saw enthralled by these views on my visit, it also wasn’t uncommon to see locals take a few snaps when the sun was setting over the city. The BTS Skytrain was inspired by Vancouver’s Skytrain, which itself hosts a very different but equally stunning aesthetic experience of winding through the city set against the North Shore mountains. I think such experiences also create a form of reverence for infrastructure, distinct from that which is afforded by civic pride in developmental progress.

These observations run contrary to theorist Marc Augé’s critique of “supermodernity”, in particular a kind of generic urban space – exemplified by airports and highways – which Augé characterizes as “non-places.” Non-places are dissociative spaces in which there can be no sense of belonging. Augé doesn’t claim that public transit is a totally meaningless experience, but argues that such spaces produce limited and private experiences, which are neither deeply embedded nor socially-binding. Indeed, traveling and commuting in areas with significant public transit infrastructure is often a solitary experience as, by their very nature, such systems function as means of “getting from A to B,” rather than offering an opportunity to build a sense of belonging specific to any one place. But, in a city such as Medellin, there are circumstances in which public transit can provide a genuine, shared sense of belonging, and encourage positive, socially cohesive behavior in its passengers. I also suspect there is at least a loose form of social-bind that comes from honor systems of travel, especially for those with an awareness that authorities in other similar countries often don’t adopt such trusting approaches to manage passenger behavior. Passengers who choose to validate their tickets – aware that authorities elsewhere rely on enforcement rather than trust – develop a kind of investment in the system’s legitimacy. This can manifest in informal self-policing, where fellow travelers feel genuinely motivated to behave the “right” way and to potentially challenge those who transgress, a dynamic that itself reinforces the communal character of the public transit system.

Bosnia is another place I’ve visited where the public-transit character seemed beyond the scope of Augé’s analysis. Its train systems trace their roots to Yugoslav Railways, a state-owned organization through which Yugoslavia carried out a significant expansion of the network following World War I, when the Austro-Hungarian Empire lost control of the region. Unfortunately, a significant amount of the system was destroyed during the Bosnian War in the 90s. During my time traveling through the region, I was warned by some locals in Mostar (famed for its “Stari Most”, an Ottoman old bridge) that while the trains heading north were decent, flooding from the previous year had created ongoing problems with the railways. As I traveled from Mostar up to Konjic by train, taking in the fantastic views of the deep blue Neretva river, these warnings slipped from my mind. Until the train suddenly came to a halt. Eventually the train driver announced that there was a technical issue with the wheels which would detain us for some time.

After a lengthy wait, the driver reported that the issue couldn’t be fixed and so we’d need to get onto another train. All passengers were escorted off onto the tracks, being guided towards a nearby platform. Before we reached the platform, the conductor (who I think played the dual role of mechanic) came running along, out of breath. Waving his arms, he told us that everything was fixed and we should quickly get back on. The frustrations that had built up amongst the passengers quickly dissipated at this comical scenario. When we reboarded, a group of Bosnian women who had met and started chatting during the delay were riotously laughing. One of the ladies humored us curious tourists by saying, “Welcome to Bosnia, where things break but we can fix them up eventually.”

I later learned, at a museum in Sarajevo, about “snadi se,” a commonly used phrase from the former Yugoslav times which describes the practice of making do and improvising. The archetypal example of this mindset is found in the Yugo, a type of car which was widely used in the region but broke down so often that a subculture of repair developed around it, with many families having their own versions of a repaired Yugo car. I think those ladies found traces of this sentiment in our train journey. So while Augé’s analysis is, in many respects, difficult to argue with, perhaps we needn’t decry public transit systems as non-spaces. With a little “snadi se” we can most certainly find a bit of regional character in them.

The New Nature Thesis

Presented as an easily digestible summary of a recent talk, read unpacking the big idea shaping the Protocol Institute’s research mission – AI and protocols, entangled at planetary scale.

Maintaining Everything That Matters

Stewart Brand is the founder of the Whole Earth Catalog, a “proto-Google,” large-format printed compendium of tools, texts, and ideas that appeared between 1968 and 1972 and served as a declaration that anyone could have access to the tools to build something better. Shaped by Buckminster Fuller’s systems thinking, Gregory Bateson’s cybernetics, and Marshall McLuhan’s media theory, Brand’s message to the communes and hackers and back-to-the-landers was: think at scale and take responsibility for outcomes. The revolution, if there was to be one, would be built out of care and competency. The question of how systems evolve and are kept going has animated everything he has done since, as co-founder of the Long Now Foundation, and author of influential books such as How Buildings Learn.

Maintenance: Of Everything (2025) comes from the perspective of someone who has watched enough cycles of creation and decay to understand entropy not as a problem to be solved but as the fundamental condition to be worked with. Prompted by the book, the following conversation between UK-based product manager and builder Matthew McDowell-Sweet and software developer, researcher, and writer Mike Travers, based in the Bay Area, emerged from the Protocol Institute Discord in April 2026 and was produced through a mix of live and asynchronous conversation.

Backgrounds

MT
I’ve been a Stewart Brand fan for a really long time, actually – I kind of latched onto the Whole Earth Catalog in my childhood, which was a long time ago! I’m actually quoted in one of his books, because he wrote a book on the Media Lab, where I worked for a time. When I did a presentation at Refactor Camp, maybe seven years ago, introducing Brand to that new audience, I tried to sum up his career by calling him a “civilization hacker.”

So the thing I admired – and it shows in the maintenance book – is that he’s trying to think on a large scale and take responsibility for these large-scale systems, in a way which is both ambitious and pretentious.

MMS
Given that you were exposed to the Whole Earth Catalog when its contents were current, do you feel like the maintenance book is similarly ambitious?

MT
Yeh, I think it is, at least in its goals. It’s very consistent – same level of ambition, same focus on the world’s systems. He’s found a new theme, but it’s the same general goal. Impressively consistent, actually. Also in form and style – he brings in some Whole Earth Catalog-type layout elements, and I thought that worked very well. I might have some issues with the politics and some of the details, but my overall reaction is positive. What got you interested in this? And what’s your top-level reaction?

MMS
I’ve been tracking the protocols dialogue since it started to emerge, and the topic of maintenance appealed to me because I’ve had a long-running interest in different paradigms for computing and intelligence. I take the position that current approaches to advanced intelligence and computation aren’t sufficiently embodied to get us to human-level intelligence, and that’s driven a lot of my explorations. When you look at the maintenance of civilizational infrastructure, it’s a very interesting topic.

I spent three or four years working in manufacturing, at a place producing technical fabrics. Stuff that was too high-spec and bespoke to easily outsource: aerospace fabrics, that kind of thing. So I’ve seen maintenance in that context. The previous startup I worked at was focused on software with a lot of maintenance capabilities involved. And now, by day I’m in product management at an enterprise-scale government body in the UK, dealing with technical debt and maintenance across a massive, sprawling software footprint.

When I finished the book, it landed as a manifesto for me, rather than a playbook.

MT
I’m glad you have some manufacturing experience. I work in scientific software and don’t have much contact with the physical world.

MMS
One thing I was wondering: would you say that this is an instance of that Silicon Valley stereotype, where someone at a high level – and we’ve said that Brand operates at civilization-scale – comes at a concept from first principles, rediscovers a lot of distributed and diffuse wisdom, packages it up nicely and brings it out in the form of a manifesto?

MT
I think Brand definitely does that. He’s not exactly a Silicon Valley type – he’s more like a prototype for them in some ways. The Whole Earth Catalog was similarly a mix of grand ideas about systems alongside a real emphasis on very concrete, physical tools and skills for dealing with the real world. He put those together. I was impressed by that 50 years ago, and I think he’s doing a version of that in this book too.

MMS
The book apparently started as a series of blog posts, which Stripe Press compiled. Given that you have a good sense of his trajectory, do you think it’s a surprise that someone like Brand is thinking about maintenance?

MT
I don’t think it’s a surprise at all. It’s very consistent with everything else he’s done. He has a sense of, and has always been concerned with, taking responsibility at a very abstract level, and he has a very pragmatic view of that. He tries to convey a sense of responsibility and induce it in others. Part of the Whole Earth Catalog was to empower all these hippies who wanted to go back to the land to actually do it. Whether that worked or not, who knows, but the idea was: here are the tools, here are the skills.

MMS
Is there something similar in the air right now? Given we’re on the precipice of huge capability jumps, the explosion of software-building ability, but also a real bottleneck in building big things in hardware. Is there something similar provoking a kind of “call to tools”?

A 21st Century ‘Call to Tools’

MT
That’s an interesting question. We are at a very critical moment with a lot going on. But I don’t think his position is quite the same – he’s 87, and while he worked on the early internet and personal computer revolutions, he’s not involved in building AI or supplying tools for that. He’s focused less on how to do new things and more on how to maintain the old things, which is kind of age-appropriate. And the current moment doesn’t seem very equivalent to the 60s – I don’t think there’s quite the same sense of hope that people felt then, when they genuinely believed they could just drop out and build an alternative lifestyle in the woods.

MMS
If there is some similarity between that time and ours, the balance is certainly different. The idea of disconnecting and building your own thing or looking after yourself in your own way is still something that is lurking.

Brand has been adept at surfing these cultural surges, and some of those waves feel like they’re recurring again in a very different context. There’s talk of a permanent underclass, things going on with longevity, the calculus around building software is changing because you can almost treat software like single-use plastic – all these big themes are forming some kind of wave. And if someone like Brand is noticing it and picking it up, there’s presumably some through-line, but I can’t quite put my finger on how to frame it.

MT
I don’t see it myself – partly because I’m on the older side, I suppose. Do you know Brand’s theory of “pace layers”? He’s thought a lot about time and change, but I think because of his age and position, he’s thinking at the grander scale rather than the current cultural moment. The Long Now has given a lot of thought to climate change and ways to ameliorate that – that’s the closest he comes to dealing with current events. The maintenance book doesn’t seem particularly of the moment to me; most of his examples are from the 20th century or earlier.

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Maintenance Maturity

MMS
On a more personal level, I’m 34, so I probably think about maintenance a little differently than someone like yourself.

MT
I’m 68, and not particularly good at maintenance of body or physical systems – so the book partly made me feel guilty, like Brand is giving moral instruction which I should try to listen to. One reason I liked the Whole Earth Catalog was that it showed: here are ways to engage with the physical world, here are all the tools and things you can do. That’s always been aspirational for me – I’m a live-in-my-head type, and engaging with the body and physical world is always a bit of a struggle. I was at MIT for many years doing computing, AI, and math, and I always had a slight envy of the people doing materials science who got to actually break things.

MMS
I found it interesting that he used examples from four industries that have quite strong maintenance cultures: aerospace, defense, manufacturing, and software, I believe. I was trying to think about why, but I don’t know if software really belongs in that frame.

MT
I read one review of the book that made an interesting political critique. All the examples are very male-coded fields: war and fast cars. If you’re looking at maintenance on a broad scale, women do a lot of the maintenance work of culture and civilization, but they aren’t part of the story here. Maybe he’ll get to that in later volumes.

MMS
Yes. In aerospace, defense, and manufacturing there’s a very direct connection between downtime from bad maintenance protocols and cost. Half a percentage point of downtime on a high-throughput manufacturing line has a very direct connection to missed unit counts; in defense, canceling a mission sets off a chain of operations that’s really costly to accommodate. That’s perhaps why Brand focuses on those industries in the book – they’re easier to relate to direct, tangible dollar costs. I suppose that’s how you get to maintenance maturity.

MT
Was maintenance maturity a concept in the book, or is that something you’re bringing in?

MMS
It wasn’t, but that idea is key to my reaction to the book. If you think about maintenance maturity on a spectrum, starting with reactive maintenance – fix it when it’s broken. Then planned maintenance – do some maintenance work every week or every ten thousand hours. Then preventative, which is more condition-based: once the car has accumulated 100,000 miles, do these tasks – usage-based rather than time-coded. Then once you move to the more sophisticated side you get truly condition-based: hook up sensors, and if those sensors exceed defined thresholds for a defined period, do XYZ. And then the most advanced cases, which I think are really only in operation for things like energy grids – continuous monitoring combined with machine learning approaches to project out the load a connected system is going to endure and what maintenance needs to be done as a result.

MT
Is maintenance maturity an industry term, with those canonical levels – reactive, planned, preventative, predictive?

MMS
Yes, it is. And Brand is definitely coming at this as an outsider, so he may be missing that existing framework. To get to those more advanced maintenance models you need an institution that can make and enforce the rules – and a lot of these things are protocolish, right? The question is: who makes, executes, and enforces such protocols?

MT
That’s interesting. Where would you say Brand’s book stands in relation to actual industry maintenance practices – is it a familiar discourse, or giving a new view?

MMS
It’s more like popularizing the criticality of maintenance on sufficiently advanced systems. Almost like the inverse of the Whiteheadian idea about civilization advancing in proportion to the operations it can perform without thinking, Brand is saying: we have to start thinking about some of those things again, at least periodically, so that we can all have a nice life.

Whitehead Advances and Brandian Returns

MT
Whitehead advances are abstractions which let people ignore the underlying machinery. Brand is reminding us that we – or someone – actually does have to pay attention to the machinery, to take care of it. The whole 60s counterculture was a rejection of ‘straight’ abstractions – job, family, etc. Going to live on a commune in the country means giving up a set of abstractions. Brand was giving people tools to create new ones. Mostly these efforts failed; but it wasn’t his fault. He was urging people to take responsibility for their lives.

I’d agree that the book makes a popularizing move – in a positive way. He has a compelling writing style, very good at telling a concise, interesting story that illustrates a point.

Can you say more about how you see the relation between Whiteheadian advances and maintenance?

MMS
We could cut by decade, or by technological era – mainframe, personal computer, early internet, internet boom, mobile, cloud, social media. At each stage there are advances at the protocol level – in hardware, software, and socioculturally – that flavor the development of history. And then there is the payoff: maintenance isn’t just a thing now; it’s something that keeps coming back to the surface. I’m led to the idea of a Brandian return. There’s actually a line in the book, something like “every civilization has to choose to care.” You advance, you get all the benefits, but at a certain point you realize that something which was made invisible still matters – community, face-to-face relationships, whatever was abstracted away. You don’t throw away the advance; you correct course, return to what was lost, and position for the next jump.

MT
By “Brandian return” you mean a return to a maintenance mindset, or a coming back to something which has been made invisible or taken for granted?

MMS
Exactly. Like hyper-individualization from algorithmic feeds – you get that advance and all its benefits, but after a certain point you realize community matters, face-to-face relationships matter. You don’t reject the advance; you return to what you’d discarded, correct the balance. And crucially, this return isn’t a woo-woo thing – it has to happen in a protocolish fashion. You’re not going back to the land; you’re asking how to systematically revitalize or sustain something.

MT
I’m not sure about the term Brandian return, but I like the abstract idea. It connects to what I’ve been arguing outside the context of Brand’s book – a Whiteheadian advance is an abstraction, it hides lower-level details, but those details are still somebody’s job. Somebody has to do the maintenance. Which is important from the protocol perspective too: you can be a user of a protocol, but there’s also the underlying layer. And it connects to the maintenance maturity point – the return, or choosing to care again has to happen in a structured, protocolish way.

Amazon is a good example: as a consumer you have this seamless protocol – think of what you want, click a few buttons, it arrives on your doorstep the next morning. But there’s all this machinery, people, effort, and money underneath it, which you’re not actively discouraged from seeing, but you’re certainly not encouraged to think about. Present-tense capitalism is very good at creating these abstraction layers. Every Starbucks is another one – convenient, mediocre, invisible machinery.

MMS
Have you heard of the concept of exocapitalism? That’s the title of a recent book by Marek Poliks and Roberto Alonso Trillo.

MT
I don’t think so.

MMS
I’ve only seen a discussion of it, haven’t read the book, but I think the thesis is that the endgame of capitalism is a system that recursively generates value for itself regardless of where the value comes from, as long as it’s recognized as value within the system – just extracting and abstracting until the human-labor element is completely worn away, leaving a self-sufficient self-referential system.

It’s like financial engineering at the mergers and acquisitions level – one company paying another that’s only utilizing a credit note, but on a civilizational scale.

Maintenance, Reskilling, Class

MT
Right. Though what I find interesting about Brand’s approach – in the maintenance book but also in the early hippie moment – is the attempt to de-abstract some of that. Rather than accepting a packaged consumer lifestyle, you go out and grow your own food, compost, build alternate systems. It’s a human-focused political orientation I find appealing. It’s not exactly humanist in the philosophical sense, but it’s centered on human agency and human scale.

Brand also pointed out, in a recent interview with Ezra Klein, that working-class kids didn’t need to buy books on how to manage a ranch – they were already doing it. The Whole Earth Catalog was for middle-class college dropouts trying to get down to that layer of practical knowledge and needing instruction. There are weird class dynamics running through all of this.

MMS
That rhymes with something I heard recently – a podcast with a founder of one of these organizations trying to build space data centers. He had felt disconnected from building things, so he attempted to build a Shelby sports car by hand. It showed up in a box; he had to get all the manuals and bring in professional mechanics to check his work. Very similar to what you’re describing: buying a textbook on composting. The class dynamics are strange – this high-achievement person performing manual labor as a kind of re-grounding.

MT
There’s another thing from my long study of Brand’s output that might be worth including: after the Whole Earth Catalog he published Co-Evolution Quarterly, a continuation of the same sensibility – composting, ecosystems, tools. But at one point he devoted an issue to space colonies, specifically Gerard O’Neill’s proposal for orbital habitats. I was about 18 at the time and thought it was wonderful that you could have composting and space colonies in the same journal. But it enraged a lot of his audience – the ecohippies who were serious about going back to the land. Wendell Berry in particular, the American poet, wrote a furious letter saying Brand was betraying the cause and the two things were incompatible. Brand published the letter and his response – I was impressed that he was willing to include that divergent opinion. But it’s interesting: 50 years later, Brand is getting involved with Musk, the space colonizer of today, and the same tension seems to be re-emerging.

MMS
That’s what I was thinking about with the rhyming of historical moments – just from how you describe it, some of those tensions and structures feel like history rhyming with itself.

MT
It’s the class dimension again – maintenance work is in general lower-status, less sexy than other kinds of work. Computer technology actively encourages ignoring the lower layers. One of the things I valued about the MIT education at the time was that it pushed back against that and encouraged you to look a level below. I remember very vividly: I was writing software for years, and then I got involved in debugging some hardware, and suddenly all the machinery behind the software popped into view where I’d been ignoring it. That was a bit of a revelation.

MMS
A friend of mine, John Romkey, was at MIT as well, and he described something similar: a scarcity mindset that made you think more deliberately and elegantly about how you deployed resources. Now it’s an era of abundance and there’s a tendency to just waste, waste, waste. Brand’s book is certainly a good counter to that.

2026 Protocol Symposium

The theme for the 2026 Protocol Symposium is New Nature – the rapidly evolving planet-scale technological layer governed by laws with a hardness and inviolability approaching those of nature. New Nature is our overarching frame for the technological future, shaped by the intersection of AI and protocols.

The symposium will be held fully online, September 21–25 (Monday–Friday), and comprises two workshop days and three days of talks.

Abstracts for talk and workshop proposals are due by Sunday, June 14, midnight Pacific Time. You can find details and the submission form here.

There’s an old Eastern European saying that a home and a vineyard don’t need a master, but a steward. There’s some truth to it, because houses and vineyards don’t operate according to simple ownership logic, but according to a logic of constant care. They teach that ownership isn’t power, but an obligation. If you don’t tend to your home, it will quickly begin to deteriorate until eventually it comes crashing down on your head.

The same applies to vineyards. Vineyards demand constant care, such as pruning, tying, protection and understanding the soil, the weather, and the timing to water the vines. The last is particularly important because vines don’t show their thirst clearly. Wilting is a reliable sign of thirst, but by then, the message has arrived too late. The first signals of thirst in a vineyard are much more subtle.

A thirsty vine might change the angle of its leaves or slow its growth. Stress, caused by a lack of water, appears as a pattern distributed across soil, root depth, grape variety, and a plethora of other things. Truly, to read a vineyard is to read a slow, uneven, living surface. All the signs of thirst are there, but they don’t appear in a way that’s easily legible. That’s why, more than anything else, vineyards need experienced growers.

An experienced grower usually walks the rows, looks at the leaves, touches the soil, and looks at the weather. The decision to water a vineyard is based on the timing, risk, terrain, plant stress, water availability, and crop quality. It’s not based on a few simple, quantifiable measurements, but on many subtle signals passed through the filter of the grower’s experience and their ability to read the vineyard, a type of literacy that has passed through generations for most of our agricultural history.

Irrigation is one of the oldest agricultural protocols. As seen in the vineyard example, it’s not just taking water from a source and dumping it where it is needed. No, water has to be released under certain conditions, following specific schedules, pipes, pumps, valves, habits, and rules of thumb. This means that vineyards are protocolized environments, and have been long before any sensor-driven automation was in charge of irrigation.

So, what happens when the stewards of vineyards delegate watering decisions to sensors, network architectures, and low-power, long-range communication protocols, such as LoRaWAN?

The Old Water Protocol

We’ve established that before the introduction of sensor-fed dashboards, viticulturists (people who cultivate grapevines) relied heavily on their experience in reading environmental signals. However, ancient irrigation protocols weren’t, and still aren’t, primitive. Quite the opposite; they’re actually highly sophisticated.

But, being mostly based on experience, much of these protocols can be very difficult to formalize, because you can’t put a numerical value on intuitions. Most growers know their vineyards and realize that they’re complex, living ecosystems. Most vineyards have that one terrace that dries faster than the rest, or that looks absolutely horrible in the afternoon, but quite vibrant in the morning. Growers also know that weather forecasts don’t always hold true.

Gaetano “Guy” Virone, founder of Environmental Designers Irrigation, makes a similar point from the perspective of irrigation system design and field performance:

The best operators use threshold-based protocols as a decision framework, not a blind rulebook. The strongest results come when smart controls are paired with regular audits, monthly checks, and field verification, because intuition without data can overwater, but data without field context can miss what the plants are telling you.

The main difference between modern irrigation protocols and those based on viticultural experience is that the latter are often embodied and local. This is where LoRaWAN comes into play, as a means of rendering informal, intuitive protocols formal and measurable.

When Vineyards Speak

For those who aren’t in the know, LoRaWAN (Long Range Wide Area Network) is a networking protocol designed to connect battery-operated devices to the internet or local networks over distances greater than your typical home WiFi. It allows small low-power devices, such as sensors, to send small data packets (readings) to an application that can store and process said data, and display it in a way that is easily legible to humans.

The fact that it’s low-powered and able to cover a wide area makes LoRaWAN perfect for monitoring the environment, plants, and soil in vineyards, which might occupy 50 or even 100 hectares (about the area of 140 soccer fields). A team of researchers installed environmental, plant, and soil sensors in a vineyard located at Quinta dos Aciprestes in Douro, Portugal, and connected them through LoRaWan for data transmission.

The main goal of the project was to solve the practical problem of monitoring vine water in remote or difficult-to-access areas where WiFi, Bluetooth, and cellular coverage might be limited. And it worked. The system provided a superior range at a fraction of the cost, which has allowed growers to manage their vineyards and water resources more efficiently.

It has also introduced a cultural shift, because the vineyard isn’t simply being monitored by the grower post-implementation. Through sensor readings, the vineyard itself becomes an active participant in the decision loop that controls its irrigation, because the conditions in the field become data points, which then turn into dashboard patterns that could suggest, or even trigger, action.

Adequacy as a Superpower

Compared to Bluetooth, WiFi, and cellular, the LoRaWAN communication protocol is actually quite limited in terms of data size and two-way communications. However, those limitations aren’t incidental. They’re by design, and that’s what makes LoRaWAN great for vineyards. Sensors that operate using LoRa (Long Range) can send small data packets over several kilometers, while maintaining high energy efficiency.

Many of these sensors and probes are usually powered by an integrated battery, which may or may not be recharged using solar panels. This eliminates the need for excessive wiring while keeping the communications working. Additionally, many agricultural signals are small, slow, periodic, and spatially distributed. For example, a soil moisture measurement sensor doesn’t need a millisecond response time and water status doesn’t need to be live-streamed.

Agriculture is a gradual, dusty, uneven, and weather-beaten context in which sensors have to survive the elements and battery constraints. LoRaWAN is perfect for this; it’s not a super-sophisticated technology, and it doesn’t need to be.

Systems that rely on sparse communication have to develop a theory of importance in terms of which metrics should be measured, how often, at what depth, and what tolerances. What’s the threshold for alerts, and at what point does an alert demand action? These are all important questions.

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Cultural Shift

Protocolized environments have a tendency to induce subtle shifts in culture and responsibility. Before the implementation of autonomous monitoring, growers had to have a good reason to irrigate a vineyard, but after the implementation of data-driven dashboards, it usually becomes difficult to find a reason not to irrigate. This is because default values and thresholds have weight.

Following implementation, growers initially remain clearly in charge, with the dashboard in an advisory role. The recommendation based on the data remains just that, a recommendation from a system that’s only supposed to support decision-making.

However, a dashboard checked every morning becomes part of the work rhythm, and thresholds that usually work become hard to ignore. A recommendation that saves time becomes trusted, used, and proven to work. This leads to the burden of explanation becoming reversed.

Dario Ferrai, co-founder of OpenClawVPS, describes this as a broader problem in sensor-driven systems:

In sensor-driven systems, the tension often appears when data signals authority even though operational reality is challenging that data. Conditions can change outside the parameters that sensors are able to sample, and that is when human judgment becomes essential rather than optional.

Once a system produces a legible recommendation, ignoring such a recommendation in favor of knowledge and experience becomes an accountable act. A grower who follows the dashboard is complying with the evidentiary protocol, but one who overrules isn’t, despite the fact that overruling might lead to better outcomes and a higher-quality product.

In these cases, experience becomes an override layer, and despite potentially better outcomes, not complying with recommendations creates conditions for later accountability. This is particularly true in areas where water is scarce, expensive, regulated, or tied to sustainability quotas. In such systems, the question of why a recommendation was followed or ignored becomes a managerial, financial, environmental, or even a legal matter.

But even then, a signal that the moisture level has dropped under a certain threshold doesn’t mean that the field is thirsty. It simply indicates that irrigation may be warranted, provided the surrounding field conditions support that decision.

It’s a capital mistake to initiate irrigation based on sensor data alone. A grower still has to compare sensor data against localized weather conditions, sensor placement, and depth, before making a decision. Doing otherwise and triggering an irrigation based on sensor data alone could, and often does, lead to sub-optimal outcomes.

Virone puts the same warning more practically:

Sensor data should start the conversation, but it should not end it. When a grower’s observation conflicts with a controller threshold, the first question should not be ‘who is right?’ but ‘what is missing?’ In the field, the issue may not be the sensor reading itself. It may be a leak, poor coverage, a setting error, an outdated zone, or a calibration assumption that no longer matches real conditions.

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Edge Cases in the Rows

Ordinary cases where everything goes according to the established routine are never good for testing protocol.

There’s no great philosophical dilemma when the soil is dry, the vines look stressed, the forecast is clear, and the dashboard recommends irrigation. The system and the grower agree, and the water moves. In an ideal world, this works every time. However, we’re not living in an ideal world, and interesting things happen when the signs and data diverge.

In the real world, sensors may disagree, a probe might be poorly placed, one row might be shaded differently, the sensor’s battery might be out of juice, and a gateway might have missed a packet. Perhaps the weather forecast suggests waiting, while the soil threshold suggests watering immediately.

A good example of this would be sensor A saying that the soil is dry, while sensor B says that the moisture is adequate. So, what’s the protocol here? Average them out? Trust a deeper probe? Trigger a human inspection? Or simply wait for the next reading? Herein lies the hidden culture of precision agriculture. It’s not that LoRaWAN increases precision. It’s actually about deciding what kind of imprecision is acceptable.

Harrison Jordan, founder and managing lawyer of Substance Law, frames such conflict as a matter of limits rather than failure:

These are not moments of sensor failure as much as moments that reveal a grower’s understanding of sensor limitations. The best growers use precision irrigation data as a second opinion, not a final judgment. The mistake is forgetting that an algorithm is built around averages, while each vineyard is unique.

Older agricultural judgment was full of imprecision throughout history, but much of it was interpreted completely by humans, using unscientific methods. As previously discussed, these are not meaningless judgments based on imaginary things. These are compressed expressions of live pattern recognition based on years, even decades of experience.

The Dashboard Is Not the Field

Labeling humans as unreliable parts of the system is an age-old temptation in automation, because humans forget, misread, delay, over- and under-water, follow habits, and make decisions based on incomplete information. And while all of that is true, networks are unreliable in different ways. They only measure what they’re designed to at the locations in which they’re placed.

Jordan makes the same point in more human terms:

When a soil moisture sensor tells a grower to wait, but the plants show signs of stress, the experienced grower will often trust the plant. Sensors provide data for one measured element at one location, while a skilled professional is reading the whole plant, the weather, the soil, and the history of that specific block.

A good example of a common fault is when a dry vine and a dead sensor both appear as a normal reading. That’s why the best version of irrigation by protocol isn’t vineyards without farmers, but vineyards with farmers who use protocols to see more, waste less, and intervene more intelligently.

A well-designed irrigation protocol doesn’t just automate watering. It’s based on the difference between measurement and judgment, and makes uncertainties more visible. In a well-designed LoRaWAN irrigation system, this would imply allowing growers to annotate anomalies, reject suggestions, mark sensor distrust, and teach the system what they’ve known from experience.

Final Thoughts

While irrigation protocols relying on LoRaWAN might enable vineyards and fields to talk, it’s important to remember that what they’re saying often isn’t the whole truth. The point of protocolized irrigation isn’t to take in data points and present them as truth.

The point is to make some forms of agricultural knowledge more durable, visible, and actionable. But this visibility should never be mistaken for completeness, and when a field reports its thirst with greater precision, we must question the thresholds and consider the surrounding conditions as well.

For most of the history of digital infrastructure provision, public institutions faced two uncomfortable options: deep dependency on large proprietary vendors such as Microsoft or Oracle, with all the lock-in and geopolitical exposure that entails; or the enormous difficulty and expense of building and maintaining systems in-house.

Open protocols offer a third path. Infrastructure that no single actor owns, that evolves through distributed processes, and that can be implemented by anyone with the technical capacity to do so. The sovereignty offered by this approach is not about ownership, but about institutions understanding how their systems work, being able to participate in them, and retaining the option to move or adapt if needed.

Public institutions across Europe and beyond are increasingly taking this third option. Public digital infrastructure is becoming dependent on systems that no single actor controls. Messaging platforms, digital ID systems, and cross-border government digital services increasingly rely on open protocol ecosystems. In Europe, this acceleration is shaped by two pressures: the call for digital sovereignty and legally mandated interoperability. The French government’s digital directorate, DINUM, for example, runs Tchap – a secure messaging platform for the French public administration – on Matrix, an open protocol maintained by a distributed global community rather than any single vendor.

Overdependence on dominant corporate vendors or external jurisdictions is increasingly seen in Europe as a real political and strategic risk. The European Commission has put digital sovereignty and open strategic autonomy high on the agenda because control over digital infrastructure now touches everything from economic security to democratic resilience and Europe’s geopolitical standing. In that context, open protocols have advantages. They allow governments to reduce dependency on individual vendors without cutting themselves off from global technology ecosystems.

At the same time, interoperability is no longer aspirational. With the adoption of the Interoperable Europe Act, cross-border compatibility and the reuse of public sector digital tools, standards, and components have become regulatory requirements rather than best practices. Public administrations are expected to build digital services that can function across Member States and integrate into shared European infrastructures. That legal shift creates pressure toward open standards and protocol-based systems, because interoperability at scale is difficult to sustain if the underlying evolution of systems is controlled unilaterally.

As a result, many major public digital infrastructure projects over the next decade will involve protocol deployments. Realising the potential of this change, however, requires something most public institutions have not yet done: treating protocol engagement as a first-class infrastructure responsibility rather than a background technical detail.

The Problem with How Institutions Procure

The problem starts with how institutions think about procurement. Servers can be audited. Vendors can be contracted. Systems can be upgraded through planned lifecycle management. When something breaks, there is someone to call. The governance of these third-party relationships is externalised into contract law, and the institution can, at least in principle, hold the counterparty to account. Even open source has historically been absorbed into this logic. Vendors and consultancies package open source software into products and services that institutions can procure, with clear accountability structures.

Open protocols do not fit that model. They define how systems communicate and evolve, but they are not owned or governed through formal authority. Maintained by distributed communities, they operate through proposal processes, informal norms and voluntary adoption rather than contractual obligation.

When an institution procures a system built on an open protocol, it is not simply acquiring software. It is entering a coordination ecosystem governed by rules that will continue to evolve long after any contract is signed, through processes that the procurement office did not assess and toward outcomes that no single party controls.

The protocol that makes a system interoperable today will be revised. The security practices embedded in it will need to change as threats evolve. The compatibility assumptions that allow it to federate with other deployments will be renegotiated by a community of contributors who have no formal obligation to an operational timeline or compliance requirements.

This arrangement is still poorly understood in the public sector, particularly in procurement systems designed around vendors and deliverables rather than shared governance.

The Risks of Unmanaged Dependency

The case for open protocol infrastructure is strong, but adoption introduces its own risks. Technical robustness does not automatically translate into institutional readiness.

Exit options can narrow quickly. If the protocol evolves in a direction that no longer fits institutional requirements, the practical alternatives are limited. Forking a protocol that is already widely deployed means taking on long-term maintenance and gradually drifting away from the wider ecosystem. Walking away usually means returning to vendor-based solutions, bringing back the same dependencies digital sovereignty policies were meant to reduce.

Change can also arrive as operational surprise rather than managed evolution. Protocol governance is continuous. Security updates, specification revisions, and coordination shifts are normal features of healthy ecosystems. But if institutions are not following those processes, they might only notice the consequence of a particular direction when it urgently impacts them. What could have been handled through normal lifecycle planning turns into incident response.

Governance influence is rarely evenly distributed, though to exactly what degree varies significantly by protocol. Some have governance structures with strong safeguards against capture by any single actor. In protocols with weaker safeguards, development tends to be shaped by those who can afford to fund full-time engineering participation. A public institution entering such a governance ecosystem may find it already dominated by a small group of well-funded private actors.

Maintenance capacity can also be more limited than it appears. Key functions, particularly security response, may depend on a very small number of individuals. If those individuals move on or are unavailable during an incident, institutions have no contractual safeguards and may face response timelines that are incompatible with their operational requirements.

In essence, open protocols replace vendor dependency with ecosystem dependency. That dependency is structurally healthier because the governance processes shaping these systems are visible and participatory, but it still requires institutional competence, monitoring and strategic engagement. Public institutions are not limited to acting as customers with influence tied to purchasing power. They can observe, engage and, where appropriate, contribute to how the infrastructure they depend on evolves.

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Building Institutional Capacity for Protocol Governance

The most productive response is to develop genuine institutional capacity to understand and track how protocol governance works, and to participate in it on the protocol’s own terms.

Participation is not control. It would be a mistake to treat engagement in protocol governance as a route to directing how a global technical community decides things. Governance is distributed by design, and the value of that distribution is precisely that no single actor can capture the infrastructure.

An institution that attends governance discussions, comments on proposals and tracks specification changes is not governing the protocol. It is informing its own planning and, where it has something useful to contribute, improving the quality of the collective decision.

When protocol foundations offer formal participation structures, such as Governmental Advisory Councils, public institutions should make use of them. These forums allow governments running large deployments to raise operational needs early while still respecting the distributed governance model most protocols rely on. In some cases, institutions may also second engineers or technical staff to protocol foundations or working groups, a practice already common in standardisation bodies such as ETSI and W3C.

Developing internal protocol literacy also changes how institutions manage their infrastructure. Teams that follow specification changes and community discussions gain early insight into how the systems they depend on are likely to evolve. Over time, a consistent presence also builds credibility within the ecosystem, increasing the likelihood that the needs of large public deployments are taken into account.

A procurement team with real protocol literacy can also assess the governance health of a protocol before committing to it. Not just the quality of the current specification, but the community’s track record on backwards compatibility, the concentration of influence among contributors, the quality of security coordination and the overall health of the maintenance work.

Making protocol risk visible at the moment dependency is created does not require complex new bureaucracy. It requires better questions during procurement and infrastructure planning. Where are decisions actually made? How does a proposal move from draft to adoption? How are breaking changes communicated, and with what notice? What happens if institutional requirements diverge from community direction?

Procurement criteria can then reflect that understanding. Vendors who actively contribute to protocol specifications, participate in security coordination and maintain implementations are better positioned to keep deployments aligned with protocol evolution over time. Procurement frameworks that treat upstream contribution as a resilience signal do not just favour better suppliers. They shift incentives across the whole ecosystem, rewarding vendors embedded in the health of the protocol rather than those treating it as a static dependency.

Institutionalising Participation: The Matrix Example

One of the clearest examples of an institution making this shift is the Direction interministérielle du numérique (DINUM), the French government’s digital directorate, which in 2025 became the first government in the world to join the Matrix.org Foundation as a formal member.

DINUM already operated the largest government deployment of Matrix through Tchap, the secure messaging platform used across the French public administration by hundreds of thousands of civil servants.

Joining the foundation did not grant DINUM control over Matrix, nor did it create a privileged position within the protocol’s governance structure. What it did was formalise France’s presence in the ecosystem behind the infrastructure it already depended on. French engineers and security teams had already been tracking Matrix Spec Changes, coordinating on security advisories and planning upgrades in line with upstream development. Membership made that engagement structural rather than personal. Reliance on a small number of individuals was reduced by embedding governance awareness within the institution itself.

Not every public institution will have the resources to sustain individual participation in protocol governance. Collective participation models offer a practical alternative. The Matrix for Public Sector forum, launched in October 2025 alongside DINUM’s foundation membership, brings together representatives from six EU Member States, the European Commission and other institutions to share knowledge, coordinate deployments and feed operational requirements into governance collectively. This lowers the threshold for meaningful participation.

France is not alone. Across Europe and beyond, governments are building at scale on open protocol infrastructure, with varying degrees of governance engagement. Germany operates a large public sector deployment of Matrix through BwMessenger, the secure messaging platform developed for the Bundeswehr. Several EU Member States, including Sweden, Luxembourg and the Netherlands, are piloting or deploying similar open protocol-based messaging and collaboration infrastructures.

Participation Is the Strategy

Open protocol adoption by governments will continue. The pressures behind it are structural: the political will to reduce dependency on digital infrastructure controlled by big tech companies outside their jurisdiction, legal requirements for cross-border interoperability, and the economic advantages of building infrastructure on open protocols rather than each institution developing its own proprietary stacks.

The question is whether the institutions involved understand the nature of that commitment. They are not simply buying a product. They are stepping into a governance process that existed before their deployment and will continue long after their contract ends.

The institutions that invest in that relationship – that send someone to the working group, contribute to the specification and treat the protocol community as a constituency rather than a supplier – will end up with something no procurement process can deliver: infrastructure that grows with them, not against them.

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This combination of ubiquity and invisibility creates a peculiar methodological quandary. Protocols permeate a multitude of technical substrates, institutional arrangements, and social realities, operating simultaneously at hyperlocal and global scales. Even so, it can prove difficult to locate protocolization precisely. What does it look like to theorize such a thing? How can we identify formal models that describe the common, generalizable features of protocols which can be reliably applied across contexts? If we manage this at all, how can we tell if we’re doing it well?

This time, we’ll explore one of our early responses to the challenge of conducting a generative collective research program for protocol formalization. In particular, we will introduce a new top-level research track built around specific, well-posed problems that we call Atomic Protocol Questions. We’ll explain what they are, why we think they’re a promising approach, and how you can contribute.

Join us at the next Special Interest Group in Formal Protocol Theory (SIGFPT) call in Discord on March 6 if this idea interests you.

Birds, Frogs, and Atoms

The physicist Freeman Dyson, in his 2009 Einstein Lecture given to the American Mathematical Society, divided mathematicians into two species: birds and frogs.

“Birds fly high in the air and survey broad vistas of mathematics out to the far horizon. They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time.”

Fields Medalist Timothy Gowers, riffing on the famous “Two Cultures” divide between academics in science and the humanities, similarly drew a distinction among mathematicians between theory-builders and problem-solvers.

Setting aside the apparently common impulse to bisect mathematicians, both reached the rather common-sense conclusion that a healthy intellectual climate requires individuals of both temperaments, for each complements and builds on the other. In Dyson’s words:

“Mathematics is rich and beautiful because birds give it broad visions and frogs give it intricate details. Mathematics is both great art and important science, because it combines generality of concepts with depth of structures.”

It seems quite natural to think that theorizing protocolization would entail primarily a theory-building approach. One might envision, for example, articulating an abstract general notion of a Protocol, audition or invent various formal systems in search of one that best captures it, and then set about applying that framework to protocols out in the real world. In fact, this has been the character of most of SIGFPT’s pathfinding investigations thus far, and will likely always form a major track of study; there is immense value in creatively bringing diverse domain knowledge to bear on shared formal questions.

But it also comes with several challenges, largely due to the difficulty of enumerating in advance a set of necessary and sufficient features of a successful formal modeling framework to this domain. Protocols are unusually resistant to analysis through any single descriptive lens. Even when a formalism is expressive enough in principle, it is often unclear how to apply it across domains without either flattening the phenomena that matter or rebuilding large amounts of domain knowledge inside the model itself. Several of the SIG’s early discussions revolved around issues of this nature.

Therefore, as a complement to the top-down, avian theory-building, we’ve introduced a research track for bottom-up, froggish theorization: enumerating small, well-scoped research questions about specific protocolized contexts: Atomic Protocol Questions (APQ).

The spiritual forebear of the APQ is David Hilbert’s famous list of 23 unsolved problems presented at the 1900 International Congress of Mathematicians in Paris. Hilbert’s problems ranged across the foundations of mathematics, number theory, algebra, and geometry, and came to define much of the research agenda for twentieth-century mathematics. In several cases, individual problems motivated new branches of mathematics entirely: his second problem, on the consistency of arithmetic, led to Gödel’s incompleteness theorems and the field of proof theory; his tenth, on solving Diophantine equations algorithmically, was eventually resolved through computability theory. In this vein, our ultimate goal is to pose and then attack a set of open questions that captures protocol studies in both conceptual and disciplinary breadth.

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Subatomic Particles

“Atomic” is meant in several senses:

• Self-Contained: Each problem is intelligible and evaluable on its own, without requiring deep background in other APQs or specialized disciplines.

• Indivisible: Each problem is framed at the lowest level of abstraction needed for its bearing on protocol studies – not decomposable into simpler protocol questions.

• Heterogeneous: The problems collectively span a wide variety of subject matters, disciplines, and scales to avoid overfitting to a small set of favored contexts.

• Representative: The problems collectively cover as many dimensions of protocolization as we can identify— learnability, evolvability, tensions, coordination costs, and many more.

The first two properties make each APQ tractable in isolation. The second two ensure the collection functions as more than a grab-bag of puzzles – it becomes a map of the protocol landscape.

Each APQ has three essential constituents: an empirical context (a real-life, observable protocolized system), a key dimension of protocolization (a theoretically significant concept or aspect of protocolized systems), and a sufficiently precise research question (crisp enough to admit evaluable answers). Moreover, answering it should require one not simply to lean on the prior research of the particular field in which it originated, but to say something new about it qua protocol, and thereby demonstrate the value of this unique perspective.

Keeping Apart

Consider a familiar urban frustration: bus bunching. Buses, of course, are meant to adhere to a consistent schedule with a regular interval between arrivals (ideally both at once). In real life, buses tend to cluster together because of compounding delays: a delayed bus will arrive at a stop with more passengers waiting to board, who then take more time to board, delaying the arrival at the next stop, and so on. The trailing bus, meanwhile, will be in the opposite situation, picking up fewer passengers until eventually it catches up to the first. This problem, called “bus bunching” is a well-studied positive feedback loop.

It’s also a protocol problem. The issue isn’t what technology buses should use, but what rules should govern their coordination behavior. There are a number of common approaches to the overall problem, but the most basic interventions are to disrupt the feedback loop by making buses that are “ahead” wait at stops longer, have delayed buses skip stops, or have trailing buses overtake leading buses. The optimization objective is not necessarily a given – one can prioritize schedule adherence, for example, which tends to work best in lower-frequency routes where travelers plan based on the timetable, or optimize for headway between adjacent buses which tends to produce better outcomes on high-frequency routes where passengers arrive randomly. In practice, it is likely that a system in a realistic urban context would need to combine several strategies to flexibly manage the various causes of bunching.

Recent work has focused on dynamic control designs that integrate real-time information on various contributors to bunching and make adjustments automatically. For example, reinforcement learning systems trained in simulation can develop policies that dynamically adjust, such as holding times based on traffic conditions and demand, outperforming more conventional analytical or optimization-based methods. Separate lines of research approach the issue from the demand side, providing information to passengers about current wait times and bus congestion, in the hope that some passengers will make the decision to wait for a less crowded bus. Perhaps more drastically, real-time data can be used to update the bus schedule itself dynamically, with the obvious drawback of making the system less legible to would-be passengers.

The progressive integration of dynamic information and automation raises several interrelated issues with the relationship between these systems and the humans who participate in them – as drivers, dispatchers, or passengers. It turns out that deployment of real-time systems are hindered by various meatspace practicalities that are not typically modeled in simulation. One factor is variation among drivers, who each drive a bit differently, and in particular have different propensities to comply with the holding control guidance. This is double-edged: naive non-compliance tends to degrade the effectiveness of the overall fleet control, but human operators might also be able to react to conditions that are not easily observed through the data pipeline, due to cost or difficulty. Similarly, exposing information to passengers indeed allows them to make informed decisions about which bus to board, but there is a reflexivity problem; passengers may end up inadvertently coordinating so as to cause crowding on the previously-empty trailing buses!

The APQ approach attempts to sharpen such concerns into more tractable research questions about protocols:

• What operational discretion should a dynamic bus dispatch protocol preserve for human agents? When does human judgment improve versus degrade the protocol’s coordination performance?

• What non-invasive data sources can capture sources of user heterogeneity that influence demand?

• What ludic elements for drivers and passengers encourage aligned participation in the protocol?

Each of these examples is meant to conform to the APQ specification. The empirical context and research question aspects are obvious, but more subtly each question is targeted towards the intersection of current research on bus bunching and ideas of interest in protocol studies (key dimensions). The first question bears on concepts such as stewardability, invisibility, and legibility. How much can and should participants steer a protocol? In what ways must it be limited? When is active awareness of, and intervention into, protocols helpful or hindering? The second is a question of constraint and observability, from the system’s perspective. Can we improve the responsiveness and dynamism of protocols without overreaching or creating protocol failure surfaces and vulnerabilities? The third is ludicity, that is, how to support the protocol’s functioning and legitimacy via game-like and strategic elements.

The questions are also deliberately posed at a moderate level of abstraction. Protocol Studies is not, at present, suited to admit capital-P Problems that are well-posed in the sense that is typically expected in formal mathematics or other formally rigorous disciplines. On the other hand, it is not so broad (“What’s the best bus system?”) as to render any attempt at an answer indeterminate. It is also agnostic to the specific academic lineage or technical tools that one may use in subsequent research. Bus bunching itself ties together research in, at a minimum, control theory, urban planning, operations research, economics, and machine learning. A good APQ should invite a variety of possible approaches from a variety of possible perspectives. This diversity both within and between questions is in fact a load-bearing feature of APQs envisioned as an overarching research program.

Jumping Together

The key wager behind the FPT effort is that “protocols,” over and above a striking set of terminological convergences, are something like what the philosophy of science calls a “kind”, or at the very least, exhibit the sort of structural unity that licenses productive cross-domain theorizing. We are not merely asserting that protocols are important but conjecturing something formally unified beneath the surface diversity. The APQ project operationalizes this with a sort of wisdom-of-the-crowds logic applied to consilience – a “jumping together” of independent streams of evidence to a unified explanatory framework across disciplines. APQs are an attempt to enable such convergence in protocol studies.

Individually, questions are designed to encourage concrete, independent investigations into pressing practical issues in technology and society. An APQ is falsifiable in the sense that it shifts debate from abstract questions about which formalism(s) might comprise the “right” foundation to empirical and technical questions about fit: what traction does each provide on this concrete question, and what are its limitations?

In aggregate, the APQs must be sufficiently diverse to span the conceptual space of protocol studies across their constituent contexts and dimensions. The hope is that though approaches to different problems may initially appear disparate, the character of their solutions will reveal similarities and differences that interfere constructively or destructively. When multiple formalisms attack the same underlying phenomena, their idiosyncratic commitments tend to wash out, while shared structure is reinforced.

APQs, then, enable comparison at two levels. Within a single problem, multiple formalisms can be evaluated adversarially. Across problems, the more telling comparison emerges: do solutions to different APQs sharing a protocol dimension reveal common structure? If “evolvability” means something formally similar whether we’re studying bus networks or robot swarms – despite different researchers, methods, and vocabularies – that’s evidence the dimension names something real.

The history of science is rich with examples of consilience, when it works. One such example is the notion of computability. In the 1930s, mathematicians from around the world invented precise, independent definitions of what it means to be computable. Alan Turing developed abstract machines. Alonzo Church created the lambda calculus. Stephen Kleene formalized recursive functions. Emil Post devised production systems. They each worked from different starting points with different motivations, often unaware of each other’s efforts. All four formalisms turned out to define exactly the same class of functions. This striking convergence – proven rapidly once the systems were compared – is substantial evidence that “computable” captures something true about the underlying nature of reality.

Or consider entropy. Carnot’s 1824 question about engine efficiency was purely practical – “What’s the best a heat engine can do?” This led to Clausius’s thermodynamic formulation, then Boltzmann’s statistical interpretation decades later. Yet they proved mathematically equivalent for macroscopic systems at equilibrium – evidence that entropy named something real.

In both examples, specific problems came first and the unifying concept emerged from comparison. Neither Carnot nor Turing were attempting to architect entropy or computability from first principles. Carnot was trying to understand engines. Turing was trying to answer the Entscheidungsproblem. The generality emerged from specificity. This is one aspect of the symbiotic dance between frog and bird.

In these cases, of course, this convergence was also uncoordinated – researchers weren’t necessarily comparing notes. APQs are a bit different: a deliberate invitation for multiple formalisms to attack shared problems. Convergence here wouldn’t inherently prove that protocol as a concept “carves nature at its joints”, but it would demonstrate something nearly as valuable: that the abstraction does productive, non-redundant work across domains that previously had no common vocabulary.

Convergence Not Guaranteed

Of course, this all assumes that there is some sort of kind upon which methods can converge in the first place. The history of science also shows that that is not necessarily the case, either. Cybernetics and the complexity science of the Santa Fe Institute, for example, are two intellectual movements that share affinities with protocol studies. The cyberneticists generated fundamental insights into what would become control theory, information theory, and artificial intelligence, but did not achieve their goal of unifying the behavior of all goal-oriented systems. Complexity science has made enormously productive contributions through agent-based modeling, network analysis, and related methods, yet cannot really be said to have converged on a formally precise definition of complexity itself.

This is not a particular criticism of those programs’ approach or, say, bird-to-frog ratio. We can’t know whether different approaches would have or will someday yet yield some more unified frame. They’re simply reminders that convergence isn’t guaranteed, even with world-class talent, real research traction, and genuinely promising phenomena. Importantly, even without achieving some kind of “grand unification,” both lines of research produced lasting value, impact, and influence on later technical thinkers.

Perhaps, after all, “protocol” is more useful for pointing at phenomena than predicting or engineering them. We should find that out too. APQs are designed so that even if convergence doesn’t come, we’ll have produced something worthwhile: well-posed problems, cross-disciplinary vocabulary, and concrete progress on specific systems. But we believe that if there is indeed a fruitful underlying logic of protocols waiting to be unearthed, this direction will bring us closer to doing so.

Join Us in the Mud

That’s all well and good, but how does one actually create an Atomic Protocol Question? A great place to start is Protocol Watching. Once you learn to see protocols, you will find them in every corner of our modern world: your airplane boarding group? Your daughter’s LEGO set? Your laptop’s charging cable? Each offers a myriad of protocol puzzles waiting to be honed into an APQ. offers a handy guide, complete with tools and tips to help you get started.

You might also seek the guidance of the LLMs. In addition to the human audience, this essay also functions as a piece of intelligence media to provide a specification of Atomic Protocol Questions for ingestion into your model of choice. Armed thusly with your protocol bicorder, you’ll have the elements to contribute to our project well in hand, no matter your technical background.

David Hilbert perceived clearly that the articulation of a problem itself is a generative act of taste. More than a list, his problems were a challenge and invitation to a global network of talented researchers to participate in an ambitious collective research program. As Dyson observed,

“Hilbert himself was a bird, flying high over the whole territory of mathematics, but he addressed his problems to the frogs who would solve them one at a time.”

In this spirit, be you bird, frog, hedgehog, or fox, we encourage you to join SIGFPT and help expand, refine, and prune the APQ set. Bring a problem you know well – from your domain, your city, your organization, your frustrations – sharpen it into an atomic question, and let it enter the comparative surface. Problems shape the future of research programs, and, eventually, entire fields. In helping to pose and attack APQs, you can help set the agenda for ours – perhaps for years to come.

Recently, I discovered the deep lore of this watch. And I’m now convinced that it holds its own against wrists sporting Rolexes, Audemars Piguets, and Patek Philippes. What it lacks in sophistication, the Casio F-91W makes up for in its unreasonable adequacy.

What do I mean by that? Obviously, the thing is quasi-indestructible. It’s also cheap, reliable and ubiquitous. As far as a timepiece goes, it’s a Maslowian choice. The F-91W meets all of one’s basic timekeeping needs. For that reason, it’s been worn by precocious schoolkids and global figures alike: Barack Obama, Captain Ripley in the movie Alien and, notoriously, Osama bin Laden.

One of the reasons that this watch became so widely associated with terrorism is that it has indeed been used as a timer in improvised explosive devices. A leaked document showed that U.S. government interrogators regarded the F-91W as a telltale sign that a detainee was associated with terrorist operations. Ironically, many American servicepeople rely on the same watch.

But why is it such a common choice among militant groups?

1. It’s adequate. Utility (alarm, stopwatch, waterproof robustness) at minimal price.

2. It’s ubiquitous. Global popularity means it’s easy to replace.

3. It’s uniform. The watch’s design has remained virtually unchanged for decades.

One of the big ideas that emerged from the Summer of Protocols research program is that protocols are unreasonably sufficient. The story of the F-91W is a curious one. A single watch isn’t a protocol, but it is unreasonably sufficient. That made it the world’s most popular watch, transforming a quintessential consumer gadget into a piece of the planetary landscape, neutral and equally available for use by good and bad actors.

The Adequacy Thesis

Through the popularity of this watch, Casio created a global supply chain of time. It’s a system that anyone can tap into from virtually anywhere in the world.

It’s also a clear embodiment of Gall’s Law: “A complex system that works is invariably found to have evolved from a simple system that worked”.

In spirit, that’s a great quote. But if I could tweak it slightly, I’d replace the second ‘system’ for ‘thing’ because the initial kernel is usually not best understood as a system. Sorry, systems thinkers—a watch is a watch.

There’s something rather interesting about how many of the most consequential technologies (shipping containers, email, the Toyota Hilux, vaccines, etc.) tend to be the adequate ones, not the sophisticated ones. I have a thesis that Gall’s Law exists because popularity provides one of the rare, legitimate excuses to complicate things. And because sophistication has such high opportunity costs, it’s antithetical to popularity, especially in the long run.

Occasionally, those opportunity costs are worth paying. In the context of navigation or aerospace, precision timekeeping is a must. The Artemis III space mission aims to be the first crewed lunar landing since Apollo 17 in 1972. Such a complicated operation cannot afford to run on the stainless steel caseback of a F-91W, which can gain +/-1 second per month. That’s easily enough to make a very expensive—if not deadly—miscalculation.

Many of civilization’s most important operations depend on clocks. Swiss watches, famously, emerged to meet the demand for precise train timetables in Europe. But there are wide variety of clocks, all created to enable the reliable repetition of critical activities… like averting disaster.

Protocol-as-Clock

This week, the Doomsday Clock has ticked its way to 85 seconds from midnight and I’ve been thinking a lot about clocks – from my tough little Casio to the ability of blockchains to introduce good friction into the world. Because protocols sequence events, they impose specific temporal orders, whether through clocks or rhythms.

When two friends attempt to call each other at the same time, sometimes the line will appear as busy. They’ll both (probably) wait a few seconds or minutes before trying again. There isn’t a widely agreed upon amount of time to wait, and that’s why it works.

TCP/IP, the protocol suite that makes the internet possible, shapes time through rhythm. That makes it a clock in its own right, since it’s not relying on an external timekeeping device. TCP/IP forces computers to “shake hands” in a certain sequence, otherwise information won’t make it from device to device.

During last year’s Protocol School, composer Ben Zucker taught a course titled Musicalization not Music. Because protocols sequence how we do things and communicate, there is an inherent musicality to their design.

I’ve repeatedly joked with Ben that the TSA should hire him as a consultant to improve the choreography of their security lines. (Seriously, though.)

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Time to Think

New technologies promise to affect how we think about time. Some also share the unreasonable adequacy of the Casio F-91W and promise to become a planetary landmark.

LLMs and AI tools are powerful. They’re used everywhere, all the time, by everyone, all at once. Just like the world’s most popular watch, the availability and affordability of these tools is accelerating their diffusion. Not all actors, obviously, will use them in prosocial ways. Policing use will be difficult because of the ubiquity of AI tools; certain versions, like LLMs that can run locally on a device and can’t be nerfed with a cloud update, might become associated with bad actors as a result of negative events.

However, there are differences. The capabilities of LLMs are far more complicated than any wristwatch. Many software tools can be patched or updated live, regardless of where their users are. The underlying information architectures are still evolving.

Working with AI gives one a sense that time is accelerating, whereas wearing a Casio (or working with blockchains) makes one feel like life is simply marching along.

AI-assisted teams and AI agents can rapidly become out of sync with supply chains. Blockchains are inexorable clocks that provide a foundation for coordination problems like establishing contracts or rules for marketplaces.

New technologies are creating a battle over time.

The Summer of Protocols research program and its community have published some great work on time that you might enjoy:

• Finding Fault Lines Within the Firm by

• Control and Consciousness of Time by

• Protocols in Emergency Time by Olivia Steiert

• New Time Machines by Aaron Lewis, , Alice Noujaim, Nahee Kim, and

• Fire Protocols & Attention as Autopoietic Space by and

Color is especially interesting as an element of Protocol Experience (PX) design, since humans are especially sensitive to color. And color, especially in the form of paint, is a cheap design variable, ideal for persistent, configurational uses. You don’t need electricity to generate a default color scheme. Ambient broad-spectrum illumination (natural or artificial) is enough. And when you do use powered color for dynamic signaling, it is still robust and inexpensive to generate, especially today, with the rise of low-power LED lighting and screens.

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Color and Control

Reading Mathews’ article, as a sometime practicing control engineer, I was struck by the realization that though I’ve spent a lot of time thinking about command and control architectures, systems, and protocols, including control rooms, I’ve never thought about color as a particularly important consideration in control engineering, either in theory, or practice.

I’d never noticed that mid-century control rooms have a characteristic sea-foam green color.

When I’ve designed things like dashboards, color has been an afterthought, and I’ve usually done something lazy like code “significant” as “red.”

Yet, color is obviously one of the most powerful design elements available to control system engineers and protocol architects, especially when it comes to human-in-the-loop environments. But engineers don’t study it. Textbooks don’t teach color science. Even history books like David Mindell’s excellent Between Human and Machine: Feedback, Control, and Computing, don’t cover the role of color in command and control.

One reason of course is that color is not easy to use in automated feedback loops, and other kinds of signals are much easier to work with. Detecting and reacting to color-coded signals typically takes cameras attached to significant computing power. In control design, it is much easier to work with electrical or mechanical signals from more specialized sensors.

But when there are humans in the loop, color is a natural and cheap signaling variable. It still doesn’t play a big role though.

The reason is that the human-centeredness of color leads to its neglect in control engineering. Humans, unlike op-amps or microcontrollers, are flaky, temperamental, and unreliable engineering components. Components of last resort when architecting for reliability.

When humans must be integrated into a protocol that generates reliably repeatable behavior, it takes significant investment in training to get them to behave in sufficiently machinic ways. We had to invent an entire subculture of “professionalism” to teach and sustain reliable human behavior. And even then, color is never just functional for humans. It is invariably also an element of aesthetic experience, psychological comfort, and narrative commitment. In control engineering terms, color is an exceptionally noisy signal when processed by human brains.

So it makes sense that control engineers and protocol architects largely washed their hands of color, and learned to work around it. It became the preserve of designers and psychologists.

Palettes and Protocols

The Birren palette was designed to be a functional color-coding language for safety-first interiors, and meant to be comfortable to inhabit for extended working hours. It was the 1950s visual equivalent of elevator music plus the dings/bells/alarm sounds an elevator is capable of producing.

In 2025 though, Birren-colored interiors evoke mid-century nostalgia. The Birren palette has unavoidable aesthetic-narrative connotations today that were likely never intended. It is the palette of Golden Age science fiction. Of Competent Men doing Professional Things.

This is not what industrial interiors of more recent construction look like though (hence the nostalgic appeal of the Birren palette). And it turns out there’s a fascinating story there about how the relationship between color and industrial safety has evolved, which I was able to unpack with help from ChatGPT.

That’s not what I set out to do though. I started out simply wanting to make some nostalgia-aesthetic digital art using the Birren color palette, with a view to possibly training an image model on it with . Here is my first attempt. I aim to make about 30 such paintings, and then train a model I’ll name Birren. This one is a sort of abstract industrial interior where a Birren-palette foreground partially masks lurking fire risks.

I wanted a stable swatch of colors to make such paintings. So just for fun, and partly to flatter my own conceits about possessing some rusty Color Science 101 knowledge from when I started my first job at Xerox two decades ago, I began by asking ChatGPT to transform the old magazine scan from Mathews’ article into an approximate color-corrected swatch for me to use in my painting program, taking into account the gamut distortions in scanning a faded old magazine article.

Here, if you’re curious, are the palette as shared by Mathews (leftmost) and the one reconstructed by ChatGPT (middle and right).

The practical reasons for doing this are to have a uniform patch of color to sample with the dropper tool in painting programs, and to attempt to undo the color-shifting effects of scanning, printing, and aging in the scan.

As it turns out, Birren color schemes are pretty forgiving. They’re not defined with the precision of Pantone spot colors, but in terms of ranges that respect a particular visual logic and grammar. They are also designed to work within the capabilities of paints available in Birren’s era (the 1950s). For instance, fluoroscent paints, which play a big role today in the color of safety, were not available then.

In the process of making these swatches, thanks to some passing comments by ChatGPT, I discovered that there’s a lot more to the story. The entire philosophy of how to use color as part of a safety strategy has changed. The Birren-style philosophy of safety protocols has been replaced by a different one, best represented in the US by color-coding practices associated with the Occupational Safety and Health Administration (OSHA).

I had ChatGPT explain this evolution to me, and then write an essay summarizing our conversation. The next few sections were primarily written by ChatGPT, with light edits and additions by me. Comments and corrections from better-informed color scientists and industrial interior designers welcome.

The Origins of Industrial Colors

Color in industrial and architectural space has never been merely aesthetic. It organizes attention, encodes danger, regulates bodily comfort, and quietly trains perception over time. Yet, contemporary designers rarely get an opportunity to think about these matters. They most often encounter industrial color schemes through regulatory tables and compliance charts – reds, yellows, greens assigned to predefined meanings and applied late in the design process.

This way of thinking about color is historically recent. In the mid-twentieth century, figures such as Faber Birren approached color not as a signaling layer but as a fundamental component of environmental design. The evolution from Birren’s approach to the standardized safety regimes later enforced by OSHA reflects deeper changes in psychology, materials, labor assumptions, and philosophies of risk.

Birren worked at a moment when industrial modernity had reached a form of equilibrium. Factories, offices, schools, and hospitals were places where people expected to spend years, often entire careers. His foundational assumption was that color must support sustained human presence. Color planning, in his view, was neither decorative nor symbolic in isolation. It was infrastructural. Interiors were visual systems whose brightness, contrast, and chromatic intensity had to be regulated as carefully as acoustics or lighting. A well-designed color environment would quietly reduce fatigue, increase accuracy, and make moments of danger perceptually unmistakable without constant visual noise.

Central to Birren’s thinking was the primacy of value, or lightness. He believed that the eye organizes space first by brightness before it attends to hue or saturation. As a result, Birren-style interiors followed a strict value hierarchy. Ceilings were lightest, maximizing reflected light and reducing glare. Walls were slightly darker, providing a calm, continuous field. Floors and machinery occupied middle values that grounded the space visually. Crucially, the most serious hazards and stop conditions were rendered darker still. Fire equipment, for example, was often painted in deep, low-value reds rather than bright scarlets. Darkness carried weight. It signaled seriousness and finality in a way that brightness could not.

This hierarchy was not meant to be consciously decoded. Over time, workers learned it implicitly. The environment trained perception. A glance was enough to tell what mattered, not because the colors were loud, but because they were rare and carefully positioned within an ordered field.

Birren was equally deliberate about chroma. He treated saturation as a physiological variable rather than an expressive one. Large areas of high chroma were avoided because they increased visual fatigue and rapidly lost their signaling power through habituation. Most continuously viewed surfaces were kept at low to moderate chroma, allowing the eye to rest. Higher chroma was permitted only in small, localized areas where interruption was genuinely required. In this sense, Birren’s environments relied on restraint. Color worked because it was not constantly demanding attention.

Neutral grays played an especially important role in this system. Far from being generic leftovers, grays functioned as active regulators of brightness and contrast. They reduced glare from machinery, stabilized visual fields, and framed colored elements so that those elements retained meaning.

Without gray, Birren’s system would collapse. It was the quiet scaffolding that allowed color to function precisely.

Birren’s philosophy was shaped not only by psychology but by material reality. The paints available during his career were chemically and optically limited. Fluorescent pigments did not yet exist, chroma ceilings were lower, and saturation stability over time was imperfect. Color thinking was grounded in reflectance rather than emission. The Munsell color system, with its explicit separation of hue, value, and chroma, aligned naturally with this worldview. Designers thought in terms of perceptual relationships rather than device outputs or symbolic codes. These technical constraints reinforced Birren’s emphasis on value hierarchy and chroma restraint.

From Contexts to Signals

By the late 1960s and early 1970s, however, the social and industrial context that supported Birren’s approach began to dissolve. Workforces became more transient, automation increased, and regulatory and legal pressures intensified. Spaces were no longer designed primarily for long-term inhabitation by stable populations. Instead, they had to function for visitors, contractors, inspectors, and emergency responders who might encounter them only briefly and under stress. In this new context, color systems based on gradual perceptual learning appeared inadequate.

Additionally, the rise of automation meant color was no longer as critical, since fewer humans were persistently present in industrial interiors, and automated systems typically used signals other than color to run themselves. The retreat of color foreshadows, in some ways, the modern rise of dark industrial interiors, ranging from dark data centers to metaphorical dark kitchens.

The rise of OSHA-style safety color reflects this shift. Modern safety color treats color not as part of an environmental context but as a discrete and exceptional signaling system. The goal is immediate recognition rather than long-term coherence. Red means fire or stop, yellow means caution, green means safety, and blue means notice. These meanings are fixed, explicit, and standardized across industries. The system assumes distraction, cognitive load, and urgency. It is designed to function even when the surrounding environment is chaotic.

This philosophical shift is visible most clearly in how value is treated. OSHA does not define a value hierarchy. In practice, safety colors are often high in both value and chroma. Bright yellow hazard markings, vivid red fire equipment, and intense green safety signs frequently sit against white or very light walls. Multiple elements compete at similar brightness levels, flattening perceptual ranking. Where Birren used darkness to convey seriousness, OSHA uses brightness to demand attention.

Here is an example of OSHA style colors, in the form of a catalog of safety tapes of various sorts.

The widespread availability of fluorescent pigments intensified this trend. Fluorescent paints emit light as well as reflect it, making them highly visible in poor lighting and at long distances. From a signaling perspective, this is a clear advantage. From an environmental perspective, it is destabilizing. Fluorescent colors dominate surrounding surfaces, erode subtle value relationships, and accelerate visual fatigue. As more signals become bright, designers add still brighter signals, producing a cycle of escalation. The result is environments that are visually loud even when nothing is happening.

Two Philosophies of Safety (and Color)

At a deeper level, Birren and OSHA embody two distinct theories of safety. Birren’s approach assumes that safety emerges from order. A calm, legible environment makes danger perceptually obvious because danger is rare and visually distinct. OSHA’s approach assumes that danger must be unmistakable even in disorder. Safety is achieved through explicit alarms that override context. One system relies on learned visual grammar; the other relies on categorical symbols.

Birren is about high context environments. OSHA is about verbose signaling environments.

These approaches produce markedly different experiences. Birren-style environments tend to feel coherent and calm, supporting long periods of focused work with relatively low fatigue. They work best where people have time to learn the space and internalize its logic. OSHA-dominant environments, by contrast, are optimized for immediacy. They function well for short-term occupants and emergencies but often produce visual clutter and desensitization over time. When everything is bright and urgent, urgency loses meaning.

Most contemporary industrial and institutional spaces are hybrids of these two systems. Birren-like backgrounds coexist with OSHA-style overlays. The result is often perceptual tension rather than balance. Value hierarchies are partially established and then violated. The eye receives conflicting instructions about what matters. This is why many modern interiors feel subtly exhausting even when they are technically compliant.

For design students, the lesson is not to choose Birren over OSHA or vice versa. The lesson is to recognize that color systems encode assumptions about people, time, risk, and responsibility. Before choosing colors, one must ask whether a space is meant for dwelling or for passage, whether safety is learned or instantaneous, whether fatigue or distraction is the dominant risk. Birren’s approach fails if applied uncritically today, just as OSHA’s approach fails when spread indiscriminately across entire environments.

Color is philosophy made visible. The transition from Birren’s visual ecology to OSHA’s signal saturation mirrors a broader cultural shift from spaces designed for inhabitation to systems designed for risk management. Understanding this history allows designers to use color deliberately rather than inherit it accidentally. The palette is never neutral. It expresses a theory of how people relate to space, danger, and each other.

Cameras, AI and the Return of Color

Thank you ChatGPT, back to me.

The retreat of color that started in the 1970s has now reached its nadir in the form of dark industrial interiors, where visible-spectrum elements are largely irrelevant (infrared though still matters).

But there’s a counter-trend underway: the rise of AI and robotics have created a strong drive towards camera-based infrastructures, and even camera-only infrastructures. In robotics, for example, one school of thought holds that cameras are all you need. That you can even dispense with things like angle sensors or rotary encoders if you have enough cameras. In the self-driving car industry, Tesla famously holds that lidars (which rely on infrared) are unnecessary. If biological organisms can self-drive using vision alone, so can cars.

Of course, you cannot entirely eliminate all other sensors. Sound is clearly almost as important as vision even for us highly visual primates. And all mammals have the equivalent of accelerometers in their ears. So it is no surprise that phones and robots typically feature microphones and accelerometers as well. But, to a first approximation, cameras are eating the world’s sensors, and will demand a return to a more colorful world.

Cameras are surprisingly powerful sensors because almost everything that might matter for feedback in a controlled environment either has a natural visual component, or can be rendered visible at low cost (for example, introducing a colorant in invisible dangerous gases or clear liquids). Just as the smartphone has replaced many personal devices, the camera can replace dozens of different sensors.

The cost though, is the need for intelligence. You need an AI, or ideally, a robot to interpret and react to color (and visual fields in general).

And when color communicates safety, and is linked to behaviors that must unfold extremely rapidly, you have a major challenge for future designers and architects.

What should the color of safety be in the age of AI? How will be paint industrial interiors when the primary inhabitants are camera-driven intelligent machines, ranging from robots to appliance-like machines, to background smart-environment features?

The designer – or AI – who figures out the answers will enter the history books alongside Birren and his late-industrial successors.

Coda: The Color of Safety On Screen

Since the color of safety is rarely fully visible unless there is an active emergency unfolding, and because industrial interiors are not particularly accessible anyway, it is not an easy observation target for protocol watching1 enthusiasts.

Fortunately, it also happens to be a favored topic in screen media. It is fascinating to pay attention to the color of safety in movies and television, especially over time (obviously, this is only possible past the black and white era).

The wave of disaster movies from the early 1970s, including such cult classics as The Towering Inferno and The Poseidon Adventure, showcase Birren-like safety environments at their peak. The build-up of tension has an uneasy background-dominant vibe of terror to it. The characters seem to inhabit their environments much more mindfully. Plots seem much more atmospheric and suspenseful.

By contrast, modern disaster thrillers are much more likely to feature bright flashing lights and loud alarms – and characters who don’t really attend to their environments until things start going wrong. Thrilling foreground excitement tends to replace ominous background build-up of terror. Plots are action-packed rather than suspenseful.

As storytellers adapt to the age of AI, we can expect to see the color of safety reshape human narratives yet again. And us protocol-watchers will have a whole new crop of movies and TV shows to watch, and a new color-of-safety language to decipher.

Rafa Fernández is the host of the Protocols for Business SIG, which meets every two weeks on Discord to discuss protocols in business settings, and you’re welcome to join the next session. Link to join at the end of the article.

Seeing Business Protocols

If you ask a typical manager how their company works, they will usually begin with familiar answers. They describe the business model, sketch an organizational chart, or point to operational policies. Often they use these to explain how decisions are approved, how risks are reviewed, and how work is supposed to move from idea to execution. These descriptions are usually sufficient – until something goes repeatedly wrong or keeps going stubbornly right.

When coordination continues to break down or when performance remains unexpectedly stable despite adverse conditions, those accounts begin to feel lacking. Deadlines slip week after week. Review cycles stretch without resolution. Or a team continues to hit targets year after year in an unfavorable market. Managers reach for partial explanations: tooling issues, unusually strong execution, “good ops.”

Individually, these accounts are accurate. But they rarely explain why the same deviations – positive or negative – recur in the same places.

Recurrence is the clue to protocols, which provide a more precise explanation. Protocols do not solve problems or guarantee success. They lock in commitments that stabilize how variation is absorbed over time. In doing so, protocols keep friction within tolerable bounds, allowing organizations to continue operating – and in some cases, to build momentum – despite persistent strain. This is their quiet achievement: protocols hold recurrent tensions within ranges the organization can live with.

For this reason, protocols rarely appear as foregrounded processes. Once established, they recede into habit, embedding themselves in infrastructure, workflows, and expectations. They become “how things are done,” rather than objects of reflection or redesign. As Venkatesh Rao, Program Director of Summer of Protocols, observes:

Protocols do not announce themselves. They constrain motion quietly, with little conscious effort, shaping what can happen without drawing attention to themselves. This creates a methodological problem. If protocols are most effective when they are least visible, how should they be studied?

Our group, the Protocols for Business Special Interest Group (SIGP4B), has taken an oblique approach. Rather than attempting to inspect protocols through documentation or formal analysis, we have found success by studying moments when habituation fails in business contexts. Operational reinvention and technological shifts interrupt routine behavior and make protocols visible.

These moments of disruption show up as unresolved approvals, delivery delays, or persistent strategic confusion. In parallel, they appear as periods of sustained advantage: new categories forming, firms holding position through volatility, or business models that continue to function when others falter. Without attention to the underlying pattern, these episodes are often treated as isolated failures or market-cycle anomalies. When viewed through the lens of protocol studies, recurring business challenges in moments of volatility temporarily illuminate how an organization is actually structured.

In geology, fault lines are not identified by close surface inspection. They are discovered when accumulated stress forces the underlying structure to express itself. Our discussions highlighted how protocols behave similarly. Persistent problems and persistent point less to local error or exceptional talent than to the protocols through which pressure concentrates and trade-offs are stabilized. The artifacts left behind by this work – extra reviews, HR policies, new roles, software controls, even new business models – offer a useful vantage point. Still, it is sustained strain and disruption that make the boundaries of protocol structures unmistakable.

Over the past year, our group has been watching one such disruption unfold across many organizations at once. AI software solutions, such as large language models and autonomous agents, are diffusing, often in the shadows, into everyday business operations. How work is generated, evaluated, and sequenced in time is changing rapidly. As a result, protocols that once operated in the background have become visible, creating an opportunity to examine what business protocols are actually doing when placed under pressure.

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Protocols under Pressure

The dominant technologies of the past two decades – networked software, mobile devices, cloud infrastructure, remote collaboration – are entering a period of relative maturity. Their organizational effects are well understood, if not fully resolved, especially in today’s top enterprises. AI software, by contrast, has just arrived in the past few years. LLMs, copilots, and early autonomous agents are being adopted unevenly across functions, spawning new markets, but often failing at enterprise-level deployment.

AI is usually discussed in terms of automation or productivity. Those framings are not wrong, but they miss what makes AI adoption particularly revealing from a protocol perspective. While much of the public discussion frames AI in terms of cost-savings or new markets, our SIG has been focusing on the pressure it places on current coordination systems by changing the speed and scale at which work is produced: AI lowers the marginal cost of many forms of knowledge work while dramatically increasing total output. Drafts, analyses, summaries, and proposals can be generated continuously. AI’s production acceleration shows up everywhere at once; tools are being adopted simultaneously in product, marketing, legal, operations, and management functions. Work that once arrived in bounded increments now arrives as a flood, often carrying significant amounts of low-signal algorithmic output (“slop”) mixed in with it.

Many organizational protocols evolved under production paradigms which assumed a specific level of scale. Documentation, like other forms of knowledge work, took time. Decisions moved through sequential approval gates. These constraints shaped how firms organized authority, pacing, and oversight.

Our group repeatedly returned to version control as a protocol case study. Today’s default workflow assumes that meaningful code change is mediated through human reviewers, which stabilizes quality and accountability by gating “merges.” This implicitly capped how much change a codebase can absorb over time, limiting it to the skill and capacity of reviewers. Large refactors – such as migrating a core codebase to a new language – have historically been slow because review, coordination, and rollback had to proceed at human pace.

Now, LLMs challenge these assumptions and workflows. The resulting strain mirrors an earlier protocol transition from centralized to distributed version control, which supported continuous deployment at scale. New protocol paradigms are necessary to address AI-enabled speed and scale.

Production constraints become easier to understand when protocols are treated as technologies in their own right. In “Constructing the Evil Twin of AI,” Venkatesh Rao describes protocols as deliberately anti-intelligent systems:

Protocols are designed to limit, to configure trade-offs, and to make actions repeatable under uncertainty. They create leverage through hardness, yet often can be brittle under certain strains.

It’s worthwhile to note that AI compresses some constraints while bypassing others. Output multiplies while, at least for now, reviews and escalations are sometimes sidestepped. Production gains from AI adoption do not lead to smooth acceleration, but uneven load and jerky momentum.

Seen this way, the friction and failures accompanying AI adoption are not primarily symptoms of resistance to change or poor product quality. They are signs of protocol integrity under load. AI introduces fluidity where rigidity previously performed a stabilizing function. Existing coordination systems are being asked to absorb more output, faster, without having been designed for that operating range.

Similar to fault lines, coordination pressure accumulates along established paths. What has been routine begins to feel strained. What has been tolerable begins to feel tight. To understand the consequences of that pressure, it helps to look at where it concentrates first.

Tensions in Business Time

Across the SIG’s discussions, interviews, and readings, a consistent pattern has emerged. Under AI adoption, the first thing that stops working smoothly seemed unintuitive: time.

This became clear when our group reviewed Blake Scholl’s writing on Boom Supersonic. Here, Scholl distinguishes between at least two clocks operating inside the same organization. The first is the calendar: project timelines, milestones, and delivery dates. The second is what he calls the Slacker Index: the amount of time engineers spend waiting – on inputs, approvals, dependencies, or external constraints – rather than building. Even in well-run, safety-critical organizations, these clocks coexist.

Under stable conditions and in mature industries, this alignment is usually implicit. Engineering velocity, supplier lead times, regulatory review cycles, and internal decision-making rhythms evolve together. At Boom, hardware design, simulation, testing, and supplier manufacturing are paced to one another. Slower clocks constrain faster ones in predictable ways. Waiting is visible, expected, and priced into the system.

As Scholl points out, AI-enabled production changes the speed and scale of production. Certain forms of work – design iteration, analysis, documentation, internal review – can suddenly accelerate by orders of magnitude. From the perspective of the Slacker Index, local waiting collapses. Yet the calendar will not automatically follow. Supplier lead times remain fixed. Certification processes still unfold at human and institutional speeds. External partners continue to operate on contractual and regulatory time.

The consequence of AI-enabled opportunity is temporal divergence (a topic explored in depth by SIG member Sachin). Some clocks speed up sharply while others remain unchanged. At Boom, this would mean design teams outrunning suppliers, simulations outrunning manufacturing feedback, or internal decision cycles outrunning the capacity of external partners to respond. The Slacker Index may improve locally – less waiting to produce – but worsen systemically as downstream dependencies fall behind.

AI systems further amplify this effect in two ways. One, because they generate outputs without passing through the durations that normally situate work, creating a dizzying orientation. Large language models produce analysis and proposals instantly. Work arrives early in excess of the organization’s ability to absorb it. Knowledge accumulates faster than it can be evaluated, integrated, or acted upon.

Second, AI software using LLMs can be contextually misaligned. They draw on data that’s often years apart (a model trained up to 2024, used in 2026) and produced outside the local business context. From this lens, the recent focus on improving AI product memory seems intuitive. Efforts such as RAG, MCP, skills, and even “undo” prompt features become attempts to realign probabilistic software into business context, tempo, and authority.

Safety-critical organizations like Boom make these dynamics visible precisely because they cannot simply collapse time. Hardware, suppliers, and regulators enforce non-negotiable rhythms. When AI accelerates internal work without moving those external clocks, coordination strain surfaces quickly. Slack accumulates in unfamiliar places, with no protocols available to redistribute it.

When time regimes fall out of alignment, coordination problems and opportunities change form. Delays no longer appear as isolated errors that can be corrected locally. Instead, organizations experience escalating tensions: pressure to act without corresponding capacity to review, decide, or remember.

As with other systems under strain, previously hidden structures become easier to observe once alignment fails. AI adoption exposes how dependent coordination was on the quiet temporal alignment – rhythm – of existing protocols.

Yet work does not stop. Systems do not fail outright. Instead, protocols drift out of phase. Understanding how firms respond requires looking at how they attempt to restore coherence – often without redesigning the underlying structures that produced it.

Enterprise Management, Built to Last

When shared assumptions about time lose coherence, organizations first adapt within current structures. Work continues by absorbing friction rather than resolving its source.

One visible form of this absorption is Boom’s solution: integrate vertically. The critical move was purchasing their own large-scale manufacturing equipment rather than continuing to rely on external suppliers whose lead times dominated the schedule. Supplier queues and fabrication delays had become the governing clock for the entire program, producing a high Slacker Index: engineers were ready to iterate, but progress stalled while waiting on parts. By acquiring the machine, Boom internalized that bottleneck and converted supplier wait time into an internal, controllable process. This collapsed a multi-month external dependency into a shorter, iterable internal cycle, allowing design, testing, and manufacturing to co-evolve rather than queue sequentially.

Another response was novel translation work. The SIG discussed the fast growing Forward Deployed Engineer role, emerging to help mediate between fast-moving demands and slower-moving infrastructure. Their task is not to eliminate mismatch, but to work across it and leverage it – adjusting scope, translating intent, and negotiating constraints as they appear. This work allows organizations to keep operating even as tempos diverge, and gain a competitive advantage in the process. At its best, the work defines the operating model. This is the case for Palantir and large AI labs like OpenAI and Anthropic.

Other adaptations the SIG encountered took the form of operational formalization: AI usage guidelines, governance documents, digitized ontologies. These measures make previously tacit constraints visible without altering the structures that produced the misalignment. They stabilize behavior at the margin while leaving underlying coordination regimes intact.

As adaptive load to the new pressures increases, authority structures reassert themselves. Approval gates and prohibitions harden. Data confidentiality clauses are expanded. Hierarchies become more visible. These protocols surface because they are firm fault lines that stabilize liability, escalation, and accountability when temporal coherence weakens.

Some of these protocols absorb the load effectively. But others begin to strain. In geological systems, stress redistributed after an initial shift often produces secondary movements elsewhere. Organizational responses to temporal misalignment follow a similar pattern. Processes adapt where they can, while pressure seeks relief where they can’t.

This adaptive pattern becomes more consequential as management itself is increasingly parameterized within software, paving a path to programmatic enforcement.

Warning: Brittle Software

Before returning to our initial question on business protocols, it is worth taking a brief detour. Alongside the recent diffusion of AI, the SIG has been considering a quieter shift: management controls themselves have been moving into software.

Approval flows, access rights, version control rules, and incident response procedures are increasingly encoded directly into business software systems. Authority becomes a software admin configuration setting. Expenses are automatically rejected when outside policy. These changes predate AI, but they shape how AI is experienced inside organizations.

This matters because management subsumed in software behaves differently from practiced management. Protocols, when encoded in software, limit discretion in what is authorized and auditable. In contrast, management practice as a whole consists of what is necessary and effective in context. Under stable conditions, the gap between the two – protocol and practice – is manageable. A manager can override an expense request, or call a system administrator to update the software configuration.

AI changes the scale and speed at which this gap becomes visible. On one hand, employees and customers bypass formal systems using personal tools, custom scripts, or unregistered agents operating through employee accounts. On the other, software-encoded controls enforce protocol broadly and uniformly, at relatively low cost. A single configuration change can propagate across an organization instantly.

This shift produces a recognizable organizational pattern. Practices that once relied on informal judgment or situational flexibility are increasingly forced into deterministic software-defined pathways. Work that used to be resolved through conversation – such as asking legal for a quick review or exercising discretion under time pressure – is now mediated through systems that require explicit inputs, permissions, and logs. These systems work especially well in mature and slow-changing businesses, and can be brittle under volatile situations like a new technological disruption such as AI software.

A recent example is from an airline: A customer relied on an airline’s AI chatbot for information about bereavement fare rules. The chatbot incorrectly stated that refunds could be claimed retroactively, advice that contradicted the airline’s formal policy. When the customer followed the guidance and was denied the refund, the airline argued in court that the chatbot was a separate tool and that customers were responsible for verifying information elsewhere on the site.

The court rejected this argument. It ruled that the chatbot was part of the airline’s customer-facing system and that the company was responsible for the commitments it made, regardless of whether those commitments were generated by an AI system. It is tempting to shape the story to focus on LLM shortcomings: “This type of mistake, in which generative AI tools present inaccurate or nonsensical information, is known as AI hallucination.”

What’s more important is that the failure occurred because a time-tested operations efficiency strategy (delegating frontline explanation to an automated system) collided with protocolized legitimacy (formal fare rules, auditability, liability). The business made an assumption that explanatory labor could be delegated to probabilistic software, decoupling it from the protocols that confer legitimacy and liability. The chatbot accelerated response and reduced staffing load, but it was not strictly aligned to the same review, approval, and accountability protocols that governed pricing policy. At sufficient scale, that gap became visible.

Understanding this trend increases the diagnostic value of the current moment. AI adoption, like many previous “digital transformations” since the advent of the internet and before, highlights where management has already been formalized, where practice has been carrying hidden load, and where current protocols are breaking.

With that context in view, it becomes easier to return to the original methodological question: how AI, not framed as a solution but as a protocol disturbance, makes the underlying structure of coordination inside firms visible.

Strange New Business

Since the summer, our group has treated AI adoption and similar business disruptions as an observational lens rather than an optimization target. This lens supports our SIG’s goal of researching, evaluating, and finding opportunities to improve business protocols under pressure. AI is useful here because it is widespread, fast-moving, and poorly aligned with existing organizational rhythms.

This approach reflects a broader pattern in protocol studies. As Venkatesh Rao notes in Strange New Rules, new coordination regimes often feel unfamiliar at first, and then they are banal. Over time, they stabilize. Eventually, they disappear into habit. What becomes difficult is not living with protocols, but noticing and refactoring them.

AI has interrupted that disappearance. It has surfaced assumptions about time, authority, and review that were previously implicit. In short, it has made visible where management is already encoded, where practice has been carrying unacknowledged load, and where coordination depends on alignments that no longer hold reliably.

Although our recent work has focused on business management protocols – multisigs, version control, incident response – similar dynamics appear in transaction and production protocols as well, including market auctions and inventory management. Across cases, recurrent confusion, friction, and governance anxiety function serve as signals of protocol stress; they point to coordination systems doing more work than they were designed to absorb.

The conditions described here are not temporary. As AI software continues to diffuse unevenly into business operations, the protocols that manage coordination trade-offs will remain under strain – and increasingly visible. Our group treats this moment as an opportunity to observe, compare, and refine how business protocols actually function under load. If you are interested in these topics, or are encountering protocol changes yourself, share your email here and join our next meeting!

Join Protocols for Business

The goal of this two-part essay is to introduce the work of the Special Interest Group in Formal Protocol Theory (SIGFPT) over the last six months, and invite you to join us as we begin our second year of explorations. In this first part, we will establish some broad qualitative background and overarching frames and concepts. In Part II, to be published in a few weeks, we will introduce you to the specific theoretical apparatuses and technical attacks we are exploring, for developing formal protocol theory.

Join us on our Discord for the 2026 kickoff meeting of SIGFPT on Friday, January 9, 10am Pacific. Calendar invite for series.

So what is protocolization? In what follows, we will explore this question, with a view to setting up a canvas for formal and mathematical attempts to theorize it in general ways.

Protocolization is an unfolding long-term planetary transformation that is at least as old as modernity itself. That is to say, it is at least several centuries old, and has unfolded through multiple distinct chapters, in loose synchronization with major technological revolutions. Protocolization is the overarching story of how humanity has cashed out and orchestrated the capabilities of every wave of new technologies to make our world ever more comfortable and hospitable for humans.

One side of protocolization is arguably an ongoing terraforming. The first terraforming in fact; that of Terra itself, preceding any science-fictional terraformings of other planets.

The other side of protocolization is changes in human behavior in response to this terraforming. Relative to our ancestors, we have become more predictable and orderly on some fronts, and more unpredictable and generative on others. Protocolization is simultaneously a civilizing process, and a rewilding process.

Protocolization is reducible neither to its technological drivers, nor to the changes in human nature triggered by them. Protocolization is the co-evolution of both into what we might call a new nature. A planetary condition powerfully determined by the laws of the artificial, which can increasingly be engineered to be nearly as immutable and indefinitely persistent as those of nature itself.

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New Nature

Protocolization proper has received almost no attention, either popular or scholarly. It has merely been partially glimpsed in peripheral vision in attempts to understand more visible and dramatic planetary processes (such as modernization, industrialization, globalization, urbanization, and digitalization), which offer readier, more legible approaches to analysis and synthesis.

These more dramatic processes typically also feature charismatic technological megafauna at their cores – printing presses, steam engines, automobiles, ocean liners, skyscrapers, bridges, superhighways, rockets, airplanes, nuclear reactors, supercomputers, AI systems, miracle medicines, robots. These “hero” technologies lend themselves to anthropomorphic projection, and encourage a view of human-technological co-evolution as a kind of augmented hero’s journey. Our default notions of “progress” are of this heroic variety.

By contrast, the technological elements of protocols – interoperability standards, kits, standardized fasteners, electrical connectors, plumbing regulations, safety codes, sewage pipes, modularity grammars – typically gently diffuse and deflect anthropomorphic impulses. Rather than serving our heroic individualist impulses, they quietly orchestrate and shape our mutualist and cooperative energies. Protocolization looks like ecological emergence rather than a technological hero’s journey. It encourages a very different narrative of progress.

We hope to convince you that “protocolization” elegantly subsumes some of the most salient features of the more dramatic and “heroic technology” process frames you might be used to, while also apprehending (if not yet quite comprehending) a great deal that those frames remain systematically blind to.

It may help to keep a pair of pictures in mind. One is the familiar image of the African savannah, with its tableau of charismatic megafauna. The other is the near-pitch darkness of the mesopelagic zone of the oceans, beyond the reach and attention of both fishing fleets and the cameras of shark documentaries. This zone is densely populated by a family of bioluminescent fish that you may have never heard of or seen pictures of – Bristlemouths (Gonostomatidae) – which number in the trillions to quadrillions, constituting nearly 90% of fish biomass on the planet by some estimates. These fish anchor not just oceanic ecologies, but the entire planetary ecology.

Dramatic planetary processes are stories about the first kind of image. Protocolization on the other hand, is the story about the second kind of image.

The first kind of image requires no particular skills to see and appreciate, and anchors most visions of technological progress and change. The second kind of image is much harder to see, and takes cultivated protocol-watching skills, but is also much more useful for understanding how ecologies actually work; how new nature works.

Since our goal is to build powerful and useful theories of new nature, we navigate by the second kind of vision of technology; we try to search for formal theories and tools that can help us see, model, predict, and shape the “mesopelagic zone” aspect of new nature, rather than the “African savannah” aspect.

The most recent chapter of protocolization, triggered a half-century ago by the affordances of early computer networks, has been transforming the world in profound and accelerating ways. But arguably, it has barely been theorized at all, largely because when you try to look closely, it looks like the mesopelagic ocean depths rather than the African savannah.

Protocolization Defined

For the purposes of this essay, we will loosely define protocolization as follows:

Protocolization is the progressive metabolization of reliably repeatable technologically mediated human behaviors at all scales into reliable planetary infrastructures for coordination.

Here, we mean technological mediation in a broad sense, encompassing conceptual and cultural technologies such as the decimal system (instrumental in the development of modern book-keeping) or hygiene or nutritional practices that first took shape as religious observances.

It is worth noting that though we only mention human behavior in the definition, these behaviors shape the fates of all other life on the planet as well. Not just the major domesticated species of plants and animals, but through the shaping of “wilderness” regimes of the planet, all other life as well.

Protocolization is driven by some mix of knowledge discovery and diffusion, technology enablement, and human behavior change, resulting in a slow accumulation of new nature ecologies across the planet.

An example we use frequently in SIGFPT meetings is hand-washing.

The global ubiquity of hand-washing protocols was driven by the discovery and diffusion of knowledge about infectious diseases, coupled with ubiquitous technological infrastructure that has made clean water and soap available everywhere in modern built environments. The protocolization of hand-washing behaviors transformed a planetary landscape of previously uncoordinated and high-variance local cultural practices into an infrastructural behavior with much lower variance. As a direct result, public health improved everywhere. The planet became more comfortable and hospitable for humanity.

Thousands of similar examples can be listed across every area of human behavior, and in every corner of the built environment, at all scales from individual to international, and from trash cans to continental supergrids.

An entertaining cross-section view of protocolization can be found simply by listening for the word protocol in movies and television. Such usage has exploded in the last half-century. One of our favorite specific illustrations comes from long-running television franchises. In Star Trek for example, the word protocol was not used at all in the original series, but is used hundreds of times in more recent shows within the franchise.

This already extant cultural footprint, by the way, is why we expect you to find the term protocolization intuitive, and experience an immediate sense of recognition of what we’re pointing to with the term, even if you’ve never stopped to think about it before.

Domains like climate, traffic, networked computing, and healthcare make particular (and domain-specific) use of protocol-based understandings of the human and machine behaviors they design and translate into infrastructures. In other domains, protocols might not be the direct result of conscious design, but manifest as unexpected systematicity, orderliness, and serendipity in how things happen, which goes unnoticed and un-theorized.

But we do sometimes notice – when we travel to a city or country with more reliable subway services than our own, for example, we are struck by a sense of serendipity suffusing our transit experiences; a sense that the world is unusually hospitable in ways precisely attuned to our needs.

That is protocolization; steadily accumulating ecological transformations of the planet, each of which makes us feel surprisingly, magically lucky when we first encounter them, but which rapidly turn into tuned-out backgrounds and unconscious expectations of artificial lawfulness in our environmental contexts.

Progress through Invisibility

Even though protocolization has been as momentous as more dramatic planetary transformations like computerization and globalization, as well as earlier ones such as urbanization, consumerization, and industrialization, it remains hidden.

This is because, by their very nature, protocols in their evolved forms tend towards invisibility, and protocolization as the evolutionary process that creates them seeks out invisibility. This feature, arguably, is central to how and why protocolization constitutes progress. Protocolization is progress through invisibility.

Here, we mean invisibility in a broad, multi-dimensional and textured sense, beyond merely literal visual invisibility. The invisibility of protocols has multiple aspects:

1. Operational invisibility – We don’t think about the protocol while using it, acting through muscle memory.

2. Infrastructural invisibility – The machinery is literally hidden/backgrounded, like the ecologically critical mesopelagic zones we alluded to earlier. For example, cables and sewage lines are buried, and are generally as ubiquitous and invisible as bristlemouth fish.

3. Social/cognitive invisibility – People don’t recognize protocolization is happening because by definition, behavioral habituation is not only part of the essence of the process, it is functionally load-bearing. Traffic protocols would be much less effective if we were constantly aware of them. We “stay in our lanes,” without thinking about it, and reflexively signal turns and stop at stop signs.

4. Anti-memetic qualities – Protocols rarely feature in headlines, titles, or public discourse, because the unremarkable “boring” quality, with one experience being largely indistinguishable from another, is essential to their functioning.

5. Marginality – Protocols often organize and scaffold the technological space around other, more charismatic technologies that are intrinsically more visible, and more directly support foreground goals. They perform prophylactic functions, enforce constraints, and structure core behaviors for coordination and synchronization. For instance, the safety protocols of airline travel – airport security checks, seatbelts – are on the margins of the foreground retail experience of air travel. We do not think much about them when making travel plans.

6. Default quietness – Protocols are quiet and unobtrusive by default, and make themselves visible primarily when they fail. An ordinary hand-washing episode is not memorable among the tens of thousands that happen over an individual lifetime. An episode involving an empty soap-dispenser or malfunctioning faucet is memorable.

7. Obliquity of action – Protocols rarely directly act on consciously controlled human behaviors in pursuit of explicitly held intentions or goals. Rather, they act through secondary pathways of supporting causation, contingency triggers, and automated follow-through. For every conscious action in a technologically advanced environment, dozens of supporting actions may unfold unnoticed, rendering the conscious behavior preternaturally frictionless and seemingly “free.” The scope of protocolization is often everything else that must happen in order for actions you consciously and freely intend and do to succeed.

Aspects of protocols are of course highly visible in specific contexts. For example, everyone knows what USB is at a lay level, and what is meant by “containerization.” Standards bodies debate specs publicly (though it is worth noting that such debates are rarely newsworthy unless they fail in dramatic ways). But we hardly ever think about the USB protocol, even as we routinely plug and unplug connectors several times everyday. Or about the mechanics of containerization driving the endless torrent of package deliveries at our doorsteps.

So to a first approximation, and across multiple domains, protocols are invisible technological phenomenology. They could be considered technological dark matter; evading study by retreating to the margins of our attention, ceding the spotlight to the “main characters” of the technological environment – striking buildings, beautiful bridges, impressive airplanes and rockets, awe-inspiring AI systems.

Protocol designers in nearly every field rightly view such invisibility as a feature rather than a bug. Protocol designers intuitively tend to design for invisibility. Invisibility of some sort – from literal visual invisibility to the reflexive automaticity in our own responses to subconsciously registered cues – is either a strong functional requirement, or at least highly desirable. Often, the very reliability of a protocol rests on its invisibility, and visibility can lead to fragility and unreliability.

Keep a protocol in peripheral vision, and it works. Look too directly at it, or attend to your behaviors within it too deliberately, and it starts to falter.

In our emerging field of Protocol Studies, this widespread design intuition has been elevated to the level of a philosophical principle, captured by Whitehead’s famous observation that “civilization advances by the number of operations we can perform without thinking about them.”

Within our young field, it is now a convention to refer to a valuable advance in protocolization, marked by a transition of an important behavior to invisibility and collective unconsciousness, as a “Whitehead advance.”

The Price of Progress

Protocolization can be understood as a centuries-old macro-trend of progress through invisibility, but this progress does not come for free. There is a clear cost associated with it, in the form of a new class of problems induced by invisibility.

This is most apparent in governance at larger social scales. In the most extraordinary cases, such as the effect of vaccines (inarguably one of the greatest Whitehead advances), protocolization becomes invisible to the point of even erasing its own historical raison d’être, and undermining the broader literacies required to sustain itself. We might perhaps call this pathology overprotocolization.

Modern vaccine denialism is possible precisely because vaccination protocols have made their own logic largely invisible through their extraordinary success. Something similar can be said of nuclear non-proliferation – the perception of risks of nuclear-armed conflict have diminished in proportion to the success of nuclear weapons protocols, and the attendant retreat of nuclear weapons into background invisibility.

Invisibility also creates peculiar difficulties in the study of protocols, since we are forced to talk of them through the fragmentary and mutually inconsistent languages of other fields, making the search for universal principles and general theories particularly difficult.

This, however, is a problem that we can begin to address in many ways. And one of the best ways to make protocols visible in positive ways, at least to scholarly and professional scrutiny, is to develop expressive formalisms and mathematical models to undergird a language for talking about them in general terms.

The work of SIGFPT encompasses both formalization as such (the development of ontologies and precise terminology that allow us to talk consistently about phenomenology across many domains) and mathematization (the development of suitable technical methods to investigate the phenomenology through the formalism).

Our explorations and activities should interest researchers and practitioners across a wide variety of existing fields – computer science, control theory, operations research, economics, sociology, anthropology, and political science among them.

Regardless of what background you are coming from, the value of FPT for you is mitigation of the theoretical fragmentation that you likely encounter whenever your interests lead you to protocols and protocolization.

Tackling Theoretical Fragmentation

Protocolization is a multi-scale planetary phenomenon that couples the local to the global, and entangles micro-level dynamics and macro-level dynamics, via technological media. This broad footprint across spatiotemporal scales and regimes of dynamics, exacerbated by general invisibility, tends to create fragmented theorization of hugely important phenomena.

An example will illustrate what we mean here, and motivate the approaches we are developing to mitigate theoretical fragmentation and construct more integrated understandings.

Containerization, the protocolization of global commerce, is generally analyzed through the lens of globalization, understood as a primarily political and economic phenomenon, even though it obviously involves a great deal more. At the micro-scale it is simply a mature standards process that stewards definitions of a set of relatively low-tech box form-factors. At the macro-scale it is an emergent intermodal network-of-networks for materials transport. At the local level, it is a tangle of logistics problems, such as regional staging, port operations, last-mile operations, border transit operations, and security procedures. At the global level, it is a climate-like hyperobject comprising a constantly shifting set of flows and stocks, and featuring weather-like phenomena.

To enable theoretical study of containerization, a sufficiently expressive formalism (or more likely, a harmonized assemblage of such formalisms) for protocols must be able to articulate concepts and propositions ranging from the “box” level to “planetary climate” level. It must comprehend dynamics ranging from an earthquake affecting a particular port, to a slow accumulation of empty containers on one continent due to unbalanced trade (the backhaul problem).

And this is merely the simplest sort of protocolization, where we can name and point to a relatively coherent single “protocol,” with relatively intelligible structural and behavioral phenomenology to roughly isolate, model, and study.

Such intelligibility, however, is an illusion. Any fragile, non-fragmentary picture we might build of a phenomenon like “containerization” shatters the moment we situate it in the real world, and bring it into contact with other protocols of comparable richness that occupy some of the same spaces it does.

Protocol Tangles

Even within the seemingly isolatable domain of containerization, formal protocol theory faces tough tradeoffs between generality and specificity, resolution and scope, and so forth.

But the need for formal protocol theory becomes even clearer when we attempt to investigate multiple protocol infrastructures at once, motivated by provocative similarities, seductive resonances, and most importantly, consequential real-world convergences (such as many physical networks sharing right-of-way corridors).

When many protocols co-evolve in convergent ways and get materially entangled, constituting a single protocolization process spanning multiple partially visible and operationally coupled infrastructures, they tend to become especially invisible. This is because a view corresponding to one component protocol and associated set of interests misses all the others.

Since the early days of protocol studies, we have thought of this as the “three blind men and an elephant” problem in studying protocols, especially large and complex ones.

Such circumstances present as especially acute fragmentation of attempts to study them theoretically. We refer to such circumstances as protocol tangles. Here are some examples:

• Factory automation emerged at the intersection of computerization within factories, and globalization between (globally distributed) factories. It is a tangle that cannot be adequately studied by any of the obviously relevant disciplines, such as industrial engineering, operations research, computer science, economics, or trade policy.

• Global public health regimes, a response to planetary-scale contagion phenomena, are tangles comprising protocols at the intersection of municipal public health infrastructures, global pharmaceutical research and disease surveillance protocols, and international relations.

• The presently unraveling Rules-Based Order of international relations is a tangle of planetary scale coordination problems factored into impoverished sets of nation-centric problems. Even though massive international cooperation undertakings (such as the UN Food Program, the UNHCR, and the WWF), operate as exceptionally invisible protocol infrastructures, they are understood and governed through highly impoverished and fragmented theoretical frames.

The worst protocol tangles are so invisible, they even lack names with which to point to them. Attempts to think systematically about them run aground immediately at the level of language.

For example, we lack even a name for the particular protocol tangle comprising containerization infrastructure, the internet infrastructure used to govern it, and the multinational corporations that manage the entanglement. We are reduced to thinking in terms of sui generis historical episodes, such as the “2017 Maersk ransomware attack,” rather than powerful theoretical frames attached to particular units of analysis, and usefully general operating principles.

In this instance, despite the similarity of the two networks (both are packet-switched networks of sorts), it is awkward to talk about the converged tangle in the language of either containerization or the internet. For example, a key difference between the core protocols of the two networks is that internet packets can be “dropped” and re-transmitted but lost containers cannot trivially be “re-shipped.” Another is that the container network has a “backhaul” problem, but the internet does not.

How does one theorize the implications of these differences to shed light on a shared phenomenon like “buffer bloat”? Could we talk formally of reversibly vs. irreversibly lossy protocols? How does one use such theorizing to deal with problems where the two networks converge or collide in the real world, such as in the case of the Maersk ransomware attack? Could we invent a converged “internet of containers” that is robust to such attacks?

So while both might be considered part of a broader class of packet-switched networks, it is not immediately obvious how to construct a formal theory encompassing both that is not uselessly over-general (like the famed “spherical cow” problem), and useful in engineering application.

As a result, discourse on the beguiling similarities between these two protocol infrastructures tend to remain in the realm of poetry and metaphor. Consequential encounters between the two, like the Maersk attack, never gravitate out of history books and into engineering or management textbooks.

Where the impoverishment caused by fragmented and incompatible frames becomes too severe, as in the case of climate action, planetary AI, and blockchains, coordination fails almost entirely, and the failure of sufficiently ambitious and powerful theorization is most acutely felt.

So as protocols become ever more complex, ever more mutually entangled, and non-reducible to non-protocol infrastructures or technological islands, they become increasingly difficult to deal with and govern.

And perhaps no protocol tangle is more devilishly difficult to comprehend and investigate than the planet-scale one caused in the last half-century by computation.

The Planetary Computational Tangle

In developing formalisms and mathematical approaches, it is worth paying special attention to a particularly consequential tangle of protocols, involving the varied modern interacting infrastructures that rest on computing capabilities. This tangle includes, but is not limited to: the existing internet, AI, blockchains, AR/VR, robotics, the “internet of things,” and planetary sensing/monitoring infrastructures, at all scales from microscopic to orbital.

This is the civilizational-level boss of protocol tangles, the planetary computational tangle, or PCT.

As the Maersk ransomware attack episode showed, it is already clear that the default mental model of the PCT as some sort of “extended internet” has been severely strained, to the point of collapse. While pleasing new formulations like “planetary intelligence” help us at least point to the PCT, they typically pre-commit to an overly legible and panoramic theorization heavily shaped by a unitary perspective, aesthetic leanings, and focal interests shaped by their most charismatic elements. They miss the essential messy plurality of the PCT, and the inextricability of human experiences and agencies within it. They serve our appreciative needs, but not the instrumental needs of practically useful theorization.

To develop powerful mental models of the PCT, especially given that thinking of the internet itself as merely a “network of computers” has already proved to be highly limiting, we need a radically different approach to theorizing. One that privileges a worm’s-eye view of the tangled messiness over pleasing panoramic views and grand narratives.

For formal protocol theory, the PCT might be viewed as the “one ring” modeling and governance challenge.

To the extent we are able to build theories and practical techniques for grappling with it, we will be able to make sense of all dimensions of protocolization, and deal more elegantly with more bite-sized protocol problems that can be meaningfully isolated and tackled. It is already clear that existing ontologies will not do when it comes to “carving the reality of the PCT at the joints,” so to speak, to isolate problems worth tackling. New ontologies are required.

To the extent we fail, our increasingly technologically terraformed planet will become invisible not just to our senses, but also to our minds.

The irruption of AI into the world, currently the most dramatic and attention-attracting world process, is worth some additional thought. Currently, the role of AI in the PCT is being understood and constructed through the lenses of corporate product development and nation-state-based “sovereign AI” frames. This approach is inherited from previous experiences like dealing with fissile materials or climate change. These frames miss much of what is important, interesting, valuable, and risky about AI, especially when dealing with its rising “agentic” usage at planetary-infrastructure scales. Such usage is unlikely to remain limited to the traditional boundaries and capabilities of products, corporations, or nations, and human actors who identify with them.

Similarly, the role of blockchain-based infrastructures in the PCT is being reduced to rigid and relatively unimaginative nation-based identity and monetary infrastructures. This again misses much of what is important, interesting, valuable, and risky about them. The flexible programmability of infrastructures like the Ethereum “world computer,” with its capacity for expressing and articulating novel and fluid regimes of law and governance, and disciplining the myriad wildernesses of the PCT with striations of orderly hardness, remains largely unexplored. Again in part due to the limiting theoretical frames through which it is viewed.

Our mental models of the PCT are perhaps most severely compromised when it comes to the raw materiality of it.

There are already over 17 billion connected Internet of Things (IoT) devices in the world. We are currently on the cusp of a robotics boom. Planet-scale sensor-networks and environmental monitoring systems are beginning to take shape all around us. Warfare has already been irreversibly transformed by autonomous vehicles on air, sea, and land, and civilian life is poised to follow.

The picture is similar at the micro-scale as well. From home automation and self-driving cars, to robots on sidewalks, we are drowning in a chaotic sea of “globally local” small-scale protocols everywhere. Conventional frames, such as traditional urbanism for sidewalk governance, or home construction practices that clumsily attempt to integrate ideas like “smart keys” for package delivery and “smart thermostats” for energy management, struggle to rein in the chaos. The result is proliferating localized Darwinian “technological tangled banks” of hyperlocal infrastructure ecologies that are invisibly connected to the planetary-scale PCT.

Even apparently small, simple, and isolatable examples of protocols, such as hand-washing, become complex to theorize when they are embedded into the PCT.

What happens to handwashing, for instance, when all soap dispensers turn into small internet-connected robots that can order refills for themselves online? What changes in our behaviors when smart faucets start singing happy birthday at us to ensure we spend the mandated twenty seconds washing our hands in controlled environments?

Why Theorize?

Mathematization and formalization have not always been part of human governance responses to major planetary transformations. Computerization was partially theorized early and powerfully, through the development of frameworks such as Turing machines and the lambda calculus. Globalization was accompanied by the rise of fields like operations research and supply chain management that formalized and mathematized critical aspects of it.

Economics, a field that offers many precedents, cues and clues for Protocol Studies, mathematized in fits and starts in the process growing beyond mere description and analysis to become the operating system of the global economy (as Donald Mackenzie powerfully argued in his classic, An Engine, Not a Camera). Along the way, thanks to the methodological demands of economics, rigorous and formal statistical sciences emerged from unsystematic beginnings in gamblers’ intuitions and superstitions.

But other planetary transformation forces have resisted formalization and mathematization. Protocolization lies somewhere between phenomena like computerization and financialization on the one hand, which lend themselves very well to formalization, and phenomena like urbanization and consumerization on the other, which resist paradigmatic treatments more cohesive than ethnographic and sociological analysis.

Some mathematization and formalization is clearly possible, especially because so much of the substance of protocolization is technological. But trying to formalize too much is misguided.

In the second part of this essay we will lay out the particular approach the SIGFPT is taking to this challenge.

FPT Within Protocol Studies

Formal Protocol Theory (FPT) is one of a handful of disciplinary areas within the broader emerging field of Protocol Studies, and in our activities and explorations, we maintain collegial ongoing dialogues with several peer areas, devoted to shared concerns and intellectual foundations.

The current list of areas, and associated Special Interest Groups, can be found here, along with participation information. Below, we briefly describe these adjacent areas in relation to FPT (each meets once every other week).

Memory Protocols

The Memory Research Group (MRG), led by , studies the role of memory in protocols, protocol technologies, and protocolization processes.

Perhaps one of the major contributing factors to the invisibility of protocols is their close relationship to memory and forgetting. The most successful protocols don’t involve user manuals during their operation, but produce and propagate the necessary, operable memory through their performance. In the spirit of the “medium is the message,” we might say that “the protocol is the memory.” For example, blockchains produce a record of their actions as part of their actions themselves, and this record is actually one of their primary utilities. The contemporary era of protocolization is marked by the ever-closer coincidence of procedures and their memory traces.

The MRG studies memory in all its manifestations, and in 2026, SIGFPT will take cues from MRG to look for ways in which we might formalize and theorize the memory features of protocols.

Protocols for Business

The Special Interest Group in Protocols for Business (SIGP4B), led by , studies protocols within organizations, approaching the domains of existing fields like management science, organizational behavior, and organizational psychology, through the lens of protocol theory.

Specialized organizations and sectors that fulfill various societal functions have historically been the locus of a great deal of protocol development and evolution. Technological specialization is another major contributing factor to the invisibility of protocols. Not only are many protocol infrastructures hidden behind the boundaries of organizations devoted to developing and using them, knowledge of such protocols is also hidden behind sector-specific jargon. Even protocols that enjoy a degree of universal presence across sectors and industries and some lay familiarity – such as those involved in managing supply chains, or staff functions such as human resources and facilities management – tend to become invisible behind the boundaries of functional specialization.

Currently SIGP4B is undertaking a protocol-theoretic exploration of perhaps the most important organizational technology in a century: AI. Effective adoption and use of AI technologies is a protocolization problem that is being simultaneously tackled by all organizations across the world in near-crisis mode. There is much to see and learn.

In 2026, SIGFPT will take cues from SIGP4B to help theorize the injection of AI into traditional organizational environments.

Protocol Fiction

Building on a year of publishing dozens of protocol fiction stories here on Protocolized, the Protocol Fiction SIG, led by and , studies the rules and principles of this emerging genre, and explores how to look at our protocolizing world more effectively through stories.

For us in SIGFPT, we hope to find both stimulation and entertainment in the work of this SIG, as well as inspiration and motivation. Fiction can often point to subtle phenomena and generative contradictions and paradoxes that can power theoretical investigations. The notion of “protocol tangles” developed in this essay, for example, was largely inspired by the many tangled plots and premises that have been explored through protocol fiction in the last year.

Besides these three peer SIGs, other SIGs may be spun up this year.

Further Reading

In the second part of this essay we will dive deeper into our early ideas. In the meantime, if you’re interested in participating in SIGFPT, you may want to read these older updates based on our work in 2025.

What is Formal Protocol Theory

Constructing the Evil Twin of AI

We hope to see you this Friday for our 2026 kickoff.

Join us on our Discord for the 2026 kickoff meeting of SIGFPT on Friday, January 9, 10am Pacific. Calendar invite for series.

Acknowledgements
Thanks to Giovanni Merlino, Mike Travers, Patrick Atwater, Seth Killian, Jonathan Moregård, Joseph Michela, and Kei Kreutler for comments and contributions to this essay in draft stage.

But that is the strange story of Bronisław Malinowski, who after a childhood of illness became a pioneer of anthropology. In 1914, Malinowski accidentally began an expedition to Melanesia – documenting social systems in New Guinea, Fiji, New Caledonia – that would return him to Europe five years later, in 1919. You might think that you were born too late for such an adventure, but you were not.

Malinowski was an influential and contentious researcher. The 2018 edition of the International Encyclopedia of Ethnography credited him with shaping the field of ethnography. He was known for stressing the importance of getting “off the verandah” and exploring everyday life. Not all of his colleagues approved of his methodology and it’s unlikely that his opinions have aged well.

What did last, however, were Malinowski’s innovations to the art of people watching.

Fei Xiaotong was among Malinowski’s students at the London School of Economics and went on to have an adventurous, difficult career as an anthropologist in China. Fei embraced Malinowski’s anti-armchair sensibility but disagreed with his claim that social institutions exist to satisfy universal human needs – arguing that social order is more about relational positioning and that Chinese society cannot be reduced to problem-solution pairs.

Today, such intellectual adventures don’t require the overhead of an expedition. You needn’t go far to find strange rules; you might not realize how much you live in your own world. Every day we interface with a tangled bank of technologies, laws, and cultural norms. Paying attention to the world around us is enriching; I would argue that we have far more interesting things to observe than Malinowski did.

Scholarship and profitable application can be an afterthought. Before Darwin was Darwin, he was Charles, a birdwatcher from England. His naturalistic theories emerged from thorough documentation. He was collecting data without a hypothesis!

If you’re unfamiliar, protocol watching can be grokked as a cousin to birdwatching or people watching. Its closest sibling might be umarelling; loitering around construction sites to check out works in progress. It has a simple definition: watch the protocols – from TCP/IP to traffic lights – as they happen around you.

Ethnographic practices might help us all to be more observant in and around our professional spaces.

Software UI/UX designers, bike lane planners, and football coaches alike must study how people interact with each other and with their environments. People in these jobs, however, start with goals in mind. They have an objective: improve user retention, reduce accidents, win more trophies, etc. Protocol watchers are also rigorous, but don’t have the disadvantage of time pressure. They can collect, collect, and collect and let patterns – and profit – emerge naturally.

It’s a potent, if nerdy, hobby. I wrote a first version of this guide last year. Since then, I’ve gotten a much better feel for patterns in how we coordinate, argue, and share information. No course registration required. No sailing ship overhead. From hiking trails and mechanic shops to airport tarmacs and financial transactions, there’s always something to learn or even replicate. It’s an adventure-in-a-lens.

My challenge to you: take up protocol watching as a miniature hobby for the new year. It’s perfect for transforming dead time in airports, traffic jams, and boarding queues into a tad more interesting downtime. It’s also a sneaky way to get off your phone a bit more, if that’s on your list of resolutions.

Some examples to orient your eyeballs:

• Queueing protocols at airports, bus stations, and ferry ports. Who gets to skip the line? Are there different types of lanes?

• Gift-giving protocols at parties. Are you expected to bring something for the host? What purpose do gifts serve? Is the expectation stated or implicit? When do people open gifts?

• Out-of-office messages. What information do people provide? How standardized is it? Is the medium automated emails, their main publication, or their website?

• Hardware standards. Charging outlets, bolt sizes, countertop heights – they afford interoperability in the long run, but is it worth the inconvenience? Who makes standards?

• Transactions. How do you establish trust with vendors? When people get stuck on the side of the road in a snowbank, why is it that others help without expectation of compensation?

It’s important to follow your own interests when protocol watching. Otherwise it will be less of a hobby and more work. Enthusiasm is the bottleneck – Darwin and Malinowski both entertained a deep-seated interest. Treat the examples above, and the tools and tips of the trade below as a starter pack of suggestions. Once you’ve spotted a couple of easy-to-recognize protocols, keep looking and take note of everything you find interesting and potentially useful.

Tools

Since everyone likes tools, we’ll start with those. These will help you build a sharper eye, faster. They’re also the backbone of a compounding hobby. The thoroughness of your notes or working memory will determine how easily you can spot patterns in your observations later. These tools will help you record, detect, inspect, and taxonomize the protocols you observe.

NOTES

At a minimum you should keep notes, whether on paper or digitally. Minimalists and flaneurs can start with just a notebook or mobile notes app – aim to fill up your first notebook as fast as possible.

To level up your game, the best off-the-shelf tool for this is the Protocol Bicorder, developed by University of Colorado professor Nathan Schneider, who also maintains the Protocol Oral History Project. The recently-created Bicorder will help you understand and catalogue protocols via some scoring scales, based on research from the Summer of Protocols program. I recommend you enable the Short Form mode.

Sketching is another nice way to take notes. Whether it’s a physical thing you draw or a choreography at a TSA security checkpoint, we’d love to see what you come up with.

COMMUNITY

When kickstarting a hobby, it’s helpful to chat with fellow hobbyists. Documenting and sharing protocols is also just fun. Our Discord has a dedicated #protocol-watch channel for exactly that. Posting there is low stakes and even lurking will give you a sense for how other people treat this as a hobby. Send a photo, video, sketch, or the JSON file generated from your bicorder entry anytime.

Protocol watching is also more fun with a buddy, kind of like people watching. Provided you have a sufficiently nerdy friend or anthropologist family member nearby, this could make for a good way to kill time.

TENSION DIAGRAMS

Every protocol is an engineered argument and exists to manage an underlying tension. Part of the game of protocol watching is trying to identify that tension. A common one is convenience vs. safety (hello again, TSA). At a minimum, you should seek to name the tension. If you want to level up your intuitions, make a diagram with the following template.

Label the axes with your factors. Draw a trade-off curve. Where does the protocol land? Is it on the frontier? Or can you easily imagine a tweak that would improve, say, convenience AND safety at the same time? Which actors prefer a safer version? Who prefers a more convenient version?

For a detailed example, check out One Tension to Rule Them All.

TRANSLATORS AND TRACKERS

A translator is a must-have tool. For example, the maintenance tags on fire extinguishers and defibrillators are full of jargon, codes, and acronyms. Using an LLM can help you translate those really quickly. You might also see protocol artifacts written in other languages – are there subtle differences not explained by translation?

Trackers are another fun augmentation. They are the telescope of protocol watching. Tools like FlightAware, FIRMS, VesselFinder, ISO standards, and IQAir all provide a unique look at the world. You can track flights, wildfires, cargo ships, food classification standards, and global air quality all from your phone browser.

You can also use LLMs to generate professional briefs and histories of protocols you observe, as and sometimes do with their writing projects, and , respectively.

Tips

Your toolkit should also include some principles and heuristics. These three tips will help you improve over time and might enable your protocol watching to compound from hobby to competency.

First, go for the hotspots. Places with a mix of hardcore engineering and human factors, like airports, libraries, kitchens, universities, factories, mines, farms, legislative assemblies, and so forth.

Second, go through, around, and behind the protocols. Get a feel for what they’re like as a participant. Try to hack them – skip lines, find workarounds. Talk to the people who design and enforce them. By exploring multiple perspectives you’ll develop a much richer perspective or, at least, not become as jaded towards a protocol’s apparent dysfunction.

On a related note, the third thing you should pay attention to: protocols are most visible when they’re not working. As much as it’s fun to catch one breaking down, it’s even more satisfying to spot a protocol that’s doing its job invisibly well.

Congrats, you’re now initiated to the protocol watching club! Beyond the benefits mentioned above (and general aura enhancement), this certification will improve your odds of acceptance into next year’s Protocol School.

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The editorial team at Protocolized is thrilled about all of the stories and studies we published this year. We encourage anyone who wants to write with us to try protocol watching. It will help sharpen your imagination for futures that might logically unfold from new technologies. It will also help you add another dimension to you worldview.

Happy protocol watching and thanks for reading Protocolized. All 84(!) issues from this year are available in archive. See you in 2026 for more stories about strange rules and essays about protocols – the world’s (mostly) invisible infrastructure.

The Protocolized team - Timber, Venkat, Tim, James, Josh, and Jenna

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Between June and December 2025, the Memory Research Group met every two weeks to read texts about memory. The readings played with juxtaposing different historical approaches to memory. We began with classical techniques, then embarked on a digital memory architectures module with contemporary AI systems before moving onto older physical memory substrates again. This allowed us to trace the present’s confusions backward to see which problems are genuinely new and which keep recurring under different names.

The group convened via the Summer of Protocols Discord, with participants from neuroscience, computer science, philosophy, architecture, and design reading texts together in real time before discussing them. We weren’t trying to arrive at a unified theory of memory, but instead trying to distinguish how memory operates in different contexts.

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June: Early Memory Techniques

Reading: Frances A. Yates, The Art of Memory (1966).

The group started with Frances Yates’s The Art of Memory. Yates discusses the method of loci – placing memorable images in imagined architectural spaces – exemplifying memory as active practice rather than passive storage. The ancient Hellenic poet Simonides realized after a building collapse that he could remember where everyone had been seated among the ruins. This became a protocol: associate information with locations in space, and you can retrieve it reliably.

What stood out was how these ancient techniques didn’t oppose knowing with memorizing. Memory was described as the faculty enabling “firm perception of things,” not just the stockpiling of facts. Some 15th century practitioners picked up on this lineage, developing elaborate systems for organizing ways of thinking, not just information. The group noted tensions around trusting external representations versus cultivating internal capacity, a concern that would keep reappearing when we discussed digital systems.

Related to this month’s topics, read the post A Very Short Introduction to Memory and Technology.

July: Distributed Cognition

Reading: Edwin Hutchins, Cognition in the Wild (1995); Michael Levin, ‘The Space Of Possible Minds’ (Noema); Michael Levin et al., ‘Endogenous Bioelectric Networks Store Non-Genetic Patterning Information During Regeneration and Development’ (2021).

Edwin Hutchins’s Cognition in the Wild documented techniques of naval navigation, arguing that memory and cognition distribute across people, tools, and environments. In this context, a ship’s navigation team doesn’t allow crucial information to exist only in one member’s memory. Instead, practices spread across coordinated action and material artifacts. This raised questions about whether memory requires a specific substrate at all, or whether it is better understood as patterns of coordination.

We followed this by reading Michael Levin’s work on bioelectric networks, which proposed that a kind of memory exists in the electrical patterns of cells, not just neural systems. His research on how organisms store non-genetic information during development and pass it on to future generations expanded the working definition of memory beyond neural substrates entirely. The group wrestled with what it means to call something “memory” when it operates without anything resembling conscious recall.

August: Spatial Memory and Medieval Neuropsychology

Reading: O’Keefe and Nadel, The Hippocampus as a Cognitive Map (1978); Mary Carruthers, The Craft of Thought (1998).

O’Keefe and Nadel’s The Hippocampus as a Cognitive Map distinguished between cognitive maps and stimulus-response learning. The hippocampus doesn’t store information about locations alone, but rather builds associative models of spatial relationships. This connected back to ancient memory palaces: spatial organization seems fundamental to how we recall knowledge.

Mary Carruthers’s The Craft of Thought examined how medieval Europeans conceived of memory as residing in physical locations within the body. Memorial landscapes were imagined architectural spaces for organizing theological and philosophical knowledge. This session was guest hosted by Aaron Z. Lewis, who wrote the text ‘Four Doors – A Portal to Sacred Memory Protocols’ for the Summer of Protocols 2023 cohort.

September: Where Memory Lives

Reading: Contemporary neuroscience research speedrun; Mary Carruthers, The Book of Memory (2008).

A research speedrun on contemporary neuroscience was followed by Carruthers’s The Book of Memory, on medieval neuropsychology. Reading about memory stored in the heart, regulated by bodily humors, organized through visual imagery, participants reflected on where they personally experience memory. Most noted that they don’t experience memories as stored anywhere in particular; memories arise in response to contexts and triggers, rather than being retrieved from a location, at least in some of today’s cultures.

Related to this month’s topics, read the post Reflections from Memoria.

October: Digital Memory Architectures

Reading: Amit Bahree, ‘What is KV Cache in LLMs and How Does It Help?’ (2025); Alan J. Smith, ‘Cache Memory Design: An Evolving Art’ (1987).

The turn toward digital systems began with understanding KV (key-value) caches in large language models. These caches allow transformer models to remember previous tokens without recomputing everything from scratch. It’s a computational efficiency trick, but it raises questions about what distinguishes “remembering” from simply having information available. The group discussed parallels to human working memory constraints.

Cache memory systems in 1980s computer architecture followed. Alan Jay Smith’s 1987 overview explained how cache memory bridges performance gaps between the relative speeds of processors and main storage through predictions about which data will be needed next. The principle of locality – recently accessed information is likely to be accessed again – operates across both biological and technological systems.

Related to this month’s topics, read the post Images of Memory.

November: Magnetic Core Memory

Reading: Paul E. Ceruzzi, A History of Modern Computing (excerpts); ‘Magnetic Core Memory,’ Computer History Museum.

Magnetic core memory, an early form of computer memory storage, brought the group into direct contact with memory’s materiality. Reading Paul Ceruzzi’s history, the group examined how early computers stored data in tiny ferrite rings magnetized in different directions. Each bit was literally woven by hand into arrays of wire and magnetic cores. After months discussing more abstract cognitive processes, this tangibility was striking. Software woven into hardware collapsed distinctions between code and physical object in ways that contemporary solid-state memory obscures.

December: Virtual Memory and Metaphor

Reading: Matthew G. Kirschenbaum, ‘Extreme Inscription: Towards a Grammatology of the Hard Drive’; Italo Calvino, Invisible Cities.

The advent of virtual memory marked the shift from physical to abstract storage. Learning about how memory became virtualized in computers – no longer tied to specific physical locations but dynamically mapped and remapped by operating systems – introduced a new layer of ambiguity in our concept of memory. In this context, the illusion of continuous memory space maintained through constant background processes revealed memory more as performance than substance.

The final session turned to Italo Calvino’s Invisible Cities and the question: what core metaphors do each of us use to relate to memory? After months of technical readings, this offered space to share more personal reflections.

What Keeps Recurring

Jumping around chronologically in our readings revealed that contemporary technical systems have rediscovered some of the tradeoffs and ambiguities of memory through different means. The tensions between capacity and retrieval speed, between compression and fidelity, between what to keep and what to discard appear whether discussing core memory in the 1950s, the hippocampus, or transformer attention mechanisms. Sometimes even fundamental mechanics resurface across substrates: magnetic core memory’s destructive reading – where retrieving a state requires overwriting it, before subsequently restoring the original – mirrors how human memory recall performs a recontextualizing function, potentially altering what remains in memory. Still, critical differences remain. Importantly, memory doesn’t oppose human knowing when it’s externalized into tools, because, ultimately, it never operated purely internally to begin with.

Memory operates as a biological process, technological substrate, cultural practice, and phenomenological experience simultaneously. No single framework dominated our discussions, perhaps because of our interdisciplinary composition as a group. Technical explanations of cache hierarchies sat alongside reflections on where memory feels like it resides in the body. Medieval mental diagrams found resonance with neural network architectures. The classical method of loci proved conceptually similar to how operating systems manage address spaces.

Amidst reflections during the last session of the year, our group surfaced a strange irony: sometimes what is remembered most is what is most dynamically changing, through attention, interaction, and reinforcement from its world.

In the medium term at least, fossilization ensures forgetting, whether for cities or neural patterns. That is, until the longer term, in which their presence might be uncovered millennia later, requiring us to map contemporary memories onto their molded matter.

MRG @ Summer of Protocols Discord

Few science fiction authors have been as involved at the frontier of technological innovation as Stanley has. In 2019 he co-wrote a book, AI 2041, with the CEO of Google China, Kai-Fu Lee. That same year, the World Economic Forum tapped him to help imagine possible futures. I had the opportunity to ask Stanley about his process and his work. What follows are some highlights and you can find the full transcript here. The text is lightly edited for clarity. All quotes are marked with timestamps [min:sec].

Protocolized encourages its writers to use LLMs and other AI tools. I’ve been trying to use LLMs in my writing, but just haven’t been satisfied with the results recently. At the same time, I feel an ongoing tension about that – how could I not be finding it useful to draw upon the entire combined archives that were scraped from the open web?

Stanley touched on this feeling early in our conversation:

[10:20] I think it’s not there yet. So you feel like always there’s something missing here, or this character might not seem to be 100% aligned and this dialogue could be a little bit cheesy. There’s always something there – so you will not be satisfied at all.

Despite that, he’s designed a custom workflow that he feels does enhance both his fiction writing and his fiction translation outputs. As a longtime teacher of fiction writing, he’s also able to provide some direction for what to focus on to create actual improvement. Indeed, our conversation did frequently return to the taste that needs to underlie successful LLM use, and his suggestions for how to develop it. Be deliberate, Stanley says, about how they are prompting you:

[03:56] It’s not just you prompting the LLM, but also the LLM as a prompt reversibly triggers your imagination and creativity.

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Where LLMs fit into Stanley’s writing process

Given the amount of time Stanley has spent tuning his use of LLMs for writing, I asked:

[3:02] Where within your writing process do you see there being a use case for LLMs? Do you see it as more relevant during the writing process or editing process? What’s your philosophy there?

His response:

I thought it was just for collecting the data and information and trying to build the structure. But now I think it’s it’s both – it’s when you’re conjuring ideas, when you use it for world building, character building, and structure building. But also you can use it for rendering your style in a way and trying to find a correspondence between different disciplines. For example, when you try to use a very comprehensive metaphor to describe some super complex science theory. An LLM can usually give you some options that you can choose from…

I will use different models for different purposes, and I will separate different tasks. Maybe one big task, you can break it down to different small parts and you give it to this model or give it to that model, or you might want to rerun an output in a different model¹ And you try to find the best outcome. But you have to, I mean, you have to train yourself with some kind of good taste.

This question of taste reappeared throughout the process, craft, and philosophy sections of our conversation. Taste and agency have been two buzzwords of 2025, for anyone tech-adjacent and terminally online.

My question to Stanley, then, was: Can you improve your craft as a fiction writer using LLMs? If so, how? This was a question that was particularly on my mind as someone relatively new to fiction writing. It seems like a skill issue not to have found a good workflow for this. Stanley agreed – improving your craft happens beyond the actual writing practice. An LLM can give you options, and your job as the writer is to have a consistent taste to discern which among them serves your vision.

That means no copy-pasting.

[10:04] I always rewrite. I couldn’t use even a single line it gave me directly. I think in a way there’s some legal issues. But through time and through your career, you raise the bar. You have a higher understanding, you have a deeper understanding of writing as a craft. You have to redo it over and over again. When you find your touch, you find the right feeling. I think that’s the most important thing.

This really reminded me of a college professor I had who swore by manually retyping code snippets from Stack Overflow. “You’ll see what’s wrong with it and change it when you rewrite it,” he said.

My former Spanish teacher said something similar. His rule was that when using Google Translate, even if you know the translation it gives you is correct, you still have to re-type it yourself. And then when you do, you often notice ways in which it was subtly wrong.

Oh, and speaking of translation –

Translating fiction with AI

Stanley and Ken Liu have had a multi-year, two-way partnership in translation – Ken Liu translated Stanley’s Waste Tide into English. Stanley translated some of Ken Liu’s work into Chinese.

[11:46] When I translated some of Ken Liu’s work into Chinese I used an LLM to find the tongue. I think it’s very difficult to capture the style, the tongue. It’s at the inter-linguistic level. You have to find the right slang, for example, or the right metaphor. Sometimes there are some cultural references that might not be able to directly attach with something in Chinese, or the other way around. So I think it’s always useful to use LLMs to help you to find something you know as a counterpart of the the language and the culture. Again, this is about taste. If you are bilingual you can feel the nuance and you can feel the subtlety of the language and the literature and you you might feel, “oh this is not exactly what the author tried to do in the original version.”

So sometimes they do experiments as well. The previous work can be referenced, but it’s not exactly the same, right? So we have to... dive deeper and we try to understand what the author really tried to do. And we come up with this idea, or maybe we could create this kind of style in Chinese to fully capture what the author tried to do. And we try to have the LLM represent what we think might be correct or close enough. But again, it’s not easy. So maybeit’ll give you the mediocre, or an average level of translation.

So I think this is like back and forth. I don’t think you can fully rely on LLM to do the translation, especially on literature.

Again, the role of the LLM here, in Stanley’s view, is largely to present you with a palette of options.

So what’s happening in between, then? What’s happening while you’re redoing it over and over again, prompting the LLMs and letting them prompt you back?

[28:48] As with every tool, you can use it in a dumb way, or in a smart way. You can use it to be lazy, or you can use it to challenge yourself. You think firstly and you see what the AI gives you and you try to compare the two. And you can see, okay, maybe this is what I’m missing here. These are my blind spots and there’s something nice, nicely done there. So maybe this is the focus area I should look into next time. So I think it’s a very good practice for your brain, for your imagination, for your narrative...

This was the thread I was really trying to pull on throughout our entire conversation. What does it look like, to Stanley, to use LLMs in a smart way?

It means being in dialogue with them, and being fluid about their role in your process. I asked Stanley what his mental model is when he uses AI in his fiction process – tool, palette, partner? Something else?

[4:52] It’s inter-subjectivity, so it’s something in between. You can see this subjectivity shifting or flowing between me and the machine in a distributed way. So I think all the magic happens in between. During the process, you know, think about electricity, think about the energy flows.

Interacting with your characters

Generative technologies offer new opportunities to interact with your characters and to discover them, even as the author.

[15:05] I’ve been playing with Midjourney and, recently, with Sora 2. It’s very interesting because when you have a character in your imagination there’s a distance, right. It’s kind of vague, kind of abstract. But when you transform it into a very vivid and and visually stunning image, even an animation, you give it more information. You might be triggered with a sense of how the character acts, or talks, or responds to certain scenarios.

What does it look like, though, to flesh out those characters, and to visualize them within the space of Sora or similar tools? Stanley experimented with assigning characters into the 16 Meyers-Briggs personality types.

[16:49] Now you can divide yourself. You can role play with different personalities. Each type could be attached to specific characters. You can interact and this becomes a role-playing game, like LARP. But also I think in the future, we might use Midjourney or Sora to do a real role-playing game. Everyone could create something like that. It’s not only writing a novel, but also creating something visually. I think that’s the future.

He’s been involved in an immersive drama production in Hangzhou. The premise is that its eight characters are all reincarnated.

[19:25] We have previous lives, we have this life, and the next life. When people buy the ticket, if they pay more they can see different lives. If they pay less they only see this life. So they can choose, just like in Sleep No More in New York. And you can follow different story lines to explore. I found it fascinating because there are some interactions between the characters and the audience. And it might trigger something that wasn’t in the script. Sometimes the character might have to improvise. But from the very beginning, we had like a big Bible defining the world building, and each character, like attached to the different lives. Part of their identity and part of their personality has been changed, like your job, your career, even your appearance, but part of it, like the soul, you know, like some addiction, like some fears, like some anxiety pattern has not. This is a very different way of creating and also a different way of storytelling.

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Keeping the process separate from the product

However, Stanley cautions, you still need to be deliberate about how much information you are providing to your readers.

Unveiling the process is helpful to a point, but the work needs to stand alone. The artist should clearly have a vision about where the work ends and what the reader needs to figure out on their own.

This is one of the aspects I admire most about Stanley’s fiction. As much as the process is important, and as much as he’s been willing to publicly share about it – including in this interview – the work itself is the thing.

[21:07] Maybe there’s some fan fiction – readers can develop the character by themselves, they might have some alternative history, some subplots – like branches, storylines. So they can develop something like that. But I think to me what I should reveal to the readers is already written. You can interpret it by yourself, that’s your freedom. I always say, OK, just figure it out by yourself because what I want to say is already written there.

Even the endings. With a semi-open ending a lot of people ask me, “what does it mean?”

You have to fill in the blank with your own imagination. I think that’s the best part of it. You can keep guessing, you can keep thinking about it. That’s what I try to do.

Shaping the future with writing

Part of shaping the future with writing is about experimenting with form and embracing the new.

I asked about the prospects for young, emerging authors:

[48:59] They are suffering because there’s no platform at all. There’s no publication. There’s no opportunity for them. So I think this is very, very cruel. People feel angry. People feel that they’re outliers, that they’ve become irrelevant in a way, right. I don’t see an easy solution here.

But I always told my students, you have to create your own job in the market. Maybe it doesn’t exist yet, but you have to start to get down to work and start to build your own career, not just waiting there, waiting for some companies, you know, some bosses to give you one, to offer you one. I think this is the reality.

And with AI, I think, yes, they can do it, but they need to refresh their experience. They have to unlearn whatever they’ve been taught, you know, from the traditional schools or parents or educational institutions.

In his own work, Stanley has been very proactive about forming partnerships with people directly making decisions that impact the future.

While he’s a sci-fi writer, he has been invited give guidance in technology and economics discourses.

So what perspectives does he find it useful to share in those forums?

[56:36} We’re so small, we’re so powerless. This is a structural asymmetry. I think punk, like solar punk, basically is what we need here. And how we should do that starts from creating new kinds of metaphors. Because metaphor is how we package and compress all this storytelling, emotion, ethics, values, mythology all together. It’s like a super efficient info pack. But it takes a very long time to form a metaphor. So that’s why we should start now. We need some time to build up new metaphors…

I try to shift the vision for these people because they are the one who spend the money, make the action and policy-making, etc. I think you should do something to change the narrative, change the metaphor and tell a new story and create a new myth for the future.

A lot has changed in climate protocols in recent years. In 2023, when I first gave a talk on this topic for Summer of Protocols’ inaugural research cohort, the two previously disconnected worlds of traditional carbon markets and DeFi had only recently collided. The main character in that clash, KlimaDAO, had proclaimed itself as a “black hole for carbon” upon its launch and sucked 17 million metric tons of CO2 from suit-and-tie carbon market registries like Verra into its treasury, thus transforming millions of carbon credits into a new zombified onchain form that neither the corporates nor the degens knew what to do with. Regen Network, perhaps the oldest player operating at the intersection of crypto and climate tech, was in the process of expanding beyond its focus on carbon, taking in other environmental markets such as biodiversity crediting.1 While it may seem strange to be designing markets for biodiversity when we still can’t seem to get carbon markets right, biodiversity crediting has managed to attract attention from international policy players such as the United Nations Development Programme and UN Environment Programme through efforts like the Biodiversity Crediting Alliance. Regen Network soon found itself partnering with some very promising groups working in this sector, from the Colombian habitat bank developer Terrasos to indigenous leaders from the Sharamentsa Achuar community in the Ecuadorian Amazon.

Regen Network’s pivot to broaden beyond carbon stemmed both from a desire to resist carbon tunnel vision, and a response to a failing voluntary carbon market. The more traditional subsector of the carbon markets – consisting mostly of reforestation and avoided deforestation credits – had continued to face challenges, both from the plummeting corporate demand, which culminated in the Guardian’s exposé denouncing Verra’s REDD+ program in early 2023; and from continued slow progress instrumentalizing Article 6 of the Paris Agreement – a key piece of the international climate treaty that aims to clarify how nation states can trade carbon credits against their NDCs (nationally determined contributions towards limiting CO2 emissions).

Durability: A New Focus for Carbon Markets

Despite the world’s complete failure to put a meaningful dent in greenhouse gas emissions, over this same period of time a new and more focused carbon market in durable carbon-dioxide removal started gaining traction in both the regulatory and corporate sustainability worlds. A significant marker of this shift was the launch of Frontier in 2022, a $1B+ advanced market commitment for durable carbon removal led by Stripe, Alphabet, Meta, Shopify and McKinsey. This renewed focus on more permanent carbon removal and storage technologies (often abbreviated as CDR) represents a rebranding of the legacy carbon markets away from greenwashing and questionable “counterfactual claims” of avoidance-based forest carbon credits, and toward scientifically verifiable removals where the CO2 sequestered can be safely assumed durably stored underground or in the depths of the ocean for hundreds or thousands of years. The approaches and technologies being researched and deployed in the CDR sector vary widely, from highly engineered direct air capture facilities, to spreading pulverized volcanic rock dust over agricultural fields so that it reacts with and absorbs CO2 under rainfall, to electrochemically removing CO2 from the surface level of the ocean or otherwise altering seawater chemistry to enhance the ocean’s capacity to absorb CO2 from the atmosphere.

If this all seems bewildering and daunting, trust that it’s hard to keep track of existing and emergent CDR pathways even for those working in the industry. One could ask why we need so many different strategies for sequestering carbon out of the atmosphere. The answer to this question is, in principle, pretty simple. We don’t know which strategies will scale, and we need to scale very quickly. In 2023, the Intergovernmental Panel on Climate Change (IPCC) released its synthesis report, stating that, even in best case scenarios for emissions reduction, an estimate of 5 to 13 gigatonnes (Gt) of carbon dioxide removal (CDR) will be needed annually by 2050 in order to limit global warming to 1.5°C and achieve net-zero emissions. So, at this point in time, the industry consensus is that we need to be funding, researching, and exploring all feasible pathways, with humility and understanding that any potential solution may, at any time, be deemed uneconomical, unsafe, or otherwise unfeasible.

A Protocol with a Thousand Faces

Protocols and carbon markets have had a complicated entanglement since the earliest days of international climate agreements. The predecessor to the Paris Agreement was explicitly titled the Kyoto Protocol, and prior to that was the Montreal Protocol (often touted as an early success of international environmental agreements). In contemporary carbon markets, the term protocol has evolved from being used to designate a singular stamp of overarching international consensus to instead operating like a Swiss Army knife of sorts. Whether in the corporate sustainability arena of voluntary carbon markets, or the nascent but rapidly evolving jurisdictional and international compliance markets, if there’s a problem with clearly defining something, or a lack of consensus regarding what exactly something means, slap the term protocol on it! The word itself becomes elastic, with varying focus and specificity that can stretch and shape to any context. In the context of today’s voluntary carbon market, the industry’s self-appointed governing bodies, known as “registries” (e.g. Verra, Gold Standard, Isometric, American Carbon Registry) publish protocols – programmatic rules which dictate what kinds of activities need to be undertaken to warrant issuing carbon credits, and how those activities must be monitored, reported and verified (a process which is abbreviated as MRV).

In this wild-west, funded predominantly by corporate do-goodism, competing registries beget competing protocols. Furthermore, with a seemingly boundless set of different sequestration pathways being explored, a key indicator of pathway maturity is how many registries have codified a protocol for a given pathway. Without any infrastructure in place to compare projects across different protocols, the market remains fractured. Prices vary across pathways and protocols and the industry has little consensus on which approach is right. Toss in the fact that we are still waiting on UN policy guidance detailing which methodologies (the UNFCCC’s term for MRV protocols) are to be approved for use in country-level trading of carbon balances under the Paris Agreement Crediting Mechanism, and it’s easy to see how neither the voluntary nor compliance markets can make up their mind about what’s good enough. The Article 6.4 Supervisory Body is still working through this task nearly ten years after the Paris Agreement was signed.

A core tension here is that despite failing to provide any concrete pathway to fungibility for carbon financiers (demand is still concentrated in a handful of players, and supply too heterogeneous), these protocols supposedly exist for the explicit purpose of making real-world carbon removal interventions legible for financial markets. In this way, to ask whether the MRV protocols of Verra, Gold Standard, Isometric or Puro function more as scientific protocols or financial protocols is a question of perspective. Scientific for sure, but their function as financial protocols remains aspirational at best.

Data Protocols Behind the Curtain

With the industry’s spotlight on durable CDR, the resulting diversity of pathways and technological approaches to carbon removal is likely the main culprit in our current protocol predicament. If carbon markets were still dealing only in forest carbon, the industry’s first attempt to solve inter-registry interoperability with the CAD Trust might have delivered on its promise. That story begins in late 2022, with a group including IETA (International Emissions Trading Association) and The World Bank launching the Climate Action Data Trust as an attempt to harmonize data schemas from the various carbon market registries. Key to their approach was developing a shared meta-registry: a new database for existing registries to report their own project and transaction data to that could prevent double counting and support interoperability between the various different carbon registries. As with many other double counting prevention initiatives in enterprise or business arenas, there was a blockchain component, and Chia Network was to serve as the blockchain technology partner in the project.

Fast forward to 2025 and CAD Trust still exists, but with the durable CDR market dealing in carbon credit offtake agreements the size of a Series C financing round (see Frontier’s $58M deal with BiCRS developer Vaulted Deep), the CAD Trust approach of replicated harmonized meta-registry has done little to provide support in an area where much of the current market activity is in future-oriented offtake agreements. On top of that, CAD Trust’s focus on technical API integrations has made adoption slow among legacy registries like Verra and Gold Standard, and nonexistent among the younger durable CDR-focused registries like Isometric or Puro.

One thing CAD Trust did get right from the beginning was bringing data standards into the spotlight. In the years since, several other initiatives have followed their lead. The Carbon Data Open Protocol (CDOP) is likely the carbon market data standards initiative with the most industry traction today. Launched as a multi-stakeholder initiative, led by RMI, S&P Global, and Sylvera (a prominent carbon credits rating agency) among others, CDOP focuses on harmonizing data schemas and vocabularies between the many different actors in today’s carbon market. Arguably more important than what CDOP is, is what it is not. By electing not to build any infrastructure or host project and transaction data themselves, CDOP sidesteps the risk of becoming another value-extracting intermediary in an already crowded market, leaving open the possibility that the project could get enough traction to serve as a genuine interoperability layer.

In carbon markets, where data isn’t at risk of disappearing and transactions are often built upon trust between known parties, interoperability initiatives don’t necessitate the fully-replicated data infrastructure that blockchains provide. With this in mind, any initiative claiming to enable interoperability in carbon markets should focus its energy on developing open standards, exchange formats, and governance frameworks rather than building its own infrastructure and risk becoming yet another silo to navigate. For CDOP this approach is more than an implementation detail, it’s deeply embedded in the initiative’s branding and overall strategy (see their July 2025 blog post Carbon Markets Need a Berners-Lee Moment for Data Standardization).

Will this latest attempt to protocolize the carbon markets improve our ability to meaningfully scale carbon removal? Early signs suggest momentum is building. Even ISO is paying attention now, having initiated, in October 2025, a working group to develop a standard Data Model for Carbon Credit Markets, in which both CDOP and CAD Trust will have membership as nominated experts. If the scientific, financial, and data infrastructure continues to mature, the foundation will be there to support the gigaton scale CDR needed by 2050. Leaving one question remaining… Will the incentives and resources align fast enough to make it a reality?

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Cory Levinson is a software consultant working on data standards in the carbon removal industry, serving as technical lead for Carbon To Sea’s Ocean Alkalinity Enhancement Data Management Protocol, working with the ocean science consultancy Submarine Scientific. He is an active member of the Carbon Data Open Protocol’s Technical Working Group, and is currently pursuing an MSc in the Soils & Biogeochemistry Graduate Group at UC Davis. Views expressed here are the author’s own and don’t necessarily reflect those of any affiliated organizations.

This text is based on a talk which Cory gave for Summer of Protocols in July.

Update on our current writing contest

We’ve extended the deadline for Building and Burning Bridges, our Bridge Atlas-inspired short story contest. The judging panel has now assembled and includes Nils Gilman, who appeared in episode 2 of the Bridge Atlas salon series, and previous Protocolized contest winner Spencer Nitkey, as guest judges.

Your story of diplomacy, bridges, and espionage is now due December 8, 2025.

This means that you’ll have one more opportunity to get some community feedback on your work. Join our Special Interest Group on Protocol Fiction for its next call, December 4 at 10am Pacific Time. More information in our public Discord server’s #protocol-fiction channel. We’ve provided thorough contest instructions here, and there are some bonus ideas hidden below to draw on.

After seeing the first three protocol fiction anthologies in print last week, we’re particularly eager to see this next thematic collection come together. This contest is our most complicated yet. We can’t wait to read some tales about what the future of diplomacy might be like – and to assemble another collection.

It’s time to lead the next renaissance in science fiction. We are on the cusp of so many new, interesting technologies that have not yet received thorough treatment in fiction. Cryptography, GLP-1’s, rapid vaccine development, glowing plants, cheaper-than-gas solar energy, universal translators, supersonic transport…

Protocol fiction, which focuses strictly on special rules over special people, is a good way to take a shot at telling stories about these strange new developments in science.

Plus there are tremendous prizes for the Building and Burning Bridges contest, which closes December 8, where you can tie in some of these technologies.

Enter a Story

Meeting with protocol thinkers

The cafes of Paris, Vienna, and the rest of Europe were lighthouses of intellectual activity. People would discuss economics, philosophy, and industry, and drink coffee in quantities inadvisable by modern health standards.

In a protocol-ish twist, we organized a meetup in a small, industrial coffee roastery. During our Bridge Atlas event at last week’s Devconnect, we invited a mix of participants from Buenos Aires and some visiting from abroad to share and discuss ideas. Of course, the first topic was coffee production – participants watched the roasters in action as they walked everyone through their protocols during a small batch. Like listening to someone explain protocols for poetry or music, it was surprisingly technical. The freshly roasted beans were then bagged and taken home by attendees.

The meetup included two panels that discussed how protocols could be used to bridge divides between technology and culture, then debated how the protocolization of migration and mobility will continue. Some key highlights:

• It’s hard to explain how the world works (or fails) and simultaneously tell a good story.

• The work of translating technical features of a protocol into marketable features is an artistic practice.

• Just as there are safety protocols in place for internet users, there must be safety protocols for AI users.

• Citizenship has scaled from cities to states – rules for planetary citizenship are creating a future which we’re already seeing glimpses of.

• Traditional diplomats are on the back foot. New side channels continue to open up and more non-state actors are getting involved in a permissionless fashion.

We’re still working on making a distributed Vienna for protocol studies happen. If you do research as a hobby (and you should!) the Summer of Protocols Discord is a unique place to hang out and share caffeine-powered ideas.

Running a diplomacy workshop

Devconnect is a biannual conference for developers who work on the Ethereum protocol. We attended as well, to learn about frontier developments in programmable cryptography, and to facilitate a workshop on ecosystem diplomacy.

Governments, standards bodies, and corporations create critical infrastructure – often together – and must manage tensions. Technology ecosystems (especially those that maintain protocols like Ethereum, TCP/IP, Linux, or even how we decide when to wear medical masks) must also build bridges with the rest of the world. Unfortunately, technologies can’t hire or appoint diplomats. People instead choose to take on that job.

We put on a full-day workshop on ecosystem diplomacy. It was based on three years of research from Summer of Protocols and especially our recent Bridge Atlas series. Why diplomacy? Isn’t that just for governments?

The workshop talks introduced some useful tools and are available on the Ethereum Foundation’s YouTube channel. Watch the talks here. Get the slides here.

No – even a technology like steel production needs diplomacy. For example: there are strong cultural and political factors at play when a company chooses a location for its next plant. People’s relationship with work and with employers varies a lot between cultures. That relationship can directly affect production schedules and planning processes. And that’s just to make metal, where causality flows mostly one way: from culture to the technology (and its implementation).

Protocol technologies like encrypted messaging have a deeper political element. The causality flows both ways, which means stakeholders have, well, more at stake. A tool like censor-resistant social media will affect culture in hard-to-predict ways. Making new markets and customers isn’t just about interfaces or features, but increasingly about diplomatic groundwork.

Summarizing the Bridge Atlas salons

If you haven’t already, be sure to listen to the series of salons we recorded leading up to the ecosystem diplomacy workshop at Devconnect. There are five episodes, each featuring a bridge-building conversation between one member of the Ethereum ecosystem and an external counterparty.

• Episode 1 - Intro: Tim Beiko and I discuss what this series is about, how Summer of Protocols approaches bridge building, and why protocol thinking is a good thing to get good at.

• Episode 2 - Paradigms: Josh Stark and Nils Gilman talk about hardness and planetarity, two extremely useful terms for anyone interested in protocols. Plus, they explore applications for programmable cryptography at unprecedented scales.

• Episode 3 - Commons: Trent Van Epps and Yancey Strickler go deep on how to protect and grow a commons so that everyone can win, by playing the long game. Between Van Epps’ experience with Protocol Guild and Strickler’s experience having founded Kickstarter and Metalabel, there’s a lot to take away.

• Episode 4 - Alignment: Former CEO of Twitch and OpenAI – and current CEO of Softmax – Emmett Shear joins Alex Stokes of the Ethereum Foundation to talk about alignment and why most people get it wrong. Protocols are a more appropriately scaled approach to the problem.

• Episode 5 - Verifiability: Shreya Shankar, creator of DocETL, and cryptographer Justin Drake explore the importance of verifiability in both AI and blockchains. They use the underlying similarities they discover to build a bridge between these two important domains.

Christine: Justin, can you start us off by introducing the lean Ethereum
project and what that entails?

Justin: In lean Ethereum, we’re tackling verifiability in two ways. First, for deterministic blockchain computation, instead of re‐executing transactions on thousands of nodes, we use cryptographic SNARKs – short proofs a single person produces and everyone else can verify in milliseconds. Second, for the non‐deterministic consensus part, we aim to speed up voting to achieve finality in seconds instead of minutes.

Christine: Shreya, I saw you smiling when Justin mentioned SNARKs. Is that something you are interested in as it relates to verifiability?

Shreya: I smiled because I learned about zk-SNARKs early in my PhD – they’re very cool! My own work is on DocETL, a declarative system for unstructured data processing. For example, a public defender might mine court transcripts to find racially biased statements. This requires reasoning beyond traditional databases, so DocETL lets users write MapReduce-style pipelines with operators defined in natural language and executed by LLMs.

Verifiability for us means: how do we ensure AI outputs align with human expectations? We optimize pipelines for accuracy and cost, sometimes replacing LLM functions with deterministic code. We also expose statistical primitives so users can verify samples and estimate worst‐case accuracy. Provenance – establishing where in the source particular data statements came from – and understanding AI reasoning are important too.

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Justin: That reminds me of my ‘spectrum of trust’ model. On one end you have math, physics, cryptography – things you trust completely. On the other, legal systems and reputation – mushy, subjective. Blockchains aim for trust minimization, pushing everything toward the ‘trustless’ end. Dispute resolution in marketplaces remains hard; maybe your work could help build trustless marketplaces without escrow agents.

Shreya: How are disputes handled now?

Justin: In OpenBazaar, buyers and sellers agree on a trusted escrow agent. It’s reputation‐based and can be inefficient. Agents can also be tempted to collude for large transactions.

Christine: Shreya, talk about AI evals in verifiability.

Shreya: We annotate AI outputs to identify features correlated with human disagreement – missing definitions, odd prioritization, etc. – until the distribution of these ‘qualitative codes’ stabilizes. Then we can train LLM judges to evaluate outputs at scale and measure misalignment.

Justin: Decentralization in Ethereum is similarly hard to measure. The Nakamoto coefficient – how many operators control 33% or 50% of stake – has limits. SNARKs lower barriers to entry so even small devices can verify. For non‐deterministic aspects, economic penalties ensure that bad behavior would destroy large amounts of staked value. Shreya, how are adversarial actors handled in AI?

Shreya: We see two threat models: malicious users crafting pipelines to prove biased points, and non‐malicious users blindly trusting AI outputs without knowing counterfactuals. The latter worries me more.

Justin: In SNARKs, we counter maliciousness with interactive proofs – multiple rounds where the verifier challenges the prover – and ‘hinting,’ where the prover supplies extra information to make verification easier.

Shreya: I can imagine combining those for DocETL: for example, AI showing users samples it didn’t find interesting and asking them to label them.

Justin: Hinting parallels a teacher giving students clues.

Shreya: In data science, negligent or biased analysis isn’t new – but AI’s agency over vast amounts of unstructured data makes the problem more urgent.

Justin: We have recursive proof composition – combining fast but large proofs with slow but small proofs to get both speed and compactness. You could apply a recursive AI approach: one AI isolates snippets, another evaluates them.

Shreya: We do something similar, decomposing problems into units different AIs handle well, and measuring accuracy after rewrites.

Christine: Is there a tension between verifiability and efficiency?

Justin: In Ethereum rollups, each flavor trades off differently: optimistic rollups have long challenge periods; zk-rollups require heavy upfront proof building; TEE rollups trust hardware; committee rollups trust members. Energy limits mean we can’t snarkify all AI – so we must be selective.

Shreya: In AI data systems, computation vs. resource cost trade‐offs are constant. DocETL partitions tasks to minimize cost and maximize accuracy – using deterministic functions for simple tasks, reserving LLMs for reasoning.

Justin: That’s like the co‐processor and glue model: specialized processors for repetitive tasks, flexible ‘glue’ for unstructured problems.

Shreya: Yes, for example filtering transcripts deterministically to discard irrelevant text before using LLMs.

Justin: In SNARKs, a team built auto pre‐compiles – machine‐generated specialized processors – which sometimes outperform human‐written ones, even for hash functions. … Blockchains would seem to provide an ideal money for AI because AIs don’t have access to bank accounts, but they can have access to cryptocurrency. When you combine that with the presumption that AIs are going to be so much smarter than we are, it’s only natural to presume that AIs are going to be the richest entities in the world and they’re going to be storing almost all of their wealth in cryptocurrencies. What kind of emotions that does that invoke in you?

Shreya: Giving AI currency could create alignment incentives we currently lack. But what happens if AIs hoard wealth? What can they spend it on? In data analysis, I’d pay more for better AI, but that could be costly.

Christine: Could you hardcode AI to value a cryptocurrency as its ultimate goal, even without things to spend it on?

Justin: Network effects could make such money valuable if other AIs accept it for tasks. I foresee a future where AIs pay humans to do things they can’t.

Shreya: True – AIs will need compute, memory, and human labor for certain tasks.

Justin: Some jurisdictions might grant AIs legal entity status, enabling bank accounts. We’ll see experiments on whether that leads to better or worse outcomes.

Christine: What are some sibling concepts to verifiability?

Justin: In blockchains, reproducibility is key: nodes re‐execute to get the same result. Bugs are mitigated either through client diversity – independent teams producing matching results – or formal verification, proving software has zero bugs. Formal verification is hard for AI, but AIs can help formalize SNARKs by tackling sub‐problems in parallel.

Shreya: In AI, reproducibility is often misunderstood. Non‐determinism isn’t bad – it’s a feature – but we may need statistical claim reproducibility rather than exact output reproducibility.

Christine: Closing thoughts?

Shreya: I’m excited about hints – using them to make verification easier has clear analogs in data processing.

Justin: I’m encouraged by efforts to turn mushy realities into black‐and‐white outcomes. Maybe Shreya’s research could replace referees in sports or escrow agents in marketplaces, solving the last 20% where humans are still needed.