Posts Tagged ‘future’
“Every happiness is the child of a separation it did not think it could survive”*…
A third and final (for awhile:) post on AI and what it might mean… Sunday’s post was a consideration of what we might lose if AI becomes central to our lives, a modern echo of Socrates’ lament on the spread of writing. Indeed, we are in a moment of high dudgeon over AI: its potential impact on the environment, on jobs. on the economy more broadly, and in the wake of recent statements from foundational model companies, on their governability(“rogue AI,” though do also see here), and thus on the prospect of our very existence (though do also see here).
Yesterday’s post was an example of thinking that starts from the implicit assumption that AI is coming, then asks how we might govern it. It’s worth noting that even if there is an AI Bubble about to burst, AI could still loom large in our longer-term future. Investors hobbled in many earlier bubbles (e.g., railroads, telecom, the dot.com crash, et al.) were directionally absolutely right about the longer-term promise of the technologies that excited them, just wrong to believe that the early entrants (and their specific offers) in which they were investing were the vehicles to realize that promise.
Today, we stick with the assumption that AI will continue to develop and to change the world around it. Some suggest that AI and its impact are moving us from literacy back to orality (the loss of which so concerned Socrates). But others suggest that AI (and the advances that it enables) could usher in a new Axial Age, a time of huge and ultimately positive transformation, on the other side of which we– humanity– will be… different, as different from the people we were in, say 1999, as Shakespeare was from Socrates.
But what might that be like? It’s relatively easy to imagine disasters that amount to the things we know collapsing; it’s much harder to envisage positive futures filled with altogether new things, futures the contents of which are, essentially by definition, things we’ve not imagined, much less experienced.
Following, from Vinkatesh Rao, one of the most thoughtful approaches I’ve yet come across. He starts with mathematics– with the recent successes of AIs in that field– locates that work in the history of math (in particular axiomatics), then suggests a way of understanding AI’s “success”:
… A theorem prover does not search the space of all possible sequences of logical symbols. It plays inside a world humans have painstakingly designed.
This makes recent achievements in AI mathematics look slightly different.
Perhaps we have been too quick to interpret them as evidence that AI has acquired access to the wild source of mathematical intuition. A significant part of the achievement may instead be that humans spent a century converting increasingly large regions of mathematical wilderness into exceptionally good game boards: semi-closed environments in which the relevant state can be represented, legitimate moves can be made, accumulated technique can be reused, and success can be verified…
Rao then explains how similar trajectories have accrued in the application of AI to programming and to physics, concluding…
Across mathematics, programming and physics, the same pattern keeps appearing. First there is clever performance. Then somebody finds a representation that captures what the clever performers are doing. Then general machinery accumulates around the representation. Then the machinery becomes architecture. Eventually much of the play becomes mechanical.
The creativity has not disappeared. It has moved.
There is an obvious objection to this story.
Perhaps what I am describing as progress is merely legibility.
James C. Scott famously described [and here] the tendency of modern states to simplify complicated social and natural realities into representations that administrators can see and manipulate: cadastral maps, standardized names, scientific forests, censuses, planned cities.
The representation is useful precisely because it throws things away. A forest seen by a forester interested in timber yield is not the forest experienced by the people, animals, fungi and plants living in it. The administrative forest has become extraordinarily legible along a narrow set of dimensions by becoming blind along others.
Every game performs a similar operation. Before you can play, you must decide what the pieces are. You must decide what constitutes the state of the game, what counts as a move, what consequences follow from moves, and what counts as winning. Everything else becomes environment.
A rigid body in a mechanical simulation is not actually rigid. A point mass is not a point. A frictionless surface does not exist. A program described by its types still runs on processors with caches and memory hierarchies. A theorem encoded in Lean does not contain the diagram that gave somebody the idea for it.
Formalization is compression.
In physics this is explicit. Reality becomes a model, the model becomes mathematics, and the mathematics becomes a numerical representation. Information is discarded at every step.
The miracle is not that nothing is lost. The miracle is how often we can throw almost everything away and still retain what matters…
…
… This suggests an important asymmetry. A productive mathematical ecology wants porous epistemic boundaries and impermeable verification boundaries. On the way in, metaphor, hallucination and category mistakes can be useful. On the way out, they are forbidden.
A poem can inspire the theorem. The poem cannot prove it…
… A domain becomes playable when enough of its phenomenological complexity can be compressed into something like a state; when there is some reasonably stable repertoire of actions; when interactions can be repeated; when outcomes provide feedback about better and worse play; and when the environment is stationary enough that lessons learned yesterday remain useful tomorrow.
None of these conditions needs to be perfect. Poker contains hidden information. Markets change their own rules. Hunting happens in an uncontrolled environment. Conversation does not have an explicit score.
Playability is a matter of degree.
Nor is playability the same as formalizability.
Children learn social games without writing axioms. Hunters learn landscapes. Merchants learn markets. Political operators learn institutions. Formalization is merely one unusually powerful technology for increasing playability.
So are money, measurement, domestication, standardization, simulation, bureaucracy and digitization. They do related things. They compress states. They stabilize interactions. They make outcomes comparable. They create memory. They allow experience to accumulate.
This suggests a hypothesis:
Far more of reality than we might reasonably have expected can be carved into semi-closed environments in which experience accumulates, performance improves through repeated play, and success eventually becomes sufficiently legible to automate.
Call this the Curiously Playable Universe hypothesis…
…
… If playability were merely a property of AI-friendly digital environments, none of this would be particularly interesting.
But the pattern is ancient. Consider agriculture.
Hunting and gathering takes place in a relatively wild learning environment. Animals move. Weather changes. Useful plants appear where they appear. The training distribution is supplied by nature.
Agriculture does something profound to the learning problem: It changes the environment.
Fields stabilize locations. Planting stabilizes cycles. Domestication alters organisms. Irrigation alters water availability. Storage alters time. Property regimes and markets stabilize incentives.
Humans did not merely become better at learning nature. They made nature easier to learn.
The wild game becomes domesticated.
Eventually industrial agriculture pushes large portions of the process toward automaticity: standardized breeds, standardized feed, controlled environments, mechanized planting and harvesting, precisely measured yields.
Then the game moves upward.
Instead of merely optimizing how an organism is cultivated, we begin optimizing the organism itself. Selective breeding becomes genetics becomes genomic selection and genetic engineering.
The old player becomes a game piece.
It is tempting to map this progression onto contemporary machine-learning terminology. Hunting looks vaguely like reinforcement learning in a difficult environment. Agriculture introduces something like shaped rewards and a controlled training distribution. Factory farming begins to resemble a regime of relentless verification against measurable outputs.
The analogy should not be pushed too literally. Human cultures have always involved teaching, imitation, norms and complicated reward systems.
But structurally the direction is unmistakable: increasing control over the state representation, action space, feedback signal and training distribution.
Commerce underwent another version of the transformation.
Exchange begins embedded in relationships, obligations, reputation, kinship and local knowledge. Credit can be intensely personal. Value is contextual.
Money performs an astonishing act of compression.
Heterogeneous goods and obligations become comparable through a common medium. Markets then stabilize arenas in which repeated exchange produces prices. Accounting makes states more legible. Contracts formalize future obligations.
Eventually finance builds games on top of the game.
A derivative can be a claim on the future value of another asset. Options put prices on possible future prices. Markets become arenas for expectations about expectations.
Play moves upward.
None of this required artificial intelligence. Humans have been turning wildernesses into games for thousands of years.
What AI changes is the cost of achieving playability in a domain.
Historically, making a domain playable was expensive. Somebody had to invent the categories. Somebody had to measure the variables. Somebody had to standardize the procedures. Somebody had to construct the institutions that made interactions repeatable.
And the resulting representation had to be relatively explicit because ordinary software was brittle.
Machine learning relaxes that requirement…
Rao muses on where this effect might accrue…
Coding is perhaps the clearest domain currently passing through the transition.
Programming was unusually playable before generative AI arrived.
Compilers already supplied verifiers. Type systems constrained legal moves. Unit tests supplied rewards. Version control recorded trajectories. Continuous integration repeatedly evaluated outcomes. Package ecosystems created enormous libraries of reusable moves.
Generative coding systems arrived in a landscape generations of programmers had inadvertently prepared for them.
As implementation becomes cheaper, the game moves upward toward specifications, architectures, product decisions and the increasingly important question of what software should exist in the first place.
Robotics is a more difficult frontier.
The physical world is phenomenologically unruly. Objects deform. Friction varies. Things break. Lighting changes. People walk into rooms. Drawers stick.
Simulation, cheap sensors, multimodal models and increasingly capable world models are gradually making physical environments more playable, but reality continually leaks through the representation.
This is why robotics may be one of the most important tests of the Curiously Playable Universe hypothesis.
Can enough of ordinary physical reality be compressed into stable learned representations to permit the same cycle of domestication and automation?
Science will probably fragment according to playability rather than according to our traditional rankings of intellectual difficulty.
Protein structure turned out to be unusually playable.
Drug discovery is less so because chemical promise must survive biology, organisms, clinical trials and human heterogeneity. Materials discovery may become increasingly playable through the combination of simulation and automated laboratories. Ecology may remain stubbornly wild because every useful abstraction excludes interactions operating at another scale.
Medicine contains both extremes. Image interpretation and molecular design can be made relatively game-like. Caring for an elderly person with five interacting conditions, family constraints and changing preferences is another matter.
Governance may become a particularly strange frontier.
States have spent centuries making populations legible through names, addresses, laws, property records, taxes, censuses, bureaucratic categories and standardized procedures. In that sense the modern administrative state is already a vast game-making machine.
AI will make more of its internal operations automatic.
But political systems contain an unusual source of wilderness: the pieces know they are pieces.
People respond strategically to measurements, categories and incentives. A rule changes the behavior it was intended to regulate. A metric becomes a target and stops being a good metric (Goodhart’s Law). Political actors learn to play the machinery designed to make them legible.
This may push governance upward toward new games involving states themselves: transnational protocols, financial systems, supply chains, standards regimes, platform governance and other structures that do not fit comfortably inside the old nation-state game board.
Some domains may resist for much longer.
Child-rearing is difficult to make playable because the objective changes as the child changes. Friendship has no stable score. Diplomacy involves adversaries whose models include models of your model of them. Entrepreneurship often consists precisely of discovering a game nobody realized existed.
Frontier science operates where we do not yet know which measurements matter. Cultural creation is evaluated by audiences who change partly because of the works being evaluated. Political legitimacy is altered by attempts to measure and optimize it.
These are not necessarily domains AI cannot enter. They are domains in which the wilderness fights back…
He continues…
… We keep asking what it means that machines can now play chess, write programs, fold proteins, prove theorems, steer robots and discover molecules. The question presumes that the remarkable new object is the machine—that some mysterious substance called intelligence has finally become “general” enough to flow from one human province into another.
But perhaps the stranger discovery concerns the provinces: Wildernesses can be reliably turned into game boards. We domesticate the territory, become skilled at its game, build machinery that plays it better than we can, and move outward to construct another game around the first, domesticating a larger scope.
This happened to fields and livestock long before it happened to chess. It happened to trade before it happened to programming. It happened to mechanics before it happened to mathematical proof.
Artificial intelligence did not invent this strange property of reality. It is merely making it difficult not to notice.
The deepest surprise of the AI era may turn out not to be that intelligence was easier to manufacture than we thought. It may be that the universe is far more playable than we imagined…
And he concludes with a consideration of writing…
… Writing sits somewhere awkwardly in the middle of the playability spectrum. It is obviously more playable than friendship or political legitimacy. There are stable artifacts, accumulated techniques, recognizable genres, repeatable operations and abundant feedback. Sentences can be revised. Arguments can be tested for consistency. Stories can be checked for continuity. Editors can compare two versions and usually say something useful about why one works better.
But writing lacks the feature that makes Lean such an extraordinarily good game board: a verifier. There is no kernel that accepts Middlemarch and rejects a bad novel. The relevant state is incompletely represented, the available moves are effectively unlimited, and the reward function wanders around outside the text in readers, institutions, historical circumstances and cultures that change partly in response to what gets written. Recent attempts to extend verifiable-reward techniques to writing therefore have to manufacture approximate evaluators out of principles and pairwise judgments rather than simply checking an answer. Writing is playable, but imperfectly and unevenly so.
The unevenness is becoming easier to see because AI is revealing which kinds of writing were already more game-like than we realized. A corporate memo, SEO article, product description, technical explanation or conventional news report has relatively strong constraints: purpose, audience, format, facts, length, house style, perhaps measurable outcomes. Formulaic genre fiction has a looser but still recognizable game board of beats, tropes, pacing, character functions and reader expectations. Even fiction once assumed to depend heavily on irreducible human voice is proving surprisingly susceptible to systematic generation and variation. Writing is not one game but a family of wilderness activities at very different stages of domestication.
At the other end lies writing whose purpose is partly to alter the terms by which it will be judged. As Walter Benjamin observed, “all great works of literature establish a genre or dissolve one.”
A genuinely new literary form, a strange essay, a foundational work of philosophy, or a piece of criticism that gives its readers a concept they did not previously possess cannot simply optimize against an existing reward function. Its success may consist in creating a new one. This is writing at its most wilderness-like, and perhaps why arguments about AI writing become confused when “writing” is treated as a single capability. Producing competent prose, satisfying a genre contract, developing an argument and inventing a form are different games with different degrees of playability…
… The meta-game emerging above writing may therefore involve constructing—and strategically revealing—the game boards on which execution takes place. If competent prose becomes cheap, more resources can be directed toward finding the question, assembling unlikely source domains, inventing the useful distinction, designing sequences of inquiry, recognizing when an analogy is productive, deciding what belongs together, and establishing the criteria by which a finished object ought to succeed. The writer becomes somewhat less like a person manufacturing sentences and somewhat more like an architect and player of generative constraints.
That does not mean prose becomes irrelevant. A game board badly realized is still a bad essay, just as a brilliant architectural plan does not eliminate the need for a building. Nor does it mean the higher-level activity is permanently reserved for humans. AI systems may themselves become increasingly capable of inventing representations, designing probes, discovering genres and constructing new games. The point is only that automaticity at one level does not end the activity. It displaces its frontier.
Writing may therefore be undergoing the same transition described throughout this essay, only messier and in public. Some of its old games are becoming startlingly playable. Some are approaching automaticity. New games are forming above them. And beyond those remains the poorly mapped territory from which the next game board might emerge…
Eminently worth reading in full: “The Curiously Playable Universe,” from @vgr.bsky.social.
And further: “Our Eukaryotic Moment,” in which Rao urges us to keep our minds open– to resist “premature ontological closure in conceptualizing AI.” A taste:
… About two billion years ago, life underwent a change in architecture. Until then, the planet was ruled by relatively simple prokaryotic cells: bacteria and archaea, tiny packets of chemistry bounded by membranes, carrying their genetic material directly in the same cellular space where much of the business of life took place.
Then something happened, or rather, several things happened in an order that remains uncertain. At some point, an ancestral archaeon cell entered into a permanent symbiosis with a bacterium capable of unusually powerful energy metabolism.
That bacterium eventually became the mitochondrion.
Somewhere in the same long evolutionary transition, genetic material became enclosed within a nucleus, separating the storage and regulation of hereditary information from much of the cell’s everyday chemistry. Internal membranes proliferated, cytoskeletons became more elaborate, and a new kind of cell emerged: the eukaryote….
… Eukaryotic cells became larger, more internally differentiated and capable of forms of organization unavailable to their ancestors. Much later, and independently in several lineages, some evolved multicellularity. Cells themselves became components of larger entities, differentiating and cooperating in increasingly elaborate ways. From the eukaryotic architecture eventually came forests, mushrooms, octopuses, hummingbirds, whales and us.
Eukaryotes were more than just “better bacteria.” They represented a different organization of life, capable of sustaining far more complex structures..
We may be living through an analogous transition now. The emergence of AI, in the particular form that it has appeared (deep learning), is arguably the eukaryotic moment in human cultural evolution, understood in memetic terms, with humans playing the role of mitochondria, and AI the role of the nucleus…
Meantime: “After We Stop Grieving AI: Cyborgs, Centaurs and Cyberpunks” (via Patrick Tanguay) See also: “Diving Deeper Into Digital Transformation” from John Hagel (source of the image above).
As the estimable Tim O’Reilly suggests, we’d probably be wise to take a breath, to treat AI as a “normal technology”: “Technology doesn’t force us… it merely opens the door.”
* Rainer Maria Rilke, Sonnets to Orpheus, Part Two, XII
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As we crane to conceive what’s beyond our cocoon, we might recall that it was on this date in 1947 that the Association for Computing Machinery (or ACM) was founded. In one sense, computer science dates back to the 17th century and the mechanical calculators of Wilhelm Schickard and Gottfried Leibniz; in another, to the Difference and Analytical Engines of Charles Babbage (and the “programming” of Ada Lovelace). But the ACM can claim to be at least a key stone in the foundation of the field we now knw as “Computer Science”– the first modern “learned society” devoted to the emerging field. (Computer Science began to be established as a distinct academic discipline in the 1950s and early 1960s. The world’s first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science department in the United States was formed at Purdue University in 1962.)
“The unpredictable and the predetermined unfold together to make everything the way it is.”*…
Thinking– worrying– about the future occupies more and more of our mindshare. How do we ready ourselves for the impacts of the playing out of the myriad uncertainties we face? Your correspondent’s approach-of-choice has been scenario planning (see, e.g., here and here), a method of thinking through and making sense of those unknowns. But as we do that, we have to think against the backdrop of “pre-determined elements”– forces that are going to accrue no matter how the uncertainties resolve, no matter which scenario unfolds.
Old friend and colleague Art Kleiner has dropped a thoughtful– and provocative– reminder of just how important understanding pre-determined elements is…
Pierre Wack, the scenario pioneer who built Royal Dutch Shell’s celebrated foresight practice, sometimes explained his methods by talking about the Ganges river in northern India. If there are heavy monsoon rains over the Himalayan headwaters, you can tell with certainty that there will be a flood five days later at Allahabad, which is 650 miles downstream. Five days after that, he said, the floods would reach Benares.
“Now the people down here in Benares don’t know that this flood is on its way,” he said, “but I do. Because I’ve seen it! This is not fortune telling. This is not crystal-ball gazing. This is merely describing future implications of something that has already happened.”…
… Most of us, peering ahead, fix on anxieties and uncertainties that may or may not happen: elections, technologies, and potential crises. We imagine what might happen, and get into the habit of thinking that our fate depends on this contingency. For instance, we pin our hopes on a particular candidate getting into office.
An alternative [your correspondent would suggest: “a critical complement”] is to look at the predetermined elements in our world as the playing field. When we recognize the true certainties, we can leap ahead to framing our choices and modulating our expectations. For example: We know it will take a long time to mitigate the effects of the climate crisis, so we invest accordingly in renewable energy. We also know our efforts to manage artificial intelligence need to happen practically overnight, so we work to rapidly build the necessary skills.
There are two kinds of predetermined elements. The first-order trends are basic and happening now. They follow directly from events that already took place — children already born, tons of carbon already in the air, debts already incurred. The second-order ones arise from the combination of first-order forces. Their effects are less predictable, but we can’t avoid the pressures they will place on us.
Taken together, they tell us the world of the 2030s will be markedly different from today and from most predictions being made today. For system leaders, a good list of predetermined elements gives you a start on developing scenarios that help you move to a creative orientation: creating the future you want.
I do a lot of work with scenarios, particularly at New York University’s Interactive Telecommunications Program, where I teach a graduate-level course on the future of media and technology. Here is my list of predetermined elements facing us today…
[Art shares a meaty– and bracing– list of both first- and second-order “pre-determineds.” He concludes…]
… The persistence of the ordinary. Against all the above sits the most underrated predetermined element of all: most people, most days, will live recognizable lives. The school, the clinic, the shop, and the family table endure because institutions change far more slowly than the forces acting on them. This is not complacency; it is the buffer that keeps the surprises survivable — and the reason that system leadership is generally local.
It’s as if we’re all driving down a treacherous highway. We notice the accidents and cars being towed off the shoulder, and the road rage as cars cut each other off. We don’t pay attention to all the drivers who stay in lane, leaving enough space between themselves and the car in front of them. Many of those drivers have experienced past accidents; they don’t want any more. If there were more of them, the road wouldn’t be nearly so scary. Uncertain: the prevailing attitudes and what it takes to bring people to a more system-oriented perspective...
[He then turns to the implications of his insight…]
… The discipline of scenario thinking is a discipline of attention. It tells us where to pay attention. Which predetermined elements affect us most? Which opportunities should we focus on? And which changes do we care about most urgently?
It is humbling and steadying at once: humbling because so much of the future is already decided, steadying because so much of it depends on what we do together.
The predetermined elements provide a working map. The first-order forces — the aging, the warming, the sun, the grid, the genome, the debt — are the ground on which the next decade must be built. The second-order combinations — cities, pressures, robots, possible relief — are where our work takes place…
Art’s conclusion is worth underling: first-order predetermineds are the terrain on which we will have to build our future; second order-pre-determineds are (a large part of) the agenda of issues we’ll have to address as we do; uncertainties are the unpredictable “weather” in which we’ll have to do that– guided throughout by our values and the hope that powers them. As Dennis Gabor said: “The future cannot be predicted, but futures can be invented.”
We already know much of what’s coming in the 2030s: “The Futures We Can’t Avoid.” Eminently worth reading in full.
* Tom Stoppard
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As we buckle up, we might recall that it was on this date in 1927 that “The Cyclone,” a wooden roller coaster in Luna Park at Coney Island, opened to the public. It wasn’t the first roller coaster at Coney Island; but with total track length of 2,640 feet, a maximum height of 75 feet, and cars that reached 60 miles per hour on a ride, The Cyclone became a signature attraction. Operating still, it was declared a New York City designated landmark in 1988 and was placed on the National Register of Historic Places in 1991.
“All the cities of the world are going to expand. We need to have a better understanding of what makes good urban habitat for home sapiens.”*…
“When did our vision of the future become so constrained, tired, and even dystopic?” Julien Crockett talks with Bruno Carvalho, the author of The Invention of the Future: A History of Cities in the Modern World, about the history of city planning and how urban design intertwines with a society’s prognostications and projections…
Cities have long been places of possibility—places where it seemed that we could break from the past and create an entirely new future. As Bruno Carvalho observes in his new book, The Invention of the Future: A History of Cities in the Modern World, this mindset is a key feature of what it means to be “modern”—a sensibility toward the present and the future as it relates to the past.
Yet, as Carvalho’s wide-ranging history details, a break from the past does not assume a positive vision of the future. In fact, Carvalho begins his book with the question “Where did the future go?”—the title of a debate between the venture capitalist Peter Thiel and anthropologist David Graeber. Both imagine that the only cities of tomorrow are on Mars. When did our vision of the future become so constrained, tired, and even dystopic?
Carvalho’s book returns to a recurring paradox we have faced since the Enlightenment: the better our capacity for creation and prediction, the more limited our ability to imagine a new future. He marvels, though, that “we know some of what is coming: urbanization and climate change, life and death. Between all that, there is a lot of space for reinvention.”
In our conversation, we look to the past to help us think through what reinvention might look like and discuss what it means to plan for a radically different future. We also discuss the legacies of Silicon Valley, the construction of New York City, urban futures moving from the West across the Pacific, and whether Carvalho is optimistic about what’s to come…
Eminently worth reading and pondering: “Where Did the Future Go?” from @lareviewofbooks.bsky.social.
* Jan Gehl
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As we undertake to understand the urban, we might recall that it was on this date in 455 that the Vandals entered Rome, which they plundered for the next two weeks. It was, as sackings went (this was Rome’s third, of four altogether), relatively “light”: while the Vandals (who had destroyed all of Rome’s aqueducts on their approach) looted Roman treasure and sold many Romans into slavery, their leader Genseric acceded to the pleas of Pope Leo that the Vandals refrain from the wholesale slaughter of Rome’s population and the destruction of the Eternal City’s historic buildings.
“The future is already here — it’s just not very evenly distributed”*…
… nor, perhaps, as widely read as it should be. “Urubos” is here to help…
The Extrapolated Futures Archive is a reverse-lookup for speculative fiction. Describe a situation you are facing, and find the SF stories that already worked through the implications.
The catalog connects stories (novels, novellas, short stories, films) to the speculative ideas they explore: thought experiments about technology, governance, biology, society, and more. Every idea is tagged with domains, scenario types, and outcome types so you can filter by the kind of future you are thinking about.
How to use it:
- Search by title, author, synopsis keywords, or idea descriptions
- Filter by domain (AI, biotech, climate, space, governance…), scenario type, outcome, decade, or series
- Browse ideas to find transferable thought experiments, then follow links to the stories that explore them
- Browse stories to see what speculative ideas a particular work contains
- Book Club discussions (marked with 📖) offer section-by-section roundtable analyses by AI personas modeled on SF authors
- What-If Query (via the What-If Query page/link) lets you describe a real-world scenario in plain text and get ranked matching ideas
The archive is designed for decision-makers in government, industry, and NGOs who want to widen their thinking by surfacing fictional precedents for novel real-world challenges…
Over 275 ideas, which cluster into 20 different “domains,” explored in over 1,900 stories, via over 3,500 links…
Mapping real-world scenarios to the science fiction stories that explored them first: “Extrapolated Futures Archive“
* William Gibson
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As we ponder prescience, we might spare a thought for Charles Hoy Fort, the prolific chronicler of paranormal phenomena; he died on this date in 1932. Fort collected accounts of frogs and other strange objects raining from the sky, UFOs, ghosts, spontaneous human combustion, stigmata, psychic abilities, and the like, publishing four collections of weird tales and anomalies during his lifetime: Book of the Damned (1919), New Lands (1923), Lo! (1931), and Wild Talents (1932). So influential was Fort among fellow-questers that his name has become an adjective, “Fortean,” often applied to unexplained events… The Truth is Out There…
“The greatest danger in times of turbulence is not the turbulence – it is to act with yesterday’s logic”*…
Jennifer Pahlka— the founder and long-time leader of Code for America, the former US Deputy Chief Technology Officer, the author of Recoding America, and the cofounder and board chair of the Recoding America Fund— has dedicated her life to improving governance and government services. Here, she reflects on a core lesson that she has learned…
I got into government reform sixteen years ago, though I didn’t think of it as reform at the time. I thought of it as just trying to make a few specific things work better. Since then I’ve worked at the local, state, and federal levels, on benefit delivery, on national defense, on a handful of things in between. I’ve worked alongside a lot of people whose own paths in this work have run the gamut. Collectively we’ve seen a lot. I think we’ve learned a lot about what we often call the operating model of government.
But the government we have — the operating model it runs on, the rules and structures and assumptions that shape how it hires, procures, and delivers — was built for a world that no longer exists, and the distance between that world and this one is growing. We are approaching the kind of moment when that gap stops being a management problem and becomes a true legitimacy crisis. (Many will say that moment has already come.) It’s time to start asking whether the theory of change most of us have been operating under — incremental improvements off a pretty poor baseline — was ever going to get us to a government capable of meeting fast-changing needs. It hasn’t yet, and if we don’t do something differently, it won’t.
Kelly Born at the Packard Foundation recently shared with me a framework called the Three Horizons, originally developed by Anthony Hodgson and adapted widely in systems-change work. In it, Horizon 1 is the currently dominant system. It’s functional enough to persist but failing in critical ways, especially for people with less power. Horizon 3 is the future system you’re working toward, already visible in patches of practice that embody different values and different ways of working, but far from the norm. Horizon 2 is the turbulent middle where change agents work.
But the key insight is that not all Horizon 2 work is the same. Some H2 innovations genuinely create the conditions for the new system to emerge. Call those transforming H2, or H2+. Others, however inadvertently, extend the lifespan of the failing system by relieving the pressure that might otherwise force structural change. Call those sustaining H2, or H2-. Both feel like reform, but they have very different long-term implications.
H2- work is attractive because it usually produces real value in the short run. H2+ work can take a long time to pay off, and the path is rarely clear. In a stable environment, you can get away with a lot of H2-. In an environment where the underlying system has become truly untenable, the difference between the two starts to matter a great deal. I think that’s where we are now…
[Jen describes a few projects that illustrate patterns that play out over and over in the category of H2-, the work that sustains the status quo…]
… The H2- work I’m describing has been done in good faith by people. I am one of those people. Code for America, which I founded and where I spent more than a decade, is in important respects capacity substitution. USDR, which I also helped start, is as well. The healthcare.gov rescue (which I didn’t actually work on but tried to provide moral support for) was the rescue-and-rebuild cycle. For much of the past fifteen years, the H2- path was arguably the right call. When there was no political space for structural change, demonstrations were a good way to build the evidence base and develop the field.
I think we are in a different moment now. This moment is defined by disruption. I count three kinds.
Contingent disruption — pandemics, climate events, geopolitical shocks, financial crises — is unpredictable in its specifics but very predictable in its category: large, fast-moving, high-stakes demands that fall disproportionately on government. COVID was not an anomaly. The next version won’t look the same.
The most recent disruption to federal government, however, was political. Whatever the cost of its methods, DOGE made the brittleness of the current operating model impossible to ignore and created political openings for structural arguments that previously had no traction. The reform field did not create this moment. But it can shape what comes out of it.
AI brings structural disruption. This is a transformation already underway in the material conditions of work, economy, and administration. AI creates dramatic change in both the needs and conditions government must respond to and the ways in which it can respond at the same time. Yes, I certainly mean a social safety net not nearly fit to handle the levels of unemployment that are likely coming our way, and yes, I mean possible upsets in the balance of power between agencies and the vendors they rely on, but that’s barely scratching the surface.
AI is not only an exogenous shock that government will have to absorb. It is also moving the bar on what counts as acceptable service in the first place. People are already using AI to understand their medical bills, navigate insurance denials, and draft appeals for benefits they were wrongly denied. Soon they will expect to apply for SNAP or file their taxes by uploading a paystub and answering a few plain-language questions, not by filling out even the best-designed web form. The forty-page PDF used to feel intolerable. The well-designed web form will start to feel that way too, and faster than the last transition did.
And service delivery is only the most visible piece. The same expectation shift is going to hit regulation, permitting, enforcement, how quickly an agency can respond to a new problem, how a legislature decides whether a law is working. If a small team with the right tools can map a regulatory regime in a week, the timelines we have now, in which rulemaking takes several years–or even multiple presidential terms–become indefensible. If an advocate can stress-test a policy against thousands of edge cases before it gets enacted, the standard for what counts as due diligence in lawmaking starts to move. The bar is rising on the whole surface of what government does, not just on the forms people fill out.
Not everyone wants this shift to happen. Public sector unions have secured laws in several states forbidding the use of AI in service delivery, won contracts requiring union consent before autonomous vehicles can operate, and pushed legislation mandating staffing levels that the work no longer requires — as my colleagues Robert Gordon and Nick Bagley have documented. The concern for workers caught in this transition is legitimate. But blocking government’s transformation while the world around it moves on is not a strategy for protecting those workers. It exacerbates public frustration with government, weakens the case for investing in it, and leaves the people who most depend on public services with a system increasingly unfit to serve them.
So the gap we have been measuring, between what government delivers and what the public considers a basic level of competence, is widening from both ends at once. The system is straining to clear the old bar at the same moment the bar is rising.
In this environment, the benefits systems that struggled to scale during COVID will be asked to scale again. The regulatory processes that can’t move quickly will be asked to respond to developments they weren’t designed to anticipate. The civil service system that can’t attract the people it needs now will need to attract people with skills that didn’t exist a decade ago.
If I had to pick, it’s AI that drives this disruptive moment. But I don’t have to pick. You could just as easily imagine climate shocks, or the next pandemic, or an escalation of the current war. Truly, some combination of all the above is not that unlikely. Reasonable people may disagree about the size and shape of the disruption AI will bring, but betting against disruption generally seems deeply unwise at the moment.
If you buy that argument, then we must acknowledge that a reform field largely dedicated to H2- work is not what the moment calls for. In a stable environment, H2- work that buys time for a failing system might be much-needed, and might be a missed opportunity for transformation. In an environment where disruptions of all kinds are accelerating, it becomes a compounding liability. Extending the lifespan of a brittle system just means the system eventually fails more spectacularly. More people get hurt. More people look for alternatives to democracy.
That doesn’t mean we need to throw everything out and start over. For the reform ecosystem, it means existing actors need incentives to align their work toward structural transformation, new actors with adjacent expertise need to be welcomed into the fold (especially advocates and lobbyists, given how little influence muscle the field has today), and connections need to be made both upstream and downstream of where we’ve been focused. It means articulating competing H3 visions from a wide range of ideological and practical perspectives and debating them among, including the project that sparked this line of thinking, which Kelly funded and FAI and New America are currently working on. It means designing funding and partnership structures that reward structural ambition while staying grounded in meaningful near-term progress. Funders and grantees share responsibility for creating the conditions under which a diverse set of actors can aim higher by working together, and connecting the dots upstream.
For this to work, it can’t be a zero sum game. Government capacity is wildly neglected in philanthropy despite its high leverage. (Good luck naming an issue philanthropists care about that doesn’t benefit from increased government capacity.) Could the field stop doing some H2- work? Sure. That would free up some existing resources for more H2+ work, which has been too little of the field’s mindshare and resources to date. But that is not the path forward — it wouldn’t get us where we need to be. We need more resources, full stop. We need to make the case to philanthropy for greater investment in the entire field (that’s part of what Recoding America Fund is trying to do) and make the case to government leaders, including electeds, to invest in better plumbing, so that the investment in H2+ work isn’t coming at the expense of the essential life support…
[Jen outlines some of the key principles that animate H2+ efforts, then ponders “doing different things differently”…]
… I realized early last year that while I’d spent the bulk of my career trying to drag government into the Internet Era, that work has to change now. We are entering a new era, and if those of us who fought the last fight don’t adapt to the conditions and expectations of this one, we’ll make exactly the mistake the people who resisted internet-era ways of working made. We’ll become the blockers — the ones holding on to old ways of working because that is what we are used to and that is what we are good at.
None of which means rescue work should stop, or that demonstrations are worthless, or that capacity substitution isn’t helpful and needed. Some H2- work, done deliberately and named honestly, is best understood as experimentation: we’re running it inside the failing system precisely because that’s where we’ll learn what a new operating model has to do. That’s a different kind of work from rescue that produces learning incidentally, but both can be valuable.
But the field needs a shared frame clear-eyed enough to ask, with each investment: does this move the system toward H3, or does it prolong H1? That question should be driving how resources, talent, and attention get allocated now, not because the prior work was mistaken but because the moment is different and the cost of extending the status quo is too high. There will have to be work that sustains the status quo, but what tradeoffs are we willing to make?
But insisting we ask the question does not mean that answering it is easy: there is no objective set of criteria that distinguishes one from the other. What may look like H2+ to some may seem like H2- to others, and part of that depends on your particular vision of that third horizon (more on that in the coming weeks.) Some may see work as contributing to a transformation, and therefore H2+, but towards an undesired H3 state. Grappling with how to answer this question is work we all need to be doing…
… Some things haven’t changed. The community is still full of good, smart people with enormous insight into a very difficult problem. We’ve just run out of time to do it the way we’ve been doing it. A brittle system that gets propped up through manageable shocks will eventually meet a shock it can’t survive, and we are moving into a period where the shocks are neither manageable nor hypothetical. Every H2- intervention that returns the system to “good enough” is now a bet that good enough will hold. It’s a bet I no longer think we can afford to make.
The window for H2+ work has not been open like this before. It will not stay open indefinitely.
Eminently worth reading in full.
What DOGE coulda, shoulda been: “A Three Horizons Framework for Government Reform,” from @pahlkadot.bsky.social.
* Peter Drucker
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As we face forward, we might recall that it was on this date in 1970 that President Richard Nixon formally authorized the commitment of U.S. combat troops, in cooperation with South Vietnamese units, against North Vietnamese troop sanctuaries in Cambodia.
Secretary of State William Rogers and Secretary of Defense Melvin Laird, who had continually argued for a downsizing of the U.S. effort in Vietnam, were excluded from the decision to use U.S. troops in Cambodia. Gen. Earle Wheeler, Chairman of the Joint Chiefs of Staff, cabled Gen. Creighton Abrams, senior U.S. commander in Saigon, informing him of the decision that a “higher authority has authorized certain military actions to protect U.S. forces operating in South Vietnam.” Nixon believed that the operation was necessary as a pre-emptive strike to forestall North Vietnamese attacks from Cambodia into South Vietnam as the U.S. forces withdrew and the South Vietnamese assumed more responsibility for the fighting. Nevertheless, three National Security Council staff members and key aides to presidential assistant Henry Kissinger resigned in protest over what amounted to an invasion of Cambodia.
When Nixon publicly announced the Cambodian incursion on April 30, it set off a wave of antiwar demonstrations. A May 4, protest at Kent State University resulted in the killing of four students by Army National Guard troops. Another student rally at Jackson State College in Mississippi resulted in the death of two students and 12 wounded when police opened fire on a women’s dormitory. The incursion angered many in Congress, who felt that Nixon was illegally widening the war; this resulted in a series of congressional resolutions and legislative initiatives that would severely limit the executive power of the president.
– source
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