Posts Tagged ‘future’
“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
“The present is pregnant with the future”*…
The estimable Tim O’Reilly uses scenario planning to create an insightful look at AI, our futures, and the choices that will define them…
We all read it in the daily news. The New York Times reports that economists who once dismissed the AI job threat are now taking it seriously. In February, Jack Dorsey cut 40% of Block’s workforce, telling shareholders that “intelligence tools have changed what it means to build and run a company.” Block’s stock rose 20%. Salesforce has shed thousands of customer support workers, saying AI was already doing half the work. And a Stanford study found that software developers aged 22 to 25 saw employment drop nearly 20% from its peak, while developers over 26 were doing fine.
But how are we to square this news with a Vanguard study that found that the 100 occupations most exposed to AI were actually outperforming the rest of the labor market in both job growth and wages, and a rigorous NBER study of 25,000 Danish workers that found zero measurable effect of AI on earnings or hours?
Other studies could contribute to either side of the argument. For example, PwC’s 2025 Global AI Jobs Barometer, analyzing close to a billion job ads across six continents, found that workers with AI skills earn a 56% wage premium, and that productivity growth has nearly quadrupled in the industries most exposed to AI.
This is exactly the kind of contradictory, uncertain landscape that scenario planning was designed for. Scenario planning doesn’t ask you to predict what the future will be. It asks you to imagine divergent possible futures and to develop a strategy that improves your odds of success across all of them. I’ve used it many times at O’Reilly and have written about it before with COVID and climate change as illustrative examples. The argument between those who say AI will cause mass unemployment and those who insist technology always creates more jobs than it destroys is a debate that will only be resolved by time. Both sides have evidence. Both are probably right at some level. And both framings are not terribly helpful for anyone trying to figure out what to do next…
[O’Reilly explains the scenario approach, then applies it to our future with AI (see the image above), astutely assessing the conflicting signals that we’ve experiencing; he explores the “robust strategy” for our uncertian future (strategic choices that make sense regardless of which future unfolds); then he concludes…
… I’ll return to the theme that I sounded in my book WTF? What’s the Future and Why It’s Up To Us.
Every time a company uses AI to do what it was already doing with fewer people, it is making a choice for the lower half of the scenario grid. Every time a company uses AI to do something that wasn’t previously possible, to serve a customer who wasn’t previously served, to solve a problem that wasn’t previously solvable, it is making a choice for the upper half. These choices compound, for good or ill. An economy that uses AI primarily for efficiency will slowly hollow itself out.
Looking at the news from the future, both sets of signals are present. The question is which will dominate. AI will give us both the Augmentation Economy and the Displacement Crisis, in different measures in different places, depending on the choices we make.
Scenario planning teaches us that we don’t have to predict which future we’ll get. We do have to prepare for a very uncertain future. But the robust strategy, the one that works across every quadrant, is to focus on doing more, not just doing the same with less, and to find ways that human taste still matters in what is created. As long as there is unmet demand, as long as there are problems we haven’t solved and people we haven’t served, AI will augment human work rather than replacing it. It’s only when we stop looking for new things to do that the machines come for the jobs…
Eminently worth reading in full. Indeed, speaking as a long-time scenario planner, your correspondent can only wish that everyone who wields “scenarios” applies the approach as appropriately, adriotly, and acutely as Tim has: “Scenario Planning for AI and the ‘Jobless Future‘,” from @timoreilly.bsky.social.
* Voltaire
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As we take the long view, we might send formative birthday greetings to Mark Pinsker; he was born on this date in 1923. A mathematician, he made impoprtant contributions to the fields of information theory, probability theory, coding theory, ergodic theory, mathematical statistics, and communication networks. This work, which helped lay the foundation for AI-as-we-know-it, earned him the IEEE Claude E. Shannon Award in 1978, and the IEEE Richard W. Hamming Medal in 1996, among other honors.









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