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Solarpunk future 🤟🌻

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'Dear Alice' Decommodified Edition | Solarpunk anime ambience with no ads

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Realidade aumentada mesmo!

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Mojo Vision: This is the first AR contact lens

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Formula 1 Pit Stops 1950 & Today [SD, 480p]

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The age of disruption was born from ingenuity and problem solving (the engineer), but as it matures, what it needs are more poets and creatives (the philosopher). We’re all hungry for a more mature discourse with a focus on ethics, tradeoffs, and unintended consequences of how technology is reimagining and remaking the world.

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The age of disruption was born from ingenuity and problem solving (the engineer), but as it matures, what it needs are more poets and creatives (the philosopher). We’re all hungry for a more mature discourse with a focus on ethics, tradeoffs, and unintended consequences of how technology is reimagining and remaking the world.

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Moonshot thinking has three components: Mindset: see possibilities amidst challenges and constraints Methodologies: know how to frame and reframe for resonance Capabilities: what will be needed to actually launch the moonshot

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Moonshot thinking has three components: Mindset: see possibilities amidst challenges and constraints Methodologies: know how to frame and reframe for resonance Capabilities: what will be needed to actually launch the moonshot

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Inside Alphabet’s X: Nurturing Radical Creativity

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Alphabet Inc.’s X (formerly Google X) creates the most daring innovations to tackle the world’s biggest problems. CEO Astro Teller joins Azeem Azhar to explain how he creates a culture of radical creativity and nurtures the audacity to fail.

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Playbook for Strategic Foresight and Innovation

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Free chapters on LinkedIn from a great book about Moonshot Thinking

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Free chapters on LinkedIn from a great book about Moonshot Thinking

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?
— Laura 🌲 ⛰️ (@LauraDeming) March 10, 2022

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?

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What are the most striking and beautiful representations of technology in film, outside of the human computer interaction in the original Iron Man?

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In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies.

In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions - positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.

Excerpted from The Work of the Future: Building Better Jobs in an Age of Intelligent Machines by David Autor, David A. Mindell and Elisabeth B. Reynolds. Reprinted with permission from the MIT PRESS. Copyright 2022.
THE ROBOTS YOU CAN SEE: DRIVERLESS CARS, WAREHOUSING AND DISTRIBUTION, AND MANUFACTURING

Few sectors better illustrate the promises and fears of robotics than autonomous cars and trucks. Autonomous vehicles (AVs) are essentially highspeed wheeled industrial robots powered by cutting-edge technologies of perception, machine learning, decision-making, regulation, and user interfaces. Their cultural and symbolic resonance has brought AVs to the forefront of excited press coverage about new technology and has sparked large investments of capital, making a potentially "driverless" future a focal point for hopes and fears of a new era of automation.

The ability to transport goods and people across the landscape under computer control embodies a dream of twenty-first-century technology, and also the potential for massive social change and displacement. In a driverless future, accidents and fatalities could drop significantly. The time that people waste stuck in traffic could be recovered for work or leisure. Urban landscapes might change, requiring less parking and improving safety and efficiency for all. New models for the distribution of goods and services promise a world where people and objects move effortlessly through the physical world, much as bits move effortlessly through the internet.

As recently as a decade ago, it was common to dismiss the notion of driverless cars coming to roads in any form. Federally supported university research in robotics and autonomy had evolved for two generations and had just begun to yield advances in military robotics. Yet today, virtually every carmaker in the world, plus many startups, have engaged to redefine mobility. The implications for job disruption are massive. The auto industry itself accounts for just over 5 percent of all private sector jobs, according to one estimate. Millions more work as drivers and in the web of companies that service and maintain these vehicles.

Task Force members John J. Leonard and David A. Mindell have both participated in the development of these technologies and, with graduate student Erik L. Stayton, have studied their implications. Their research suggests that the grand visions of automation in mobility will not be fully realized in the space of a few years.15 The variability and complexity of real-world driving conditions require the ability to adapt to unexpected situations that current technologies have not yet mastered. The recent tragedies and scandals surrounding the death of 346 people in two Boeing 737 MAX crashes stemming from flawed software and the accidents involving self-driving car-testing programs on public roads have increased public and regulatory scrutiny, adding caution about how quickly these technologies will be widely dispersed. The software in driverless cars remains more complex and less deterministic than that in airliners; we still lack technology and techniques to certify it as safe. Some even argue that solving for generalized autonomous driving is tantamount to solving for AGI.

Analysis of the best available data suggests that the reshaping of mobility around autonomy will take more than a decade and will proceed in phases, beginning with systems limited to specific geographies such as urban or campus shuttles (such as the recent product announcement from Zoox, an American AV company). Trucking and delivery are also likely use cases for early adoption, and several leading developers are focusing on these applications both in a fully autonomous mode and as augmented, "convoy" systems led by human drivers. In late 2020, in a telling shift for the industry from "robotaxis" to logistics, Uber sold its driverless car unit, having spent billions of dollars with few results. The unit was bought by Amazon-backed Aurora to focus the technology on trucking. More automated systems will eventually spread as technological barriers are overcome, but current fears about a rapid elimination of driving jobs are not supported.

AVs, whether cars, trucks, or buses, combine the industrial heritage of Detroit and the millennial optimism and disruption of Silicon Valley with a DARPA-inspired military vision of unmanned weapons. Truck drivers, bus drivers, taxi drivers, auto mechanics, and insurance adjusters are but a few of the workers expected to be displaced or complemented. This transformation will come in conjunction with a shift toward full electric technology, which would also eliminate some jobs while creating others. Electric cars require fewer parts than conventional cars, for instance, and the shift to electric vehicles will reduce work supplying motors, transmissions, fuel injection systems, pollution control systems, and the like. This change too will create new demands, such as for large scale battery production (that said, the power-hungry sensors and computing of AVs will at least partially offset the efficiency gains of electric cars). AVs may well emerge as part of an evolving mobility ecosystem as a variety of innovations, including connected cars, new mobility business models, and innovations in urban transit, converge to reshape how we move people and goods from place to place.

TRANSPORTATION JOBS IN A DRIVERLESS WORLD

The narrative on AVs suggests the replacement of human drivers by AI-based software systems, themselves created by a few PhD computer scientists in a lab. This is, however, a simplistic reading of the technological transition currently under way, as MIT researchers discovered through their work in Detroit. It is true that AV development organizations tend to have a higher share of workers with advanced degrees compared to the traditional auto industry. Even so, implementation of AV systems requires efforts at all levels, from automation supervision by safety drivers to remote managing and dispatching to customer service and maintenance roles on the ground.

Take, for instance, a current job description for "site supervisor" at a major AV developer. The job responsibilities entail overseeing a team of safety drivers focused in particular on customer satisfaction and reporting feedback on mechanical and vehicle-related issues. The job offers a mid-range salary with benefits, does not require a two- or four-year degree, but does require at least one year of leadership experience and communication skills. Similarly, despite the highly sophisticated machine learning and computer vision algorithms, AV systems rely on technicians routinely calibrating and cleaning various sensors both on the vehicle and in the built environment. The job description for field autonomy technician to maintain AV systems provides a mid-range salary, does not require a four-year degree, and generally requires only background knowledge of vehicle repair and electronics. Some responsibilities are necessary for implementation - including inventorying and budgeting repair parts and hands-on physical work-but not engineering.

The scaling up of AV systems, when it happens, will create many more such jobs, and others devoted to ensuring safety and reliability. Simultaneously, an AV future will require explicit strategies to enable workers displaced from traditional driving roles to transition to secure employment.

A rapid emergence of AVs would be highly disruptive for workers since the US has more than three million commercial vehicle drivers. These drivers are often people with high school or lower education or immigrants with language barriers. Leonard, Mindell, and Stayton conclude that a slower adoption timeline will ease the impact on workers, enabling current drivers to retire and younger workers to get trained to fill newly created roles, such as monitoring mobile fleets. Again, realistic adoption timelines provide opportunities for shaping technology, adoption, and policy. A 2018 report by Task Force Research Advisory Board member Susan Helper and colleagues discusses a range of plausible scenarios and found the employment impact of AVs to be proportional to the time to widespread adoption. Immediate, sudden automation of the fleet would, of course, put millions out of work, whereas a thirty-year adoption timeline could be accommodated by retirements and generational change.

Meanwhile, car-and-truck makers already make vehicles that augment rather than replace drivers. These products include high-powered cruise control and warning systems frequently found on vehicles sold today. At some level, replacement-type driverless cars will be competing with augmentation-type computer-assisted human drivers. In aviation, this competition went on for decades before unmanned aircraft found their niches, while human-piloted aircraft became highly augmented by automation. When they did arrive, unmanned aircraft such as the US Air Force's Predator and Reaper vehicles required many more people to operate than traditional aircraft and offered completely novel capabilities, such as persistent, twenty-four-hour surveillance.

Based on the current state of knowledge, we estimate a slow shift toward systems that require no driver, even in trucking, one of the easier use cases, with limited use by 2030. Overall shifts in other modes, including passenger cars, are likely to be no faster.

Even when it's achieved, a future of AVs will not be jobless. New business models, potentially entirely new industrial sectors, will be spurred by the technology. New roles and specialties will appear in expert, technical fields of engineering of AV systems and vehicle information technologies. Automation supervision or safety driver roles will be critical for levels of automation that will come before fully automated driving. Remote management or dispatcher, roles will bring drivers into control rooms and require new skills of interacting with automation. New customer service, field support technician, and maintenance roles will also appear. Perhaps most important, creative use of the technology will enable new businesses and services that are difficult to imagine today. When passenger cars displaced equestrian travel and the myriad occupations that supported it in the 1920s, the roadside motel and fast-food industries rose up to serve the "motoring public." How will changes in mobility, for example, enable and shape changes in distribution and consumption?

Equally important are the implications of new technologies for how people get to work. As with other new technologies, introducing expensive new autonomous cars into existing mobility ecosystems will just perpetuate existing inequalities of access and opportunity if institutions that support workers don't evolve as well. In a sweeping study of work, inequality, and transit in the Detroit region, Task Force researchers noted that most workers building Model T and Model A Fords on the early assembly lines traveled to work on streetcars, using Detroit's then highly developed system. In the century since, particularly in Detroit, but also in cities all across the country, public transit has been an essential service for many workers, but it has also been an instrument facilitating institutional racism, urban flight to job-rich suburbs, and inequality. Public discourse and political decisions favoring highway construction often denigrated and undermined mass transit, with racial undertones. As a result, Black people and other minorities are much more likely to lack access to personal vehicles.

"Technology alone cannot remedy the mobility constraints" that workers face, the study concludes, "and will perpetuate existing inequities absent institutional change." As with other technologies, deploying new technologies in old systems of transportation will exacerbate their inequalities by "shifting attention toward what is new and away from what is useful, practical, and needed." Innovating in institutions is as important as innovating in machines; recent decades have seen encouraging pilot programs, but more must be done to scale those pilots to broader use and ensure accountability to the communities they intend to serve. "Transportation offers a unique site of political possibility."

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Hitting the Books: What autonomous vehicles mean for tomorrow's workforce | Engadget

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In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies.

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This is a famous picture by the artist Imperial Boy (帝国少年), who works in the anime industry. I sometimes claim that the entire genre of solarpunk is simply a riff on this picture. That's an exaggeration, obviously - a few people have thought very seriously about the design principles of solarpunk - but the influence and appeal of Imperial Boy's design is undeniable. And other solarpunk art, while often lovely, tends not to immediately look like the kind of city you'd want to build; often it's either a picture of a high-tech farm, or some variant of "put a tree on the side of every building".

Planting a bunch of trees on buildings is a popular (and controversial) idea. The premise is simple - we need to live in dense urban areas, but forests are nice, so why not a mashup of the two? But when you actually do it, while the results are generally fine, somehow it doesn't seem to capture the magic of Imperial Boy's illustration.

So if it's not just "trees on buildings", where does the Imperial Boy picture get its magic? Looking at it carefully and trying to analyze what I like about it, I think that much of it is about architecture, and even more of it is about the use of urban space - about how the structures in the picture shape the kinds of things you'd do if you were there. For example, here are five things I like:

1) Open, walkable multi-level retail. The building on the right, which appears to be some sort of retail space, has a second floor that you can walk to from the outside. It appears to have a veranda on the second floor and possibly another on the third floor, where a bunch of trees are planted. These are places you can walk to, where you can presumably sit, admiring the city, surrounded by pleasant greenery - an oasis of calm in a bustling metropolis. It's not a perfect match, but it reminds me of Tokyu Plaza Omotesando Harajuku, which sits at one of the busiest intersections in downtown Tokyo:

It also reminds me a bit of Daikanyama, or of various trendy shopping centers and train station plazas in Japanese cities where they've tried to integrate lots of greenery into the design.

2) River with low bank. Urban rivers are nice, but if the banks are very high it still feels like a concrete chasm, even when lined with the nicest plants. Here's Nakameguro in Tokyo:

The river in the Imperial Boy picture looks more like the River Walk in San Antonio:

If you can be sure the river's not going to overflow its banks and flood your shops, that's pretty nice.

3) Walkable streets. There's a small cobblestone open space in front of the shops, and a guy is able to just sit there reading a newspaper or something. There are a lot of plazas like this around the world; my favorites tend to be in Seattle and Vancouver, but just for fun here's a picture of Triangle Park in Osaka:

More generally, people like walkable urban spaces where they can stroll around to shop and hang out and see other people.

4) Varied architecture. The round-topped buildings in Imperial Boy's picture are fun and exotic. But even more importantly, there's a lot of variation in the design of the structures. Some edges are rectilinear, some are curved. Some facades are smooth, some are broken up. There are various kinds of windows. The skyscraper in the background is actually pretty ugly in many ways - its tiny, deep-set windows make it look a bit like a prison. But since it's just one element of the jumble of styles, you don't really notice it. I don't know much about architecture, but I do believe that a variety of styles practically always beats one uniform style. In fact, I think San Francisco's residential areas generally do a great job in terms of switching up styles from building to building:

5) Shade. It's nice to sit and walk in the shade much of the time. The trees in Imperial Boy's picture are nice because they provide shade to the people at street level. But one problem with the "put a tree on it" approach to solarpunk is that trees don't really tend to grow out at a steep angle from the sides of buildings. The better way to create shade is just to have buildings have lots of overhangs and to put a bunch of big, spreading trees at ground level.

There's one more thing that lots of people probably like about the city Imperial Boy depicts - it isn't very crowded. Of course, if you want to create a dense city, you're going to have to have to pack a lot of people into a small amount of space. But cities can be designed so that there are crowded parts and less crowded parts, even near the city center. Japan does a good of this; America, less so.

So that's really the secret of Imperial Boy's magic. Not some biopunk science fiction that merges foliage and concrete, nor some cyberpunk construction method that creates buildings in improbable shapes. It's just good urban design - something we could have right now if we wanted. Except that "if we wanted" actually means "if a huge set of institutional and cultural barriers to dense, walkable development and high-quality architecture didn't exist", which of course they do.

So how do we, as a society, make ourselves want dense, walkable, well-planned cities and high-quality urban architecture? I think we need to draw more pictures.

When people imagine density, they tend to imagine either something famous (Manhattan) or the closest thing to them (a four-or-five-story wooden apartment complex). That's not great, though, because those are examples of what our current, broken institutions allowed us to build, rather than what we might build if we had better institutions. For example, those boxy apartment buildings are built that way in order to maximize the number of people they can fit onto a plot of land, since it's so rare that they can get a building approved (and because of height restrictions and/or excess construction costs). Here's a visual explanation by Alfred Twu:

None of these are going to look amazing (the only apartment buildings that look truly nice on their own are small walkups), but I think Europe's is the best out of these three. For high-density neighborhoods, of course, use a mix of styles:

When it comes to housing, one powerful tool is the design competition. Christopher Hawthorne, Los Angeles' Chief Design Officer, came up with a brilliant idea to hold a design competition to draw pictures of denser housing. The result was Low-Rise L.A., whose winners you can see here. Here's a Twitter thread and Slate piece:

This works pretty well for housing, because it's easy to capture the feel of a housing idea in a single piece of artwork, or a few pieces. For cities as a whole, it's much more difficult. America needs to build its cities around trains a lot more than it does, and around cars a lot less, but the only train-centric place we really have is NYC. And Manhattan is a fine place, but there are lots of train-centric cities in the world far less hectic and imposing.

You can gaze at pictures of those places all day (and in fact you should), but everyone knows they were built under a very different set of institutional constraints and cultural preferences. A denser San Francisco or Houston or Miami just wouldn't look like Amsterdam or Tokyo or Paris. Instead, we need more artists to draw pictures of what the future of those cities could look like, if they densified and added more transit.

That's a big undertaking. One example I know of, interestingly, comes from a kids' movie - the fictional city of San Fransokyo, created for the Disney movie Big Hero 6. It's basically just a denser San Francisco with a few pseudo-Japanese stylings, but it looks great:

Now, that may not look exactly like the future city you want to create. But only by drawing a bunch of these futures can we convince the people of our cities that density and transit and mixed-use development won't turn their cities into Manhattan clones or dystopian superblocks or whatever else their fevered imaginations run to whenever they hear someone say the word "density".

To create the future we must first dream the future. Private foundations that are interested in pro-density politics should give a bunch of money to people like Christopher Hawthorne, who should then scour the country for a hundred different Imperial Boy type artists to draw pictures of the futures of American cities.

(They won't be quite as good as Imperial Boy himself, of course...but then again, who is?)

Update: Alfred Twu has an amazing blog post in which he envisions a kind of dense development where tall buildings are set back behind shorter ones.

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This is a famous picture by the artist Imperial Boy (帝国少年), who works in the anime industry. I sometimes claim that the entire genre of solarpunk is simply a riff on this picture. That's an exaggeration, obviously - a few people have thought very seriously about the design principles of solarpunk - but the influence and appeal of Imperial Boy's design is undeniable.

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s the car with the blacked-out windows came to a halt in a sidestreet near Tübingen's botanical gardens, keen-eyed passersby may have noticed something unusual about its numberplate. In Germany, the first few letters usually denote the municipality where a vehicle is registered. The letter Y, however, is reserved for members of the armed forces.

Military men are a rare, not to say unwelcome, sight in Tübingen. A picturesque 15th-century university town that brought forth great German minds including the philosopher Hegel and the poet Friedrich Hölderlin, it is also a modern stronghold of the German Green party, thanks to its left-leaning academic population. In 2018, there was growing resistance on campus against plans to establish Europe's leading artificial intelligence research hub in the surrounding area: the involvement of arms manufacturers in Tübingen's "cyber valley", argued students who occupied a lecture hall that year, brought shame to the university's intellectual tradition.

Yet the two high-ranking officials in field-grey Bundeswehr uniforms who stepped out of the Y-plated vehicle on 1 February 2018 had travelled into hostile territory to shake hands on a collaboration with academia, the like of which the world had never seen before.

The name of the initiative was Project Cassandra: for the next two years, university researchers would use their expertise to help the German defence ministry predict the future.

The academics weren't AI specialists, or scientists, or political analysts. Instead, the people the colonels had sought out in a stuffy top-floor room were a small team of literary scholars led by Jürgen Wertheimer, a professor of comparative literature with wild curls and a penchant for black roll-necks.

After the officers had left, the atmosphere among Wertheimer's team remained tense. A greeting gift of camouflage-patterned running tops and military green nail varnish had helped break the ice, but there was outstanding cause for concern. "We'd been unsure about whether to go public over the project," recalls Isabelle Holz, Wertheimer's assistant. The university had declined the opportunity to be formally involved with the defence ministry, which is why the initiative was run through the Global Ethic Institute, a faculty-independent institution set up by the late dissident Catholic, Hans Küng. "We thought our offices might get paint-bombed or something."

They needn't have worried. "Cassandra reaches for her Walther PPK" ran the headline in the local press after the project was announced, a sarcastic reference to James Bond's weapon of choice. The idea that literature could be used by the defence ministry to identify civil wars and humanitarian disasters ahead of time, wrote the Neckar-Chronik newspaper, was as charming as it was hopelessly naive. "You have to ask yourself why the military is financing something that is going to be of no value whatsoever."

In the end, the launch of Project Cassandra saw neither paint bombs nor sit-ins. The public, Holz says, "simply didn't take us seriously. They just thought we were mad."

harges of insanity, Wertheimer says, have forever been the curse of prophets and seers. Cassandra, the Trojan priestess of Greek myth, had a gift of foresight that allowed her to predict the Greek warriors hiding inside the Trojan horse, the death of Mycenaean king Agamemnon at the hands of his wife and her lover, the 10-year wanderings of Odysseus, and her own demise. Yet each of her warnings was ignored: "She's lost her wits," says Clytaemestra in Aeschylus' play Agamemnon, before the chorus dismiss her visions as "goaded by gods, by spirits vainly driven, frantic and out of tune".

The idea that novelists are modern-day Cassandras - "speaking always truths, never grasped as true" - may sound positively esoteric. There are tons of listicles on the internet hailing books that predicted events before their time, but most of these are accidental acts of clairvoyance by sci-fi writers describing futuristic technological equipment.

In 1914's The World Set Free, HG Wells wrote about atomic bombs whose radioactive elements contaminate battlefields - three decades before Hiroshima and Nagasaki. British author John Brunner's Stand On Zanzibarfrom 1968imagined Europe's states forming a collective union, China's rise as a global power, the economic decline of Detroit and the inauguration of a "President Obomi".

And, naturally, there is George Orwell's Nineteen Eighty-Four, in which a one-party state uses "telescreens" to identify people from their expressions and heart rate - written more than half a century before the NSA's Prism surveillance programme and China using facial recognition software to track its citizens.

But Wertheimer says great writers have a "sensory talent". Literature, he reasons, has a tendency to channel social trends, moods and especially conflicts that politicians prefer to remain undiscussed until they break out into the open.

"Writers represent reality in such a way that their readers can instantly visualise a world and recognise themselves inside it. They operate on a plane that is both objective and subjective, creating inventories of the emotional interiors of individual lives throughout history."

We were interested in what hit a nerve. Was a book heaped with awards? Or banned and the author had to leave the country?

His favourite example of literature's ability to identify a social mood and cast it into the future is a retelling of the Cassandra myth by the East German novelist Christa Wolf. Kassandra, published in 1983, casts Troy as a state not unlike the late-stage German Democratic Republic, succumbing to the paranoia of a Stasi-like secret police as it veers towards a not-so-cold war. Kassandra, cursed with the gift of prophecy, is also a cipher for the author's own predicament: she foresees the decline her society is heading for, but her warnings are ignored by the military patriarchy.

If states could learn to read novels as a kind of literary seismograph, Wertheimer argues, they could perhaps identify which conflicts are on the verge of exploding into violence, and intervene to save maybe millions of lives.

t first glance, Jürgen Wertheimer looks like the least ideal candidate to drag literature out of the ivory tower. Born in Munich in 1947, to a Jewish father who had escaped from Dachau concentration camp and spent the rest of the war in hiding, he started university at the height of the 1968 student movement that energised a generation of young postwar Germans, but steered clear of the political activism that came with it.

Wertheimer, who says he is scared of crowds, has never joined a march in his life. Instead, he has spent time in libraries to work on his dissertation on Stefan George, the poet of the German wing of the l'art pour l'art movement: the idea that art must serve no function other than that of being art. Apart from a doomed love affair with Munich's less successful football team, 1860 Munich, literature remains both his vocation and sole hobby, the 74-year-old says with a husky Bavarian drawl so melancholy, it suggests that passions can also become burdens.

Colleagues say Wertheimer's air of apolitical world-weariness belies an agile, pragmatic mind. "I think he simply didn't take part in 1968 because everyone else was," Holz says. "He's always been a bit of an outsider who liked doing things his own way." At the end of the 2000s, when students across Germany rose in protest against plans to introduce university fees and a more efficient, UK-style bachelor's-master's system, Wertheimer found himself on the barricades, speaking up in support of the protesters.

As more and more university funding was diverted to Tübingen's "cyber valley" rather than the arts and humanities departments, and literary study was increasingly under pressure to prove it could be socially useful, Wertheimer discovered his activist zeal.

On 15 December 2014, he posted a letter to Ursula von der Leyen - now president of the European Commission, then Germany's defence minister. He drew her attention to the fight against Boko Haram in northern Nigeria - a terrorist group whose name is often translated as "western education is forbidden" and which has targeted schools and burned down libraries. Clashes of arms, he wrote, were usually preceded by wars of - and sometimes on - words, and therefore words could also be used to prevent them. He would relish the opportunity to put the theories he had developed in this field "into practical use in the framework of the German military's foreign deployments".

'It was a small project that created a surprising amount of useful results': Jürgen Wertheimer (centre) with researchers Isabelle Holz and Florian Rogge. Photograph: Bettina Flitner

Von der Leyen never personally responded to Wertheimer's offer. But in the spring of 2015 he received a letter from the defence ministry's politics directorate, inviting him for a meeting. Two years of talks and meetings later, it commissioned the Tübingen professor to run a pilot project to test his idea of literature as an "early warning system". Cassandra's dry run was to demonstrate how the war in Kosovo and the rise of Boko Haram could have been predicted through the study of literary texts.

In the summer of 2017, Wertheimer hired a small team of researchers, including Florian Rogge, an unassuming master's student working on anti-democratic satire in the Weimar Republic, and Holz, who was writing her PhD on the literary inspirations of the Baader-Meinhof group. A room where the team would convene each week was fitted with giant military maps and shelves of literature from Nigeria, Algeria and Kosovo. "We felt we had been handed a unique opportunity," Holz recalls, "to show literature was capable of more."

redicting unpredictable crises has become the west's great obsession in the 21st century. It stumbled into the millennium on the back of two genocides, in Bosnia and Rwanda, that were later widely thought to have been preventable if the international community had responded with more resolute shows of force. It then rushed into a war in Afghanistan, and launched an ill-conceived intervention in Iraq, often riding roughshod over the advice of experts who had studied political division and sectarian conflicts on the ground.

The terrorist attack on New York's twin towers that triggered the rush to Afghanistan is an example of what Nassim Nicholas Taleb described in his 2007 book The Black Swan. "Black swan events" are rare, have an extreme impact and seem predictable - if only in retrospect. In a world becoming ever more interconnected, Taleb argued, such events were increasing exponentially.

The book would become a bestseller, not least because the unpredictable crises kept coming: the Lehman Brothers collapse in 2008, the European debt crisis in 2009, the refugee crisis of 2015, Brexit and Trump in 2016, a global pandemic in 2020. Even when Covid-19 has been hemmed in, it seems inevitable that another crisis will follow. The only thing governments can do is try to anticipate which direction it will come from, and shovel resources in that direction in advance. "With the wisdom of hindsight" was the go-to excuse of the Bush and Blair era; the next generation of global leaders want to attain the gift of foresight.

To an extent, this is nothing new: giving warning of impending crises is what foreign intelligence agencies are meant to do. What is new is that information that was once accessible only to spies is now freely available online and can be openly harvested by other arms of state. Big data is where most governments of today hope to find the tea leaves that allow them to read the future. America and the Scandinavian countries lead the field in conflict prediction through machine learning. In Britain, the Alan Turing Institute is trying to harness AI to give policymakers advance warnings through its Global Urban Analytics for Resilient Defence - or Guard - project.

Germany, which had contributed to the Afghan war in only a modest capacity and stayed out of Iraq, has invested some £43m into figuring out if it can use data tools to predict international conflicts. It has set aside a further £2.6bn to expand the approach until 2025. The centrepiece of its attempt at geopolitical clairvoyance is a megadata management platform developed at Munich's University of the German Federal Armed Forces. It is called Preview - a forced acronym for "Prediction, Visualisation, Early Warning" - overseen by Carlo Masala, a professor of international politics. Preview sucks up information that could give a hint about where on the planet a crisis is about to erupt: RSS feeds of news websites, data banks tracking military conflicts, civil protests or car bombs going off. Broader structural clues are thrown into the mix: GDP per capita, regional educational structures, climate change data.

All this raw information is fed into Watson, IBM's artificial intelligence platform, which helps convert it into maps highlighting potential trouble spots: green indicates stability, orange highlights instability, red warns of a conflict on the verge of escalation. One German official says the AI prediction system had already given Angela Merkel's government a few months' warning of the rebel insurgency in Mozambique's northern Cabo Delgado province, where security forces are battling with militants trying to set up an Islamic state. But the early warning system is still in development: the aim is to eventually be able to predict conflicts 12-18 months in advance.

Germany remains more wary than other nations of outsourcing strategic assessments to algorithms: "There are some analysts in the US who believe that AI can eventually replace human prognostics altogether - we don't believe that," says Masala, a lively and communicative German-Italianwho enjoys poking fun at his country's earnest moralising on matters military on social media (his Twitter profile is a picture of Tyrion Lannister, Game Of Thrones's permadrunk military strategist). "The algorithm may be able to do 84% of the work, but we see it as a support system for human analysts, not a replacement."

Watson, the question-solving supercomputer that sits at the heart of Preview's crisis prediction effort, takes a second to process 500 gigabytes, the equivalent of a million books. But Watson isn't great at reading between the lines. A literature professor seemed perfectly placed to complement the computer's linear skill set.

ven with his team of three researchers, the challenge Wertheimer had been set seemed overwhelming. "At the beginning of the project, we thought we'd spend most of our time sitting in libraries reading books," recalls research assistant Florian Rogge."But we realised quite quickly that there were simply too many books, written in languages we didn't speak."

The team considered text mining: scanning books for emotive words and phrases to build a cluster map of feelings associated with specific issues, geographical regions or political figures. They consulted the IT department. The problem was that text mining requires books to be digitised, with the researchers' chosen search terms defined in advance. "It would have bulldozed over irony, ambivalence or metaphors, all the literary aspects we were interested in," Rogge says. "We wanted the books to tell us something we didn't already know."

The group decided instead to focus on what it calls "literary infrastructure": what happens around the text? How is it being received? "We became interested in what hit a nerve," Rogge says. "Was a book heaped with awards and state prizes? Or was it banned and the author had to leave the country?" Kuwait, for example, saw a rise of novels about the situation of the stateless Bidoon minority after 2010. Many of them were censored or banned shortly after their publication, prefiguring the crackdown on Bidoon protesters in 2019.

Reading books in translation proved an inefficient way to pick up such trends. Rogge says he ended up skim-reading no more than 30 novels over the course of the project. Instead, Wertheimer's team reached out to writers and literary critics in regions they were interested in. The response was surprisingly enthusiastic. The novelist Wole Soyinka sent links to articles in the Nigerian press and supplied contacts to other writers. Kosovar writer Beqë Cufaj organised a colloquium at his country's embassy in Berlin. Hearings in Paris and Madrid were attended by novelists from Algeria, Morocco, Egypt, Israel and France, most of whom volunteered to pay for their attendance out of their own pockets.

Our systems could already predict a conflict a year in advance. But five years - that was something new

In 2018, weeks after the Bundeswehr officers had travelled to Tübingen, Wertheimer presented his initial findings at the defence ministry in Berlin. He drew attention to a literary scandal around Jovan Radulović's 1983 play Dove Hole, about an Ustashe massacre against their Serbian neighbours, and the expulsion of non-Serbian writers from the Serbian Writers' Association in 1986. In the years that followed, he showed, there was an absence of tales about Albanian-Serbian friendships or love stories, and a rise in revisionist historical novels. Literature and literary institutions, he told the military men, had "paved the way for war" a good decade before the start of the bloodshed of the Kosovo war in 1998.

Carlo Masala was at the presentation. "At the beginning, I thought: this is crazy shit," he recalls. "It won't fly." But Masala, who had spent a part of his academic career studying the conflict in Bosnia, remembered how the hardening tensions in the regions had been preceded by a decline in interfaith marriages. "In Kosovo, it seemed, you could detect similar early warning signs in the literary scene."

"It was a small project that created a surprising amount of useful results," says one defence ministry official who attended the presentation. "Against our initial instincts, we were excited."

In its bid for further government funding, Wertheimer's team was up against Berlin's Fraunhofer Institute, Europe's largest organisation for applied research and development services, which had been asked to run the same pilot project with a data-led approach. Cassandra was simply better, says the defence ministry official, who asked to remain anonymous.

"Predicting a conflict a year, or a year and a half in advance, that's something our systems were already capable of. Cassandra promised to register disturbances five to seven years in advance - that was something new."

The German defence ministry decided to extend Project Cassandra's funding by two years. It wanted Wertheimer's team to develop a method for converting literary insights into hard facts that could be used by military strategists or operatives: "emotional maps" of crisis regions, especially in Africa and the Middle East, that measured "the rise of violent language in chronological order".

hey had the books; but how could they feed their findings into a machine?The challenge to bridge the intellectual divide between science and the humanities in the space of 12 months fell to Julian Schlicht, who was brought in to Project Cassandra in September 2019 to work out how literary criticism could be converted into data points. A 30-year-old student of politics, sociology and Islam studies who was researching the Taliban's innovation strategies for his MA, he was the only member of the team without a background in literary studies.

"I was probably among the sceptics when I joined," Schlicht says in hindsight. "Coming into this project with a background in politics, I thought their approach was... quite cheeky. There was a moment after the first meeting when I thought: how is this meant to work?"

Wertheimer's researchers had become increasingly interested in Algeria, a country that had remained mostly silent during the Arab spring. Only 51.7% of voters in the north African country had turned out to vote in its 2014 presidential election, suggesting political apathy or weariness, borne of violent memories of the civil war that shook the region in the 1990s: Algeria was broadly classified as a "stable" state.

Jürgen Wertheimer: 'Modern politicians are much like their ancient counterparts: they prefer not to know.' Photograph: Dominik Gigler/The Guardian

Trends in Algerian book publishing, however, hinted that something was about to change beneath the surface. Amar Mezdad's 2014 novel Yiwen Wass Deg Tefsut ("A Spring Day") follows a group of people who join a demonstration that is violently dispersed. Saïd Sadi's 1991 diary novel Algérie, L'Échec Recommence was reissued in the northern Algerian Kabylia region in 2015, revisiting the Berber spring of the 1980s. And there was Boualem Sansal's 2015 novel 2084: La Fin Du Monde, an Orwell-referencing dystopia in which an Islamist dictator uses religion to control the language and minds of his people. A former high-ranking government official who has criticised the rise of political Islam in Algeria, Sansal's books had been banned in his home country since 2006, but were still widely read - making this a prime example of literature's ability to touch a nerve.

But turning these atmospheric signals into information that could be of use to military or political decision-makers proved a challenge. "We realised there was a clash between how someone from the humanities and a scientist would go about drawing up a map," Schlicht recalls. "A cultural historian will say: my expertise tells me this region is red and that region yellow. A scientist, however, asks: how do we work out when the yellow region turns orange?"

The researchers developed a risk score system with nine indicators for each book: thematic reach, censorship of the text, censorship of the author, media response, scandals around the text, scandals around the author, literary awards for the author, literary awards for the the text, and narrative strategy. In each category, the book was assigned a score between -1 and +3: the higher the score, the more "dangerous" the text.

In some cases, negative scores were necessary. A book told from several perspectives, such as from two opposing enemy camps, was assigned zero or -1 points in the narrative strategy category. Zoltán Danyi's 2015 novel The Carcass Remover, for example, scored only 12 points, mainly because it reflected on the Yugoslav wars without using black-and-white depictions of heroes or villains. "We realised that literature can also help solve or lessen conflicts," Schlicht says. "Not every book divides opinion."

Dystopian fiction from Algeria scored much higher. Body Writingby Mustapha Benfodil, a 2018 collage novel made up of the diaries and scribbles of a fictional astrophysicist killed in a mysterious car accident on the day of the presidential election, spoke of the desire to create order out of the chaotic memories of the Algerian civil war in the 90s, expressing a yearning for democratic change. Twenty points, the Project Cassandra researchers decided.

Had the book had more impact, it would have scored higher: La Faille by Mohamed-Chérif Lachichi, a 2018 thriller portraying violence in Algerian prisons, a corrupt legal system and a growing protest movement, scored 22 points because it represented a case of a well-known and widely reviewed author questioning the status quo. The highest-scoring work in the project's 300-book data bank was Sansal's 2084: Wertheimer's team assigned it 25 points.

Project Cassandra's scoring system had several flaws. Its indicators were malleable: should rave reviews or hatchet jobs contribute to a book's score only when they were published in its country of origin? Was a book with 24 points really twice as dangerous as a book with 12 points? Converting scores into emotional heat maps came with additional headaches: what does a dystopian Algerian novel set in a parallel universe really tell you about the different political allegiances of Algiers and Oran? "Translating literature into numbers is difficult; you constantly end up making compromises," Rogge says. "You need to score a lot of books in order to even out the imprecisions."

When Azerbaijan gave anti-Armenian books to Georgian libraries, the project predicted conflict. A year later, war broke out

But the literary seismograph's instincts proved reliable. In February 2019, two years after Wertheimer's team had identified Algeria as a region of interest, civil protests broke out in Algiers and several other cities, culminating in the resignation of President Abdelaziz Bouteflika. When Wertheimer handed over his team's findings in the summer of 2020, they met the defence ministry's formal requirements for taking their methodology to the next level. Project Cassandra had established a tangible link between literature and empirical historical events. Plus the cancellation of trips and seminars because of the pandemic meant the study had come in almost £34,000 under budget.

A meeting with Operations Command was scheduled. "We all had our hopes up," Holz remembers. "We couldn't wait to apply our findings in the field."

Then Wertheimer received a phone call. Project Cassandra would be discontinued in winter 2020.

n the fallout, some blamed the pandemic, which had placed unforeseen strain on government budgets. Others pointed the finger at the five-year reshuffle cycle for government posts, which meant one of the most supportive officers had been dispatched to Moscow and the new guard knew little about Cassandra's original promise. Via official channels, the defence ministry is tight-lipped: "Retrospectively we like to emphasise how much we appreciated this innovative approach," is all a spokesperson could say on the matter.

"Of course it was a massive blow, even more so because we hadn't expected it," Holz says. Behind the scenes, some officials remain baffled. Crisis prediction through literature, one says, would have been a "self-fulfilling success story". It came with a free network of enthusiastic, expert volunteers, who could not only help identify conflicts several years in advance but provide diplomatic corps with the right counter-narratives to quell it. Perhaps, another official pondered, Project Cassandra was simply too good to be true, and would have risked showing up the more costly undertaking of data-driven programmes.

In the Greek myth, Cassandra's warnings go unheeded because the Trojan priestess has been cursed by the god Apollo, angered after being turned down for sex. In Christa Wolf's modern adaptation, the Trojan generals know she is speaking the truth, but ignore her regardless. "King Priam prefers to remain ignorant out of political calculation," Wertheimer says. "I used to believe modern politicians were different, that they simply didn't know better. It turns out they are much like their ancient counterparts: they prefer not to know."

In one of his last reports to the defence ministry, towards the end of 2019, Wertheimer had drawn attention to an interesting development in the Caucasus. The culture ministry of Azerbaijan had recently supplied libraries in Georgia with books carrying explicit anti-Armenian messages, such as the works of poet Khalil Rza Uluturk. There were signs, he warned, that Azerbaijan was ramping up propaganda efforts in the brewing territorial conflict with its neighbouring former Soviet republic.

War broke out a year later: 6,000 soldiers and civilians died in a six-week battle over Nagorno-Karabakh, an enclave of Azerbaijan populated by ethnic Armenians. Turkish president Recep Tayyip Erdoğan used the war to bolster his strongman image, hailing Armenia's defeat in December as a "glorious victory". Russia, traditionally allied with Armenia, successfully leveraged the conflict to consolidate its influence in the region. Germany and the EU, meanwhile, looked on and stayed silent: being able to predict the future is one thing, knowing what to do with the information is another.

Wertheimer isn't bitter about the end of Project Cassandra, he says: "I'm not a bitter person, only melancholy." At any rate, the project may still have a future after all. The German interior ministry has commissioned his team to investigate the hidden scars of the country's reunification process. There have been talks with the EU representative for foreign affairs and security policy, Josep Borrell, about docking Cassandra in Brussels. Wertheimer says he is interested in applying his method to analyse geopolitical tensions in Ukraine, Lithuania and Belarus.

Above his writing desk in Tübingen hangs a map of the world with travel visas pinned to the white spaces next to the continents. "Literature is at the centre of my life," he says, "but only because I believe it can be a springboard into the real world."

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'At first I thought, this is crazy': the real-life plan to use novels to predict the next war

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s the car with the blacked-out windows came to a halt in a sidestreet near Tübingen's botanical gardens, keen-eyed passersby may have noticed something unusual about its numberplate. In Germany, the first few letters usually denote the municipality where a vehicle is registered. The letter Y, however, is reserved for members of the armed forces.

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Pablo Rodriguez

Pablo Rodriguez is a computer scientist and the founding CEO of Alpha, Telefónica's Moonshot innovation facility in Barcelona. He's worked at several Silicon Valley startups, at Microsoft Research in Cambridge, and at Bell Labs in New Jersey. He's written more than 150 scientific publications and has 30 patents. Pablo tweets at https://twitter.com/pabloryr and is on LinkedIn at https://www.linkedin.com/in/pablorr/. He is part of the VOYAGERS health-tech community.

launched Europe's closest equivalent to Google [X] - a "Moonshot Factory" called Alpha for the Spanish telco Telefónica. Here's how to do real corporate innovation
We can't predict the future, but we can build for it. In the last decade, a new form of innovation has emerged to reinvent businesses, design towards a future we want, boost our collective ambition, become more sustainable and impact the world for good. A form of innovation that anticipates the next big disruption, albeit very difficult to achieve: Moonshots.

Moonshots used to happen in governments and universities. Places that could take long-term risks - to create the Apollo 11 Moon landing programme (the original Moonshot), the Internet, the Human Genome Project.

Now some corporations are embracing the ability to take those long-term risks - not least because the speed of change means "the long term could soon be the next term".

I've been a research scientist, a repeated startup entrepreneur in the Valley, a builder of corporate innovation labs. Running Moonshots has been the most fascinating journey of all.Moonshot thinking started in Silicon Valley with X (Google's sister company at Alphabet) - self-driving cars and Internet balloons. I ran Alpha, the first European Moonshot factory, established in Barcelona by Telefónica in 2016.

We defined a Moonshot as something that will affect hundreds of millions of people, have a net positive effect on society through deep tech, and be scalable to a billion-euro business.

At Alpha, we consistently held to the mantra: "Fall in love with the problem, not the solution".

The Moon (the problem) is the grand challenge that's hard to solve for. It typically falls in one of the 17 United Nations Sustainable Development Goals (SDGs), such as energy access, reducing chronic diseases and improving learning. It takes a fair amount of work, however, to define a concrete, well-designed problem in any of those spaces, one that's validated with deep human insights. When you find and solve for such a Moon, you can provide huge social value to society as well as big business opportunities.

The tough part is picking the right Shot (the solution to the problem). At Alpha we focused on technology-driven shots: finding a maturing technology (typically in labs or universities) ready to be accelerated. Now, there's a danger that you may fall in love with a great shot, and lose track of the Moon you're daring to reach. So fall in love with the problem, not the solution - or you may end up with a beautiful sophisticated hammer, but no nails to hit.

Moonshots have much in common with startups, but they're not startups. A Moonshot organisation needs capital and corporate muscle behind it to take things to scale and provide the patient investment required - as opposed to quick venture-capital returns expected in startups, which can stifle innovation. In fact, Moonshots are too early or too audacious for most VCs, typically taking far higher technical risks than at a startup.

Here are some lessons from my five years setting up and running Alpha on what it takes for a Moonshots to succeed. Alpha's culture was of frugal operations, short-term proof points and intermediate delivery focus, driven by a cash-strapped funding model, with numerous exercises in prioritisation and idea-filtration. The whole Moonshot organisation - including its talent - had to build itself from scratch at a distance from the parent organisation.

If a Moonshot was easy, someone else would have solved it before. Your team needs to understand the journey will be damn hard. You'll want to quit many times, when you'll need to remind yourself that it's supposed to feel like a struggle.

That's because this form of innovation follows a complex system of interactions. Like epidemics, earthquakes or financial crises, innovation emerges from the connections and interactions between all of us and typically follows a power-law (or long-tail) distribution. If you plot its impact distribution on a logarithmic scale, it will follow a straight line.

For instance, the distribution of occurrences of earthquakes vs their intensity follows this kind of power-law-distribution scale. Plot the number of earthquakes in any year, and you'll record millions of 2.5-sized quakes, which we don't even feel.

But about once a year you'll record an 8.0 earthquake, which causes major disruption. You don't know where or when such an earthquake will occur, only to expect it roughly once per year. Innovation is the same.

If you plot the number of innovations and their impact, you'll find many incremental innovations that keep your business running. But from time to time, an innovation will transform everything. This is Moonshot radical innovation. It's difficult to pick losers and winners upfront: innovation is a probability game, not a deterministic one, Moonshots are difficult because the odds are against you. Failure isn't necessarily due to ineptitude: Moonshot innovation follows the science of complex systems, and many will fail.

Moonshots are typically incubated for five to ten years, building on research done at labs and universities. To beat the probability odds, persistence is key. Don't expect results in three to five years.

Incremental innovation typically has far shorter and faster returns. I implemented such incremental-innovation transformation at Telefónica over almost ten years to drive intrapreneurship and corporate change (making an elephant dance, which has been turned into Harvard Business Review case study - thanks to Open Innovation Prof. Henry Chesbrough at Berkeley).

There is really nothing wrong with this kind of innovation: many startup incubators and corporate-innovation arms follow this model. But Moonshots fall into the category of transformational or disruptive innovation. They require you to dive into the far-fetched future as opposed to iterating from the present. You need to commit to the journey, with persistence, patience, and belief. In the beginning, Moonshot projects can be weird, extremely fragile, and exotic, and don't really fit with whatever else is going on in the parent company.

The good news is that there is literature about a golden ratio for innovation, which shows that if you invest 10 per cent of your innovation budget into Moonshots, eventually that will bring 70 per cent of your returns.

This is a story from VOYAGERS, a global community of mission-led people who share weekend adventures and commit to supporting each other. Discover more and apply to join at VOYAGERS.io

Moonshots are nifty and exciting. However, the existing business will strive to minimise risk and emphasise predictability, whether in setting budgets or paying staff on time.

To take the type of risks required by Moonshot thinking, you'll require a separate organisation outside the deterministic corporate environment (which can be shared by one or multiple corporates or funding entities). This organisation is in the business of risk-taking.

It's typically a much, much smaller organisation than the mother company. That is a good thing: if it fails, it won't bring the whole ship down.

It needs to report right to the top, and with a different command structure: you need the personal buy-in of the chairman or a board member from day one. They need to be excited, talking about it with peers, to the board, at a cocktail party. Give them regular updates. Without this commitment from the top, your Moonshot journey will eventually fail.

It took years to set up Alpha, needing alignment and coordination from across the corporation (long-term incentives plans, procurement, HR, legal, finance...). We created a special Disruptive Innovation Council.

As well as a separate corporate structure, you also need a separate and distinct culture. At Alpha this is articulated around a set of core values, including: Brutal Honesty; Curiosity to the power of We; Think Bigger; Patience/Impatience; and Sacred Trust.

Setting up a Moonshot organisation can be a Moonshot in itself, considering how risk-averse most companies are.

Moonshots should not address topics too close to the parent company. Instead, aim to create totally new businesses for industries primed for reinvention that lack a digital or innovation culture.

If you are too close to the mother company, you'll end up doing more of the same innovation already being done by the corporate R&D departments. If you're too far, the parent company may see the new opportunities as too alien.

You should be connected to the parent company as if by an umbilical cord. Distant enough to be independent, but connected enough to leverage some of its nutrients and resources.

Make sure your Moonshots can fail by themselves. In fact, they should only be killed within your Moonshot organisation, which has the fullest knowledge to know whether projects are worth continuing or not.

Doing global good and having a social impact used to be the remit of NGOs or corporate social responsibility teams - decoupled from business and revenue opportunities.

Doing well and doing good don't have to be at odds.

In fact, to do good at scale you need to make projects financially sustainable. A new social contract is emerging that reevaluates the relationships between individuals, businesses and governments. Some use the phrase "stakeholder capitalism", that is, capitalism that benefits all of the stakeholders in a given community and where people everywhere will reap the benefits of such progress.

Merrill Lynch recently identified 12 megatrends - including food security, waste management and fixing education - that offer long-term social business opportunities and will provide substantial innovation returns within socially conscious portfolios.

Philanthropy is changing too. Guiding philanthropy towards Moonshot thinking could build a serious force for measurable good and new business opportunities.

Moonshot organisations, in fact, are purpose-driven. They focus on mission and purpose, which align with team members' inner purpose and mission. A purpose-led organisation will bring people together, their heart and minds in a well-balanced dance towards impact.

Cherish the four Ps: Purpose, Profit, People, and Persistence.

Moonshots are about taking calculated risks. So you need the very best people you can find, balancing generalists and experts.

At Alpha, we had a former F18 fighter pilot, who knew how to take very well-calculated risks, alongside an accomplished graphic artist and visual storyteller who made more intuitive leaps into the unknown with unexpected insights and questions. Give them the freedom to act, and get out of their way.

Your leaders' personal purpose must be aligned with Moonshots, and your teams need to embrace diversity. It takes multiple skills and disciplines working together to solve daunting challenges - a psychiatrist, say, working with an AI engineer and a happiness expert. Your culture needs to ensure that an artist feels valued, an engineer stretched, a serial impact entrepreneur bringing her own beat.

The culture must encompass the rhythm at which each one ticks, and must keep adapting. Culture is organic: it should evolve, be challenged and iterated.

The people who change the world tend to be brave, purpose-led misfits. They're creative, they're diverse, and they need an environment that supports them. So nurture them. Unlock their human potential; understand if they are sleeping well and whether they are happy; optimise their food and workplace; give them access to a holistic doctor or group psychotherapy sessions to help them address problems, achieve consensus or improve the creative tension.

You must also embrace psychological safety: a culture where people can fail and dare. If you design against failure, you will soon become yet another corporate innovation lab.

Your mission is to create an environment where smart people can float crazy-sounding ideas which could become boldly creative solutions to tricky problems. You need to protect this culture from the corporate culture, and understand that it's a difficult, emotional journey to build a Moonshot, grow your team as individuals, and give them unique experiences. It will pay off.

Storytelling helps identify new Moonshot ideas. A Moonshot could originate with a scientific research paper, a sci-fi future scenario (eg a world where humans are augmented by AI), an upcoming trend (a change in demographics in China or Africa), or a grand challenge that is good for the world (such as the UN Sustainable Development Goals).

To identify a Moonshot, project yourself 10 years ahead. In a sci-fi prototyping workshop, you might create future fiction worlds and stories to explore how people's lives will be transformed by your solution. Make ideas concrete in the form of a science-fiction comic, or videos, that explore future capabilities of current technologies.

Once you imagine a seemingly crazy, science-fiction-like idea, you can retro-cast: track back the steps needed to reach your desired future and understand potential obstacles.

By structuring the storytelling process, you can unlock great creativity, reframe, stress-test and pivot Moonshot ideas. Storytelling helps align your team, your investors, your board, your partners, the community, and the people you are trying to help throughout this journey of hope and delivery. It makes your mission easier to share and relate to.

So tailor well-crafted stories around the Moonshot, its purpose, and your tangible intermediate delivery proof points. Storytelling will align and bring onboard your Moonshot captain, team, experts, and investors. It will seduce them and inspire them to continue.

Moonshots are as risky as cutting-edge research, but need to be executed with the focus and velocity of a startup. That's not always easy. You need the patience to let difficult solutions unravel, and the impatience to undertake them fast.

Experimenting and learning quickly is paramount. Make sure your teams know how to set up great experiments where they can de-risk loads of important unknowns with little costs. Mistakes are fine if you learn fast. Focus on the experiment, not the result. The only real failure is that of not learning anything. Embrace learning!

Prototype early and deliver intermediate proof points. Don't think too big for your intermediate experiments. If you take a small risk and fail, who cares? Build on that success of failure. Take the minimum effort to learn the most, and do it super cheaply.

Create quick objects from the future, videos with explorations of ideas, paper prototypes, and achieve intermediate Moonshot milestones - which could even become self-sustained small(er)-businesses. Spend a couple of days prototyping something very rough. If it goes nowhere, no one will bother. But if it's interesting, make the next prototype. Maybe a week's work. And then move on and on from there.

Don't try to solve all your tech or science problems first and then figure out your business. Instead, develop both the tech and the business in parallel through a product focus: release intermediate products you can quickly test and learn from that serve as evidence of progress. Finding a finance partner already in your market will help test your crazy idea, de-risk faster and give you more validation proof-points.

Rigorous evaluation should let you quickly kill ideas that don't match the Moonshot criteria. Create red teams, open clinic reviews and appoint murder boards that try to kill ideas fast, or give teams new perspectives. You need to keep yourself intellectually honest, with rigour, and be dispassionate in trying to find the reasons why the Moonshot will fail. Tackle these first. De-risk, and learn super fast.

In the early stages of a Moonshot, you need an ideation team. These are small, agile, and diverse teams that work closely with experts, labs and universities. They explore hundreds of ideas, and a handful of more mature Moonshot candidates, and are willing to put their job on the line. You're trusting them with your company's future.

Once you've identified your Moonshot, you go into the Moonshot Launch process. But before you put together a big expedition, start climbing a bit of the mountain to see if it feels right, start de-risking the path and identifying obstacles.

Engage field experts and potential Moonshot captains early in the process. You'll need a specialised Launch Captain, a senior member of the team who can temporarily mobilise resources and attract talent and partners.

The key role is the leader of the Moonshot, which we called the Moonshot Captain. A Moonshot Captain should be an entrepreneur who can take an idea all the way to scaling the business. Their mission and purpose in life should be aligned with the Moonshot, and they should be able to build a team with skills that have never before been brought together - a diverse team, not only in gender diversity, but in demographics and backgrounds, from PhDs to artists.

Let me tell you about the first Moonshot Captain at Alpha: Oliver Harrison. Oliver is a trained psychiatrist, specialising in medicine and neuroscience, who spent six years as head of public health and then director of strategy in Abu Dhabi's health authority, and five years with McKinsey healthcare. These are not skills you'll typically find in a corporation, let alone a telco.

Bringing talent from the existing core business may conflict with your culture, or will not serve your Moonshot needs. Be prepared to pass on talent if they are not a great culture fit.

To attract talent, map the personas you would love to work with. Map the archetypes that will best help meet your various goals. Get your team to do this persona-mapping exercise together, as it will help them achieve alignment and give them a stronger sense of belonging.

Keep the organisation as small as possible for as long as possible. A handful of people can do great things if empowered and flexible. They should be "confidently humble" and network effectively. Develop an apprenticeship culture, with few managers and very few organisational layers, but with a lot of distributed power. Check in regularly to see what's working and what is not. And keep resources constrained and budgets small. This will ensure focus.

As the Moonshot advances, bring in an anchor player in that space to help you de-risk faster and be close to the real world. Be ready to share risks with others, with spin-offs and joint ventures. Build a team that can think in terms of scaling from day one, and don't underestimate the effort of assembling a team of misfits.

You won't find all the talent you need in-house. But it won't be easy to identify and bring in people with the right levels of courage, entrepreneurship and creativity - and fast. So build a " knowledge talent hub" and for each Moonshot engage a wider community of experts, misfits, social entrepreneurs, Nobel-prize winners, scientists, doers and kindred spirits who might join your journey for a day, a week, or even the whole Moonshot.

Engage and interact regularly with this network. Build emotional connections with them, and trust. Talk in their language. Understand them. Know where they hang out, how they connect. They're not "talent" or "candidates" - but human beings. Think about how they'll interact with your Moonshot organisation's brand, not the other way around.

Ensure your organisation is frictionless to work with. You won't solve Moonshot problems alone. Don't be insular, or make intellectual-property protection your priority. Instead, set up open Moonshot ideation and brainstorming sessions, hackathons and open launchpad accelerators. And look outside: find out what is happening in a research lab in Lyon or Zurich (not just MIT or Stanford), in Japanese youth culture, in Boston's biocomputing community.

Reading research papers is less effective than going out into the field, co-creating, building and experiencing things together. You'll find saplings of opportunities beyond mainstream labs, in Europe, Israel, or Asia, that can serve you as feeders for your Moonshot factory.

At some stage, you'll need to use standard business metrics to measure performance (such as customer acquisition costs, or churn, paid pilots). But for most of the journey, you'll need to devise new metrics to track progress. The way you evaluate and reward your Moonshot efforts can be as inspiring as your driving aspirations to change the world.

The way you evaluate and reward your Moonshot efforts can be as inspiring as your driving aspirations to change the world we live in.

De-risk your Moonshot milestone by milestone, with relatively short deadlines. Short-term deliverables and proof points will focus your teams. So decide what you need to measure. Build a dashboard with metrics to track various dimensions for each Moonshot, from tech progress to business model. At the start, define your "kill questions" (those that if not fully answered will make you abandon the Moonshot, eg will the science work as forecast?). Address these kill questions at each milestone, so that you invest the fewest resources to answer the next most important questions, as quickly as possible.

It comes down to definition and measurement. Be super clear about the Moonshot's goals and feed these in detail into the dashboard. Think of it as a rocket-ship's dashboard, where various instrument panels let you know that the ship is heading in the right direction and not breaking up under the pressure.

Track metrics such as how much you are de-risking the Moonshot and how quickly, OKRs, KPIs, business modelling, financial indicators, quantitative and qualitative metrics that tie your money spent to the killer questions. But don't forget that performance goes beyond metrics, and is imbued with stories that involve characters, factual and emotional plotting, conflict, effort, milestones and resolutions. Think of performance as scenes, as in a play, that will evolve and contribute to the overall performance of the learning of the Moonshot.

A learning log can help track the various ways of "knowing": narrative, visual, dialogic, quantitative and qualitative. All of your learning logs will constitute the institutional memory of the Moonshot. They will become the repository for knowledge, instead of a mere list of achievements, and serve as a spirit of reflection.

Sometimes you need to trust intuition, as there are things that metrics can't tell. Seeing the energy in people at work, their passion or belief in what they are doing, how their eyes shine - this will help teams define success or failure.

Measure and nurture such energy and make sure it grows. If not, react. Have brutally honest conversations, and if something is not working understand why.

As for yourself, as a leader of Moonshots, work on the organisation's energy blockages that impede true innovation. Be a great networker, nurture trust, share vulnerabilities, beware of "good news" only and "avoid failure" behaviours, be a source of inspiration and boldness, stimulate constructive conflict, and generate creative entropy across the teams and beyond.

And be ready to unlearn and undo many paths towards learning again.
photos © alpha, futur.io
The faster we iterate to get things right, AND wrong, the higher our chances of successfully solving some of the biggest challenges facing humanity.

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Ten things I learned running a Moonshot Factory. By Pablo Rodriguez

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Pablo Rodriguez launched Europe's closest equivalent to Google [X] - a "Moonshot Factory" called Alpha for the Spanish telco Telefónica. Here's how to do real corporate innovation We can't predict the future, but we can build for it.

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An interview with Xer Phil Watson about navigating an uncertain future

A moonshot is about looking beyond where you can actually see, to an unpredictable future.

Baseball legend Yogi Berra once quipped, "It's tough to make predictions, especially about the future." By definition, the future is unknowable until it happens, and by then it's usually too late to do anything about it. But, as Xer Phil Watson says: "you can look for levers to torque the future in a particular direction."

That's what we do at X: create moonshots that take the long view. Our projects - in areas like sustainable agriculture, ocean health, and robotics - might take a decade or more before they're ready. That's not only okay: it's by design.

The first step, Phil says, is to throw out your assumptions about what's possible and be ready to reconfigure your worldview in an instant.

"When you're building things nobody has ever built before, your assumptions are almost guaranteed to be wrong," he says.

As inspiration, Phil cites the book Superforecasting: The Art and Science of Prediction, in which social psychologist Philip Tetlock and journalist Dan Gardner argue that good forecasting starts from a place of humility, insatiable curiosity, and admitted ignorance. This in itself requires a shift in perspective, especially because most people equate success with "being right." That's compounded by the Dunning-Kruger effect, a cognitive bias causing people to overestimate their own knowledge or ability, particularly in areas where they actually don't know much of anything.

"Pundits and experts with an established worldview are consistently less good at predicting how things will play out than people going in with a learning and data gathering attitude," Watson says.

If a moonshot is about looking beyond where you can actually see, Phil's telescope is a "cone of uncertainty" pointed at the future. The farther out you look, the harder it is to make accurate predictions. It's true that nobody can predict the future, but we can forecast where unconventional innovation is likely to have the most impact in the long run. To narrow the cone of uncertainty, Xers like Phil use tools like dependency analysis, techno-economic analysis, and what he calls "what needs to be true for this to succeed" assessments, to dissect ideas and evaluate the logic behind them. What is the real cost and benefit of the technology? How likely is the solution to not only work but also be practical enough to disrupt an entire industry? What are the multiple future forces that might intersect to create new opportunities or challenges in a territory?

From there, it's a matter of trying audacious things, celebrating what we've learned from our failures, and following the project wherever it leads. Just don't invest too much in what you've built, or your own assumptions, Phil explains. Otherwise you'll be reluctant to pivot to what really matters even if the project explodes in your face - sometimes literally.

"The weirdness of the universe is stronger than what our minds are willing to tolerate," he says. "So let's keep looking for where we're wrong. Life is a lot more fun if you're excited to find out you're wrong."

And eventually, you might just be right.

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An interview with Xer Phil Watson about navigating an uncertain future Baseball legend Yogi Berra once quipped, "It's tough to make predictions, especially about the future." By definition, the future is unknowable until it happens, and by then it's usually too late to do anything about it.

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10 years of moonshot taking
10 years ago Larry, Sergey, and Sebastian offered me the chance to run a grand experiment - to build a moonshot factory. Initially we only had a fuzzy idea about exactly what X was going to do, but we knew we wanted to create an organization that could invent and launch breakthrough technologies that would make the world a radically better place. And we knew X could play an important role for Google, in reaching beyond its core Internet business and beating the innovator's dilemma. I agreed to co-found X because I've always wanted to create an organization that could repeatedly generate breakthrough innovations filled with hyper-creative people and weird, out-there thinking.

What motivated me then is what motivates me now: humanity is choosing to keep much of its potential off the table, undeployed, and underutilized. Many people go to work at jobs that aren't designed to be fulfilling, and many more don't get the chance to contribute at all. Meanwhile, many powerful organizations are forced to focus on their own profitability and short term goals at the expense of everything else - leaving the status quo intact, or at best only making the world incrementally better. Yet the massive problems facing us this century need the widest array of minds, the wildest imaginations, and enormous commitments of time, resources, and attention.

The moonshot journey

X as a whole is our prototype solution to this need. We've been trying to make a factory for radical ideas, technologies, and businesses that will have a huge positive impact, and for employees who maximize their creativity, talent, and personal sense of purpose. And we hope to prove that "good for the world" can be financially rewarding, so that more organizations are inspired to operate like this. 10 years into this experiment, we're proud of our work so far, whether it's the many standalone companies and Google products that started at X or the hundreds of alumni who carry X DNA into many different companies and industries. That said, there's still more we can do.

As we hit our 10th anniversary, I've been taking stock of how we're doing at building an organization that unleashes radical creativity while remaining relentlessly focused on real technologies and real-world impact. What I've come to realize is that our main cultural battle is against fear and the strong gravitational pull toward conventional ways of thinking and behaving. All of us have been conditioned for years not to fail, not to be vulnerable, and to minimize risk. So my number one job is helping Xers reset and free themselves from these invisible but pernicious constraints so they can free up their potential. And it's a mission I want more leaders to take on, so below are some of the lessons we've learned about helping teams break away from the ordinary and into the unexpected and unknown.

An early Project Wing prototype

#1 Stop thinking you can predict the future. This is one of the biggest myths perpetuated by investors, media, and the entire "futurist" industry; no one is much better than random at knowing what businesses and products will succeed in the long run. Our approach: Try audacious things and figure out as quickly as you can when you're wrong.

It's possible to predict general territories where new opportunities are more likely to lie; for example, we think machine learning has the opportunity to unlock moonshot-sized opportunities in areas like agriculture, drug discovery, and the development of new materials. But you actually have to jump in and start trying things in order to get the details of that "Ah-ha!" breakthrough right. Sometimes you learn some harsh lessons; that's the point. Sometimes it takes dozens of iterations; one team at X right now is working on improving how people hear, and they explored 35 different ideas before they found the one we're going to pour fuel on.

#2 Take a long term view - it changes everything. We've deliberately chosen to work on hard problems whose answers are 5-10 years over the horizon. This gives us space to explore and experiment and learn more deeply. Taking the long view also enables us to think through the implications not just the applications of what we're building, and ensure we're taking into account the needs of many different people affected by any new technology. For example, we decided to tackle the longer, harder path of creating fully self-driving cars instead of launching a partially autonomous "freeway assistant" feature, because we wanted to help people who couldn't drive at all and make roads as safe as possible. By thinking long term, we can optimize individual projects and our portfolio for as much positive impact as possible, even if we can't know exactly which bets will work out in a big way.

Early tests of our self driving car

#3 Carve out space for creative and weird souls. Willy Wonka had to build a chocolate factory to house the Oompa Loompas, because they struggled to survive in the wild. And innovators and dreamers often can't thrive in typical organizations. Their constant "what ifs" and "why does it have to be this ways" can be irritating for organizations trying to lock in an execution plan and meet quarterly targets. As a result, their creative spirit is slowly bent over time to serve the status quo, or they leave, maybe branded as troublemakers (ironically, this often happens when the bosses hire "innovation consultants"). Don't get me wrong - execution matters, especially when you're operating at scale. But radical innovation needs a different cultural environment than product delivery does. That's why X was set up away from Google. It's why our moonshot projects leave X once the focus shifts from experimentation and prototyping to shipping products reliably and scaling a business.

#4 Dream like a child, test like a grownup. You can get way further than you think just by being optimistic enough to try. You'd be surprised how many people find motivation and joy in a ridiculously hard problem, especially if it has a magical-sounding solution. This is why we've often said that 10X can actually be easier than 10%. Before it seemed possible, teams at X believed in the possibility of self-driving cars, self-flying delivery drones, and stratospheric Internet-beaming balloons, and now all of these things are becoming real. We choose to see the world differently and keep our child-like sense of wonder and boundless opportunity. At the same time, we hold on to our skepticism as we run tests and learn to look dispassionately at the results, so we're ready to change course or walk away when that is the right thing to do.

#5 Look for holy s**t moments - they can get you further than clear objectives. We send our projects off to explore new spaces without heavily quantified goals because we want them feeling free to find the intriguing paths that lead to the biggest surprises. But it's hard to resist the allure of a clear milestone because humans dislike uncertainty and viscerally feel the downsides of failure far more easily than we can imagine the rewards of transformative success. To counteract this, we tell teams we want them to try a bunch of experiments and come back when they've found something that makes us say "holy s**t." A robot navigates up to a table and uses its arm to grasp and sort recyclables from trash. Or a four-wheeled buggy rolls through a field, targeting and zapping weeds with a laser. These aren't products - but they are demonstrations from a couple of our current projects of new technical capabilities that are harbingers of their ability to do even more interesting things with robotics and in agriculture in the future.

The computational agriculture team tests in the field

#6 Cultivate the ability to be passionately dispassionate. While we can't predict the future, we're trying to invent it efficiently, to maximize the impact of our effort and resources. So it's absolutely necessary to be intellectually honest and kill things that are pretty good (even if we love them!) so we can move on to even better opportunities. It's something we keep trying to get better and faster at, though it's deeply unnatural. It's never easy to kill projects. Over the years we've had to disband dozens of teams and walk away from hundreds of gorgeous ideas, including a carbon-neutral fuel made from seawater.

Entrepreneurs who are charging passionately toward their vision often need to be coached to pop their heads up regularly and switch their mindset to dispassionately assess their situation. Sometimes people say to us, "You have to keep going at this because no one else will." But that misses the point. Yes, we want to invest in far-out technologies with huge promise when they're still so risky no one else will. At some point, though, once we're satisfied that the technology has been somewhat de-risked, we need to test its commercial viability. If the market is as excited about it as we are, other companies will put money into it too, and we'll keep going. But if they won't, then maybe we've been kidding ourselves, and we should walk away and redirect our time, resources, and talent to more promising solutions.

One of Project Loon's earliest prototypes

#7 Create fearless teams of chaos pilots. The lone inventor having a eureka moment is largely a myth; innovation comes from great teams. This doesn't mean innovation by committee or consensus. It's about committing to working together and having each other's back, because where we're going, we know it's going to be dark and bumpy a lot of the time. We deliberately assemble teams from a wide range of backgrounds, cultures, and communities, so we can generate more creative ideas and ask hard questions. Former rocket scientists work alongside concert pianists and puppeteers, and marine biologists mingle with physicists and machine learning experts. Above all, we work hard to create an environment where everyone feels comfortable sharing their views and loves running toward really hard problems precisely because they're difficult.

Xers cheering a successful "rocket launch" during our celebrations for the 50th anniversary of the moon landing
Engineering a cultural moonshot
X is a moonshot in and of itself, but unlike most things at X, our breakthrough idea isn't a technology. It's our people. It's about engineering a culture and designing an organization that can overcome the powerful forces that cause humans to retreat to the comfortable and conventional. These forces stifle any efforts to innovate and problem-solve in more than an incremental way. So I'm committed to finding a set of practices, like the ones above, that help many individuals and organizations reset ourselves so audacious, purpose-driven, team-oriented collaboration can become the path of least emotional resistance - and therefore the path to solving the many challenges facing the 21st century.
Editor's note: we'd love to hear your tips, thoughts, and questions in the comments field, and we'll try to address some of them in a future blog. Want more? Here's a post from 2016 about our factory processes, a TED talk about the unexpected benefits of failure and thoughts on how to make teams fearless. In 2018, we created The Gimbal, a document we give to all new Xers that explains our ways of thinking and working, and today we're making it available to anyone .

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10 years of moonshot taking 10 years ago Larry, Sergey, and Sebastian offered me the chance to run a grand experiment - to build a moonshot factory. Initially we only had a fuzzy idea about exactly what X was going to do, but we knew we wanted to create an organization that could invent and launch breakthrough technologies that would make the world a radically better place.

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If Your Innovation Effort Isn’t Working, Look at Who’s on the Team

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From time to time people ask about the Futures Cone, and how it came about. Here is a brief history of how I came across it before adapting it to suit my use of the concept, and began using an early version of the Futures Cone diagram (i.e., with fewer categories) in 2000 when working as a foresight analyst for Swinburne University (before becoming an academic in the Master of Strategic Foresight). The text in this post is excerpted from a chapter submitted to the Handbook of Anticipation, ed. Roberto Poli (Springer International). The "formal" citation for the chapter/book is:

Voros, J 2017, 'Big History and anticipation: Using Big History as a framework for global foresight', in R Poli (ed.) Handbook of anticipation: Theoretical and applied aspects of the use of future in decision making, Springer International, Cham. doi: 10.1007/978-3-319-31737-3_95-1,
while the text in this post is from the section of that chapter entitled 'Types of Alternative Futures', pp. 10-13, and the "cone" diagram itself is: Fig. 4, p. 11.

Futurists have often spoken and continue to speak of three main classes of futures: possible, probable, and preferable (e.g., Amara 1974, 1981; Bell 1997, and many others). These have at times lent themselves to define various forms of more specialised futures activity, with some futurists focusing on, as it were, exploring the possible; some on analysing the probable; and some on shaping the preferable, with many related variations on this nomenclature and phraseology (e.g., again, Amara 1991, and many others). It is possible to expand upon this three-part taxonomy to include at least 7 (or even 8) major types of alternative futures.

It is convenient to depict this expanded taxonomy of alternative futures as a 'cone' diagram. The 'futures cone' model was used to portray alternative futures by Hancock and Bezold (1994), and was itself based on a taxonomy of futures by Henchey (1978), wherein four main classes of future were discussed (possible, plausible, probable, preferable). Some years later I found out that this idea of a cone graphic was used even earlier than Hancock and Bezold (1994) by Charles Taylor (1990), in which he wrote of a "cone of plausibility" that defined a range of plausible futures extended over an explicit timeframe, including a kind of 'back-cone' into the past. He also included "wildcards" in his approach, but other futures categories mentioned here were not explicitly depicted in the diagram given by Taylor as they were by Hancock and Bezold.

Over the years that I have been using the Futures Cone in foresight teaching and practice, I have found it useful to adapt it and add more classes to the initial few. The most recent version of the Futures Cone as I now use it is as depicted in the figure shown.

The 7 types of alternative futures defined below (or 8 if one also includes a specific singular 'predicted' future, which I generally don't do any more) are all considered to be subjective judgements about ideas about the future that are based in the present moment, so the categories for the same idea can obviously change over time as time goes on (the canonical example of which is the Apollo XI Moon landing, which has gone through most of the categories from 'preposterous' to 'projected' and thence into history as 'the past'). In brief, these categories are:

Potential - everything beyond the present moment is a potential future. This comes from the assumption that the future is undetermined and 'open' not inevitable or 'fixed', which is perhaps the foundational axiom of Futures Studies.

Preposterous - these are the futures we judge to be 'ridiculous', 'impossible', or that will 'never' happen. I introduced this category because the next category (which used to be the edge of the original form of the cone) did not seem big enough, or able to capture the sometimes-vehement refusal to even entertain them that some people would exhibit to some ideas about the future. This category arises from homage to James Dator and his Second Law of the Future-"any useful idea about the future should appear ridiculous" (Dator 2005)-as well as to Arthur C. Clarke and his Second Law-"the only way of finding the limits of the possible is by going beyond them into the impossible" (Clarke 2000, p. 2). Accordingly, the boundary between the Preposterous and the Possible could be reasonably called the 'Clarke-Dator Boundary' or perhaps the 'Clarke-Dator Discontinuity', since crossing it in the outward direction represents a very important but, for some people, very difficult, movement in prospection thinking. (This is what is represented by the red arrows in the diagram.)

Possible - these are those futures that we think 'might' happen, based on some future knowledge we do not yet possess, but which we might possess someday (e.g., warp drive).

Plausible - those we think 'could' happen based on our current understanding of how the world works (physical laws, social processes, etc).

Probable - those we think are 'likely to' happen, usually based on (in many cases, quantitative) current trends.

Preferable - those we think 'should' or 'ought to' happen: normative value judgements as opposed to the mostly cognitive, above. There is also of course the associated converse class-the -preferred futures-a 'shadow' form of anti-normative futures that we think should not happen nor ever be allowed to happen (e.g., global climate change scenarios comes to mind).

Projected - the (singular) default, business as usual, 'baseline', extrapolated 'continuation of the past through the present' future. This single future could also be considered as being 'the most probable' of the Probable futures. And,

(Predicted) - the future that someone claims 'will' happen. I briefly toyed with using this category for a few years quite some time ago now, but I ended up not using it anymore because it tends to cloud the openness to possibilities (or, more usefully, the 'preposter-abilities'!) that using the full Futures Cone is intended to engender.

The above descriptions are best considered not as rigidly-separate categories, but rather as nested sets or nested classes of futures, with the progression down through the list moving from the broadest towards more narrow classes, ultimately to a class of one-the 'projected'. Thus, every future is a potential future, including those we cannot even imagine -these latter are outside the cone, in the 'dark' area, as it were. The cone metaphor can be likened to a spotlight or car headlight: bright in the centre and diffusing to darkness at the edge-a nice visual metaphor of the extent of our futures 'vision', so to speak. There is a key lesson to the listener when using this metaphor-just because we cannot imagine a future does not mean it cannot happen...

Then there are all of the imaginable ones (i.e., inside the cone), beginning with the sub-class of those that we judge to be unreasonable, (i.e., ridiculous), or impossible-'preposterous' in my alliteration- and the further sub-class of those that we judge to be 'reasonable but which would require knowledge we do not yet possess but which we might possess in the future ' and so 'might' happen-'possible'.

Then there is the sub-class of those that we think are reasonable based on what we currently know, and so 'could' happen; thus, 'plausible'. And so on through the rest: the sub-class of futures based on the playing out of current trends-'probable'; and finally the default extrapolation of current dynamics -the (single) 'projected' future, the only class in the whole schema containing only a single future, although different people will 'project' different futures, so it is really a single-member class containing many 'single' futures, as it were. The similarly single-member class of 'predicted' future, which had a similar underlying rationale-namely, what 'will' happen depends a lot on whom you ask-is very rarely used, except to make a specific point.

The class of preferred futures-what 'should' or 'ought to' happen-can take in any or all of the classes from preposterous to projected, because these futures must be at least imaginable (so inside the cone), and because people's idea of what they prefer-and how they judge others' preferences-can range from the default projected future thought to be coming all the way outward to (what is considered) outlandish preposterous-ness.

To this set, one may also add Wildcards-by definition low probability events (sometimes referred to as 'mini-scenarios') that would have very large impact if they occurred (Petersen 1997, 1999). Since they are considered 'low probability' (i.e., outside the Probable zone), any member of any class of future outside the range of probable futures could be considered by definition a wildcard (although this usage is not common, as the focus tends to be on 'high impact' events). Thus, in this view, some wildcards are considered plausible, some possible, some preposterous, and-the scariest of all-some we have not even imagined or dreamed of yet (i.e., potential)... These last are not even classifiable as 'black swans' (Taleb 2007), but rather as, perhaps, 'scarlet splofflings' (Q: 'what the hell are they?!' A: 'exactly!').

This taxonomy finds its greatest utility when undertaking the Prospection phase of the Generic Foresight Process (Voros 2003) especially when the taxonomy is presented in reverse order from Projected to Preposterous. Here, one frames the extent to which the thinking is 'opened out' (implied by a reverse-order presentation of the taxonomy) by choosing a question form that is appropriate to the degree of openness required for the futures exploration. Thus, "what preposterously 'impossible' things might happen?" sets a different tone for prospection than the somewhat tamer question "what is projected to occur in the next 12 months?"

References

Amara, R 1974, 'The futures field: Functions, forms, and critical issues', Futures, vol. 6, no. 4, pp. 289-301. doi: 10.1016/0016-3287(74)90072-X

--- 1981, 'The futures field: Searching for definitions and boundaries', The Futurist, vol. 15, no. 1, pp. 25-29.

--- 1991, 'Views on futures research methodology', Futures, vol. 23, no. 6, pp. 645-49. doi: 10.1016/0016-3287(91)90085-G

Bell, W 1997, Foundations of futures studies, 2 vols, Transaction Publishers, New Brunswick, NJ, USA.

Clarke, AC 2000, Profiles of the future: An inquiry into the limits of the possible, Millennium edn, Orion Books, London.

Dator, JA 2005, 'Foreword', in RA Slaughter, S Inayatullah & JM Ramos (eds), The knowledge base of futures studies, Professional CD-ROM edn, Foresight International, Brisbane, Australia.

Hancock, T & Bezold, C 1994, 'Possible futures, preferable futures', Healthcare Forum Journal, vol. 37, no. 2, pp. 23-29.

Henchey, N 1978, 'Making sense of futures studies', Alternatives, vol. 7, no. 2, pp. 24-28.

Petersen, JL 1997, 'The wild cards in our future: Preparing for the improbable', The Futurist, vol. 31, no. 4, pp. 43-47.

--- 1999, Out of the blue: How to anticipate big future surprises, 2nd edn, Madison Books, Lanham, MA, USA.

Taleb, NN 2007, The black swan: The impact of the highly improbable, Random House, New York.

Taylor, CW 1990, Creating strategic visions, Strategic Studies Institute, US Army War College, Carlisle Barracks, Carlisle, Pennsylvania, USA.

Voros J 2003, 'A generic foresight process framework', Foresight, vol. 5, no. 3, pp. 10-21. doi: 10.1108/14636680310698379

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From time to time people ask about the Futures Cone, and how it came about. Here is a brief history of how I came across it before adapting it to suit my use of the concept, and began using an earl...

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How science fiction can help predict the future - Roey Tzezana

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Spanish Present Slides in Video Tutorial

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Stunning video presentations, simply produced

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