Imagined Futures



The challenging part of predicting what the future holds isn’t prediction per se, it’s getting a useful prediction from messy, noisy, uncertain data. How will technologies, societies, ideologies, systems, institutions, rules, needs, norms and all the other material and ideological forces making and shaping social reality converge? How can we have confidence in what we think, what we imagine, might happen in a day, a week, a year, a decade?

One approach is to extrapolate from things we know, whether about ourselves or others, or about a given system. Try it yourself. One of our team predicts that 5 years from now at 3pm on Thursday, November 12th 2026 they will be picking their daughter up from school. Sometime in July of 2040, another's son will in all probability be sitting his final year university exams. Another predicts that they will buy a pair of women’s fluffy socks in December 2024, because this is a fixed feature on his wife’s Christmas list.

Recommendation algos apply the same basic process, extrapolating from what they've been told about you and cross-referencing with the choices made by other people with similar characteristics, identities, and preferences. The more data they have the more confident the prediction can be. So a 10 year repeated history of fluffy sock purchases in the second and third weeks of December, for example, translates into an impeccably timed marketing message from fluffysocks.com. Knowledge that you have a son aged 4 translates into knowledge that you’ll be susceptible to university financing offers in the first quarter of 2037. Knowing that you pick your daughter up from school at 3:15pm triggers job suggestions weighted towards flexible, near-home working.

It’s tempting to think that this predictive process is driven by historical data. But it's not the historicity of the data that does the work. Rather, it’s the degree to which that data reveals something structural and potentially timeless about how we live our lives, about the most plausible of future paths.

But this process of extrapolating from the micro to the macro isn't the only way of uncovering a predictive structure and - as data gets more uncertain, complex or ambiguous - other approaches may be better. When philosophers and ethicists ask “what would you do if” ... presented with runaway trolley cars, drowning children, fat men stuck in caves, ticking time bombs, rings of invisibility, or an identity stripping veil of ignorance, the underlying assumption is that putting yourself in this imagined hypothetical state helps reveal some deeper, timeless truth about how decisions should be made and the rules that should order society. This goal to uncover generalizable truths is the same whether you happen to be a marketing strategist or a philosopher. Only the methods vary. 

It should be obvious by this point that prediction is a somewhat fuzzy concept, in large part because we - individuals, societies - have “constitutional agency”, a say in shaping the structures that guide future agency. Behavioural economics and the idea of "nudging" captures this fuzziness nicely. On the one hand behavioural scientists investigate the structural constants - including human psychology - which allow us to predict agential choices or preferences. There's a claim here about the ability to predict what can be called a natural future, a future state that will emerge organically from the status quo. On the other hand, behavioural scientists can use this understanding to design incentives that nudge or adapt preferences in particular ways, with or without awareness. The drip-drip-drip of an idea - buy this product, support this politician, protest this legislation, boycott this company - through a social feed adapts the choices you make and the future you create for yourself. The design of a train station to regulate passenger flows. Policies to wear seatbelts, tax sugar, or require companies to report their greenhouse gas emissions. Changing governing structures or systems in a purposive way creates what we can call an imagined future.

Societies have their own ways of combining natural and imagined futures. Constitutions, for example, combine extant moral instincts and norms to define a collectively imagined future for a society, a future governed and indelibly shaped by key principles. Legal rules provide a predictive hook that would have allowed Abraham Lincoln (who himself wrote: "the best way to predict your future is to create it") to believe that 100 years from his presidency the US would still have habeus corpus rules preventing people from being jailed without trial. To put this another way, laws and policies create a path dependency for the actors in a system. If we can believe that most people most of the time will follow the rules AND we believe that those rules will persist into the future, this provides a compelling basis for future prediction. 

Why this matters to us.

We love data at Adjective Ventures, but not in the abstract. Data only really becomes interesting to us as investors in the future when it’s held together in a compelling imagined narrative about why this will lead to that.

Much of the most influential recent thinking on business strategy and organisational design highlight the value of aligning around imagined futures. Consider the role of purpose. The evidence shows that businesses designed and led with a clear purpose grow more and fail less than businesses without. "Purpose" in this case is a proxy for the imagined future an organisation has set out to achieve.

Entrepreneurs more generally are united in a focus on creating new futures. The imagined future plays a well documented role here. The idea for Google Earth and Google Maps, for example, was sparked by the description of an imagined computer programme called "Earth" in the sci-fi novel Snow Crash (Neal Stephenson  1992). Radical innovators have always tended to started with an image of the future they want to create and work back from this future state. Google's moonshot programmes, Gates' philanthropic strategy and Musk's mindset at Tesla/Neuralink/SpaceX (elegantly explained here) all encapsulate an imagined futures methodology.

By the same token, venture capital has always attracted investors with a capacity to imagine future worlds. For all the due diligence processes and financial modelling in place, at crunch decision points most early stage investors implicitly revert to the question of whether a prospect has the potential to leverage or actively catalyse a future they expect or want to see emerge. 

So we at Adjective Ventures start with imagining plausible futures. Imagine a future where all manufacturing is automated and decentralized. Imagine a future where diets and medical treatments are bespoke engineered to you. Imagine a future where university education is free and accessible to anyone, anywhere in the world. Imagine a future where autonomous technologies make decisions tailored to your personal ethical committments. Imagine a future where the protection and redress offered by law and human rights are automatically triggered in the event of a violation. Imagine a future where 80% of jobs are replaced by machines. Imagine a future where 90% of spending occurs in the virtual economy. Imagine a future where we only purchase from or invested in companies with a carbon negative footprint.

The point is we're being explicit and intentional about the methodological, evidentiary role of imagined futures. That's our strategy for making qualitatively better investment decisions.

In practical terms this means we actively partner with designers, writers, musicians, poets, and artists; we rely on thought experiments, simulations and games to test product-market fit; and we unapologetically start new ventures from an imagined idea of the human-kind future we want to create, figuring out the best strategic path and partners needed to engineer this imagined future into existence.