Since Freedomlab Thinktank started sharing its internal research with a broader audience, we have published 499 articles on our website. This marks our 500th article, celebrating over five years of speculative essays, philosophical analyses, scenario thinking, zeitgeist documents, and much more. To commemorate this milestone, we invited three thinktank members to reflect on our publication history and highlight some of the most remarkable pieces.
Sebastiaan Crul: At FreedomLab, like many others today, we emphasize the need to think beyond boundaries and domains, working at the intersection of fields such as technology and society, or technology and geopolitics. We often talk about concepts like ‘relational thinking,’ ‘transdisciplinary thinking,’ and ‘systems thinking.’ These are hallmarks of our approach. However, the risk, especially today, is falling into the trap of simply echoing popular terms without truly embodying them. It's easy to declare the need for systemic or relational thinking in a complex, uncertain world, but harder to actually practice it.
That’s why I’ve particularly enjoyed writing articles that aim to provide analytical and philosophical depth to our transdisciplinary work, accommodated by concrete examples of what it means in today’s world. One of my favorite ways to do this is through movie analysis and socio-cultural examples. My series on the history of the cyborg is a good example, where I explored how the evolution of man-machine hybrids reflects broader societal changes. We’ve come a long way from the masculine Terminator cyborg, to the replicant Rachel in Blade Runner, the fully virtual lifeform Puppet Master in Ghost in the Shell, and finally to contemporary AI cyborgs like Ava in Ex Machina. Currently, I’m working on a series about AI metaphors, blending contemporary debates with strategic analysis through an introduction to the philosophy of technology.
Another approach I frequently use is applying the digital systems framework—The Stack—to specific cultural practices. One of the first times I did this was back in 2019, examining the rise of sports wearables for amateurs. In that article, I discussed the common disappointment people experience with wearables that do little more than count steps or display heart rates in real time. I speculated about what could elevate these devices to the level of professional athletes' analysis. For this to happen, we need more sensors integrated into sports clothes and wearables, platforms that support third-party apps and data integration, and, most importantly, new AI forms to analyze the data and present the results through intuitive interfaces. I imagined the potential rise of voice interfaces backed by AI-driven sports coaches that would guide you in real-time during your training session, create personalized training schedules and give diet tips. Today, as generative AI rapidly disrupts fields like marketing, graphic design, and translation, one cannot help but wonder: should sports coaches be concerned about their jobs too?
Pim Korsten: We are living in uncertain times, in which it seems to be increasingly difficult to peer into the future. Just to mention a few: the covid pandemic wrecked our healthcare as well as social and economic systems, which seems utterly unprepared for such a shock. A few years later at the end of 2022, OpenAI launched its generative AI chatbot ChatGPT, becoming the fastest-growing consumer application in history by a large margin as its capabilities took the world by surprise. And a few years before, Trump was elected US President with most polls giving him little to no chances of winning until days before the election.
At FreedomLab, we are reluctant about providing quick answers on how the future will look. Instead, we offer and try to widen the space for free thought. This freedom for thought helps to navigate uncertainty as speculate about the future to get a grip on current developments. In everyday language, ‘speculation’ means talking about things in an imprecise and unfounded manner. Something like bragging about something you do not know too much about or just uttering some random ideas on a given question. However, speculation can also be a much more precise way of thinking. Following the Kantian tradition, speculation means to reason and think beyond the input given by empirical experience. As our reason ‘transcends’ the concrete, empirical reality, it cannot give indubitable and certain knowledge. But this does not mean that we cannot say anything informative about the future; instead we try to find regular patterns and regularities that say something real about the future. And instead of just naively extrapolating historical patterns, we look at the intersection of these and see where certain trends and developments create new phenomena. For example, using the concept of ‘technological momentum’, we often use our Stack framework to see where emerging technological innovations coincide on a shared timeline. For example, when Apple introduced its first iPhone in 2007 it single-handedly launched the ‘smartphone computing era’. Despite the iPhone’s neat design and huge marketing budget, it also benefited from the convergence and integration of various technological innovations. For example, 3G wireless data made data-driven applications and web browsing possible on a smartphone, while miniaturization of hardware/computing components made these computers fit into our pockets. Capacitive touch screen technology provided intuitive user experiences, and GPS created location-based services that showed the benefits of carrying a smartphone all day every day.
Speculation helps to envision where convergences between innovations on a shared timeline create new, unforeseen things. For example, in the digital realm, we see rising momentum for a new systemic overhaul, dubbed ‘the decentralized Stack’. For example, blockchain technology and permissionless protocols provide a shared, decentralized data infrastructure without a single controlling entity, helping to create more open and secure digital systems. For example, Europe is now considering whether its digital Euro payments could be carried out on permissionless ledgers. And the ‘Merge’ on the Ethereum platform saw an increase in the development and adoption of smart contracts and decentralized applications (dApps). As we see these chances, we can speculate on the development of a decentralized Stack, as well as critical barriers and obstacles, such as scalability, user experience and accessibility, regulatory and compliance challenges and limited interoperability.
Speculation is also not just naming every other trend after each other, in a linear or analytic order; speculation is about synthesizing trends into larger themes. This means first that we consider phenomena not in isolation, but try to look at them from certain perspectives to grasp what each individual misses but collective construct. We did this for example with Francis Fukuyama’s End of History thesis, who believed (in the 1990s) that all modernizing countries would transform into liberal democracies with capitalist free markets. However, by looking at the core claims and futures envisioned by criticism of Fukuyama’s hypothesis, we can sketch certain pathways beyond the end of history, i.e. different pathways or alternatives that countries can take. As such, we try to combine different points of view and perspective to understand the fundamental trends that shape the future.
Lastly, speculation is about making thorough philosophical, conceptual analyses of a core concept and seeing what important thinkers, both historical and current, would think about a given case in point and subject them to a critical analysis. Again, critical in the Kantian sense of determining what something means and how we can speak about something, instead of just being negative or criticizing. Only then can we begin to understand what our core concepts mean and how they have ‘realized’ themselves in our shared world. This makes speculation not an unfounded practice creating castles in the sky, but deeply informed by the historical as well as metaphysical trajectories of our guiding concepts and ideas in our world. For example, we examined what the concept of ‘freedom’ means in our day and age, and question what the philosopher Hegel would think about the current state of freedom almost 200 years after his death. Indeed, we still see that his conception of freedom rings true: after the 2008 financial crisis, we are seeing a crackdown on the ‘freedom’ of markets and (financial) institutions, bringing them more aligned and under control of governments. In a much broader sense, we are seeing a reversal of the primacy of economic value and efficiency against other values that are more explicitly related to ‘freedom’: in response to China’s economic clout Western governments have boosted fiscal spending and industrial policy to maintain national sovereignty and in the name of national security. Although not explicitly mentioned by economists, it often pits our ‘democracies’ against ‘autocracies’ and therefore the need of a more active and interventionist economic policy that is a clear break with the neoliberal freedoms of the past decades. These types of analyses are thus not only observing; they also help to envision futures that we want to achieve, where we will actively take a stance beyond our thinking. As such, our thinking also tries to speculatively design a better future in our complex world.
Sjoerd Bakker: At FreedomLab, we delve into the complex relationship between humans and technology, exploring how these interactions shape our society and future. One aspect of this how different generations relate to technology, and how it impacts their everyday lives. In 2019, I examined the various ways in which we interact with technology – drawing on the work of Don Ihde – to distinguish between very casual forms of interaction with technology, in which the technology itself goes largely unnoticed (as with a pair of glasses), and interactions in which the technology itself is much more present. The latter typically happens when technology fails, and we start shouting at the machine, or when technology clearly mediates our experiences, in the case of an MRI scan, for instance.
This distinction is rather crude and, more importantly, relative to specific contexts, individuals, and, perhaps, generations too. In my piece, I stressed how Gen Z has a very natural way of dealing with smartphones (and other computers), while not necessarily understanding how these things work. Older generations, by contrast, may be a bit clumsy at times when it comes to new features and apps, but they are likely to have a far better understanding of the underlying technology. They – we, I – did not grow up with smartphones, but the phones grew up with us. And, sticking with this metaphor, we have known computers and phones ever since they were babies, we learned to change their diapers, and we have battled with them through their adolescence. So yes, Gen Z may know how to use the technology, but they hardly understand how these devices work ‘under the hood’. This is not Gen Z's fault; they simply never had a chance to take a proper look under the hood.
Luckily, generative AI presents Gen Z with a second chance, as they get to enjoy an immature technology of their own. The mediocre performance of current AI models offers Gen Z a unique opportunity to critically engage with and understand how an emerging technology mediates human experience, before it becomes seamlessly integrated and opaque.
Generative AI may have developed beyond its earliest infancy, but it is still an adolescent at best. It presents itself as an all-knowing oracle, but it is still prone to silly mistakes and questionable fantasies. To experience these growing pains firsthand, and to struggle with imperfect applications, is a chance for Gen Z – and everyone else – to learn about the inner workings of the technology. Because it is far from flawless, this is the phase in which we still look at the technology itself, instead of through it. Before it becomes self-evident and transparent, we can still observe and experience how the technology mediates our understanding of the world around us.
Later generations may only experience AI as a voice, or another kind of interface, without really knowing what or who they are dealing with. If and when AI really matures and becomes this all-knowing and all-capable assistant that we can steadily rely on, future generations are likely to engage with the assistant all the time, while barely understanding what happens behind its interface. As long as it works, and there’s no need – or possibility – to really question the answers and solutions the assistant provides. I’m sure that these kids – Gen Alpha and beyond – will understand perfectly how to use the technology, and how to integrate it seamlessly into their everyday lives. I’m also sure that they will laugh at Millennials and Gen Z'ers struggling with the technology and its latest features. However, I am also quite sure that they will overlook the many ways in which their assistant mediates their experiences and how it will present them with answers, suggestions, and practical solutions that serve not only the interests of the users but also the interests of the companies or governments that hide behind the shiny interface.
