If there’s one phrase that best expresses the two-way nature of the relationship between humanity and technology it’s probably Father John Culkin’s quote (often attributed to Marshall McLuhan): ‘We shape our tools and thereafter our tools shape us’. Humans create the technology, but that technology later shapes human behaviour, culture, perceptions, norms, and even the physical environment in which we live and work.
At the point when Elisha Otis demonstrated his safety elevator in 1854, the most desirable floors in any building were the lower ones (the ground floor or the first floor, which in Paris was actually called the ‘noble floor’). Higher floors meant more stairs, which meant lower status, but the invention of the elevator completely inverted this social hierarchy and penthouses became premium. Elevators made the skyscraper possible, which reshaped the urban environment as well as urban land economics entirely. The value of a plot of land was no longer fixed by its footprint but multiplied by the number of floors you could stack on it. Building regulations, zoning laws, fire codes, the entire legal and physical infrastructure of the modern city was redesigned around the assumption that buildings go up.
As the story of the elevator shows us, the initial debate about a new technology almost always focuses on first-order effects (safety, speed, application) but the real transformation happens at the level of second and third order effects – infrastructure, institutions, spatial design, and social norms. For AI, the current focus on automation misses the much bigger question about how organisations, workflows, skill development, career structures, and even physical workplaces will be redesigned around the assumption that AI is always available.
But there is sometimes a less comfortable nuance to second order effects. Jonathan Boymal notes that new technologies whose initial promise is freedom and efficiency may also carry with them the potential for social harm. Using the example of cars, he describes how urbanists and public health advocates from the 1930s through to the early 1960s weren’t only concerned about whether cars were safe, but also about whether automobile-centred planning would hollow out street life and shrink the everyday possibility of walking, long before those effects were widely visible. They were right of course. As cars became dominant, cities were progressively redesigned around vehicular speed. Zoning separated homes from shops and workplaces. Roads widened, footpaths narrowed, public transport was deprioritised. The built environment reshaped itself around the car, and in doing so made it increasingly difficult to live without one.
Boymal references Ivan Illich’s concept of ‘radical monopoly’ which is the idea that a technology or institution becomes so dominant that it renders all alternatives to it inaccessible or unthinkable. Technology doesn’t only reshape an environment, but more specifically it can reshape the environment to a point where non-use is penalised.
We have history here too. Before railways, every town kept its own local time based on the position of the sun. So Bristol was 11 minutes behind London. This was fine when the fastest thing moving between towns was a horse, but it made railway timetables unworkable. In 1847, the Railway Clearing House pushed British railways to adopt Greenwich Mean Time, and the rest of the country gradually followed. Once railways imposed standardised time, operating on local time became very difficult if not functionally impossible. The environment reorganised so completely that the prior way of doing things was eliminated.
Electric lighting tells a similar story. Before widespread electrification, human activity was almost entirely structured around daylight. Factories, shops, work, social life. In his book Disenchanted Night historian Wolfgang Schivelbusch famously wrote about how artificial light went beyond extending the day to create shift work, nightlife, shop windows, and how the rise of the salon changed bourgeois culture. Once cities, workplaces, and social expectations had reorganised around artificial light, opting out of the electrically extended day became a recipe for economic exclusion.
The smartphone is a more contemporary example of the same phenomenon. Banking without one is increasingly penalised as branches close and features go app-only. Navigating a city is harder without digital tickets, Google maps, QR code menus, ride-hailing. Restaurants have redesigned their kitchens and layouts around delivery apps. Hotels compete with every spare bedroom on Airbnb. The modern world is increasingly built around the smartphone.
In each case, the reshaping of the environment ends up becoming a condition of access, penalising non-use of the technology. Initial concerns around trade-offs give way to a rhetoric of inevitability. For AI, what begins with questions about the pace of development and a loss of cognitive sovereignty has become a rhetoric around inevitability and indispensability.
What’s perhaps most interesting about this is the speed at which this has happened. It took decades for car-dependent planning to become a default. Smartphone dependency built over roughly fifteen years. With generative AI, the inevitability framing arrived almost immediately. AI is so capable, so fast, and the expectation around its use so pervasive in organisational life that opting out looks eccentric, or even career-limiting. Interfaces default to AI support. Human-only workflows start to look like inertia to change rather than a deliberate choice. The framing around inevitability narrows the debate and recasts the loss of human agency as a mark of maturity rather than a design failure.
This is not an argument against AI adoption. It is an argument for paying very close attention to the terms on which it happens. These framings are choices. They feel like descriptions of an inevitable reality, but they are rhetorical moves. And with AI we are still, despite the breathless pace, early enough in the cycle that the environment has not yet fully reshaped. The organisational structures, the workflow designs, the career incentives, the skill development pathways, these things are still being decided. Not by the technology itself, but by the people deploying it and the institutional choices being made around it.
This means recognising that the choices being made now about which workflows default to AI support, which skills get invested in, which roles get redesigned, are infrastructure decisions with long half-lives, rather than experiments that can easily be reversed. It means treating every decision about where AI gets embedded not as a technology question but as an environment design question. And it means deliberately protecting space for human-only thinking in workflows before AI becomes the default, and designing career progression around deepening expertise, not just efficiency gains.
The lesson from cars, railways, electric light, and smartphones is not that environmental reshaping is always bad. Standardised time was a genuine improvement. Electric light created enormous economic and cultural value. The lesson is that once the environment reshapes around a technology, the range of available choices narrows dramatically, and the moment for deliberate design passes quickly. AI will reshape how we work. But the question is whether that reshaping happens to us, or whether we retain enough agency to shape it ourselves.
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Photo by Aleksandr Popov on Unsplash
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