On Monday I was in Southampton, giving a couple of guest lectures at the business school, including one on smart cities. As often happens in the preparation of a talk, the idea at its heart evolved and gained some resolution. What became clear in this iteration of thinking about smart cities and their successor, what I call ‘living cities’, is the difference in how their value is measured.
Smart cities have become, in many ways, an austerity era phenomenon. The idea has been around a long time but really began to get traction when marketers in tech companies caught on to the fact that cities needed to save money. Yes, there is an overlay of environmental messaging, but fundamentally cities have bought into the smart agenda because it might offset some of the impact of a population both growing and ageing, and falling government spending.
Dig in to any smart city project and look at its metrics and you will find that most are about cutting energy, fuel, and water costs. All of these have secondary values of reducing carbon emissions, but those don’t usually justify the investment. Nor do impacts on the navigability of a city, or its air quality (though that might if health care costs were more closely tied to municipal spending).
In short, smart cities are an attempt to cost-optimise static assets.
The idea of the living city that I put forward a few years ago is an evolution of the smart city. It takes the same basic infrastructure – sensors and actuators, a network layer, storage and computing, analytics – and adds a few more ingredients.
First, it adds some artificial intelligence (really just some machine learning), to understand not just how to optimise what is there, but whether these are even the right assets.
It does this by drawing from a new set of sensors. Ones that track not only the environment, but its denizens. Combining intimate personal data about mood, health, travel, and expressed sentiment, the system can understand the effect its infrastructure has on people. Are they happy? Are they productive?
There are many questions about the privacy aspects of this sort of sensing, but if we could lower the barriers to expressing our frustrations, or our joy, at good and bad service, then wouldn’t we choose to? We’re already conditioned to rate everything from our taxi drivers, to our morning coffee, to our everyday purchases.
These sensors tell the city about the need for change. Robotics give it the capability to make changes, potentially with minimal human intervention.
This is easiest to understand at a building scale. Imagine a smart building that recognises issues of noise in a shared workspace, so it can reshape the interior to create more private spaces and minimise noise travel in those that are shared. Or a smart building that realises that the entry and exit ways are poorly sized for peak times, so can adapt them accordingly.
Giant 3D printers, new materials, modular construction techniques, and future energy systems all play into this idea of a much more flexible built environment.
This creates a new challenge though: how do we moderate the decisions of the AI? This is where a change in the building lifecycle comes in. Right now construction and property operation is a very staged process with very little overlap between those stages. Once someone has completed their role in designing, or building a project, they tend not to be involved. I think we need to change the property business model to engage all of the parents of a project throughout its lifecycle. Particularly the architects who understand a building’s DNA and can steer the behaviours of the building’s AI in line with its original purpose and aesthetics.
All of this creates a much more adaptable environment. One whose value can be measured in much richer terms than just the cost of operation. We can ask whether a building, or even a whole city, is delivering on productivity, health, even joy, and steer its evolution to better meet those needs.