AI, data, and the urban economy: A renewed quest for development pathways

New developments, pathways and servification

Structural change used to be the classic recipe for growth: workers move from farming to factories, then to services – often found in urban centres. Hence, the strong historical link between structural change and urbanisation.

In wealthier economies, we’re now deep into that last stage - and pushing it further. Servification of an economy isn’t just about more services; it’s about services embedded across the economy – from logistics to education to healthcare.

But the shift isn’t smooth. On the one hand, robotisation and automation can displace routine tasks, hollow out middle-income jobs, and fuel the rise of both low-paid, unstable gig work and other advanced tech roles. On the other hand, AI appears to compress wages for medium- and high-skill workers, undermining tradtional labour market advantages. In less developed economies, these shifts may result in urbanisation without job upgrading. In more advanced economies, they create new tensions, as job stability and wage progression come under strain.

A prime example is platform-based work (e.g., Uber, Deliveroo), which has made labour more flexible, but potentially more precarious. Many governments, especially in Europe, have stepped in. Spain’s “Rider Law” reclassifies gig workers as employees. Dutch courts have challenged bogus self-employment. Meanwhile, private actors are experimenting with alternatives, like the French Mobicoop or the American inDrive, which promote cooperative ownership or fairer pricing models.

Developments, platforms, and uneven opportunity

Platforms also create new entrepreneurial pathways. The now-famous Dubai bar (a small-scale invention in Dubai) became a global brand overnight thanks to TikTok influencers. While certainly not representative, it shows how digital visibility has redefined what “scale” and “market access” mean.

But not everyone plays on equal footing. Data – the raw material of the digital economy – varies in value and accessibility. In places like the U.S., it’s monetised by platforms, but access and visibility are often skewed. Even sellers on platforms like Amazon or Alibaba require significant investment and skills to understand how algorithms work and how to appear at the top of search results. Data is now a core production input, like labour or capital. But turning it into innovation – whether as a vendor, a startup, or a city – requires infrastructure, skills, and institutional capacity. Without these, the gains of digitalisation tend to concentrate in already strong firms and privileged urban areas.

The task ahead

As we have seen, AI and digitalisation aren’t just layering new technologies onto urban economies – they are transforming their very foundations. Traditional development paths are giving way to new patterns of servification, platformisation, and data-driven production.

These shifts are redefining labour markets, putting pressure on policy frameworks, and unlocking new forms of productivity, particularly in urban contexts. But benefits are not automatic. Without targeted investment, institutional capacity, and nuanced regulation, digitalisation risks reinforcing inequalities rather than resolving them.