How the AI boom was enabled by a 1970s economic revolution
Artificial intelligence is accelerating a global economic revolution that began back in the 1970s. Researching the impacts of AI on different sectors of society highlights an important parallel moment in history: the creation of the “service economy” in the US.
In 1972, amid a period of global turmoil, a group of OECD (Organisation for Economic Co-operation and Development) economists sought to reinvent how nations thought not only about wealth but the very purpose of society. They did this by proposing a broad new category of commerce: services.
It seems hard to imagine now, but until then economists had perceived and measured trade largely in terms of goods alone. Money was made by exchanging tangible, physical products (wheat, guns, butter). To become a rich nation, the wisdom went, you needed to add unique value to your raw materials (crops, iron) by turning them into more complex products (processed foods, steel) that gave you a competitive advantage over other countries.
Instead, this new category of services lumped together a diverse range of “intangible” jobs and social goods – from teaching and driving trains to social housing and water – in a huge new economic basket. It suggested there could be common standards by which to trade in them globally, creating metrics that offered a new source of wealth for investors.
While it would be two decades until the General Agreement on Trade in Services became a cornerstone of the newly formed World Trade Organization in 1995, the reimagining of jobs and social goods as tradeable services had an immediate effect on nations around the world. It spurred a new wave of private enterprise, and changed how and why essential societal activities were provided.
It also enabled the rise of the generalist boss and the creation of the “CEO class”. To run complex sectors from public transport to healthcare required accepting a view of management as a skill divorced from the specifics of the activity being managed.
Statistics and benchmarks became more important than the particulars of the task at hand, since they determined how services were valued in the market. Consulting firms supercharged this new era of key performance indicators, audits, rankings and standardised workflows.
While trade unions and the public sometimes resisted these changes through strikes and street protests, they were largely unable to stem the tide. Many governments came to see their role less as providers of public goods, more as managers of services outsourced to the private sector. This dramatic shift in how global trade operates set the scene for how we view and measure AI today.
Services on steroids
At its core, AI technology is about seeing patterns across data that, due to scale and complexity, we humans cannot. Acting on what AI tells us can, for example, save lives through early detection of cancer. Yet within that promise, how AI is sold today looks very much like services on steroids.
The services revolution helped create common standards and means of valuation across different sectors of society. Today, when politicians and CEOs speak of AI, it is usually in terms of universal models that can be applied to almost anything, regardless of context or human values.
This understanding is only possible in a society in which many of the sector-specific challenges of, say, health services and utility companies are ironed out and glossed over by those operating and investing in them. The services approach has enabled this.
Today’s gobsmackingly high share valuations in AI-centric businesses result from global marketeers’ desire to own a piece of whichever system dominates how we create society – from accessing healthcare to finding love.
Amid strategies of mass data capture and subscription services, there is the assumption that only the private sector can be a provider – and that the solutions are largely the same. AI is the lucrative but badly defined tool with which mainly US providers are seeking to drive home their existing competitive advantage.
But this leaves us with an important question from history.
Who benefits?
Looking for parallels between what we see as AI today and the creation of the services economy points to the classic question, cui bono? Who benefits?
The invention of trade-in-services greatly expanded the range of activities in which financiers might speculate. Through pension funds and private shareholding, many people’s personal wealth grew rapidly as a result.
But it has also led to the rise of large multinational corporations, for example in energy and water utilities. Anger over rising prices and exorbitant CEO bonuses in these sectors are in part a consequence of the services revolution.
The present approach to AI is following a similar, but much-accelerated, path. The rollout of AI has not only made a small group of companies extraordinarily rich and powerful, it has created a global sovereignty crisis.
At the same time as governments are extolling the virtues of AI for service delivery, there is growing awareness that not all countries have equal control over a technology seen as critical to how society will be run.
To use and regulate AI wisely requires being clear-eyed about whether we are talking simply about technology, or a broader political project. Given the evidence of the services revolution, we believe it is time to look beyond the hype and examine more rigorously what AI actually means for different sectors of society – and what exactly it is trying to achieve.
Michael Strange does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. He is the author of Writing Global Trade Governance: Discourse and the WTO (Routledge, 2013), among other books.
Marisa Ponti does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.