The State of AI: when will we reach economic singularity?

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.

You can find earlier discussions on the US vs China, energy constraints, chatbot companions and the future of war here. And if you want to hear more about the debate on autonomous weapons listen to Helen Warrell and James O’Donnell on the FT’s News Briefing podcast here.

This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor-at-large David Rotman about the true impact of AI on the global economy.

On Tuesday December 9 at 1pm ET (6pm GMT) you can join David, Richard and MIT Technology Review editor-in-chief Mat Honan for an exclusive, subscriber-only live conversation on how AI is reshaping the global economy. Register for the event here.

Richard Waters writes

The adoption of any far-reaching new technology is always uneven, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone productivity across the economy as a whole.

At one extreme, AI coding assistants have revolutionised the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95 per cent of generative AI projects produce zero return.

That has provided fuel for the sceptics who maintain that, by its very nature — a probabilistic technology prone to hallucinating — generative AI will never have a deep impact on business.

To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox” of IT in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt.

Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull.

In the case of AI, companies need to build new infrastructure (in this case, new data platforms), redesign core business processes and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place.

The opportunities — and the challenges — are enormous. An executive at one Fortune 500 company says his organisation has carried out a comprehensive review of its use of analytics and concluded that its workers, overall, add little or no value. Rooting out the old software and replacing that inefficient human labour with AI might yield significant results. But, as this person says, it would require big changes to existing processes and take years to carry out such an overhaul.

There are some early encouraging signs. US productivity growth, stuck at 1-1.5 per cent for more than a decade and a half, rebounded to more than 2 per cent last year. It probably hit the same level in the first nine months of this year, though the lack of official data due to the recent US government shutdown makes this impossible to confirm.

It is impossible to tell, though, how durable this rebound will be or how much can be attributed to AI. The effects of new technologies are seldom felt in isolation. Instead, the benefits compound. AI is riding on the back of earlier investments in cloud and mobile computing. In the same way, the latest AI boom may only be the precursor to breakthroughs in fields that have a wider impact on the economy, such as robotics. ChatGPT might have caught the popular imagination, but OpenAI’s chatbot is unlikely to have the final word.

David Rotman replies

This is my favourite discussion when it comes to artificial intelligence. How will AI impact overall economic productivity? Forget about the mesmerising videos, hopeful companionship and agents to do tedious everyday tasks. AI’s bottom line will be whether it can grow the economy and that means increasing productivity. 

But, as you say, it’s hard to pin down just how AI is effecting such growth or how it will in the future. Brynjolfsson predicts that AI will follow a J-curve in which initially there is a slow, even negative effect on productivity as companies invest heavily in the technology, before they finally reap the rewards. And then the boom. 

But there is a counter example to the just-be-patient argument. Despite smartphones and social media and apps such as Slack and Uber, digital technologies have done little to grow the economy.

Daron Acemoglu, an economist at MIT and 2024 Nobel Prize winner argues the productivity gains from generative AI will be far less and take far longer than AI optimists think. The reason is that although the technology is impressive in many ways, it is too narrowly focused on ChatGPT and other products that have little relevance to the largest business sectors.

The statistic you cite of a lack of business benefits for 95 per cent of AI projects is telling. 

Take manufacturing. No question some version of AI could help; imagine a worker on the factory floor snapping a picture of a problem and asking an AI agent for advice. The problem is that the big tech companies creating AI aren’t really interested in solving such mundane tasks, and their large foundation models mostly trained on the internet aren’t all that helpful either. 

It’s easy to blame the lack of productivity impact from AI so far on the failings of business practices and poorly trained workers. Your example of the executive of the Fortune 500 company sounds all too familiar. But it’s more useful to ask: how can AI be trained and fine-tuned to give workers, such as nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs. 

The distinction matters. Numerous companies announcing large lay-offs recently cited AI as the reason they can be more efficient with fewer workers. The worry, however, is it’s just a short-term cost-saving scheme. As economists such as Brynjolfsson and Acemoglu agree, the productivity boost from AI will come when it’s used to create new types of jobs and augment the abilities of workers, not when it is used to just slash jobs to reduce costs. 

Richard Waters responds 

I see we’re both feeling pretty cautious, David, so I’ll try to end on a positive note. 

Some analyses assume that a much greater share of existing work is within the reach of today’s AI. McKinsey reckons 60 per cent, (versus 20 per cent for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4 per cent. Also, calculations such as these only take into account automating existing tasks; any new uses of AI that enhance existing jobs would, as you suggest, be a bonus (and not just in economic terms).

Cost-cutting always seems to be the first order of business with any new technology. But we’re still in the early stages and AI is moving fast, so we can always hope.

Further reading

• FT chief economics commentator Martin Wolf has been sceptical about tech investment boosting productivity, but says AI might prove him wrong. The downside: job losses and wealth concentration might lead to “techno-feudalism”

• Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and the course corrections that will be required

• David also wrote this piece about how we can best measure the impact of basic R&D funding on economic growth, and why it can often be bigger than you might think