The State of AI: the economic singularity | MIT Technology Review

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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioArtificial intelligenceThe State of AI: Welcome to the economic singularityThis 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 job market.
By David Rotman and Richard WatersDecember 1, 2025FT/MIT Technology Review | Adobe Stock 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. 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 job market. Bonus: If you're an MIT Technology Review subscriber, you can join David and Richard, alongside MIT Technology Review’s editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here. Richard Waters writes:
Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized 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% of gen AI projects produce zero return.
That has provided fuel for the skeptics who maintain that—by its very nature as 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. FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK In the case of AI, companies need to build new infrastructure (particularly 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 both enormous. An executive at one Fortune 500 company says his organization 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 labor with AI might yield significant results. But, as this person says, such an overhaul would require big changes to existing processes and take years to carry out. There are some early encouraging signs. US productivity growth, stuck at 1% to 1.5% for more than a decade and a half, rebounded to more than 2% 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 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 favorite discussion these days when it comes to artificial intelligence. How will AI affect overall economic productivity? Forget about the mesmerizing videos, the promise of companionship, and the prospect of agents to do tedious everyday tasks—the bottom line will be whether AI can grow the economy, and that means increasing productivity.  But, as you say, it’s hard to pin down just how AI is affecting such growth or how it will do so in the future. Erik Brynjolfsson predicts that, like other so-called general purpose technologies, 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 finally reaping the rewards. And then the boom.  But there is a counterexample undermining the just-be-patient argument. Productivity growth from IT picked up in the mid-1990s but since the mid-2000s has been relatively dismal. Despite smartphones and social media and apps like Slack and Uber, digital technologies have done little to produce robust economic growth. A strong productivity boost never came. Ask AIWhy it matters to you?BETAHere’s why this story might matter to you, according to AI. This is a beta feature and AI hallucinates—it might get weirdAn industry I care about is.Tell me why it mattersLearn more about how we're using AI. Daron Acemoglu, an economist at MIT and a 2024 Nobel Prize winner, argues that the productivity gains from generative AI will be far smaller and take far longer than AI optimists think. The reason is that though the technology is impressive in many ways, the field is too narrowly focused on products that have little relevance to the largest business sectors. The statistic you cite that 95% of AI projects lack business benefits 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.  It’s easy to blame the lack of productivity impact from AI so far on 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 AI can be trained and fine-tuned to give workers, like nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs.  The distinction matters. Some companies announcing large layoffs recently cited AI as the reason. The worry, however, is that it’s just a short-term cost-saving scheme. As economists like 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 just to 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% (versus 20% for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4%. Also, calculations like these are based on automation of 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 skeptical about whether tech investment boosts productivity but says AI might prove him wrong. The downside: Job losses and wealth concentration might lead to “techno-feudalism.” The FT's Robert Armstrong argues that the boom in data center investment need not turn to bust. The biggest risk is that debt financing will come to play too big a role in the buildout. Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and what course corrections 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. by David Rotman and Richard WatersShareShare story on linkedinShare story on facebookShare story on emailPopularWe’re learning more about what vitamin D does to our bodiesJessica HamzelouHow AGI became the most consequential conspiracy theory of our timeWill Douglas HeavenOpenAI’s new LLM exposes the secrets of how AI really worksWill Douglas HeavenMeet the man building a starter kit for civilizationTiffany NgDeep DiveArtificial intelligenceHow AGI became the most consequential conspiracy theory of our timeThe idea that machines will be as smart as—or smarter than—humans has hijacked an entire industry. But look closely and you’ll see it’s a myth that persists for many of the same reasons conspiracies do.
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The State of AI: Economic Singularity – A Complex Assessment (1287 words)

This article, a collaborative effort between the Financial Times and MIT Technology Review, attempts to dissect the evolving impact of generative AI on the global economy and, specifically, the job market. The piece, penned by David Rotman and Richard Waters, presents a cautiously optimistic, yet deeply nuanced, perspective on a technology that has sparked both immense excitement and considerable anxiety. Central to the discussion is the recognition that the initial impact of AI, particularly generative AI, has been far more uneven and less dramatic than many initial predictions suggested.

The core argument hinges on the concept of a “productivity paradox,” echoing concerns raised by Erik Brynjolfsson decades earlier. This paradox highlights the historical tendency for new technologies to initially produce little discernible effect on overall economic productivity until, after a lag, they eventually generate substantial improvements. The authors cite the 95% failure rate of AI projects – as exemplified by the widely reported MIT study – as evidence that the current wave of AI, despite its impressive capabilities, is still in its early stages of impacting business outcomes. This isn’t a dismissal of AI's potential, but rather a recognition of the time and strategic adjustments needed to translate technological advancements into tangible economic gains.

A significant portion of the analysis revolves around the specific ways companies are attempting to integrate AI. The authors emphasize that successful implementation requires a multi-faceted approach that extends beyond simply deploying AI tools. It demands fundamental shifts in corporate infrastructure, particularly the development of robust data platforms. Moreover, substantial investment in retraining workers is frequently cited as a crucial, yet often underestimated, component. The concerns raised by a Fortune 500 executive, mirroring observations across various organizations, reinforce the idea that a superficial, cost-cutting approach—simply replacing human labor with AI—is unlikely to yield significant productivity benefits.

The examination pivots to a more optimistic outlook, tentatively fueled by a rebound in US productivity growth, albeit a fragile one. The potential for AI to augment human capabilities, rather than merely replace them, is highlighted as a key driver of future gains. The success, or lack thereof, will likely depend on training workers, allowing them to specialize in areas that require uniquely human skills – critical thinking, creativity, problem-solving, etc. – while AI handles more routine tasks. The authors suggest that focusing on entirely new job creation, spurred by AI, represents a far more productive approach than simply diminishing existing roles.

Further complicating the picture is Daron Acemoglu’s perspective, who argues that AI’s productivity gains will be limited and slower than anticipated due to the technology’s narrowly focused applications. He points to the significant disparity between the hype surrounding generative AI and its actual relevance to the vast majority of business sectors. This critique underscores a critical challenge: AI’s strengths are currently concentrated in automating existing tasks, rather than fundamentally reshaping industries. The discussion around AI's potential in mundane tasks like factory floor assembly, aided by AI-powered image recognition, is presented not as revolutionary, but as a potential enhancement of existing worker capabilities.

The article incorporates a comparative analysis, drawing attention to the contrasting opinions of various experts. Brynjolfsson and Acemoglu, both recognized authorities in the field of technological change, offer distinct frameworks for understanding AI’s trajectory. Furthermore, the piece incorporates insights from MIT Technology Review’s own analysis, demonstrating a synthesis of diverse perspectives.

A cautious optimism is maintained in considering potential future gains. McKinsey’s projection of 3.4% annual productivity gains across the economy, based on a comprehensive automation of existing work, suggests a potential for substantial transformation. However, the authors acknowledge the inherent uncertainties involved.

Ultimately, the article suggests that the future of AI’s impact on the economy hinges on a strategic, adaptable, and human-centric approach. It is not a simple technological fix but, rather, a complex and protracted process of adaptation and integration.