Federal Reserve Paper Assesses Generative AI's Potential Impact on Productivity

AI Won’t Boost Human Productivity Just Yet, a New Paper From the Federal Reserve Says

The timeline for an AI productivity boom will be “inherently slow†and “fraught with risk,†according to the country’s most powerful economic institution. Generative AI is not just another tech hype cycle that is bound to die down but is instead a game-changer for human productivity, according to the Federal Reserve. The big caveat, though, is the road to get there will be “inherently slow†and “fraught with risk.†In a recent paper published by the Fed Board of Governors, researchers suggest that the hype around generative AI is probably not a bubble in the long run and that the technology will be a serious macroeconomic force, proving to have revolutionary effects for labor productivity akin to electricity and the microscope. The idea that generative AI will make the workforce more productive isn’t a groundbreaking one. It’s been lauded by corporate executives and many AI bulls alike since OpenAI’s generative AI model ChatGPT sparked the AI craze. But what’s significant is that the country’s most powerful economic institution has just voiced notable confidence in the technology’s potential. Albeit with a catch. The paper divides technological innovations into three categories. First, you have innovations like the light bulb, which dramatically increased productivity initially by allowing workers to not be limited to daylight. But once the technology was adopted widely, the lightbulb stopped providing additional value to workplace productivity. “In contrast, two types of technologies stand out as having longer-lived effects on productivity growth,†the researchers write, and AI has characteristics of both. The first are “general-purpose technologies,†like the electric dynamo or the computer. The electric dynamo was the first practical electric generator, and it continued to deliver accelerating productivity growth even after widespread adoption because it spurred related innovations and continued to improve on itself. The researchers say that generative AI is already showing signs that it fits the bill. You have specialized LLMs for specific domains like OpenAI’s LegalGPT meant to assist in legal matters, and “copilots†like Microsoft’s Copilot product, which is meant to increase office productivity by integrating generative AI into corporate workstreams. Fed researchers think even more knock-on innovations are to come, and that wave will be led by digital native companies. And it’s evident that the core technology is rapidly innovating and will likely continue to do so as companies develop the technology with an aim to achieve artificial general intelligence. In the meantime, the paper points out, the technology’s rapid growth has already given us further innovations like agentic AI and landmark AI models like Deepseek’s R1. The second type of technology is called “inventions of methods of invention,†the most prominent examples being the microscope or the printing press. Although a microscope has now become a common tool, it continues to raise levels of human productivity by enabling research and development projects. Generative AI has been helpful in simulations to understand the nature of the universe, in novel drug discoveries, and more. And the paper notes that there has been a huge spike, starting in 2023, of companies citing AI within research and development contexts and in corporate earnings calls, showing that perhaps AI’s integration with corporate innovation has already begun. Alas, this confidence comes with a caveat. AI will be a boon for economic and productivity growth, but it is unlikely to happen overnight. The Fed’s paper says the biggest challenge with generative AI right now isn’t the tech itself: it’s getting people and businesses to actually use it. While researchers are starting to adopt it more, most companies outside of tech and the scientific fields haven’t worked it into their daily operations yet, with the exception of the finance industry. And industry surveys show that AI adoption is far higher within large firms than small ones. So while generative AI is likely to boost how productive we are overall, the impact will be slow. That’s because it takes time, money, and other supporting tech like user interfaces, robotics, and AI agents to make AI really useful across the economy. The authors compare it to past big tech changes, like advances in computation, which accumulated for decades before causing a productivity boom. The timeline for that boom is still unknown. Goldman Sachs economists think AI’s effects on labor productivity and GDP growth in the U.S. will start to show in 2027 and will accelerate to a peak in the 2030s. Another risk the Fed points out comes with building infrastructure for anticipated demand. A widespread adoption of generative AI means significant need for investment in data centers and electricity generation. But investing too quickly can have "disastrous consequences†when demand doesn’t grow as expected, the Fed warns, similar to how railroad overexpansion in the 1800s led to an economic depression towards the end of the century. Despite the caveats, the Fed is confident that generative AI will be transformative for productivity. But whether that transformation continues to accelerate perpetually and have as big of an effect as the electric dynamo or the microscope will depend on the extent and speed of the technology’s adoption.

Federal Reserve economists aren't sold that AI will actually make workers more productive, saying it could be a one-off invention like the light bulb

A new Federal Reserve Board staff paper concludes that generative artificial intelligence (genAI) holds significant promise for boosting U.S. productivity, but cautions that its widespread economic impact will depend on how quickly and thoroughly firms integrate the technology. Titled "Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?" the paper, authored by Martin Neil Baily, David M. Byrne, Aidan T. Kane, and Paul E. Soto, explores whether genAI represents a fleeting innovation or a groundbreaking force akin to past general-purpose technologies (GPTs) such as electricity and the internet. The Fed economists ultimately conclude their "modal forecast is for a noteworthy contribution of genAI to the level of labor productivity," but caution they see a wide range of plausible outcomes, both in terms of its total contribution to making workers more productive and how quickly that could happen. To return to the light-bulb metaphor, they write that "some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not." Here's why they regard it as an open question whether genAI may end up being a fancy tech version of the light bulb. According to the authors, genAI combines traits of GPTs -- those that trigger cascades of innovation across sectors and continue improving over time -- with features of "inventions of methods of invention" (IMIs), which make research and development (R&D) more efficient. The authors do see potential for genAI to be a GPT like the electric dynamo, which continually sparked new business models and efficiencies, or an IMI like the compound microscope, which revolutionized scientific discovery. The Fed economists did cautioning that it is early in the technology's development, writing "the case that generative AI is a general-purpose technology is compelling, supported by the impressive record of knock-on innovation and ongoing core innovation." Since OpenAI launched ChatGPT in late 2022, the authors said genAI has demonstrated remarkable capabilities, from matching human performance on complex tasks to transforming frontline work in writing, coding, and customer service. That said, the authors said they're finding scant evidence about how many companies are actually using the technology. Despite such promise, the paper stresses that most gains are so far concentrated in large corporations and digital-native industries. Surveys indicate high genAI adoption among big firms and technology-centric sectors, while small businesses and other functions lag behind. Data from job postings shows only modest growth in demand for explicit AI skills since 2017. "The main hurdle is diffusion," the authors write, referring to the process by which a new technology is integrated into widespread use. They note that typical productivity booms from GPTs like computers and electricity took decades to unfold as businesses restructured, invested, and developed complementary innovations. "The share of jobs requiring AI skills is low and has moved up only modestly, suggesting that firms are taking a cautious approach," they write. "The ultimate test of whether genAI is a GPT will be the profitability of genAI use at scale in a business environment and such stories are hard to come by at present." They know that many individuals are using the technology, "perhaps unbeknownst to their employers," and they speculate that future use of the technology may become so routine and "unremarkable" that companies and workers no longer know how much it's being used. The report details how genAI is already driving a wave of product and process innovation. In healthcare, AI-powered tools draft medical notes and assist with radiology. Finance firms use genAI for compliance, underwriting, and portfolio management. The energy sector uses it to optimize grid operations, and information technology is seeing multiples uses, with programmers using GitHub Copilot completing tasks 56% faster. Call center operators using conversational AI saw a 14% productivity boost as well. Meanwhile, ongoing advances in hardware, notably rapid improvements in the chips known as graphics processing units, or GPUs, suggest genAI's underlying engine is still accelerating. Patent filings related to AI technologies have surged since 2018, coinciding with the rise of the Transformer architecture -- a backbone of today's large language models. The paper also finds genAI increasingly acting as an IMI, enhancing observation, analysis, communication, and organization in scientific research. Scientists now use genAI to analyze data, draft research papers, and even automate parts of the discovery process, though questions remain about the quality and originality of AI-generated output. The authors highlight growing references to AI in R&D initiatives, both in patent data and corporate earnings calls, as further evidence that genAI is gaining a foothold in the innovation ecosystem. While the prospects for a genAI-driven productivity surge are promising, the authors warn against expecting overnight transformation. The process will require significant complementary investments, organizational change, and reliable access to computational and electric power infrastructure. They also emphasize the risks of investing blindly in speculative trends -- a lesson from past tech booms. "GenAI's contribution to productivity growth will depend on the speed with which that level is attained, and historically, the process for integrating revolutionary technologies into the economy is a protracted one," the report concludes. Despite these uncertainties, the authors believe genAI's dual role -- as a transformative platform and as a method for accelerating invention -- bodes well for long-term economic growth if barriers to widespread adoption can be overcome. Still, what if it's just another light bulb?