Federal Reserve economists don’t sell AI to actually make workers more productive.

The Fed economist ultimately concludes that “modal prediction is for Genai’s remarkable contribution to the level of labor productivity,” but note that a wide range of plausible results are found in both the complete contribution to making workers more productive and how it occurs quickly. To return to the metaphor on light bulbs, they say, “Some inventions, such as light bulbs, temporarily increase productivity growth as adoption spreads, but their effects disappear when the market is saturated. That is, power levels per hour are permanently high, but growth rates are not.”

Whether Genai could ultimately become a flashy technical version of the bulb is why they consider it an open question.

genai: Tools and Catalysts

According to the author, genai combines the characteristics of “inventional methods” (IMIS) that makes research and development (R&D) more efficient with the characteristics of GPT (which triggers a cascade of innovation across sectors and continues to improve over time). The authors believe that genai is an electric dynamo-like GPT that has continuously triggered new business models and efficiency, and has revolutionized scientific discovery.

The Fed economist warned that it was in the early stages of technology development, writing, “The case of generator AI as a general purpose technology is fascinating, as it is supported by an impressive record of knock-on-innovation and ongoing co-innovation.”

Since Openai launched ChatGPT in late 2022, the authors have said that Genai has demonstrated outstanding capabilities, from matching human performance for complex tasks to translating frontline work in writing, coding and customer service. That said, the authors said they find little evidence of the number of companies actually using technology.

Limited but increasing adoption

Despite such promises, the paper highlights that most profits have previously been concentrated in large corporations and digital native industries. Research shows that genai adoption is high among large and technology-centric sectors, but small and medium-sized businesses and other functions are lagging behind. Data from job postings show only a small growth in the demand for explicit AI skills since 2017.

“The main hurdle is diffusion,” the author writes, referring to the process in which new technologies are integrated into widespread use. They note that the typical productivity boom from GPTs, such as computers and power, took decades as companies developed restructuring, investments and complementary innovations.

“The share of jobs that require AI skills is low and only a modest rise, suggesting that companies are taking a cautious approach,” they write. “The ultimate test of whether genai is GPT is
The profitability of large-scale use of genai in a business environment and such a story is currently difficult to realize. They know that many individuals use technology “probably unknown to their employers” and speculate that businesses and workers are not aware of the use of the technology as it is so routine and “not noticeable.”

Knock-on and complementary technology

The report details that Genai is already driving a wave of product and process innovation. In healthcare, AI-driven tools draft medical notes and assist in radiology. Finance companies use Genai for compliance, underwriting and portfolio management. The energy sector uses it to optimize grid operations, information technology has seen multiple uses, and programmers are using GitHub Copilot to complete tasks 56% faster. Call center operators using conversational AI also increased productivity by 14%.

Meanwhile, the continuous advances in hardware, particularly the rapid improvements of chips known as graphics processing units or GPUs, suggest that Genai’s underlying engine is still accelerating. Patent applications related to AI technology have been on the rise since 2018, coinciding with the rise of transformer architecture, the backbone of today’s large language models.

“Green Shoot” in Research and Development

The paper also finds that Genai, which strengthens observation, analysis, communication and organization in scientific research, is increasingly functioning as an IMI. Scientists now use genai to analyze data, write research papers, and even automate part of the discovery process, but questions remain about the quality and originality of the output generated by AI.

The authors highlight the increased reference to AI in R&D initiatives, both in patent data and corporate revenue, as further evidence that Genai has gained a foothold in the innovation ecosystem.

Cautious optimism – and unresolved questions

The prospect of a genai-driven productivity surge is promising, but the authors warn against hoping for an overnight conversion. This process requires significant complementary investments, organizational changes, computational and reliable access to power infrastructure. They also highlight the risks of blindly investing in speculative trends, namely the lessons learned from past technological booms.

Still, what happens if it’s just another bulb?

For this story, luck Generated AI was used to assist with initial drafts. The editors checked the accuracy of the information prior to publication.