U.S. restaurant productivity has seen a surprising surge since 2019, defying typical economic patterns during recessions. This growth is primarily driven by a substantial increase in real revenue, outpacing the rise in employment costs. The study attributes this phenomenon to a combination of factors: restaurants raising menu prices significantly, a shift in consumer spending towards restaurants from other services like travel and entertainment, and operational adjustments like reduced menus and streamlined services adopted during the pandemic that persisted even as restrictions eased. These changes have effectively raised average revenue generated per worker, resulting in the observed productivity boost.
Theophile Cantelo has created Foudinge, a knowledge graph connecting restaurants and chefs. Leveraging Large Language Models (LLMs), Foudinge extracts information from various online sources like blogs, guides, and social media to establish relationships between culinary professionals and the establishments they've worked at or own. This allows for complex queries, such as finding all restaurants where a specific chef has worked, discovering connections between different chefs through shared work experiences, and exploring the culinary lineage within the restaurant industry. Currently focused on French gastronomy, the project aims to expand its scope geographically and improve data accuracy through community contributions and additional data sources.
Hacker News users generally expressed skepticism about the value proposition of the presented knowledge graph of restaurants and chefs. Several commenters questioned the accuracy and completeness of the data, especially given its reliance on LLMs. Some doubted the usefulness of connecting chefs to restaurants without further context, like the time period they worked there. Others pointed out the existing prevalence of this information on platforms like Wikipedia and guide sites, questioning the need for a new platform. The lack of a clear use case beyond basic information retrieval was a recurring theme, with some suggesting potential applications like tracking career progression or identifying emerging culinary trends, but ultimately finding the current implementation insufficient. A few commenters appreciated the technical effort, but overall the reception was lukewarm, focused on the need for demonstrable practical application and improved data quality.
Summary of Comments ( 195 )
https://news.ycombinator.com/item?id=43364715
Several commenters on Hacker News discussed the potential reasons behind the reported productivity surge in US restaurants. Some attributed it to increased automation, such as online ordering and kiosk systems, reducing labor needs. Others pointed to a shift in consumer behavior, with more takeout and delivery orders streamlining operations and requiring fewer front-of-house staff. Skepticism was also expressed, with some suggesting the data might be flawed or that increased productivity came at the expense of worker well-being, through higher workloads and fewer benefits. Several commenters also discussed the limitations of using revenue per worker as a productivity metric, arguing that it doesn't capture changes in food quality, portion sizes, or menu prices. Finally, the impact of the pandemic and resulting labor shortages was mentioned, with some speculating that restaurants were forced to become more efficient out of necessity.
The Hacker News post titled "The curious surge of productivity in U.S. restaurants," linking to a University of Chicago working paper, generated a moderate discussion with several insightful comments. Many commenters engaged with the core findings of the paper, which suggests a significant increase in restaurant productivity, largely attributed to technological advancements like online ordering and delivery platforms.
Several commenters pointed out the potential downsides of this increased productivity, primarily focusing on the impact on labor. One commenter highlighted the precarious nature of restaurant work, noting that these technological efficiencies might translate to fewer jobs or reduced hours for existing staff, ultimately benefiting owners more than workers. This sentiment was echoed by others who expressed concern about the broader societal implications of automation-driven productivity gains, suggesting that while businesses might become more efficient, the benefits are not necessarily shared equitably.
Another line of discussion revolved around the quality of the dining experience in the face of these changes. Some commenters argued that the shift toward online ordering and delivery has led to a decline in the overall quality of food and service. They suggested that the pressure for speed and efficiency, driven by these technologies, might incentivize restaurants to cut corners, impacting the customer experience.
Furthermore, some users questioned the methodology of the study, particularly regarding how productivity was measured. They raised concerns about the potential for confounding factors, such as changes in consumer behavior or the types of restaurants included in the analysis, to influence the results. This skepticism highlighted the importance of considering the limitations of any economic study and the need for further research to validate the findings.
Finally, a few commenters offered anecdotal evidence from their own experiences in the restaurant industry, either as owners or employees. These personal perspectives provided valuable context to the academic discussion, illustrating the real-world implications of the trends described in the paper. For instance, one commenter who claimed to be a restaurant owner discussed the challenges of implementing new technologies and managing the changing expectations of both customers and staff.
Overall, the Hacker News discussion offered a multifaceted perspective on the complex relationship between technology, productivity, and labor in the restaurant industry, enriching the analysis presented in the original working paper. The comments touched upon key issues like labor displacement, quality concerns, and methodological limitations, demonstrating a nuanced understanding of the topic.