The primary economic impact of AI won't be from groundbreaking research or entirely new products, but rather from widespread automation of existing processes across various industries. This automation will manifest through AI-powered tools enhancing existing software and making mundane tasks more efficient, much like how previous technological advancements like spreadsheets amplified human capabilities. While R&D remains important for progress, the real value lies in leveraging existing AI capabilities to streamline operations, optimize workflows, and reduce costs at a broad scale, leading to significant productivity gains across the economy.
The article "Most AI value will come from broad automation, not from R&D," posits that the predominant economic impact of artificial intelligence will not originate from groundbreaking research and development, but rather from the widespread implementation and integration of existing AI capabilities across various sectors and business processes. The authors argue that while the development of novel AI algorithms and models is undoubtedly crucial, the true transformative power lies in the application of readily available AI tools to automate a multitude of tasks currently performed by humans.
This assertion is supported by the observation that many industries are already experiencing substantial productivity gains through the deployment of relatively mature AI technologies, such as machine learning for predictive analytics, natural language processing for customer service, and computer vision for quality control. The authors contend that these existing technologies, while perhaps not representing cutting-edge research, possess significant untapped potential for further automation, which can be realized through focused efforts on implementation and adaptation.
Furthermore, the article highlights the diminishing returns observed in certain areas of AI research, where significant investments in R&D yield only incremental improvements in model performance. This phenomenon suggests that focusing solely on pushing the boundaries of AI capabilities may not be the most efficient path to maximizing economic value. Instead, the authors propose a shift in emphasis towards refining existing technologies and making them more accessible and applicable to a wider range of real-world problems. This approach, they argue, promises a more immediate and substantial return on investment compared to pursuing more speculative research avenues.
The argument is further elaborated by drawing parallels with historical technological advancements, such as the internal combustion engine and electricity. While the initial inventions were undoubtedly revolutionary, their true transformative impact was realized only after they were widely adopted and integrated into various industries, powering everything from automobiles and factories to household appliances. Similarly, the authors believe that the true potential of AI will be unlocked not through the pursuit of ever more complex algorithms, but through the systematic application of existing AI capabilities to automate tasks across a broad spectrum of industries and activities. This process of widespread automation, they conclude, will be the primary driver of AI-driven economic growth in the coming years.
Summary of Comments ( 136 )
https://news.ycombinator.com/item?id=43447616
HN commenters largely agree with the article's premise that most AI value will derive from applying existing models rather than fundamental research. Several highlighted the parallel with the internet, where early innovation focused on infrastructure and protocols, but the real value explosion came later with applications built on top. Some pushed back slightly, arguing that continued R&D is crucial for tackling more complex problems and unlocking the next level of AI capabilities. One commenter suggested the balance might shift between application and research depending on the specific area of AI. Another noted the importance of "glue work" and tooling to facilitate broader automation, suggesting future value lies not only in novel models but also in the systems that make them accessible and deployable.
The Hacker News post titled "Most AI value will come from broad automation, not from R & D" has generated a moderate amount of discussion, with several commenters offering insightful perspectives on the interplay between AI research, development, and deployment.
Several commenters agree with the premise of the article, highlighting that the true value of AI lies in its widespread application across various industries rather than solely within the confines of research labs. They emphasize the importance of focusing on integrating AI solutions into existing workflows and processes to achieve tangible benefits. One commenter draws parallels with the software industry, arguing that the real impact came from applications and not the initial theoretical advancements.
Another prevalent viewpoint revolves around the distinction between "horizontal" and "vertical" AI progress. Some argue that while "horizontal" advancements, like improved large language models, are impressive, they primarily serve as enabling technologies. The real value, they contend, emerges from "vertical" progress, which involves tailoring these general-purpose AI models to address specific industry needs and challenges. This tailoring requires domain expertise and a deep understanding of the target workflows, emphasizing the importance of collaboration between AI specialists and industry professionals.
One commenter challenges the notion that research and development are separate from broad automation, suggesting that the two are intrinsically linked. They argue that continuous R&D is crucial for refining AI models, making them more robust, efficient, and adaptable to different contexts, which in turn fuels broader automation.
A more skeptical perspective questions the feasibility of widespread automation in certain sectors, particularly those requiring complex reasoning and decision-making. While acknowledging the potential of AI in automating routine tasks, they express doubts about its ability to fully replace human expertise in areas demanding nuanced judgment and creativity.
Finally, some comments delve into the potential societal consequences of widespread AI automation, including job displacement and the need for retraining programs to equip workers with the skills required to navigate the changing landscape. One commenter expresses concern about the potential for AI to exacerbate existing inequalities if its benefits are not distributed equitably.
While no single comment dominates the discussion, the collective insights provide a nuanced perspective on the complexities and potential implications of AI automation, emphasizing the crucial role of both R&D and practical implementation in realizing its full potential.