Journalist Dylan Matthews discusses the potential of large language models in journalism, the impact of AI on income inequality, and the challenges faced by news media. They explore the use of AI in writing marketing copy, the rise and fall of digital media companies, and the evolution of news media in the US. They also touch on the risks of AI, podcast listening preferences, and the future of journalism in the age of collapsing business models.
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Quick takeaways
AI can excel in explanatory and analytical journalism, but certain types of journalism with human expertise may be challenging to replicate.
AI tools can assist with generating ideas and overcoming writer's block in journalism, but distinguishing AI-written content from human-generated content remains a challenge.
AI brings both challenges and opportunities to journalism, requiring journalists to adapt their skill sets and embrace responsible AI practices for collective action amidst increasing automation.
Deep dives
The Impact of AI on Journalism
AI's impact on journalism and the print media industry is discussed in this podcast episode. The speaker highlights that while certain kinds of journalism, such as war correspondents or deep investigative reporting, may prove difficult for AI to replicate, there are other areas, like explanatory and analytical journalism, where AI can make substantial progress. The speaker also mentions the importance of credibility and expertise in journalism, as well as the role of AI in synthesizing information and filling knowledge gaps in news coverage.
Changing Landscape of Journalism and AI's Abilities
This episode explores how AI is reshaping the journalism industry. It discusses the viability of large language models (LLMs) in replacing certain journalistic roles, such as copywriting and analysis. The speaker also shares experiences with using AI tools for writing and highlights the potential of LLMs in generating ideas and assisting with writer's block. The conversation delves into the challenges of distinguishing AI-written content from human-generated content and the need to adapt to emerging technologies.
Challenges and Opportunities for Journalism
The podcast touches upon the challenges and opportunities AI brings to journalism. It raises questions about the impact of AI on job automation and the need for journalists to adapt their skill sets. The discussion also touches on the historical evolution of journalism, polarization in media, and the potential implications of a subscription-based model on audience dynamics. It concludes with a reflection on the importance of cultivating responsible AI practices and the value of collective action amidst increasing automation.
Journalism and the Future of News Business Models
The podcast discusses the challenges faced by traditional news organizations and explores potential models for sustaining journalism in the future. One model discussed is the idea of millionaires buying news outlets, experiencing losses, and then trying to limit those losses. However, this model only works for wealthy individuals and doesn't guarantee long-term sustainability. The ad-based model is also discussed, with concerns about the dominance of Facebook and Google in the digital ad marketplace. The podcast raises questions about the viability of this model and whether news organizations can compete with these dominant actors. Overall, there is uncertainty about the future of news business models and whether traditional journalism will survive.
Distributing Economic Surplus from AI
The podcast explores different models for distributing economic surplus generated by advancements in artificial intelligence (AI). The example of OpenAI is mentioned, where it started as a nonprofit but transitioned to a for-profit entity. The concept of a windfall clause is discussed, where companies would have a self-imposed tax bracket system for profits exceeding a certain threshold. The surplus beyond that threshold would be donated to charitable causes. The podcast acknowledges that the specific implementation details are still unclear, but considers it a starting point for exploring alternative ways of distributing economic surplus from AI. The potential benefits of private sector models and their ability to address global issues are also mentioned. However, the podcast acknowledges the risks of leaving surplus distribution to titans of industry and the need for careful considerations in developing the best approach.
Will large language models (LLMs) replace journalists any time soon? On what types of writing tasks do LLMs outperform humans? Have the US news media become less truth-seeking in recent decades? Or is truth-seeking behavior merely an aberration from a norm of propagandizing? How should we redistribute economic surplus from AI? Have any AI companies committed to a Windfall Clause? Instead of bothering to negotiate with us, wouldn't a superintelligent AI be able to get much more done by first wiping us all out? What are some subtler or less-well-known ways subscription models reshape incentive structures for journalists? Why is collective action so hard?
Dylan Matthews is a senior correspondent at Vox, where he cofounded Future Perfect, a section devoted to exploring ways to do good. He writes frequently about economics, philanthropy, global health, and more. You can email him at dylan@vox.com.