Justified Posteriors

The discussion unpacks whether simply scaling AI models leads to transformative results. It explores the predictable outcomes of increasing compute, data, and parameters, but questions their ultimate effectiveness. The conversation also delves into the complexities of data quality and model performance, emphasizing the importance of innovative solutions over mere resource addition. Real-world applications like translation and software development reveal AI's potential and economic implications. The dynamic between AI capabilities and human performance is examined, uncovering both challenges and opportunities.

Is social media really a trap? Hosts delve into fascinating research on college students' experiences with TikTok and Instagram, revealing a paradox where users crave connection yet feel trapped by the platforms. The discussion touches on the fear of missing out (FOMO) and how it contributes to anxiety, while also critiquing the superficial nature of online interactions. Insights on competition for attention and the mental health challenges faced by users highlight the complexities of engagement and societal expectations surrounding social media.

In this episode, we discuss Artificial Intelligence Technologies and Aggregate Growth Prospects by Timothy Bresnahan.* We contrast Tim Bresnahan's paper on AI's impact on economic growth, with Daron Acemoglu's task-replacement focused approach from the previous episode.* Bresnahan argues that AI's main economic benefits will come through:* Reorganizing organizations and tasks* Capital deepening (improving existing machine capabilities)* Creating new products and services rather than simply replacing human jobs* We discuss examples from big tech companies:* Amazon's product recommendations* Google's search capabilities* Voice assistants like Alexa These demonstrate how AI creates value through new capabilities rather than just replacing existing human tasks.* Other parts of Bresnahan's analysis:* AI works best with "low stakes" decisions where false positives aren't costly* Modularization of tasks is important for AI adoption* Capital deepening through continuous improvement of existing AI systems* Prior Beliefs:* Andrey: 20% task replacement, 80% other effects* Seth: Initially 30-50% task replacement, moved closer to Bresnahan's view after discussion* Other considerations raised:* Many AI benefits may not be captured in GDP measurements* The distinction between task replacement and reorganization can be unclear* We conclude by considering more transformative AI scenarios, questioning whether the task-based model remains useful for analyzing more advanced AI capabilities. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit empiricrafting.substack.com

Will AI's impact be as modest as predicted, or could it exceed expectations in reshaping economic productivity? In this episode, hosts Seth Benzell and Andrey Fradkin discuss the paper “The Simple Macroeconomics of AI” by Daron Acemoglu, an economist and an institute professor at MIT.Additional notes from friend of the podcast Daniel Rock of Wharton, coauthor of “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” one of the papers cited in the show, and a main data source for Acemoglu’s paper: (1) Acemoglu does not use the paper’s ‘main’ estimates of the feasibility of using GPTs to dramatically increase productivity in tasks, rather it uses more ‘experimental’ estimates from the appendix about which tasks are fully automatable. These numbers are smaller than the main texts’ which is one reason for Acemoglu’s small productivity impact estimates (2) For a paper that uses the main estimates from his paper, Daniel recommends the OECD working paper “Miracle or Myth?"🔗Links to the paper for this episode’s discussion: https://economics.mit.edu/sites/default/files/2024-05/The%20Simple%20Macroeconomics%20of%20AI.pdf-Seth and Andrey debate AI's potential effect on economic growth, with reference to Acemoglu's prediction that AI will contribute less than 1 percentage point to total factor productivity (TFP) over the next decade.-🗞️Subscribe for upcoming episodes, post-podcast notes, and Andrey’s substack:💻Follow us on Twitter:@AndreyFradkin https://x.com/andreyfradkin?lang=en@SBenzell https://x.com/sbenzell?lang=en This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit empiricrafting.substack.com

How close are we to AGI, and what might its impact be on the global stage? In this episode, hosts Seth Benzell and Andrey Fradkin tackle the high-stakes world of artificial intelligence, focusing on the transformative potential of Artificial General Intelligence (AGI). The conversation is based on Leopold Aschenbrenner’s essay 'Situational Awareness', which argues that AI's development follows a predictable scaling law that allow for reliable projections about when AGI will emerge. The hosts also discuss Leopold’s thoughts on the geopolitical implications of AGI, including the influence of AI on military and social conflicts.Sponsored by the Digital Business Institute at Boston University’s Questrom School of Business. Big thanks to Ching-Ting “Karina” Yang for her help editing the episode.-🔗 Links to the paper for this episode’s discussion: https://situational-awareness.ai/🗞️ Subscribe for upcoming episodes, post-podcast notes, and Andrey’s posts:💻 Follow us on Twitter:@AndreyFradkin https://x.com/andreyfradkin@SBenzell https://x.com/sbenzell This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit empiricrafting.substack.com