Did you know the U.S. nurse labor market is over $600 billion annually, but the dedicated software market for nurses is almost zero? In this episode General Partners Alex Rampell, David Haber, and Angela Strange discuss how AI is revolutionizing labor by automating tasks traditionally done by humans. They’ll trace the evolution of cloud eras — from the original to financial services-enabled to the current AI-enabled outcomes era — showcasing how AI is creating unprecedented opportunities, allowing startups to outpace incumbents. They also explore how this shift will reshape industries, where we are in the adoption curve and what companies need to succeed, and the gaps where the a16z Enterprise team would love to see more innovation. Resources: AI Turns Capital to Labor:  https://a16z.com/ai-turns-capital-to-labor/The Messy Inbox Problem: https://a16z.com/the-messy-inbox-problem-ai-apps-wedge-strategies/Vertical SaaS: Now with AI Inside: https://a16z.com/vertical-saas-now-with-ai-inside/BarbAIrians at the Gate: The Financial Opportunity of AI: https://a16z.com/financial-opportunity-of-ai/Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.a  

This episode was originally published on our sister podcast, web3 with a16z. If you’re excited about the next generation of the internet, check out the show: https://link.chtbl.com/hrr_h-XC We've heard a lot about the premise and the promise of prediction markets for a long time, but they finally hit the main stage with the most recent election. So what worked (and didn't) this time? Are they really better than pollsters, is polling dead?  So in this conversation, we tease apart the hype from the reality of prediction markets, from the recent election to market foundations... going more deeply into the how, why, and where these markets work. We also discuss the design challenges and opportunities (including implications for builders throughout). And we also cover other information aggregation mechanisms -- from peer prediction to others -- given that prediction markets are part of a broader category of information-elicitation and information-aggregation mechanisms.  Where do domain experts, superforecasters, pollsters, and journalists come in (and out)? Where do (and don't) blockchain and crypto technologies come in -- and what specific features (decentralization, transparency, real-time, open source, etc.) matter most, and in what contexts?  Finally, we discuss applications for prediction and decision markets -- things we could do right away to in the near-future to sci-fi -- touching on trends like futarchy, AI entering the market, DeSci, and more.   Our special expert guests are Alex Taborrok, professor of economics at George Mason University and Chair in Economics at the Mercatus Center; and Scott Duke Kominers, research partner at a16z crypto, and professor at Harvard Business School  -- both in conversation with Sonal Chokshi.  As a reminder: None of the following should be taken as business, investment, legal, or tax advice; please see a16z.com/disclosures for more important information.    Resources: (from links to research mentioned to more on the topics discussed) The Use of Knowledge in Society by Friedrich Hayek (American Economic Review, 1945)Everything is priced in by rsd99 (r/wallstreetbets, 2019)Idea Futures (aka prediction markets, information markets) by Robin Hanson (1996)Auctions: The Social Construction of Value  by Charles SmithSocial value of public information by Stephen Morris and Hyun Song Shin (American Economic Review, December 2002)Using prediction markets to estimate the reproducibility of scientific research by Anna Dreber, Thomas Pfeiffer, Johan Almenberg, Siri Isaksson, Brad Wilson, Yiling Chen, Brian Nosek, and Magnus Johannesson (Proceedings of the National Academy of Sciences (November 2015)A solution to the single-question crowd wisdom problem by Dražen Prelec, Sebastian Seung, and John McCoy (Nature, January 2017)Targeting high ability entrepreneurs using community information: Mechanism design in the field by Reshmaan Hussam, Natalia Rigol, and Benjamin Roth (American Economic Review, March 2022)Information aggregation mechanisms: concept, design, and implementation for a sales forecasting problem by Charles Plott and Kay-Yut Chen, Hewlett Packard Laboratories (March 2002)If I had a million [on deciding to dump the CEO or not] by Robin Hanson (2008)Futarchy: Vote values, but bet beliefs by Robin Hanson (2013)From prediction markets to info finance by Vitalik Buterin (November 2024)Composability is innovation by Linda Xie (June 2021)Composability is to software as compounding interest is to finance by Chris Dixon (October 2021)resources & research on DAOs, a16z crypto  Stay Updated:  Let us know what you think: https://ratethispodcast.com/a16z Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://twitter.com/stephsmithio Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.