NASDAQ CEO Adena Friedman runs one of the world's largest financial services companies, including the NASDAQ stock exchange that's home to more than 3,500 listed companies. They were also the creator of the world's first electronic stock market. Yet how does the company adapt to technology trends today, such as the blockchain? How does it deal with other headwinds in its business, from fewer listed companies to trends in passive vs. active investing? Based on a conversation that was recorded at our annual a16z Summit in November 2017, this podcast features general partner Jeff Jordan interviewing Friedman about these changes... as well as broader themes in the way markets work. They also discuss the IPO process (which Jordan has also shared his experiences and advice on) -- from what companies should be thinking about to where technology could help.

When you have “a really hot, frothy space” like AI, even the most basic questions — like what is it good for, how do you make sure your data is in shape, and so on — aren’t answered. This is just as true for the companies eager to adopt the technology and get into the space, as it is for those building companies around that space, observes Joe Spisak, Head of Partnerships at Amazon Web Services. “People treat it like magic,” adds a16z general partner Martin Casado. This magical realism is especially true of AI, because by definition — i.e., machines learning — there is a bit of a “black box” between what you put in and what you get out of it. Which may be fine… Except when you have to completely change the data being fed into that black box, or you’re shooting for a completely different target to come out of it. That’s why, observes Scott Clark, CEO and co-founder of SigOpt, “an untuned, sophisticated system will underperform a tuned simple system” almost every time. So what does this mean for organizations going from so-called “toy” problems in R&D to real business results tied to KPIs and ROI? In this episode of the a16z Podcast, Casado, Clark, and Spisak (in conversation with Sonal Chokshi) share their thoughts on what’s happening and what’s needed for AI in practice, given their vantage points working with both large companies and AI startups. What does it mean for data scientists and domain experts? For differentiation and advantage? Because even though we finally have widely available building blocks for AI, we need the scaffolding too… and only then can we build something powerful on top of it.