Love the term or hate it, the concept and reality of the "sharing economy" (or "gig economy" and so on) is here to stay. And in fact, argues NYU Stern professor and researcher Arun Sundararajan, it may even reduce the income distribution gap between the haves and have-nots in a way that previous shifts -- like the Industrial Revolution and traditional 20th century institutions -- never did. How? Because it's a new model for (crowd-based) capitalism -- one where we're increasing the segment of the population that owns the means of production. Or... have we just shifted value from traditional institutions to the platforms instead? Well, let's see what the data tells us. In this episode of the a16z Podcast, Sundararajan (who is also affiliated with NYU's Center for Urban Science+Progress and at NYU's Center for Data Science) shares the latest findings, economics research, and more from his new book on The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism. We cover the challenges of capturing this shift in GDP (as well as the challenges of GDP and measuring tech progress in general); the challenges of creating a new funding model for the "social safety net of the 21st century workforce"; the challenges of "data darwinism", reputation, and ratings; and finally, how and just who should regulate the sharing economy? The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

The mindset of "move fast and break things", while great for code, isn't exactly great for the human body. So adding computation to biology -- especially in the slow-moving pharmaceutical industry, where drug approval can take years -- brings with it both opportunities (like drastically faster discovery and assessment) and challenges (the need for hard evidence, not just soft-ware). But there's more: We don't want just better outcomes for healthcare. We want better outcomes at a cheaper price. And that's where machine learning comes in. The benefits of such computation -- i.e., software -- can provide a powerful, frictionless, and far more cost-effective tool for biopharmaceutical research ... but it requires data. So who provides that data? Is it the pharmaceutical companies, or the payers (insurance)? How are organizations incented to overcome intellectual property silos in sharing their data? Especially since it was only relatively recently, Jeff Kindler (the former CEO of the world's largest pharmaceutical company, Pfizer) reminds us in this episode of the a16z Podcast, that the FDA even allowed data to be put in computers vs. on paper. But there's a reason the self-driving car was pushed out of the software and not the auto industry, argues TwoXAR co-founder and CEO Andrew Radin -- and it has to do with the unique nature of the developer's mindset applied to novel problems. The deterministic nature of Moore's Law -- it's not a matter of if, but when -- plays a role too, observes a16z bio fund general partner Vijay Pande. There are things that big data and simulations will be able to accomplish that a hundred lab experiments on animals can't. Still, the two mindsets will have to merge, so we can move fast ... but without compromising quality, safety, and reliability. That's the big difference between computer science and biology after all. image: mattza/ Flickr